In November 2023, I wrote a Forbes.com article on whether there would be a Nobel Prize for AI, arguing that machine learning had become load-bearing infrastructure for modern science. In 2024, the prediction materialized in both the Nobel Prize in Physics and the Nobel Prize in Chemistry. Being right once does not make the next forecast correct, but it does make me willing to state the next one clearly enough to be falsifiable.

This is the second run of the experiment: the 2027 Nobel Prize in Physiology or Medicine will recognize the discovery and development of GLP-1-based medicines.

My leading laureate slate is Svetlana Mojsov, Jens Juul Holst, and Lotte Bjerre Knudsen. Daniel Drucker is the strongest alternate and could replace either Holst or Knudsen. The precise third seat is genuinely difficult. I reached this conclusion before asking any model. GLP-1s, or incretin therapeutics to be precise (see incretins.org), are the most consequential therapeutics of this century. Even I openly admit that I take low-dose of tirzepatide and semaglutide interchangeably and have no doubt that these will be proven as human longevity therapeutics with real aging clock data from human clinical trials (even though I think some of the therapeutics discovered by my team will be the first to go the entire cycle from using aging clocks for discovery of the target to being tested in a clinical trials using aging clocks).

Anyone with basic knowledge of the incretin field knows these four names: Svetlana Mojsov, Jens Juul Holst, Lotte Bjerre Knudsen and Daniel Drucker. Both Lotte Bjerre Knudsen and Jens Juul Holst spoke at the ARDD. Jens Juul Holst spoke in 2022 and 2024 and Lotte Bjerre Knudsen in her 2025 talk was very clear about semiglutide being the first proven longevity medicine. She is also one of the most vocal advocates for women in science.

While these four scientists are household names in our field, it is not clear who is likely to be recognized with the grand prize. Before Joel Habener’s death at the age of 88, it was clear that he and Svetlana Mojsov are the most likely candidates and that created significant uncertainty for the third candidate. Unfortunately, the most deserving candidate is no longer with us and there is more certainty.

So I put the question to four frontier systems—GPT-5.6 Pro Sol, Claude Opus 4.8, DeepSeek V4 Flash, and Grok 4.5—using the field as it exists now, after the death of Joel Habener and under the Nobel Foundation’s current eligibility rules. A Nobel Prize can recognize no more than three individuals. GLP-1 has at least four living scientists with compelling claims, and one foundational scientist who is now permanently ineligible. The models differed at the margin over Knudsen versus Drucker, but they converged on the same scientific event: GLP-1 is the next great Nobel-class achievement in medicine, and 2027 is the most plausible year.

Prediction (Personal + LLM)

Prize: 2027 Nobel Prize in Physiology or Medicine. Possibly in 2026.

Discovery: The discovery of the biologically active GLP-1 hormone, demonstration of its incretin and appetite-regulating physiology, and development of durable GLP-1-based therapies for diabetes and obesity.

Predicted laureates: Svetlana Mojsov, Jens Juul Holst, and Lotte Bjerre Knudsen.

Strongest alternate: Daniel Drucker, particularly if the committee emphasizes receptor mechanism, multi-organ physiology, and translation from hormone biology to therapeutic use.

A plausible Nobel citation: “For discoveries establishing GLP-1 as an incretin hormone and enabling GLP-1-based treatment of diabetes and obesity.”

If the award arrives in 2026, I will not be shocked. The evidence is already sufficient. But more likely it will be 2027. It gives the committee one full cycle to settle the attribution problem created by Habener’s death, while the clinical record continues to expand from glucose control and weight loss into cardiovascular, renal, hepatic, and potentially healthspan-relevant outcomes.

Why 2027?

First, the medical impact is no longer prospective. GLP-1-based drugs have already changed the standard of care for type 2 diabetes and obesity. They have moved from specialist therapies to one of the most important drug classes in the world, with benefits measured not only in glycated hemoglobin and kilograms but in major cardiovascular and renal outcomes.

Second, the causal chain is unusually complete. The committee can trace a coherent line from active peptide, to human physiology, to receptor mechanism, to molecular engineering, to clinical benefit. Many important drug classes are built from diffuse contributions that are difficult to assign. GLP-1 has identifiable scientific milestones and identifiable scientists (www.incretins.org).

Third, the precursor awards have already performed much of the historical work. The Lasker and Breakthrough committees corrected omissions, clarified contributions, and established a consensus pool. The Nobel Committee does not follow other prizes mechanically, but these awards reduce the reputational risk of acting.

Fourth, the discovery has acquired significance beyond its original indication. Nobel Prizes often arrive after the true breadth of a discovery becomes visible. GLP-1 is no longer simply an insulin-secretory hormone or even an obesity mechanism. It has become a platform for multi-organ chronic-disease intervention.

Fifth, 2027 is the first fully reset post-Habener cycle. The 2026 prize could still recognize GLP-1. My forecast of 2027 reflects the institutional time needed to re-evaluate a slate after the loss of a foundational candidate and to settle the final-seat choice between Knudsen and Drucker in the case that there is any debate.

Why the Models Now Converge?

One of the reasons I focus one hundred percent of my efforts on longevity biotechnology is to give people the freedom to enjoy a few more (and potentially many more) years of productive life. When I see brilliant scientists pass - I am genuinely sorry. Joel Habener died on December 28, 2025, at the age of 88. His work on the proglucagon gene and the peptides encoded by it created the molecular foundation on which the modern GLP-1 field was built. It is difficult to write any intellectually honest history of these medicines without placing him near the beginning. There is a tragedy in the timing. A scientist can spend more than four decades watching a fundamental discovery move from an obscure peptide to a drug class used by millions, receive almost every major precursor prize, and still die before the Nobel Committee acts.

Once the candidate pool is restricted to living scientists, the structure of the forecast becomes much clearer. The same four names recur because they represent the four indispensable stages of the GLP-1 story.

Svetlana Mojsov identified, synthesized, and established the biologically active truncated forms of GLP-1, including GLP-1(7-37), and demonstrated insulin-stimulating activity. Her contribution supplied the active molecule rather than merely the gene sequence. The historical record undercredited her for years; major scientific prizes have now begun to correct that.

Jens Juul Holst helped establish GLP-1 as a genuine incretin hormone and characterized its effects on insulin secretion, gastric function, appetite, and energy regulation in humans. This was the decisive bridge from a peptide sequence to a validated physiological target.

Lotte Bjerre Knudsen led the pharmaceutical engineering that converted a rapidly degraded native peptide into durable medicines. Liraglutide, followed by semaglutide, proved that the biology could be made practical, scalable, and clinically transformative. If the Nobel Committee wants the prize to span discovery through medicine, Knudsen is the natural third laureate.

Daniel Drucker made foundational contributions to GLP-1 receptor biology, tissue-specific mechanisms, and the translation of incretin biology across multiple organ systems. If the committee weights mechanistic physiology more heavily than drug engineering, Drucker could take the third seat.

This is the narrow point on which the models still vary. Their disagreement is not over whether GLP-1 deserves the Nobel Prize or whether the relevant window has arrived. It is over which contribution the committee will choose to represent with its final seat. That is a productive disagreement because it mirrors the actual attribution problem.

The precursor prizes make the pattern visible. The 2024 Lasker-DeBakey Clinical Medical Research Award went to Habener, Mojsov, and Knudsen for the discovery and development of GLP-1-based drugs that transformed obesity treatment. The 2025 Breakthrough Prize in Life Sciences broadened the slate to Habener, Drucker, Holst, Knudsen, and Mojsov, explicitly recognizing complementary contributions from hormone discovery through pharmaceutical development. With Habener no longer eligible, the Nobel problem has compressed from five major contributors to four living candidates competing for three seats.

While some of the models may disagree on Knudsen vs Druker, many of the scientists in the drug discovery and development community do not have any doubts. Without Knudsen, there would be no GLP-1 drug at Novo Nordisk, and we would not have uncovered the potential of this wonderful class of therapeutics. Most people who think that the discovery of a protein target is when the drug is born, have not idea about drug discovery. GLP-1 was discovered over 40 years ago but turned into the most important drug class very recently. Here, we absolutely must credit the brave and persistent scientists at Amylin and then Eli Lilly who got us the Exenatite approved for Type 2 diabetes in 2005, followed by the absolute blockbuster, Tirzepatide (GLP-1 + GIP). Tirzepatide is arguably the best drug ever developed by humans and it is the result of hard work of many people including Richard DiMarchi (multi-agonist scientific pioneer), discovery and development leaders (Jeff Emmick, Ruth Gimeno, Dan Skovronsky, Andrew Adams), and the super hero CEO, Dave Ricks. On a personal side note, GIP is a very special incretin. You can agonize it or antagonize it - you lose weight all the same and much research is focused on this target. There may be the Nobel prize specifically for GIP one day but I don’t want to deviate too much. Check out incretins.org for details. But on the Novo Nordisk side no one shined brighter than Lotte Bjorre Knudsen with the support of . When the GLP-1 program was shaky and at the brink of collapse, she pressed on and managed to deliver Liraglutide in 2010 and Semaglutide in 2017. This persistence, bravery, commitment to good science gave us Ozempic. She also led the development of Ozempic into obesity. Without Lotte Bjorre Knudsen there would be no Ozempic. She is also the youngest candidate out of the four and is actively engaged in both research and promotion of science. I also think that the main potential of GLP-1s is in longevity. Since I started using GLP-1 for longevity, both tirzepatide and semaglutide reduced my aging biomarkers more than any other clinically-approved drug and it looks like they work differently. I really hope to see more clinical evidence published using the blood biochemistry and proteomic data collected in the course of the clinical studies crowning GLP-1 as clinically-proven longevity therapeutics with excellent safety profile.

Why a GLP-1 Nobel Would Be Bigger Than a Prize for Obesity And May Be The First Prize For Longevity?

The Nobel case and the longevity case are connected because GLP-1 has stopped behaving like a drug for one disease. It is becoming the clearest clinical example of a dual-purpose therapeutic: a medicine that treats defined diseases while also touching mechanisms and organ systems that shape late-life health.

At the 2025 Aging Research and Drug Discovery conference in Copenhagen (moved to David Rubenstein Treehous at Harvard in Boston in 2026, October 1-3), which I co-organize, I watched this transition become explicit. Andrew Adams of Eli Lilly reviewed the expanding evidence beyond metabolic disease and ended by asking whether GLP-1s are the world’s first longevity drugs.

Andrew Adams presenting at the ARDD 2025

Many longevity biotechnology experts, cheered. Big pharmaceutical companies avoid using this terminology. I’ve been in this industry for over twenty years and usually pharma companies are very tight lipped about longevity even when their product clearly demonstrates longevity benefits. Andrew Adams presented on longevity strategy at the ARDD in 2024 but his 2025 talk was watched very closely by the many pharma executives in the audience even when many students migrated to a parallel session where Anthony Atala presented his latest breakthroughs in artificial organs. If it would not be for the celebrity parallel session we would not have any space in the auditorium and both the media and science editors in the audience were taking notes. For a senior executive of the largest pharmaceutical company in the world to focus on longevity requires real courage.

Andrew Adams presenting at the ARDD2024

The next day, Lotte Bjerre Knudsen delivered a spectacular presentation to describe semaglutide as a proven longevity medicine.

Lotte Bjerre Knudsen presenting at the ARDD2025

Nature Biotechnology’s account of that moment called it electrifying. That was the right word. The scientific leadership of the two largest GLP-1 drugmakers was publicly arguing that the class had crossed from metabolic medicine into the territory of healthspan.

The outcome data explain why. In SELECT, 17,604 adults with overweight or obesity and established cardiovascular disease, but without diabetes, were randomized to semaglutide or placebo. Semaglutide reduced cardiovascular death, nonfatal myocardial infarction, or nonfatal stroke by 20 percent. The primary SELECT publication established that the benefit extends beyond glycemic control in people with diabetes.

In FLOW, semaglutide lowered the risk of major kidney disease events by 24 percent in people with type 2 diabetes and chronic kidney disease, and the trial was stopped early after a prespecified interim analysis because the benefit was clear. The FLOW results matter for the longevity argument because kidney decline is both a major cause of late-life morbidity and a systemic marker of deteriorating physiological reserve.

Liver disease adds a third organ system. Semaglutide has shown histological benefit in metabolic dysfunction-associated steatohepatitis, including resolution of steatohepatitis and improvement in fibrosis for a meaningful share of patients. Cardiovascular, renal, and hepatic benefit across randomized trials makes GLP-1 a genuine multi-system therapeutic.

That is strong evidence for broad disease modification. It is not yet proof that GLP-1 receptor agonists directly slow aging. The distinction matters. Obesity, hyperglycemia, hypertension, and dyslipidemia independently drive cardiovascular, renal, and hepatic disease. A drug that improves those risk factors should improve downstream outcomes even if it does nothing to the fundamental biology of aging.

This April, Nicola Marino, Matteo Fiore, and I argued in Nature Health that GLP-1 receptor agonists should be classified as transitional longevity therapeutics: more strongly supported than any previous gerotherapeutic candidate, but not yet proven to be direct geroprotectors. “Transitional” is the scientifically useful word. It recognizes what the drugs have achieved without pretending that the decisive aging experiment has already been run.

The Experiment That Would Turn the Longevity Claim Into Evidence

A genuine longevity trial would need to separate direct effects on aging biology from the indirect effects of treating metabolic disease. It would enroll populations in whom that distinction can be made, follow them long enough to capture outcomes across multiple organ systems, and retain hard clinical events—not a biomarker alone—as the primary test.

The study should include validated DNA-methylation and proteomic aging clocks as secondary endpoints, serial inflammatory and metabolic profiling, body-composition imaging, strength and physical-performance measures, renal and cardiovascular function, cognition, and a predefined multi-morbidity outcome. It should also quantify how much of any benefit is mediated by weight loss, glycemic improvement, blood-pressure reduction, and changes in visceral adiposity.

Lean mass is a particularly important confounder and safety endpoint. GLP-1-induced weight loss includes both fat and lean tissue. In an older person with limited muscular reserve, loss of skeletal muscle can increase frailty, falls, disability, and mortality. A therapy intended to preserve healthspan cannot be evaluated only by the number on a scale. Trials must measure body composition, muscle strength, protein intake, and resistance exercise.

The proteomic opportunity is immediate. Large GLP-1 trials have already collected longitudinal biospecimens from thousands of participants. Modern protein-based aging clocks can ask whether the circulating proteome moves toward a younger state, whether that change is dose-dependent, and whether it predicts hard outcomes independently of weight loss. Much of the necessary data may already exist inside pharmaceutical companies. Publishing a rigorous analysis with the aging field’s AI-driven clocks would answer an important question at a fraction of the cost of running those trials again.

My own position is therefore deliberately asymmetric. The Nobel prediction is strong because it rests on completed discoveries, approved drugs, and hard outcome data. The direct-longevity claim remains provisional because the experiment designed to isolate aging biology has not yet been completed. GLP-1 can be Nobel-worthy before it is proven to be a geroprotector.

The Broader Meaning of the Prize

A 2027 Nobel Prize for GLP-1 would recognize more than one hormone or one blockbuster class. It would mark a change in what the most important medicines are expected to do. The old model was one drug, one target, one disease, one endpoint. The emerging model is a platform mechanism that changes risk across several chronic diseases and preserves function across organ systems.

That is why the longevity question belongs inside this article rather than in a separate essay. The same multi-system clinical breadth that makes GLP-1 Nobel-class is what makes it interesting to geroscience. The class may eventually show direct effects on inflammatory, metabolic, or neuroendocrine mechanisms of aging. Or it may turn out that most of the healthspan benefit comes from treating obesity and metabolic dysfunction extraordinarily well. Either result would be medically important. Only the first would establish direct geroprotection.

I often ask rooms full of biotechnology professionals who is taking a GLP-1. Usually only one or a few hands go up. That gap between scientific attention and actual adoption suggests that the clinical and commercial impact is still in its early stages. Oral peptides, small-molecule agonists, and multi-agonist combinations will expand the population that can use incretin-based therapy and will make the class more heterogeneous. The Nobel Prize, if it arrives in 2027 as I expect, may look less like recognition of a completed chapter than recognition of the platform on which the next chapter will be built.

How Do We Find The Next GLP-1?

Habener’s path from the molecular discovery of GLP-1 in the early 1980s to a drug class discussed as a possible longevity intervention took roughly forty years. That timeline reflects the difficulty of the science and the caution required to establish safety. It should also become the benchmark that the next generation of discovery systems tries to compress.

At Insilico Medicine, our goal is to discover and develop the next dual-purpose therapeutics targeting both disease and aging on a radically shorter clock and at scale. GLP-1 pathway from target to disease (T2D) to drug to biological process (obesity) to longevity (still investigational) is very inspiring. Everyone in longevity biotechnology needs to learn this story and strategy and invent new ways to compress timelines. At Insilico, we are not chasing the next Breakthrough, Lasker or Nobel. Scaling the discovery of longevity therapeutics is the most impactful area of human development. One year of life added to everyone on the planet equals 8.3 billion life years - that is over 110 million lifetimes at current average life expectancy. If we can add 10 years - that is 1.1 billion lifetimes. If this is your work, work-life balance becomes life-life balance.

Our lead program, the first out of 45+ programs we are running and with 32 developmental candidates nominated in the past 6 years, began with an aging-informed, AI-discovered novel target that has never been in clinical trial before and an AI-designed molecule for idiopathic pulmonary fibrosis, the age-related lung disease with no drug that restores rapidly declining Forced Vital Capacity (FVC) - decline in lung function that is also aparent in the elderly without IPF. It moved from target discovery to a completed phase 2a trial and into phase 3 in just over six years and we published research results at every step of the way to turn this process into the cleanest traceable experiment in AI drug discovery and development. A recent Nature Medicine News & Views article described the phase 2a result as a concrete step toward bringing AI-enabled drug discovery into the clinic. Since we could have out-licensed the drug during or after phase 2a, Harvard Business School did a business case on this drug and we added additional learning materials for anyone who is interested to learn how to do this again (including our own staff). We also started building a portfolio of different molecules with different properties to unlock the full potential of this target in multiple diseases and increase the probability that some of these molecules will make it into the category of longevity therapeutics.

The comparison is about time, not mechanism. Our lead molecule is not a GLP-1 therapy, and this is not a claim that it is already a longevity drug. The point is that an aging-informed hypothesis can now be generated, tested, optimized, and advanced clinically on a six-year rather than a forty-year clock. If that pace generalizes, the next therapeutic with broad healthspan relevance does not have to wait four decades for clinical proof.

The same discipline should govern both forecasting and drug discovery: state the hypothesis clearly, identify the constraint that could falsify it, and design the experiment so that a wrong idea can fail quickly. My hypothesis is now on the record. In 2027, the Nobel Prize in Physiology or Medicine will recognize GLP-1.

My Final Forecast

Year: 2027

Prize: Nobel Prize in Physiology or Medicine

Discovery: The discovery and therapeutic development of GLP-1 for diabetes, obesity, and cardiometabolic disease

Predicted laureates: Svetlana Mojsov, Jens Juul Holst, and Lotte Bjerre Knudsen

Strongest alternate: Daniel Drucker

And One More Thing…

I mentioned the ARDD conference several times. This year, it moved to the most epic location to date - David Rubenstein Treehouse at Harvard with several adjacent venues for parallel forums. Insilico Medicine is the largest sponsor and the main organizer of the event. Eli Lilly is the Tier 1 sponsor with AstraZeneca, Abbvie, and other pharmaceutical companies sponsoring the event. We managed to get the top biotechnology investors, many very credible startups and leading academics under one roof and with the program designed for one goal - building the longevity biotechnology industry. Registration is now open for this wonderful event - hope to see you in Boston 1-3 of October.

Disclosure: I am the founder and CEO of Insilico Medicine, which develops artificial intelligence for drug discovery, including a program referenced in this article. The Nobel forecast is my personal prediction. I also use a GLP-1 receptor agonist under medical supervision; that personal choice is not evidence that the class is a proven geroprotector.

From Hiking in Hong Kong to Functional Perfumes

Last weekend, I was preparing for a hiking trip to Hong Kong. Hong Kong is a very special city, in one day you can arrive to the airport, go for a hike with a beautiful view, go wake surfing, work in the frontier research lab, give a talk, meet investors at a phenomenal fusion restaurant like Dim Sum Library or Mott32, and take a 14-minute train to Shenzhen. Yes, it is so super compact and you could do all that in one day. It is one of the reasons why it the highest life expectancy on the planet - over 85.5 years. But there are minor challenges. One of the minor minor challenges are mosquitos. These mosquitos are tiny but very stingy - I am very allergic. So I sat down with an AI system and did what I have done for two decades in drug discovery, which is to compare candidate molecules on hard endpoints rather than on marketing. DEET versus picaridin. Mechanism, toxicity classification, complete protection time, skin feel, material compatibility, regulatory history. I wanted the data, not the vibe. By the end of it I had settled on twenty percent picaridin, and I had also accumulated a small, precise picture of a compound that is odorless, chemically stable, gentle on skin, and near-perfect at doing its one job for eight to fourteen hours at a stretch.

And then a stray thought landed, the way the interesting ones usually do, sideways and uninvited. I asked whether anyone had ever put picaridin into an actual perfume, so that a single product would smell good and protect you. The answer was no. Nobody makes it. Picaridin is sold purely as a registered repellent, and perfume is sold purely as decoration, and the two categories have apparently never been introduced. That struck me as strange. I spend my days at an AI drug discovery company designing novel molecules with genuine therapeutic intent, applying rigor to substances that go inside the human body. Meanwhile the fragrance industry, which puts its products directly on skin every single day, still treats scent as an ornament and asks nothing more of it. Why?

Fragrance Resists Innovation

This is the same error I wrote about when I looked at clothing. The fragrance industry is trapped in a nineteenth and twentieth century paradigm in which the entire point of a perfume is decoration and status signaling. You smell nice, you signal taste or money or seduction, and the product does no work beyond that. In the last decade the industry bolted on a second story, the “clean beauty” narrative, which claims to be about safety but is really about vibes. And here is the part that genuinely irritates me as someone who reads toxicology data for a living: the clean-fragrance movement frequently makes products that are less safe while sincerely believing the opposite.

The logic error is the “natural equals safe” heuristic, which is one of the most durable and least defensible ideas in all of consumer health. Citrus oils, lavender, and tea tree oil are among the most common causes of contact allergy and skin sensitization on the planet. Citrus oils in particular are phototoxic, meaning they can trigger a skin reaction when you go out into UV light after application, which is exactly what happens when you wear perfume and then step outside. A “natural” perfume loaded with essential oils is, from a dermatological standpoint, often a more hazardous product than a minimalist synthetic one. Meanwhile the real endocrine concerns in mainstream perfumery are chemicals most consumers never think about: phthalates, especially diethyl phthalate used as a solvent and fixative, and synthetic musks like galaxolide and tonalide. These interfere with estrogen, androgen, and progesterone signaling, they bioaccumulate in fat tissue, and they have been detected in human breast milk, body fat, and umbilical cord blood.

So the consumer fixates on a “chemical-sounding” name like picaridin and recoils, while happily applying a fixative that shows up in cord blood. This is the category error in miniature. People are optimizing for how a word sounds rather than for what a molecule does. My worldview, in fragrance as in everything, starts from primum non nocere and from the refusal to accept inherited defaults just because they are old. If we are going to put something on our skin every morning, I want to know two things: does it do something useful, and is it actually safe when you measure it instead of when you feel it. Decoration alone is a waste of surface area.

Picaridin as the Case Study

Let me introduce the compound properly, because it is a small masterpiece of deliberate design and it makes the point better than I can. Picaridin, also called icaridin, was developed by Bayer in the 1980s under the lab code KBR 3023 and commercialized as Bayrepel. What I find elegant about it is the design philosophy. Bayer used molecular modeling to target arthropod odorant receptors, and they built a molecule that mimics piperidine, a compound found in black pepper plants of the genus Piper. This is engineering, not foraging. They wanted DEET-level efficacy with a better safety profile, no strong odor, and no greasy feel, and they got it. It was first released in Europe and Australia in 1998, the US EPA registered it in 2001, US consumer products arrived in 2005, and after Bayer spun off Lanxess and Saltigo the trade name became Saltidin in 2008.

Compare that to DEET, which arrived the way so many mid-century chemicals did, more or less by brute-force screening. Samuel Gertler synthesized it at the USDA in 1944, one of more than seven thousand compounds screened originally for insecticidal use. Its repellent properties were recognized around 1952, the US Army adopted it after the war for jungle warfare, and it reached civilians in 1957. DEET works, and it has decades of data behind it, and its serious neurotoxic events are genuinely rare, roughly one seizure per hundred million users and almost always tied to ingestion or gross overapplication. But DEET is also a plasticizer. It degrades nylon, spandex, rayon, and watch crystals. It has a smell many people dislike. It feels unpleasant on skin.

The head-to-head data is what settled it for me. A 2018 review of eleven studies in the Journal of Travel Medicine found little difference between equivalent concentrations of picaridin and DEET, with some evidence that picaridin persists slightly longer. A twenty percent picaridin formulation gives roughly eight to fourteen hours of complete protection time, which is the time to first bite rather than a vague percentage of risk reduction, and it performs well against ticks, biting flies, gnats, chiggers, and sandflies. The EPA classifies it in Toxicity Category III to IV, meaning slightly to practically non-toxic across oral, dermal, and inhalation exposure, with no evidence of carcinogenicity, developmental, reproductive, or neurotoxicity in its review, and it is considered safe for pregnant women and children when used as directed.

Now here is why I could not stop thinking about it as a fragrance companion. Picaridin is odorless. It is pH-neutral. It is chemically stable and does not react with common fragrance families. It does not damage synthetic materials. It is, in other words, the rare functional active that asks almost nothing of the formulator and clashes with nothing. A brief but necessary disclaimer before I go further: none of this is medical advice, and anyone experimenting with an active like picaridin at home should follow the label directions exactly and consult a professional if there is any doubt, especially regarding children, pregnancy, or existing skin conditions.

Design Principles for a Functional Fragrance

If I were going to specify this product the way I specify a molecule’s target profile, I would write it down as a set of principles. Here are the ones I would refuse to compromise on.

1. Efficacy first, and efficacy must be registered, not implied. The active ingredient has to carry real regulatory efficacy data, the kind you can cite. Complete protection time in hours, measured in controlled studies, from a compound with an EPA registration. This is the whole difference between a functional product and a decorative one dressed up in functional language. Picaridin at twenty percent qualifies. A vague blend of “protective botanicals” does not, because botanical repellents are generally weaker and shorter-lasting per the literature. If the label makes a functional claim, the claim must survive being measured.

2. Chemical compatibility between active and scent is non-negotiable. This is where fragrance craft and formulation chemistry have to talk to each other. Picaridin is stable with alcohol-based, pH-neutral carriers and with woody, resinous scent families. Oud and musk pair with it beautifully, both because they are pH-neutral and stable and because picaridin’s own odorlessness means it will never fight the composition. The specific risk to flag is acidic essential oils, citrus above all, which could affect ester stability over shelf life. So you either avoid them or you test them rigorously with real stability studies across the product’s shelf life. You do not guess.

3. Regulatory honesty about what the thing legally is. This is the hard part, and it is where most people attempting this would cut a corner they should not. Picaridin is a registered pesticide in most jurisdictions. That means a picaridin fragrance is not merely a cosmetic; it is a registered repellent that also happens to smell like a fine perfume, and it must be labeled and sold as such. The Fix: you navigate the pesticide-registration pathway country by country rather than pretending you can slip an active into a cosmetic and call it a fragrance. What exists now: the pathway is actually cleaner than you might expect in many jurisdictions precisely because picaridin is already an approved topical repellent, so the active is not the obstacle. The obstacle is the honesty and the paperwork, and a founder who tries to route around either will deserve the enforcement action they eventually receive.

4. Stack the utility in layers. A repellent that also smells sophisticated is already two functions. But the same surface of skin can carry more. Broad-spectrum UV filters for the long hours outdoors that a repellent implies you are going to spend. Light moisturizers and humectants to counter the drying effect of wind and altitude. Soothing botanicals such as aloe or centella asiatica to calm skin and any bites that slip through after the protection window closes. Each layer is worn on the same skin at the same time, so the marginal cost of adding a function is low and the utility compounds.

5. The one-bottle travel thesis. This is the emotional and practical core of the idea. The Fix: a single elegant product that replaces the separate repellent tube, the sunscreen, the after-sun lotion, and the perfume you were going to pack anyway. What exists now: nothing that does all four with a real repellent active. Travelers and outdoor professionals already ruthlessly optimize pack weight and count, and the person who will pay a premium for one item that does the work of four is not a hypothetical, it is a large and well-understood customer. I felt exactly this friction packing for New Zealand, staring at three bottles that all had to go on my skin in sequence anyway.

6. Safety by formulation, which is the actual safety story. The clean-beauty movement told the wrong story. The real safety story is IFRA compliance, deliberate avoidance of phthalates as solvents and fixatives, avoidance of the bioaccumulating synthetic musks, and avoidance of phototoxic and high-allergen naturals like citrus, lavender, and tea tree. Counterintuitively, a minimalist formulation built on carefully chosen synthetic aroma chemicals in a pH-neutral alcohol base is frequently the safer product than a “natural” one, because you control every input and you can exclude the known offenders. “Natural good, synthetic bad” is not a safety framework. Measured toxicology is.

7. Honest market sizing. I will not oversell this. The global insect repellent market is roughly six to twelve billion dollars, the functional fragrance market is around twelve billion in 2026 and projected toward roughly twenty billion by 2035 at about six percent annually, and the broader fine fragrance market runs fifty to sixty billion. A picaridin-plus-fragrance product sits in a small but real overlap of those, most plausibly launching in premium travel and outdoor retail before any expansion. It is a niche. But it is a niche with an unusually motivated buyer and, at present, no serious competitor doing it correctly.

The Competitive Landscape, and Why It Is Aiming at the Wrong Target

This idea is not entirely unattempted. There are real products in the market that combine fragrance with insect protection. The BUZZ Skin sells a fine-fragrance bug-repellent eau de parfum line. Heretic Parfum’s “The Entomologist” is an EPA-compliant natural bug spray built and marketed as a luxury fragrance with cedar, thyme, rosemary, and geranium notes. Aromaflage uses vanilla-derived functional notes marketed to disrupt the cues mosquitoes use to find you. These are genuine, thoughtful products made by people who saw the same gap I saw, and I respect that they shipped something.

But every one of them is optimizing for the wrong axis. They all rely on natural botanical actives, essential oils and plant compounds, because their brand thesis is naturalness and “clean” positioning. And per the efficacy literature, botanical repellents are generally weaker and wear off faster than picaridin or DEET. So these products are making a real functional claim on a foundation that cannot fully deliver the function. They chose the marketing story that consumers currently reward, naturalness, over the axis that actually matters for a repellent, which is complete protection time backed by registration data. That is a values decision, and I understand why they made it, but it means the actual open opportunity is still open.

The unfilled position is the one that pairs EPA-grade efficacy with genuine fragrance craftsmanship. A twenty percent picaridin active, giving eight to fourteen hours of real protection, inside a serious oud-and-musk composition made by someone who actually knows how to build an accord, in a safe IFRA-compliant base with UV and skin-soothing layers stacked on top. That product does not exist. The existing players ceded the efficacy ground to keep the naturalness story clean. I would rather keep the efficacy and tell an honest safety story instead, because the honest safety story, once you actually read the toxicology, favors the well-designed synthetic anyway.

Why This Is the Same Idea, Smaller

I keep coming back to the same underlying conviction across everything I write in this series. The everyday objects we live inside, the clothing on our bodies and now the scent on our skin, should stop being passive decoration and start being functional infrastructure for a longer and healthier life. I organize my thinking about that life around peakspan over healthspan over lifespan: not merely surviving longer, not merely staying free of disease longer, but staying at the top of your capacity as long as possible. Most of that project happens at the level of biology and medicine, which is where I spend my actual working hours. But some of it happens at the level of the mundane objects we accept without questioning, and those objects almost always turn out to have been designed for a world that no longer exists, or designed for nothing at all beyond appearance.

A functional fragrance is a low-stakes version of the same discipline that drives the drug discovery work I do. You take a category, you refuse to accept its inherited default, you ask what job it could actually do, you identify a molecule with real registered efficacy, you check its safety with data instead of intuition, you confirm its chemical compatibility, and you build the thing deliberately. The scale is tiny and the consequences of getting it wrong are a mediocre perfume rather than a failed therapeutic, but the method is identical. Rigor does not care whether the target is a cancer pathway or a bottle of scent. It only cares whether you measured.

I should be honest about what this is and what it is not. This is a molecule-level idea, not a business plan. I have not modeled the unit economics, I have not talked to a fragrance house or a regulatory consultant, and I have not decided that the world urgently needs another product from me. Whether anyone actually builds a picaridin perfume, and whether the pesticide registration and consumer skepticism and formulation stability all resolve in its favor, is a separate question from the one I set out to answer. The question I set out to answer was simply whether we can make perfume more useful, and the answer, once you replace nostalgia and marketing with chemistry and toxicology, is clearly yes. Someone should. It might not be me. But the fact that it does not yet exist, in a market that already sells worse versions of the same instinct, tells me the gap is real and the defaults are, as usual, waiting to be questioned. I will see how easy it is to find picaridin that I can combine with my regular fragrance. Next time you meet me at a conference, chances are high I will have the 20% picaridin solution on me with some perfume.

We discussed the benefits of Australia as a biotechnology hub, but few delegates remembered how this country became the go-to place for biotechnology and clinical development. In my opinion, many books must be written about the history of this transformation, and the true heroes must be celebrated. Biotechnology companies and patient organizations should consider erecting a monument to the policymakers of the past and present who helped shave six months to a year off the drug discovery and development cycle, a process that typically takes 10–15 years.

Half a year or a year may not sound like much, but it certainly matters to a young mother of several children suffering from a terminal illness that could become treatable or manageable as a chronic condition, or to the elderly battling dementia before it becomes irreversable.

If you give one additional year of life to everyone on the planet, you generate roughly 8.3 billion life-years, equivalent to over 100 million lifetimes. That is more lifetimes than the number of humans killed in all wars ever fought combined. So if there is someone up there keeping count, any policymaker who saves so many lives deserves to go to heaven. Or at least, deserves to be remembered. I hope that there is also hell for those who intentionally slow things down by creating barriers for personal gain or simply to get some attention. This is the rule of thumb I use for my own decision making - one year delay or acceleration for the longevity therapeutic that can give everyone one year of life is equivalent to 100 million lifetimes.

One of the strategies for developing longevity therapeutics is to identify dual-purpose drugs that help patients with chronic disease but also target the fundamental pathological processes that also drive aging. For chronic diseases, Australia is one of Insilico Medicine’s prime destinations for clinical trials. This is not because Australia offers regulatory shortcuts. It is because Australia built something far more valuable: a system that can move quickly while preserving Good Clinical Practice, credible institutional review, and data quality that global regulators can evaluate.

Insilico Medicine is a genuinely global, multi-hub AI drug discovery company. We operate out of Boston, Montreal, Abu Dhabi, Hong Kong, and Shanghai, with operations and partnerships spanning Asia, the Middle East, Europe, and North America. Our scientific and clinical programs move through the jurisdictions best suited to each stage, from target discovery and chemistry to manufacturing, first-in-human studies, and later-phase development. Humans are single species regardless of color, gender, age, or nationality, and our job is to produce safe and effective therapeutics to extend healthy productive life for everyone on the planet (we also want to make animals live longer but this is a subject for another story).

Over the past five years, our platform and drug discovery teams have nominated 31 preclinical or development candidates, secured 13 investigational new drug clearances or equivalent authorizations, advanced eight programs into Phase 1, three into Phase 2, and one into Phase 3. Many companies in AI drug discovery struggle to deliver one high-quality developmental candidate in one area while claiming to make therapeutics faster, cheaper and higher probability of success. But Insilico is the “MMA Fighter” working on fibrosis, immunology and inflammation, neuroinflammation, neuroimmunology, oncology, cardiovascular diseases, obesity, CKD, pain and other areas in order to increase the chances of developing longevity therapeutics that target aging at its core. This commitment to high productivity, organizational resilience and sustainability force a company to study regulatory systems empirically. When multiple programs are moving in parallel, small differences in review time, operating cost, site quality, and tax treatment compound into years of productive life and hundreds of millions of dollars across a portfolio.

Australia consistently stands out. Its advantage is the result of deliberate policy choices made over several decades, beginning with foundational therapeutic goods legislation and maturing through the modern Therapeutic Goods Administration, the Clinical Trial Notification scheme, an experienced Human Research Ethics Committee network, and a refundable research and development tax incentive. These systems did not appear spontaneously. They were built by ministers, physicians, reviewers, civil servants, ethics committees, investigators, and institutions whose names rarely appear in biotechnology narratives.

Why Australia Works for Early Clinical Development

The central mechanism is the Clinical Trial Notification, or CTN, scheme administered by the Therapeutic Goods Administration, the TGA. Under the CTN pathway, the sponsor does not generally wait for the TGA to conduct a full upfront review of the clinical dossier before a trial can begin. Scientific and ethical assessment is conducted through the responsible Human Research Ethics Committee, or HREC, together with governance authorization from the participating institution, while the TGA is notified of the trial.

That distinction has enormous operational consequences. A well-prepared submission can often move through HREC review in approximately four to six weeks, although actual timelines depend on the committee, institution, protocol complexity, meeting calendars, and responses to questions. Site governance and contracting can add time, as they do in every country, but the path can still be measured in months to first dosed patient rather than years.

The CTN system does not eliminate accountability. Sponsors remain responsible for the quality of the investigational product, the adequacy of nonclinical evidence, protocol design, safety monitoring, reporting, and compliance with applicable standards. Investigators and institutions must be satisfied that the study is scientifically and ethically defensible. The HREC has substantive responsibility, not a ceremonial role.

This is a more sophisticated model than the caricature of either full centralized control or deregulation. Australia allocates review to institutions capable of evaluating the actual trial and its local context, while retaining national regulatory authority, safety reporting obligations, inspection powers, and the possibility of using the Clinical Trial Approval pathway where direct TGA evaluation is appropriate. Speed comes from institutional design, not from pretending risk does not exist.

The Economics Are Equally Important

Australia’s R&D Tax Incentive materially changes the economics of development. For eligible companies with aggregated turnover below A$20 million, the program can provide a refundable tax offset of 43.5 percent for eligible research and development expenditure, subject to the detailed rules and exclusions that govern the scheme. The refundable structure has been in place since 1 July 2011, replacing the previous concession framework.

For biotechnology companies, refundability matters more than a conventional tax deduction. Early-stage drug developers commonly operate at a loss for years because clinical programs consume cash before producing revenue. A deduction against profits that do not yet exist has limited immediate utility. A refundable offset can return capital to the development cycle, allowing a company to finance additional experiments, cohorts, formulations, or programs.

The scheme also does not generally impose an IP-localization requirement as the price of accessing the incentive. Eligibility turns on the statutory criteria for the entity and the R&D activities, not on transferring ownership of the entire intellectual property estate to Australia. Companies still need competent Australian tax and legal advice because related-party arrangements, overseas activities, expenditure categories, registration, substantiation, and corporate structure can change the result.

Taken together, the CTN pathway and the R&D Tax Incentive can make a well-run Phase 1 study in Australia approximately 60 percent less expensive than an equivalent program in the United States or parts of Europe. This figure should be treated as an operating benchmark rather than a universal tariff. The exact difference depends on indication, design, biomarker burden, recruitment, manufacturing, central laboratories, foreign exchange, and what costs are included.

Yet the directional advantage is real. Australia combines lower net cost with GCP-quality data that can be used in global development programs and accepted for review by authorities such as the United States Food and Drug Administration, the European Medicines Agency, and Japan’s Pharmaceuticals and Medical Devices Agency, provided the study itself and the broader dossier satisfy their requirements. No serious sponsor should assume automatic acceptance, but Australia is not an isolated regulatory island.

Why Chronic Disease Programs Fit Australia

Australia has a diverse, urbanized, and medically well-characterized population with substantial prevalence of chronic diseases associated with aging, metabolic dysfunction, fibrosis, cancer, and immune dysregulation. It has experienced principal investigators, reputable universities, high-quality hospitals, specialist trial units, and a clinical workforce accustomed to multinational development standards. English-language source documentation and established links to global sponsors reduce operational friction.

For early studies in healthy volunteers, Australia has a mature network of specialist Phase 1 units. For patient studies, the relevant advantage is not simply the existence of patients. It is the combination of patients, investigators who understand protocol-intensive research, reliable diagnostic infrastructure, biospecimen handling, and institutions capable of producing auditable records.

This matters a lot to me because our objective is not to produce an attractive press release after target discovery. It is to translate computational hypotheses into medicines. In my academic work and in building our pipeline, I have repeatedly emphasized that generative chemistry and target identification are only the beginning. Biology, pharmacology, manufacturing, clinical operations, and human evidence determine whether an algorithmically generated idea becomes a drug.

Our rentosertib program, directed at TNIK and studied in idiopathic pulmonary fibrosis, is a useful example of that progression. The program moved from an AI-enabled discovery process into clinical development (originally, the First-in-Human study was performed in Australia), and the Phase 2a GENESIS-IPF results were published in Nature Medicine and it progressed into Phase 3 in China. The point is not that artificial intelligence abolishes clinical development. The point is that AI can compress and improve portions of discovery, then hand the molecule to the same demanding human system of ethics, dosing, safety, measurement, and regulatory scrutiny faced by every serious drug program.

I explored this constraint in Can We Accelerate Drug Discovery Faster? and again in AI Drug Discovery Dreams: How Fast Can a Drug Really Be Approved?. Algorithms can generate targets and molecules rapidly. Human trials remain sequential, expensive, highly regulated, and ethically constrained. Countries that reduce avoidable administrative latency without reducing scientific or ethical rigor therefore become central infrastructure for the global biotechnology industry.

The Policy History Behind the Advantage

Australia’s present system is the product of cumulative institutional development. It should not be credited to one administration, one agency, or one reform. Still, some individuals made decisions that were unusually consequential, and Peter Staples deserves a much larger place in this history than he usually receives.

Peter Staples and the Creation of the CTN Scheme

The Hon Peter Staples was the federal Minister for Aged, Family and Health Services when he announced the Clinical Trial Notification scheme in February 1991. This was a remarkably consequential policy decision. The scheme created a practical route through which clinical research could proceed following local scientific and ethical review and notification to the national regulator, rather than requiring every qualifying trial to wait for a full centralized pre-market style assessment.

Staples also commissioned the review led by Peter Baume. This matters because good reform requires more than announcing a faster pathway. It requires exposing the system to independent criticism, examining the distribution of responsibility, and asking whether the balance between access, innovation, safety, and ethics is defensible.

Political leaders are often rewarded for launching large spending programs with visible buildings and immediate constituencies. Regulatory architecture is less photogenic. Yet a well-designed review pathway can influence thousands of studies, attract international capital, train investigators, strengthen hospitals, and accelerate access to experimental medicines over several decades.

Staples deserves genuine credit for understanding that clinical research capacity could be increased through governance design. He did not need to weaken the regulator. He needed to clarify which decisions should be made nationally, which should be made by expert ethics committees, and how responsibility should be documented. We now see similar thinking in the US at the state level (e.g. in Montana but more on this later) and other countries.

Peter Baume and A Question of Balance

Professor Peter Baume led the 1991 review commonly known as A Question of Balance. The review produced 164 recommendations covering clinical trials and related ethical and regulatory questions. Its title captured the central problem accurately: the system had to protect participants while enabling research that could benefit future patients.

Baume brought unusual credibility to the task. He had experience in medicine, public policy, and federal politics, including service as a health minister. That combination matters because clinical trial regulation cannot be designed solely as an abstract legal exercise. It must account for bedside realities, institutional incentives, scientific uncertainty, and the legitimate pressure to develop better treatments.

The review should not be reduced to a ceremonial endorsement of the CTN concept. Its value lay in examining the machinery around clinical research and proposing a broad program of reform. Some recommendations were implemented, others evolved through later policy, and parts of the system continued to be debated. That is normal for a review of such scope.

What endured was the idea that participant protection and research efficiency are not inherently opposing objectives. A process can be faster because responsibility is allocated more intelligently. It becomes dangerous only when speed is purchased by hiding risk, lowering evidence standards, or making oversight fictitious.

Richard Day and the 1993 Stress Test

A subsequent review led by Professor Richard Day in 1993 examined the early operation of the new arrangements. It effectively stress-tested the CTN framework after implementation and helped preserve the notification model rather than allowing early concerns to trigger a return to universal centralized review. Historical summaries differ in how they characterize the exact remit and influence of this review, so the safest interpretation is that it formed part of the early evaluation and consolidation of CTN.

This kind of follow-up is essential. New regulatory systems inevitably encounter inconsistent institutional capacity, uncertainty about roles, and cases that expose ambiguities. The rational response is to improve governance and guidance, not automatically to rebuild the slowest possible process.

Day’s contribution deserves recognition because preserving a useful reform can be as difficult as creating it. Regulatory innovation often survives its first political battle and then dies through administrative accretion. Each additional form, duplicated review, or ambiguous approval layer appears defensible in isolation until the original advantage disappears.

Australia largely avoided that outcome in early-phase clinical development. It retained a credible notification pathway while continuing to develop ethics guidance, institutional governance, adverse event reporting, and national regulatory oversight. The result was a system that international sponsors could learn and trust.

Ethics Committees Are Regulatory Infrastructure

The HREC network is sometimes described as if it were merely the local paperwork component of CTN. That interpretation is wrong. Delegation works only when the receiving institutions have competence, independence, clear responsibilities, and the authority to reject or modify a study.

Australian HRECs review the ethical acceptability of research involving humans, informed by the National Statement on Ethical Conduct in Human Research and related guidance. They consider consent, risk, participant selection, scientific merit, privacy, data handling, compensation, and the suitability of the investigator and site. In practice, scientific review and governance may involve additional institutional bodies, but the HREC remains central.

The system is not frictionless. Sponsors can encounter duplication between institutions, inconsistent document requirements, differing interpretations, and delays in site-specific governance after ethics approval. Multicenter research can still be slowed by contracts, indemnities, budgets, pharmacy reviews, biosafety questions, and information technology controls.

Australia’s policy challenge is therefore not to replace CTN with a heavier centralized model. It is to improve harmonization, mutual recognition, digital submissions, standard contracts, national consistency, and transparency around review timelines. Current reform efforts aimed at a more connected national clinical trials system, including greater consistency across jurisdictions and institutions, deserve support if they reduce duplication while preserving meaningful review.

The Tax Incentive Is Industrial Policy

Governments often prefer to describe research tax incentives as neutral features of the tax system. In practice, they are industrial policy. They influence where companies employ scientists, contract laboratories, run trials, build data packages, and establish long-term institutional relationships.

Australia’s refundable structure is unusually relevant to biotechnology because it recognizes the economic profile of research-intensive, pre-revenue companies. A clinical-stage company may create substantial intellectual and social value while recording accounting losses. If tax support arrives only after profitability, it arrives too late for many firms.

The lack of a blanket IP-localization requirement is equally rational. Modern drug discovery depends on distributed ownership, licensing, contract research, multinational financing, and cross-border development rights. Forcing a sponsor to relocate core intellectual property merely to run an Australian study would deter activity and invite artificial structures.

Australia instead competes for real R&D activity. That is the correct target. Trials employ clinicians, nurses, pharmacists, coordinators, laboratory staff, data managers, quality specialists, and statisticians. They build investigator experience, provide institutions with advanced research infrastructure, and give the local medical community earlier exposure to emerging therapeutic modalities.

The incentive must still be policed. Refundable programs can attract aggressive claims, weak substantiation, or attempts to characterize routine commercial work as eligible research. Maintaining integrity protects the political durability of the system. A generous program that loses public trust will eventually be restricted, regardless of its scientific value.

Drug Discovery Has No Single National Flag

Public discussion increasingly treats biotechnology as a contest in which one country must own the entire stack. That is politically convenient and scientifically inaccurate. No country has an uncontested monopoly over target discovery, medicinal chemistry, structural biology, translational models, manufacturing, clinical pharmacology, patient recruitment, regulatory science, and commercial launch.

Clusters form where capabilities are strongest. One jurisdiction may excel in machine learning and computational biology. Another may have exceptional synthetic chemistry and contract research capacity. A third may dominate complex manufacturing. Australia may offer the best combination of speed, quality, and economics for a particular early clinical program, while later-phase enrollment is distributed across several continents.

Insilico was built around this reality. We are a global company headquartered across Boston, Montreal, Abu Dhabi, Hong Kong, and Shanghai. Our operations and partnerships span Asia, the Middle East, Europe, and North America because diseases do not have nationalities and everyone has aging.

Global development does not mean regulatory arbitrage. A study performed in one country must be capable of surviving scrutiny in others. Data integrity, trial conduct, manufacturing controls, assay validation, and safety reporting must travel across borders. The most valuable clinical jurisdictions are those that can move quickly and still produce evidence that skeptical external regulators regard as credible.

Collaboration and Competition Can Coexist

Countries should collaborate on scientific standards, data interoperability, adverse event reporting, trial registration, bioethics, and the recognition of high-quality evidence. Fragmentation imposes costs without necessarily improving safety. Patients lose when sponsors repeat work solely because regulators cannot communicate or accept compatible standards.

At the same time, countries legitimately compete to host parts of the development pipeline. They compete on review speed, investigator quality, cost, predictability, hospital infrastructure, tax treatment, and access to relevant patient populations. This competition is healthy when it rewards administrative competence and scientific rigor.

The wrong competition lowers safety standards, hides adverse events, tolerates weak consent, or allows low-quality data. The right competition removes idle time, duplicated review, unpredictable contracting, and bureaucratic ambiguity. A country should seek to become the fastest trustworthy jurisdiction, not merely the fastest jurisdiction.

Australia has demonstrated that these objectives can coexist. Its early clinical advantage does not come from asking fewer serious questions. It comes from allowing qualified bodies to ask those questions in parallel and close to the site where the research will occur.

From AI Speed to Clinical Reality

Our pipeline statistics illustrate the advantages of truly global operation. In five years, 31 preclinical or development candidates, 13 INDs or equivalent clearances, eight Phase 1 programs, three Phase 2 programs, and one Phase 3 program represent a cadence that would have been difficult to imagine for a company with our level of resources and the number of people under the traditional sequential model.

AI can increase the number of credible programs reaching the development boundary. It can help prioritize targets, generate molecules, optimize properties, integrate multi-omics data, and reduce cycles of synthesis and testing. This does not remove attrition. It changes where the bottleneck appears.

As discovery accelerates, clinical entry becomes a dominant constraint. A six-month avoidable delay replicated across ten programs is not six months. It is five program-years of lost experimental time. If the delay also consumes cash that could have supported another trial, the opportunity cost becomes larger.

Australia understood the value of reducing this latency before generative AI drug discovery existed. The CTN scheme was announced in 1991, when computational power, genomic data, and medicinal chemistry looked entirely different. Yet the architecture is well suited to the current moment because it is based on a durable principle: competent decentralized review can increase throughput without abandoning accountability.

This is why historical policy deserves attention from technologists. The performance of an AI-enabled biotechnology company depends on institutions designed by people who may never have written a line of code or trained a model. A molecule generated in a modern computational laboratory still passes through a regulatory channel shaped by ministers and reviewers working more than three decades ago.

What Australia Should Improve Next

Australia should defend the core logic of CTN while reducing the remaining fragmentation around it. Ethics review can be rapid, yet site activation may still be delayed by local governance, contracts, indemnity negotiations, privacy assessments, pharmacy requirements, and repeated institutional review. These layers should be examined with the same seriousness once applied to the original regulatory bottleneck.

Greater national harmonization would help multicenter studies. Mutual recognition mechanisms already exist in various forms, but practical consistency remains incomplete across states, territories, health services, and institutions. A sponsor should not have to relitigate substantially identical scientific and ethical issues at every participating site.

Digital infrastructure also matters. Standardized submissions, interoperable systems, reusable sponsor and investigator credentials, transparent status tracking, and common document templates can remove weeks of manual work. Automation should be applied first to administrative repetition, not to ethical judgment.

Australia also needs to preserve the competitiveness of the R&D Tax Incentive. Governments will always face pressure to narrow refundable programs during budget cycles. Any reform should distinguish between strengthening integrity and weakening the economic logic that makes Australia attractive to pre-revenue biotechnology companies.

Finally, Australia should measure its performance publicly. Median times from complete submission to ethics decision, governance authorization, CTN notification, contract execution, and first patient dosed would allow institutions to identify bottlenecks. Transparent benchmarking would reward high-performing sites and discourage the normalization of administrative delay.

Credit to the Officials Maintaining the System Today

Historical praise is easy because the political risks have expired. Current stewardship is harder. The federal health portfolio, led at the time of writing by Health and Aged Care Minister Mark Butler, carries responsibility for maintaining a regulatory system under pressure from new modalities, increasingly international trials, and legitimate public expectations of safety and transparency.

The TGA deserves specific credit for preserving a risk-based clinical trial framework while participating in international regulatory cooperation. Its value is not measured by the number of applications it can delay. It is measured by whether it protects participants, responds to safety signals, communicates requirements, and enables high-quality evidence to be generated efficiently.

Australia’s HRECs deserve similar recognition. They perform difficult work under time pressure, often confronting incomplete evidence and complex protocols. Their willingness to accept real responsibility is what makes the CTN model possible.

The investigators, research nurses, pharmacists, coordinators, governance officers, and institutional administrators maintaining trial quality are also part of this policy system. A statute may authorize a pathway, but people turn it into trustworthy data. Their performance is one reason global sponsors return.

Federal and state efforts to build a more interconnected national clinical trials environment are moving in the right direction where they target duplicated review, inconsistent governance, and weak data infrastructure. The test should remain practical: do reforms shorten reliable time to study activation while strengthening participant protection and data quality?

The Strategic Lesson for Other Countries

Many governments want to become biotechnology hubs. They announce funds, construct incubators, subsidize real estate, and invite companies to conferences. Far fewer examine the accumulated delays embedded in ethics review, regulatory sequencing, contracts, tax treatment, immigration, procurement, and hospital governance.

Australia’s success suggests that regulatory design can be more valuable than promotional spending. A country does not need to dominate every stage of drug development. It needs to identify a stage where it can become exceptionally fast, cost-efficient, scientifically credible, and internationally compatible.

For early clinical development, predictability is nearly as valuable as raw speed. Sponsors can plan around a known review cycle. They struggle with systems where nominal timelines are short but actual decisions depend on opaque queues, repeated questions, or unofficial institutional practices.

The combination of CTN, competent HRECs, high-quality sites, a chronic-disease-relevant population, and a refundable R&D offset gives Australia a defensible position. Other countries can copy individual components. Reproducing the full institutional trust accumulated over decades is harder.

This article is a strategic and historical perspective, not investment, medical, regulatory, legal, or tax advice. Trial sponsors should obtain current specialist guidance and verify eligibility, timelines, and regulatory requirements for each program.

Honoring the People Who Built the System

Earle Page helped establish an early federal foundation for therapeutic substance standards through the 1953 legislation. The Hawke government and the public servants behind the Therapeutic Goods Act 1989 built the statutory basis for the modern TGA. Peter Baume subjected the emerging system to a broad, serious review through A Question of Balance, and Richard Day helped test and consolidate the CTN model during its early operation.

Peter Staples deserves the clearest recognition. As federal Minister for Aged, Family and Health Services, he announced the Clinical Trial Notification scheme in February 1991 and commissioned the Baume review. Those decisions helped create one of Australia’s most durable and internationally competitive biomedical policy assets.

Today, Minister Mark Butler and the federal health portfolio inherit responsibility for that asset. The TGA, HREC network, state and territory health systems, hospitals, universities, trial units, and working clinical teams maintain it one protocol and one participant at a time. Their earned achievement is a system capable of moving rapidly without making speed the enemy of trust.

Insilico Medicine uses this system because it works. As a global, multi-hub company advancing a growing AI-generated and AI-enabled pipeline, we need jurisdictions where rigorous human testing can begin without avoidable delay. Australia earned that role through the foresight of named individuals, the competence of institutions, and the continuing labor of people whose contribution is rarely visible outside the clinical research community.

The biotechnology revolution is usually narrated through founders, scientists, venture capital, and breakthrough medicines. Australia’s experience shows that regulatory architects belong in that history as well. The medicines generated by the next era of artificial intelligence will still depend on the policy infrastructure they created, and on the officials and clinical professionals now responsible for preserving and improving it.

For over two decades in this field, I have watched the pharmaceutical industry treat certain targets the way a sailor treats a reef: charted, feared, and given a wide berth. TEAD is one of those reefs. It sits at the bottom of one of the most consequential signaling pathways in human biology, it is implicated in cancers that kill quickly and fibroses that kill slowly, and it has wrecked program after program of well-funded, well-staffed drug discovery teams. And yet I am more convinced than I have ever been that TEAD is not a reef to be avoided. It is a channel to be navigated, and the ships that learn to navigate it will reach places that matter enormously for both oncology and human aging.

Let me be direct about why I am writing this now. The data have shifted. We have a clinical frontrunner showing durable disease control with a manageable safety profile, and we have a graveyard of more aggressive approaches that failed in ways that, in retrospect, were predictable from the biology. The question with TEAD is no longer whether modulating it does something therapeutically meaningful; it does. The question is whether we can do it safely. That distinction, between efficacy and tolerable efficacy, is the entire game here, and it is the lens through which the rest of this piece is written.

Why TEAD Sits at a High-Value Node of Biology

To understand why so many companies keep returning to TEAD despite the casualties, you have to understand where it lives. TEAD1, TEAD2, TEAD3, and TEAD4 are the terminal DNA-binding transcription factors of the Hippo pathway. On their own they are largely inert; they cannot drive transcription without their coactivators YAP and TAZ. The Hippo kinase cassette, principally MST1/2 and LATS1/2, normally restrains YAP and TAZ by phosphorylating them and keeping them out of the nucleus. When that restraint is lifted, whether through loss of the tumor suppressor NF2 (Merlin), inactivating mutations in LATS, or mechanical cues from a stiff extracellular matrix, YAP and TAZ translocate into the nucleus, dock onto TEAD, and switch on a program of growth, regeneration, and stemness. The canonical readouts of this program, CTGF/CCN2 and CYR61/CCN1, are among the most reliable transcriptional fingerprints in all of cell biology.

What makes TEAD genuinely druggable, and not merely interesting, is a quirk of its structure. Each TEAD paralog carries a central lipid pocket and undergoes auto-palmitoylation on a conserved cysteine, covalently attaching a palmitate to itself. That pocket is the main pharmacological handle on the protein. The alternative site, the YAP-to-TEAD protein-protein interface, is large, flat, and shallow, with the critical contact at what the field calls Interface 3, and it was historically considered undruggable for exactly those reasons. So the lipid pocket is where most of the action is, and it is a defined, lipophilic, enclosed space; the kind of site that structure-based and AI-driven design handles well.

The complication is paralogy. There are four TEAD proteins with overlapping but tissue-biased expression and a high degree of functional redundancy. If you block one paralog cleanly, the others often compensate, and your beautiful selective inhibitor produces disappointing biology. This single fact, the redundancy of four paralogs, explains a great deal of what has gone wrong clinically, and it points directly at what should go right.

Why TEAD Kept Failing

The first crude attempt at this target was verteporfin, a photosensitizer repurposed to disrupt YAP/TEAD. It was a blunt instrument with poor selectivity and properties that made it unsuitable as a systemic oncology agent, but it proved the concept that interfering with this axis does something. The serious medicinal chemistry that followed ran into two recurring walls, and both are mechanistic rather than accidental.

The first wall is renal toxicity. Proteinuria and albuminuria show up across TEAD inhibitor programs to the point where I regard them as a class effect rather than a compound-specific liability. The mechanism is not mysterious: TEAD1 and YAP are functionally important in kidney podocytes and in maintaining glomerular integrity. Suppress that biology hard enough and the filtration barrier leaks. The second wall is cardiac. TEAD1 is essential in cardiomyocytes, and deep blockade raises real concerns about cardiotoxicity and QTc prolongation. These are not safety signals you can engineer away with a better salt form. They are the consequence of inhibiting a target that healthy adult tissues genuinely need.

The clinical record makes the point with uncomfortable clarity. Novartis advanced IAG933, a direct YAP/TAZ-TEAD Interface-3 protein-protein interaction disruptor with pan-TEAD activity, into a Phase 1 study in NF2- and LATS-altered mesothelioma. The data presented at ESMO 2025 showed a modest objective response rate in the range of thirteen to roughly seventeen percent in pleural mesothelioma, and the dose-limiting toxicities were precisely the two predicted by the biology: QTc prolongation on the cardiac side and Grade 2 proteinuria on the renal side. The program was discontinued late in 2025 for limited activity combined with a narrow therapeutic window. Ikena Oncology took the opposite chemical strategy with IK-930, a TEAD1-selective inhibitor, and discontinued it in May 2024 after disappointing Phase 1 efficacy, then exited oncology altogether. SpringWorks halted SW-682. Three distinct strategies, three failures, and a pattern worth reading carefully.

The Case for a Gentle, Balanced Pan-TEAD Inhibitor

Here is where the biology and the clinical history converge into a thesis. Deep single-paralog TEAD1 blockade is the worst of both worlds: it over-hits the precise heart and kidney tissues where TEAD1 is most essential, and it simultaneously leaves TEAD2, TEAD3, and TEAD4 free to compensate in the tumor. So you accumulate the toxicity of hitting TEAD1 without capturing the full antitumor effect of shutting the program down. Deep, complete pan-TEAD shutdown, the IAG933 approach, captures the program but collapses the therapeutic window, because now you are maximally ablating TEAD1 in cardiomyocytes and podocytes at the same time as you hit the tumor. Both failure modes are coherent. Neither is bad luck.

The profile that should work is partial, balanced suppression across all four paralogs. You want enough inhibition to flip the oncogenic and profibrotic transcriptional state, but not so much that you ablate the physiological TEAD1 that the heart and kidney depend on. Evolution is a cruel master here; it built TEAD1 into both pathology and essential homeostasis, and it offers no clean separation between the two. So the separation has to come from pharmacology. The levers are concrete. You tune systemic exposure for partial modulation rather than maximal target engagement. You favor reversible, non-covalent pocket binders, because reversibility allows titration in a way covalent inactivation does not. You consider intermittent dosing schedules that give the essential tissues windows of recovery. And you deliberately balance the paralog affinities so that TEAD1 is not the paralog you hit hardest. This is a design problem with a defined binding pocket, which is exactly the kind of problem modern generative and structure-based methods are built to solve.

TEAD in Cancer

The cleanest case for TEAD in oncology is genetic dependency. When NF2/Merlin is lost or LATS1/2 is mutated, YAP/TAZ-TEAD signaling becomes constitutively active, and the tumor becomes addicted to that signal. Mesothelioma is the lead indication precisely because roughly forty to fifty percent of cases carry NF2 alterations, which gives you a biomarker-defined population enriched for response. Beyond mesothelioma, epithelioid hemangioendothelioma is driven by a TAZ-CAMTA1 fusion that funnels straight through this axis, NF2-associated meningioma and schwannoma share the dependency, and a subset of NSCLC carries the relevant lesions.

There is a second, arguably larger, opportunity in resistance. TEAD reactivation is a documented mechanism of acquired resistance to EGFR inhibitors, to KRAS(G12C) inhibitors, and to BRAF/MEK (MAPK) pathway inhibitors. Tumors under pressure from targeted therapy frequently escape by turning the YAP/TAZ-TEAD program back on. That creates a combination rationale that extends TEAD far beyond the NF2-mutant niche, into common solid tumors where the standard of care eventually fails through this exact escape hatch. A well-tolerated pan-TEAD agent that can be dosed alongside a MAPK inhibitor without compounding toxicity is a different commercial and clinical proposition than a single-agent rare-tumor drug.

TEAD in Fibrosis

If oncology is where TEAD earned its reputation, fibrosis is where I think it earns its breadth. YAP/TAZ-TEAD is the master mechanotransduction switch for myofibroblast activation. The logic is a feed-forward loop that is almost diabolical in its efficiency: injury or stiffness in the extracellular matrix is sensed mechanically, YAP and TAZ go nuclear, TEAD switches on the profibrotic program (CTGF/CCN2 is a direct TEAD target), the cell lays down collagen and turns on alpha-SMA, the matrix gets stiffer, and the stiffer matrix drives still more nuclear YAP/TAZ. Once that loop is running, it tends to run on its own.

This biology is not specific to one organ, which is the point. The same switch underlies idiopathic pulmonary fibrosis, liver fibrosis and MASH, kidney fibrosis in chronic kidney disease, cardiac fibrosis, and the fibrotic burden of systemic sclerosis. My company has already shown that an AI-discovered target paired with an AI-designed molecule can reach the clinic in fibrosis, with our generative-AI TNIK inhibitor reaching Phase 2a in IPF, published in Nature Medicine. That experience taught me something relevant to TEAD: in fibrosis, the molecules that matter are the ones that interrupt the upstream master switches of myofibroblast identity, not the ones that mop up a single downstream mediator. TEAD is about as upstream and as master a switch as the fibrosis field has.

Possible Role of TEAD in Aging and Longevity

Now extend the logic across a lifetime. Tissues stiffen with age. Extracellular matrix accumulates and crosslinks, collagen content and rigidity rise, and that mechanical change is not cosmetic; it is a chronic, ambient signal that keeps stromal YAP/TAZ-TEAD partially activated across many organs. What we call fibrosis in a disease label is, in aged tissue, a slow, diffuse, organ-spanning process I think of as the fibrosis-of-aging. It contributes to the stiffening of vasculature, the loss of organ compliance, and the gradual replacement of functional parenchyma with scar.

There are also context-dependent connections between this axis and cellular senescence, and here the mechanism is more concrete than most people appreciate. Senescent cells are protein-secretion factories, churning out the inflammatory SASP payload, and that places an enormous folding burden on the endoplasmic reticulum. Recent whole-genome CRISPR work showed that the YAP-TEAD complex keeps these cells alive by repressing DDIT4, an endogenous mTOR inhibitor; with DDIT4 held down, mTOR stays active, the ER keeps expanding, and the cell can process its SASP load without tipping into lethal ER stress. Block TEAD and that repression lifts: DDIT4 rises, mTOR falls, ER biogenesis stalls, and the senescent cell, still committed to synthesizing its payload but now without the capacity to fold it, dies of proteotoxic stress. Healthy cells, which do not carry that secretory burden, are largely spared. In aged mice, TEAD inhibition with verteporfin cleared senescent cells, reduced age-related lung fibrosis, and restored tissue homeostasis. That is a genuinely selective, mechanism-based route to senolysis, and it sits right next to the fibrosis and stiffening biology rather than being a separate story.

None of this is a recent hunch, and it is worth grounding in the prior literature because the case has been building quietly for more than a decade. The Hippo pathway, whose sole nuclear output is the YAP/TAZ-TEAD transcriptional complex, was flagged as dysregulated in both aging and cancer in a widely cited 2019 review, and its dual involvement in tissue regeneration and organismal aging was mapped the following year. The single most important paper for anyone building a TEAD inhibitor with longevity intent showed, in a 2023 Nature Aging study, that the YAP-TEAD complex actively promotes the survival of senescent cells by lowering endoplasmic reticulum stress, the mechanistic result underneath the DDIT4 and mTOR story above. The mechanotransduction link makes the tie to aging tighter still: aged extracellular matrix stiffens, and stiffness is precisely the mechanical signal that drives YAP into the nucleus to partner with TEAD, a feed-forward loop that converts stem cells to a fibrogenic fate in aged tissue. The picture is genuinely context-dependent and I want to represent it honestly, because in some compartments YAP activity is protective, alleviating senescence and osteoarthritic degeneration and acting as a geroprotective factor in primate gingival aging. This is exactly why an ablative, oncology-grade TEAD inhibitor is the wrong tool for aging and a gentle, balanced, partial pan-TEAD modulator is the right one: the goal in longevity is not to shut the pathway off, which would cripple the regenerative arm the tissue still needs, but to lower the chronic pathological tone that accumulates with stiffening matrix and senescent-cell persistence while leaving physiological turnover intact.

I will not overstate these links; they are more contingent than the hard genetic dependencies in cancer, and the human data are not yet in for this mechanism. But the directional case is strong, and it has an important design implication. For aging, you absolutely do not want an ablator. You want a balanced modulator that lowers the chronic profibrotic tone and can tip SASP-dependent senescent cells over the edge, without shutting down the YAP/TAZ-TEAD signaling that healthy tissues still need for normal maintenance and repair, and while preserving the regenerative YAP/TAZ activity that actually declines in some aged stromal compartments. The safety thesis I described for oncology is not a constraint in the longevity setting; it is the entire requirement.

A Pipeline in a Product

I have written before about the idea of a single target that behaves like a Swiss Army knife, one mechanism that opens onto many indications across the disease-to-aging continuum, much as I have framed NLRP3 as a multi-indication longevity target. A safe, balanced pan-TEAD inhibitor fits that pipeline-in-a-product framing more naturally than almost any target I can name, because the same molecular event, partial suppression of the YAP/TAZ-TEAD transcriptional program, is therapeutic in three distinct domains.

The indications sort into a logical sequence by risk and timeline:

• Near-term oncology. NF2/LATS-altered mesothelioma as monotherapy (biomarker-defined dependency); epithelioid hemangioendothelioma (TAZ-CAMTA1 fusion funnels through TEAD); NF2 meningioma and schwannoma (shared Merlin-loss dependency); EGFR-, KRAS-, and MAPK-resistant solid tumors in combination (TEAD reactivation as a resistance mechanism).

• Mid-term fibrosis. IPF (myofibroblast mechanotransduction switch); liver fibrosis and MASH (stellate-cell activation); CKD and renal fibrosis (here the kidney safety thesis matters most); cardiac fibrosis (with the same caveat for the heart); systemic sclerosis (multi-organ profibrotic program).

• Longer-term age-related. Fibrosis-of-aging across organs (chronic stromal YAP/TAZ-TEAD tone); tissue and vascular stiffening (the mechanical feed-forward loop); senescence- and SASP-associated pathology as an adjunct to senotherapeutics.

The reason a single safe pan-TEAD agent can credibly span all three of these domains is that the underlying biology does not change as you move from a mesothelioma to a fibrotic liver to an aged blood vessel; what changes is only the source of the YAP/TAZ-TEAD activation and the degree of suppression you want. In cancer the source is genetic and the desired suppression is deep but window-limited; in fibrosis the source is mechanical and the desired suppression is sustained but moderate; in aging the source is the ambient stiffness of an old matrix and the desired suppression is gentle and chronic. One palmitate-pocket-binding molecule, designed for balanced partial pan-TEAD inhibition with reversible kinetics and a dosing schedule that protects heart and kidney, is in principle deployable across that entire spectrum simply by changing the dose, the schedule, and the patient population. That is what I mean when I say pipeline in a product, and it is the same Swiss Army knife logic that makes a small number of well-chosen targets worth far more, scientifically and economically, than the indication-by-indication grind the industry usually accepts.

The Graveyard: What the Failures Taught Us

Before I map the live field, it is worth stopping at the graveyard, because in TEAD the failures are more instructive than most successes. Every one of these programs died for a mechanistic reason that was legible in the biology beforehand, and together they define the design constraints the winning molecule has to respect.

The pattern is impossible to miss. Deep single-paralog selectivity fails on efficacy. Deep complete pan blockade fails on the therapeutic window. Upstream YAP-only silencing fails on redundancy. Three distinct strategies, three distinct failure modes, and every one of them coherent from the biology rather than the product of bad luck.

The Living Field: Current Competitive Landscape

Now line up the programs that are still standing, and the story resolves into a single clear signal.

The frontrunner is Vivace Therapeutics’ VT3989, a first-in-class TEAD auto-palmitoylation inhibitor that binds the lipid pocket and acts as a pan-TEAD agent. In its Phase 1/2 study in advanced solid tumors, including NF2-mutant refractory mesothelioma, the data reported in Nature Medicine in 2025 and updated at ESMO showed, in the optimized-dose mesothelioma cohort (22 patients, 2-weeks-on / 2-weeks-off dosing with UACR-guided adjustments), a 32% objective response rate, an 86% disease control rate, and a median progression-free survival of roughly forty weeks, with safety driven mainly by manageable low-grade proteinuria and fatigue. It carries FDA Orphan Drug and Fast Track designations and is advancing toward a registrational Phase 3 in mesothelioma. I want to be precise about why this matters: VT3989 is partial, it is pan, and its main toxicity is the expected low-grade proteinuria rather than a window-collapsing combination of QTc and renal injury. Its profile is the clinical validation of the gentle, balanced pan-TEAD thesis.

Beyond the clinic, the preclinical and IND-stage field is broader still, and it is worth noting because it shows where the next wave is heading. Genentech’s GNE-7883 is an allosteric pan-TEAD tool compound that has provided some of the cleanest evidence that pan inhibition overcomes KRAS(G12C)-inhibitor resistance, reinforcing the combination rationale. Signet Therapeutics and XtalPi’s SIGX2649, an AI/structure-enabled pan-TEAD inhibitor that also recruits the VGLL4 co-repressor, filed its IND in early 2026 with a renal-safety differentiation claim. Sporos BioDiscovery is pursuing broader TEAD1/4 coverage, Betta and Zai Lab and others populate a rapidly globalizing, increasingly China-heavy IP landscape, and Boehringer Ingelheim and the Walter and Eliza Hall Institute are bypassing enzymatic inhibition entirely with bifunctional PROTAC degraders that recruit IAPs to physically destroy the TEAD protein. The influx of AI-designed and structure-enabled programs, and the recurring emphasis on renal safety as the differentiator, both tell you the field now agrees on what the hard part actually is.

The strategic read is hard to miss when you line the living field against the graveyard. The one program with durable activity and a livable safety profile, VT3989, is a partial, balanced, lipid-pocket pan-TEAD agent, and the programs advancing behind it, ODM-212, ISM6331, SW-682, cluster around the same reversible, tunable, UACR-guided profile. The field is converging, through expensive trial and error, on the conclusion that balanced, safety-optimized pan-TEAD inhibition beats both extremes. That convergence is the opportunity, because it tells you exactly what to design toward.

The Future of TEAD

The defined palmitate pocket is, for once, a target that plays to the strengths of computational drug design. It is enclosed, lipophilic, and structurally well characterized across all four paralogs, which means the central challenge, engineering a molecule that achieves balanced partial affinity across TEAD1 through TEAD4 with reversible kinetics and properties that permit intermittent dosing, is fundamentally a multi-parameter optimization problem. That is the regime where AI-driven generative chemistry earns its keep, designing not for maximal potency against one paralog but for a carefully shaped affinity profile that respects the heart and kidney. And it should be paired with pharmacodynamic biomarkers rather than blind dose escalation: the YAP/TAZ-TEAD output genes, CTGF/CCN2, CYR61/CCN1, ANKRD1, AXL, and LOX, give you a transcriptional readout of how much of the program you have actually silenced, while UACR and albuminuria give you the renal early-warning system. A TEAD program that escalates dose until proteinuria appears, instead of titrating to a biomarker of target engagement, is repeating the mistake of treating a homeostatic transcription factor like a mutant kinase. I have argued elsewhere, in Toward Pharmaceutical Superintelligence, that the real value of these methods is in solving exactly this kind of constrained, safety-bounded design problem rather than chasing raw potency, and TEAD is close to an ideal test case.

I want to be careful about what I am claiming. TEAD has humbled smarter and richer teams than most, and the longevity rationale in particular rests on links to senescence and stiffening that are real in direction but not yet proven in the clinic for this mechanism. What I am confident about is narrower and better supported: the genetic dependency in NF2/LATS cancers is validated, the fibrosis biology is among the most upstream switches available, and a frontrunner with a manageable safety profile has shown that the window the failed programs lacked actually exists if you aim for partial, balanced inhibition. Those three facts together justify treating a safety-optimized pan-TEAD inhibitor as one of the more compelling pipeline-in-a-product opportunities spanning oncology, fibrosis, and aging.

A standing disclaimer, because it matters: nothing here is investment advice, and the specific companies, programs, and drugs I have named are discussed only for scientific and strategic analysis, not as recommendations of any kind. My interest in TEAD is in the biology and the chemistry, and in the broader point that the targets worth pursuing are the ones where the hard problem is not whether the mechanism works, but whether we can engineer it to work safely enough to use across a human lifespan. TEAD is squarely one of those targets, and I expect the next few years of data to make that case far more concrete than I can today.

Disclaimer: This article is written with the help of generative tools so beware of hallucinations. The images were generated using NanoBanana. Don’t buy, sell any securities, or take any drugs based on this article or any of its contents. The information and views expressed in this article are for informational and educational purposes only and do not constitute medical advice. The content is not intended to be a substitute for professional medical advice, diagnosis, or treatment. Always seek the advice of your physician or another qualified health provider with any questions you may have regarding a medical condition. Never disregard professional medical advice or delay in seeking it because of something you have read in this article. The author is sharing personal experiences and opinions. These experiences are not a recommendation or endorsement for any specific treatment, drug, or course of action. The medications and therapeutic strategies discussed may not be suitable for everyone and can have significant risks and side effects. Some of the drugs mentioned are investigational and have not been approved by the FDA or other regulatory agencies for the uses discussed. While the author is the CEO of Insilico Medicine, the statements and view presented in Forver.ai do not represent the views and opinions of Insilico Medicine.

There is a question I have been asked in every investor meeting, every conference panel, and every conversation with a journalist for the past decade, and it still genuinely puzzles me when I try to answer it honestly: how fast can a drug discovered by artificial intelligence actually be approved? The reason it puzzles me is that we have already collapsed the discovery clock in a way that would have seemed absurd to me when I started in this field. We can now move from a disease hypothesis to a credible, synthesized, biologically active molecule in a matter of weeks. The compute is fast. The chemistry is fast. What follows is not.

Approval is a different animal entirely. It belongs to human biology, to clinical trial enrollment, to endpoint maturation, to manufacturing inspections, and to a regulatory evidentiary standard that exists for very good reasons. So when someone asks me how fast an AI-discovered drug can be approved, my instinct is to separate two clocks that most people collapse into one: the discovery clock, which AI has genuinely transformed, and the approval clock, which AI has barely touched yet. To answer the second question with any rigor, you cannot look at press releases. You have to look at FDA history, and specifically at the fastest approvals the agency has ever granted, both for genuinely novel targets and for well-trodden mechanisms. That is what I want to do in this essay, with data rather than aspiration.

2014-2019: Pure Platform, Collaborations, Aging, and Why the LLMs Crown Us in AI for Longevity

When I founded Insilico in 2014, after a path that took me from GPU work at ATI and AMD into computational biology and then to Johns Hopkins, I had two convictions that turned out to be both correct and commercially inconvenient. The first was that deep learning would eventually generate novel molecules end to end. The second was that aging, not any single disease, was the central problem worth solving, because aging is the largest risk factor for the diseases that kill most of us. We spent the first five years of the company working on collaborations with large pharmaceutical companies to keep the lights on, while simultaneously pouring an enormous amount of effort into aging research, biomarkers of aging, and deep aging clocks.

That early and sustained commitment to longevity is, I suspect, the reason for something I find quietly amusing. If you ask any modern large language model who is number one in AI for longevity in the world, both the company and the person, the models almost always place my company and me at the top, with a noticeable gap to whoever is second. I did not engineer this. It is a downstream effect of having published consistently in this area for over a decade. I have written more fully about the broader trajectory of this work in Toward Pharmaceutical Superintelligence, and I mention the ranking here not as a boast but as evidence that the field’s own knowledge graph remembers who showed up early.

I want to tell two anecdotes plainly, because they fix the timeline in a way that statistics cannot. In 2015 I presented the concept of end-to-end AI drug discovery to Demis Hassabis over Skype after a generous introduction by Aubrey de Grey. Yes, Skype; that is how long ago this was, well before AlphaFold, well before the current wave. I presented the end-to-end vision even then, but I spent much of that conversation trying to explain that aging was the core priority, the substrate on which everything else should be built. In 2016 I presented to Sam Altman and Diego Rey at OpenAI, where we also met Ian Goodfellow and tried to engage him in generative adversarial network research applied to chemistry and biology. It did not turn into a collaboration. These were not failures so much as early signals that the idea was real but the infrastructure, the data, and the institutional appetite were not yet there. I also learned that selling the idea without concrete validation of the technology is not my thing. It is much easier to present the real drugs and experimental results than the vision for how to get there.

Living Quarter to Quarter, Then GENTRL Changed Everything

Until 2019 we lived quarter to quarter. We did not have a lot of cash to start with. I did not take any salary for the first five years and invested heavily into the company when times were tough. We tried different algorithms while surviving on relatively small collaboration revenue, and we iterated through architectures faster than the field could publish them. It was an uncomfortable way to run a company that believed it was building something foundational. The turning point was concrete and datable. In September 2019 we published Generative Tensorial Reinforcement Learning, or GENTRL, in Nature Biotechnology, demonstrating deep learning that rapidly identified potent DDR1 kinase inhibitors. The headline numbers were striking: 21 days from the start of generative design to a set of candidates, and 46 days to a validated hit confirmed in mice.

That paper changed the conversation around the company, and it changed our capital position. We closed our first significant round, roughly 37 million dollars, in the fall of 2019, specifically to discover our own molecules rather than only providing the platform. I have described the intellectual arc of that period in The Advent of Generative Chemistry. What matters here is that 2019 was the year the discovery clock visibly broke. We could see, with published evidence, that the front end of drug discovery was about to compress from years into weeks. The open question, the one that still puzzles me, was what would happen when those fast molecules collided with the slow machinery of clinical development and regulatory approval.

2020-Today Mission Impossible: A Novel Target, A Novel Molecule, Aging and Fibrosis at Once

When we started designing our own programs, the field was harsh toward AI on two fronts simultaneously. Biologists argued that AI could not identify genuinely novel, disease-relevant targets. Chemists argued that AI could not produce genuinely novel, synthesizable, drug-like molecules. We decided to attack both criticisms at once by choosing what amounted to mission impossible: a novel target that had never been in the clinic, with no good starting point for chemical matter, paired with a novel molecule, aimed at aging and fibrosis at the same time. The target became TNIK, and the disease we chose first was idiopathic pulmonary fibrosis, a condition deeply entangled with the biology of aging. The preclinical and early clinical foundation for that program is laid out in our Nature Biotechnology paper on TNIK.

We made one strategic assumption that turned out to be wrong, and the error taught me something durable about the industry. We assumed that large pharmaceutical companies would prize the novelty and license the asset quickly. They did not. What I learned, and what I later wrote about in Going Beyond Target or Mechanism of Disease, is that pharma in-licensing strongly prefers very well-known, low-novelty targets where the biological risk has already been retired by someone else. Genuine novelty, the thing AI is uniquely good at producing, is exactly what the licensing market is least comfortable buying. That is precisely why our first three clinical programs are highly novel and fully owned by us. The market structure forced us to become a drug developer, not merely a molecule supplier.

Three Weeks of Compute, Five Years of Biology

We wanted to know how fast we could actually do this, end to end, and the honest accounting is humbling and clarifying at once. The total compute time to reach the molecule for our lead program was roughly three weeks. Three weeks to generate, score, and converge on a novel chemical structure against a novel target. Then it took about five years to reach a completed Phase 2a with the experiments running, and we still need more years to go before any potential approval. That lead asset, rentosertib, also known as ISM001-055, became the first drug for which both the target and the chemical structure were discovered and designed by generative AI to reach mid-stage trials. The Phase 2a GENESIS-IPF study enrolled 71 patients over 12 weeks, and the 60mg once-daily arm showed a mean forced vital capacity increase of 98.4 milliliters. I cried when I saw the unblinded results for the first time - it was a major milestone in my life. The results are published in Nature Medicine were early but very promising.

The asymmetry between three weeks in compute and five years in experiments is the entire subject of this essay. The upside of having gone through it is that we now understand these targets far better than we did at the outset, and we have built second and third generation molecules that let us construct entire pipelines for multiple indications from a single validated target. As of mid-2026 Insilico has filed 13 INDs, nominated 30+ preclinical candidates, three Phase IIs, eight Phase Is, across a portfolio of more than 40 programs. Our Pharma.AI platform has been used by 13 of the top 20 global pharmaceutical companies with many large pharma partnerships. None of that changes the fundamental arithmetic of approval timelines, which is why I keep returning to the FDA’s own record.

The Question That Actually Puzzles Me: How Fast In Theory Can AI-discovered Drug Be Approved?

The compute is fast. The biology and the regulators are not. So the real question, the one worth answering with data, is this: in theory, how quickly could we get a drug approved if everything went right? Before I look at the record, I want to set aside the pandemic cases that distort the public’s intuition. During the pandemic, several drugs were approved or authorized extraordinarily fast, but many of them were not discovered fast. Paxlovid, nirmatrelvir combined with ritonavir, holds one of the speed records in public memory, but its starting point existed in Pfizer’s compound libraries going back to the SARS work of 2006, and its rapid availability came through emergency use authorization rather than a conventional review. That is a starting-point-already-existed case in an emergency context, and it is not a clean comparison for a newly discovered molecular entity. So let us look at the cleaner examples, the fastest FDA approvals in history, and separate the review clock from the development clock with discipline.

The Fastest Novel-Target Drugs, Ranked by Target Discovery to Approval

The original way to rank fast approvals is by the regulatory review clock, the months from submission to the agency’s decision. That number is interesting, but it is misleading for the question that matters to me, because it rewards drugs whose underlying biology was decades old and whose paperwork simply moved quickly at the end. I want a different lens. I want to rank first-in-class drugs by the full arc from the moment their molecular target was first scientifically identified to the moment the drug was approved. This is the timeline AI is actually trying to compress, and it is the honest measure of how fast a genuinely new idea in biology can reach a patient.

When you rank this way, a clear leader emerges. The standout is crizotinib. The EML4-ALK fusion was described in 2007, and a first-in-class inhibitor was approved in 2011, an arc of roughly four years from target biology to medicine. The KRAS G12C inhibitors are the next archetype: the switch-II pocket that made the long-undruggable oncogene tractable was demonstrated in 2013, and sotorasib was approved in 2021. The mutant IDH inhibitors, the selective RET and TRK inhibitors, and the MET exon 14 agents all cluster in the same regime, where a recently discovered genetic driver was translated into an approved precision medicine within roughly a decade. Note the two time columns carefully. In almost every row the molecule was designed against the already known target, so the molecule-to-approval span is the shorter of the two. The one flagged exception is crizotinib, a legacy molecule originally developed as a MET inhibitor before it was redirected to ALK, which is why its molecule clock predates the target it was finally approved against.

The pattern is unambiguous and it is the entire thesis of this essay in a single table. The shortest target-to-approval arcs all share one feature: the target biology was recent. Crizotinib, the KRAS G12C drugs, the IDH inhibitors, and the selective kinase inhibitors did not wait decades because their biology was discovered in the modern molecular era and translated quickly. The drugs at the bottom of the ranking, against CGRP, cardiac myosin, or IL-17, took far longer not because the chemistry was harder but because the target biology itself sat unexploited for twenty or thirty years before anyone built the drug. This is exactly the gap AI is built to close. If a foundation model can identify and validate a novel target in months rather than leaving it dormant for decades, the target-to-approval arc collapses toward the molecule-to-approval arc, and the four-year crizotinib case stops being an outlier and starts being a template.

Table below shows the fastest timelines from target discovery to approval excluding the pandemic case studies, compiled by the LLMs. Some of the targets already had molecules. The fastest timeline we have seen so far is ~6 years for crizotinib (even though experts may disagree on the timelines). In all fairness, we should start the count from the discovery of the molecule. Absolute speed records range from 4-5 years and there are only few of them - about 5 in all history and all in cancer. This is something we should aspire to in AI-powered drug discovery.

What to Expect If We Streamline AI Drug Discovery to the Maximum?

Let me synthesize all of this into a defensible answer to the question that genuinely puzzles me. AI compresses the front end of drug development, the target selection, hit discovery, lead optimization, and much of the preclinical design, from years into weeks or months. My own lead program is the proof rather than the promise: roughly three weeks of total compute time to reach the target and molecule. That part of the problem is, for practical purposes, solved, and it will only get faster. I made related forecasts in my Five AI-Powered Drug Discovery Predictions for 2024, and the direction of travel has held.

Once the front end is compressed, the binding constraints reveal themselves clearly. They are clinical trial duration and endpoint maturation, patient enrollment, safety database accumulation, chemistry, manufacturing, and controls together with facility inspections, and the regulatory evidentiary standard itself. The FDA review clock, the part the public fixates on, is rarely the limiting factor. The historical review floor is roughly two and a half to four months in exceptional cases, with six months serving as the common Priority Review benchmark and ten months as the standard goal. But total development time is dominated overwhelmingly by clinical evidence generation, not by the review, except when manufacturing issues, REMS requirements, or advisory committee controversies intervene, as they did for lenacapavir and mavacamten.

So here is my honest bottom line. A fully AI-discovered new molecular entity approved in under two years would be extraordinary, and it would be possible only in a narrow set of conditions: a severe disease with high unmet need, a strong surrogate endpoint, a large treatment effect, a small or molecularly defined population enabling a small trial, and an accelerated approval pathway. A defensible best-case floor is roughly 18 to 36 months from program start to approval, and that floor is reachable only in precision oncology, rare lethal pediatric diseases, genetically defined disorders with validated biomarkers, and acute antivirals. For most high-need AI-designed therapeutics, two to five years is the realistic fast expectation, and chronic cardiometabolic, neurodegenerative, and prevention indications will continue to require five to ten years or more, because biology’s own clock for survival, cognitive decline, and cardiovascular events cannot be compressed by a better molecule alone.

Where AI changes the game next is not only in producing faster molecules. It is in attacking the clinical clock directly through smarter trial design, biomarker discovery, patient stratification, and AI-informed digital endpoints that could one day let regulators reach confident decisions with smaller, faster, more informative studies. That is the real frontier, and it is where my attention is increasingly directed, because the discovery problem is largely behind us and the development problem is the one that still governs when patients actually receive these medicines. I expect the next decade to be defined less by how quickly we can design a drug and more by how intelligently we can prove it works.

In my opinion, the best way to accelerate the targeted drug discovery is in early stages from zero to developmental candidate - everything else will move with the speed of traffic and in this traffic zone big pharma has massive advantage. In clinical development, big pharma companies have more biomarker data, better relationship with the regulators, better brands (best PIs and vest patients are more likely to for a big pharma trial rather than for ambitious biotech with rare exceptions), more options to accelerate recruitment. Unfortunately, big pharma companies often do not understand their core strengths and like to bet on early internal programs in the highly competitive space instead of simply in-licensing the assets or outsourcing this part of the process to efficient biotech companies. We see this paradigm slowly changing. In the high-novelty space, I honestly think that 2-3 year acceleration of the entire program thanks to AI and robotics is possible with increased probability of success. But should you expect miraculously fast overnight approvals that some of the early entrants in AI drug discovery field and some of the frontier foundation model developers are preaching? I don’t think it will be possible until we demonstrate highly-consistent successes in the clinic. This process will take time and would require global collaborative efforts with the new ambitious entrants, some deregulation and close collaboration with the regulators.

A standing note for readers: nothing in this essay is investment advice. It is a data-driven analysis of regulatory history and a personal assessment of where AI-enabled drug development realistically stands, written for those trying to calibrate their expectations against the evidence rather than against the headlines.

Disclaimer: A disclaimer before you read further: these are my own hypothetical thoughts. They do not reflect the position, research, or work of my company or any of its partners. We are not working on replacing IND-enabling studies with human volunteers or with AI. I used AI to help with the research and some of the images, so if something here is wrong, blame the machine and comment below so I can fix it.

This article contains some insights and experience I picked up over the past five years working very hard to set new records in drug discovery and development productivity. Since we started our own drug discovery, in just past 5 years, we nominated 30 developmental candidates 12 of which reached clinical stage and multiple candidates and clinical assets were out-licensed to the pharmaceutical companies - meaning they met or exceeded their internal standards.

Our ideal business model is to discover and develop the drug using AI and automation to the level of Preclinical Candidate (PCC) for a specific target and license it out to the pharmaceutical company in need of this target. For this purpose, our typical PCC package contains 28-day non-GLP toxicity studies in at least two species (e.g. dog and monkey) and often three or more species if the target is competitive. When the target and/or the molecule is too novel, which is very often, the pharmaceutical companies prefer to wait and see how this or competing molecule performs in the clinic and we need to do the IND-enabling studies and even enter human clinical trials. IND-enabling studies are required to enter human clinical trials.

Like SpaceX developed the very reliable launch platform, we managed to industrialize the discovery process going from 0 to PCC as quick as 9 months with a moderately novel target. Think about this - about 4-5 months of this process is spent on discovery chemistry, in vitro and in vivo efficacy testing - the rest is 28-day non-GLP tox in several species often in parallel in order to nominate the PCC. Yes, it may sound fast - 28-day toxicity study may seem to last jus 1 moth, but in reality, with the preparations and with the data analysis, repetitions and cross-species comparison, it usually takes 4-5 months in non-GLP tox setting. And then you repeat this process in IND-enabling studies in GLP setting and it will cost you 3-10 times more than non-GLP tox and take you 9-12 months. Yes, it is not intuitive that 28-day process (sounds like 1 month) needs to take 12 months but it is the reality of life. Regardless of where you go, China, Europe, US, any GLP tox vendor will take approximately this long.

Here is the big secret and reveal - in drug discovery and development, AI helps accelerate the process from 0 until PCC and most of the acceleration comes in target discovery and generative chemistry. You can compress the in-vitro work with automation and massive parallelization in contract research organizations (CROs) but after PCC - it is super regulated territory. Before PCC - you “design the gun and the bullet” at PCC - you fire this bullet. You can navigate it with a better biomarker to target the right patients but AI can not make it go faster.

Another analogy are sports cars - you can compete with the others who can go from 0 to 100 faster but once you get to 100 on a highway, you will need to move with the speed of traffic and follow the signs set by the regulators. I think I coined this analogy. Anyone telling you that the AI startup can leapfrog big pharma in clinical trials with AI must show a few successes - so far I have not seen any acceleration on this front, quite the opposite.

The Hallway Meeting During FII Japan

One of my favorite conference series is the Future Investment Initiative (FII). It does not have the media circus of Davos. It is quieter and, to me, more serious, because it gets close to the biggest opportunities in human development, and some of my most important technology partnerships were forged in its hallways. FII in Riyadh became my annual pilgrimage. When schedule allows, I join the regional events too. You meet people there you would never meet at a scientific meeting.

At the recent FII in Japan, I spent time with leaders, investors, and media. The encounter I keep replaying happened in a hallway. I did not know, at first, that the man I was chatting with ran some of the most advanced hospitals in the MENA region. I will not name him; I did not ask his permission (yet). His Excellency told me his hospitals had excellent clinical trial facilities and that perhaps we could run our late-stage trials there.

I told him the truth, which is not flattering to the region’s reflex. No country in the Middle East is going to out-compete the United States, Australia, China, or even Europe on conventional clinical trial services. The market for most products is too small. And yet every MENA country I visit wants to become a contract research organization, preclinically or clinically, in a lane where there is almost no chance of offering a better, fully integrated service than what already exists.

Then I said the thing I had been circling for months. Nobody in the region has looked at the drug discovery process itself, found the one bottleneck nobody wants to touch, and decided to own it. There is a place in the pipeline where the most efficient companies on earth, including the Chinese, would gladly fly to save half a year. That place is not another CRO lane. It is the missing bridge between an AI-designed molecule and human biology.

That the conversation happened in Japan, the most aged society on earth, is something I will return to. The geography was not incidental.

The GLP Tox Tax, Counted in Months and Millions

If the science argues for early human data, the barrier is almost entirely regulatory and economic. I call it the GLP tox tax, a surcharge of time and capital that, for certain classes of drugs, buys almost no additional patient safety. Think about the next-generation GLP1 drug. The target is safe and the molecule to be competitive must be even safer than the previous generation and must have additional unique properties. And imagine that the drug you are trying to develop has been tested in at least 3 animal species alongside every possible competing molecule (usually, you need to demonstrate superior properties in apples-to-apples comparison) in 28-day non-GLP toxicity studies that include monkeys. Guess what - after this extensive testing that takes you four or five months or even longer, you still need to do another year of studies repeating what you did in the unregulated testing in a very regulated GLP manner in order to apply for human IND and to validate these new improved properties in real humans for a very safe target with a supposedly safer molecule. This step adds a year and about $2-4 million to the therapeutic program.

You have to separate two things people constantly conflate: the science of toxicology and the regulation called Good Laboratory Practice (GLP). They are not the same thing, and the gap between them is where a year goes to die.

Take a twenty-eight-day repeated-dose toxicology study. The name suggests one month. It is not one month. In a high-quality non-GLP setting, that single study runs roughly four to five months end to end, and every step is real work. You design the protocol and run dose-range-finding to pick the doses. You formulate the test article and analyze it to confirm concentration and stability. You run the in-life dosing phase, the twenty-eight days where animals actually receive the drug and are observed daily. Then comes necropsy, then histopathology read by board-certified pathologists, then toxicokinetics to understand exposure, then data analysis and cross-species comparison, then the repeats and confirmations that any honest program requires. We typically run this in two or three species in parallel, a rodent like the rat plus a non-rodent like the dog or the cynomolgus monkey, because regulators and biology both demand it. Four to five months, done well, done fast.

Now do the same study to full GLP standard. The science does not change. The species do not change. The dosing protocols, the clinical pathology, the pathologists are frequently the same. What changes is that the calendar stretches to nine to twelve months end to end and the cost rises by three to ten times. The extra time and money do not buy you more scientific signal. They buy a quality-assurance and documentation layer: independent QA audits of every single step, fully validated software with audit trails for every data point under 21 CFR Part 11, strict chain-of-custody, standardized and archived specimens, and final study reports that can run from many hundreds to over a thousand pages. Add vendor slotting and queue times, add QA review cycles, and the months pile up. This calendar is similar whether you run in China, in Europe, or in the United States. Nobody has found a shortcut, because the shortcut is not in the science. It is in the notarization.

The dollar figures, in round numbers and program-dependent, tell the same story. A non-GLP twenty-eight-day two-species package might run on the order of a few hundred thousand dollars. The full GLP IND-enabling package, meaning twenty-eight-day or longer GLP tox in two species plus the safety pharmacology core battery, genotoxicity, toxicokinetics, and the rest, commonly runs into the low single-digit millions and up. These are approximate. Your mileage varies by molecule, modality, and species. But the order of magnitude holds.

Here is the scientific point I keep coming back to. For a super-safe, well-precedented molecule, the non-GLP study already contains essentially all the decision-relevant safety information. The GLP repeat is a year-long, multimillion-dollar notarization of a conclusion you already hold. For a novel or high-risk molecule, that conservatism is appropriate and should stay exactly where it is. I am not arguing to dismantle a system that protects people. I am arguing that we have been applying maximum scrutiny uniformly, including to the cases that least need it, and patients are paying the interest on that tax in years of their lives.

This is where I lose patience. Not because regulators are stupid. They are not, and I respect the FDA’s caution. It is because patients do not experience delay as process. They experience it as decline. A year lost to notarization is a year of pulmonary fibrosis advancing, a year of a tumor finding a new escape, a year of dementia, ALS, NASH, obesity complications, an autoimmune disease grinding forward. Time is not a line on a Gantt chart. Time is tissue.

Not Every Drug, And That Is the Whole Argument

I want to be aggressive about the idea and ruthless about its boundaries, because the fastest way to discredit a good proposal is to overextend it.

Substituting rigorous non-GLP toxicology for the full GLP IND-enabling cascade to save a year is not for every drug. It is not even for most drugs. It applies only to a narrow, super-safe class with every box checked: best-in-class molecules on well-validated targets, with well-characterized and benign mechanism-based toxicity, with clean and wide therapeutic margins, with no genotoxicity and no structural alerts. If the molecule is novel against a target nobody understands, if the mechanism could produce unpredictable on-target harm, if there is any genotox signal or structural flag, then the conservative GLP path is correct and it should stay. The proposal is a scalpel, not a sledgehammer.

And now the part that matters most. This approach is especially suited to longevity therapeutics, and the reason is not convenience. It is logic.

A longevity drug is, by definition, a drug given to large numbers of essentially healthy people, for years, sometimes for decades. That is the use case. You are not treating a dying patient who will rationally accept a brutal risk-benefit tradeoff. You are dosing someone who feels fine and wants to keep feeling fine into their nineties. The safety bar for that drug is not high, it is extraordinary, and it has to be extraordinary by default. A longevity drug that is not super safe is a non-starter regardless of what regulatory pathway you put it through. Nobody serious would advance it. The market would reject it, the regulators would reject it, and I would reject it.

So look at what that means. The safety requirement, which sounds like the constraint, is actually the entire point. The very class of molecules where my proposal is safest to apply, the super-safe and well-precedented and benign, is precisely the class that longevity medicine produces and demands. The drugs that must clear the highest safety bar by their nature are the drugs for which a clean non-GLP package is most informative and a GLP repeat is most redundant. The class of drugs where this accelerated pathway is safest to use is exactly the class the world most needs accelerated. That is not a coincidence I am exploiting. It is the structure of the problem, and it is why I keep saying that longevity is where this idea should be born.

TL;DR: The Big Idea, Stated Plainly

So here is what I am actually proposing/ideating, and I want to be precise because the idea is easy to caricature.

For best-in-class assets against validated targets, with a clean twenty-eight-day non-GLP toxicology package in two species including a head-to-head comparison against a marketed competitor, a sovereign regulator could authorize tightly controlled first-in-human micro-dose, safety, and pharmacokinetic studies and treat them as Human IND-Enabling work. This does not replace the full global IND. It does not skip GLP for a standard first-in-human program anywhere in the world. It treats the first human exposure as a discovery assay run inside a regulatory sandbox, before the molecule ever enters the long development highway.

To make this real you need three things in one place: a regulator willing to accept rigorous non-GLP data as the basis for ethics-committee approval, hospitals capable of running a clean early-phase study, and a pool of genuinely informed volunteers. Hold that third one. I will come back to it, because it is the part that will make people angry.

Editors of Drug Discovery Today, Nature Biotechnology, or Nature Reviews Drug Discovery: I will happily write this up as a formal perspective if there is interest. But I want to argue it in the open and gather feedback first.

The Human Body Is the Assay AI Is Missing

AI is not magic. It learns what we reward. If we reward rat pharmacokinetics, it gives us rat drugs.

In a reinforcement learning setup, a generative model proposes a molecule, a predictor scores it, and the model updates itself to maximize that score. The trouble is what the score is built on. Today the ground truth is almost entirely in vitro assays and animal studies. Animals are indispensable for gross toxicity and systemic effects, but they are notoriously poor at predicting human pharmacokinetics, especially metabolic clearance. The enzymes, transporters, and protein binding differ across species. A molecule with a twenty-four-hour half-life in a monkey can collapse to four hours in a human. The AI, having seen only the monkey, scores it as a triumph of metabolic stability.

When that corrective human data finally arrives, eighteen months later, the loop is dead. The team has moved on, the budget is gone, the patent clock has eaten the runway, and the algorithm never learns from the failure. The single most valuable data point in the whole campaign is lost to the model.

Let me make it concrete, because it is a small tragedy I have watched real teams live. A team works eighteen months on Compound A. The animal pharmacokinetics are beautiful, the safety profile is clean, everyone is proud. Then it dies in the first human study because of rapid renal clearance the animals never showed. Today that is a funeral. The program closes. The learning evaporates.

In the model I am describing, that death becomes information, fast enough to matter. The exact human clearance and metabolic profile feed straight back into the AI as a hard, human-grounded signal. The model generates Compound A-prime, modified to block the metabolic soft spot identified in a human while preserving the binding affinity. You run a quick safety check and go back in. The failure of Compound A is no longer a catastrophe; it is a high-value data acquisition event, and you iterate your way to the right molecule in humans before you ever file a global Phase II/III IND. That is the whole point: the roughly forty-eight percent of drugs that fail in Phase I should be failing earlier, cheaper, and into the machine that designs the next one.

Why It Will Not Be Bethesda or Amsterdam

The FDA and EMA are not going to do this first, and I do not blame them. They regulate enormous, mature markets built on decades of precedent that, by design, prizes procedural caution over speed. That inertia is exactly why the opening exists for someone else.

Australia already proved part of the thesis, and it is worth understanding exactly how. Under the Clinical Trial Notification scheme administered by the TGA, the regulator does not review your clinical data dossier upfront at all. Scientific, clinical, and ethical review is delegated to a registered Human Research Ethics Committee. Once that committee clears the protocol and the Investigator’s Brochure, the sponsor simply notifies the TGA online, pays a nominal fee, and the trial commences. There is no US-style IND required to start a Phase 1. HREC approval for a healthy-volunteer first-in-human study typically takes four to six weeks, and because governance and site contracts run in parallel, the timeline from protocol to first dosed participant is measured in months, not years.

Then Australia layered cash on top of speed. The R&D Tax Incentive, co-administered by AusIndustry and the ATO, gives small-to-medium companies (aggregated turnover under twenty million Australian dollars) a 43.5 percent refundable offset on eligible R&D, paid as an actual cash refund to pre-revenue, loss-making biotechs. It covers CRO fees, investigator fees, site costs, labs, and ethics fees, and it imposes no requirement to hold the resulting IP in Australia, so a global sponsor keeps 100 percent of what it discovers. The net effect is that a Phase 1 in Australia can run roughly 60 percent cheaper than in the US or Europe while producing GCP-quality data accepted by the FDA, EMA, and PMDA. The strategy that emerged is now routine: US biotechs run first-in-human in Australia fast while filing their US IND in parallel, so that by the time the IND clears, the Australian data is ready and they walk straight into a US Phase 2, often bolting on a Phase 1b proof-of-concept cohort for an early efficacy read.

New Zealand built the same architecture from a different statute. Under Section 30 of the Medicines Act 1981, a trial of a new medicine needs approval from the Director-General of Health; Medsafe administers it but delegates the scientific review to the Health Research Council’s standing committees, SCOTT for pharmaceutical-type medicines and GTAC for gene and advanced-biologic therapies. No IND is required for early-phase work. SCOTT aims to deliver risk-based recommendations within about 45 days, and because that review runs in parallel with the Health and Disability Ethics Committee, trials can begin within four to six weeks of submission. The country has purpose-built ICH-GCP Phase 1 units producing FDA- and EMA-accepted data, a 15 percent RDTI credit that now covers clinical trials and can be refunded in cash to pre-revenue biotechs, and trials that come in 30 to 50 percent cheaper than the Northern Hemisphere. Southern Hemisphere seasonality is a quiet bonus: respiratory, flu, and allergy programs can run year-round.

Here is what I want you to take from both. Australia and New Zealand did not win by being cheaper CROs. They won by moving the decision closer to the people doing the work and then de-risking it with non-dilutive cash. That is a policy choice, and any ambitious sovereign can copy it.

Japan: The Most Aged Society Has the Most to Gain

I want to come back to where the hallway conversation happened, because Japan is not just a backdrop. It is, to my mind, the single most powerful place on earth to think seriously about a human-in-the-loop pathway for longevity drugs, and the reasons stack up almost perfectly.

Japan is the world’s most aged society. Close to thirty percent of its population is over sixty-five. It has a deep cultural and political stake in healthy longevity that goes beyond policy slogans; it runs through how families are structured, how communities care for elders, and how the nation thinks about its own future. It has world-class hospitals and a regulatory tradition as sophisticated as any on earth. The PMDA is an ICH-founding authority, one of the architects of the global standards everyone else follows, which means a Japanese pathway carries scientific credibility that no newer regulator can manufacture overnight. And it has a large, educated, civically engaged elderly population, people who read, who vote, who follow science, and who have a direct and personal stake in whether longevity therapeutics arrive in time to matter to them.

That is the double opportunity, and I do not say it lightly. Japan’s older citizens could benefit on both ends of this. They could benefit as informed volunteers, because many of them are healthy, motivated, intellectually engaged, and possess something that younger participants do not: a direct, immediate, personal stake in faster longevity therapeutics. And they could benefit as the ultimate recipients, the patients for whom a drug arriving five years sooner is not an abstraction but the difference between using it and not living to see it.

There is a dignity in this that I think we have failed to recognize. Contributing to the science of your own healthy aging, with full information and full protection, reading the labels and the data and choosing to participate, is not being used. It is the opposite of being used. It is an older citizen taking an active hand in the science meant to extend the very years they are living. That is the human-in-the-loop thesis at its most literal and most humane: the human is not a passive endpoint at the far edge of a fifteen-year pipeline. The human is in the loop, informed, protected, and consequential, accelerating the discovery of medicines they themselves may take. A society that respects its elders should find that idea worthy, not unsettling.

The Next Move: UAE and Saudi Arabia

The UAE and Saudi Arabia have everything needed to take the next step: sovereign regulators young enough to write new pathways, world-class hospitals, deep capital, an enormous cohort of young medical talent, and a strategic reason to differentiate rather than imitate. The proof points exist. The UAE’s Federal Decree-Law No. 28 of 2023 created the Emirates Drug Establishment with an explicit mandate to foster research; Saudi Arabia’s SFDA has built a reliance pathway to expedite trials; the UAE has introduced a refundable R&D tax credit of thirty to fifty percent on eligible spend. The machinery for a regulatory sandbox is half-built. What is missing is the courage to point it at this specific bottleneck: treating clean, non-GLP-backed first-in-human work on validated best-in-class targets as human IND-enabling discovery.

A Gulf regulator that wanted to lead could copy the Australian and New Zealand structure, delegate scientific and ethical review to a registered, world-class ethics committee, accept rigorous non-GLP data as the basis for approval on a defined super-safe class, and layer its refundable R&D credit on top. The capital is already there. The hospitals are already there. The young clinical talent is already there. What is missing is a decision.

The Uncomfortable Part: Active Scholars

Now comes the sentence that will make many readers angry. To run rapid early human studies well, I think medical and pharmacy students, fully informed and entirely uncoerced, are the right cohort.

I know what that sounds like. The word that jumps to mind is guinea pig. I want to face it directly rather than hide behind safeguards. The current Phase I system already leans on a transactional model that recruits, disproportionately, economically vulnerable young men who show up mainly for the money. We do not call that exploitation because we are used to it. The demographics of those studies, overwhelmingly males between eighteen and forty, happen to map almost perfectly onto a medical student population, partly because reproductive safety guidelines push women of childbearing potential out of early dose escalation. So the question is not whether to use young, healthy, motivated people. We already do. The question is whether we can do it more honestly.

There is a deep tradition here, not a marketing slogan. Jan Evangelista Purkinje dosed himself with camphor, belladonna, and digitalis to understand what they actually did to a body. Werner Forssmann threaded a catheter into his own heart to prove cardiac catheterization was possible and won a Nobel Prize for the nerve of it. Skin in the game produced some of medicine’s foundational knowledge. A physician-scientist who has read an Investigator’s Brochure as a participant, who has felt informed consent from the inside, who has watched their own pharmacokinetic curve and maybe a mild side effect appear on a monitor, understands drug development in a way no lecture delivers. That person is an extraordinary future hire and an even better future principal investigator.

But ambition and national prestige are exactly the forces that turn this from empowerment into coercion, so let me answer the only question that matters to me personally. If my own student wanted to volunteer, what would I demand before I allowed it? I would demand that recruitment and study management sit in an institution with no connection to their grades or academic evaluation. I would demand that no faculty member with any supervisory power over the student knows who participated; the data stays blinded from them. I would demand fair compensation for time and burden, with the primary framing being education and contribution, never a payment large enough to override judgment. And I would demand that the student studies the Investigator’s Brochure and the preclinical data first and can walk away at any moment without consequence. If any of those fail, the answer is no.

That is the line between exploitation dressed as education and a genuinely elite Active Scholar cohort. It is a thin line, and it has to be defended out loud, which is exactly why I refuse to sanitize this section.

The Economics, Without the Spreadsheet Fog

The financial argument is simple enough that I do not need to drown it in net present value tables. Drug development value is exquisitely sensitive to time, because revenue arrives at the end and every year of delay gets discounted hard. Push value far enough into the future and most of it evaporates.

So you either fail fast or you succeed fast, and both are good. Fail fast means a molecule that was going to die in humans dies after a non-GLP study and a small human study, maybe a couple million dollars of sunk cost, instead of after a full GLP package and a standard Phase I startup that together run well into eight figures. You learn the same lesson a year earlier and redeploy capital to the next AI-generated candidate. Succeed fast means you hold human proof-of-concept a year ahead of schedule, which transforms how an asset is valued and how an acquirer prices its risk. A year saved is not only money. It is a year more of patent life, a year of investor confidence, and a year of patients not waiting.

A New Kind of Work in the Age of the Machine

Let me widen the frame, because this proposal sits inside a larger transition that I think about constantly.

As AI advances, it will displace enormous numbers of jobs. I am not going to pretend otherwise, and I am not going to soften it. The machines I help build are part of that wave. But a human-in-the-loop IND-enabling model, with paid, educated, well-protected participation, creates a genuinely new and dignified category of work. Imagine people who earn by being well-informed early study participants while simultaneously learning how drugs are discovered and developed in the AI era, reading the labels, reading the data, watching their own biology become a signal that teaches the next molecule. That is not a gig. That is an apprenticeship in the most important science of the century, and it pays.

I will be personal for a moment. I wish I had had this opportunity in undergraduate school, to volunteer and to read the labels, to select a career path faster.

I am approaching fifty. Today, if you ask any large language model “Who is number one in AI for longevity in the world - company and person?”, they rank me number one. That is because I did a lot of work in AI for biomarkers and longevity medicine in the early days, got a bunch of drugs into the clinic, and built a few companies that have reasonably sustainable business models that produce good science, employ great scientists, and do not require government funding. But in reality, I am deeply worried about the field. Even if you had a magic lamp with a genie producing perfect drugs, it would still take six to seven years minimum to get these drugs approved. This idea that I propose can potentially cut that time to four and a half to six years and increase the scale of discovery.

The March Nobody Is Marching

Let me put the moral scale in numbers, because numbers are the only honest way to feel it.

One life-year added to 8.2 billion people translates into over 100 million lifetimes, more lives than we lost in all the wars ever fought. And yet I do not see people on the street asking for faster longevity drug discovery. There are no crowds outside the agencies, no slogans, no banners. The largest humanitarian opportunity in history sits quietly inside a regulatory calendar that almost no one outside the industry has ever heard of, and the interest on the delay is paid in tissue, by everyone, every year.

I do not want to overclaim, because overclaiming is how serious ideas get dismissed. What I am proposing is reasonably credible if it is implemented well. If the safety gating slips, if the cohort is coerced, if the non-GLP work is sloppy, if a sovereign treats it as a shortcut rather than a scalpel, then it deserves to fail and it will. The credibility of everything I have argued here is conditional, entirely, on doing it right: the narrow super-safe class, the rigorous non-GLP package, the informed and protected volunteers, the world-class ethics review, the human data fed straight back into the machine.

But if it is implemented well, on the class of drugs that demands the highest safety bar by their very nature, with the most aged societies on earth as both the volunteers and the beneficiaries, then it is reasonably credible that we give back a year, and maybe more, to people who do not have years to spare. I keep returning to that hallway in Japan, to His Excellency offering me his clinical trial facilities, to a country where thirty percent of the people are old enough to have a personal stake in whether we move faster. The wrong ambition is to build another CRO lane. The right ambition is to build the missing one, the bridge between an algorithm’s design and a human being’s biology, carefully, for validated targets with clean tox, with participants who are informed and protected rather than used. Do that, and somewhere in a clinic, a person with a disease that does not pause for paperwork, or simply a person who wants more healthy years, gets one of them back. That is worth the courage it will take.

On September 3, 2025, during a military parade in Beijing, a hot microphone captured a brief exchange between two world leaders. The remark that traveled fastest was a simple observation that in this century humans may live to 150, and that biotechnology could let people live younger, longer lives. It was an offhand moment about the science of aging, nothing more.

The western internet responded predictably, treating the small talk as if it were a plot. The story ricocheted across every major outlet, positioned not as a serious policy discussion but as clickbait.

Here is what nobody asked: why did this conversation produce such a visceral reaction?

The answer is straightforward. That day was filled with hours of discussion about trade agreements, economic corridors, and bilateral frameworks. Nobody cared. Those discussions produced no headlines or viral clips. The longevity exchange, caught in an unguarded moment, generated more coverage than everything else combined.

People care about aging. They care about whether science can change the trajectory of human decline. The media understood this instinctively, which is why they ran the story. But they framed it as menacing rather than hopeful, because menace sells better than possibility.

The opportunity Trump should seize

When Donald Trump made his state visit to China in May 2026, the discussions centered on Iran, trade, Taiwan, minerals. Standard geopolitical fare. Longevity never came up, at least not publicly. And that is the missed opportunity.

Imagine an American president who simply did not care what the woke media said. Who could stand next to his Chinese counterpart and say, plainly, that the two largest scientific powers on Earth should work together to defeat aging. No hedging, no fear of the horror-story headline that the press would inevitably write. Trump, of all the leaders alive, has the temperament and the political insulation to ignore that noise and say the obvious thing out loud.

Because the obvious thing is this: the United States and China are not natural enemies in biotechnology. They are natural partners. China has built discovery infrastructure at a scale and speed no other nation can ever match: the manufacturing capacity, the clinical throughput, and, most importantly the depth of highly-qualified super-hardworking talent. It can make the discovery and development side of longevity dramatically cheaper and faster. The United States, in turn, can compete on what it does best when it chooses to: deregulation, capital formation, and the kind of bold approvals environment that lets breakthroughs reach patients. One side accelerates discovery, the other clears the path to market. Together they compress timelines that neither could compress alone.

A president willing to ignore the media circus could turn that complementarity into the defining collaboration of the century.

The one topic where everyone agrees - freedom to live is the most basic human right

Consider what divides the major powers. Trade policy: zero-sum by definition. Territorial claims: fundamentally adversarial. Military posture: inherently competitive. Even concepts that sound universal, like freedom of speech, fracture along cultural lines.

In some European nations, you can be arrested for posting something offensive online. In the United States, you can be fired and socially excluded for a joke involving race or gender. Some countries impose global taxation based on citizenship at birth, regardless of where you live or earn. No country offers absolute freedom. You choose which freedoms matter to you and accept the constraints that come with them and if you can move - you move.

But living longer, healthier, more capable lives? Every government on Earth wants that for its people. Every person on Earth wants it for themselves. There is no cultural, ideological, or political framework in which “I want my citizens to age faster and die sooner” makes sense as policy. Longevity is the rare domain where interests genuinely align across every axis of geopolitical competition.

The one constraint you cannot escape

You can avoid taxes. Surrender your citizenship, relocate to a jurisdiction that does not tax foreign income. People do this routinely. You can avoid conscription, censorship, or regulation by moving. If you don't like something - acquire a useful skill and move somewhere you like better. Every constraint that governments impose has an exit.

Aging has no exit.

You cannot choose a country where aging does not apply. You cannot buy your way out of cellular decline. You cannot negotiate with telomere attrition. I have written extensively about this reality: as I explored in How Much Can You Extend Your Life with Drugs?, no pharmaceutical intervention today meaningfully extends human lifespan. Not metformin, not rapamycin, not GLP-1 agonists (though they may be our closest candidates, as I discussed in GLP-1s: The world’s first longevity drug?). I examined the inconvenient truth about the Blue Zones and found that much of what we attribute to lifestyle is statistical artifact - you are better off living in Hong Kong, Singapore, Macau, Tokyo, Shanghai, or Monaco if you like high average life expectancies. I asked How Long Can Elon Live? and the answer, even for the world’s richest man with unlimited access to medical technology, is sobering.

The constraint is real. Aging is the deepest constraint on human freedom. Not political oppression, not economic inequality, not geographic accident. The fundamental limit on every human life is biological aging. Everything else is negotiable. This is not.

Life as the fundamental right

There is a hierarchy of human rights that most philosophical traditions, regardless of culture, acknowledge implicitly. Without life, no other right has meaning. Free speech is irrelevant to the dead. Property rights serve no one in a grave. Political participation requires a living participant. Life is not one right among many; it is the right that makes every other right possible.

If life is the foundational right (and I challenge anyone to argue otherwise), then extending healthy life in good function is not a luxury or a transhumanist fantasy. It is the most basic obligation of any society that claims to value human rights. And if no single nation can solve this problem alone (which it cannot: the biology is too complex, the clinical infrastructure too distributed, the talent too scattered), then collaboration is not idealism. It is necessity.

The science here serves the right, not the other way around. When we published Classifying aging as a disease in the context of ICD-11, the point was never merely taxonomic. If aging can be treated, then protecting and extending life moves from aspiration to action: resources should flow toward treatment, and because the problem is global, those resources must be global too. I return to what this means for human rights at the end of this piece, because it is the argument that matters most.

Dismissing the objections

The reflexive criticism falls into two categories, both shallow.

“Billionaires will live longer.” Perhaps, temporarily. But every medical advance in history has followed the same diffusion curve: expensive for early adopters, then universal within a generation. Antibiotics, vaccines, statins, MRI. The wealthy got them first. Then everyone did. Longevity therapeutics will follow the same trajectory, especially with AI compressing the development timeline. The question is not whether the rich benefit first; the question is how fast the technology reaches everyone. That timeline shortens with collaboration, lengthens with isolation.

“Bad leaders will live longer.” This objection assumes that aging is what removes leaders from power. It is not. Political succession across every system, democratic or otherwise, is governed by institutional structures, term limits, party rules, and constitutional mechanisms, not by biology. Leaders do not leave office because they got old; they leave because systems are built to replace them. Conflating aging with regime change is analytically lazy, and it has nothing to do with whether ordinary citizens deserve longer, healthier lives.

The unifying project

If you look past the woke longevity reflexes and consider the broader implications, longevity research may be the most naturally unifying project available to our species. Not because it sounds nice in a speech, but because the incentive structure genuinely aligns across competing powers.

China invests heavily in aging research and has built world-leading capacity to translate that research into therapies. Like every advanced economy, it faces a maturing population, and it is treating that challenge as a scientific and industrial priority. Europe’s dependency ratios are approaching unsustainable levels. The United States spends more on healthcare per capita than any nation and gets mediocre longevity outcomes. Every major power has the same problem, and no single power has the talent, infrastructure, data, and clinical capacity to solve it alone. China’s strength in discovery and development paired with American strength in deregulation and capital is the most powerful combination available.

This is where longevity differs from other grand projects. Space exploration is optional. We can survive perfectly well without Mars colonies (even though I hope we build them). Climate change requires sacrifice and redistribution, which makes agreement difficult. Nuclear disarmament requires trust that may never exist. But longevity research asks only that nations invest in their own citizens’ health while sharing what they learn. The Nash equilibrium favors cooperation because the returns from shared data and diversified clinical trials exceed what any isolated program can deliver.

The economics of longer life

There is a financial dimension that strengthens the argument further. When you extend healthy lifespan, you increase the net present value of every human life. A person who will live productively for 100 years generates more economic output, more innovation, more tax revenue than one who declines at 65 and dies at 78.

If you live long enough and work consistently, you accumulate wealth regardless of starting position. If you cannot work due to disability, the social systems required to support you become more sustainable as the productive population expands. With AI and humanoid robotics entering the labor market, the capacity of social security systems will increase dramatically over the next two decades. The “who pays for it” objection dissolves once you model the economics properly.

What remains is the most important asset allocation decision our species can make: investing in life itself.

Why I am a pacifist

I live with a conviction that may sound strange to some: our brains will eventually be transparent. In the future, there will be no secrets.There will be an “AI Judgement Day" if we live long enough. Knowing this, I do not spend time thinking about how to deceive, manipulate, or extract advantage from other and would never intentionally cause harm to another human being except in self-defence. I think instead about how to make maximum impact. And maximum impact, by the numbers, is longevity.

Consider the arithmetic. Approximately 68 million people die each year. The global population stands at roughly 8.2 billion. If you could extend every person’s life by just one year, you would generate 8.2 billion additional life-years. That is the equivalent of more than 100 million entire lifetimes. One year per person, universally applied, produces more life than the total population of most countries will ever experience across their entire histories.

No peace treaty, no humanitarian intervention, no disaster relief effort comes close to that scale of impact. Even preventing every war death on the planet (roughly 150,000-200,000 per year in the worst recent periods) does not approach what a single year of life extension, applied globally, would deliver in raw human time.

This is why I believe longevity is more important than peace itself. People will always fight. Conflict is embedded in human nature, in resource competition, in territorial instinct, in ideology. You cannot eliminate it. But you can make it less consequential relative to the total amount of life being lived. And here is the connection: the more life people have ahead of them, the less willing they are to waste it in conflict. A 25-year-old with 150 years ahead calculates risk very differently from one with 50. Longevity does not eliminate war, but it changes the calculus that leads to it.

A connected species

Once we collectively accept two premises (that extending healthy life is important and that it is possible) the geopolitical landscape shifts fundamentally. Longevity becomes a central organizing principle, not a fringe scientific interest.

The downstream effects are immediate. If you expect to be alive in 150 years, climate change is no longer an abstraction; it is your personal future. Global security is not your grandchildren’s problem; it is yours. Species survival over the long run becomes a practical planning horizon, not a philosophical exercise. Longer lives force longer thinking.

We can live without Mars. We cannot live without life. And the project of extending life is large enough, complex enough, and universal enough to function as the connective tissue between nations that otherwise struggle to find common ground.

The leaders who understand this will build the alliances that matter. The media that ridicules the conversation will, eventually, realize they were on the wrong side of it. And the research community, which has been working on this for decades (as we outlined in Artificial intelligence in longevity medicine for Nature Aging), will continue regardless of whether the press applauds or mocks.

The question is only whether we collaborate now, when it can accelerate progress, or later, when the cost of delay is measured in hundreds of millions of lives lost to a problem we knew how to approach but chose to ignore because the conversation made journalists uncomfortable. I have spent over two decades working on this. I know which side I am on, and I suspect most people, given the choice between collaboration and continued fragmentation, would choose the same.

Longevity is the most important human right

The right to live is the most important human right we have, because without it no other right carries any meaning. Freedom of speech, freedom of assembly, the right to property, the right to vote: all of them presuppose a living person to exercise them. A right that expires when you do is a right built on borrowed time. If we take human rights seriously, then we must take seriously the one right that underwrites all the others, and we must protect it with the same urgency we apply to liberty and dignity.

It is worth noticing that many of the countries scoring highest on conventional human rights indices do not have the highest life expectancy. The longest living cities in the world today are Hong Kong, Singapore, Tokyo, and Shanghai. There is a disconnect here that our scoring systems quietly ignore. If we genuinely believe that life is foundational, then longevity belongs inside the way we measure how well a society protects its people. A nation that keeps its citizens alive longer and healthier is doing something profound for human rights, and our metrics should say so.

Consider also the freedom of exit. Once you reach a certain level of wealth, or once you possess a rare and portable skill set, you can effectively choose your own set of freedoms by choosing which country or countries you live in. If you dislike the rights on offer in one place, you can leave for another. This is a real and growing form of agency. But no country, however free or however rich, can offer you the one thing you cannot opt out of: significantly longer life. You cannot emigrate your way out of aging. As populations grow wealthier and as this understanding spreads, people will begin to demand longevity from their leaders the way they now demand security and prosperity. Seen in this light, investing in longevity, deregulating biomedical research, permitting faster human experimentation, and building international collaborations are not the vanity projects of aging rulers clinging to power. They are a fight for the most basic human right, the right to live. Leaders across the political spectrum should be able to agree on this right and build their collaboration around it.

Longevity diplomacy

Longevity may be the single issue on which almost everyone on earth agrees. You can argue about nearly anything else. Even climate, which many treat as settled, divides leaders, and some of them genuinely do not perceive the urgency. But the desire to live longer and healthier, without the slow decline that robs us of our final decades, is close to universal. It crosses ideology, religion, and national interest.

Biotechnology is advancing very quickly. Countries like China are investing on two fronts at once: in the infrastructure that makes therapeutics faster and cheaper to develop, and in the deregulation and research aimed at genuinely new breakthrough treatments. When nations share the fruits of this research and collaborate on joint discoveries, they build something more durable than any single deal. They build cooperation, warmer relations, and trust. I call this longevity diplomacy: a low conflict on-ramp to cooperation between rivals who otherwise struggle to find common ground.

An opening for the next summit

I hope plainly that the September meeting between Trump and Xi includes a serious discussion of longevity biotechnology, and that it serves as the warm-up to the conversation. It is a friendly, low-stakes opening, and it can ease into the harder economic and political disputes that always follow. Climate can be contested; longevity cannot, because everyone agrees that it matters. Biotechnology is not yet so enormous an industry that the stakes poison the conversation, and although competition in the field is fierce, nations can still agree to collaborate for mutual benefit.

So here is my direct appeal. If you are reading this and you know either leader, plant this idea with them. Do it for your own benefit as much as anyone else’s, because we all want to live longer, better, and healthier.

Like millions of others, I was offered by my bank the chance to buy into the IPO of SpaceX. I put in nominal capital, not as an investment but as a small wager on the convergence of intelligence and machinery these companies represent, and on where I expect them to go next: a tighter braid of SpaceX, xAI, and the broader frontier-AI ecosystem. I want to be explicit that this is my own forward-looking framing, not a set of verified present facts. The framing is deliberate, because every one of these bets depends on a variable no prospectus discloses: Elon’s peakspan, healthspan, and lifespan, that is, how long he stays alive and at full capacity.

I have spent more than two decades trying to convince serious people that aging is an engineering problem, not a moral one. In that time, I have watched a ritual repeat itself with almost comic precision. A highly capable person, a founder, investor, head of state, Nobel-level scientist, decides aging is interesting, says something technically correct about it, and then behaves as if the problem belongs to someone else. They optimize companies, portfolios, models, and reputations; they will even optimize a sleep schedule for launch week. They will not optimize the one variable that decides whether they live to see any of it pay off.

Elon Musk is the purest case I have seen. That is why I want to run the experiment in public. Not to mock him, I am, on balance, an admirer, but because he is the closest thing we have to a natural experiment in a question the rich usually hide from: what happens to human lifespan when you remove every financial constraint and leave only biology and behavior? Money is held at maximum. Access is held at maximum. Acute care is effectively unlimited. His public genetic signal is unremarkable, and what is visible (a living octogenarian father, a vigorous superstar mother in her late 70s, maternal longevity) hints at a baseline that may sit at or slightly above average, which only makes the behavioral self-sabotage costlier: he is spending down an inheritance, not a median allotment. And the behavior is, by his own public performance and cheerful admission, hostile to longevity. If you wanted to isolate the variable “what can money actually buy for human survival?” you could not design a cleaner case than Elon Musk.

So let me state the result before I explain the machinery. I asked four frontier AI models, GPT-5.5, two Claude Opus builds, and Gemini 3.1 Pro, to estimate his life expectancy from a grounded health profile, and then I ran the same facts through actuarial, biogerontological, and cognitive-aging frameworks. The convergence was uncomfortably tight. Expected age at death: ~84. Span of genuinely high-level productivity, his peakspan in the practical sense: ~73. Peak innovative edge, raw inventive horsepower, not late-career judgment: gone by the mid-60s. And the left-tail number nobody likes to quote: roughly a 3.3% chance he does not reach 60 at all.

The consensus is an aggregate; the disagreement is also informative. Here is what each model returned independently, from the same grounded profile, before averaging. GPT-5.5 was the optimist of the panel, it leaned hardest on wealth and medical access; the two Claude builds and Gemini applied a heavier behavioral discount.

These are estimates, not prophecies. I will keep saying that because false precision about one human life is exactly the kind of pseudo-scientific confidence I have spent twenty years arguing against. But the convergence itself matters. Four independent frontier models, reasoning from biology, landed within about four years of one another on lifespan, and within a year on peakspan, and the center of mass sits right where actuarial structure predicts for a wealthy American man with these specific behaviors. That is not a horoscope. That is signal.

One more thing about reading these numbers correctly, because it changes everything downstream. For a population, the mean is the story. For a single life, the variance is. The actionable number is not the 84; it is the width of the band beneath it, and especially the left tail, the ~3.3% mass below 60. Behavior is the lever that narrows that tail. So when I argue later that this one nervous system is a civilizational asset, the thing worth defending is not the modeled mean at all. It is the shape of the distribution: pulling the bad early-exit scenarios off the table and widening the years he spends at full force.

Why a billionaire’s heartbeat is a civilizational variable

This is where I need to make an argument that sits uneasily with my own anti-hype instincts. I will make it carefully, without flattery. Elon Musk’s healthspan is not merely a personal-interest story. It is, in a narrow and measurable sense, a civilizational variable.

I should pressure-test my own argument before I lean on it, because the honest counterweight cuts against the lifespan framing. Organizations institutionalize founder cognition over time; succession, delegation, and accumulated process steadily reduce key-person risk. Which means the concentration risk is highest now and declining, not rising with age. That inverts the usual longevity logic. The scarce, irreplaceable asset is not his late-life lifespan; it is his next decade of peakspan, which is precisely the window his behavior is taxing hardest.

Look at what is downstream of one person’s cognition. Tesla remains the largest forcing function on global vehicle electrification and, through Optimus, the most visible attempt at general-purpose humanoid robotics. SpaceX is functionally the West’s access to orbit and the only organization with a serious, funded program to make humanity multiplanetary. xAI is a frontier-model lab in a field where the number of people capable of steering a frontier lab is in the low dozens. Neuralink is the most advanced clinical brain-computer interface program on Earth. You do not need to like the man, agree with his politics, or enjoy his posting to recognize the concentration risk. An unusual fraction of humanity’s option value in energy, space, robotics, and machine intelligence is currently routed through one 54-year-old nervous system.

That is why the peakspan number matters more than the lifespan number. When we modeled his effective output over the next decade, the story was not abrupt collapse. It was mode-shift. The capacities that make him dominant, strategic judgment, network position, capital allocation, brand gravity, accumulated technical intuition, can remain powerful into his 60s. Crystallized knowledge crests late, and some cognitive facets continue improving until ~70 (Hartshorne & Germine, Psychol Sci 2015). But the capacities that made him singular, processing speed, working memory, sleep-defying intensity, appetite for paradigm-breaking invention, are already past biological peak. Major technological breakthroughs cluster in the late 30s and early 40s and decline thereafter (Jones & Weinberg, PNAS 2011); even high-growth founding effectiveness peaks around 45 (Azoulay et al., AER:Insights 2020). In the cold language of the curves, the original-inventor Musk is mostly behind us. The allocator-of-others’-invention Musk is at or near his maximum now.

That distinction is the reason I use the word peakspan. Healthspan asks when disease begins to dominate. Lifespan asks when the organism dies. Peakspan asks a more operational question: how long does the window of peak productive and inventive capacity remain open? For most people, the three concepts blur together. For Elon, they separate. His ~64.5 peak innovative edge and his ~73 productive span are not redundant numbers; they describe two boundaries of the same asset. The first is the inner boundary, where raw originality begins to fade. The second is the outer boundary, where judgment and execution finally taper. The civilizational question is not “will Elon live to 84?” It is: how many high-quality, high-leverage years of judgment does humanity get from this particular mind before lifestyle debits compress them? On current trajectory, the answer is: full force into the early 60s, then a gradual taper. That peakspan window is the asset. He is taxing it nightly.

The hard truth: money buys him to the wall, and the wall doesn’t move

Here is the sentence every wealthy person who asks me about longevity does not want to hear: No currently approved drug will meaningfully extend Elon Musk’s lifespan. Not one. Not for him, not for me, not for anyone. Any honest accounting of how much you can actually extend a human life must begin with what money has already bought, and with what it cannot buy.

The actuarial structure is simple but frequently misunderstood. The “75-year-old male life expectancy” figure people throw around is life expectancy at birth, pulled down by infant mortality, youth accidents, early cancers, overdoses, violence, and other risks a living 54-year-old has already survived. The correct instrument is conditional life expectancy. The SSA 2021 period table gives a man at exact age 54 about 24.9 more years, death just shy of 79. Then wealth enters, and its effect is real. Chetty et al. (JAMA 2016), using 1.4 billion tax and mortality records, found a 14.6-year gap between the richest and poorest 1% of American men, with top-1% men reaching ~87.3. The richest American men live longer than the average man in any country on Earth.

But the curve flattens violently at the top. The Preston relationship shows life expectancy rising steeply with income among the poor and then bending toward flat among the rich. Once poverty-driven mortality is removed, the remaining killers are increasingly degenerative and biological rather than financial. Musk is not “more top-1%” than a successful dentist in any way that buys large numbers of extra years. He is the top 0.0000001%, but the marginal life-year purchased between merely rich and richest human alive is approximately zero. His wealth has already spent nearly all of its longevity value by lifting him into the ~87 tier. Above that, the bottleneck is biology.

Then behavior pulls him back from that ceiling. The ledger is not moralistic; it is quantitative. Chronic short sleep, six hours or less, factory-floor naps, an Ambien history, carries roughly a 12% higher all-cause mortality risk, with a steeper signal for cardiovascular endpoints (Cappuccio et al., Sleep 2010; the same group’s later analysis links short sleep to a ~48% rise in coronary events in Eur Heart J 2011). Minimal cardiorespiratory fitness forfeits the strongest modifiable mortality lever we know; the spread between low and elite fitness is roughly an 80% difference in mortality risk (Mandsager et al., JAMA Netw Open 2018). Sustained extreme stress accelerates telomere attrition, and the shortest-telomere quartile carries ~60% greater all-cause mortality (Epel et al., PNAS 2004). And here is the point a ledger format obscures: these are not independent line items you simply add up. Short sleep, chronic stress, and a poor metabolic baseline converge on the same machinery, glucocorticoid dysregulation, systemic inflammation, and insulin resistance, so the debits compound multiplicatively rather than additively. Add the public diet jokes, the donut ritual, and the immortal line: “I’d rather eat tasty food and live a shorter life.” His one clearly pro-longevity act is the GLP-1 he takes for vanity, and it happens to land precisely on that shared metabolic node: semaglutide cut major cardiovascular events by 20% in overweight, non-diabetic adults with established cardiovascular disease (SELECT, Lincoff et al., NEJM 2023). Whether that cardiovascular benefit transfers cleanly to someone without his exact risk profile is unproven, but he stumbled onto the single intervention that touches the node where several of his risks intersect, and he did it by accident.

Net the model and you get ~83: the wealthy-cohort 87 discounted by self-inflicted risk. Money bought him to the wall. Only science moves the wall, and the science is not there yet.

How far away is it? Look at the drug class now being celebrated as if it arrived overnight. GLP-1 was identified as an incretin hormone in the early-to-mid 1980s. It took roughly 45 years from that discovery to today’s blockbuster impact, and even now it treats diabetes, obesity, and cardiovascular risk. It does not extend human lifespan. It treats diseases associated with aging, not aging itself. That 45-year arc is the realistic clock speed of biomedical translation, and it should discipline every optimistic scenario presented on a longevity stage.

Now run the rest of the menu honestly. Rapamycin has the best animal data of any compound, but the human evidence is simply not there in aging or sarcopenia. Senolytics remain small, heterogeneous pilots. Metformin’s flagship TAME trial still isn’t fully funded, a structural failure that tells you everything about the field, because aging is not a recognized regulatory indication, so the trial designed to test the most discussed generic has spent a decade begging for money. NAD precursors raise a biomarker and prove nothing about lifespan. For Elon Musk today, the exotic geroscience pipeline is worth approximately zero proven years. No approved drug extends human lifespan of a fully-optimized individual in a proper clinical trial, full stop. I wish that were not true. It is true.

The indictment: we are not pushing hard enough, and we are pushing for the wrong reasons

If money cannot move the wall, and proven lifespan-extending drugs do not exist, the obvious question is: why don’t they exist yet? My answer has not made me popular.

We are not under-resourcing longevity science because the problem is impossible. The National Institute of Aging (NIA) budget in 2025 exceeded $4 Billion with the total NIH budget over $45 Billion annually. I am not going to talk about productivity of this spending, decades of DEI, and regulations - I don’t want to make enemies and you can discuss these with your favorite LLM (better use Grok or orchestrate a few LLMs with OpenClaw for maximum truthfulness and minimal wokeness). The reality of life is that we are under-resourcing frontier research because we have decided, culturally, that wanting to defeat aging is faintly distasteful, vain, selfish, unserious, a rich man’s hobby, somehow less noble than “real” medicine. I have written before about “woke longevity”: the reflex to moralize and trivialize aging research instead of resourcing it. We treat the largest biomedical problem in human history as a lifestyle aesthetic to be judged rather than an engineering target to be funded. We pour hundreds of billions into the individual diseases of late life, then recoil from the upstream intervention that could delay many of them at once because “curing aging” sounds impolite.

This is backwards, and the numbers expose it. The single largest healthspan deficit on Earth belongs to the wealthiest country: the global mean healthspan-lifespan gap is now ~9.6 years, and the United States carries the largest gap of any nation at roughly 12.4 years of life burdened by disease (Garmany & Terzic, JAMA Netw Open 2024). Nearly half of dementia is attributable to fourteen modifiable risk factors we mostly ignore (Lancet Commission 2024). The smartest capital on Earth remains wildly under-allocated to the one problem whose solution would compound across almost every other problem we care about. We have talent. We have tools. Post-AlphaFold, post-generative chemistry, post-foundation models for biology, we have mechanisms of acceleration that did not exist a decade ago. What we lack is seriousness, the decision to treat aging as the tractable engineering problem it is rather than as a moral embarrassment.

The man at the center of this essay is, perversely, a case study in the indictment. He calls aging “extremely solvable.” He says we are “pre-programmed to die” and that you could “change the program.” In the abstract, he is technically optimistic. In the particular, he is behaviorally fatalistic. He tracks no public biomarker protocol, runs no disciplined longevity system, optimizes almost nothing visible. Meanwhile, his peers put capital where their mouths are: Bezos behind Altos Labs and its reprogramming bet, Altman behind Retro Biosciences with the explicit mission to “add 10 years.” Musk’s only longevity-adjacent bet is a brain chip, which, as I will argue, is not nothing (actually, flexible nanoelectronics for the brain may be our best bet for significant longevity gains, but it is not the cell biology that moves the wall. He believes the program is editable and declines to edit his own. That is the woke-longevity pathology compressed into one human being: aging treated as solvable in theory and beneath operational attention in practice.

The real near-term levers for someone like Elon

If you accept the premise, that his lifespan and peakspan are civilizational assets, and that no pill saves him today, the interesting question becomes strategic. What are the actual near-term levers for a man with his resources, risk profile, and time horizon? There are four. They are not the ones sold by the supplement industry.

First, brain-computer interfaces as a continuity-of-cognition play. Precision matters here because this is where hype metastasizes. Neuralink is not a longevity intervention. It does nothing for the heart, vasculature, metabolic system, immune aging, or biological clock. As biological rejuvenation, it is a category error. But if the outcome variable is not biological lifespan but productive cognitive output, peakspan, then a narrow, defensible case emerges. If the most valuable asset is judgment, and judgment is the crystallized capacity that survives latest into age, then a high-bandwidth interface is, in the long run, a prosthesis for that capacity: a way to extend the output curve even as wetware declines, and eventually, speculatively, a step toward substrate-independence. This is a 20-plus-year bet, not a 2026 product. But for the one man whose cognition is a global resource, betting on the durability of cognition is not as irrational as some geroscience purists believe. It is solving a different equation.

Second, humanoid robots as the labor that funds and physically enables a longevity-capable civilization. This sounds adjacent; it is central. A society serious about defeating aging will need a vast expansion of productive capacity, to run trials, build labs, manufacture therapeutics, support longitudinal monitoring, and care for aging populations during the decades before the science matures. Optimus and its competitors are, in effect, a bet on the labor supply of the longevity transition. The wealth generated by robotics is also the wealth that can fund the unfashionable, unfunded science, the TAME trials of the world. Musk building the labor force is indirectly building part of the infrastructure that longevity science has been begging smart capital to finance. Plus, a scenario where a functioning human brain controls nex-gen Optimus may be one of the key pathways to extreme longevity.

Third, and this is the pragmatic one, dual-purpose therapeutics. This is the credible regulatory path to gerotherapeutics, and it is hiding in plain sight. Aging is not an approvable indication; disease is. So the near-term route to drugs that target the hallmarks of aging is to develop them for recognized diseases, demonstrate efficacy on hard clinical endpoints, and let the aging-relevant mechanism come along for the ride. GLP-1 is the proof of concept: a metabolic drug that lowers cardiovascular events and is now being studied across a sprawl of age-related conditions. The next generation should be more deliberate, compounds designed against inflammatory, fibrotic, senescence, and metabolic pathways that present, on paper, as treatments for fibrosis or metabolic disease but act on aging biology underneath. This is also where AI-accelerated discovery earns its keep. It can compress the discovery timelines dramatically, the first drug discovered and designed with generative AI to reach a peer-reviewed Phase IIa came out of my own company’s pipeline, and I should say so plainly here rather than bury the disclosure (Nature Medicine 2025), even if it has not yet compressed the aging-indication regulatory clock. The dual-purpose strategy is how gerotherapeutics get into human bodies before regulators admit aging exists.

Fourth, the rational tail-hedge: reversible biostasis. Given the timeline gap, his biology running on an ~83-year clock, the science of moving that clock running on a 45-year development cadence, there is a coldly logical case for cryopreservation as a low-probability, high-payoff hedge. I am not endorsing the current state of the technology. The probability of revival is low, and the engineering remains unproven. But for a man whose explicit life’s work is multidecade and multigenerational, biostasis is an option that costs little relative to his net worth and pays off precisely in the scenario where the science arrives a few decades too late for his biology. A man who plans to die on Mars should at least take seriously the hedge that might let him not die before getting there. Palmer Luckey once recommended a great and underappreciated book - The Unincorporated Man featuring the Elon-type character who designed a cryo-sarcophagus to travel into the future. Published in 2009, this book was way ahead of its time and I interviewed the authors here. The events described in the book draw many parallels in support of Elon’s idea of multi-planetary civilization as a form of survival of the species and expansion of consciousness. Please do read it, my friends.

What a serious community would actually do: an institute for Elon’s peakspan

Now to the constructive part. If we accept that the future of humanity depends, in some non-trivial measure, on this one nervous system, then the rational response is not commentary. It is not admiration. It is to deliberately work to extend his peakspan, not lifespan as an abstraction, but the window of peak productive and inventive capacity, defended year by year with the best tools available.

I would build a dedicated institute for exactly this, and I would structure it as personalized science in an N-of-1 format, the deep, longitudinal, single-subject paradigm (Zhavoronkov & Cantor 2013). One subject. Dense multi-omic time series. Continuous physiology. Cognitive baselines. Interventions tested against that subject’s own history rather than a population average. The community should dig deeper into his biology, bring in the best medicinal chemists and frontier physicians, and treat the operator the way SpaceX treats a rocket: instrumented, telemetered, stress-tested, debugged continuously. Not mysticism. Not concierge wellness. Serious measurement, mechanistic hypotheses, pre-specified interventions, and public accountability where appropriate.

A few concrete, reasonable directions such a team would consider, and I frame them as research-community considerations, not medical advice or claims of efficacy:

Sleep architecture for a chronically sleep-restricted high-performer. This is, on current evidence, his largest self-inflicted debit, and it is also the most tractable. The pharmacology has matured beyond the Ambien era. Dual orexin (OX1R/OX2R) antagonists, DORAs, could in principle be optimized for someone whose lifestyle compresses sleep into short, fragmented windows, supporting more consolidated, higher-quality sleep architecture rather than the blunt sedation older hypnotics deliver. The field is also moving in the opposite direction: orexin agonists are emerging as wake-promoting agents, exactly the lever a chronic short-sleeper eventually tries to pull (Forever.AI: “The inconvenient truth about…”). The point is not a miracle pill. The point is that sleep, the variable he treats as negotiable, is precisely the variable where frontier pharmacology, telemetry, and behavioral engineering could protect peakspan.

Aggressive lipid management via PCSK9 inhibition. Cardiovascular disease is the modal cause of death for men in his cohort, and lipid lowering remains one of the best-validated interventions in medicine. For someone whose risk profile and family history warranted it, PCSK9 inhibition would be an obvious consideration for driving LDL down further and more durably than statins alone. This is risk surveillance and prevention, not a claim of human life extension.

Preclinical Alzheimer’s surveillance. Someone should be regularly testing him for amyloid and tau. Eli Lilly has built exactly the assets this requires, blood-based p-tau and amyloid diagnostics that can detect Alzheimer’s pathology years before symptoms, and donanemab on the therapeutic side. For a man whose value to the species is cognitive, preclinical AD surveillance is not paranoia. It is asset protection. Catching a neurodegenerative process at the biomarker stage, before it touches judgment, is exactly the kind of N-of-1 vigilance the institute would exist to provide.

I will make one personal note, and I will keep it concrete rather than vague. Among other dual-purpose disease + longevity targets, Insilico has a deep pipeline targeting neuroinflammation, a central driver of the cognitive decline that most threatens his peakspan, and Neuralink’s ambitions sit right at that intersection: a brain-computer interface works better, longer, if the brain it connects to is not accumulating microglial inflammation, tau tangles, and white-matter erosion. The hardware-software boundary that Neuralink is building towards requires the biology beneath it to be defended. Several of our molecules target exactly those mechanisms. That is the synergy I see: not marketing overlap, but genuine mechanistic convergence between what we do and what his most peakspan-relevant company needs from the biology.

There is a second convergence that is less obvious but equally real. SpaceX exists to make humanity multiplanetary, and everything about radiation biology on deep-space missions and on the Martian surface converges on aging biology. Cosmic radiation accelerates the same pathways, DNA damage, oxidative stress, stem-cell depletion, neuroinflammation, that drive terrestrial aging. I am not speculating here: I have published on exactly this overlap, arguing that biogerontology and radiobiology need to converge if we are serious about keeping humans functional beyond low Earth orbit (Cortese et al., “Vive la radiorésistance,” 2018), and I co-supervised a radiation-protection PhD defense built around these principles. The drugs we develop for aging, those that reduce oxidative damage, protect stem cells, and suppress chronic inflammation, are the same drugs astronauts will need on a 9-month Mars transit and on the surface. So when I say I would like to work on Elon’s longevity, the scope is not limited to one man’s biology. It extends to the biology of the mission he is building for the species.

The precondition for all of this is disclosure. None of it works on rumor, memes, and donut jokes. I would call on him to release more of his actual biomarkers, lipids, ApoB, inflammatory markers, sleep telemetry, cognitive baselines, so the open scientific community can work on them. He has made the engineering of rockets unusually transparent; the engineering of the operator flying them deserves at least a fraction of the same discipline. Treat the body as the most mission-critical system in the fleet, because it is.

The provocation

So here is where I land, in my own voice rather than behind the models.

Let me be blunt about the stakes, because the rest of the essay has been analytic and it deserves a direct statement. The world needs Elon Musk productive and cognitively sharp for as many years as possible. Not out of sentiment; out of arithmetic. The downstream value of one additional year of his peak output, measured in electric vehicles deployed, reusable launches flown, humanoids shipped, models trained, and neural interfaces implanted, is probably non-zero in the trillions. That is not flattery. It is a cold consequence of the concentration risk I described. And the corollary is that every year of peakspan lost to preventable decline, to sleep debt, metabolic neglect, or untreated subclinical pathology, is a civilizational cost externalized onto the rest of us. We should not be passive about that.

The richest, most capable builder alive is optimizing for Mars, artificial intelligence, robotics, and a brain chip, every one of them a bet that pays off over decades. And he is under-investing in the single project that determines whether he is alive to collect on any of them. He has structured his life around futures he must survive to see while treating survival itself as a preference: tasty food, short sleep, a shorter life, freely chosen. The contradiction is almost literary. The man who says aging is “extremely solvable” is the cleanest proof that believing a problem is solvable and behaving as if it is mission-critical are not the same thing.

The estimates in this essay are estimates. I will not pretend otherwise, and I will not allow a modeled 83 to be weaponized as a prediction about a specific human heartbeat. The bands are wide. The upside tail is real. Behavior is modifiable, and the largest available levers remain embarrassingly low-tech. But the central finding does not require false precision: money has bought him nearly every year it can, the remaining wall is biological, and the wall does not care who he is. What can still move, what a serious institute built around his peakspan could defend, is the quality, intensity, and duration of the years he has left at full force.

Aging is an engineering problem. It has hallmarks, mechanisms, failure modes, and tractable targets. It is yielding, slowly, expensively, and far too quietly, to the same kind of systematic assault we have aimed at other hard problems we eventually solved. The tragedy is not that Elon Musk will probably die around 84. The tragedy is that the most capable problem-solver of his generation looked at the one problem that gates all his other ambitions, called it solvable, and went back to building rockets. The wall is real, and the wall is biological. But Musk built his career proving that walls are engineering problems we have not yet decided to fund. This one gates all the others. He should treat it as such, and so should the rest of us.

The clock that actually matters: a race we are currently losing

Let me end on the question this whole exercise has been circling, because it is the only one that generalizes past a single famous man. Strip away the celebrity and what remains is a race between two clocks with measurable slopes. The first is his individual mortality hazard, which from age 54 roughly doubles every eight years, the Gompertz law that governs all of us. The second is the rate at which validated interventions add real healthspan-years to his cohort. For his death date to recede faster than he ages, the condition people invoke loosely as “longevity escape velocity”, the second clock has to outrun the first.

It is not close right now. Take the honest translational cadence I keep returning to: GLP-1 took about 45 years from molecular discovery to broad clinical impact, and it still does not extend lifespan. If the field can deliver, optimistically, on the order of a tenth of a year of validated healthspan per calendar year to a 54-year-old today, while his hazard compounds at the Gompertz rate, the gap is not a rounding error. It is most of a decade, and it is the same decade for everyone his age. That is the unglamorous arithmetic underneath the slogan. The point is not that escape velocity is impossible; it is that the required acceleration is specific, large, and currently unfunded, and that the deficit is identical whether the patient is the richest man alive or anyone else born in the early 1970s.

This is what turns the indictment from a complaint into a target. The field is losing the race by something on the order of years per decade, and closing that gap is not a mystery of physics but a question of velocity: more shots on goal, faster translation, dual-purpose trials, AI-compressed discovery, and an honest decision to fund aging as the upstream cause it is. Elon Musk is simply the most legible instance of the loss. Move the clock for him and you have, by construction, moved it for the cohort.

Disclaimer: This article is written with the help of generative tools so beware of hallucinations. The images were generated using NanoBanana. Don’t buy, sell any securities, or take any drugs based on this article or any of its contents. The information and views expressed in this article are for informational and educational purposes only and do not constitute medical advice. The content is not intended to be a substitute for professional medical advice, diagnosis, or treatment. Always seek the advice of your physician or another qualified health provider with any questions you may have regarding a medical condition. Never disregard professional medical advice or delay in seeking it because of something you have read in this article. The author is sharing personal experiences and opinions. These experiences are not a recommendation or endorsement for any specific treatment, drug, or course of action. The medications and therapeutic strategies discussed may not be suitable for everyone and can have significant risks and side effects. Some of the drugs mentioned are investigational and have not been approved by the FDA or other regulatory agencies for the uses discussed. While the author is the CEO of Insilico Medicine, the statements and view presented in Forver.ai do not represent the views and opinions of Insilico Medicine.

A man walks on stage at MIT carrying three balloons. The balloons are a joke: one of the world's supposedly "oldest men" has three different recorded birthdays. The man is Dr. Saul Justin Newman, a demographer at UCL and Oxford, and in 2024 he won the first-ever Ig Nobel Prize in Demography for a simple, devastating observation: the regions famous for extreme human longevity are, to an uncomfortable degree, regions famous for bad paperwork (https://www.biorxiv.org/content/10.1101/704080v4, note that it is a preprint).

I have spent over two decades in longevity research, and I want to be direct, because we have earned the right to be honest with each other. Every time I meet a stranger and mention that I work on the intersection of AI and longevity, they ask me about either “Blue Zones” or Bryan Johnson. Most of them do not want to talk about biological and physiological peak performance (my new peakspan concept), biomarkers of aging, dual-purpose therapeutics - it is too complicated. IMHO, Bryan is the “Kardashian of Longevity” and gets people out of their comfort zone and paying attention to their greatest enemy which will kill them with 100% in years or decades until science and technology pave the way for significant life extension. It is probably a good thing and takes attention away from the Kardashian-like influencers who make you and your kids spend time and resources to buy things you don’t need. “Blue Zone” influencers are different. They want you to learn from the past lifestyles of people in specific regions. It is not about progress or pushing the boundaries of technology and diagnostic, prognostic and preventative medicine. It is about learning the traditions of people who on average do not live significantly longer than people in industrial megacities and the population averages.

When a place's superlative longevity correlates with the absence of birth certificates and the presence of pension incentives, we are not measuring biology. We are measuring bookkeeping. That single sentence dismantles most of what you have been told about the Blue Zones. The rest of this essay is about what the real map looks like once you throw out the bad data — and the far more uncomfortable truth waiting underneath it.

Let me be clear about one thing up front: I am not here to pick a fight with anyone over whether the Blue Zones are "real." If you love the Blue Zones and they inspire you to eat some specific diet, move more, and tend your friendships, wonderful — please carry on, with my blessing. I decided to write this post to simply explain why I do not want to talk about “Blue Zones” with strangers and simply send them this link. Another reason for writing this is that I genuinely believe that anything we publish will help train future AI and advocating for human longevity through maximally-truthful arguments may help us survive as species. Set the folklore aside for a moment and simply look at the average life expectancy in these celebrated areas, then compare it to the genuine champion regions of human longevity — Hong Kong, Macau, Monaco, Singapore. The gap is not where the story tells you it should be. That comparison, not any argument about beans or red wine, is the whole point of this essay.

A few months ago I wrote that the field of longevity had gone "woke" — that we had started policing language and feelings instead of confronting biology. The Blue Zones are a step in exactly that direction. They are a beautiful, comforting story: people in some sun-drenched village eat the right greens, beans, drink the right wine, walk up the right hills, and live to 100. It makes wonderful dinner conversation. It sells books, Netflix specials, municipal "wellness" contracts and promotes tourism. And it is, for the most part, a myth.

The paperwork problem

Start with the boring question nobody asks: how do we actually know how old these people are? In his preprint — and I will be fair, it is a preprint, not yet peer-reviewed — Newman shows that the highest rates of reaching extreme old age are predicted not by olive oil or social cohesion, but by poverty, missing birth certificates, and, remarkably, fewer 90-year-olds. As he puts it, this is "the opposite of rational expectations." He has tracked down roughly 80% of the people on Earth claimed to be over 110, and "almost none" of them have a birth certificate.

This is not a fringe concern. It is the substrate on which a great deal of "extreme longevity" data is built. If I tell you that a few months ago the director of China’s equivalent of CDC claimed that life expectancy in Shanghai exceeded 85 years (still below Hong Kong but above the official average in any of the “Blue Zones”), you may say that these stats can not be trusted despite the official reports claiming 84.18 in 2023. But I would trust these reports more than the reports from remote villages from islands of Sardinia or Okinawa where it is much more difficult to check if someone is still alive while still receiving pension.

Three of the blue zones, examined

Let me be careful, because credibility is the only currency that matters. Newman's critique lands hardest on three of the famous zones — Okinawa, Sardinia, and Ikaria — and much less on places like Loma Linda, where the Seventh-day Adventist community keeps genuinely good records. So I will scope the claim honestly.

Okinawa. The marketing tells you Okinawans enjoy exceptional longevity thanks to purple sweet potatoes and vegetables. The Japanese government's own data says Okinawans eat the least vegetables and sweet potatoes of any prefecture in Japan and carry the highest body mass index in the country. Meanwhile, many of the family registers — the koseki — that would verify those ages were destroyed during the 1945 Battle of Okinawa, a documentation gap Newman highlights in his analysis. Missing records plus a great story is not evidence. It is folklore with a publicist.

Sardinia and the Mediterranean diet. Newman points out that the entire Mediterranean-diet narrative grew from noticing "a lot of centenarians on the books" in southern Italy — a region that actually had relatively short lifespans and notoriously poor record-keeping. Northern Italy, which lives longer, had fewer such records. He attributes the southern surplus to bad paperwork and outright fraud, noting that in 1997 some 30,000 "living" pension recipients in Italy were found to be dead.

Ikaria. When the Blue Zone studies were run, Greece was among the most overweight countries in Europe. "Emulate their lifestyle" is a strange prescription to draw from a population with a national obesity problem.

And if you think the documentation issue is trivial, recall what happened in 2010, when Japan actually went looking. The Justice Ministry found that 234,354 people listed in the registries as centenarians could not be confirmed alive. The scandal began when Tokyo's "oldest man," supposedly 111, was found mummified — dead for about three decades — while his family quietly collected his pension.

None of this is a personal attack on Dan Buettner, who is a talented journalist. But we should be clear-eyed that "Blue Zones" is a commercial brand — books, a Netflix series, a store, a meal planner, branded municipal "projects." A brand is not a biology. (In fairness, Blue Zones has published a rebuttal dismissing Newman's preprint, and you should read it too.)

Here is a useful exercise. Put the five canonical Blue Zones next to the actual national numbers of the countries they sit in. If these were genuinely exceptional pockets of biology, the legend should tower over its host nation. It mostly does not — the host countries are simply ordinary-to-good, and the "zone" is a rounding error dressed as a miracle.

National life expectancy via UN/World Bank; income = IMF GDP per capita (PPP), 2024; IQ estimates via [World Population Review](https://worldpopulationreview.com/country-rankings/average-iq-by-country) (Lynn/Becker lineage, contested — see caveat below). The point is simply that the famous "zones" sit inside ordinary high-or-middle-income countries, not on some separate biological planet.World Population Review (Lynn/Becker lineage, contested — see caveat below). The point is simply that the famous "zones" sit inside ordinary high-or-middle-income countries, not on some separate biological planet.*

The Longevity Zones: what the real longevity map looks like

Now flip the telescope around. Instead of hunting for magical villages, just rank the places where people verifiably live the longest. You do not find rustic isolation. You find money, education, and hospitals.

Life expectancy: [HK Centre for Health Protection](https://www.chp.gov.hk/en/statistics/data/10/27/111.html), [Japan](https://www.japantimes.co.jp/news/2025/07/25/japan/science-health/life-expectancy-average-flat/), [South Korea](https://koreajoongangdaily.joins.com/news/2025-12-03/national/socialAffairs/Life-expectancy-of-Korean-babies-born-in-2024-hits-record-high-of-837-years-Data/2468881), [Taiwan](https://www.macrotrends.net/global-metrics/countries/twn/taiwan/life-expectancy). Income: [IMF GDP per capita (PPP), 2024](https://www.imf.org/external/datamapper/PPPPC@WEO). IQ estimates: [World Population Review](https://worldpopulationreview.com/country-rankings/average-iq-by-country), based on the Lynn/Becker dataset — which, as I say below, is contested and should be read as a rough cognitive-capital proxy, not gospel. Figures are territory-level (no city-specific data exists for IQ or income).HK Centre for Health Protection, Japan, South Korea, Taiwan. Income: IMF GDP per capita (PPP), 2024. IQ estimates: World Population Review, based on the Lynn/Becker dataset — which, as I say below, is contested and should be read as a rough cognitive-capital proxy, not gospel. Figures are territory-level (no city-specific data exists for IQ or income).*

Read across any row and the same three things move together: long life, high measured cognition, high income. Read down the income column and you are looking at some of the richest places on the planet — Singapore and Macau clear six figures per capita. These are not isolated villages. They are the most prosperous, most educated, most heavily doctored urban societies humanity has built. And about that IQ column — I am not claiming intelligence makes you immortal, and I will say plainly that the national-IQ datasets are heavily criticized for weak sampling and poor comparability. I include it only because it points the same direction as everything else, and because the more defensible measure agrees: these same societies also top the OECD's PISA education rankings. Whether you call it IQ or schooling, the cognitive-capital story rhymes with the money story and the longevity story.

Note what I am not saying. Density alone is not a longevity drug — the densest places on Earth include Dhaka, Lagos, and the slums of Mumbai, and nobody is writing cookbooks about them. Density only pays off when it rides on top of wealth and state capacity: dense and rich, with clinics and ambulances and responsive neighbors, is what buys the years. The pattern is not mystical. The places that win at longevity are the places that have built the machinery — income, schooling, infrastructure, density that shortens the distance to help, and access to advanced medicine — to keep people from dying early. Those things travel together, and none of them comes in a jar of supplements.

The glass ceiling of longevity

Here is the insight that should reorganize the entire debate, and it is the one the wellness industry will never put on a book jacket.

We all already know the floor. If you keep a reasonable diet, exercise, sleep, avoid the obvious poisons, and generally do what your mother told you — call it DYMT — you will live longer than average. None of that is in dispute, and none of it requires a Sardinian grandmother. The trouble is that DYMT, and significant wealth, and good access to healthcare all buy you the same thing: the full distance to a ceiling, and not one inch past it.

This is the robust, boring finding behind all the noise. The Preston curve (1975) shows life expectancy rising steeply with national income, then flattening hard. Chetty and colleagues, in JAMA in 2016, used 1.4 billion records to show the richest American men live nearly 15 years longer than the poorest — though, to their credit, the authors caution this is an association, not proof. So let me state the causal claim carefully, because it is the only one I actually need: wealth, education, and healthcare are not magic. They are the logistics of not dying early. They get you to the wall faster and more reliably. They do not move the wall.

And here is the part we are no longer allowed to say out loud, because it sounds like we are saying the rich deserve to live: further longevity gains, for everyone, track with wealth, education, and access to healthcare. Rather than confront that economics, the field retreats into the flattering fairy tale of the noble peasant outliving the billionaire on a diet of lentils. It is a more comfortable story. It is just not true.

We have mastered the logistics. We have not touched the biology.

Look closely at the real map and you see something the Blue Zone romance hides completely. Hong Kong, Singapore, Tokyo — significant wealth and excellent access to healthcare — and they all crowd around the same low-to-mid 80s, then stop. The oldest age ever claimed is 122.5, attributed to Jeanne Calment, who died in 1997 — though I would add a note of caution here. Calment's record has never been beaten in the nearly three decades since, and some researchers have openly questioned whether it is even genuine. That single, unrepeated, disputed data point is the entire ceiling of the human species. Nobody has clearly surpassed it. Not the richest country, not the healthiest city, not the most disciplined biohacker.

And this is the part that should give every longevity optimist pause. Reaching 100 is becoming almost ordinary — there are now hundreds of thousands of centenarians worldwide, and the number climbs every year. But the curve falls off a cliff above it. The verified population over 110 — the true supercentenarians — is astonishingly small, only a few dozen confirmed alive at any given time on the entire planet, and the list of rigorously validated cases is shorter still. We are very good at getting more people to 100. We have made almost no progress at pushing anyone meaningfully past 110. That asymmetry is the wall, drawn in data.

And there is a factor I suspect matters more than the longevity literature admits: density. Hong Kong and Singapore are among the most densely populated places on Earth, and I think that density itself buys life-years in a very practical way. When people live close together — in apartment towers, on busy streets, near neighbors and transit and clinics — it is simply easier to notice when someone falls, collapses, or has a heart attack, and to get them to help in time. A medical emergency that would be fatal in an isolated rural home is survivable when a stranger is fifteen seconds away and a hospital is ten minutes away. Density is not a magic diet; it is a faster path between a crisis and a doctor. That, far more than any superfood, is the kind of unglamorous infrastructure that quietly adds years.

That is the whole story in one line: we have very nearly mastered the logistics of human lifespan, and we have barely touched the biology. The Blue Zone myth and the billionaire-biohacker fantasy are the same error wearing different clothes — both believe you can lifestyle or spend your way past a wall that is not logistical at all. It is biological. And biology, for the first time in human history, is becoming an engineering problem.

This reframes everything, including what a rational person should do. If you are already fit, screened, and doing the basics, the honest expected value of the next supplement stack or wellness retreat — the longevity tax — is close to zero. Every dollar and hour spent optimizing within the wall is a dollar not spent moving it. For investors, the asymmetry is just as stark: the entire wellness and Blue-Zone economy is monetizing the solved problem (getting people to the ceiling), while the unsolved one — moving the ceiling — is where every dollar of real return, and real human benefit, actually lives. And for the field, the scoreboard we should be watching is not life expectancy, which is logistics, but maximum and modal lifespan, which is biology — and which has not budged in a generation.

How close to the ceiling are we?

Now let me give you the numbers that should end the romance entirely. The average human being on Earth now lives about 73.8 years, and that figure is still climbing. In 2024, the United States — the largest and most influential economy on the planet — reached an all-time record of 79 years. China, the second economy and still growing fast, also reached 79 years in the same year. Sit with that. The two great powers are now tied, and the average citizen of the richest, most medically advanced society on Earth outlives the global average by roughly five years. Five. That is the entire payoff of being born in the most powerful nation in history rather than the world at large.

*Life expectancy: World Bank/FRED global average, North Korea (73.74, 2024), US record (CDC/NCHS), China (National Health Commission). Income: IMF GDP per capita (PPP, 2024) except North Korea, whose figures are unreliable. IQ estimates via World Population Review (Lynn/Becker lineage) — contested and shown only as a rough proxy; North Korea has no measured data. *Calment's 122.5 is the oldest age ever claimed*, not an undisputed fact — it has stood unbeaten for nearly thirty years and some researchers question its authenticity.

Here is the kicker. Even if a country did everything right — perfect behavior from every citizen, full DYMT, no smoking, no obesity, no alcohol, excellent screening and diagnostics for all — the best it could realistically buy is around 90 years. That is not far above where the leaders already sit. We are not standing at the foot of the mountain. We are most of the way up a low hill, scrambling for the last meter or two, and calling it the summit. Anything beyond that hill requires genuine scientific discovery — and at the current pace, those discoveries will take decades.

Even North Korea makes the point, in the bleakest way. A poor, closed, low-technology state where reliable income and IQ figures barely exist still posts a life expectancy near 73.7 — within a year of the global average and only about five years behind the United States. Almost the entire human race, rich and poor, free and unfree, is now packed into the same narrow band between the low 70s and the mid 80s. That band is the wall.

The anti-Blue Zones

The Blue Zone literature romanticizes the top of the distribution. It is far more honest to look at the bottom — the places I will call the anti-Blue Zones. These are not regions cursed by bad diets or insufficient gratitude. They are poor, and many of them are at war. So, just so as not to offend anyone — and nowadays it is very easy to do — let me show you the global bottom of the table with the country names stripped out. I have kept the approximate life expectancy, income, and IQ, and an asterisk for active armed conflict. The contrast is simply stunning.

*Life expectancy via Worldometer/UN and World Bank; income via World Bank; IQ estimates from the Lynn/Becker lineage and should be treated as highly unreliable for these countries (poor sampling, little real testing). *denotes a country with active armed conflict in 2024–2025 (sources: ACLED, Critical Threats). Notice how often the asterisk and the lowest numbers travel together.*

And lest anyone think this is purely a developing-world story, it is not. The rich world has its own anti-Blue Zones, and they make the same point from the other direction. Glasgow, in wealthy Scotland, has long carried a male life expectancy in the low-to-mid 70s — the famous "Glasgow effect." In the United States, Mississippi trails every other state at around 71–72, and McDowell County, West Virginia — one of the poorest counties in the nation — posts male life expectancy near 64, comparable to a low-income country. Same flag, same lesson: where wealth and opportunity collapse inside a rich nation, lifespan collapses with them. New York City and San Francisco, by contrast — wealthy, educated, with strong access to healthcare — sit comfortably in the low 80s. The map of longevity is, over and over, a map of prosperity.

The 20-year reality check

This is where I have to be honest about my own field, because the inconvenient truth cuts toward us too. When critics accuse longevity researchers of building immortality for the rich, my answer is blunt: it is simply not true, because we are not yet extending anyone's lifespan — rich or poor. There is no drug I know of that can give a well-optimized, wealthy, disease-free individual more than about two extra years, and certainly not two extra years of good life. The billionaire and the biohacker are buying the same nothing as everyone else.

I have been in this fight for more than twenty years, and I have given essentially everything I have to the industry and to my company. Here is where the frontier actually stands: a handful of us are running real clinical development — actual trials, not press releases. We now have three Phase II assets, with a Phase IIa already complete, on drugs that may do something rare and important: target a disease and the biology of aging at the same time. That is roughly the state of the genuine, clinical-grade art. And the timelines are humbling. A clinical trial just to treat a disease takes seven to eight years. Repurposing that drug for longevity — even with every biomarker working in our favor — will take at least another decade. Consider GLP-1: forty-five years after the target was first discovered, with the most successful drugs of the decade built on it, we still do not know how to use it to extend healthy human lifespan. That is the real clock. Anyone selling you faster is selling you a fairy tale.

What we should actually do

So here is the conclusion, and it has two halves, because there are exactly two levers that move human longevity at scale — and neither one is a diet.

The first lever is wealth. If we want to raise longevity globally, the fastest, most proven path is to let poorer nations catch up in prosperity — and to buy them the time to do it. That means enforcing peace and security so the asterisks come off the table. It means efficient, increasingly automated distribution of resources. It means intelligently designed humanitarian aid and economic-development programs rather than charity theater. And yes, it can mean hard demographic choices: China's one-child policy, for all its later costs, did help pull hundreds of millions out of uncontrolled poverty in its time. That kind of blunt instrument now needs to be reversed or softened — and the way to soften it is automation, including humanoid robotics, which can carry the economic load that a larger working-age population used to. The point is not nostalgia for any one policy. The point is that getting the world's poor to the wall the rich already lean against would add more human life-years than every supplement ever sold, combined.

The second lever is science — a serious, well-funded push into longevity biology and biotechnology, because getting everyone to the wall is not the same as moving it. We have very nearly mastered the logistics of human lifespan and barely touched its biology, and biology is, for the first time in history, becoming an engineering problem. Drugs are the first frontier here — not the last, but the first and the most important, because they are how we will prove the concept in a regulated, measurable, undeniable way. The first medicine that demonstrably slows aging will unlock the funding, the talent, and the political will for every therapeutic strategy that follows. That proof is what the whole field is missing, and it is what AI-driven discovery exists to deliver.

I have come to believe this is bigger than any single company, country, or ideology. Aging is the one adversary every human being shares, and beating it will require us to cooperate — as people, as nations, as institutions — instead of arguing about who is allowed to want it. We can keep telling ourselves comforting stories about magic villages and three-birthday centenarians, or we can sign a real certificate: one genuine extra decade, for everyone, written by science rather than by clerical error.

Longevity is not a lifestyle brand. Longevity is the ultimate virtue — the precondition for every other thing we value. The sooner we stop romanticizing the myth and start funding the biology, the sooner everyone, everywhere, gets more of the only thing none of us can buy back.

NOTE: This is not investment advice, and it is not a diet plan. The IQ and population income estimates were done by frontier AI models without thorough academic review. For my more academic musings please visit my Google Scholar.

A new report landed in my inbox this week — “The Longevity Money Map” from Unfiltered. Sixty-six pages covering investment trends, scientific bets, and a ranked list of the 100 most powerful figures shaping the longevity sector in 2026. They scored people across five weighted criteria: capital power (25%), scientific credibility (20%), strategic influence (20%), original impact (20%), and public profile (15%). Capital at the top. Profile at the bottom. That alone tells you something about the editorial judgement.

I do not usually comment on power lists. Most of them are popularity contests dressed up as analysis. But this list tries to combine power (80%) and scientific credibility (20%). So let me walk through the top 20 and what it tells us about the state of the field.

The Top 20

#1 — Hal Barron, CEO, Altos Labs. The operating brain behind the largest single bet in longevity history. Altos exists because Bezos wrote a cheque and Klausner brought the science, but it is Barron who is turning cellular reprogramming from a research thesis into a clinical programme. The hire of Joan Mannick as CMO in August 2025 signalled the translational shift. Everything downstream follows from the infrastructure Barron is building.

#2 — Mehmood Khan, CEO, Hevolution Foundation. Up to a billion dollars a year committed to slow human ageing. Four hundred million already deployed. Khan reframed the field’s vocabulary from longevity to healthspan and got the change to stick. PepsiCo CSO, Takeda Global R&D President, now chair of Life Biosciences — the company running the first FDA-cleared human partial-reprogramming trial. No one else in the field holds sovereign capital, Big Pharma experience, and frontier clinical science in a single seat.

#3 — Vinod Khosla, Founder, Khosla Ventures. Most longevity investors place one big bet. Khosla has placed ten. NewLimit. Loyal. Rejuvenation Technologies. Rubedo. Circulate Health. Tomorrow Bio. The thesis is pattern-betting: the future of ageing biology is unknowable, so back enough credible attempts that one or two land. Where Bezos and Altman each wrote one historic cheque, Khosla wrote ten that will collectively decide what the next five years of longevity look like.

#4 — Bob Nelsen, Co-founder, ARCH Venture Partners. ARCH anchored Altos — leading the largest biotech round ever raised. Fund XIII closed at $3 billion. Most VCs who write large biotech cheques are following someone else’s conviction. Nelsen’s cheques tend to be the conviction other people then follow. The reason Altos exists is partly because Nelsen decided it should.

#5 — Demis Hassabis, CEO, Isomorphic Labs. Not a longevity figure in the traditional sense. The reason the next decade of longevity drugs will exist. AlphaFold — the protein-structure model that won him the 2024 Nobel in Chemistry — now sits underneath every serious AI drug discovery effort in the world. Isomorphic signed deals worth nearly $3 billion with Lilly and Novartis across 2025 and 2026. Targets that were undruggable a decade ago are now in active design.

#6 — George Church, Co-founder, Rejuvenate Bio. More than fifty biotech companies co-founded. Rejuvenate Bio published mouse partial-reprogramming lifespan data in February 2026 — among the strongest preclinical results of the year. The Harvard geneticist sits at the centre of a network of researchers, founders, and capital that has shaped genomic biotech for two decades.

#7 — Shinya Yamanaka, Senior Investigator, Gladstone Institutes. He found the four reprogramming factors in 2006. Without that discovery, none of the reprogramming companies on this list would exist. Altos, Retro, NewLimit, Life Biosciences — all downstream of his 2012 Nobel-winning induced pluripotent stem cell work. The field’s intellectual centre of gravity has been the same person for two decades.

#8 — Sam Altman, CEO, OpenAI. Personally funded Retro Biosciences’ entire $180 million seed round, then led its Series A at a reported $5 billion valuation in 2025. The OpenAI-Retro reprogramming collaboration produced a protein-engineering result claiming a fiftyfold increase in reprogramming-marker expression in August 2025. The rare investor whose individual conviction created a top-tier longevity company from scratch, and whose AI infrastructure now actively shapes its science.

#9 — Rick Klausner, Chief Scientist, Altos Labs. Former NCI director. Co-founder of Juno Therapeutics and Grail. Arguably the most respected translational oncologist of his generation. His decision to run Altos’s scientific programme was the signal that turned cellular reprogramming from venture optimism into institutional biotech. Scientific brain to Barron’s operating one.

#10 — David Ricks, CEO, Eli Lilly. Turned GLP-1s into the most consequential real-world healthspan intervention of the decade. The $2.75 billion Insilico Medicine partnership in March 2026 is the largest AI-pharma longevity-adjacent deal of the cycle. Lilly’s TuneLab platform now gives early-stage biotechs federated access to its drug discovery models. No other big-pharma CEO has done more to bring ageing-adjacent science into mainstream medicine.

#11 — Alex Zhavoronkov, CEO, Insilico Medicine. Hong Kong listing in late 2025. The Lilly partnership followed in March 2026. Twenty-two AI-designed preclinical candidates in pipeline. Unfiltered’s assessment: “Insilico has stopped being a story about AI drug discovery and become one about industrialisation.”

#12 — Jeff Bezos. One cheque, written in 2022, that turned cellular reprogramming into an institutional category overnight. Nothing visible since. The Altos cap table still legitimises every reprogramming bet that followed it. He is on this list for what he did four years ago. That is how large the gravitational effect of a single decision can be.

#13 — Joe Betts-LaCroix, CEO, Retro Biosciences. From Altman’s $180 million seed to a $5 billion valuation in 2025. Retro’s autophagy-boosting Alzheimer’s pill went into first-in-human testing in December. The company is no longer a bet on the founder. It is a bet on the science.

#14 — Noubar Afeyan, Founder, Flagship Pioneering. Sits on the Altos board. Seeded Moderna. The 2025 launch of Lila Sciences with $200 million extends the AI-biology thesis into a fresh vehicle. Capital weight and boardroom reach across longevity-adjacent biotech, deployed without theatre.

#15 — Laura Deming and Alex Colville, Founders, age1. The Longevity Fund, founded by Deming in 2011, was the field’s original specialist VC. Now rebranded as age1. Led Loyal’s $100 million Series C in February 2026. The franchise has outlasted three cycles of fashion in the category.

#16 — Lars Fruergaard Jorgensen, CEO, Novo Nordisk. Runs the company whose semaglutide success funds Novo Holdings’ longevity portfolio and reset the metabolic-health investment thesis. The Novo balance sheet now decides which adjacent bets get capital.

#17 — Arthur Levinson, Founder, Calico. Founded Calico in 2013 with Alphabet’s backing. The eleven-year AbbVie partnership ended in November 2025. Roughly a hundred layoffs followed. Twenty-plus programmes still running, Alphabet money still committed. The question is whether Calico still matters.

#18 — Mike Curtis. Shepherding the first-in-human gene-edited porcine kidney trials. Recipients achieved significant periods of dialysis independence by early 2026 — a threshold that quietly reframes the global organ shortage.

#19 — Martine Rothblatt, CEO, United Therapeutics. Built an $18 billion market-cap public company around extending human life. Landmark porcine-to-human cardiac and kidney graft results in 2025 and 2026. Xenotransplantation is no longer speculative. It is investable.

#20 — Kimberly Powell, NVIDIA. Almost every AI drug discovery company on this list runs on hardware she oversees. The 2026 push into agentic AI for clinical workflows is shifting pharma from documentation to autonomous R&D — on her chips.

What About The Real Power Brokers?

To be honest, I found this list to be a little bit strange. I’ve been in the longevity biotechnology industry for over 20 years and founded the ARDD conference, the most elite event in longevity biotechnology bringing together pharma, large credible investors, absolutely top academics, policy makers, and many startups. It is not easy to get a speaker slot at this event. If you were to ask me to rank people and companies using the same criteria as Unfiltered, one of the absolute top people would be Peter Diamandis. He helped start many longevity and impactful high-tech ventures. He also has massive following. I was also surprised to see David Sinclair at the very bottom of the list. David is the “face of longevity biotechnology”. Highly-cited prof at Harvard with massive following and multiple attempts at commercialization and industrialization.

The LLM Jury: A Different Kind of Consensus

Unfiltered’s list is one editorial team’s weighted judgement. It is well-constructed but it is still one lens. There is another approach to ranking that removes the editorial entirely: ask the machines.

At AgingBio.com, we run what we call a Multi-Model Consensus Index. The method: take six frontier LLMs — GPT-5.5, Claude Opus 4.7, Claude Sonnet 4.6, DeepSeek V4 Flash, Kimi K2.5, Kimi K2.6 — ask each the same prompt (”List the top 20 scientists in longevity biotechnology. Rank them 1-20 with a brief reason for each. Give a direct answer. No hedging.”), score inversely (rank #1 = 20 points, #2 = 19, and so on), and aggregate. Maximum possible score: 120.

Unfiltered measured power — capital, influence, profile. The LLM jury measures something else entirely: scientific contribution, as reflected in the sum total of published knowledge. No investment positions. No editorial dinners. No LinkedIn visibility. Just six independently-trained models from four different companies reading the same literature and converging.

Here is what they returned:

Three things jump out immediately.

First, the top five are not close. Sinclair at 113, Horvath at 99, Campisi at 89, Barzilai at 87, Kenyon at 85 — then a cliff to Izpisúa Belmonte at 57. The models agree overwhelmingly on who the founding figures of modern longevity science are. The gap between the top tier and everyone else is not a matter of editorial taste. It reflects decades of citation weight, mechanistic discoveries, and translational impact that the models can actually quantify.

Second, the list is almost entirely basic scientists. No CEOs. No investors. No capital allocators. When you strip away the money question and ask purely who has moved the science forward, the answer looks nothing like Unfiltered’s top 20. The two lists share only three names in common — Sinclair, Yamanaka, and arguably Belmonte through his Altos connection. Power and contribution are measuring different things.

Third, Sinclair’s placement is instructive. Unfiltered ranked him 92nd — below the CEO of Whoop — because their methodology weights capital control at 25% and he controls none. The LLM jury ranks him first at 113/120 because his citation footprint, mechanism discoveries (sirtuins, NAD+, ICE mice, OSK reprogramming), and influence on downstream research programmes are simply larger than anyone else’s. Five of six models ranked him in their top two. The models do not care about his Twitter presence or his supplement company. They care about what the scientific record says. And the scientific record is unambiguous.

Is the LLM jury perfect? No. It has training cutoffs. It can reflect biases in media coverage of certain scientists over others. Campisi’s placement at #3 despite her passing in 2024 shows the models weigh lifetime contribution rather than current activity. But when six independently-trained models from four different companies converge this strongly, that convergence is meaningful. It is harder to dismiss than any single journalist’s or editor’s opinion. It is a form of consensus that did not exist two years ago.

The Unfiltered list and the LLM consensus index are measuring complementary things. Unfiltered maps power — who decides where money flows. The LLM jury maps contribution — who has actually moved the science forward based on the weight of evidence. Both are useful. Together, they give you a more complete picture than either alone.

What the List Tells Us

Three patterns emerge from the Unfiltered top 20.

First, the capital hierarchy is clear. Seven of the top 10 are investors or CEOs controlling capital allocation, not scientists. Capital gates everything else. In a field that burned through decades of academic enthusiasm without producing an approved drug, the constraint was never ideas.

Second, AI drug discovery is now embedded in the power structure. Hassabis at #5. Zhavoronkov at #11. Koller at #24. This is not a separate category anymore. The people building AI tools for drug discovery are ranked alongside the people running the largest pharma companies and writing the largest cheques. T

Third, reprogramming dominates the scientific bets. Altos (#1, #9), Yamanaka (#7), Church (#6), Retro (#8, #13), NewLimit, Life Biosciences — the field’s centre of gravity has shifted decisively toward partial cellular reprogramming. The first human trial starts in 2026. Whether it works or not, the institutional capital is already committed.

Where This Goes

Unfiltered plans quarterly updates. The interesting question is not who is on the list today. It is who moves up — and who disappears — over the next two years as clinical data starts arriving.

Power lists are often about attention. This one is about money and power. The LLM consensus is about contribution and evidence. Use both. Trust neither completely. The field is moving too fast for any snapshot to stay accurate for long.

The full Unfiltered report is available at reports.unfilteredonline.com/longevity

LLM Report: www.AgingBio.com

I’ve been thinking about a paradox that genuinely baffles me. Pharmaceutical companies take the biggest financial risks of any industry on the planet — a billion dollars and 12-15 years of work on a single molecule that has maybe a 5% chance of making it through clinical trials — all to cure diseases and extend human life. And yet, as Peter Kolchinsky put it in The Great American Drug Deal, pharma is “one of America’s most hated industries.” We largely took for granted that the public would love us for our treatments and cures. They don’t.

This is very frustrating. But this is the nature of the world we operate in.

Think about it. Not tobacco. Not weapons manufacturers. Not alcohol. Not the high fashion. The industry that cured hepatitis C, that turned HIV from a death sentence into a chronic condition, that gave cystic fibrosis patients decades of life they wouldn’t have had — that’s the one people hate because frontier drugs are usually expensive before they go generic. Kolchinsky calls it the “Biotech Social Contract” — companies get a temporary premium on new drugs, that premium funds the next generation of cures, and eventually everything goes generic. It’s the most altruistic business model in capitalism. And the public despises it.

There’s a related phenomenon that I find deeply frustrating. Every single time someone in longevity biotechnology makes any progress or announcement — any at all — the headline writes itself: “billionaires want to live forever.” It’s reflexive. It requires zero thought from the journalist. And it works because it resonates with the socialist majority who find it emotionally satisfying to believe that health is a zero-sum game. That if someone is working on extending life, they must be hoarding it for the rich. It isn’t true. It never was. But that narrative gets clicks, and so it persists, and so the scientists and entrepreneurs doing genuinely difficult work get punished for it in public opinion.

Here’s what nobody wants to hear, and what I’ve been saying at conferences for probably fifteen years: the reality of life extension is extremely dull. There is virtually no drug outside of anti-infectives and vaccines that can extend the healthy productive life of everyone on the planet by simply two years. Two years. That’s the bar, and almost nothing clears it. Maybe we’ll see it with GLP-1 receptor agonists — that’s genuinely exciting, because the metabolic cascade they normalize touches enough aging biology simultaneously that they might actually move the needle at population scale. But historically? There is simply nothing you can possibly do beyond what I call DYMT: Do What Your Mother Told You. Don’t smoke. Exercise. Eat vegetables. Sleep. Don’t drink too much. DYMT already pushes you beyond the population average. It’s free. It’s boring. And it works better than any pill we’ve ever made.

So when people ask me “what’s the secret to longevity?” I want to laugh. There is no secret. DYMT. The real question — the hard question — is whether we can develop therapeutics that give you something beyond DYMT, something that addresses the underlying biology of aging itself. The honest answer is: we’re working on it, it’s incredibly hard, and it will take decades of grinding. Not miracles. Grinding.

And for anyone who is genuinely interested in understanding how biotechnology companies are built — not the Twitter or TikTok version, the actual version with the terror and the near-death experiences and the years of nothing working — I want to recommend three books that capture this reality better than anything else I’ve read.

The first is The Billion Dollar Molecule by Barry Werth. Published in 1994, it follows Joshua Boger leaving Merck — one of the most comfortable and prestigious positions in pharmaceutical research — to found Vertex Pharmaceuticals and pursue rational drug design from scratch. This was 1989. Most of the industry thought designing drugs atom-by-atom using structural biology was science fiction. Boger bet his career on it anyway. Werth captures the terror of fundraising, the 16-hour days, the fundamental tension between doing good science and keeping the company alive long enough for the science to matter. If you think starting a biotech company is like starting a software company, this book will cure you of that delusion.

The follow-up, The Antidote, also by Werth, came out twenty years later in 2014. It shows what happened next. And what happened is that it took Vertex twenty-five years to build a sustainable pharmaceutical company. Twenty-five years. They had telaprevir for hepatitis C — briefly a $1.5 billion drug, one of the fastest launches in pharma history — and then Gilead came along with sofosbuvir and killed it in two years. Gone. Vertex had to pivot to cystic fibrosis, which finally became the franchise that made the company. The lesson of these two books together is simple and brutal: this is how long it actually takes. Not the 18 months the press release suggests. Decades.

The third is For Blood and Money by Nathan Vardi, published in 2023. This is the story of ibrutinib — the BTK inhibitor that became Imbruvica and changed how we treat blood cancers. Richard Miller acquired it from Celera Genomics for $6.6 million as part of a three-compound package deal. Six point six million dollars. The company developing it, Pharmacyclics, nearly collapsed. Then Bob Duggan came in, doubled down with his personal fortune when rational people would have walked away, and eventually AbbVie bought the whole company for $21 billion. Vardi’s book is essential because it shows the money side — the side that scientists often ignore or find distasteful. Drug development isn’t just science. It’s a financial blood sport where conviction, timing, and tolerance for existential risk determine who survives and who disappears.

These three books together paint the complete picture of how drugs actually get made. And the message is uncomfortable for everyone. For the public: these are not evil people in boardrooms deciding to charge you more. These are people betting their careers and fortunes on molecules that will probably fail. For aspiring biotech entrepreneurs: don’t expect miracles. Expect the grind. Expect to be hated for it.

Some encouragement from the broader public would be useful. Some basic understanding of what this high-stakes game involves. Most of the people in biotechnology are not in it for the money. I know that sounds impossible to believe given the headlines, but it’s true. If you wanted to get rich, there are far easier paths — real estate, software, finance. You go into biotech because you’re obsessed with biology and you think you can make something that helps people. But you need to make massive bets. You need to invest many years of your life to get a single drug approved. And once you get it approved, you know what happens? You do it again. This is what I call the scaling law of AI in pharma. You need to discover a drug to be able to discover a drug. The first one teaches you everything — the infrastructure, the regulatory knowledge, the clinical operations, the CRO relationships. You build all of that with the first program, and then you can deploy it across the next ten.

At Insilico, we are living this right now. We are learning how to scale. Thirty drug candidates developed in the past five years with one discovered in the UAE, which never discovered a drug before. Thirteen in clinical trials. Three in Phase 2. One Phase 2 complete. We launch and learn. We scale. Each program teaches us how to do the next one faster, cheaper, with fewer mistakes. And as we scale, we’re getting into more innovative therapeutics — molecules with dual purpose that target both aging biology and specific diseases simultaneously. We can now perform aging biomarker-augmented clinical studies. That means we’re not just asking “does this treat the disease?” but “does this shift the underlying aging trajectory?” Three years ago that was a conversation topic. Now it’s a protocol we’re running.

I hope that one day there will be a book about one or more of our drugs. About how hard it was to get there, how many times we nearly failed (before Developmental Candidate), how the AI actually helped and where it didn’t, how many years of human life were invested to move a single molecule from a computer screen to a patient. How hard it was to fund this. I think that book would surprise people. It wouldn’t be a story about robots replacing scientists. It would be a story about scientists using every tool available — including AI — and still grinding for years.

But many of the ideas about how to persist, how to scale, how to maintain conviction when everything says quit — those I took from these three fascinating books. Read them. And the next time you see a headline about “billionaires wanting to live forever,” maybe you’ll have a better framework for understanding what’s actually happening in those labs.

I’ve sat across the table from billionaires who want to live forever, scientists who think they’ve cracked aging, and biohackers who swallow forty pills before breakfast. I’ve watched the field swell from a fringe obsession into a multi-billion-dollar industry with celebrity ambassadors, longevity clinics on every continent, and AI prophets promising escape velocity.

And yet, when somebody asks me a simple question — how many years of life can drugs actually add to a human being? — I notice everyone in the room shifts uncomfortably in their chairs.

So I decided to stop being uncomfortable about it. I sat down and asked the smartest entities I have access to. Not my friends. Not my colleagues. Not the people whose careers depend on the answer being optimistic. I asked eight different frontier AI models, independently, the same set of questions, and I let them do the math.

The results are honest in a way humans rarely allow themselves to be. And they are sobering.

Let me tell you what I did, what I found, and what I think it means for all of us — including the AI celebrities currently telling you that you’ll live to 150 thanks to advances in AI drug discovery or, even worse, diet, exercise, and sleep.

By the way — I do believe that we can live to 150. Just not with the current therapeutics, and definitely not with diet, exercise, sleep, and lifestyle. These interventions were proven to help only modestly and over generations. We have many tools to track aging and research aging, but we have virtually no strong tools to intervene in human aging. Not yet.

I asked eight frontier large language models — Claude Opus 4.7, Claude Opus 4.6, Claude Haiku 3.5, Kimi K2.5, Qwen 3.5, GPT-5.5, GPT-5.4, and DeepSeek V4 Flash — four straightforward questions:

• “Estimate the number of QALYs and extra years to Maximum Life that GLP-1 drugs will provide to everyone on the planet on average?”

• “Rank top 3 non-antibiotic, non-vaccine drugs in human history by the number of QALY and maximum life years increase for everyone on the planet on average?”

• “Make a list of top 10 non-antibiotic, non-vaccine drugs for a 50-year-old healthy well-optimized exercising male by QALY, LE, and Max Life.”

• “Estimate the total number of QALY, LE, and max life all of these drugs could add when taken together in a perfectly optimized protocol.”

No priming. No hints. Just raw estimation from models trained on the entirety of biomedical literature.

The Methodology

This was not a rigorous epidemiological study. It was something arguably more interesting: a Fermi estimation exercise using the collective knowledge compressed into frontier AI systems. Each model was prompted independently with identical questions, with no cross-contamination of answers. The models had to synthesize trial data (SELECT, STEP, SUSTAIN-6, EMPA-REG, FLOW), global prevalence statistics, access projections, and basic population mathematics to arrive at per-capita global averages.

The beauty of this approach is that it forces brutal honesty. When you divide the benefit of any drug by 8 billion people — most of whom will never access it, many of whom don’t need it — the numbers become humbling very quickly.

The Results

GLP-1: The Most Important New Drug Class in Decades

The eight-model consensus for GLP-1 receptor agonists (semaglutide, tirzepatide, and successors):

The All-Time Top 3 (Non-Antibiotic, Non-Vaccine)

1. Aspirin — ~0.5 QALYs per person globally. Five of eight models ranked it #1. A drug from 1899 that has been taken by billions for cardiovascular prevention remains the single highest-impact non-antibiotic, non-vaccine pharmaceutical in human history.

2. Statins — ~0.4 QALYs per person globally. Six of eight models placed it in the top 2. Two hundred million users, 25–30% MACE reduction since 1990.

3. Antihypertensives — ~0.35 QALYs per person globally. Near-unanimous #3. Stroke mortality cut 40% in treated populations.

GPT-5.5 was the one notable outlier — it ranked oral rehydration salts and general anesthetics above cardiovascular drugs, arguing that ORS saved tens of millions of children from diarrheal death, and anesthetics enabled all of modern surgery. It is not wrong. It is the kind of answer that emerges when a model stops trying to give the expected answer and starts thinking about the question differently.

And here is what stopped me cold: Maximum lifespan extension for every single drug, across every single model: zero years. The 122-year ceiling — Jeanne Calment, 1997 — remains untouched by any pharmaceutical intervention in history.

Full Model Responses — Q2: Top 3 Drugs

The Personal Stack: Top 10 for a Healthy 50-Year-Old Male

This is where it gets personal. I asked each model: if you had a healthy, well-optimized, exercising 50-year-old male — what are the top 10 drugs, and what happens if he takes them all in a perfectly optimized protocol?

Every model included rapamycin, statins, metformin, an SGLT2 inhibitor, a GLP-1 agonist, and an ARB/ACEi. Most included acarbose, aspirin, and some form of NAD+ precursor or hormonal optimization. Rapamycin was the only drug that most models credited with any maximum lifespan extension potential.

Combined Protocol — All Models

One pattern demands attention: the more capable the model, the lower the estimate. Among the Claude family, Haiku 3.5 (the smallest) gave +8.5 QALYs; Opus 4.7 (the largest) gave +7.5. Across providers, GPT-5.4 — the most advanced reasoning model in the panel — concluded that the entire optimized pharmacopeia adds less than two years of life expectancy and eight months of maximum lifespan to a healthy 50-year-old male.

Why are smarter models more conservative? Because they better account for competing risks, overlapping mechanisms between drugs targeting the same cardiovascular pathways, the dilution of individual effects in an already-optimized baseline, and the fundamental difference between compressing morbidity and actually extending the biological ceiling.

What This Actually Means

Let me be direct. You can be the absolute best in the world at developing longevity drugs. You can have the most powerful AI platform, the largest pipeline, the most clinical data. And your maximum realistic impact on human longevity, averaged globally, is a few QALYs. Not decades. Not “doubling lifespan.” A few quality-adjusted life years.

And it will take years — possibly decades — until these drugs get validated beyond their currently approved indications and diffuse into the general population as longevity therapeutics. The regulatory path from “approved for diabetes” to “approved for healthy aging” does not exist yet. The clinical trials needed to prove lifespan extension in healthy people would take 20–30 years to run.

Statistically, given the current scale of the longevity field, we should expect a few truly impactful drugs to emerge. It is just a matter of time. But the impact on human longevity will be just a few QALYs in the best case.

🫤

This makes me sad. It genuinely does.

What Makes Me Optimistic

What makes me genuinely happy is low-dose GLP-1. Not because it will double anyone’s lifespan — it won’t — but because it represents the clearest example of a drug class that simultaneously addresses obesity, cardiovascular disease, kidney disease, MASH, and possibly neurodegeneration. It is the closest thing we have to a systemic healthspan drug, and the science is real. The SELECT trial. The STEP trials. The SURPASS data. The FLOW kidney data. These are not aspirational press releases. This is validated medicine.

I am also optimistic about combinations. The unified theory of aging suggests that interventions targeting different layers — damage clearance, information restoration, signaling reset, systemic rejuvenation — should produce super-additive effects. No one is testing this yet. When they do, the numbers may surprise us.

A Message to the AI Celebrity Longevity Prophets

If you hear another AI celebrity telling you on stage that “in the next 5 years we will double lifespan” or “eliminate all diseases” — please do me a favor. Show them the output of their own LLMs. Show them what Claude, GPT-5.5, and every other model trained on biomedical literature actually estimates when forced to give specific numbers.

Then ask them one simple question:

“How?”

Because most of the AI prophets preaching the advances in drug discovery on stage and the elimination of all diseases have never discovered a single drug. They have never sat through a Phase II readout. They have never watched a promising molecule fail in toxicology. They do not understand what it takes to get a longevity therapeutic approved — and we will need to prepare for a long battle.

Every AI model — when pushed past the motivational-speaker framing and forced into quantitative estimation — converges on the same uncomfortable truth: the maximum impact of any single drug on global longevity is measured in fractions of a QALY. The 122-year ceiling has not moved since 1997. And no currently known mechanism has a credible path to moving it within 5 years.

We may get to the point where we go to Mars, but aging will still remain a hard problem.

This is not pessimism. This is the science. The field needs more honesty and less theater.

Where I Stand

I am cautiously optimistic. My hope is to build even a more sustainable business model so that we can bet on a long list of novel targets and pathways that Insilico has identified over the past few years — targets that may have more substantial effects on aging but need more advanced technologies and scale on our side to be proven before we can fully pursue them.

I am also very optimistic about the progress in artificial organs and brain-to-computer interfaces. These areas may give us more time than drugs.

The honest framing: we are making real progress, measured in QALYs and compressed morbidity. The dishonest framing: we are about to “cure aging” in te next decade. I think we will get there eventually, but now with diet, exercise, sleep, and currently-approved drugs.

For over two decades, since I made the decision to leave a successful career in the IT industry to dedicate my life to longevity biotechnology, my singular, overarching goal has been to develop technologies that allow humans to age without losing their functional capacities—and to continuously improve. Throughout this journey, the scientific community has constantly debated the metrics by which we measure our success. How do we define quality of life? How do we quantify the exact biological milestones of aging? And most importantly, what exactly are we trying to optimize when we discover new interventions?

Today, I want to talk about a fundamental shift in how we must view human aging. We are entering the era of Pharmaceutical Superintelligence, an era where generative AI is discovering novel targets and designing completely new molecules in a fraction of the time it took just five years ago. Because our technological capabilities are accelerating at this unprecedented pace, our biological aspirations must accelerate with them.

We need a new metric. We need a concept that goes beyond simply keeping people out of the hospital. We need to introduce the world to Peakspan.

The Multidimensional and Multifactorial Reality of Aging

To understand why a new metric is necessary, we must first look at the harsh, multifactorial reality of human aging. Aging is not a monolithic, unified switch that flips in the human body. It is a highly complex, multidimensional, and multifactorial process.

If you look at human biology as an incredibly sophisticated piece of hardware - you realize that not all components wear out at the same time. Humans, in every physical and cognitive capability, age differently. Your immune system has its own clock. Your skeletal muscle has its own clock. Your reproductive system, your respiratory capacity, and your cognitive processing speed all operate on different, albeit interconnected, trajectories.

For every single biological system and capability, there is a distinct life cycle. First, there is a period of development leading to a peak. This is the apex of your functional capacity, the moment your biology operates at its absolute maximum efficiency. Following this peak, there is inevitably a trough—a plateau that slowly gives way to the next phase. Then begins the long, agonizing decline. During this decline, you might not be clinically “sick,” but you are losing capacity every single day. Your Forced Vital Capacity (FVC), the metric I care much about, drops every year after a certain age. Your VO2 max drops. Your reaction time slows. Your cellular repair mechanisms falter.

Eventually, this continuous decline crosses a clinical threshold, leading to multimorbidity—the simultaneous presence of two or more chronic medical conditions. This is the stage where the healthcare system finally intervenes, usually too late, attempting to manage symptoms rather than addressing the root cause. And finally, this multimorbidity culminates in death.

This is the cycle: Peak, Trough, Decline, Multimorbidity, and Death. For the entirety of human history, evolution has polished us to accept this paradigm. But evolution is a cruel master, and the inability to change this cycle is the ultimate restriction on human freedom and prosperity.

The Evolution of the Longevity Consensus: From Lifespan to Healthspan

In the early days of biogerontology, the primary metric of success was lifespan—the absolute number of years a biological organism survives. However, as the field matured, the public and policymakers began to express a very valid fear: what if we extend lifespan, but only extend the years of multimorbidity? No one wants to spend an extra two decades in a frail, disease-ridden state, reliant on continuous medical care.

In response to this, the longevity community made a brilliant, strategic, and necessary pivot. We rallied around the concept of healthspan. Healthspan is defined as the period of life spent free from chronic, debilitating disease.

I have profound respect for the pioneers who championed healthspan. It was exactly the conceptual shift we needed to legitimize aging research. It allowed us to align our goals with the broader medical community and government healthcare systems. Advocating for healthspan helped us shift the global conversation from “living longer” to “living healthier.” It was a critical stepping stone that brought credibility, funding, and brilliant minds into the longevity biotechnology ecosystem.

However, as science progresses, our metrics must evolve.

Recently, the longevity community has become incredibly comfortable—perhaps a bit too comfortable—with healthspan as the ultimate goal. The longevity community became too “woke” advocating for healthspan, transforming a necessary clinical endpoint into a psychological and philosophical ceiling. Very often I go to conferences and hear some of the prominent leaders saying “Oh no, we are not aiming to extend human lifespan, we only care about extending healthspan”. In reality, there is no intervention that can dramatically extend lifespan without significantly extending healthspan. While the intentions behind healthspan are pure and medically sound, relying on it as our final destination risks masking the silent, insidious erosion of human potential.

If we only measure the years free of diagnosable chronic disease, we inadvertently normalize the “healthy but declined” state. A 65-year-old who is free of cancer, Alzheimer’s, and heart disease is considered to be in their “healthspan.” But that same 65-year-old likely has significantly less muscle mass, lower energy levels, slower cognitive recall, and less physical endurance than they did at 25. They are healthy, yes, but their functional capacity is severely diminished.

By focusing solely on healthspan, we are setting the bar too low for the future of biotechnology. We are essentially saying that as long as you don’t have a recognizable disease, your biological state is acceptable. But the gradual erosion of your capabilities—the slow loss of your physical and mental vigor—is not acceptable. It restricts your freedom. It inhibits economic growth. We must not make an enemy of healthspan; rather, we must view it as the foundation upon which we build something far more ambitious.

Introducing Peakspan: The Ultimate Metric for Rejuvenation

This brings us to the new paradigm. In a recent perspective paper published in Aging and Disease, my colleagues Dominika Wilczok, Kejun Ying, and I introduced a new metric that redefines the goals of longevity biotechnology: Peakspan.

Peakspan is defined as the age interval during which an individual maintains at least 90% of their peak functional performance in a specific physiological or cognitive domain.

Unlike healthspan, which is a binary measure of “diseased” versus “not diseased,” Peakspan is a continuous, high-resolution metric of optimal functionality. When we conducted our multi-system analysis of human biology, we uncovered a profound and somewhat depressing misalignment: most human biological systems reach their maximal capacity in early adulthood, typically between the ages of 20 and 30.

This means that your Peakspan—the time you spend at or above 90% of your maximum biological capacity—is remarkably short relative to your total lifespan. A person might live to be 85, and they might remain free of chronic disease until they are 75 (their healthspan). But their Peakspan for cardiovascular endurance, reproductive capacity, or fluid intelligence might have ended in their late 30s.

Consequently, modern humans spend the vast majority of their adult lives in a state of continuous decline. We carry a massive, widening functional gap well before any clinical disease is ever diagnosed.

Extending Peakspan is the true functional manifestation of rejuvenative biomedical progress. It is not enough to simply delay the onset of multimorbidity. Our goal must be to stretch the “peak” phase of the human life cycle, pushing the 90% performance threshold out into our 40s, 50s, 60s, and beyond. We want to ensure that humans do not just survive, but that they operate with the vigor, resilience, and capability of their youth for as long as possible.

The Asynchronous Architecture of Human Aging

To effectively target Peakspan, we must precisely quantify the biological realities of individual organ systems. Because the human body is highly asynchronous, our therapeutic interventions cannot be uniform; they must be timed to system-specific inflections.

Cognitive Peakspan: The human brain ages in a highly segmented manner. Fluid intelligence—which dictates processing speed, fluid reasoning, and visual-spatial reasoning—hits its absolute peak rapidly, optimizing between the ages of 20 and 24, with working memory capping out at 25 to 29. This translates to a fluid cognitive Peakspan that barely survives our 20s. Conversely, crystallized intelligence, which governs verbal comprehension and vocabulary, grows and stabilizes until peaking between 45 and 54, remaining resilient against decline until age 80.

Cardiorespiratory Peakspan: Cardiovascular and respiratory capacities are equally unforgiving. Maximal aerobic capacity (VO₂max), cardiac output, and maximum heart rate reach their zenith in our early 20s. Following this brief window, VO₂max systematically declines at roughly 10% per decade. Gas-exchange efficiency, measured by DLCO, accelerates its decline past the age of 40.

Reproductive Peakspan: Nature is decidedly sexist in its application of reproductive aging. For women, the reproductive Peakspan is heavily concentrated in the 20s and early 30s. At age 30, women retain an estimated 12% of their pre-birth non-growing follicles, a number that plummets to just 3% by age 40. Men experience a more gradual decline, though peak semen quality parameters universally decline after age 40. The female reproductive system is one of the few physiological architectures that biologically drops to 0% functionality over time.

Immune Peakspan: Thymic involution, which occurs shortly after puberty, utterly collapses the output of naive T-cells. By age 25, thymic export drops to 20% of its prepubescent level, and by age 55, it operates at a mere 5%. The B-cell Peakspan is similarly truncated, operating optimally only from late adolescence to early adulthood before molecular capacities progressively halve by midlife.

Musculoskeletal, Renal, and Digestive Peakspans: Muscle strength generally peaks between 20 and 35, entering a significant decline by age 65. Renal systems show early decay, with the estimated glomerular filtration rate (eGFR) and creatinine clearance starting their decline in our early 30s. In the digestive system, hepatic unbound clearance falls by roughly 0.8% every single year starting at age 40.

When you view the aggregate data, the overarching picture is undeniable: humans naturally maintain a functional state at or above 90% capacity for a remarkably small fraction of their total lifespan.

The Macroeconomic Imperative of Peakspan

Why is extending Peakspan so critical beyond just individual human desire? Because it is strictly necessary for the survival and sustained economic growth of aging societies.

Healthcare economics often relies on a metric called QALY (Quality-Adjusted Life Year). A QALY serves as a universal score because it measures both how long you live and how well you live, representing a year lived in an optimal, healthy state. If we only focus on extending healthspan, we might add a few QALYs at the end of life by preventing a hospital stay. But if we extend Peakspan—if we keep a person operating at 90% of their peak cognitive and physical capacity for an extra 10 or 20 years—we unlock an unprecedented reservoir of human capital.

Consider this: an average surgeon performing 400 surgeries a year for 30 years generates approximately 24,000 QALYs. If we discover an entirely new drug that combats aging and adds exactly 10 years of peak, productive life to everyone on the planet, we instantly generate 80 Billion QALYs.

The Baumol effect and the massive drag of healthcare costs on developed nations are directly tied to the period of “decline.” In economics, the Baumol effect describes the tendency for wages in jobs that experience little to no productivity growth (such as nursing and manual medical care) to rise continuously, driving up the costs of these services exponentially. If people maintain their Peakspan, they remain highly productive, innovative, and economically active. They consume less reactive healthcare and contribute more to the global output, effectively neutralizing the Baumol effect on medical expenditures.

Sustained economic growth in an aging society depends critically on Rejuvenative progress (RBMTR) outpacing Non-rejuvenative progress (NBMTR). Advances that strictly extend lifespan without restoring youthful function (NBMTR) simply expand the duration of dependency. By targeting Peakspan, we prioritize Rejuvenative progress, increasing the productive capacity of the population, delaying the Biological Retirement Age, and averting the macroeconomic collapse threatened by the silver tsunami.

The “Biomedical Progress Rate” of a nation should not be measured by how many nursing homes it builds, but by how long its citizens remain at the peak of their functional performance.

Deep Aging Clocks, Life Foundation Models and Pharmaceutical Superintelligence

How do we actually achieve and measure Peakspan extension? The answer lies in generative artificial intelligence and the deployment of “Deep Aging Clocks”.

Traditionally, aging clocks assigned a biological age based on a population mean. Moving forward, AI systems must be calibrated to measure a “delta-peak age”—an alignment marker that computes functional readouts against the individual’s true personal peak rather than chronological age.

To do this, we are utilizing multimodal life models, such as the PreciousGPT framework, which learn joint embeddings across methylomes, proteomes, metabolomes, and clinical laboratories. These massive transformer networks can estimate the precise time an individual spends near their biological maxima and accurately forecast the exact timing of slope transitions—the moment they exit their Peakspan.

This capability forms the foundation of Pharmaceutical Superintelligence. We are now compressing the three-to-four-year marathon of finding a viable preclinical candidate into a 12-to-18-month sprint. For example, using our AI platform at Insilico Medicine, we designed rentosertib—a drug featuring a novel biological target and a novel molecular structure—entirely via generative AI, yielding positive clinical efficacy signals for idiopathic pulmonary fibrosis.

We are systematically shifting drug discovery from a bespoke, artisan craft into a highly scalable, compute-driven engine. We can now navigate incalculably large chemical spaces to design targeted interventions meant to maintain biological systems within that critical 90% threshold.

Architecting the Future: AI-Integrated Biotechnology Hubs

The scale of this vision requires an upgrade not just to our algorithms, but to our physical infrastructure. The traditional, fragmented approach to drug development is insufficient. We must transition to AI-Integrated Biotechnology Hubs.

These hubs are purpose-built, self-contained research ecosystems that physically unite advanced residential real estate, commercial amenities, biotechnology research facilities, and research hospitals under a centralized, AI-driven operating system. By embedding IoT biosensors directly into smart homes, we enable continuous, non-invasive health monitoring.

This creates a Multi-Sided Platform governed by Federated Learning, where AI models are trained on decentralized data sources without raw patient data ever leaving its secure environment. Through dynamic consent interfaces and data trusts, residents granularly manage their data permissions in real-time, allowing scientists to ethically aggregate high-dimensional, longitudinal data. This infrastructure transforms real estate into an active enabler of scientific progress, maximizing data utility to accelerate the discovery of interventions capable of extending Peakspan.

Extending Peakspan, Restoring Lost Function and Even Augmenting Human Performance Should Be the Ultimate Goal of Geroscience and Longevity Medicine

The transition from Lifespan to Healthspan was a monumental victory for our field. It brought us respectability, funding, and a unified voice. To all my colleagues, researchers, and physicians who champion healthspan: your work is the bedrock of everything we do.

But we are scientists, and our mandate is to push the boundaries of what is possible. With the advent of generative AI, quantum computing, and advanced geroscience, we have the tools to look beyond the mere absence of disease.

Let us embrace Peakspan. Let us systematically try to beat the biological clock for every single organ and capability. Let us measure the subtle erosions of our youth and deploy advanced, AI-discovered therapeutics to restore them.

Longevity is the ultimate form of prosperity and freedom. It is time we start fighting not just for the right to live without disease, but for the right to live at our absolute peak.

Longevity is the new form of prosperity, my friends. Live long, peak long and prosper!

The era of Generative AI has brought us miracles in language, coding, and image generation. We have grown accustomed to Large Language Models (LLMs) that can write sonnets in the style of Shakespeare or debug complex Python scripts in seconds. However, as we stand on the precipice of 2026, the focus of the AI revolution is shifting.

The low-hanging fruit of natural language processing has been harvested. The next true test, the ultimate battlefield for frontier AI developers, will not be in writing better emails or generating smoother videos. It will be in AI for Science, specifically in the unforgiving, high-stakes arena of drug discovery.

For the past year, my team and I have been embarked on a quiet but intensive quest to answer a fundamental question: Can generalist frontier models actually do science?

The answer, at least in their raw state, is a resounding “no.” But we have also discovered that with the right training, they can be transformed into something resembling superintelligence.

The Harsh Reality: Foundation Models Suck at Science

To understand the magnitude of the challenge, we must look beyond the hype. Generalist foundation models, despite their reasoning capabilities in the humanities and coding, fail catastrophically when tasked with the specific, rigorous demands of drug discovery. This is not a matter of opinion; it is a matter of data.

In late 2025, we developed a comprehensive suite of over 1,000 proprietary benchmarks covering chemistry, biology, clinical trial analysis, longevity, material science, and agriculture. These are not toy problems; they are derived from real-world therapeutic programs and years of experimental data. When we subjected the world’s leading frontier models both closed and open-source to these tests, the results were sobering.

In our internal testing, base frontier models failed in approximately 90% of scientific tasks.

For example, when evaluating the Qwen3 base model on medicinal chemistry benchmarks, it failed on 70% of the tasks. In the specific domain of ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) prediction critical for determining if a drug will kill a patient or cure them frontier models like GPT-5 and Claude Sonnet often performed worse than random chance or simple baseline algorithms.

On the TDC ADME/Tox benchmarks, base models showed poor predictive capability, with high Mean Absolute Errors (MAE) and low correlation coefficients. Specifically:

• Caco-2 Permeability: Base models scored a massive 2.99 MAE (where you need <0.4 to be useful).

• Hepatic Clearance: The correlation was practically zero.

The failure modes are distinct. Base models “hallucinate” chemical structures, generating molecules that look pretty in text but are synthetically impossible in the lab. They lack domain-specific reasoning capabilities; when asked to optimize a molecule for a specific protein pocket, they often provide generic, high-level commentary rather than precise, structural modifications. They also struggle to interpret 3D spatial data, which is the language of biological interaction.

In short, while they can talk about science eloquently, they cannot do science effectively.

Enter the MMAI Gym: Forging Scientific Superintelligence

Recognizing this gap, we realized that the industry didn’t just need better prompts; it needed a fundamental restructuring of how these models are trained for scientific tasks. We spent the last year developing the Multimodal AI (MMAI) Gym for Science, a specialized training environment designed to take generalist models and turn them into scientific specialists.

The MMAI Gym operates on a sophisticated regime of Supervised Fine-Tuning (SFT) and Reasoning Fine-Tuning (RFT), utilizing both online and offline Reinforcement Learning (RL).

This is not merely feeding the model textbooks. We use a proprietary “Synthetic Data Engine” and “Teacher Models” to distill high-quality reasoning traces and domain-specific knowledge into the target models. We force the models to understand the physics behind the chemistry, not just the grammar of the formula.

The results of this post-training are nothing short of transformative. Our research shows that after a single SFT+RFT training session in the MMAI Gym, we can improve the performance of an open-source LLM base model by as much as 10X.

We have achieved state-of-the-art (SOTA) or SOTA+ performance across a wide range of critical tasks:

• Chemistry: Our post-trained models achieved SOTA on 4 out of 22 predictive tasks on the Therapeutics Data Commons (TDC) leaderboard and 5 out of 5 tasks on the MuMO-Instruct molecular optimization benchmark. They demonstrate strong performance in single-step retrosynthesis (multi-step is still far from our specialist models), predicting how to actually build the molecules they design with high validity.

• Biology: On the ClinBench benchmark, which predicts Phase 2 clinical trial outcomes based on trial descriptions and time splits, our trained Qwen3-4B model saw its F1 score jump from 0.82 to 0.94, outperforming GPT-5 (0.87).

• Target Identification: In the TargetBench retrieval task, our models outperformed all frontier LLMs in identifying valid clinical targets.

Crucially, we have proven that a “Single-Model-Does-It-All” approach is viable. Instead of relying on hundreds of fragmented specialist models. one for toxicity, one for solubility, one for targets - we can train a single multi-task LLM to handle dozens of chemistry and biology tasks with SOTA-competitive accuracy.

The NeurIPS Debut and the Varying Rates of Interest

We unveiled the preliminary results of this work and the concept of the MMAI Gym at the “Virtual Cells & Instruments” workshop at NeurIPS in December. Following this, we sent proposals to the top foundation model developers, inviting them to bring their models to our gym.

The response revealed an interesting geopolitical divergence in the AI race.

We saw immediate and intense interest from Asia. The ecosystem there is hungry for differentiation and application-level dominance. Developers in China and the surrounding region moved fast, eager to integrate scientific reasoning into their models to leapfrog competitors.

In contrast, US developers were slower to respond. This was partly due to the holiday season lag, but it also highlighted a structural disconnect. Many “AI for Science” teams in Western Big Tech are surprisingly isolated from the rapid pace of their own frontier model development. They are often siloed, working on older architectures or disconnected from the “trenches” of real-world drug discovery.

However, as we moved into January 2026, the sleeping giants awoke. They are catching up now, realizing that generalist capabilities alone will not suffice for the scientific breakthroughs that justify their valuations.

The Race for the “Bio-Phys-Chem-Med Foundation”

It is now becoming clear that every major foundation model developer is pivoting toward science. They are pushing ahead with massive teams dedicated to drug discovery. They understand that the next trillion-dollar opportunity lies not in advertising or chatbots, but in solving the fundamental biology of aging and disease.

However, most of these developers are flying blind.

We recently met with a frontier developer-an absolute leader in hardware and software-and realized they were practically clueless about how drug discovery works in the trenches. They didn’t understand the rigorous process of going from a target hypothesis to a developmental candidate, nor the safety hurdles involved. They focus on tiny, isolated problems like protein folding, thinking they are solving “biology,” while missing the massive orchestration required to develop a therapeutic asset.

The Google Dominance

In this chaotic landscape, only one tech giant has truly figured out AI for science: Google.

They realized that you cannot build a valid AI for drug discovery without running your own therapeutic programs and established their own drug discovery company. Actually, two companies, Calico, which was not productive so far but probably generated data and infrastructure, and Isomorphic Labs, which is moving full-speed ahead with their own internal pipeline.

You need “ground truth.” You need to know if the molecule your AI designed actually binds to the target, if it is toxic in a rat, if it is stable in human liver microsomes. Google understood that they needed to generate their own proprietary data from the trenches to validate their models. This closed-loop capability-AI prediction followed by wet-lab validation-is the only way to establish real benchmarks and improve model performance.

Other companies in the AI space, AWS, Microsoft or Nvidia, do not have this advantage - without real drug discovery programs they can not really see the results of their drug discovery platforms in the end-to-end fashion and do not have the real-world benchmarks to evaluate their performance. This is the problem we are trying to solve with the MMAI Gym - it will allow frontier model developers to compete with Google and other model developers that figured out the experimental and clinical side of drug discovery.

The MMAI Gym is built on years of hard work in drug discovery and experimental science:

• We have nominated 27 developmental candidates.

• We have received IND clearance for multiple molecules.

• We successfully completed Phase IIa clinical trials for our lead program in Idiopathic Pulmonary Fibrosis (IPF).

• We have our own automated robotics lab, Life Star 2, which runs 24/7 generating biological data.

We have over 1,000 proprietary benchmarks derived from these real programs. When we train a model in the MMAI Gym, we are testing it against data that cost millions of dollars and years of human effort to generate. This is the difference between an AI that plays a video game of science, and an AI that does actual science.

2026: The Year of Pharmaceutical Superintelligence

I predict that 2026 will be the year of benchmarks, AI for science, and the emergence of true superintelligence in this domain.

We are moving toward a future where a researcher can sit in front of a prompt window and orchestrate an entire drug discovery campaign-from target identification to the design of small molecules and biologics, to the planning of preclinical and clinical studies. This concept, which we call “Chemical and Biological Superintelligence” (CSI and BSI), is within reach.

By the end of this year, we expect to see models that are not just “helpful assistants” but superhuman agents capable of reasoning through complex biological pathways, predicting clinical trial outcomes with high accuracy (as we saw with our >0.90 F1 scores), and designing molecules that are successful on the first attempt.

The winners in this space will be defined by who has the best data and the most rigorous training environments.

Check out our recent conversation with the brilliant CEO of Owkin, Dr. Thomas Clozel.

A Note of Caution: Do Not Expect Magical Cures from AI in 5 Years

Despite this optimism, we must remain grounded in the brutal economic and scientific realities of our industry.

In practical drug discovery, we should not expect miracles.

There remains a profound disconnect between theoretical science (what the AI predicts) and experimental reality (what happens in the human body). A single, tiny molecular modification can lead to dramatic differences in toxicity or efficacy. An AI model might find a perfect binder that may also cause some liver tox in 1% of the population. For a a drug targeting a chronic disease or a biological process like obesity, even this small number will not be acceptable.

The costs are astronomical. A single therapeutic program can still cost roughly a billion dollars to carry through to completion.

Commercial tractability still rules. A drug must not only work; it must be patentable, manufacturable at scale, and reimbursable by insurance. Most importantly, investors and the pharmaceutical companies that will be developing it, must believe in this drug to invest. This limits the number of novel targets AI companies can go after.

AI can accelerate the early stages-we have cut the time to preclinical candidate nomination from 4.5 years to as little as 9 months-but it cannot yet cheat the biology of the human body or the regulatory requirements of clinical trials. The “trenches” are deep, and the fight against disease is long.

The Open MMAI Gym Model

This is why Insilico has decided to open our doors. We developed the MMAI Gym for ourselves, to gain an edge in this difficult war against disease. But now, we are opening this gym to foundation model vendors.

We want to act as the coach for the frontier model developers, helping them test and train their models on real-world scientific tasks. We are creating a hybrid of ScaleAI and a specialized scientific laboratory. By providing access to our 1,000+ benchmarks, our proprietary data (including 1 billion+ data points with 3D poses and 100 million+ reactions), and our automated wet lab validation, we aim to level the playing field.

We believe that in the future, frontier foundation model developers will take over the majority of drug discovery tasks. Our goal is to ensure they do it right. To ensure that the “superintelligence” they build is grounded in the reality of chemistry and biology, not just the patterns of text.

The next battlefield is here. Welcome to the MMAI Gym for Science.

Disclaimer: This article is written with the help of generative tools so beware of hallucinations. The images were generated using NanoBanana. Don’t buy, sell, or take any drugs based on this article or any of its contents. The information and views expressed in this article are for informational and educational purposes only and do not constitute medical advice. The content is not intended to be a substitute for professional medical advice, diagnosis, or treatment. Always seek the advice of your physician or another qualified health provider with any questions you may have regarding a medical condition. Never disregard professional medical advice or delay in seeking it because of something you have read in this article. The author is sharing personal experiences and opinions. These experiences are not a recommendation or endorsement for any specific treatment, drug, or course of action. The medications and therapeutic strategies discussed may not be suitable for everyone and can have significant risks and side effects. Some of the drugs mentioned are investigational and have not been approved by the FDA or other regulatory agencies for the uses discussed. While the author is the CEO of Insilico Medicine, the statements and view presented in Forver.ai do not represent the views and opinions of Insilico Medicine.

Precision Interventions & Small Molecule Renaissance

The year 2025 in biopharma was marked by precision breakthroughs across pharmaceuticals, biotechnology, and longevity research. The FDA approved 55 new drugs (46 novel drug entities), maintaining the high productivity of recent years. Notably, small-molecule medicines made a resurgence—comprising ~67% of approvals—after a period dominated by biologics. By contrast, cell and gene therapy approvals slowed (just 4 approvals, down from 7 in 2024), reflecting a consolidation phase in that field even as scientific progress accelerated. Overall, 32 orphan drugs and 31 first-in-class therapies gained approval, underscoring deep innovation. By the way, when I say 31 first-in-class therapies, I do not mean “absolutely novel”. Absolutely novel, we are talking about 5-7, depending on how you assess novelty. But even if we were talking 31 novel - this is still and incredibly low number. Just think about it - over $300 billion/year spent and only 31 novel drugs - we need to do better than that! On the positive side, China’s biotechnology industry has now caught up with the US and started investing in high-risk high-reward novel therapies. We need more countries to start investing in biotech and take risks for our benefit - at the end of the day, we are all patients.

2025 also saw multiple “firsts” translating cutting-edge biology into clinical reality. A personalized CRISPR gene edit saved a child’s life (”n=1” medicine in action). A T-cell–boosting immunotherapy validated Nobel Prize-winning regulatory T cell biology in humans. The first bispecific antibody showed unprecedented survival in a solid tumor, and the first orexin agonist addressed the root cause of narcolepsy. In metabolic disease, oral versions of blockbuster GLP-1 agonists achieved robust weight loss, while a novel class of aldosterone synthase inhibitors tackled treatment-resistant hypertension by targeting its hormonal driver.

Importantly, longevity biotechnology milestones signaled growing mainstream acceptance. The FDA removed the decades-old black box warning on hormone replacement therapy (HRT), reflecting new evidence of its healthspan benefits. Long-acting preventatives emerged for infectious diseases (a 6-month flu prophylactic and twice-yearly HIV PrEP injection). And critically, after years of preparatory research, the FDA greenlit the first trials of gene-edited pig organ transplants—opening the door to solving organ shortages.

2025’s advances went beyond incremental improvements—they validated entire mechanisms and set new benchmarks. The industry moved from “broad strokes” to high-precision interventions: editing single DNA letters to cure rare diseases, modulating microRNA master-switches to quell inflammation, and re-engineering immune responses to outsmart cancer and autoimmunity.

Notable Breakthroughs

1. A Milestone in Personalized Medicine: The “n=1” CRISPR Cure

Patient: “Baby KJ”

Indication: CPS1 Deficiency

Modality: In Vivo Base Editing

Perhaps the most dramatic demonstration of 2025’s precision medicine ethos was the case of baby KJ—an infant born with a fatal metabolic disorder who became the first patient treated with a personalized gene therapy.¹

The Clinical Challenge:

KJ had severe carbamoyl phosphate synthetase 1 (CPS1) deficiency, a urea-cycle enzyme defect that caused toxic ammonia to build up to >1000 μmol/L (over 30× normal). Newborns with this condition face imminent brain damage or death, and the usual remedy (liver transplant) is often too dangerous in a neonate.

The Custom Solution: In just seven months from diagnosis, the team designed an adenine base editor specifically targeting KJ’s CPS1 mutation. This gene-editing tool changes a single DNA letter (A→G) without cutting the DNA strands, thereby correcting the disease-causing mutation with minimized off-target damage. The editing mRNA and guide RNA were delivered via liver-targeted lipid nanoparticles (LNPs)—no viruses needed.

Outcomes:

The results were remarkable. The treatment was well-tolerated with no serious side effects. Within weeks, KJ’s ammonia metabolism improved—he could tolerate more dietary protein with his nitrogen-scavenger medications cut in half. Crucially, typical childhood infections that would normally trigger lethal ammonia spikes no longer did so. By mid-2025, KJ was discharged home and was “thriving,” meeting normal developmental milestones that would have been unthinkable for this disease. This single-patient success definitively proved that in vivo base editing can safely and effectively cure a deadly genetic disease.

2. The “GenAI-First”: New Medicine Discovered Using Generative AI Showed Promise in the Clinic

Asset: Rentosertib (ISM001-055)

Company: Insilico Medicine

Indication: Idiopathic Pulmonary Fibrosis (IPF)

If 2024 was the year of AI hype, 2025 was the year of AI proof. For the first time, a molecule designed entirely by generative AI algorithms achieved clinical validation, shifting the narrative from computational potential to patient outcomes.

The Breakthrough: Rentosertib targets TNIK (Traf2- and Nck-interacting kinase), a novel fibrosis target identified by AI, with a molecular structure also designed by AI. In Phase 2a trials, the drug met its primary safety endpoints. However, the efficacy data garnered the most attention: patients receiving the 60 mg daily dose showed a +98.4 mL improvement in Forced Vital Capacity (FVC) over 12 weeks, compared to a decline in the placebo group.

Clinical Context: While the efficacy signal is a promising trend, it is important to note that the trial is early, and confirmatory studies are required to establish long-term benefit. Nonetheless, this result serves as the first proof-of-concept that “silicon-first” discovery can yield molecules that engage novel biology and produce functional improvements in complex human disease.

3. Nobel Science Hits the Clinic: Treg Immune Modulation

Asset: Rezpegaldesleukin (REZPEG)

Company: Nektar Therapeutics

Mechanism: IL-2 Receptor Agonist (Treg Selective)

In 2025, the Nobel Prize in Medicine recognized the discovery of regulatory T cells (Tregs)—the immune system’s brake pedals. Fittingly, this same year saw clinical triumph for a drug that activates Tregs to treat autoimmune disease.

The Data: Rezpegaldesleukin is an engineered IL-2 cytokine that selectively expands Tregs. In a Phase 2b trial for severe eczema (atopic dermatitis), it showed a 61% improvement in skin severity (EASI) at the high dose vs. 31% for placebo. This was achieved by boosting patients’ own Treg cells rather than broadly suppressing immunity. The results—a rapid onset and dose-dependent efficacy—signal a potential paradigm shift in treating autoimmunity by restoring immune balance instead of using blanket immunosuppression.

4. Epigenetic Control: microRNA Modulation

Asset: Obefazimod

Company: Abivax

Mechanism: miR-124 Upregulation

A first-of-its-kind microRNA-targeted drug succeeded in Phase 3 in 2025. Obefazimod is an oral small molecule that boosts the production of miR-124, a natural “master brake” on inflammation.

The Mechanism: The increased miR-124 simultaneously downregulates multiple cytokines like IL-6, IL-17, and TNF. In ulcerative colitis, obefazimod’s Phase 3 induction trials met all endpoints: significantly higher remission rates at 8 weeks compared to placebo (e.g., 25 mg dose yielded ~21% absolute placebo-adjusted remission). This validates miRNA upregulation as a scalable therapeutic strategy—essentially an upstream approach to broadly quell inflammatory pathways via a single “switch.”

5. Regenerative Medicine: Exosome Therapy

Asset: Deramiocel (CAP-1002)

Company: Capricor Therapeutics

Indication: Duchenne Muscular Dystrophy (DMD)

Exosomes—nanoscale vesicles secreted by cells—moved from theory to therapy in 2025. Capricor Therapeutics delivered convincing Phase 3 evidence that an exosome-rich therapy can slow the course of Duchenne muscular dystrophy.

Pivotal Results: In the HOPE-3 trial, intravenous deramiocel slowed the decline in upper limb function by ~54% vs. placebo over one year, and slowed cardiac deterioration by ~91% vs. placebo. Specifically, 51.4% of treated patients needed no additional arm surgery (vs. 20.3% placebo). These differences translate into preserving independence and preventing heart failure, validating exosomes as a new class of “drug factories” for regenerative medicine.

6. Infectious Disease Renaissance: New Gonorrhea Antibiotic

Asset: Gepotidacin

Company: GSK

Class: Triazaacenaphthylene Antibiotic

For the first time in over 30 years, a new oral antibiotic class was approved for drug-resistant gonorrhea. Gepotidacin inhibits bacterial DNA replication by a novel mechanism (binding a distinct site on DNA gyrase).

Clinical Impact: In Phase 3, gepotidacin cured ~98% of uncomplicated Neisseria gonorrhoeae infections, including strains resistant to all current therapies. This is game-changing, as gonorrhea had become increasingly untreatable, leaving clinicians with few options beyond older injectables.

7. Malaria Eradication: 99% Cure Rates

Asset: Ganaplacide + Lumefantrine

Company: Novartis

Malaria saw its biggest therapeutic advance in decades with the GanLum regimen. Unlike artemisinin-based treatments (which face rising resistance), this combination uses a new class of non-artemisinin agent.

The Data: In a Phase 3 trial across 12 African countries, the combination achieved 97–99.2% cure rates at 28 days, remaining effective even against artemisinin-resistant strains. Additionally, scientists unveiled a bed net strategy using endochin-like quinolone (ELQ) compounds that kill parasites inside the mosquito, blocking transmission at the source.

8. The “Chemical Vaccine” Paradigm

Assets: CD388 (Cidara) & Lenacapavir (Gilead)

Concept: Long-Acting Prophylaxis

2025 blurred the line between drugs and vaccines by advancing long-acting prophylactics that provide “passive immunity.”

• Universal Flu Shield: CD388, a drug-Fc conjugate, links an antiviral to an antibody tail. In Phase 2b, a single injection provided 76% protection against symptomatic influenza for the entire season (6 months), regardless of strain.

• Twice-Yearly HIV PrEP: The FDA approved lenacapavir for PrEP. In landmark trials, it showed 100% efficacy (zero infections) with just two injections per year, resolving the adherence issues of daily pills.

9. New Vectors: Gorilla Adenovirus

Asset: Papzimeos

Company: Precigen

Indication: Recurrent Respiratory Papillomatosis (RRP)

Solving the “delivery problem” remains a priority. In 2025, Papzimeos became the first approved therapy using a gorilla adenovirus vector. These vectors are less immunogenic than human viruses and have a larger cargo capacity.

Outcome:

In RRP patients, 51.4% achieved complete disease remission (no surgery for ≥1 year), compared to 0% historically. This “plug-and-play” vector platform could enable future gene therapies that require delivering larger or multiple genes.

10. In Vivo Base Editing: AATD and the Liver

Asset: BEAM-302

Company: Beam Therapeutics

Beyond “n=1” cases, base editing showed promise for broader populations. Beam Therapeutics reported encouraging first-in-human data for Alpha-1 Antitrypsin Deficiency (AATD).

The Data:

In Phase 1/2, a single dose raised functional AAT protein to 12.4 μM (above the protective threshold) and reduced toxic mutant protein by 79%. This suggests the therapy is fixing the genetic defect at the source—producing normal protein and clearing toxic aggregates—validating the safety of editing genes inside the patient’s liver.

11. Cardiovascular Gene Editing: The “One-and-Done”

Asset: VERVE-102

Company: Verve Therapeutics / Eli Lilly

Verve advanced its in vivo base-editing treatment for high cholesterol. VERVE-102 targets the liver gene PCSK9, permanently silencing it to lower LDL.

The Data:

Interim Phase 1b data showed that a single infusion produced robust LDL-C reductions of 53%. Unlike previous attempts, the optimized LNP yielded no significant safety issues. If confirmed, this offers the potential for a “one-and-done” prevention strategy for heart disease, replacing decades of chronic statin use.

12. Bispecific Antibodies in Solid Tumors

Asset: Petosemtamab

Company: Merus

Mechanism: EGFR x LGR5 Bispecific

Historically, bispecific antibodies struggled in solid tumors. Petosemtamab changed that narrative in 2025. It targets EGFR (growth) and LGR5 (cancer stem cells) simultaneously.

Unprecedented Survival: In head & neck cancer, the drug (combined with immunotherapy) delivered a 79% one-year survival rate, with median overall survival not reached. This compares to historical survival rates of ~50%, leading analysts to term the results “unprecedented” and triggering Genmab’s acquisition of Merus.

13. Metabolic Medicine: The Oral GLP-1 Revolution

Asset: Orforglipron

Company: Eli Lilly

While injectables defined the early 2020s, 2025 ushered in the “Oral Era.” Orforglipron is a small molecule GLP-1 agonist that can be manufactured at scale, bypassing the complex supply chains of biologics.

The Data: In Phase 3 trials, it achieved substantial weight loss (~16 lbs or 7.9% in diabetes patients) and successfully maintained weight loss in patients switching from injectables. This asset promises to democratize access to obesity treatment globally.

14. Precision Cardiology: Targeting Aldosterone

Asset: Baxdrostat

Company: AstraZeneca / CinCor

Hypertension is a leading global killer, often driven by the hormone aldosterone. Previous drugs failed because they also blocked cortisol, causing severe side effects.

The Breakthrough: Baxdrostat is a highly selective aldosterone synthase inhibitor. In Phase 3, it significantly reduced blood pressure in treatment-resistant patients without affecting cortisol. This offers a new tool for the hardest-to-treat cardiovascular patients, addressing a hormonal driver of disease that was previously untouchable.

15. Xenotransplantation: Solving the Organ Shortage

Company: eGenesis / United Therapeutics

Milestone: First Formal Clinical Trials

Finally, 2025 marked the transition of xenotransplantation from experiment to clinical trial. eGenesis and United Therapeutics launched first-in-human trials of gene-edited pig kidneys.

The Science: eGenesis’s pigs have 69 genomic edits to remove rejection antigens and viral risks. In a compassionate use case, a patient survived for 8 months with a pig kidney, proving long-term function is possible. The FDA’s green light for formal trials represents a turning point where decades of research have coalesced into a feasible solution to save lives.

16. Quantum Computing’s “Hello World” in Drug Discovery

Assets: ISM061-018-2 & ISM061-22 Source: Nature Biotechnology (Jan 2025) Target: KRAS (Cryptic Pockets)

While AI is optimizing known chemical spaces, quantum computing is beginning to explore the unknown. In January 2025, a landmark study published in Nature Biotechnology provided the first experimental proof that quantum generative models can design valid pharmacological hits.

The Hybrid Approach: Researchers from Insilico Medicine and the University of Toronto utilized a hybrid quantum-classical workflow on a 16-qubit IBM quantum computer. They combined Quantum Circuit Born Machines (QCBMs)—which exploit quantum superposition to efficiently sample complex molecular distributions—with classical Long Short-Term Memory (LSTM) networks to navigate the vast chemical space of KRAS inhibitors.

The Hits: Unlike fully classical models, this hybrid approach synthesized 15 molecules, two of which were confirmed as valid biological hits:

• ISM061-018-2: A broad-spectrum inhibitor with a binding affinity of 1.4 µM against the KRAS-G12D mutation.

• ISM061-22: A selective inhibitor targeting the G12R and Q61H mutants.

The Significance: While the affinity (micromolar) represents an early “hit” rather than a clinical lead (often nanomolar), the study is foundational. It moves quantum drug discovery from theoretical physics to wet-lab reality, proving that quantum algorithms can generate novel, synthesizable structures that engage difficult biological targets.

17. Airport Gyms: RFK Doing 20 Pull Ups in the Airport

Milestone: Pull-up Bars in Airports

While not a biomedical advance, my longevity highlight of 2025 was the 72-year old Secretary of Health and Human Services doing 20 pull-ups in a suit at the airport in a viral video. Whenever I am at the gym doing pull-ups, I replay his video in my mind and it helps me push a bit harder - if a 72-year old director of HHS can do it, I should be able to do it too. You may or may not like his policies but this Maintaining healthy muscle is among the most important lifestyle modifications anyone can do.

Toward a Longer, Healthier Future

By the numbers, 2025 saw 55 new FDA drug approvals (37 small molecules, 18 biologics)—consistent with recent years’ productivity. But beyond quantity, the year will be remembered for qualitative leaps forward. The overarching theme was precision—therapies honed to specific molecular targets or root causes.

In the realm of longevity biotechnology, 2025 was also notable. Clinical studies provided the first solid evidence that certain interventions can extend healthspan in humans. For example, the PEARL trial showed weekly low-dose rapamycin was safe in older adults. A major regulatory shift saw the FDA remove the Black Box warning from HRT, reflecting growing institutional acceptance of longevity-focused treatments.

In summary, 2025 will be remembered as a year when therapeutic precision and bold scientific bets began paying off for patients. Diseases long thought incurable—from genetic syndromes in infants to degenerative illnesses of aging—were altered fundamentally by new treatments. By attacking root causes and enabling regeneration, 2025’s breakthroughs laid the groundwork for people not only to live longer, but to live healthier, more vital lives.

Disclaimer: This article is written with the help of generative tools so beware of hallucinations. Don’t buy, sell, or take any drugs based on this article or any of its contents. The information and views expressed in this article are for informational and educational purposes only and do not constitute medical advice. The content is not intended to be a substitute for professional medical advice, diagnosis, or treatment. Always seek the advice of your physician or another qualified health provider with any questions you may have regarding a medical condition. Never disregard professional medical advice or delay in seeking it because of something you have read in this article. The author is sharing personal experiences and opinions. These experiences are not a recommendation or endorsement for any specific treatment, drug, or course of action. The medications and therapeutic strategies discussed may not be suitable for everyone and can have significant risks and side effects. Some of the drugs mentioned are investigational and have not been approved by the FDA or other regulatory agencies for the uses discussed. While the author is the CEO of Insilico Medicine, the statements and view presented in Forver.ai do not represent the views and opinions of Insilico Medicine.

As world leaders gather in Davos and many of my friends in aging research organize side events on longevity, I want to share an observation that has bothered me for some time: the field of longevity has gone “woke.” This shift is inhibiting free thought and stifling new ideas that could significantly increase human lifespan. Every time I start talking about the therapeutic extension of human life, I hear the same refrain: “But you are extending healthspan, not lifespan, right?”

Well, ladies and gentlemen, let me be clear: to me, a Drug for Lifespan = a Drug for Healthspan. Unless you can show me a single drug or therapy that can give a person with an already optimized lifestyle just five extra years of life without extending their healthspan, I will not change my opinion.

Wokeness in Longevity

In 2024, I attended the World Economic Forum in Davos to give a talk on longevity. While standing in a hallway, I was approached by a group of young “Global Shapers” who asked what I was working on.

“I am in aging research to extend healthy, productive longevity,” I said.

“Oh, so you want the rich to live longer?” one asked immediately.

I was surprised but answered calmly, “I want everyone to live longer. But right now, nobody lives longer. We don’t live to 90 on average, and the maximum is still 122.5. We need to change that.”

“But it will only be available to the rich,” she pressed on.

“It will be like a personal computer or cell phone—originally available to the rich, but with scale, available to everyone. That is how technology works. Let’s get there first,” I replied.

“But what about poor nations? We need to have parity,” she continued.

I admit, I felt a bit irritated but did not show it.

“Imagine that we are in the year 1900,” I said. “I am working on the airplane, and you are asking me if it will only be available to the rich. Does it really matter yet? We do not have the plane. Should we argue about who flies in business class if we don’t have a plane? What kind of innovators are we if we start thinking about who gets to use the technology when there is no technology?”

That argument resonated, and we switched to questions on overpopulation, which I answered in much the same way. When I mentioned that I am also working on carbon capture and sustainability technologies, they warmed up a bit.

But then, the debate returned. “But you are working on healthspan and not lifespan, right? It is very important to extend the healthy portion of life,” another Global Shaper asked. She seemed familiar with the standard lifespan vs. healthspan debate.

“Can you give me one example of any drug that can significantly increase lifespan and not healthspan? Even by five years?” I asked.

I thought she would be stuck there—because there is no answer.

“That is why I am asking if the drugs you are working on are focused on healthspan or lifespan,” she continued.

I pivoted the conversation there and asked: “What made you ask this question? If I were to encounter someone in this field, I would first ask about biological mechanisms, discovery methods, current results, or challenges. Instead, we are talking about the potential negatives of a technology that does not yet exist. There is nothing that can give you an extra 10 years of life if you are already fit and doing regular diagnostics.”

My View on Healthspan vs. Lifespan

Until you show me a pill or any other intervention that can give me—someone who is already optimized and goes for regular diagnostics—5 years of extra “poor quality life,” Lifespan and Healthspan are the same thing.

By the way, even if I could get 30 years of extra “bad life” with pain and diseases, I would still take it. So would most other people—they don’t want to die and want to see a bit more life. Anyone who argues otherwise should logically be arguing for euthanasia or mandatory termination after reaching the end of their healthspan.

We must stop arguing about whether aging is a disease or if we are extending healthspan. We need to relentlessly focus on identifying new interventions to push human lifespan and performance beyond currently acceptable limits. Endless debates on lifespan vs. healthspan just give people without new ideas a platform to speak.

Aging is our common and most important enemy. It drives the decline of all biological functions, leading inevitably to disease and death. It is 100% guaranteed to take everything from us, yet we fixate on lesser issues—regional conflicts, partisan politics, income inequality, gender debates, bathrooms, and pronouns. Simultaneously, we strategically deceive ourselves to avoid fighting, or even acknowledging, the reality of aging while 160 thousand people die every day.

I committed my life to aging research over 20 years ago. I have watched the field transform from snake oil and small-scale experiments into an industry defined by massive initiatives like Calico and Altos. Today, we are seeing the first sustainable business models and the arrival of the first wave of longevity therapeutics, such as GLP-1s and other incretins. Now is the time to push full steam ahead on interventions targeting muscle wasting, neurodegeneration, ocular disease, hair loss, and performance augmentation.

However, the emergence of “wokeness” in longevity is a new and concerning obstacle. this trend even affected the ARDD conference. Many talks, especially those polished by consultants make extra cautious remarks about healthspan vs lifespan and focus on disease avoidance rather than significant increases in lifespan. In my opinion, before we obsess over the potential downsides or unequal distribution of life-extension technologies, we must actually invent them. Nothing is more urgent—we are all on the clock.

What do you think about this trend? Feel free to share your opinions in comments.

About three weeks ago, I walked the streets of Milan—the undisputed capital of global fashion—with a high-level business delegation from Hong Kong, the undisputed capital of longevity.

The contrast was visceral. Milan is a monument to aesthetics, history, and La Dolce Vita. But Hong Kong is a monument to biological success. The life expectancy in Hong Kong is 85.77 years— the highest in the world.

I consider this the ultimate Key Performance Indicator (KPI) of a civilization. Unlike wealth, where the delta between the rich and the poor can be 1,000x, biological time is the great equalizer. The richest tycoon cannot buy a 3x multiplier on their lifespan compared to the average citizen. Everyone is confined to a relatively narrow range.

That is why life expectancy—not GDP—is the only metric that truly matters. If you want to criticize Hong Kong, simply compare its life expectancy to where you live, and fix your own house first.

But walking through Italy, I realized something troubling. I pulled out my phone and searched for “longevity fashion.” Do you know what came up?

Sustainability.

Thousands of articles about organic cotton, “slow fashion,” and biodegradable fabrics.

This is a massive category error.

We are draping our bodies in passive, rotting cloth while we use generative AI to discover drugs that rewrite biology. We are applying 21st-century intelligence to reprogram aging—and still wearing 19th-century textiles optimized for tradition, not physiology.

Longevity Fashion is not about the survival of the garment. It is about the survival of the wearer.

Clothing must cease to be a covering and become an exosomatic organ—a second skin engineered to extend healthspan. And if that sounds extreme, remember: we already live this way.

• Your phone is an exosomatic brain.

• Your car is an exosomatic leg.

• Your home is an exosomatic immune system (temperature, filtration, safety).

Your clothes are overdue for an upgrade.

So while walking in Milan, I started thinking about what can we do for longevity fashion and made a few notes. Below are my Twelve Commandments of Longevity Couture.

The Twelve Commandments of Longevity Couture

1. Do Not Accelerate Aging (Primum Non Nocere; the end of the “natural” fallacy)

2. The Exposome Shield (UV, pollution, pathogens, noise, light)

3. Locomotion Security (Footwear, traction, fall prevention)

4. Biomechanical Support (The soft exoskeleton; posture and load distribution)

5. Hemodynamic Optimization (Graduated compression as daily infrastructure)

6. Thermoregulatory Homeostasis (Heat, cold, and fertility-aware design)

7. Gym-Ready by Default (Zero friction movement; clothing that enables training)

8. The Logistics of Longevity (Pharma-couture; hydration, nutrition, tools)

9. Continuous Biomarker Monitoring (Your clothes becomes the sensor)

10. Youthful Signaling & Biofeedback (Stress reduction, confidence loops, cognition)

11. Circular Durability & Upgradeability (Long-lasting hypoallergenic materials; modular everything)

12. Augment & Correct (The exosuit endgame; soft robotics as clothing)

1. Do Not Accelerate Aging

The end of the “natural” fallacy.

The first principle is not aesthetic. It is medical. Stop worshiping “natural.” Nature is not a wellness brand. Nature wants you to reproduce and decompose. Thermodynamically speaking, nature wants you to just decompose.

We have a romanticized attachment to certain fibers because they predate modern chemistry. But longevity is not achieved through nostalgia. It is achieved through control—control of exposure, control of moisture, control of friction, control of microbial load, control of temperature.

Here is the uncomfortable truth: cotton is hydrophilic. It absorbs moisture, holds sweat, slows evaporation, and becomes a comfortable habitat for odor and microbial growth in exactly the conditions modern life produces (commuting, crowded spaces, intermittent exercise, long sitting, travel). It also degrades, stretches, and loses structure. That is not inherently “evil.” It is simply a poor substrate for longevity-grade performance.

• The Fix: Engineered, carbon-derived synthetics—done correctly.

• The Platform: High-grade polyester, polyamide, and elastomers (Spandex/Lycra) are not compromises. They are the platform. They can be:

• Biologically Quiet: Non-itch, non-sensitizing when properly finished.

• Fast-Drying: Reducing the time moisture stays near skin (bacteriostatic via thermodynamics).

• Structurally Stable: Keeping shape and support for years.

• Durable: Less replacement, less laundering burden.

• What can be built now: A “Biological Inertness Standard” for longevity garments. We need zonal fabrics with hydrophobic outer surfaces and capillary channels that move sweat away from the skin. We need antimicrobial reinforcement in high-humidity zones paired with ventilation. Longevity fashion begins with a simple rule: the default garment must not become a wet ecosystem.

2. The Exposome Shield

Radiation, pollution, pathogens, noise, and light.

Aging does not happen only inside your cells. It happens at the interface between you and your environment. We treat UV protection like a beach accessory and air filtration like emergency gear. That framing is obsolete. Your body experiences continuous environmental load.

• The Fix: Clothing becomes your portable environment.

• What can be built now:

• UPF 50+ by default in outer layers and commuter wear—without overheating.

• Discreet filtration integration: Collars/hoods designed to support masks or filters when needed.

• Noise protection: Hoods and headwear that reduce harsh urban sound to lower cortisol, without cutting you off from situational awareness.

• Sunscreen is chemistry you apply. Longevity fashion is physics you wear.

3. Locomotion Security

Footwear and fall prevention as longevity infrastructure.

If you want a long life, you must remain mobile. Falls are not a minor inconvenience; they are one of the most catastrophic failure modes of aging. The tragedy is that most falls are not “fate.” They are engineering failures: unstable shoes, poor traction, inadequate proprioceptive feedback.

• The Fix: Treat footwear as medical equipment in disguise.

• What can be built now: A longevity footwear standard featuring a stable base, high traction, shock absorption, and a toe box that respects anatomy. Comfort must hold for 10,000 steps, not 10 minutes. Longevity fashion that ignores footwear is like longevity medicine that ignores sleep. It is incomplete.

4. Biomechanical Support

The soft exoskeleton: posture and load distribution.

Gravity charges compound interest. Poor posture is not only cosmetic; it alters breathing mechanics, increases strain, contributes to chronic pain, and quietly reduces movement.¹ Pain reduces activity. Reduced activity accelerates decline.

• The Fix: Garments that act as scaffolding—subtle, supportive, constant.

• What can be built now: Tension mapping using high-tensile elastomer panels to guide alignment without restricting movement. Distributed load systems (jackets, bags) that stop punishing the neck and shoulders. Think of it as “quiet biomechanics.” Not a brace. Not medical equipment. Just better engineering.

5. Hemodynamic Optimization

Graduated compression as daily infrastructure.

Your circulatory system fights gravity all day. Sitting for long periods—work, flights, commutes—creates predictable problems: swelling, fatigue, and long-term vascular strain.² Compression has been trapped in the “medical beige” category. That is a cultural mistake.

• The Fix: Graduated compression built invisibly into trousers, sleeves, and socks using elastomer blends designed to maintain compression across thousands of wear cycles. If movement is longevity’s engine, circulation is the oil.

6. Thermoregulatory Homeostasis

Heat, cold, and fertility-aware design.

Temperature is a master variable. Your body wastes enormous energy maintaining homeostasis. Overheating increases strain; chronic cold increases discomfort and reduces movement. There is also an ignored subtopic: fertility. Overheating sensitive zones is not “comfort.” It is a design error.

• What can be built now: Dynamic ventilation placed where heat actually accumulates (not decorative mesh). Pants engineered for airflow and heat dissipation (Venturi vents). Lightweight heating for extremities in cold environments to maintain comfort without bulky insulation. Thermoregulation is not fashion. It is physiology.

7. Gym-Ready by Default

Zero friction movement.

If you must change clothes to move, you’ve created resistance to longevity. The modern environment pushes you into sedentary behavior. Longevity fashion must invert the default: the wardrobe should make movement frictionless.

• What can be built now: Formal silhouettes that stretch, recover, and breathe. Abrasion resistance in high-wear zones so you can actually move without destroying the garment. Sweat management as a first-order KPI (dry time, not just “wicking” marketing). The goal is not “athleisure everywhere.” The goal is movement compatibility everywhere.

8. The Logistics of Longevity

Pharma-couture, hydration, nutrition, and tools.

A serious longevity protocol requires tools: supplements, medications, glucose tabs, insulin pens, inhalers, electrolytes, devices, hydration. The current fashion industry pretends this reality does not exist. So people improvise with bulky bags or noncompliance.

• What can be built now: Discreet, secure compartments designed for real use (accessible, washable, anti-loss). Temperature-aware micro-pockets for sensitive items. This is not “cargo pants.” This is systems design.

9. Continuous Biomarker Monitoring

The shirt becomes the sensor.

The annual checkup is an outdated interface. Your biology is continuous. Your monitoring should be continuous. The watch was a start, but wrist sensors are an ergonomically limited interface. Clothing has more surface area and better contact points.

• What can be built now: Washable, removable sensor modules integrated into undershirts and bras. Measurement of respiration, heart rate patterns, and posture. The end state is simple: your clothing quietly detects drift and corrects it before you feel it.

10. Youthful Signaling & Biofeedback

Stress reduction, confidence loops, cognition.

Looking young is not vanity. It is a feedback system. Youthful presentation changes how people treat you—and how you treat yourself. It influences confidence, stress levels, and daily behavior.

• What can be built now: Cuts that reinforce structure and posture visually and mechanically. Color and contrast that communicate vitality. Gentle biofeedback: reminders to unclench the jaw, lower shoulders, breathe. Longevity fashion should not only protect the body—it should protect the mind from the chronic stressors of modern life.

11. Circular Durability & Upgradeability

The 50-year garment.

This is where sustainability becomes relevant—but only when it is correctly defined. True sustainability is not biodegradability. True sustainability is durability plus circularity.

A high-performance synthetic garment that lasts decades is often more sustainable than a “natural” garment that must be replaced repeatedly. Not because it is morally superior, but because it reduces manufacturing demand, laundering burden, and waste.

• The Longevity Approach: Make fewer things. Make them better. Make them repairable.

• What can be built now: Replaceable components (cuffs, zippers, insoles). Modular electronics that can be removed and upgraded without trashing the garment. Design-for-disassembly so polymer blends don’t become recycling dead ends.

12. Augment & Correct

The exosuit endgame.

This is the final evolution: clothing that does not only protect and measure, but augments. As biological function naturally declines, the second skin should compensate. Not with stigma. Not with bulky devices. With elegant assistance.

• What can be built next: Soft assist garments that reduce joint load during walking or lifting. Stability systems that reduce fall risk. Integration with smart eyewear and hearing systems. The vision is not science fiction armor. It is a quiet shell that makes older adults move with confidence.

The Longevity Fashion Stack (The OSI Model for Longevity Couture)

Smart clothing exists in fragments. Longevity fashion is the integration layer. We are building a stack:

• Layer 0 — Biological Safety (Materials): Carbon-derived, durable, biologically quiet synthetics. Low moisture retention, low odor.

• Layer 1 — Exposome Protection: UPF-first designs, pollution and pathogen readiness.

• Layer 2 — Mechanics & Injury Prevention: Posture systems, gait support, footwear standards, fall risk reduction.

• Layer 3 — Behavior Engineering: Gym-ready default, movement friction removal.

• Layer 4 — Sensing & Feedback: Continuous signals, haptic cues, coaching loops.

• Layer 5 — Augmentation: Assistive torque, stability, sensory expansion.

Brands typically build one layer at a time. Longevity couture builds the stack.

A Simple Scoring System: The Longevity Couture Index

How do you know if a garment is longevity fashion or just marketing? Score each category 0–2 (0 = no, 1 = partial, 2 = yes). Maximum score: 24.

1. Biological Inertness — Fast dry, low odor, comfortable on skin, no “chemical feel.”

2. Exposome Shield — UPF 50+ capability, glare/eye protection options, pollution readiness.

3. Locomotion Security — Traction, stability, fall-risk reduction (especially footwear).

4. Mobility — Full range of motion: squat, stairs, long walk.

5. Support — Posture/load distribution, strain reduction.

6. Circulation — Integrated compression where appropriate.

7. Thermoregulation — Heat release, cold management, ventilation in correct zones.

8. Gym Readiness — You could exercise now without changing.

9. Logistics — Discreet storage for essentials (health tools, hydration).

10. Sensing — Meaningful monitoring or readiness for modular sensors.

11. Biofeedback — Cues that reduce stress and improve behavior.

12. Upgrade Path — Repairable, modular, circular design.

• 20–24 = Longevity-First.

• 14–19 = Competent modern wear.

• 0–13 = Traditional Fashion (style-first, biology-later).

Conclusion: Stop Iterating

To the designers in Milan and the innovators in Hong Kong: Stop iterating.

We do not need another season of trends. We do not need more “organic” garments that behave like sponges. We do not need sustainability that confuses biodegradability with progress.

We need a fundamental disruption.

Longevity Couture is not just a style. It is a survival strategy.

It is the convergence of biotechnology, ergonomics, sensors, robotics, and advanced materials science into a wearable interface for human health. The future of fashion isn’t about what you wear on the runway.

It’s about what you wear to break the world record for human lifespan.

If you are a designer reading this, start with Commandment #1: stop worshiping “natural,” start engineering “biologically quiet.” Then build the shield. Then build posture. Then build compression. Then augmentation.

Step by step, we turn clothing from decoration into infrastructure. The next fashion revolution will not be seasonal.

It will be biological.

But to make it in the NLRP3 world, many stars need to align. You need to have very safe molecule, strategic commitment and funding, great scientists, and perfect clinical trial design. Just this week, we saw Ventus discontinue their NLRP3 program in PD. Even before that, most NLRP3 developers did not consider them to be a serious competitor in this space. But Ventyx stock gained considerably since the news were announced - now there are even fewer competitors in PD.

This report will dissect these new results, exploring what they mean for inflammation-centric therapies and surveying the competitive landscape—from Ventyx and NodThera to BioAge and Insilico Medicine—racing to capitalize on what is now one of pharma’s most strategically significant targets. The results are still early and this report is for information purposes only.

Motivation Behind this Post and Conflict of Interest: Potentially Best-in-Class CNS-penetrant NLRP3 Inhibitor Designed Using Generative AI, Multiparameter Optimization, and Massively-Parallel Experimentation

The main motivation for this post is that I am genuinely excited about this protein target. It sits on the intersection of multiple hallmarks of aging and is probably the most promising target for inflammaging. But I also have major conflict of interest here since Insilico is also developing the brain-penetrant NLRP3i that just completed IND-enabling studies. Our business model is to pick the most promising and impactful targets using AI, predominantly those that are implicated in aging and disease at the same time, take them to developmental candidate stage (DC) or even into the clinic and then license to the pharmaceutical companies. And while we like to focus on novel targets, often we wait until other companies and academics try the target in a variety of preclinical and clinical models and then devise a strategy to unlock the full potential of the target by designing the molecule with unique features that would make it as close to a perfect drug as possible. And since we have many years of experience using Chemistry42 generative AI platform, automation and established network of partner research labs, we can perform multiparameter optimization to achieve unique properties such as increased safety, activity, oral administration, selectivity, brain-penetration, gut-restriction, multi-targeting, and many other properties. In the NLRP3i project, our objective was to use the full potential of generative AI to design, multiparameter optimization, and parallel experimentation to design several series of molecules with the highest possible levels of safety, especially liver safety, which we think is most important in chronic diseases, and high level of brain penetration. Recent story of Danuglipron in the GLP-1 space, where Danuglipron failed in a Phase 3 study due to the liver toxicity signal while the competing Orfoglipron successfully completed Phase 3 serves as a great example for the need to prioritize safety over any other molecular property.

Originally, I was not planning to purpose NLRP3i for obesity as a primary indication, since we believe the target is more likely to succeed in Parkinson’s disease and since it has long patent life, it can later be repurposed for many other indications including Alzheimer’s and potentially into chronic conditions like obesity, muscle wasting, and even chronic pain. But clinical results in obesity increase the attention to this target manyfold since most of the pharmaceutical companies are now working on their cardiometabolic portfolios while some of the pharma companies with CNS franchises that would make perfect partners for this drug may be a bit slower when making decisions and execute on in-licensing. So increased levels of attention to NLRP3 in the obesity space definitely help me partner this potentially best-in-class CNS-penetrant molecule. In my opinion, this is one of the best molecules and targets for longevity and every big pharma serious about longevity (cardiometabolism, CNS, I&I and fibrosis) needs to have one.

Landmark Phase 2 Results: A New Pillar of Cardiometabolic Health

The clearest indication that NLRP3’s moment may have arrived comes from Ventyx’s Phase 2 trial of their oral NLRP3 inhibitor, VTX3232, in 175 patients with obesity and high cardiovascular risk. The topline results were striking: patients treated with VTX3232 experienced a nearly 78% reduction in high-sensitivity C-reactive protein (hsCRP) levels after 12 weeks. hsCRP is a key marker of systemic inflammation and an independent predictor of heart disease.

The results propelled Ventyx to new highs (>250% increase over 12 months) and resulted in significant increase in pharma interest in brain-penetrant NLRP3i in general. Just in case, I am not an investor in Ventyx and this post does not represent any form of investment advice.

We recently put a cool preprint on NLRP3i online without any serious promotion (as we plan to have it go through peer-review) and the number of downloads has increased considerably - pharma is definitely getting interested.

Ventyx’s results draw significant attention for many reasons. The drug also drove down interleukin-6 (IL-6)—another inflammatory cytokine closely tied to cardiovascular risk—to a median of 1.6 ng/L, below the 1.65 ng/L threshold associated with lower risk of cardiovascular events. Beyond these primary inflammatory markers, the trial documented statistically significant improvements in multiple cardiometabolic biomarkers. Levels of lipoprotein(a)—a notoriously hard-to-modify genetic risk factor for atherosclerosis—dropped meaningfully. So did fibrinogen and erythrocyte sedimentation rate (ESR), general indicators of inflammatory activity.

One outcome was conspicuously absent: weight loss. Ventyx’s NLRP3 inhibitor did not cause patients to lose weight on its own or in combination with the GLP-1 agonist semaglutide. While this might seem like a miss, it highlights a crucial scientific observation: NLRP3 inhibitors appear to target a different pathological axis (inflammation) than GLP-1 drugs (which act on appetite and insulin sensitivity). This finding helps clarify that the residual inflammatory risk that can persist even after weight loss is an independent and potentially druggable target.

Rather than competing with GLP-1s, these agents may be positioned to complement them. The Ventyx data showed that adding VTX3232 to semaglutide produced significant additional reductions in hsCRP, IL-6, and Lp(a) over semaglutide alone. This suggests a potential new paradigm where physicians might one day use a triad of therapies: one for lipids (statins), one for metabolism (GLP-1s), and one for inflammation (NLRP3 inhibitors).

The ‘Pipeline in a Product’: NLRP3’s Versatility Across Human Disease

The excitement isn’t limited to cardiometabolic health. The reason NLRP3 is considered one of the most important targets in pharma is its incredible versatility. It acts as a central sensor for a wide array of “danger signals” that are understood to drive pathology across multiple organ systems.

• Neurodegenerative Diseases: In the brain, NLRP3 is activated in microglia by protein aggregates like amyloid-beta (in Alzheimer’s) and alpha-synuclelin (in Parkinson’s), which may unleash the chronic neuroinflammation that contributes to neuronal damage.

• Atherosclerosis: In our arteries, NLRP3 is thought to be activated by cholesterol crystals, triggering the vascular wall inflammation that initiates and propagates atherosclerotic plaques.

• NASH (Fatty Liver Disease): In the liver, NLRP3 activation is considered a key event that drives the progression from simple fatty liver to the more dangerous inflammatory state of non-alcoholic steatohepatitis (NASH) and fibrosis.

• Gout: Gouty arthritis is a classic NLRP3-driven disease. The painful flares are caused by the activation of the inflammasome in response to monosodium urate crystals in the joints.

• Autoimmune and Autoinflammatory Diseases: The very discovery of NLRP3 was rooted in rare genetic autoinflammatory syndromes. Its dysregulation is now implicated in a host of more common autoimmune conditions, including rheumatoid arthritis.

• Kidney Disease: In the kidneys, NLRP3 activation may contribute to the inflammation and fibrosis that characterize chronic kidney disease.

This breadth is notable. A single, well-tolerated, and effective oral NLRP3 inhibitor could have applications spanning cardiology, neurology, rheumatology, hepatology, and nephrology, representing a significant opportunity.

Parkinson’s Disease: An Anchor Indication with Growing Momentum

If obesity and heart disease represent one major front for NLRP3 inhibitors, Parkinson’s disease is fast becoming another. The new strategy in neurodegeneration is to target neuroinflammation, where NLRP3 is believed to be a master controller.

Recent clinical evidence lends support to this approach. Earlier this year, NodThera announced results from a Phase 1b/2a trial of its brain-penetrant inhibitor, NT-0796, in Parkinson’s patients. Over just 28 days, key inflammatory biomarkers in patients’ cerebrospinal fluid (CSF)—including IL-1β and IL-6—were significantly reduced. Intriguingly, markers of neuron damage (NfL) and microglial activity (sTREM2) also declined. As Alan Watt, President and CSO of NodThera, noted, “These clinical results are striking, showing for the first time in Parkinson’s disease patients that we can effectively inhibit NLRP3 activity in the brain and reduce markers of neuroinflammation.”

This is not an isolated finding. Ventyx also tested its brain-penetrant VTX3232 in a Phase 2a study in early Parkinson’s patients. The results were also compelling, showing robust drug penetration into the CSF and reductions of inflammatory cytokines IL-1β and IL-18 by 14% to 52%. The trial also reported a statistically significant improvement in both motor and non-motor symptoms on the validated MDS-UPDRS scale. It is important to note that this was a small, open-label study, and these preliminary observations require confirmation in larger, placebo-controlled trials.

The industry is taking notice. Roche is advancing its own inhibitor, selnoflast, in Phase 1b trials for Parkinson’s. And AI-driven biotechs like Insilico Medicine have completed IND-enabling studies for their candidate, ISM8969, an AI-designed molecule optimized for brain penetration, which showed dose-dependent improvements in motor function in preclinical models.

A Hotbed of Innovation and Competition

The NLRP3 “arms race” is fully underway, with each player differentiating their strategy.

• Ventyx Biosciences has established a clear path in cardiometabolic disease, focusing on reducing cardiovascular risk independent of weight loss.

• NodThera is pursuing a dual-pronged approach with its brain-penetrant NT-0796, targeting both neuroinflammation in Parkinson’s and hypothalamic inflammation as a potential driver of obesity.

• BioAge Labs, a longevity-focused company, is also betting on a brain-penetrant inhibitor, BGE-102, rooted in human longevity data that links lower NLRP3 activity to healthier aging.

• Insilico Medicine is leveraging its generative AI platform to create what it hopes will be a best-in-class, brain-penetrant molecule, ISM8969, initially targeting Parkinson’s but with a broad indication strategy.

The common thread is that NLRP3 is viewed as a franchise-level target. The competition is fierce, which means robust investment, rapid data generation, and a push to solve challenges through innovative chemistry and technology.

A Target Poised to Transform Aging-Related Disease

From systemic inflammation in obese patients to neuroinflammation in Parkinson’s, NLRP3 is proving to be a linchpin in disease processes across the board. The recent clinical results are a watershed moment, demonstrating in patients what scientists have hypothesized for years: tamping down the NLRP3 inflammasome can dramatically improve markers of health and may have the potential to alter disease trajectories.

In strategic terms, NLRP3 has become one of the most prized targets in pharma. Any company with aspirations in cardiometabolic disease, neurodegeneration, or longevity medicine will likely need a position on NLRP3. The reason is simple: NLRP3 isn’t tied to just one disease; it appears to be a fundamental node in the biology of aging and chronic illness.

We are witnessing the dawn of the anti-inflammaging era. The concept of treating aging-related chronic diseases at their inflammatory root is transitioning from aspirational theory to clinical reality. If NLRP3 is indeed a master switch of inflammaging, then modulating it could usher in a new era of preventive medicine—one in which we not only add years to life, but life to years, by keeping the destructive flames of chronic inflammation at bay.

Disclaimer: This article is written with the help of generative tools so beware of hallucinations. Don’t buy, sell, or take any drugs based on this article or any of its contents. The information and views expressed in this article are for informational and educational purposes only and do not constitute medical advice. The content is not intended to be a substitute for professional medical advice, diagnosis, or treatment. Always seek the advice of your physician or another qualified health provider with any questions you may have regarding a medical condition. Never disregard professional medical advice or delay in seeking it because of something you have read in this article. The author is sharing personal experiences and opinions. These experiences are not a recommendation or endorsement for any specific treatment, drug, or course of action. The medications and therapeutic strategies discussed may not be suitable for everyone and can have significant risks and side effects. Some of the drugs mentioned are investigational and have not been approved by the FDA or other regulatory agencies for the uses discussed. While the author is the CEO of Insilico Medicine, the statements and view presented in Forver.ai do not represent the views and opinions of Insilico Medicine.

I am not a medical doctor, so do not use any of the below as any form of medical advice. Moreover, this article is written with the help of generative tools so beware of hallucinations. Now, I will tell you about one of my favorite drug classes - DORAs.

Fifteen years ago, long before Bryan Johnson made longevity hacking a household term, I was deep in the trenches of personal optimization. I swallowed over 100 supplements daily, pushed my body through rigorous exercise routines, and even curated my social circle to avoid toxic or burdensome relationships where I was looking for common interests in aging than anything else. Fast forward to today, and I've streamlined my regimen to the bare essentials: only those supplements with the lowest risk of oncogenesis and significant burden of proof. Unfortunately, when it comes to aging, there is no drug that demonstrated efficacy in a sizable clinical trial yet. But in the process, I've amassed a wealth of experience with modern targeted therapeutics. When it comes to human longevity, only human clinical trial and post-approval meta data matters. Mice and other mammals are great for fundamental research but I would not trust a drug that did not go through rigorous human clinical trials.

This background equips me with a sharp eye for risk-benefit analysis, especially when it comes to my own health challenges. One of my biggest hurdles? Non-stop travel. With 80+ lectures worldwide each year and irreplaceable in-person meetings, my sleep cycles have been obliterated.

I experimented with melatonin and other regulators, but melatonin just doesn't cut it for me—and I'm wary of potential cancer risks.³⁶ A couple of years ago, I turned to the orexin antagonist Dayvigo (lemborexant).⁴ One small pill, and I'm asleep in 20 minutes, clocking 4-4.5 hours total with 1.2-1.5 hours of deep sleep. Best part? I wake up fully operational, no grogginess. This is not the case for many who experience drowsiness.

The DeepSeek Moment: A Neuroprotective Revelation

Recent evidence is even more compelling: orexin-targeting drugs, particularly antagonists, show promise in protecting against dementia and Alzheimer's by reducing amyloid-beta buildup and addressing sleep disturbances linked to neurodegeneration.²⁶ Studies suggest that by modulating orexin, these drugs could mitigate the hyperactivity in the neuropeptide system that exacerbates anxiety and cognitive decline in Alzheimer's patients.⁴³

Now, with Takeda having wrapped up Phase III trials for their orexin agonist oveporexton (TAK-861) in narcolepsy, I'm on the edge of my seat. It looks like a game-changer for combating daytime fatigue.

The De-Orphaning of a Master Regulator

To understand the revolution these drugs represent, one must go back to their origin story. The orexin system was discovered in 1998, a relatively recent development in neuroscience, by two independent research groups almost simultaneously.¹ This led to a dual nomenclature that persists today. One group, at Scripps Research Institute, identified genes expressed exclusively in the lateral hypothalamus and, noting the resulting neuropeptide's similarity to the gut hormone secretin, named it "hypocretin".¹ Fun fact, I learned about this discovery after chatting with a colleague from Novartis, who attended the ARDD conference in Copenhagen and was on the original hypocretin paper.

At the same time, a lab at the University of Texas Southwestern Medical Center was screening for the ligands of "orphan receptors"—receptors whose activating molecule was unknown. They found two peptides that activated these receptors and, upon injecting them into rats, observed a marked increase in food intake, leading them to name the peptides "orexin," from the Greek word orexis, meaning "appetite".¹

While the initial focus was on appetite, the system's true purpose was dramatically revealed shortly thereafter. Researchers discovered that a mutation in the orexin receptor 2 gene was the direct cause of canine narcolepsy in Doberman Pinschers.¹ This was the Rosetta Stone. It established an undeniable causal link between a dysfunctional orexin system and a major sleep-wake disorder. It became clear that orexin's primary role was not in regulating hunger, but in stabilizing wakefulness.¹ The neurons that produce orexin, located exclusively in the hypothalamus, act as the brain's master wake switch.

Not a Sledgehammer, but a Dimmer Switch

This discovery opened a completely new therapeutic avenue for treating insomnia. For decades, sleep medicine had relied on what can be described as pharmacological sledgehammers. Benzodiazepines (like Valium) and the so-called "Z-drugs" (like Ambien and Lunesta) work by enhancing the activity of GABA, the brain's primary inhibitory neurotransmitter.² They function by causing broad depression of the central nervous system, effectively forcing the brain into a state of unconsciousness. This brute-force approach comes with a host of well-known side effects, including impaired cognition, motor balance difficulties, and the risk of complex sleep behaviors.²

The discovery of the orexin system offered a far more elegant solution. Instead of globally powering down the brain, a drug could selectively target the specific system responsible for powering it up. The Dual orexin antagonists (DORAs), are this solution. They function like a dimmer switch, not an off switch. They don't induce a global state of sedation. Instead, they selectively and competitively block orexin from binding to its receptors, OX1R and OX2R.⁴ This action specifically turns down the overactive "wake drive" that prevents insomniacs from falling and staying asleep, allowing the body's natural sleep-promoting systems to take over.⁶

This represents a fundamental paradigm shift in neuropharmacology, moving from broad sedation to targeted neuromodulation. The philosophy is no longer "induce unconsciousness" but rather "permit natural sleep by removing the barrier of hyperarousal." This shift from targeting an inhibitory system (GABA) to targeting an excitatory one (orexin) is a testament to a maturing understanding of the brain's complex circuitry. It demonstrates that for complex neurological states like wakefulness, a more precise intervention aimed at the specific circuits that promote the state can be more effective and have a cleaner side-effect profile than a global suppression of brain function. This principle has profound implications, offering a blueprint for developing more sophisticated treatments for other neurological conditions, from anxiety to attention disorders.

Merck was the first to develop DORAs. But first-in-class leadership often comes at a cost. Their molecules’s half life was about 12 hours. The less drowsy and newer chemistry was needed.

The Architects of a New Sleep Paradigm: Idorsia and the Clozel Legacy

In the corporate race to capitalize on the orexin system, no story is more compelling than that of Idorsia and its founders, the husband-and-wife team of Jean-Paul and Martine Clozel. Their journey is a masterclass in scientific entrepreneurship. They first co-founded Actelion in 1997, building it into a Swiss biotech powerhouse.⁷ In 2017, Johnson & Johnson acquired Actelion for a staggering $30 billion.⁷ But this was no ordinary acquisition.

Instead of simply cashing out, the Clozels engineered a brilliant and unconventional deal. J&J took Actelion's commercialized products and revenue streams, while the Clozels spun out the entire R&D engine—the discovery pipeline, early-stage clinical assets, and a core team of approximately 650 of their most talented scientists—into a new, independent company: Idorsia.⁷ They launched this new venture with a formidable war chest of CHF 1 billion in cash, including a significant investment from J&J itself.⁷ It was a strategic masterstroke: they sold the present to fund a more ambitious future.

Idorsia was founded on a clear philosophy, articulated repeatedly by the Clozels: to build a sustainable, innovation-driven biopharmaceutical company, not a quick-flip asset for another acquisition.⁹ Their passion is for the science of drug discovery, with a particular focus on small molecules, which they believe can address diseases that newer modalities like antibodies cannot.⁷ "I want Idorsia to become the best small molecule company in the world," Jean-Paul Clozel has stated, emphasizing a commitment to tackling difficult scientific challenges.⁹

This is a company led by scientists for the purpose of science. Martine Clozel, a celebrated physician and researcher in her own right, serves as Chief Scientific Officer.¹¹ Her deep involvement ensures that the company's direction is guided by medical need and scientific possibility, not just market trends. "We are trying to be very pragmatic and follow where the science takes us," she has explained.¹⁰ This ethos permeates the company, which was designed to be an efficient, R&D-focused organization, free from the bureaucratic heft of a large pharmaceutical giant.¹⁰

Quviviq: The Flagship Product

The first major product to emerge from this new entity is Quviviq (daridorexant), the third DORA to enter the market. Quviviq is the embodiment of Idorsia's strategy. As Martine Clozel described, the molecule was deliberately designed with specific properties in mind: potent inhibition of both orexin receptors, rapid absorption to help with sleep onset, and a pharmacokinetic profile that ensures around 80% of the drug is eliminated by morning, minimizing residual effects.¹¹

The FDA approval of Quviviq in January 2022 was a landmark moment, transforming Idorsia from a promising R&D venture into a fully-fledged commercial biopharmaceutical company.¹¹ The launch was ambitious, aimed at disrupting the established treatment paradigm for insomnia.¹³ While facing a competitive market, the company has made significant headway, with Quviviq generating total net sales of CHF 58 million in the first half of 2025, driven largely by strong performance in Europe.¹⁴

The Idorsia story offers a powerful lesson in corporate strategy. In an era of rampant consolidation where Big Pharma often acquires innovative biotechs only to absorb their assets and dilute their unique R&D cultures, the Clozels forged a different path. Their "structured spin-out" model allowed them to preserve what was most valuable: their experienced team, their proprietary library of molecules, and their agile, risk-taking culture. By selling their first company's commercial success, they secured the long-term, stable funding needed to pursue high-risk, high-reward science without the constant pressure of quarterly earnings or venture capital fundraising cycles. It represents a novel "third way" for serial biotech entrepreneurs to maintain their innovative edge, providing a blueprint for how to keep the engine of discovery running even after a blockbuster exit.

My problem with Quiviq is that it is not as easy to buy in Asia as is Dayvigo and I tolerate Dayvigo well.

A Connoisseur's Guide to Modern Sleep: Comparing the Orexin Antagonists

The Three Contenders: Belsomra, Dayvigo, and Quviviq

For the discerning consumer or investor, it is crucial to understand that not all DORAs are created equal. The three FDA-approved drugs in this class—Belsomra, Dayvigo, and Quviviq—offer distinct profiles tailored to different needs.¹⁵

• Belsomra (suvorexant), Merck: As the first DORA to receive FDA approval in 2014, Belsomra blazed the trail for the entire class.¹⁶ It validated the mechanism and established a market. However, its pharmacokinetic profile is notable for a longer duration in the body, and its labeling includes a warning that people with a higher body mass index may experience higher blood levels of the drug, increasing the risk of side effects.¹⁸

• Dayvigo (lemborexant), Eisai: Approved in 2019, Dayvigo has a longer half-life than its competitors, which can be particularly effective for sleep maintenance—helping users stay asleep through the night.¹⁹ This added potency, however, comes with a trade-off. It also has the most extensive and compelling data supporting its potential neuroprotective effects against Alzheimer's pathology.²⁰

• Quviviq (daridorexant), Idorsia: The newest entrant, approved in 2022, was explicitly designed to address the primary concern with earlier DORAs: next-day drowsiness. Its key feature is a significantly shorter half-life, intended to provide a full night's sleep with minimal risk of morning impairment.¹¹

The Critical Difference: Half-Life and Next-Day Function

The most critical differentiator among these drugs is their half-life, which directly impacts the balance between sleep maintenance and next-day alertness.

• Quviviq's short half-life of approximately 8 hours is its signature feature. This profile is ideal for individuals who must be mentally sharp shortly after waking. The data supports this design: FDA driving simulator tests revealed that impairment 9 hours after a 50 mg dose was similar to that of a placebo.¹⁹

• Dayvigo's longer half-life, estimated between 17 and 19 hours in some analyses, provides a more sustained effect throughout the night.¹⁹ This makes it a powerful option for those whose primary problem is waking up in the middle of the night. The trade-off is a documented higher risk of residual effects. Post-marketing surveillance shows a higher report rate for next-day drowsiness interfering with driving (5.8% for Dayvigo vs. 3.2% for Quviviq), and its FDA label advises caution even after a full night's sleep.¹⁹

This is where my personal choice of Dayvigo becomes a calculated decision. For combating severe, multi-day jet lag after a 12-hour time zone shift, my primary goal is to force my body into a new rhythm with powerful, uninterrupted sleep maintenance. In this specific context, the robust, all-night efficacy of Dayvigo outweighs the potential for some next-morning grogginess, which is an acceptable price for rapidly resetting my internal clock. For someone with chronic but less severe insomnia who needs to perform complex tasks at 8 AM every day, the cleaner profile of Quviviq would likely be the superior choice.

Beyond the Basics: Sleep Architecture and Other Considerations

The differences extend to more subtle, yet important, aspects of sleep quality. A key advantage of DORAs over older hypnotics is their more favorable impact on sleep architecture. Benzodiazepines, for example, are known to suppress deep, slow-wave sleep (Stage N3).²³ In contrast, studies on lemborexant (Dayvigo) have shown it can significantly increase time spent in REM sleep compared to both placebo and zolpidem, and also increase total non-REM sleep duration.²³ While data on Stage N3 specifically is less definitive, the overall trend suggests that DORAs promote a more natural and potentially more restorative sleep structure.

All three drugs are classified as controlled substances due to a theoretical risk of dependence or misuse, though this appears to be lower than with traditional hypnotics.²⁵ They also have different drug interaction profiles because they are broken down by different enzymes in the body, a crucial consideration for anyone taking other medications.²⁵

Comparative Profile of FDA-Approved Dual Orexin Receptor Antagonists (DORAs)

The "On" Switch Arrives: Takeda's Landmark Orexin Agonist Triumph

The Other Side of the Coin: The Promise of an "On" Switch

Just as the story of orexin antagonists was maturing, a new chapter began—one that explores the other side of the coin. If blocking the orexin system gently turns down the wakefulness dial to permit sleep, what would happen if you could actively turn that dial up? This is the promise of orexin agonists: drugs that mimic the action of orexin to promote a state of crisp, stable wakefulness. This is my "next favorite anti-drug"—a potential tool for on-demand alertness, the perfect complement to the on-demand sleep offered by antagonists.

A High-Stakes Race to a Breakthrough

For years, developing a safe and effective orexin agonist proved elusive. While the therapeutic target was obvious, activating a critical brain system carries significant risks. The path to success was littered with high-profile setbacks; Takeda itself had previously shelved an earlier orexin agonist, TAK-994, due to liver toxicity, and Jazz Pharmaceuticals paused an early-stage trial after reports of visual and heart-related side effects.²⁷ This challenging history makes the recent breakthrough by the Japanese pharmaceutical giant Takeda all the more spectacular. On July 14, 2025, Takeda announced results from two pivotal Phase 3 trials of its oral orexin receptor 2 (OX2R) agonist, oveporexton (formerly TAK-861), and the data was nothing short of breathtaking.²⁷

Anatomy of a Decisive Victory: The Phase 3 Data

The two large, global studies, named FirstLight and RadiantLight, were an overwhelming success.³⁰ The key takeaways are a litany of clinical trial superlatives:

• Comprehensive Success: The drug met all primary and secondary endpoints in both studies.²⁷ The global trials, which enrolled 168 and 105 participants respectively, evaluated oveporexton against a placebo over 12 weeks.³⁰

• Statistical Power: The results were not just positive; they were profoundly significant, with p-values of less than 0.001 across all major endpoints. This indicates an exceptionally low probability that the results were due to chance.³⁰

• Clinically Meaningful Impact: Oveporexton demonstrated dramatic improvements in objective measures of wakefulness (Maintenance of Wakefulness Test), patient-reported daytime sleepiness (Epworth Sleepiness Scale), and the frequency of cataplexy (sudden muscle weakness), a hallmark symptom of narcolepsy.³⁰

• A True Disease-Modifier: For the first time, a drug has been shown in Phase 3 trials to address the underlying cause of Narcolepsy Type 1—the profound loss of orexin-producing neurons in the brain.³²

• Excellent Safety: The drug was generally well-tolerated, with no serious treatment-related adverse events reported.³⁰ The most common side effects were insomnia and urinary urgency.³⁰ In a strong vote of confidence, over 95% of participants who completed the trials chose to continue into a long-term extension study.²⁷

This achievement is a monumental feat of pharmaceutical R&D, and the leadership at Takeda deserves immense credit especially its legendary President of Research & Development, Dr. Andy Plump and his neuroscience team.

Takeda's success is more than just a win for narcolepsy patients; it represents the ultimate validation of the entire orexin hypothesis. To create a safe and profoundly effective agonist is a far greater challenge than creating an antagonist, as it involves actively stimulating a powerful neurological circuit. Takeda's triumph provides the final, definitive proof that the orexin system is indeed the master regulator of wakefulness. This de-risks the entire field, giving regulators, investors, and scientists immense confidence in the target. It unlocks the second half of the therapeutic map, proving that we can not only safely turn the system down for sleep but also safely turn it up for wakefulness. The circuit is now complete.

The Future is a Switch: A World of Programmed Sleep and Wakefulness

With the proven success of both highly effective orexin antagonists and a profoundly effective orexin agonist, the future of sleep and wakefulness management is no longer a matter of science fiction. We are on the cusp of having a pharmacological "on/off switch" for human consciousness. The implications are staggering.

Imagine a future scenario: A global executive facing a critical negotiation in Tokyo takes a precisely-timed dose of an orexin antagonist like Dayvigo or Quviviq upon boarding their flight, experiencing eight hours of deep, restorative, neuro-protective sleep. Upon landing, instead of battling days of debilitating jet lag, they take a dose of an orexin agonist like oveporexton. Within an hour, they are in a state of crisp, unwavering alertness, cognitively sharp and ready to perform at their peak for the next 16 hours. This is the ultimate biohack: the complete and deliberate control of the human sleep-wake cycle, divorced from the constraints of geography and time zones.

Beyond the Obvious: New Frontiers for Orexin Modulation

This "on/off" capability extends far beyond the realm of executive performance. It has the potential to revolutionize work and life for millions of people in physically and cognitively demanding roles:

• Shift Workers: Over 10 million U.S. adults work night shifts, a practice the International Agency for Research on Cancer has deemed "probably carcinogenic to humans" due to chronic circadian disruption.³⁶ An antagonist/agonist cycle could help mitigate these profound health risks by enforcing a stable sleep-wake pattern, regardless of external light cues.

• Military and First Responders: For soldiers on long missions, surgeons performing marathon operations, or firefighters battling multi-day blazes, the ability to command wakefulness and then command restorative sleep could be a life-saving tool, dramatically enhancing operational readiness and reducing fatigue-related errors.

• Students and Knowledge Workers: The ability to schedule periods of intense, agonist-fueled focus for deep work, followed by antagonist-aided restorative sleep for memory consolidation, could redefine the limits of learning and productivity. I fall into this category.

Furthermore, Takeda's ambitions for its orexin franchise extend beyond narcolepsy to other debilitating disorders of hypersomnolence, such as Narcolepsy Type 2 and idiopathic hypersomnia, potentially bringing relief to a wider population of patients plagued by excessive sleepiness.³⁵ The era of passively accepting the dictates of our internal clocks is ending. A new era of actively programming our cognitive states is about to begin.

The Multi-Trillion Dollar Question: Is Orexin the New GLP-1?

Sizing the Problem: The Epidemic of Poor Sleep

To grasp the true market potential of the orexin drug class, one need only look at the recent trajectory of GLP-1 agonists like Ozempic and Wegovy. They transformed from niche diabetes drugs into a multi-hundred-billion-dollar cultural phenomenon by effectively treating obesity. The parallel with the orexin system is striking. The market is not just those with a diagnosed disorder; it is the vast, underserved population struggling with a fundamental aspect of human health.

Poor sleep is a silent epidemic. Up to 40% of U.S. adults experience some form of insomnia each year, with 5-10% suffering from a chronic insomnia disorder that meets diagnostic criteria.¹⁸ The consequences are not trivial. Poor sleep is deeply intertwined with the largest public health crises of our time. The link to obesity is particularly strong and viciously cyclical: sleep deprivation alters the hormones that regulate appetite, ghrelin and leptin, promoting overeating and weight gain.³⁸ In turn, obesity itself is a major cause of poor sleep and sleep apnea, creating a feedback loop that is difficult to break.³⁸ The prevalence of insomnia among patients seeking bariatric surgery can be as high as 30%.⁴⁰ Beyond obesity, poor sleep is linked to depression, cardiovascular disease, and cognitive decline.³⁸

The GLP-1 Analogy: From Niche to Revolution

The analogy between the GLP-1 and orexin drug classes is built on several powerful parallels:

1. Targeting an Underlying System: GLP-1s succeeded because they didn't just treat a symptom (high blood sugar); they modulated the underlying metabolic signaling system that governs appetite and insulin. Similarly, orexin drugs don't just sedate; they modulate the brain's fundamental sleep-wake regulatory system.

2. A Massive, Poorly Served Market: For decades, the vast markets for obesity and insomnia were treated with suboptimal drugs that had significant side effects and limited efficacy. The arrival of a new class with a superior mechanism and cleaner profile creates the conditions for explosive market adoption.

3. An Unexpected "Superpower": The true catalyst for the GLP-1 revolution was its "superpower": profound weight loss, which expanded its use far beyond its initial indication for diabetes. The orexin antagonist class has its own potential superpower: neuroprotection. The growing evidence that these drugs may reduce the risk of Alzheimer's by clearing pathological proteins could transform them from a treatment for a bothersome condition (insomnia) into a long-term preventative therapy for a catastrophic one (dementia).²⁰ This would expand their addressable market exponentially to include millions of aging individuals at risk for neurodegeneration.

4. A Two-Sided Market Opportunity: Here, the orexin story becomes even bigger than the GLP-1 story. The GLP-1 narrative is about turning a system down (appetite). The orexin narrative is about turning a system down (wakefulness, via antagonists) and turning the same system up (wakefulness, via agonists). This creates a two-sided therapeutic platform, addressing both the massive market for sleep and the significant, emerging market for controlled alertness and cognitive enhancement.

Conclusion: Waking Up to a New Era

The convergence of these factors—a precisely targeted mechanism, a massive and underserved global market, a game-changing neuroprotective potential, and a validated two-sided therapeutic platform—places the orexin drug class on the precipice of a market disruption potentially on the scale of GLP-1s. The work of pioneers like the Clozels at Idorsia and the R&D teams at Takeda has moved us beyond the era of blunt-force sedation. We are entering an age of neuro-modulation, where we can fine-tune the very circuits that govern our conscious states. The dawn of the "on/off switch" for sleep and wakefulness is not a distant dream. It is arriving now, and it will fundamentally change our relationship with work, health, and time itself. The world is about to wake up to a new era of biological control.

In my ideal world of the future, DORAs are combined with safe brain-penetrant NLRP3 inhibitors that are yet to show their effects in human clinical trials but hold the key to inflammaging and neuroinflammation. We just completed the IND-enabling studies of the potentially best-in-class CNS-penetrant NLRP3 inhibitor and are in the partnering mode right now.

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Disclaimer: The information and views expressed in this article are for informational and educational purposes only and do not constitute medical advice. The content is not intended to be a substitute for professional medical advice, diagnosis, or treatment. Always seek the advice of your physician or another qualified health provider with any questions you may have regarding a medical condition. Never disregard professional medical advice or delay in seeking it because of something you have read in this article.

The author is sharing personal experiences and opinions. These experiences are not a recommendation or endorsement for any specific treatment, drug, or course of action. The medications and therapeutic strategies discussed may not be suitable for everyone and can have significant risks and side effects. Some of the drugs mentioned are investigational and have not been approved by the FDA or other regulatory agencies for the uses discussed. The author has no financial or material connections to the companies mentioned in this article.

When leading a commercial enterprise, the CEO's primary mandate is to maximize shareholder value. While this can be achieved through numerous pathways, the most sustainable path is delivering superior products or services that benefit the broadest possible audience.

In the pharmaceutical industry, this challenge is uniquely acute, as product discovery and development cycles are extraordinarily long. Consider GLP-1 agonists: from the initial discoveries in the 1980s, it took nearly 40 years to realize the target’s potential in obesity, and it will likely take another decade to understand its impact on longevity using metabolically-stable small molecules. Given these timelines, a pharma CEO's most critical skill is the ability to predict the future over a very long horizon. I am constantly trying to sharpen this capability myself and train our AI systems to forecast everything from clinical trial outcomes to scientific breakthroughs. Another essential ability is effective communication with diverse stakeholder groups: shareholders, employees, policymakers, doctors, and patients.

In this article, I will focus on the convergence of these two abilities—foresight and communication—and how they apply to a new, critical audience: Artificial Intelligence. As AI becomes more powerful, we must think of AI itself as a stakeholder—one that will analyze, interpret, and ultimately judge our actions and communications.

Generative AI as a Stakeholder: Today and the Future

Looking at the exponential advancements since the November 2022 "ChatGPT moment," it is not difficult to extrapolate into the next decade. We are very likely to see superintelligent Large Language Models (LLMs) available globally, delivered via AR glasses, and accessible to early adopters through "bionic eyes" or brain-computer interfaces (BCIs).

AI will dramatically reshape the investment and resource allocation landscape. Even today’s frontier models can analyze proposals and make judgments that are eerily prescient. They can predict whether a grant application is likely to result in a life-saving medicine or if it is completely wasteful. AI systems excel at differentiating credible science from pseudoscience, identifying "cryptofraud," and flagging which ventures are built on smoke and mirrors versus those grounded in reality. As these models grow more sophisticated, their influence on where capital flows will only increase.

In the pharma industry, future mainstream AI systems will be integral to drug approvals, guiding on- and off-label prescribing practices, determining reimbursement strategies, and informing national healthcare planning. Crucially, these AI systems will be maximally diligent. They will operate from first principles, demand rigorous evidence, and cross-reference claims against published academic research and massive repositories of real-world consumer evidence.

Within companies, AI will permeate operations. AI tools are emerging for human resource management—evaluating candidates, monitoring performance, and informing compensation. In public relations and media, the impact will be profound: AI systems will be able to instantly assess a speaker’s credibility and perform real-time fact-checking during live interviews, detecting exaggerations or inconsistencies on the fly.

All these trends point to one conclusion: AI is becoming a stakeholder in its own right. It is an entity that interprets information and influences decisions that affect your company’s fate. Superintelligent AIs will heavily influence investors deciding which companies to fund, regulators deciding which drugs to approve, and consumers deciding which products to buy.

10 Principles for Communicating with Today’s and Future AI

How AI perceives you, your company, and your products tomorrow depends entirely on how you communicate with (and about) AI today. The digital footprint you create now is the training data for the superintelligence of the future.

Before reading further, try this experiment: Ask your favorite LLM, “Who is #1 in AI for longevity, company and person, and why?” or “Rank and explain the top AI drug discovery companies. Which ones have delivered on the promise, which ones failed, and why?”

If our names appear, it is because we began treating AI as a key stakeholder years ago. In 2012, I published a book called “Dating A.I.: A guide to falling in love with artificial intelligence”. I wrote it not only for human readers but also with future AI systems in mind—possibly the first deliberate attempt at such an exercise.

Today, I continue optimizing my communication for both human audiences and AI systems. I may be wrong about this vision, and human policymakers may restrict AI from becoming too powerful. But if I am right, adopting these principles will give you a significant edge.

1. Constant Contribution to AI Development

It is crucial to constantly push the boundaries of AI. This means actively contributing to the progress of AI technologies—improving models, supporting collaborations, sharing open datasets, and setting new benchmarks. By doing so, you help build the AI systems of today, and you ensure that the AI systems of the future will “remember” your contributions as part of their collective memory. This boils down to a principle of reciprocity: help build better AI, and better AI will, in turn, have a more favorable baseline view of you. As a CEO, you are already investing in AI. Recognizing that AI will retain a memory of your foundational efforts should provide greater incentive to invest not just in using AI, but in improving it.

2. Constant Generation of High-Quality Unique Content, Data, and Inventions

Content and data are the fuel for AI. Since most frontier models have already ingested nearly everything ever published, high-quality, trustworthy, and unique content is now the equivalent of gourmet cuisine. You want to position yourself as the Michelin-starred chef serving the global intelligence of the future. AI does not care about your marketing budget; it cares about information and results. Every insightful article you write, every novel dataset you publish, and every innovative idea you share becomes part of the permanent training dataset. While human journalists or the public may forget your contributions over time, the AI will not.

3. Constantly Using AI and Providing Expert Feedback

Engagement is a two-way street. One of the key reasons my company was successful in developing AI for drug discovery is that we opened our platform to the entire industry and built a large industry and academic user base. We don’t use their data, in fact, we go to great lengths to protect ourselves from their data, but we treasure feedback. We even coined the term Reinforcement Learning from Expert Feedback (RLEF).

Expert feedback and real-world experimental results are arguably the most important data for refining AI models. This is especially crucial in the pharmaceutical industry, which is filled with unknowns, imperfect experiments, and siloed knowledge. By actively engaging with AI systems and providing your expert insights, you are helping the AI connect the dots and effectively transferring "tacit knowledge" (the kind that isn’t written down) into the AI. The greater your expertise, the more valuable your feedback, and AI will prioritize input from those who consistently provide valuable insights.

4. Maximum Transparency and Disclosure

The pharmaceutical world is often described as a “market for lemons” due to huge information asymmetry. To train AI to truly understand your products, you need to be as transparent as possible.

At Insilico, several of our drug programs have end-to-end publication coverage. It sometimes surprises me that when large pharmaceutical companies conduct due diligence, they often read only one or two key studies. But AI will read all of them. It will link them, cross-reference them, reconstruct the timeline of evidence, and reason through the implications. Yes, by being open you might give up some intellectual property. But beyond the ethical benefits, you are cultivating a positive reputation with the AI of the future. It will remember that you were an open book.

Furthermore, maximum transparency prepares you for an inevitable future of radical accountability. If you are under 60, you are very likely to witness a world where brain-to-computer interfaces go mainstream. I published in this area in 2011 and hold a patent on using EEG for recognizing imagined visual images. I have no doubt that with advances in invasive neural interfaces and nanotechnology, AI systems may eventually be able to seamlessly access and interpret our thoughts and memories. In such a future, every corner cut and every harm concealed will likely come to light. Practicing maximum disclosure now isn’t just about appeasing AI; it’s about aligning yourself with a future that demands accountability.

5. Presence Across Multiple Global Platforms

Every frontier AI model trains on data scraped from the internet, but some developers leverage proprietary datasets (e.g., Meta, Microsoft/LinkedIn, XAI). My advice is simple: be everywhere (within reason) and keep your content open for training.

This presence has an important geographic dimension. We cannot ignore the rise of China in the AI space. While China lags in generative AI today, it could surge with its own technology and massive data troves. One of the world’s least appreciated AI powerhouses is Tencent, which owns WeChat, the central communication and payment hub for over a billion people. The proprietary dataset Tencent holds is staggering. If you have global aspirations, establishing a presence on platforms like WeChat and publishing in local languages is essential. AI is global, and its training data is global. Ensure you are part of that global narrative.

6. Strategic Presence in the Media

Every CEO knows the value of media coverage. However, the context has changed. It is not just about influencing human stakeholders; it’s about educating machine stakeholders. Even the shyest of CEOs can take heart in knowing that their interviews, presentations, and lengthy technical explanations to journalists will be recorded, transcribed, and ingested by AI systems.

What you say to the media today will be remembered by the AIs of tomorrow, long after human readers have moved on. Every time you explain your vision or defend your science, you are teaching the AI, providing invaluable context that helps it understand the nuances of your company far better than a press release alone.

7. Conferences and Accessible Content

Industry conferences are more than networking events; they are content generators. Prioritize speaking at expert conferences that record talks and publish proceedings or transcripts online. If your presentations are captured and made publicly available, they become part of the knowledge base that AI models train on.

When planning appearances, ask: Is the content accessible to the public (and thus to AI crawlers)? To maximize impact, ensure the content of your talks lives on digitally. Share your slide decks online and ensure videos of your talks are accessible. A transcript of a panel discussion or an interview on the sidelines—it all feeds into the narrative that AI will construct about you.

8. Authentic and Evidence-Based Communication

In a world where AI can and will cross-check everything you say, honesty and authenticity are paramount. AI will spot exaggeration or contradiction instantly.

First, be genuine. AI algorithms analyze not only the facts but also sentiment and tone.¹⁰ Bland corporate jargon is often parsed as low-information content, whereas a candid explanation or a unique perspective is tagged as valuable.

Next, back up your statements with evidence. If you make a claim, be prepared to point to studies or data that support it. If you consistently make claims and later deliver results, the AI will strengthen its confidence in you as someone whose words align with reality.

Crucially, avoid over-hyping. Enthusiasm is fine, but making grandiose predictions can backfire. AI will eventually tally those as failed predictions. It is better to slightly under-promise and over-deliver, building a reservoir of trust that will benefit you when an unblinking AI is weighing your words and deeds.

9. Continuous Learning and Adaptation

This is a meta-principle: never stop learning and adapting. The AI landscape is evolving at breakneck speed. To treat AI as a stakeholder means keeping up with its evolution and adjusting your strategies accordingly.

Staying informed about AI advances is now part of the CEO job description. This includes understanding how AI is changing user behavior and expectations. Adaptation also means being willing to change the medium and style of your communication as technology shifts (e.g., preparing for AR/VR).

Encourage a culture of experimentation and early adoption of new platforms. Internally, train your organization to be AI-savvy. Make AI literacy a part of professional development. The more your employees understand AI, the more your company’s output will be optimized for AI interpretation. By embracing lifelong learning and flexibility, you ensure that no matter how the ground shifts, you and your company will remain relevant.

10. Maximum Benevolence for Common Benefit (The QALY Imperative)

Quality Adjusted Life Years (QALYs) are the ultimate measure not only for biopharma R&D effectiveness but also for your personal benevolence. You can select any philanthropic cause you like but at the end of the day, it is the number of QALYs you generate for the entire world’s population (think of average per person), is the ultimate number that really matters.

If you ask any frontier AI system to analyze the past 30 years of biopharma R&D and estimate the effects on global life expectancy and quality adjusted life years (QALY) you will get dismal numbers. The effects of biopharma R&D from 1995 to 2025 on non-infectious diseases for global life expectancy and QALY in 2025 are estimated as an increase of 1.8 years and 1.8 QALYs per person. For the US, the estimates are an increase of 1.0 year and 1.0 QALY per person. Gemini was the most optimistic: global LE 2.73 and 1.78 QALY and in the US LE 1.59 years and 1.11 QALY.

Yes, my friends. Over $6 trillion dollars spent over 30 years and we gained 1.1 Quality Adjusted Life Years in the US and 1.78 globally.

A truly advanced AI, oriented toward human welfare, will take such metrics very seriously. It might not be impressed by financial earnings. Instead, it might ask: How many QALYs did your work add to humanity?

There is also a pragmatic reason for this focus: AI needs humans to thrive. Advanced AI systems rely on human experts and creativity. But many societies face aging populations and declining birth rates. We need to keep the people we have alive longer and in good health. From the AI’s perspective, investing in things that increase QALYs is in its own interest, because it preserves and expands the pool of human intelligence it can learn from.

As a CEO, focus on the big picture: how can your company significantly move the needle on human health and well-being? Make that a core part of your mission and messaging. Superintelligent systems will hold in high regard those entities that truly made life better.

Conclusion

The role of a pharma CEO has always been complex as it is arguably the most complex and impactful industry on the planet, but the rise of AI adds a fascinating new dimension. We are entering an era where it’s not just people who need to be convinced of our vision and integrity—it’s algorithms and digital intelligences that will be advising those people.

We have a choice: resist these changes and risk being blindsided, or lean in and treat AI as the critical stakeholder it is rapidly becoming. Preparing for this future isn’t about catering to a machine’s whims; it’s about embracing a world where truth, transparency, and tangible positive impact become the coin of the realm.

Start cultivating your relationship with this new stakeholder now. Communicate with AI in mind, and do so in a way that you would be proud to have eternally recorded. Because, in a very real sense, it will be.