AI as a Stakeholder: What Every Pharma CEO Should Know About Communicating with AI

Communicating with AI today and in the future may require new levels of intensity, transparency, and communication mediums

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.