The world’s most valuable AI companies – Anthropic, OpenAI, and Google – are eyeing drug discovery. They believe their large language models offer a faster way to discover druggable targets with a higher likelihood of becoming medicines. They also have the risk tolerance and capital to see if they can succeed. Andrew Dunn, reporting for the industry trade publication Endpoints, illustrates this point well:
“It [Anthropic] has gone on a 100-day sprint to enter the life sciences, buying a stealth-stage biotech and hiring scientific stars like the Nobel Prize-winning scientist John Jumper. It is trying to win pharma’s trust and money by selling a bioscience-tailored version of its flagship large language model, Claude. And it’s also building its own biology research team with ambitions that — at least to some degree — resemble a drug-developing startup.”
The biggest threat may not be AI-native drug discovery itself. It may be the perception that AI-native companies are positioned to outperform traditional biopharma—and the pressure that perception creates long before the science is settled.
The immediate risk isn't that Anthropic, OpenAI, or Google’s DeepMind will suddenly replace pharmaceutical companies. It will take years to assess whether their models are effective. Crucially, drug discovery accounts for only a small part of the process needed to bring a drug to market. No AI company can replace the sector’s decades-long experience in drug development, regulatory process, commercialization, or sales.
Many companies – especially large pharmaceutical organizations – are already consistently integrating AI into their business and communicating effectively about it. But while we wait, the rise of AI-native drug discovery will create new communication challenges.
What if investors, partners, policymakers, regulators, and patients come to believe that AI-native companies will discover and develop better medicines faster — and that traditional biopharma companies are unprepared to meet that possibility? What if they view AI-native companies as competitors rather than collaborators? What if markets don’t need definitive proof to start pressuring narratives?
In some industries, this isn’t hypothetical. New AI narratives have already taken hold and put immediate pressure on share prices.
Earlier this year, Citrini Research published a speculative memo. It asked readers to imagine that it was June 2028. Its premise was counterintuitive and unsettling: what if AI’s biggest believers are right about the technology? The memo described an “intelligence displacement spiral”: AI improves, companies cut white-collar workers, displaced workers spend less, consumer businesses weaken, companies invest more in automation, and AI improves further. It was plausible enough to rattle markets and put pressure on companies whose business models appeared vulnerable to AI disruption.
The memo did not necessarily prove that companies like IBM, DoorDash, or American Express had weak narratives. It revealed something subtler: their narratives may not have been as strong, resilient, or scenario-ready as leadership believed. These technology companies have done a great job learning from this experience. An article from The Wall Street Journal noted that companies like Salesforce were “punished by investors convinced that their expensive off-the-shelf products would soon be replaced with cheaper AI-concocted versions. That hasn’t happened. It isn’t likely to happen any time soon, either.”
Today, pharmaceutical and biotechnology companies have a moat. But is it visible, credible, and easy for stakeholders to understand?
Markets, media, employees, partners, and policymakers do not wait for scientific certainty before forming judgments. In technology-driven sectors, perception often precedes conclusive evidence. This is especially true when the story is intuitive: AI companies are faster, more data-driven, less encumbered by legacy systems, and built to automate complex work. For biopharma, this could create a dangerous gap between operational reality and external perception.
Ask this question: If a Citrini-style memo on AI-native drug discovery mentioned my company, would we be ready? Pressure-test your corporate narrative against an AI-native competitor: Can you explain why your approach to discovery and development remains advantageous if AI-native companies claim superior speed, cost, or probability of success?
Explain the full drug development continuum: Companies should discuss not only discovery but also the entire path from biological insight to approved and accessible medicine: translational medicine, clinical trial design, regulatory strategy, manufacturing, commercialization, and lifecycle management.
Avoid generic AI-washing: Saying “we use AI” is inadequate. Stakeholders will want to know how AI strengthens the company’s existing expertise, where it improves decision-making, and why the company is better positioned by combining technology with institutional experience.
Prepare audience-specific answers: Investors may ask about capital efficiency and competitive advantage. Partners may ask whether AI-native companies are better collaborators or future competitors. Policymakers and regulators may ask about oversight, evidence, and accountability. Patients may ask whether AI-discovered medicines can be trusted.
Make the moat tangible: The best narratives translate abstract strengths into specific, defensible claims: what the company knows, what it can do, what it has learned from past development programs, and why that matters in the real-world process of getting medicines approved and used.
AI-native drug discovery is not an existential verdict on pharma. The industry is already adapting and placing greater emphasis on the strength of its moat. If anything, it may become a powerful accelerant for companies that integrate advanced AI tools with deep biological knowledge, clinical development expertise, regulatory experience, and commercial reach.
But those advantages will not explain themselves.
The companies best prepared for the AI-native drug discovery era will not be those that reflexively defend the old model or attach AI language to existing stories. They will clearly and consistently explain why their model remains essential — and how it is evolving.
AI will likely be one of the defining topics at the 2027 J.P. Morgan Healthcare Conference, and investors will expect more than a generic commitment to innovation. They will want to understand how AI changes your competitive position, where it creates advantage, and why your company is best positioned to succeed.
NEXUS PRO helps healthcare companies answer those questions before they face them in the market. By testing corporate narratives with real stakeholder audiences, NEXUS PRO identifies where messages resonate, where skepticism emerges, and how competitors' AI stories could challenge your position. The result is a clearer, more resilient narrative built for investors, analysts, policymakers, partners, and patients.
Before your leadership team walks into JPMorgan, ask a simple question: If an analyst published a viral note tomorrow arguing that AI-native companies will outperform traditional biopharma, would your narrative hold up?
If you're not certain, let's test it. Schedule a NEXUS PRO briefing with the Narrative team to see how your stakeholders respond before the market does.
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Adam Silverstein is a Managing Director at Narrative Strategies, where he brings nearly two decades of experience in healthcare communications, public relations, and thought leadership. To learn more about how Narrative is helping healthcare companies communicate with their stakeholders about AI, email Adam at asilverstein@narrativestrategies.com |