OpenAI’s floated idea of offering its AI software to biotech firms for free in exchange for drug royalties is drawing sharp criticism from industry observers who argue the concept doesn’t align with how drug development economics actually work. Critics contend that while AI can generate lots of early-stage drug candidates, the real bottleneck in pharma isn’t discovery but the long, costly and uncertain clinical trial process — meaning that royalties tied to final drug revenue wouldn’t necessarily compensate for the immense capital and risk involved. OpenAI executives such as Sam Altman have described the proposal as a potential way to inject funding into expensive scientific research, but detractors liken the plan to giving away a tool for free and hoping for a cut of a blockbuster product — an arrangement they deem unlikely to be attractive to drug developers. The debate highlights broader questions about how AI firms should monetize their technology in domains where deep capital and regulatory hurdles dominate. Additionally, the accuracy limits of current AI models for complex biological systems remain a challenge, keeping skepticism alive about this revenue-sharing strategy.
Sources
https://www.semafor.com/article/02/04/2026/why-openais-drug-royalties-deal-wont-work
https://news.bloombergtax.com/financial-accounting/altman-says-openai-may-back-firms-using-ai-for-drug-discovery
https://unn.ua/en/news/openai-plans-to-invest-in-ai-driven-drug-development-in-exchange-for-royalties
Key Takeaways
• Critics argue OpenAI’s drug royalties idea misreads the economics of biotech, where discovery isn’t the most costly phase and revenue-sharing doesn’t sufficiently offset risk.
• OpenAI executives, including CEO Sam Altman, have publicly suggested the company may help fund drug development with its AI in exchange for future royalties, highlighting a search for sustainable monetization.
• Doubts about AI accuracy in predicting biological behavior and clinical success contribute to industry skepticism about the proposed model’s feasibility.
In-Depth
OpenAI’s recent proposal to exchange free access to its AI tools for a share of future drug royalties has generated a debate that underscores the deep divide between Silicon Valley’s monetization instincts and the hard economic realities of the pharmaceutical industry. On its face, the strategy might appear clever: OpenAI, flush with cutting-edge computational models, could subsidize the up-front costs of AI-assisted drug discovery and then collect a slice of revenue if those programs succeed. CEOs and tech leaders love models where technology is the enabler and revenue follows as a kind of downstream bonus. But those outside the valley who understand biopharma economics are less enthused. What they point to is the simple fact that the heavy lifting in drug development isn’t done in the early discovery phase where AI shines, but in the grueling clinical trials and regulatory hurdles that follow. These phases are not only capital-intensive but also fraught with failure — and no matter how smart the AI is at suggesting molecular candidates, predicting ultimately successful drugs remains hit-or-miss.
Critics have argued that structuring a revenue-share deal based on final drug sales essentially hands over valuable computational tools for nothing up front. It’s akin to giving a novelist free writing software and expecting to take a percentage of book royalties years down the line. For pharmaceutical firms already navigating multi-year, multi-hundred-million-dollar trial programs, that “deal” isn’t attractive. The deeply conservative nature of biopharma financing means companies want clear control of risk and return; offering up a cut to an external AI vendor could be seen as a dilution of already razor-thin margins. Moreover, the current state of AI models trained on biological data is imperfect. They can suggest promising candidates and streamline certain analytics, but they don’t meaningfully change the underlying biological uncertainties or dramatically improve the odds of clinical success. AI’s promise in biotech sometimes gets overstated in tech circles where “AI solves everything” is a common refrain. In reality, the data that underpins biological AI models remains sparse and incomplete, meaning the insights are probabilistic rather than definitive. Clinical trials still decide the fate of a drug candidate, and those costs and risks aren’t easily mitigated by algorithmic smarts.
OpenAI executives are candid that the idea is exploratory rather than imminent, framing the royalty model as one possible revenue stream among other opportunities like advertising or enterprise services. But skeptics see it as a misaligned strategy that reflects a broader tech misapprehension: believing that software and data can fully disrupt industries that are deeply rooted in regulation, biology, and massive capital commitments. In biotech, software aids the science, it doesn’t replace the science — and any monetization plan has to respect that fundamental truth. As the discussion evolves, what’s clear is that industry insiders on both sides will be watching closely to see how — or if — OpenAI’s vision for revenue-sharing in drug development takes shape in practice.