Prof. Derya Unutmaz

Derya Unutmaz argues that frontier AI has crossed from productivity tool to peer-level scientific collaborator, making non-use a competitive—and in medicine, ethical—liability. He describes o1 Pro and Deep Research as “almost on my level,” says he no longer trusts even his expert knowledge without consulting AI, and claims “there’s not a single area” of his research workflow that remains untouched. Nathan Labenz keeps one guardrail: for medical care, he currently wants both doctor and AI, not either alone.

The strongest evidence for machine-generated novelty is AI’s ability to transfer concepts across otherwise unrelated domains. Inspired by Battle Royale games, Derya asked o1 Preview to generate ideas for engineered T cells competing inside tumors; it proposed ideas he had not considered, extending the analogy around resources, exhaustion, competition, and killing cancer cells. He calls this one of his first “early AGI moments” and predicts that AI will soon be “much more innovative and creative than we are.”

Deep Research extracted a biological insight from aging data that Derya’s team had struggled to fully interpret despite his 30-plus years studying the relevant T cells. Comparing gene-expression programs in young and elderly subjects, it inferred that declining immunity was not merely about fewer naïve T cells: the cells considered naïve were themselves no longer naïve. Derya says the insight “recapitulated everything I’ve done in the past 30 years in one sentence,” revealing qualitative and epigenetic aging inside the cells.

The near-term economics are already disruptive even before further model progress. Derya used Deep Research to draft a patent application—including patent searches, chemical analysis, primary claims, and secondary claims—in roughly half an hour, versus what he estimates would have cost more than $10,000 through lawyers. He also describes an AI-assisted grant as probably the best he and a colleague had written, with better ideas and much less friction.

The practical moat is not elaborate prompt engineering but an opportunity-seeking mindset, scientific tolerance for failure, and repeated iteration. Derya writes naturally, asks models to improve ten ideas sequentially, or stages ten rounds between an enthusiastic scientist and a skeptical one. Because his work involves hypotheses that fail roughly 90% of the time, his advice is to treat AI as an intellectually honest colleague and ask, “What can I get this thing to do that’s valuable?” rather than searching for a mistake that proves it weak.

Biology’s accumulated datasets may become a large, underexploited asset as reasoning models acquire longer contexts and stronger causal insight. Derya is reopening data generated five or ten years ago because it may be a “gold mine”; current tests include roughly 1,000 genes, with 10,000 next. He says o1 Pro has already suggested Parkinson’s disease drug targets, with the work intended to go into clinical trials, while the longer-term destination is a personal digital twin spanning genomics, proteins, microbiome, metabolism, physiology, and symptoms.

AI may reward agency more than credentials, compressing the value of traditional scientific training while widening the gap between self-directed and passive workers. Derya would potentially choose an AI-enabled, passionate 19-year-old over a 40-year-old with 20 years’ experience, and says the conventional four-to-five-year PhD plus two-to-three-year postdoc path is becoming obsolete. His blunt qualification is that this will not lift everyone: he argues that perhaps “0.001% of humanity truly innovates,” but AI can give capable people without credentials the means to act.

On existential risk, Derya favors an ecology of regulator AIs over attempts to make any single model perfectly constrained. Borrowing from immune regulation, he argues that excessive guardrails destroy useful function, while competitive defender systems could police malicious models; his greater fear is humans training AI to destroy other humans. If society survives a “very, very painful” transition, he forecasts a golden age within roughly 10–15 years—abundant resources, cured diseases, reversed aging, and eventually the thousand-year lifespan he would spend exploring space.

🔗 Original source & video: Frontier Models for Frontier Science with Prof. Derya Unutmaz, Immunologist & ChatGPT Pro Grantee