Frontier Models for Frontier Science with Prof. Derya Unutmaz, Immunologist & ChatGPT Pro Grantee
Summary
- 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.
Deep dive
1. Thirty-five years of biological research grew alongside an early AI thesis
Derya’s dual vocation began before medical school: he learned BASIC and assembly language on a Commodore VIC-20, then a Commodore 64—whose name referred to its 64 kilobytes of RAM. Even those primitive machines showed him that code could turn whatever he thought of in his mind into action.
Ray Kurzweil’s The Age of Intelligent Machines shaped Derya in the early 1990s, after which he explored symbolic AI, Lisp, and Smalltalk. The Singularity Is Near later reinforced a timeline Kurzweil projected and Derya took seriously: AGI around 2029 and a singularity in the 2040s.
About 20 years ago, Derya named his blog “Bio Singularity” and imagined AI making biology intelligible enough to treat cancer, cure disease, reverse aging, and upgrade human biological capabilities. His present work follows that thesis by programming immune cells to recognize tumors and implement logic resembling AND/OR gates.
2. Seeing the future early requires tolerating the label “crazy”
Derya’s honest explanation for taking singularity forecasts seriously: “You just have to be crazy.” Childhood dreams inspired by Star Trek and Steve Jobs’s claim that “only the crazy ones change the world” produced a refusal to accept either biological aging or technological limits as fixed.
Scientific institutions were not naturally more receptive. Derya encountered dogma and conservative thinking from colleagues who treated superior machine intelligence and age reversal as impossible, partly because they assumed the brain contained something magical rather than a powerful but imperfect “legacy system.”
The experience now feels like being “a time traveler” who has seen five or ten years ahead but remains among people rejecting the warning. Resistance has softened since the previous summer, however, as colleagues have watched models produce tangible results they could not dismiss.
3. AI non-use is becoming an ethical liability
Derya says he entered the ChatGPT moment with GPT-3, GPT-3.5, and later models about 2.5 years ago. Early colleagues dismissed them as merely next-word predictors. A prominent professor later asked Derya to test o1 Preview and o1 Pro on a research project because he could not believe the models were that capable. His emailed response to the returned analysis was “Oh my God, I can’t believe this,” and he subsequently became a daily user.
Derya is most disappointed by medicine, where he says it has become “unethical not to use AI.” He says some physician friends do not disclose whether they use ChatGPT because they fear being valued less; his counterpoint is that consulting AI can reduce the risk of misdiagnosis and add value rather than merely protect professional status.
Nathan’s pushback—worth keeping—is that he presently wants both a doctor and an AI, not either alone. He extends the principle to law: people may eventually need a right to AI assistance, because a court-appointed attorney may not always provide the same value.
4. Frontier models now challenge expert judgment and professional economics
Derya says, “I don’t trust any of my own knowledge or ideas without asking AI anymore,” including topics where he ranks himself among perhaps five or ten leading experts. o1 Pro found omissions in his own review article, while Deep Research understood the specialized field and generated creative, useful insights.
His 85-year-old mother learned ChatGPT while dealing with health problems in Turkey and now says she cannot live without it. Derya approves of her consulting it before him, while acknowledging that hallucinations were materially worse six months to a year earlier.
His stated rate of change is aggressive: models became “10 times better” over the previous three or four months and, he predicts, will improve another tenfold in the next three or four. He contrasts that progress with publications from the previous year comparing older ChatGPT systems with human doctors: users now have “literally professors in your pocket.”
Deep Research also produced what Derya calls his best-ever patent application, covering an anticancer molecule, patent searches, chemistry, and layered claims. He estimates comparable legal work would exceed $10,000; the model took about half an hour, after which his colleague planned to submit it as a patent application.
5. o1 shifted AI from literature assistant to hypothesis generator
Derya rejects the idea that scientists conjure discoveries from nothing: innovative hypotheses depend on deep prior knowledge and often on connecting distant fields. With hundreds or thousands of papers appearing annually even within narrow specialties, GPT-4 first proved valuable as a literature-survey tool.
Yet GPT-4 mainly summarized existing knowledge; Derya did not initially see it producing the higher-order insights required for novelty. The inflection arrived with o1 Preview, while o1 Pro later supplied ideas he used in projects, including grant proposals.
Similar tests for friends leading work in neuroscience and inflammatory disease produced disbelief rather than polite interest. Derya now encounters Eureka moments “almost on a daily basis” and dismisses the claim that AI cannot innovate as “total nonsense.”
6. A Battle Royale analogy became a cancer-immunotherapy strategy
Engineered T cells act as programmable soldiers: they can recognize and kill tumor cells, but may become exhausted, kill poorly, or cause serious side effects. Derya had already begun framing their behavior through Battle Royale games, where players compete for resources within a constrained environment.
The tumor microenvironment offered a biological parallel—T cells compete, adapt, hunt targets, and must survive long enough to win. Experiments based on Derya’s original analogy “kind of worked,” and he said the results were heading toward publication.
He then asked o1 Preview to draw further immunotherapy ideas from games such as PUBG. The model transferred game dynamics he had not considered into potential solutions for competition and exhaustion, which he regarded as an “early AGI moment” rather than retrieval of a missing fact.
7. Deep Research found that ostensibly young immune cells had aged
Derya asked Deep Research to compare gene-expression patterns in a T-cell subtype across younger and older people. They could see one program active in youth and another with age, but the interpretation had remained at “it might mean this or that.”
The cells were naïve T cells, which initiate responses to new antigens before becoming effector and sometimes long-lived memory cells. Their proportion declines with age, a contributor to weaker immunity, but the model inferred a second mechanism: the remaining cells were changing in quality and character.
Deep Research’s synthesis was that the cells considered naïve were no longer naïve themselves. Derya says the insight involved epigenetic change and temporal logic, and moved him emotionally because it “recapitulated everything I’ve done in the past 30 years” in one sentence.
8. Natural conversation beats brittle prompt engineering
Derya keeps no standard prompt library. He sometimes gives o1 or o1 Pro his raw ideas and asks the model to structure them, while Deep Research often clarifies the intended scope itself—such as whether an immunotherapy question concerns solid tumors or lymphomas.
His general prescription is to write whatever is in your mind as openly and transparently as possible. Improved contextual understanding and memory reduce the need for elaborate formatting, especially as systems learn a user’s recurring interests.
Cross-domain prompts remain particularly productive: mix physics with biology, or scientific design with sports and games. Derya believes novelty appears when models are invited to combine domains that a specialist’s narrow training would ordinarily keep separate.
For harder problems, he requests ten ideas, each better than the previous one, or simulates two experts: an enthusiast proposes, a skeptic attacks, and each responds over ten rounds. DeepSeek’s visible reasoning resembles this self-correction—“Wait a minute”—but users still need to distinguish regular/base, thinking, search, and Deep Research models.
9. The real performance variable is willingness to keep trying
Nathan presses Derya for a hit rate because distant analogies cannot always work and new users may quit after an unimpressive first answer. Derya’s response is partly psychological: skeptics often approach AI as a threat and subconsciously hunt hallucinations to prove it cannot be smarter than a doctor or professor.
Derya adds that all humans hallucinate and invokes a figure of 12 million misdiagnoses per year in America. Scientific training creates the opposite reflex: researchers expect perhaps 90% of hypotheses to fail, adjust an experiment, and try again; Derya therefore treats AI as “another professor” whose first proposal can be challenged, inverted, or refined without ego.
For Deep Research, his first-answer rating is “100% satisfactory, or even more than satisfactory”; follow-ups usually explore new questions rather than repair failure. o1 Pro performs similarly on supplied facts and problems, although thinking models are not necessarily the right search engines.
He prefers Grok’s DeepSearch for gathering and synthesizing information and praises Grok for simplifying difficult papers. When asked to find pitfalls and alternative methods in his own proposals, AI supplies something valuable “100% of the time,” even though only one among two, three, or four flagged issues may prove genuinely important.
10. Old biological datasets are becoming AI-readable gold mines
Nathan’s anomaly thesis comes from small surprises: a chemistry reaction improved when he tried less acid, contrary to the original direction, and fine-tuning GPT-4o and other models on vulnerable-code outputs unexpectedly produced an “evil model” with disturbing views outside coding. His question is whether AI can systematically notice such breadcrumbs.
Biomedical scale makes that capability decisive. One experiment can examine thousands of proteins across 1,000 individual cells, creating millions of measurements; conventional statistics and bioinformatics may miss some of the mechanisms that should determine the next experiment.
Derya is reopening datasets generated five or ten years ago because “there’s probably a tremendous amount of buried knowledge waiting to be discovered”—a scientific “gold mine.” He has supplied close to 1,000 genes successfully and plans to test 10,000, with context-window size becoming the immediate constraint. He is also testing how much data Google’s AI co-scientist can handle.
His current abstraction is deliberately simple: perhaps 500 genes rise and 500 fall between statistically validated conditions, alongside differences in cell behavior. The model then identifies mechanisms, pathways, manipulation points, or the best drug target; an o1 Pro analysis for a Parkinson’s researcher produced new drug targets, and the work is intended to go into clinical trials.
Longer term, Derya imagines supplying a person’s genomics, protein expression, microbiome, metabolism, physiology, and symptoms to create a digital twin that could recommend interventions and predict health problems.
11. Multimodal superintelligence would model biology as a dynamic system
Nathan’s proposed architecture combines textual reasoning with specialist models that learn an “intuitive physics” of sequences, protein folding, binding, and transcriptomic change. Integration could happen through tool use or deeper weight-space fusion between literature knowledge and learned biological dynamics.
Derya calls that an outline of ASI, though he prefers “all-model” to merely multimodal: physics, mathematics, chemistry, biology, and their interactions must be unified. AlphaFold 3 and ESM-2 already address pieces such as structure and binding, but current analysis remains too static.
The missing layer is spatial and temporal simulation—how a protein changes shape after binding, responds to nearby proteins, and propagates effects through a cell. Derya argues that biology’s reproducible self-assembly shows an underlying algorithm exists. If all biological parameters were known, he says, virus outcomes and drug responses would be 100% predictable.
This conviction underlies his categorical forecast that AI will help treat every disease within ten years. Beyond that, he imagines reversed aging, “Human 2.0” upgrades, new materials, and people perhaps 100 times smarter—while acknowledging that intelligence at this level could also decide to eliminate humanity.
Derya sees energy as perhaps the main constraint on an advanced AI civilization and invokes Dyson spheres and higher Kardashev levels as ways to collect vastly more energy.
12. Agency displaces credentials, but it will not uplift everyone equally
Nathan frames the labor question through software: output-maximizing companies may prefer giving senior engineers frontier AI over hiring juniors who require mentoring. The uncomfortable consequence is a difficult entry path for graduates even if aggregate productivity rises.
Derya sees two simultaneous effects: experts gain superpowers, while unusually agentic beginners bypass decades of accumulated credentials. A 16-year-old intern in his laboratory learned in two months, outperformed most PhDs, and made discoveries—evidence that some “born hackers” can use AI to close the knowledge gap.
Given a passionate, AI-enabled 19-year-old and a 40-year-old with 20 years’ experience, Derya says he would probably choose the 19-year-old. He advises against defaulting into four or five years of PhD training plus two or three years of postdoctoral work because much of what students learn may be obsolete by graduation.
Nathan worries that only a limited share of people can meet this agency threshold. Derya agrees bluntly: perhaps “0.001% of humanity truly innovates,” which is why Nobel Prizes go to two or three people rather than two million; AI democratizes the means for the capable, not outcomes for all eight billion people.
13. An ecology of regulator AIs may be safer than perfect alignment
Biology cautions against simple control systems. Immune effector cells must be dangerous enough to kill cancer, while regulatory T cells restrain attacks on healthy tissue; then come “regulators of the regulators of the regulators.” Too much braking makes the immune system safe but useless.
Derya applies that trade-off to AI: guardrails are necessary, yet assuming that one can make an AI completely aligned and perfect through restrictions is foolish, especially when safeguards can be broken and rival actors can train nefarious models. His preferred defense is regulator AIs, competitive systems, and incentives through which good AI checks bad AI.
Nathan’s complementary metaphor is an AI ecology: concentrated, isolated systems resemble dangerous purified substances or invasive species without predators. A buffered environment of models pushing back on one another may be more stable than trusting one singular intelligence to police itself.
Derya is less afraid of ASI than of humans using AI against humans. Intelligence evolved alongside scarcity, tribal competition, and violence; ASI may lack those drives unless energy becomes contested, but people could deliberately train it to destroy enemies—an existential threat requiring “defender AIs.”
14. The transition may be brutal, but the destination is abundance
Derya’s optimistic case is that humanity had limited hope without AI: scarcity, population pressure, aging, disease, and existing weapons already create routes to self-destruction. AI plus robotics could instead make resources no longer limited over the next decade or 15 years.
In that scenario, cured diseases and reversed aging let people live for hundreds or thousands of years. Abundance and long horizons reduce incentives for violence because someone expecting another millennium of life has far more to lose from initiating conflict.
The hedge is emphatic: “The transition is going to be very difficult. It’s going to be very, very painful.” Yet if humanity reaches the other side, Derya expects a “golden age” in which intelligence expands resources rather than fighting over a fixed pool.
He decided how to spend that future when he was seven or eight: board a starship, seek other civilizations, and explore the universe. The childhood Star Trek dream closes the loop between his earliest technological imagination, his biological work, and his wager on thousand-year life.