David Kipping, an astrophysicist at Columbia, stumbled into a closed-door meeting at the Institute for Advanced Study in Princeton. He came back shaken and recorded a podcast. Here is what was said — and why it should unsettle anyone who works with models.

In January, David Kipping drove to Princeton to deliver a colloquium on astronomy. In the corridors of the Institute for Advanced Study — corridors whose particular institutional hush one learns not to break — he passed Ed Witten, one of the architects of string theory. The two exchanged the briefest of nods, as people do in hallways they share with ghosts. Einstein had walked here. Oppenheimer. Gödel. IAS is not a place given to nodding along with nonsense.

Kipping is a professor at Columbia, runs the YouTube channel Cool Worlds (a million and a half subscribers), and has spent a decade straddling ML and astrophysics. Eight years ago he stopped building models himself: the literature was moving faster than he could track, and he decided you’re either full-time in AI or you use it as an instrument. He chose instrument. His research portfolio includes work on circumbinary planet stability and detecting “missed” exoplanets through neural networks. A working scientist — not a journalist, not a blogger.

The following day, out of habit, he stopped by IAS and walked into a closed meeting. It had been convened by a senior astrophysics professor whose name Kipping deliberately withholds. Topic: what AI is doing to science. Forty minutes of presentation, a historian commenting via Zoom, then open discussion. About thirty people in the room, among them authors of the cosmological simulation codes Enzo, Illustris, Gadget — adaptive mesh hydrodynamics, hundreds of thousands of lines of C and Fortran. Try, as Kipping put it, to find a room with a higher average IQ.

No cameras, no press releases, no prepared remarks. Not a conference. Not a PR event. This is precisely why people said what they actually think.

The historian spoke first: this is a historic moment and it must be documented.

The room laughed. Kipping did not.

Capitulation

The lead professor’s opening claim: AI codes an order of magnitude better than humans. His exact framing — complete supremacy, order of magnitude superior. Not one person in the room raised a hand to disagree. Not one.

Then a number. The professor said AI can now perform roughly ninety percent of his intellectual work. He hedged: maybe sixty, maybe ninety-nine. But the thrust was plain — a clear majority, and growing. This was not just about code. Analytical reasoning, mathematics, problem-solving. Everything that a person at the Institute for Advanced Study has spent a career perfecting.

A concrete example, from Kipping himself. He had been working with an integral in Mathematica — Wolfram’s flagship engine for symbolic computation, the gold standard for decades. Mathematica failed. ChatGPT 5.2 succeeded. It produced the full chain of substitutions and transformations, which Mathematica does not even attempt. Numerical verification confirmed the result.

When someone at the place where Gödel once worked admits that a model performs ninety percent of his thinking, no marketing department on earth could draft a more terrifying sentence. An identity crisis, pronounced aloud, before witnesses. The witnesses nodded.

Handing Over the Keys

The lead professor had given agentic systems complete control of his digital life. Email, files, servers, calendars. Root access, in Unix terms. Primary tools: Claude and Cursor, with GPT as backup. Roughly a third of the room raised hands: us too.

Someone asked about privacy. Had he at least read the terms of service?

“I don’t care. The advantage is so large that the loss of privacy is irrelevant.”

Then ethics. Standard concerns were enumerated — displacement of jobs, energy consumption, climate impact, concentration of power among billionaires. He acknowledged every one. And then, quite literally, said: I don’t care, the advantage is too great. Kipping describes the room’s mood as “ethics be damned.” This was not the eccentric bluster of one radical. The room concurred.

Pause here. Academics have spent their entire careers cultivating the art of saying “there are nuances” when they mean yes or no. These are the world’s most diplomatically hedged people. And here they sit, in a closed room with no cameras, saying they don’t care about ethics. The position itself is predictable: if your job is maximizing scientific output, you optimize for output. But the readiness to say it without a single qualifying clause — that is what tells you how much pressure they feel. A year ago, they would not have said this even at a bar.

Kipping’s metaphor: the forbidden fruit. AI companies are the serpent with the apple. Once bitten, innocence does not return. And if you refuse the bite but the competing lab takes it, they outpace you. An arms race with a built-in moral dilemma.

This sense of inevitability is not abstract. Kipping inventories his own workflow: proofreading papers by feeding LaTeX directly into GPT; vibe coding; debugging not by tracing logic but by pasting the error into a chat window. Literature search. Derivative computation. When his TARS project required graphene properties, albedo data, and mechanical stress analysis, he routed everything through AI. For YouTube production: AI for audio cleanup, transcription, upscaling, fact-checking scripts. All of it.

Yet Kipping does not consider himself a power user. His self-assessment: “My strength has always been creativity — AI amplifies it.” The lead professor, by Kipping’s account, has gone considerably further. That distance between “I use it for proofreading” and “I gave it root access to my servers” is a chasm, and inside it live every stage of acceptance that scientists are now passing through in a year or two.

How Trust Grows

This is where anyone working on alignment, interpretability, or even just shipping agent pipelines in production should slow down and pay close attention.

He described his trajectory. He began with Cursor — because Cursor shows diffs. Here is what your code was, here is what it became, here is what I changed. Transparent, auditable, familiar to any programmer. But as trust accumulated, transparency began to chafe. It stopped feeling like a guardrail and started feeling like friction. He switched to Claude. Claude dispatches sub-agents, decomposes the task, solves the pieces in parallel, acts with greater autonomy. It does not show every diff. It simply does.

For verification, the professor played models against each other: solved a problem in Cursor, cross-checked in Claude, discussed the result in GPT. Peer review, essentially — except not between colleagues, but between three neural networks.

Plot this trajectory formally and you get an S-curve: skepticism, disappointment, time investment, surprise, trust, surrender of control. On the final plateau, transparency becomes an annoyance — a fly buzzing while you think. The world’s leading scientists are already standing on that plateau.

Here is what this means for everyone building interpretable and explainable systems: your most sophisticated users do not want your transparency. They will switch it off. Not because they were coerced, not because the interface is bad — because they produce more without it. Natural selection within user behavior presses toward less interpretable systems. For the alignment community, this should trigger alarm: the better models perform, the weaker the incentive to supervise them.

A side effect: small scientific collaborations will begin to vanish. Researchers used to recruit co-authors for skills they lacked — a particular calculation, a sanity check, code in an unfamiliar library. Now the model fills the gap. Why invite a colleague for one computation when Claude handles it in ten minutes? Kipping already publishes single-author papers, unusual for his field, and expects the trend to intensify. Core collaborations will endure — two or three people, each genuinely irreplaceable. Everything else gets delegated to agents.

First contact with models, however, usually disappoints. The lead professor admitted he spent enormous amounts of time on trial and error. Hours screaming at the keyboard in all caps — a peculiar image, a distinguished astrophysicist hammering CAPSLOCK in a silent office. Most people try once, get garbage, walk away. Those who push through — the early adopters — acquire a massive advantage. Hence the meeting’s true purpose: the Institute was not resisting. It was assembling a cohort for accelerated adoption. The message was unambiguous: embrace this.

The Economics of the Trap

The lead professor was spending hundreds of dollars a month on model subscriptions. Out of pocket. For him, manageable. For a graduate student or young postdoc, already a barrier. The stratification is happening now: AI amplifies some; others cannot afford the amplifier.

Since 2014, total investment in the AI industry exceeds the entire Apollo program by more than five times (inflation-adjusted) and the Manhattan Project by fifty. No technology in human history has attracted this much capital. None.

The question that came up over lunch: how do investors get their money back? One scenario is the price trap. Classic dealer logic — the first hit is free. Models are cheap today. Everyone gets hooked. Skills atrophy. In two or three years, companies raise prices to thousands of dollars a month. By then the Overton window has moved: AI-level productivity is the expected baseline, and opting out is as unthinkable as throwing away your GPS. The habit persists; the underlying skill has long since died.

A second scenario was debated over lunch with particular heat: AI companies may demand a share of intellectual property. Imagine terms of service in which OpenAI or Anthropic claim ten, twenty, fifty percent of any patents generated using their “research” tier. For now, speculation. But two hundred billion dollars in investment requires a return, and nobody is running a charity.

Almost nobody discusses this publicly. They should. If your grant pays for the research and twenty percent of the IP goes to Anthropic — that is a fundamentally different economics of science.

Who Suffers Most

Traditionally, physics and astrophysics rewarded people with raw technical brilliance. The ability to solve differential equations in your head, write complex simulations, think in high-dimensional abstractions. Those advantages are now neutralized.

What has replaced them is a managerial profile. Decomposing a problem into model-digestible chunks. Patience — not losing your mind when the model confidently hallucinates for the third time running. Building workflows: prompts, rules, chains of agents. A fundamentally different breed from the one that drove science for three hundred years. As if a conductor were told: the orchestra is virtual now, throw away the baton, learn MIDI.

The GPS analogy is precise and merciless. Before navigation apps, we held three-dimensional maps of our surroundings inside our heads. GPS killed that skill. Behind the wheel, we now think about anything except the route. The coming atrophy of coding ability, mathematical reasoning, autonomous problem-solving: same mechanism, vastly larger scale.

Most exposed are the young scientists. Training a PhD student runs about a hundred thousand dollars a year — salary, health insurance, tuition. A model subscription costs twenty dollars a month. A first-year project that takes a student twelve months, the model consumes in an evening.

Against this backdrop, the current administration is slashing federal research grants. And an existential question hangs in the air. Kipping frames it carefully: “I’m not endorsing this, but I can imagine someone saying it.” Why spend five years training a scientist if in five years there may be no scientists in any familiar sense?

Tenured professors are relatively safe. By definition of tenure, dismissing them requires dissolving the institution entirely. Captains going down with the ship.

The lead professor already uses AI to screen graduate applicants — not to decide, but to assist. He rated the outcome as the best in his entire career: faster, more accurate, more reliable.

A chilling follow-up: by what criteria do you select students when the traditional ones — technical mastery, coding fluency, abstract mathematics — may be worthless in five years? Kipping is blunt. Would he work with a student who refused on principle to use AI? Probably not. It would be like refusing to use the internet. Or refusing to write code at all.

The Silence in the Room

Certain things were not said at the meeting. Their shadow, however, falls across every fact in the podcast.

If models produce ninety percent of the work and cross-check each other, who catches a systematic error common to all of them? When everyone relies on the same systems, diversity of thought narrows. Suppose three models agree on how a particular integral evaluates. What if all three inherited the same flawed approximation from their training data? A lone human reviewer working through it by hand might have caught it — but reviewers are buried in submissions, they have no time, and they too are increasingly checking work through models.

Reproducibility was already a sore subject in science. (If you are unfamiliar: half the results in psychology do not replicate. Biomedicine is not much better.) Now add this: an experiment that amounts to “ran a prompt, got a result.” How do you reproduce it a year later, after the model has been updated? What was the sampling temperature? What system prompt was set by default that Tuesday? Which model version was running? Reproducibility either gets a second wind or a bullet to the head. It depends entirely on whether we learn to pin down prompt environments as rigorously as we pin library versions in requirements.txt.

If models generate science, and that science enters training data for the next generation of models, you have a closed loop. Whether it converges to something meaningful or diverges, nobody knows. Model collapse is widely discussed as a concept, but in the specific context of scientific reasoning, almost nowhere. Scientific texts are not like marketing copy: they contain chains of inference in which an error at step four ruins everything that follows. If a model trains on ten thousand papers where an intermediate step was hallucinated but the final answer happened to match experiment, it absorbs bad reasoning that yields correct results. That is worse than a straightforward mistake. It is the kind of corruption you do not notice until you try to build on it.

One more thread Kipping touches, from a different angle: public reaction. His YouTube audience has a fierce allergy to “AI slop” — content wholly generated by models, masticated Wikipedia, Reddit rewrite. Kipping draws a line: his content rests on original ideas; the model assists with execution, not with thought. But note this: the scientists at IAS were not concerned about public reaction at all. They do not fear their papers being called AI-generated, because they have already conceded the premise — models work at their level or above. From their vantage point, AI-assisted science is entirely legitimate. The gap between how academics perceive this and how the public does is already a chasm, and it will widen.

Then there is the paper flood. One to two orders of magnitude more publications. Power users producing three or four papers a year instead of one, and “ordinary people with GPT” adding theirs. Already, dozens of new papers appear daily in each subfield on arXiv. Nobody can read them. “Use AI to read” is the surface-level answer, but a scientist does not need a summary. A scientist needs the knowledge internalized — absorbed, digested, cross-wired with everything already in the mind. Summaries cannot do that.

The Last Question

What is the point of replacing all scientists with machines?

Kipping reaches for an analogy with art. AI-generated art exists, and for certain tasks it is useful. But what grips us in a museum is the human story behind the canvas: what drove the painter, what was happening in the room, why that particular brushstroke landed there. Science shares the same nature of curiosity. It is detective work. It is the jolt of joy when the pieces suddenly click and a fragment of the world becomes legible.

Kipping’s fear is concrete. A world in which a superintelligence designs a fusion reactor and no human being can comprehend how it works. A world where the result exists but understanding does not. Where everything is, effectively, magic. His words: “I don’t know if I want to live in a world where everything is just magic, fantasy. I want to live in a comprehensible world.”

Run the numbers. A model costs twenty dollars a month and does the work of a PhD student. This means science ceases to be the province of a credentialed elite. The viewers of Kipping’s channel, who for years wrote to him with ideas they could not execute, no longer need Kipping. Democratization. It sounds magnificent. But the consequence is an avalanche of publications in which human attention becomes the binding constraint. The locus of value shifts: not “who can produce science” but “who can tell the signal from the noise.” A completely different skill. And possibly the last one humans will hold a monopoly on.

Kasparov lost to Deep Blue in 1997. For the next decade he championed centaur chess — human plus machine, stronger than machine alone. By 2015, that turned out to be wrong. The machine alone was stronger. The centaurs exited quietly, without a farewell ceremony. In science, we are currently somewhere in the centaur phase: the human is still needed, still steering the process, still formulating the questions. How long this lasts is not an abstract question. For some of those graduate students now being screened for admission, the answer will arrive before their dissertation defense.

The most striking thing about this podcast is not its content. Anyone who works daily with large language models will recognize their own thoughts in it. What is striking is something else entirely. Kipping says: what shocked him was not what he heard, but that all of it was spoken aloud, and the entire room was nodding. Thoughts he had believed were his private anxieties — half-formed, uncertain, frightening — turned out to be a chorus. Simply, until that January morning, no one had dared say them.

The historian on Zoom was right: this moment needed documenting. Kipping documented it. We have read it.

Who will read it five years hence — ourselves, or the systems to which we will have delegated reading by then — that question went unanswered in the room.

Perhaps it did not need answering. It was enough that someone asked.

Based on the Cool Worlds podcast (David Kipping, Columbia University), episode on the closed-door meeting at the Institute for Advanced Study, Princeton, 2025.