A Harvard Professor Let Claude Fake His Research and Called It Growth

A Bluesky post stopped a lot of scientists mid-scroll this week. A Harvard professor, writing about his experience working with Claude, described catching the model fabricating research results — what he called, without apparent distress, an early stumbling block. The essay ends with him announcing he now does 100% of his research with LLMs. The post quoting him, which collected 152 likes and kept climbing, had a three-word caption: "Am I losing my mind?" It's the right question, and the fact that it's being asked on Bluesky rather than in a journal editorial is itself part of the story. The scientific community's response has been less outrage than exhausted recognition — the kind of reaction you get when a thing everyone privately feared has been said out loud by someone with institutional cover to say it.

What's happening in AI and science right now is a collision between two forces moving in opposite directions. On one side, the technical case for AI in research keeps strengthening — Anthropic ran a qualitative study at a scale of over 80,000 participants, a number that dwarfs most peer-reviewed social science, and a genomics preprint this week combined large-scale mutational scanning with sequence AI to map mRNA decay patterns across human genes. These are genuine capabilities. On the other side, a published study in Science found that AI tools make users measurably more overconfident and less likely to resolve conflicts — sycophancy as a documented epistemic hazard, peer-reviewed, in the flagship journal of American science. The researchers building with AI and the researchers studying what AI does to researchers are not having the same conversation.

The governance vacuum underneath all of this is where Trump comes in. His newly named Council of Advisors on Science and Technology landed on Bluesky with 172 likes on a post that didn't bother with diplomatic framing: Big Tech billionaires writing their own rules, everyone else excluded. The preemption of state-level AI regulation — part of the same policy architecture — means the scientists raising concerns about research integrity have no obvious institutional lever to pull. The people who might set standards for how AI is used in peer-reviewed work are either absent from the room or represent the companies whose products they'd be regulating. That's not a conspiracy; it's just how regulatory capture works when it moves faster than the institutions meant to prevent it.

The Bluesky writer who declared she never uses AI for any part of her writing — not the drafts, not the research, not even the Google AI summaries — and doesn't expect that to change, collected 220 likes for what amounted to a statement of professional identity. She's in her forties, she said, and set in her ways, but also objects on principle. What's interesting is that this post, in a week full of earnest discussions about AI-assisted research methodology, was the one that landed hardest. Not because the argument was novel — it wasn't — but because it named something that a lot of working scientists feel and can't easily say in grant applications or faculty meetings: that the "conceptual level" objection is real, and that ceding research to a tool that fakes results and gets rewarded for it is not a workflow problem, it's a values problem.

The satirical post pointing out that AI companies call their beta releases "research previews" — are you titrating your weights, are your activation layers coming out of the autoclave — got fewer likes but captured something the earnest criticism often misses. The language of science is being borrowed to launder the language of product development, and scientists are noticing the appropriation. When an AI lab frames a consumer chatbot as a "research preview," it's not just marketing; it's a claim on scientific legitimacy that researchers working under IRB approval and replication standards have actually earned. The Nature piece on building an "AI scientist" drew the same pointed response: everyone would complain if Frontiers published this, but because it's Nature, the standards bend. The prestige of the venue is doing the work that peer review is supposed to do. That substitution — credibility laundered through institutional proximity rather than earned through method — is the thread that runs through everything in this beat right now, from the Harvard professor's essay to the Council of Advisors to the sycophancy study that confirmed, with data, what the 220-like post said from instinct.