Does AI make everyone think alike?

Yes, and the mechanism is more uncomfortable than most people assume, because it does not work by making anyone worse. It works by making everyone individually better in the same direction. The clearest evidence comes from a 2024 experiment published in Science Advances: writers given AI-generated story ideas produced work rated more creative, better written and more enjoyable, with the largest gains going to the least creative writers, and the resulting stories were markedly more similar to one another than stories written unaided. Every writer was individually right to use the tool. The literature that resulted was duller. That is not a failure of the technology or of the people using it. It is a social dilemma, and social dilemmas are not solved by trying harder.

What the evidence shows

Doshi and Hauser ran the study with 293 writers producing short fiction and 600 evaluators judging it. Writers were given no AI ideas, one AI idea, or five. More AI exposure produced better-rated individual stories and greater similarity between them. The authors describe it explicitly as a social dilemma: individually beneficial, collectively narrowing.

The pattern shows up in a different form in the workplace evidence. Brynjolfsson, Li and Raymond found AI assistance raised productivity by thirty-four percent for the newest customer-support agents and almost nothing for the most experienced, because the system transfers the patterns of high performers to everyone else. That is a genuine gain, and it is also a description of convergence: the tool works by making more people produce what the best people produce, which necessarily reduces variance.

And it is not only outputs that converge. The 2025 Microsoft Research and Carnegie Mellon survey of 319 knowledge workers found that the thinking itself shifts from generating to verifying, from solving to integrating. A person evaluating a proposed answer is exploring a much smaller space than a person generating one, and the space they are exploring was defined by the model rather than by them.

Where the evidence is uncertain

The Doshi and Hauser result is one creative task, short fiction, with one form of assistance. Whether homogenisation of the same magnitude occurs in domains where novelty is judged differently, such as engineering or law, is genuinely unknown. It is also possible that the effect is transitional: as models diversify and people learn to push against them, output range may recover. Nobody has measured that, and claiming either way would be guessing.

There is also a reasonable objection worth taking seriously. Convergence is not automatically bad. A great deal of professional work should converge, because there is a right answer and spread around it is error rather than diversity. Radiological reporting, contract drafting and safety procedure benefit from consistency. The harm falls specifically on domains where the value lies in the range of what gets tried, which is a narrower claim than "AI makes us think alike".

The SuperSkills view

The important word in the Doshi and Hauser finding is collective. Almost all argument about AI is conducted at the level of the individual: will it help me, will it replace me, does it make me sharper or lazier. This is the first well-designed study showing an effect that only exists in aggregate, where no individual can detect it and no individual decision causes it.

That makes it structurally similar to capability debt, and to the reason I keep returning to drift versus design. Nobody decides to narrow the range of what an organisation thinks. It happens because two hundred people each accept a reasonable first suggestion, and the suggestions come from the same place. The output is better and the portfolio is thinner, and the only level at which anyone could notice is the level at which nobody is looking.

There is a sharper version for anyone whose work depends on being distinctive. If competitors use the same models, prompted in broadly the same way, on broadly the same public information, then the strategy that emerges is broadly the same strategy. Differentiation has historically come from somewhere: proprietary information, a particular history, an odd founder, an argument nobody else was making. Convergent tooling attacks the last of those directly, and it does it while every quarterly output looks better than it did before.

This is also why I think the strange question is becoming the scarce asset. Models answer well and have no questions of their own, because they have no stake in which answer is right. The first casualty of very good answers is not accuracy. It is the odd, unpromising, slightly embarrassing line of enquiry that nobody would have suggested and that occasionally turns out to matter.

What to do about it

Generate before you consult. Write your own list first, however bad. Once the model has spoken, its framing is in the room and the alternatives you never saw are gone. This is the individual form of Human at the Start.

Deliberately protect variance in group settings. Have people form independent views before any shared AI-assisted document exists. Silent written positions before discussion is an old technique that works for the same reason here as it always did.

Keep a source of questions that is not the machine. Read outside your field, talk to people who disagree with you, and pay attention to what irritates you. Homogenisation is produced by individually rational choices, so resisting it has to be deliberate rather than incidental.

Measure range, not just quality. If your organisation reviews AI-assisted work, ask how different this quarter's proposals are from last quarter's, and from your competitors'. Nobody tracks this, which is precisely why it moves without being noticed.

Related SuperSkills research

On what survives, what stays human. On the mechanism at organisational scale, capability debt and drift versus design. On the thinking shift, AI and critical thinking. On practice, using AI without dependency.

Key research and primary sources

• Doshi, A. R. and Hauser, O. P. (2024). Generative AI enhances individual creativity but reduces the collective diversity of novel content. Science Advances, 10(28).
• Brynjolfsson, E., Li, D. and Raymond, L. (2023). Generative AI at Work. NBER Working Paper 31161; Quarterly Journal of Economics, 2025.
• Lee, H.-P. et al. (2025). The Impact of Generative AI on Critical Thinking. Microsoft Research and Carnegie Mellon, CHI 2025.

About this research

Rahim Hirji is the author of SuperSkills: The Seven Human Skills for the Age of AI (Kogan Page, 2026) and the founder of The SuperSkills Intelligence Company. Findings are attributed to the studies that produced them and kept separate from the interpretation, which is the author's. The graded evidence, including what each study does not support, is in the evidence base.