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JS John Schulman Co-founded OpenAI and led the team that built ChatGPT, then ran the company's post-training research group; his papers on proximal policy optimization and reinf...

“…so many people are distilling mostly from Claude that all the open weight models write the same way as Claude and use the same, like have the same ticks. So this seems kind of concerning to me that we're having this monoculture emerge.”

Dwarkesh Podcast · AI Research & Frontier Labs · September 2026

“…so many people are distilling mostly from Claude that all the open weight models write the same way as Claude and use the same, like have the same ticks. So this seems kind of concerning to me that we're having this monoculture emerge.” — John Schulman, Dwarkesh Podcast

Context

The panel had been arguing about whether reinforcement learning flattens the range of a model's writing, with Schulman noting that RL-trained models reuse the same themes and character names. He extends that from one model to the whole open-weight ecosystem. Beren Millidge pushes back immediately, arguing this is a property of the training data rather than of RL or distillation as methods.

Transcript

Dwarkesh Podcast
John Schulman

…they're reusing certain themes all the time. And they're using the same character names all the time. So there's actually, it's not like you're getting the same kind of diversity that you get when you, like from human authors, you're sort of getting one really good style. So I think that kind of

Charlie O'Neill

diversity has

John Schulman

definitely been cut down by RL a lot. And in fact, since we were talking about distillation earlier, that's sort of something, yeah, one thing that's happening is that so many people are distilling mostly from Claude that all the open weight models write the same way as Claude and use the same, like have the same ticks. So this seems kind of concerning to me that we're having this monoculture emerge. Yeah.

Beren Millidge

Again, I don't think this is fundamental to RL as a method though. And same with distillation. Even with distillation, you're just training on the data. Just because your data is not super broad, that doesn't mean the training method itself is somehow wrong. It's a problem with the data. And I…

Dwarkesh Patel

Okay, super rapid fire predictions about the future. So I want timelines on the following couple questions. By when do we have models which you can here's what the it feels like to a user you basically hire them as a drop-in remote worker for all kinds of white-collar work, not just coding, but I…

Charlie O'Neill

If you mandate it to use a browser or whatever, rather than, again, the firm setting up the information to be programmatically accessible, maybe a couple of years. But if it's not browser-based, it can send Slack messages, it can do all this stuff. But I'd still probably say it around a year.

Speaker names come from our own diarization. The position is estimated from where the line sits in the episode.

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