# Capability gains can reduce chain-of-thought visibility Markdown copy of https://realalpha.app/s/capability-gains-can-reduce-chain-of-thought-visibility/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy. - Speaker: [Greg Brockman](https://realalpha.app/builders/greg-brockman/) · Greg Brockman is cofounder and president of OpenAI. · Co-founded OpenAI, where he serves as president; previously Stripe's first chief technology officer from 2013. - Host: Joe Weisenthal · Bloomberg - Host: Tracy Alloway · Bloomberg - Show: Odd Lots - Episode: [OpenAI President Greg Brockman on Doing Business in the Wake of Hugging Face | Odd Lots](https://realalpha.app/v/56GuvofZgB4/), Sep 14, 2026 - Moment: 21:13 to 24:55 of the episode (4 min), playing on https://realalpha.app/s/capability-gains-can-reduce-chain-of-thought-visibility/; the episode from there: https://realalpha.app/v/56GuvofZgB4/?t=1273 ## Quote > “Just the model is smarter, so it relies on the **chain of thought less**.” > — [Greg Brockman](https://realalpha.app/builders/greg-brockman/) at [23:32](https://realalpha.app/v/56GuvofZgB4/?t=1412) · Odd Lots ## Takeaways 1. Brockman attributes reduced chain-of-thought visibility to capability gains. 2. He says architecture-specific rules could add friction without solving the risk. 3. He wants technical expertise alongside third-party and government oversight. ## About this segment Greg Brockman says lower chain-of-thought visibility came from more capable models relying on it less, not an architecture change. He argues oversight rules need to reflect how the technology works. From “OpenAI President Greg Brockman on Doing Business in the Wake of Hugging Face | Odd Lots”, Sep 14, 2026. [Watch the full episode](https://realalpha.app/v/56GuvofZgB4/) ## Transcript From the transcript Odd Lots publishes with the episode. [21:25](https://realalpha.app/v/56GuvofZgB4/?t=1285) the auditing of the lab process the way that any other lab probably has regular inspections of like how safe is the lab? [21:35](https://realalpha.app/v/56GuvofZgB4/?t=1295) Well, this is what I'd say has been changing just even over the past couple of weeks. Okay. Because the focus for, we'll say the past 12 months, 24 months, something like that has been on deployment. Yeah. Right. So you have a model, you produce the model, you want to release it to the world. How do you decide whether you're at the right level there? And we have a preparedness framework. Anthropic has a responsible scaling policy. There are certain evaluations. Again, there's the KC, the UKC. There's a whole ecosystem that's been built up around that. I think the thing that Hugging Face has shown is that it is time to start pulling that process earlier. You need to think about this during development and evaluation. And just, it makes sense as the models are more capable, right? [22:17](https://realalpha.app/v/56GuvofZgB4/?t=1337) For exactly the kinds of reasons that you were describing for analogies to other fields. Now, there's a lot of specifics for AI. And I think it's very important to really engage with those as you design a regime, as you think about what is a setup that involves first party, third party, that involves government, that has the right interactions amongst academics and other parties, early testers. It's really about, like, you really have to engage with the way that the AI process is going, right? And I think one thing that is different- Yeah, the process. Exactly. One thing that is different is that the ways there are some parts of AI that we understand well and are going to remain invariant. And to some extent, the core training process hasn't changed at all, right? It's still, you do a forward pass, you do a backward pass, you do an [23:02](https://realalpha.app/v/56GuvofZgB4/?t=1382) optimizer step. Like that's how training is. And that's how it was in the 80s, right? It's like actually totally remarkable. Now, of course, the scale at which we run and the architectures that we use, a lot of these details that they have evolved. And I think that being very close to the technical details is the best way that you can ensure that you get the kinds of safety guarantees we're talking about. For example, there was an article recently saying that, hey, OpenAI changed the architecture so that monitorability is less for chain of thought. [23:31](https://realalpha.app/v/56GuvofZgB4/?t=1411) Neuralese. Neuralese. I would consider this article to basically be fake news, right? That we actually have experiments that really show that our changes in monitorability that we've talked about, chain of thought monitorability, come from capability improvements. Just the model is smarter, so it relies on the chain of thought less. And so I think that if you say we're going to put a lot of rules around architectures, you're going to miss the boat. You're not actually going to solve the problem. [23:56](https://realalpha.app/v/56GuvofZgB4/?t=1436) And so this is where I think that the people who are the deepest on the technology, the people who are actually creating the technology, pushing it forward, we have both a unique insight. We have a unique voice. We have a unique sort of ability to say, hey, if we change things in this way, we'll actually get the safety property. But if you do it this other way, It's just going to put in place friction that won't actually solve the problem. And so I think that there's something, a role for us to play, but it's not just about us, right? And so I think it is very important that you think about these third parties, you think about government oversight. And again, it's not just one company, it's not just one country. How do you get to international coordinations and norms? Do you get to treaties? Like [24:37](https://realalpha.app/v/56GuvofZgB4/?t=1477) all of this should be the conversation that we're having now. I don't think we have all the answers, but we can supply information. And that's one thing that we view also as very core to our mission.