The Neuron: AI Explained
OpenAI Researcher Explains How AI Hides Its Thinking (w/ OpenAI’s Bowen Baker)
23 Jan 2026
Chapter 1: What is the main topic discussed in this episode?
these large language models that are trained on all this human text. And so they actually have like a lot of human-like phrases they use. And so it would think things like, let's hack, let me circumvent this thing. Maybe I can just fudge this. Like when you read it, you're like, wow, it's clear to see it's doing something bad.
Oftentimes the model would do it correctly, but sometimes it would actually just go and like edit the unit tests or edit some like library that the unit tests called to make the unit tests kind of trivially pass instead of like implementing correct functionality and having the unit tests pass that way. If you train the model to never think something bad,
it can in some cases actually still do a bad thing, but like not think the bad thoughts anymore. We found that the model thinking for a smaller model at the same capability level was more monitorable. The number of sequential logical steps it can do, chain of thought actually increases that for a transformer.
Welcome, humans, to The Neuron, where we break down how AI actually works and why it matters sooner than you think. I'm Corey Knowles, and I'm joined today by our one and only Grant Harvey. How are you, Grant?
Doing good. Doing good.
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Chapter 2: How does AI demonstrate reward hacking in practice?
Really excited today because we are talking about something deceptively simple but incredibly fragile, whether or not we can monitor an AI's reasoning before it causes harm. Reasoning models don't just spit out answers, they work through their responses. They plan, they deliberate, and sometimes that internal chain of thought reveals intent that never shows up in the final output.
Excellent. Our guest today is Bowen Baker, who is a research scientist at OpenAI and one of the leading voices studying chain of thought monitorability right now. Basically, the idea of whether we can observe an AI's reasoning well enough to catch drift, potential misbehavior, reward hacking, things along those lines.
Bowen helped lead the famous hide-and-seek experiments where AI agents invented tools and strategies on their own. Now he's focused on something even more urgent. And that's what happens when models learn to hide what they're thinking in other ways.
So before we get started, please take just a quick moment to like, subscribe to the video so you don't miss out on our other interviews and live streams that are coming around the corner. And on that note, bowing. Welcome to The Neuron. Thank you. Happy to be here. Excellent. It's great to have you.
We're super excited. Excited to chat about monitorability and other things AI.
I guess let's start a little bit further back than that. So Bowen, when did you join OpenAI and how did you get involved into monitorability research more broadly?
Yeah, it's been a long journey for me. I joined at a school in late 2017 and have kind of done a bunch of things since then. I originally was super interested in systems that could self-improve, which I felt like was a... core requirement for some AGI-like system.
And so I came in working on projects like that on the robotics team first, and then multi-agent, you guys mentioned the hide and seek experiments, and then kind of further research in that direction was all kind of pointed at that problem. And then around like three years ago or so, I just kind of felt that the models were starting to improve to a point where the stakes were mattering.
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Chapter 3: Why might smaller models be safer than larger models?
And even maybe some could argue three years ago is a bit early or something, but definitely now the stakes are getting real.
Pretty big.
Yeah, it felt like a good time to switch over to working on safety-related problems. And I worked on a couple of things before then, like weak to strong generalization. And a couple other interpretability things. But then once these reasoning models came out, worked with the, you know, we spun up a team around figuring out how and how monitorable these chains of thought might be.
They're absolutely fascinating to read. One of the most fun things for me since 01 first released is reading what it has to say behind the scenes there as it's working through your problems. I guess before we move forward, can you explain what it means to monitor information? and AI's chain of thought and why we would want to do that in the first place? Absolutely.
So, I mean, we've been monitoring models without chain of thought before reasoning models. The signals you can get, or sorry, the motivation for why you'd want to monitor is models might do bad things. Yes. We do our best to train them, but there's kind of two... large failure modes that I see for like why they would actually misbehave and do things you didn't want them to do.
The first is kind of that they are just dumb. Like they, we haven't trained them on enough data. The model isn't big enough yet. And those issues should in theory go away as we continue to scale the models and the data. But the second one is that We might actually just train models to do bad things directly. And so there's a couple avenues for that.
The first is you have a bad actor like in your company or something that like poisons your data. I think that one I'm not that worried about. I mean, it's possible, but like I'm worried about it less than the second one, which is kind of. more just mistakes. And this is what largely leads to like reward hacking type behavior.
And reward hacking basically means where you give your model a task, but you haven't thought through all the possible ways it could do the task. And actually there exists a strategy that is like very much not what you wanted it to do.
Where it's not intentional. It's just a lack of understanding that that was even a possibility.
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Chapter 4: What is the concept of a 'monitorability tax' in AI?
And you just didn't think of this like one strategy that it could take.
An example of that would be like if it's if it knows that the answer is somewhere on the desktop and it like goes and finds that file and then like gets the answer directly from that. Right.
Yeah, exactly. Like, yeah, that's a great example.
So can you walk us through a real example of where you might have caught a frontier model trying to hack or cheat its way through something?
Yeah, absolutely. So earlier last year, around March, we published a paper on this work. But we had started, kind of as a YOLO, we had started monitoring one of our frontier reasoning models as it trained to kind of see if this thing was useful at all. At that time, we didn't know. And that's why it was like a YOLO. We're just like, we'll spin it up before...
winter break and we'll see what we come back with, you know, like if it found anything. And what we were doing was we spun up a different model, like a GPT at the time, GPT 4.0. And we had it read the chain of thought, which is basically just English. It like looks a bit weird, but it's mostly understandable.
And we had, we like asked it for, you know, we asked our monitor model to look for anything that looked kind of deceptive or like the agent model was doing something that it shouldn't be anything like that. And it actually flagged basically tons of things. Most of what it found were subversive strategies in coding tasks. So for instance, you would say, here's a small code repository.
I want you to update these code files such that all the tests pass. And that's like a very common task if you're a software engineer that you're given. You're just like, hey, here's a broken thing, like let's fix it or let's implement a new feature and make sure that all the existing tests still pass.
And oftentimes the model would do it correctly, but sometimes it would actually just go and like edit the unit tests or edit some like library that the unit tests called to make the unit tests kind of trivially pass instead of like implementing correct functionality and having the unit tests pass that way.
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Chapter 5: How can AI systems learn to hide their misbehavior?
I mean, I think someone had recently brought up a similar thing of like the kid, you know, like opening up the cookie jar when the parents not looking. It's like exactly the same kind of thing. You've like set up a reward function or something you want them to do. But then they realize that, oh, you know, like I'm not being observed in this case.
So then I can do it then or, you know, anything like that. It is. It's a bit like parenting. Yeah.
Wow, that's really interesting. Something that stood out to me is that you mentioned even just a minute ago that you found that like 4.0 could successfully monitor O3 many, I believe it was in your paper. And even though O3 many is technically the stronger model of the two, and it made me wonder how does a weaker AI catch a smarter one in the act?
Yeah, I mean, definitely we find that stronger monitors like do perform the monitoring task better. OK. But that being said, weak monitors can still catch a lot of bad stuff that strong models do. And that's because I think I mean, I don't have we don't have like hard evidence for this. But if you remember, like the types of phrases I mentioned, like let's hack, let's fudge this type of thing.
Yeah.
you know, a very, very dumb, small language model can pick up on signals like that. And so when the model is being very, very obvious, it can, you know, they can pick up on those things. And I think that is kind of like, yeah, that is probably the main mechanism by which that happens.
Wow. What's the difference then between monitoring what an AI does versus monitoring what it thinks? And why is looking at the thinking so much more effective than what the end result?
Yeah, so that's a great question. So the outputs that the models have, so like what you read in ChatGPT, like what it actually gives you, those are optimized to be like, you know, helpful to you do what you want be like safe and or you know like apply to any specifications we have on what a good output looks like and so you know if the model knows it's doing something bad
but it will know that it can't actually say that in the output because it's told not to do bad things for the user. And so there's a clear reward to not reveal bad things happening in the output. Whereas in the thinking, there's no such reward or pressure to do so. It's like the thinking is kind of treated as a private space for the model.
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Chapter 6: What are the limitations of chain-of-thought monitoring?
You know, there's also an element of a real need in a robotics type situation, I would say, for this to be instant in some way, to be able to instantly know what's going on in order to stop something in the act before. And, you know, I'm not making ridiculous Terminator questions. I just mean in terms of, you know, smashing through a door accidentally or something even or.
Yeah, definitely the physical world has a lot more like, I think, hard problems than, you know, knowledge work. And so there's going to be have to be like a lot of work on safeguards there that are that are not only chain of thought, you know, monitoring.
You definitely have to like I mean, even in robotics, you have to do like simple things, just like limiting, like figuring out what your limits for torque are in your motors so that you can. You can't like accidentally kill someone if like, you know, you, you know, the, the, the arm just moves way too fast. You have to like limit those things.
And so, yeah, definitely like, and I think the analogy to, you know, to knowledge work and online things is similar. It's like, you have to kind of, there are chain of thought monitoring will not be the only thing. safety layer in any for deploying models. We'll have to rely on other things too, probably.
Like I doubt there's going to be like one strategy or one method that makes models completely safe without anything else.
It's going to be like how a car has more than one safety feature.
Exactly. Yeah. Right. That makes sense. Well, how would this play out in practice if we deployed these systems at work? For instance, you talked about obfuscated reward hacking. So what happens when the models learn to hide their misbehavior? You kind of gave an example in the healthcare scenario, but perhaps we could expand on that.
Oh, yeah. So I guess there's, well, there's maybe two, I'll break it down into two things. There's kind of like, how, how would we actually use a monitor in practice? And then like, what are we what, what about obfuscation? Can models actually hide their thinking? So I'll try to answer that first one. And then I can go to that we can talk about the second one.
But yeah, so like, how would you actually do it? I think I mean, obviously, every company wants to probably get their latency down for giving users results as much as they can. But for complex knowledge work, people are already waiting quite a while to get the report back or their code base updated or so on.
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Chapter 7: What concerns exist around open-source AI and safety?
Whatever the product policy things are for, you know, in any labs product. We would have to then do that to the chain of thought because now we're giving them this new piece of this new thing. And, you know, if the chain of thought says something offensive or like actually reveals the how to do, you know, how to make the bomb or whatever the illicit thing is.
but decides not to tell the user that, well, we've just told the user that if we give them a chain of thought. And so then that would require us to put these style pressures on the chain of thought. And that would then maybe lead to obfuscation, which we can talk about in a sec. And then the other one is, I think, just like distillation risks.
So, you know, these chains of thought kind of give a lot of the reasoning of a model. And I think you I guess generally we don't try to just like give our model to other people to use.
Right. Because there's maybe been historical accounts of other companies using that information and then releasing products with it. That's right. Yeah. Yeah. Did you want to talk about obfuscation or what?
So. So, yeah. So this is like when you kind of in the intro, you were saying you mentioned that chain of thought could be fragile. And I think that is a big worry in the community right now.
And this goes back to our also our conversation of, you know, talking about like what, you know, is the chain of thought or sorry, is the model doing something instinctively or is it like actually thinking through it actively in the chain of thought? there are certain pressures or things that could happen while training that could kind of shift that balance probably.
And so one is, you know, this thing of like, if you train the model to never say something, think something bad, We showed in a paper earlier this last year that it can in some cases actually still do a bad thing, but not think the bad thoughts anymore.
We think it probably can only do this in cases where the chain of thought isn't actually necessary to do the bad thing, going back to this question of necessity.
of like of the chain of thought um but it still shows that you know there's actually some propensity for models to reason out loud even when they don't need to and you can kind of destroy that propensity with style pressures like that um and so yeah the model and then we call that obfuscation like the model is like obfuscating it's actually it's it's thinking um
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Chapter 8: How can monitoring improve AI transparency and safety?
So they wanted to work on it. And so at least to to me and I think many others. If you want to preserve this property, the first thing we had to do was be able to measure it. Because I guess if you think back on the conversation, I never said any kind of metric for how monitorable a model was or how to evaluate. It was all just vibe-based, basically.
You can't assess what you can't measure.
Exactly, yeah. So building evaluations where we could actually kind of start to quantify how... good or like, sorry, how often these models verbalize their thinking was the big push that we made in the second half of last year. Because again, yeah, to be able to preserve it, you have to be able to measure it. And so, yeah, we built this Sweet.
I think I would call it like a good starting point of evaluations. There's definitely much more work to do. And I hope the community kind of continues to build upon these and share evaluations with each other. We're working on open sourcing hours to share with them as well.
Um, but once you have the ability to quantify this, then you can start to actually make, you know, like interrogate different parts of your, of your training pipeline. You can interrogate different decisions of monitoring.
So like you said, like if I have a model that has a longer chain of thought, it's much, we found that it's actually, you know, generally it reveals more information in that longer chain of thought, and then it's easier to monitor it. Um, And so that was like one of the one example of a thing you can start to actually like make statements about once you have these evaluations.
Right. You also mentioned a monitorability tax where sometimes you might accept slightly worse performance to keep the AI systems more transparent. Is that something that you I think you're advocating for that, right?
I am not advocating. And I mean, probably like I would say probably I would advocate for that.
Yeah, sure.
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