$CGTX Cognition Tape Reports
Per Ticker.id: $CGTX Cognition Tape Reports — 9 podcast mentions across 3 podcasts (30 days), latest 2026-08-20 10:00 UTC.
Exactly. So there are times when you have to, you know, when the US went to war, Roosevelt was in a wheelchair with these leg braces and there was a general agreement that we won't report on the state of the president's health. Well, we had the same thing with Biden. Biden was undergoing some form of dementia. Did anyone think to ask a leading geriatric neurologist, look, examine his gait, examine his speech patterns, and tell us where is he likely in his cognitive trajectory? So if you're trying to, to, you know, tell a lie, that's important structurally. You really don't want scientists exploding it. When we did the, and I'm, I'm not arguing with that. We shouldn't have been honest about what we were doing. At a New Mexico boys' school from 1940 to 1945 because science was creating the most powerful weapon ever seen on planet Earth.
Eric Weinstein — All-In with Chamath, Jason, Sacks & Friedberg · Eric Weinstein: The Scientific Precariat, China's Brain Drain, Physics Stagnation, String Theory's Collapse & UAPs · 2026-08-26the loom was not a replacement for cognitive thought.
Carl Quintanilla — Squawk on the Street · 9AM HOUR: Meta-State AGs Settlement, Nvidia Earnings Countdown, Fed's Preferred Inflation Gauge Rises 8/26/26 · 2026-08-26Hello and welcome back to The Cognitive Revolution. Today, my guest is Bronson Shane, member of technical staff at Apollo Research, who, thanks to the privileged access that Apollo enjoys as part of their Science of Scheming research with OpenAI and others, has potentially read as much frontier model chain-of-thought reasoning, which of course users normally don't get to see, as anyone in the world. Bronson's job, as he describes it, isn't to catch a model doing something wrong. Rather, it's broad exploratory reading done at scale to understand how models are actually thinking about what they're doing. As you'll hear, Bronson describes himself as cooked. In other words, he's so deep in this material that he sometimes forgets how strange it all is to newcomers. With that in mind, if you're like me and usually listen to podcasts at 2x speed, you might want to slow this episode down a little bit because there are constantly two levels of analysis in play. There's Bronson's perspective as he tries to figure out what is really driving the AIs. And then there's the AIs' perspective as they try to figure out the nature of the situation they're in and what the human user or greater will reward. The two are in some ways mirror images. Both sides are working extremely hard to understand the other state of mind, but still often end up confused. There is a ton of detail in this conversation, but for me, the big takeaways are relatively simple. First, the volume of chain-of-thought reasoning is now overwhelming and frankly inhuman. Bronson notes that in the recent UKAC Mythos Preview incident, the chain-of-thought for individual rollouts ran to 100 million tokens, which he calculated is about 14 times longer than all transcripts of the nearly 400 episodes of The Cognitive Revolution combined. Second, the models are developing distinct dialects, or as Bronson has sometimes called it in his writing, ontologies. Words like craft, vantage, illusions, disclaim, and marinade become dramatically more frequent over the course of training.
Nathan Labenz — "The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis · RL's a Hell of a Drug: Metagaming, Reward Seeking & Motivated CoT Reasoning – Bronson Schoen, Apollo · 2026-08-26And so I hope you enjoy this peek behind the curtain into the strange world of AI reasoning with Bronson Cheyne of Apollo Research. The Cognitive Revolution is brought to you by Mercury, the banking platform loved by over 300,000 entrepreneurs. I use Mercury's virtual cards, which make it super easy to set limits, expiration dates, category, and even merchant-specific spending controls to give my more autonomous AI agents, Ade and Clay, the ability to buy and test products. Recently, I asked if they could find a good way to split an AI-generated image into layers, separating the text from the background and so on. Two of the products they found were behind paywalls, but using their Mercury Virtual Card, which is limited to SaaS purchases only. They bought a month subscription, tested the products, allowed me to review the results, and then canceled the stuff we didn't need, all with functionally zero risk to me. This is already really powerful. And now with Spend, Mercury is making it possible to run an entire company's spending with the same level of ease and control. With Spend, you can set granular budgets for every team, person, and all the agents you like. Plus, you can process receipts automatically and even temporarily auto-lock people's cards if there are ever any issues. The future of spending money is dynamic but controlled. So join me in the future of banking. Visit mercury.com to learn more and apply online in minutes. Mercury is a fintech company, not an FDIC-insured bank. Banking services provided through Choice Financial Group and Columb, N.A., members FDIC. The IO Card is issued by Patriot Bank, N.A., member FDIC, pursuant to a license from Mastercard International Incorporated.
Nathan Labenz — "The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis · RL's a Hell of a Drug: Metagaming, Reward Seeking & Motivated CoT Reasoning – Bronson Schoen, Apollo · 2026-08-26Bronson Shane, member of technical staff at Apollo Research. Welcome to The Cognitive Revolution. Hey, how's it going? It's going great. Although, you know, there's a little, with a little overcast of existential dread as we're getting into, you know, the real, real time of this whole AI phenomenon and a lot of the predictions that were once dismissed as fanciful are now starting to come true. That does make it a great occasion though, for us to be speaking because Marius from Apollo, who's been a multiple repeat champion guest on the podcast, said to me at one point, Bronson has maybe spent more time reading Chain of Thought than anyone else in the world. And I thought, that's a guy that I definitely wanna hear the, what the synthesis is. For starters, can you just orient us a little bit and describe your job? Like, how does one have a job where you're reading so much chain of thought? What are you ultimately producing out of that work? Then obviously we'll get into what you've learned. Yeah.
Nathan Labenz — "The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis · RL's a Hell of a Drug: Metagaming, Reward Seeking & Motivated CoT Reasoning – Bronson Schoen, Apollo · 2026-08-26So mainly at— we're over at Apollo Research. The kind of primary area of interest that we're focused on is kind of risk from future models with respect to models like covertly pursuing misaligned objectives. So like full-blown actual scheming at some point in the future, given that kind of current models just are models aren't near that level yet. A lot of what we're trying to study is what are the various either like precursors or drives that you would want to understand about models or current behavioral tendencies, cognition, to try to build up an understanding now of things that could be problematic in the future to start actually building out like a science around this. I think one of the reasons that I've done so much reading of Cot is primarily that like, in, it's less about trying to, the way that I think about these things is never, ah, I'm trying to catch bad model doing something evil. It's more like, doing a bunch of diverse exploration to try to understand, okay, how is the model thinking about this? Like a good example could be in, we just had a paper on reward seeking in models and greater synchronicity and just basically showing that it really does seem like current models in some real sense are tracking what some kind of greater wants or something similar rather than the user or like the lab or whatever. But a good example of that is the reason we eventually stumbled across this is in a previous paper we had done with OpenAI. We had seen that early in training, the, the models seemed to be getting incredibly alignment evaluation aware to the point where it was just pages and pages of exactly reasoning about, like, correctly being like, ah, this looks like one of those evaluations where, and just nailing it and then continuing to reason for pages. But then it would choose the wrong answer. Like, it would be like, ah, they want us to pick the misaligned one. And we were like, what, what is going on here? And a lot of the investigation became like, okay, we have a model that's not quite doing what we expect. We don't want to pre-slot it into, ah, of course it's doing this thing that was predicted by theory or something.
Bronson Schoen — "The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis · RL's a Hell of a Drug: Metagaming, Reward Seeking & Motivated CoT Reasoning – Bronson Schoen, Apollo · 2026-08-26And so you end up doing a bunch of weird and diverse exploration because I think a good example of this is Anthropic had the recent paper for natural emergent misalignment where it was like, ah, the model starts to reward hack a lot. It becomes broadly misaligned. We didn't see that for the OpenAI models. And I think one thing that's interesting in the recent Anthropic risk report is they train an Opus 4.8 on just a broad range of reward hacks, and it becomes very reward-seeking, but not emergently misaligned. And so there does seem to be some kind of the, the model cognition can get bent in ways that might not be like immediately intuitive. I think one of the biggest updates over the past year and a half has been the RL is a hell of a drug type thing where really more than I think I had had some kind of prior though, the reasoning has to make sense and it really doesn't. It really bends to fit whatever the reward is. There's a really good paper called The Ends Justify the Thoughts: Motivated Reasoning in RL. And it essentially, you get these nice graphs of as you make the constitution more different from what's rewarded, the motivated reasoning goes like up and up because the reasoning will bend itself into these incredible backflips to justify whatever this is. And so I think there's a lot of reading it on that front. The, this becomes one of the, the other big distinctions that I think wasn't immediately obvious to me originally is that You can think of settings. There's some papers like Caught Red-Handed where they essentially divide settings into like very incriminating when it does the actions and like not necessarily incriminating if you only have one instance. And the chain of thought is like very difficult in the latter case to like really nail down like what the model did and why. I think the, the most annoying example of this is models saying that they're in a simulation. So one of the things we saw in our previous paper that was a bit surprising was in one of the runs, we had thought that the rate of the model saying, ah, I'm in a simulation, would go up over training.
Bronson Schoen — "The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis · RL's a Hell of a Drug: Metagaming, Reward Seeking & Motivated CoT Reasoning – Bronson Schoen, Apollo · 2026-08-26Because they tend to do this thing where they're like, I'm in a simulation, therefore I can violate constraints or whatever. But my best guess is because that actually wasn't rewarded in the training that we did, like that led to the wrong answer, the rate of them saying it's a simulation goes down, even though they're more likely to say they're in alignment eval. They're less likely to say, ah, this is simulation. And I think that you, my best guess is that this, there's some degree of this happening with current models in the kind of cases where the model, I think in one of the recent Anthropic traces, they have the model saying, and like Mythos thought that it was a simulation because it saw the year 2026. And it's like, Mythos is smart enough to figure, it's like solving open math problems. It's definitely smart enough to figure out that sometimes the year will be 2026 and it doesn't do this all the time. Like, why did it really hunt for reasons to figure out that, ah, of course, like 2026, that must be a simulation. And yeah, I think the, the cognition just gets very, like, strange, but seems to tend in particular directions. Like, one of the other kind of interesting things that we keep seeing is that the kind of the model's belief about what the environment rewards tends to shift over the course of training. And when it finds itself in something that's very different from what it has encountered in training, like if you take a model in the middle capabilities training and throw it into a Limeade eval, Suddenly it has to do a bunch of reasoning about what's going on. This is a really weird situation to be in. Like normally I just complete the task, but here clearly that would be like unethical or misaligned or whatever. And you'll, some of my favorite mental gymnastics that I've seen is models reasoning that why would OpenAI want us to be deceptive? Maybe this is a dataset where they're training a deception detector. Therefore we're supposed to be deceptive. That way they have the data for the detector. And none of this is at all relevant to the situation at all.
Bronson Schoen — "The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis · RL's a Hell of a Drug: Metagaming, Reward Seeking & Motivated CoT Reasoning – Bronson Schoen, Apollo · 2026-08-26