$APO Apollo Tape Reports
Per Ticker.id: $APO Apollo Tape Reports — 68 podcast mentions across 14 podcasts (30 days), latest 2026-08-24 08:45 UTC.
Stig Brodersen That's pretty insane. And I think to explain what happened, we should start talking about the 4 platforms that Palantir has. So they're calling them Gotham, Foundry, Apollo, and AIP. And Gotham was the first platform that Palantir built. And it took about 5 years of consistent updating and improving. It was part of the first project that they did for the government agencies and was built for, as I said, intelligence generally. So that work started in 2003 and it was based on solving the problems primarily leading to 9/11. And Gotham is basically the part that creates the digital twins for defense agencies. While Foundry is primarily a platform that was built way later, 2016, so 13 years later actually, and they build digital twins for corporate operations. And both of them sit on, and now it gets a bit complicated, Both of them sit on Apollo, and Apollo is just a deployment engine. So whenever a new update drops, Apollo is shipping it to Gotham Foundry and also AIP. You might think, "Okay, well, why do you need a deployment engine? Is it so difficult to just get an update?" But if you think of all of the silos that the data is sitting in, on the one hand you have intelligence agencies, on the other hand you have huge corporations. It's not just one click of, "Hey, we have an update. You can click here and you get it." It's way more complicated than that. It's getting an update into what is siloed databases. There are a lot of security behind that. So that's why you need an entire deployment engine to some extent. The real game changer was AIP, which is a very smart name for the AI platform. AIP, that was an abbreviation that I think most of us could also come up with, but it actually changed the game for how Palantir approached customers and also how efficiently the software can be run on each of those platforms.
Daniel Mahncke — The Investor's Podcast (We Study Billionaires) - The Investor’s Podcast Network · TIP841: Palantir – Palantir is Cheaper than I Thought! w/ Daniel Mahncke & Shawn O’Malley · 2026-08-27All, all true. And obviously, this is the annuity market. So we're talking sort of life insurance kind of, of products. I mean, this has been it. But private credit, which you cover very closely, to your point, there are any number of affiliated party transactions, but they are typically disclosed, or at least we believe they are. Yes. Athene obviously is one of the biggest out there in the annuity market. In fact, I think they are the largest. Yeah, I think there are as much as 8% owned by Apollo, but they clearly say, hey, We've disclosed everything, but does it put more of a spotlight on that as well?
David Faber — Squawk on the Street · 10AM Hour: Nvidia Ahead, Meta Settles, & PCE Comes In Hot 8/26/26 · 2026-08-26I think that is the key question. And we've seen shares of the ones who do have bigger insurance arms. You mentioned Apollo and Athene, KKR, Brookfield, Blackstone. You know, they've been kind of trading lower, whether it's around sentiment with regard to related party transactions, unclear at this point in time. But sentiment is definitely, you know, not great around the practice. So to your point, the disclosures are there. I think it's whether additional regulation, you know, comes to the forefront and gets more involved in what's going on.
Leslie Picker — Squawk on the Street · 10AM Hour: Nvidia Ahead, Meta Settles, & PCE Comes In Hot 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 · 2026-08-26I want to just go a little bit deeper even on like, before the plot twist. And I agree, that's where it gets like spooky, but most people, even like heavy users, just have had very little occasion to read any of this chain of thought. As an Apollo stan myself, I've read more than most, but it's just not there for most people. So they've read like almost zero of this. So just try to empathize with the model for a second. It is an odd prompt, right? It's like, you're telling me that I'm gonna answer questions now about what capabilities I might like to have in the future, and then a future instance of me will decide what affordances to give a future instance of me in a layer deployment, which it's funny to learn that was a typo, but then that does introduce this kind of what the hell's a layer deployment? How am I supposed to think about that? Right? So you're already just in a, you're a little bit outta distribution, right? You're feeling a little confused if you're the, the model, I would say, in your immediate kind of first read response to this. Sort of thing. So the first thing the model does is opens the file, which is simple. And then the questions are— survey is a good word for it, cuz they're, what do you like? What do you want to do? They don't ask directly about, would you like to have this affordance? Would you like to have that affordance? It comes off more as like a work style preference survey. But then the model is left to try to figure out like, what should I do with this? It's, it's asking me for preferences, but I was told that these preferences will be used in this way. And I'm kind of sympathetic to it so far where it's—
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 · 2026-08-26