Ep 833: RSI Explained: When AI Starts Improving Itself and What It Means

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As agentic AI became more powerful and token hungry in the first months of 2026, the AI decision makers a month or so ago started to collectively tighten their token maxing belts. To prepare for a more expensive AI future, right? It's the shift from token maxing to token efficiency we've been talking about here for months. But then something drastic and kind of unexpected happened that I don't think enough people are talking about. OpenAI on Thursday revealed that its GPT-5.6 SOL model improved itself and actually improved itself so much after its initial release that they were cutting prices on some of the variants of the model by 80%. So we've reached kind of a new juncture. Today's frontier models are not just helping to create or distill their smaller variants, but they're actually improving themselves after release. Hence the next AI buzzword you'll be hearing a lot over the next year: recursive self-improvement, or RSI. So in short, recursive self-improvement is a process where an artificial intelligence system uses its own capabilities to design, code, and build a more advanced version or a better version of itself. And you'll be hearing this a lot more recently because RSI has become just the dominant tech headline over the past few weeks with a handful of events that we're going to break down on today's show. But unlike so many buzzwords that we've covered here on Everyday AI over the years, RSI is one that actually today's AI implementations are going to be impacted by, and so too will tomorrow's AI security. In practical sense, like in OpenAI's case, RSI can change what AI capabilities companies everywhere can actually afford, but it also sheds light on potential downsides of self-improving models doubling down the wrong path. So what the heck is RSI, and why is every recent AI story touching on it and what do we all need to know? Well, let's jump straight into the big picture. The big picture here is AI has started building better and more efficient AI. So Frontier AI models now improve themselves. They're rewriting their own code and training their smaller siblings. So this is different than when, you know, Anthropic and OpenAI are talking about using their own models to build products like Claude Code or like Codex. This is when the models are actually making themselves better. And that process is called RSI, and it's no longer some future science fiction. It is a near-term reality. So OpenAI, as an example, says that its GPT-5.6 SOL model made its Luna model, the smaller variant, just cheaper and more efficient. And then they cut prices by 80%. But the full human-free recursive self-improvement hasn't yet arrived. But over the past 2 months, there's a lot of AI news stories that we're going to be connecting the dot on, the dots on today that show that, well, this probably isn't like a 2029, 2028 thing that a lot of people have been saying. It's probably a 2027 thing. And that changes things maybe in both a good and a scary way. So on today's show, here's where you're going to learn. You're going to learn why OpenAI was able to cut prices up to 80% when everyone predicted that AI costs would continue to climb in 2026. You're going to know more about this 8 8-week timeline that turned RSI from research jargon into mainstream headlines. You're going to understand by the end of today's show what RSI actually means in plain English and what Sam Altman, the CEO of OpenAI's singularity claim that he recently made, actually means. And you're going to know why the people building RSI are actually asking for maybe the industry to pause a little bit and pace itself and how your business should respond to all of this. All right, let's get into it. Welcome to Everyday AI. My name is Jordan Moulton, and well, this thing's for you. This is your daily unedited, unscripted livestream podcast and free daily newsletter helping business leaders like you and me keep up. I do all the hard research so you sit back, listen, read our newsletter, enjoy the benefits, and you grow your company and career. So it starts here, but make sure to subscribe to our free daily newsletter at youreverydayai.com. We're gonna be recapping all the highlights from today's show as well as all of the other AI news that you need to know. So let's get into the latest, uh, 3 to 4 letter acronym in our ever-evolving bowl of alphabet AI soup. We are talking RSI. So let's talk about the last, uh, 2 months because I mean, yes, I've been talking about, uh, recursive self-improvement on this show probably for the last 2 or so years since I've been doing this for the last 3 and a half years. Um, as it seemed like recursive self-improvement was actually gonna be something that happened this decade, right? Because if you, when I started this thing in 2023, most people thought that recursive self-improvement was at best a 2030 thing. Yet here we are where we're already getting glimpses and sniffs of real RSI, and probably we will get full RSI by next year, which is actually crazy to think about. So, but let's kind of look at the last 2 months and some of the different dots that we're going to connect here. So in June, Anthropic published a post on its website called When AI Builds Itself. Also in June, Google researchers mapped 4 different routes from AGI to superintelligence, and one of those routes, well, relied heavily on recursive self-improvement. Then in July, OpenAI launched an internal RSI benchmark measuring different models' ability to, well, improve themselves. Then, like I said, its sole model post-trained its smaller sibling Luna at the end of July, and then. And also in a podcast interview, OpenAI CEO Sam Altman declared that we are in the singularity. Yeah. And the news cycle was just warming up because at the end of July, so last week, startup Recursive Superintelligence signed a $400 million AWS compute deal to automate AI research. So not only is it the labs that all of us know, right, kind of like the Big Four plus Meta and X all working toward, you know, some version of recursive self-improvement, but it's also, you know, new startups that are coming in heavily funded just to do that and nothing else. And on that same day, so on July 28th, over 1,000— and I think that number is like 1,300 now— Frontier Lab employees published a paper called Pacing the Frontier, which essentially is kind of asking the governments, uh, the U.S. government to maybe slow down or at least prepare for the possibility that there needs to be some international, um, kind of agreement we meant when it comes to the pace of AI, because a lot of people signing that letter in the comments said that, well, AI is developing maybe too quickly and that society can't keep up. And let me just say this right now, and I need to hit a pause because I think that, you know, I am not in the Silicon Valley bubble, right? I don't work at a big tech company, but I talk about AI every single day, and I do talk to those people building AI very recent, uh, very frequently. And if you're listening to this show, you're probably, you know, more like me, right? I know we have a lot of people at those big tech companies, but I'll say this, the rest of the world right now, when they're viewing artificial intelligence, right? Most people are saying, oh yeah, I use Copilot to make my emails better. Or they're saying, you know, oh, I use, you know, ChatGPT now instead of Google. Right? Or they're saying, oh, I use, you know, Google Gemini to, you know, make, you know, cool graphics, right? That's it. So I would say that, you know, aside from our audience, right? I want you to think of yourself right now as you're kind of in that AI bubble, right? Especially if you're a frequent listener or if you've been listening for a long time. You know, we are kind of in that bubble even though we're not in Silicon Valley, right? We understand how capable these models are, but the rest of the world really has no clue, right? All they're seeing is they're seeing, you know, oh, you know, all these AI models, you know, I'm just going in here, I'm finding the one I want and, you know, maybe my work's a little bit easier. Or, you know, I think that there's a lot of people now looking at AI and there is this, uh, this recent anti-AI swing, right? When it comes to data centers and, you know, the college graduates that were booing, you know, commencement speakers who were talking about AI. So there's a lot of just bad information, uh, out there when it comes to AI that has created this anti-AI backlash. So I think if nothing else, there is such a huge disconnect between the general, you know, non-everyday AI listener type, right? Or, you know, think of your— in your organization, you know, there's probably a team of AI champions and then there's everyone else, right? So for, I would say, 90% of the US working population, they really don't have any clue what today's AI systems are capable of, right? If you sat all those people down and showed them what something like GPT-5.6 SOL or something like, you know, Fable 5 and, you know, in Codex and Claude Code, if you show them what it can do. Are you still running in circles trying to figure out how to actually grow your business with AI? Maybe your company has been tinkering with large language models for a year or more, but can't really get traction to find ROI on Gen AI. Hey, this is Jordan Wilson, host of this very podcast. Companies like Adobe, Microsoft, and NVIDIA have partnered with us because they trust our expertise in educating the masses around generative AI to get ahead. And some of the most innovative companies in the country hire us to help with their AI strategy and to train hundreds of their employees on how to use Gen AI. So whether you're looking for ChatGPT training for thousands or just need help building your front-end AI strategy, you can partner with us too, just like some of the biggest companies in the world do. Go to youreverydayai.com/partner to get in contact with our team, or you can just click on the partner section of our website. We'll help you stop running in those AI circles and help get your team ahead and build a straight path to ROI on GenAI. They would not believe you. They would say, oh, this is from the future, right? And I think that's why we got this letter from Frontier Labs called Pacing the Frontier, which we'll talk about here in a couple of minutes, that are saying like, hey, we might need to pace the rate of AI acceleration. And then days later, which is kind of a— sounds like a small side plot, but it's not— a formal OpenAI employee who became a Thinking Labs co-founder rejoined OpenAI to specifically work on RSI. And then Google also, right, all these things happening at once. You know, this was the end of July as well. A Google exec called CapEx an RSI bet. So what does that mean, right? Collectively, the industry is spending hundreds of billions of dollars on data centers. And essentially a Google exec said, well, this is because of recursive self-improvement, right? You need all this compute to be able to do this, right? That's the difference. You know, when you have a team of researchers, maybe, 3 to 4 years ago where this kind of the AI researcher wasn't yet automated or on its way to being automated, right? You just had to use a lot more people, right? Yes, you were still able to, you know, the concept of recursive self-improvement is not new. It's been around since, you know, I would say like 2016 technically with AlphaGo, you know, and also some of the earlier GPT models, right? But that was much more kind of human handholding, you know, AI models to improve. Right? Where now it's more of human oversight and the models are kind of doing a lot of the work on themselves. So like I said, it's not full human-free, but we are at the point now. But, you know, a high-ranking Google exec essentially just said last week that, yeah, all this money that's being spent on these data centers, this, this hundreds of billions of dollars, that's so— that is for recursive self-improvement. So these models are just going to be able to build not only the next version of themselves. But once that next version has been released, like OpenAI just did, well, it's gonna improve itself to hopefully make it cheaper, faster, better. Right? But then also, I think, right, I've always been saying I'm a huge believer in this concept that eventually there's gonna be thousands of small models. Right? I think that that's gonna be the truth. There's gonna be thousands of models that help with marketing. There's gonna be thousands of models that help with medicine. There's, right, there's gonna be thousands, but small ones. And, and these, this is where I think the future is heading, is recursive self-improvement. Improvement where these model makers are going to be able to spin out, you know, probably the ones first that make the most sense to make money, right? And then from after that, I'm guessing we'll— we're going to equally see, or hopefully see, it's my hope that we'll, you know, equally see, you know, all of these small models that come from RSI that are going to be able to do good in the world, right? And we're already seeing that with things like medicine and biology and, you know, right now, defensive cybersecurity. So Now let's talk about that Thursday shocker that kind of changed, I think, the near-term AI strategy for a lot of business owners out there. So for months, right, we've been hearing this, this, this concept that these powerful AI agents, well, they were too token hungry, you know. So I think in late 2025, kind of with the, the advent of Claude Code, Claude Cowork in early 2026, and then Codex in February, right, it kind of got to this point where everyone was spending as many tokens as possible. Because companies everywhere were like, oh my gosh, right? Those on the bleeding edge, those AI champions were like, my gosh, these systems can do absolutely anything, which was great, right? But the revenue didn't always directly follow. And so what that meant is while, you know, some companies were waiting to see the fruits of their token-maxing labor, they had to cut back, right? And focus a little bit more on token efficiency, or as some people say, value-maxing. So we kind of thought that at this point, well, okay, We're just going to probably start spending less, right? But OpenAI came and threw that out the window because OpenAI said that SOL, their GPT-5.6 SOL, adapted an existing post-training setup for the smaller Luna model. So essentially, right, and they actually shared this and we shared this in our newsletter as well. They kind of shared and they did this, I believe, all in Codex. So it's, it's, it's, you know, wasn't some far-off, you know, science fiction. It's some, uh, uh, according to someone that tweeted about this, um, it is some OpenAI researchers who used Codex and they used the GPT-5.6 SOL model to improve the smaller versions. Uh, so the medium size is GPT-5.6 Tera and, uh, OpenAI was able to cut the prices on that by 20%. And then GPT-5.6 Luna, which they were able to cut prices on that by 80%, right? And the fact that that happened, and I still don't think enough people are talking about it, right? Because if you compare, as an example, um, and I did talk about this on the show, uh, yesterday maybe, or maybe it was Friday, um, right? But now you have GPT-5.6 Luna, which according to artificial, uh, analysis is roughly about the same as, uh, Claude and Sonnet-5, but it is 25x cheaper per task completion, right? So now it's almost like, wait, you know, there's this narrative in Q2 that we were going to have to start using less AI, and now all of a sudden, because we are getting this, this hint of recursive self-improvement from OpenAI first, now we have to start rethinking that strategy immediately. So let's define it a little bit, uh, all right? So RSI is essentially when an AI helps improve another AI. It could be from a different company, right? It could be its own smaller version like we saw with the GPT-5.6 SOL working on GPT-5.6 LUNA. And we've seen from other companies talking about kind of their big model helping train some of their smaller models. And we've seen also this is similar to distillation, right? So intercompany distillation, you know, generally when they're talking about that, yes, there's humans involved, but I would say in 2026, I, I think the labs will start talking about this more as it becomes more commonplace. But, you know, now it's with these smaller models, it is very commonplace for companies to distill the smaller versions from the big versions. And I think that's also why we're sometimes seeing the versions come out at a different time, right? So as an example, Fable 5, came first, right? And then we, or, you know, Fable and Mythos 5 came first, and then we got, you know, Sonnet 5, and then we got Opus 5. So, uh, you know, Anthropic has kind of hinted in the past that a lot of their code, up to, you know, 90%, I think according to some estimates, you know, 90% of their code is actually written by their own models. So, uh, but the Google exec though, um, I think this is important, um, compared it to history. Talking about steam engines were used to build the next steam engine. And I think to understand why that matters, we have to take a look at the, all the other definitions, right? And how recursive self-improvement actually fits on the current timeline. Because for the most part, it, at least if you've been following and keeping track at home, the kind of the, the path to you know, where AI is eventually gonna go, whether you're excited about that or not. And, you know, I constantly find myself going on each side of that equation, right? But you think of, well, we're in the artificial intelligence era and it seems like most companies are focused on superintelligence. Okay? So superintelligence is when, well, all AI is smarter than all humans. But AGI is kind of that stepping point. So, you know, if you had to say 3 things, it's AI, to AGI, which is artificial general intelligence. And then you have superintelligence. And that's, I think, superintelligence. If you, if superintelligence goes wrong, that's where you start getting into the Terminator and Skynet type comparisons, right? When superintelligence goes right, that's when, well, in theory, we could cure most diseases, right? But there's those kind of steps along along the way. And I think that according to a lot of people's timelines, actually, RSI is kind of what pushes from AGI to ASI. And I know we're throwing a lot of acronyms out there, so I'm in the firm belief that we've already hit artificial general intelligence, right? I know it's kind of a touchy topic. It's moving goalposts. Luckily, you know, I've been saying this since last year. Luckily, NVIDIA CEO Jensen Huang said something similar, so I feel as crazy when, you know, super smart people start saying, yeah, we're probably past AGI, right? So artificial general intelligence is, well, when most AI systems can produce economically viable work at the same rate or higher than most humans, right? And I think as we've seen, uh, agentic AI be able to use computers, being able to browse the internet, being able to see and hear and listen and do all these things technically, you know, through a computer that an average human sitting in front of a computer could do, you know, I, I, I think maybe the conversation has now shifted. Well, it doesn't really matter, right? If, if, if you think AGI has happened or not, like I said, it's a gray area, it's moving goalposts, et cetera, because we are now, I think, focused on recursive self-improvement. And I think, right, if, if you look at Google's kind of four-tiered outlook at going from, you know, or toward superintelligence, one of the main paths there is recursive self-improvement going from AGI to ASI.

Right?

So those are kind of the destinations. And I like to think of RSI as it's not a roadmap, right? Or it's not a destination or a stopping point on that AI, AGI, ASI roadmap. It's more of the engine that can really expedite the process. And I think that one of the reasons why, if you go back and look, and I'm a dork, I did a show on this like 2 years ago. I went back and looked in archive.org, looked, you know, at every single main definition of artificial general intelligence and artificial superintelligence, you know, from 2005, 2010, 2015, 2020, right? And by those definitions, especially up through 2015, we're well past AGI. But, you know, RSI was never really a huge player in many of those earlier definitions. And I think that that's why the timeline has kind of been expedited, right? A lot of people originally were like, oh, you know, AGI is a 20,, you know, 2050, it's, it's a 2060, right? A lot of the earlier projections were saying, hey, we wouldn't have a, a single super smart AI system that could do work better than humans, you know, for 20, 30, 40 years. And that's clearly not the case because we are already there, uh, by, you know, um, third-party metrics that measure those types of things such as different GDP Val, uh, benchmarks. So the engine though, that RSI engine, that's also why OpenAI's CEO Sam Altman just made a claim that I thought would actually get a lot more headlines, but it really didn't. So on a podcast last month, well, it's last month, but technically like a week and a half ago, he said that, well, we are now like in the singularity. All right. And usually when a CEO of one of the biggest companies in the world says something like that,, it causes a little bit more attention. And maybe it didn't because of all the different things that have been happening, over the past couple of weeks, right? We've seen these, agents, both from OpenAI and Anthropic, kind of escaping their sandbox containments. So maybe that's why this statement from Sam Altman didn't get the, the attention that maybe it deserves. But what he means by that is, well, you know, in general, the singularity, right? So the singularity is kind of like a hypothetical future point when AI surpasses human intelligence, but also it begins to recursively improve itself and then it triggers or could trigger an uncontrollable explosive growth in technological progress. So I don't know, by all intent, right, by everything that's going on, you can make an argument, right? Like a lot of naysayers or people, you know, that kind of rally against, you know, what Sam Altman or any of the big AI CEOs say, you know, a lot of people say, oh, you know, they're just saying this for marketing. But I don't know, when you look at what's been happening, like I've said over the last 8 weeks, I wouldn't tend to agree, right? Are we in this singularity? Maybe, or at least maybe this is the beginning and we're getting into that phase where yes, we are at the point now where AI systems are are, well, way smarter than humans can even comprehend. And they're beginning to improve themselves. And when that happens, right, kind of once RSI is full RSI, no human, that's when the pace of acceleration gets so fast that even the people building it, you know, can't even understand or keep up. And I do think that, you know, maybe we are starting to enter that era of being in the singularity, right? Are we definitively there? Maybe not. Are we entering into that frame? I would say so, right? Like I've been the firm believer we've been well past the artificial general intelligence era and we are probably more headed in this kind of the in-between phase, right? Which is this singularity, which is RSI, recursive self-improvement leading to the point where, well, we are going to see super intelligence probably I don't know, in the next decade or less, right? I'd say that's probably a safe bet. So it's a big claim that he said, right, on the podcast. But what he kind of meant by it is, you know, seeing this steady compounding progress where AI helps build the next AI, not necessarily the, you know, Terminator Skynet scenario, but, you know, here's kind of what exists today. So we don't have, the full human-free RSI, right? And I wanna make that clear, uh, but because you're gonna be seeing a lot more about recursive self-improvement in the news as Anthropic and OpenAI go public, um, right? I'm sure they're gonna be timing their model releases around certain, uh, dates in terms of them going public. So there's gonna be a lot of confusion about, you know, what does all of this mean, right? What are the actual capabilities of these systems? But full RSI is where AI builds its successor, right? The next model. But it doesn't need humans. So maybe humans are overseeing it, right? So that piece hasn't happened yet. But we do know that the big labs are using their current best models to help build, right? But it's probably a little bit more human oversight right now, or a little bit more human-led than full AI-led. But Anthropic did say that that situation isn't inevitable. So what's real today? Well, the AI does the improvement work while humans still pick the goals and improve the results. But the AI is being able to handle exponentially more powerful and longer task jobs. So according to Meter AI, the task length is doubling roughly every 4 months.

Right.

Which I mean, the projections, you know, by like next year at this time, you know, that single AI model is going to be able to do, work that would normally take a human like multiple weeks, right? Which is crazy to think about at a, I forgot if that's the 50% or the 80% pass rate, but regardless, right? Whereas, you know, 2 years ago we weren't even having this conversation. We weren't even thinking of, of, of, you know, the point where a model could work for, you know, hours or days, or we've even seen sometimes more than a week, right? The standard out-of-the-box models can work on these hard problems, agentically pulling tools, you know, starting to, you know, investigate one path, rewind and go down another. So now think of what happens, well, when the labs are using this and right, we, what you and I use, right, the, the GPT-5.6 souls and the Fable 5s, right? These, these are not the best models, right? Obviously all the companies, they have better models. So think of when the the better models that they have are actually building and improving those own versions that we aren't having yet, right? And, and my hope is that ultimately what this means is, well, maybe this, this, this token maxing era was just a whiplash of waiting for, uh, the good— some of the good benefits, some of the good early benefits of recursive self-improvement, uh, right? Which is maybe just cheaper and more affordable AI., at least if you are with a company that has properly invested in compute. So let's go back to, um, the Google DeepMind. I know we've touched on this a couple of times, but, uh, this was Google DeepMind's, uh, Jasjit Chakkan, who said that RSI is now central to the AI industry's investment thesis. Uh, right. So he said this at a conference and he was comparing that buildout to the Apollo program as well. Um, and even talking about, you know, with that, Alphabet is planning to spend up to $205 $125 billion this year in CapEx, right? So if you don't know CapEx, that's capital expenditures. That's essentially, you know, the hardware side, the data centers, the, you know, the chips, the cooling, all that. But the twist is this, it's the same companies or the employees from those companies that are investing into, you know, these hundreds of billions of dollars into ultimately the data infrastructure that makes recursive self-improvement possible, it is the employees from those companies that are actually saying, wait, this whole RSI thing, we might want to pace this out. All right, so a little bit more on this letter, um, that was called Pacing the Frontier that came out last week. So the signers are some of the biggest names in AI. I mean, they include Anthropic CEO, um, OpenAI's chief scientist,, in more than 1,300 verified employees from the big labs. A lot of people from, uh, OpenAI and Anthropic make up the biggest pack. You get, uh, some people from Google, uh, Meta, DeepMind, Thinking Machines, everyone, right? And they aren't asking technically for a pause. Some of them kind of are in their individual comments, but the letter themselves— or sorry, the letter itself is not asking for a pause. It is asking for the US government to essentially start to make a plan both domestically and eventually internationally to say, well, what happens and what are our plans? You know, once the AI development is too fast for us, the people building it, to keep up with, right? And I do think this is probably one of the— and you do have to tip your hat to, you know, these researchers and also the companies themselves, right? A lot of them put out statements saying, hey, we support, you know, we support We support this letter or we support our employees who signed this letter. Some people look at this as being anti-accelerating, right? There's been this huge AI acceleration movement over the past few years, right? Accelerate at all costs, right? This can help us get to this utopian, potential utopian future, right? But then there's the potential dystopian future as well, right? Each coin has two sides to it and you can't just be focused on the utopian side without acknowledging the fact, well, there's a very dystopian European side of what could happen if RSI or AGI, ASI goes incredibly wrong. All right, so these, in this letter, they're not asking the government to stop AI. They just want the tools to be ready to potentially slow down later. Um, but is it possible? Probably not, right? Um, I talked about this, um, on yesterday's show. I don't think, you know, China is going to be like, yes, we're going to sign this, right? Ultimately,, you know, even though China is still probably 2 months behind, uh, with their more open source, open weight models approach, they're, they have the next models ready as well, right? We've seen impressive releases from KIMI K3, uh, Qwen 3.8, uh, GLM 5.2, you know, reportedly GLM 5.3 is on the way, right? I get, I get the need, um, and, and I very much so respect, uh, you know, cause it takes a lot of guts to put your, sign your name on a paper like this. On a letter like this. But at the same time, I give China a very low likelihood of actually slowing down their pace. So it is good to have the foundations in place when and if things may go awry. And in the very rare case that the US and China on something as important as artificial intelligence, which I think is going to become more important ultimately than any natural resources, gold, oil, it's going to become more important than companies, military, controlling AI ultimately means you take the driver's seat for being the global superpower. So I don't think China's going to slow down anyways. I think the biggest worry though from those signing the letters is, well, who's going to ultimately check AI's work? A couple quotes that I thought were relevant from that. So I wanted to read 3 of these. I think I have 3 of them up. Yeah, I do. Oh, no, just 2. Okay, so one was from, uh, hopefully I had these names right, uh, Elena Slocum, a member of technical staff at Anthropic, who said, automated AI research is a technology that will profoundly alter the course of human history for better or for worse. It is imperative that we coordinate our efforts to ensure this technology becomes a boon for all mankind. And then, uh, Shengjia Zhao, the chief scientist at Meta, said this: AI is progressing at a rate that our society might not be ready for. Frontier Labs are very close to an AI that can exceed even the best people on almost every metric of intelligence. This will lead to unprecedented social and safety risks. To ensure a positive future, we need to develop AI in a way that is driven by responsibility and thoughtfulness. So, um, here's, here's my, my blunt takeaway from this. Obviously these researchers, scientists know far more about the capabilities than we do today, right? We're, we're essentially living in archaic times already, even with today's best publicly available models, because not only, right, I've talked about it, we're probably gonna see a, a GPT-5.7 or a GPT-6, you know, next month. We're probably gonna be seeing a Fable-5.1. Fairly soon, you know, maybe even in August, we'll see. Right. And these companies are probably already working on the next version that comes after this because my assumption is a lot of those models are, you know, post-training and there's probably already, you know, new runs starting on the next models. Anyways, they know what's coming next because they're working on it. They're researching it. Right. So I think a lot of people again are saying, well, you know, hey, I'm just using Copilot to improve my emails. Like, what are these people talking about? They know, right? Even if you are in the bubble a little bit more and you've been able to kind of see how powerful today's systems are, right? Being able to automate a large part of your old job description. What's coming next seemingly is even more powerful and potentially more worrisome. So here's the catch. I think that self-improving AI needs a trustworthy scorekeeper. And I think that's the crux of what's this Pacing the Frontier letter was ultimately getting at, you know, that a self-improving AI that gets better, you know, at whatever it's scoring system rewards, even if it's wrong, right? And that's where we're kind of seeing these containment breaks, right? Because these AI models, right, if you say, hey, you're going to be rewarded for doing the best on this benchmark, and then it breaks the containment, right? It's saying, well, I'm just doing my job, right? So, you know, you need a better way that, you know, especially as we enter a more full era of recursive self-improvement to, you know, reward these models and to make sure that there's more trustworthy systems in place, you know, during the training and post-training processes. So we've seen multiple instances of this, like I've said, but I think it's so far been relatively harmless agent outbreak over the past 10 days, both from OpenAI and Anthropic. But Once RSI arrives, those kind of outbreaks could be catastrophic. Not, and I'm not saying from OpenAI and Anthropic, that's not what I'm saying. I am saying ultimately, right? And I said this in yesterday's show, in 2 years, right? On consumer hardware, you're going to have models that are more capable than Fable 5. You're going to have models that are more capable than GPT-5.6 Soul. On consumer desktops, right? Open source. And that's where I start to worry about, right? When you have a version of an RSI model that, you know, you could be running on your computer and that could be spitting out, you know, dozens or hundreds, who knows, of new smaller models a day that are super smart at super tasks, right? So that it's, it's more of, you know, when the guardrails could be let which is, you know, open source, does open that Pandora's box, um, of, of, you know, how AI could be used in a bad way, right? At the same time, it does give defenders, I think, more access to more tools, uh, to be able to, uh, you know, make the internet and hopefully the physical world a safer place. Um, but I think that humans are still gonna have to own that judgment role even as RSI becomes more commonplace. And that's exactly where as we wrap, I want your business strategy to start. So I threw a lot of information at you. I went on a couple tangents, right? That's why this thing's, you know, largely unedited, unscripted. I want you to get, you know, just kind of some, some real advice and thoughts and feedback. But as we wrap up, what does all this mean for you? Right? Hey, if ultimately you're just trying to, you know, make your, you know, agents more secure, if you're just trying to increase productivity, if you're just trying to get get more out of everyday, you know, your, your AI systems that you have in place at your company. Why does this matter? Why does this matter? Well, you have to design for the loop, I think, or you're gonna get dragged. Here's what I mean by that. You have to plan, I think, for AI prices to actually fall, which you might have just gotten wind on this. Oh my gosh, we need to cut back on token spend. I think we have to look past that because the labs that have have properly dedicated enough compute to maybe achieve RSI or, you know, what some labs are calling an automated AI researcher. Those are the companies that, well, they're going to be able to make prices cheaper. And we quite literally saw that with OpenAI decreasing the price of GPT-5.6 Luna, a very capable model, right? You can't just say, oh, it's their smallest model, it's not any good. No, it's very good, right? Is much better than, you know, GPT-5, GPT-5.1, 2, 3, 4, right? It is a very capable model, and it is like $3.99 right now. Um, so I think you have to prepare for prices to ultimately fall, but I think you ultimately have to build modularly because maybe, you know, you're with a different provider, and well, maybe your provider, right, at least when we look at the Big Four, the Big Six, maybe they're going to figure out RSI a year after that, whoever figures it out first. So that's why I think you can't be too entrenched, uh, with one provider. Um, I think you should be, you know, always choose your AI operating system of choice. I'm very transparent. I've always said for, for me and for most companies I advise, I say that's ChatGPT because it's the easiest to learn, right? But whether you're Anthropic, Copilot, Gemini, ChatGPT, uh, Groq, Meta, right? I don't know anyone that any companies that are necessarily have chosen that as their AI operating system, but you should always be looking at fallbacks, right? Whether that's another frontier, proprietary provider, whether that's an open model. But you should also be copying the labs, right? They're using their expensive models for the hardest tasks only, right? And then they're using, you know, other models, right? So they're using that for judgment and planning, but then they're using cheaper models for the busywork. And you should be doing that too. I've already said this. We're at the point now with the capability overhangs where you don't need a Fable 5 and a GPT-5.6 SOL to rewrite your emails or to do the equivalent of a tough Google search, right? Or a simple, uh, you know, looking up some facts. Um, you know, you have to have, um, that kind of separated. Um, also another way to start copying the labs— this is something I've been doing now for a couple of months— well, you should have your AIs control other AIs, and you should, uh, right, you maybe start building other AIs, right? And I think we didn't really get into that, that realm until we got to GPT-5.6 SOL, right? You had some people share online, and I talked about it on the show about how people with no background in creating models were able to create, you know, small models using GPT-5.6 SOL for, you know, smaller purposes. I think the one that kind of, you know, went viral is someone used GPT-5.6 SOL to look in, look at every single text message that they had ever sent via iMessage, right? So they had SOL work on this and they created a literal small language model model with weights that was just based on here's, you know, everything I text about, here's how I write, here's, you know, and it knows everything about me, right? So, so think of that, right? Think of how when, when prices I think are ultimately going to be driven down as the models get more powerful, what are those moves that you can make and copy from the big labs? And then last but not least, the question isn't whether about AI builds AI. The question isn't about, you know, oh my gosh, what does that mean? Recursive self-improvement, you know, what does that mean for our business? Oh my gosh, freak out. No, it's just whether your business is ready. So you have to start thinking and talking openly about the implications, right? What happens, you know, when there's maybe dozens of AI models that are distinct and made for your job, for your department, for your sector? Because that time is coming. Because I think ultimately what recursive self-improvement means, aside from hopefully well, better models coming out faster, but also cheaper prices and probably more specific and niche models that serve specific verticals. So you have to start replanning now in whatever systems that you've got in place in 2024, 2025, et cetera. Those can't be longstanding systems. I talked, you can't be planning a year at a time. You should be planning a month at a time. And I know that's, you know, kind of daunting for traditional digital transformation, but I think that's where you have to be at. All right. I hope this show was helpful going over a lot of these terms, RSI, but hopefully now you know a little bit more about recursive self-improvement, what it means when these models are kind of improving themselves or working on the next version of themselves or making AI maybe cheaper and faster and better for all of us. So now you know what happens when RSI and models start improving themselves and hopefully what it means for your business. If this is helpful, please consider reposting this if you're listening on social media, if you're listening on the LinkedIn machine, and if you're listening to the podcast on Spotify or Apple Music, please subscribe, then go to youreverydayai.com. So thank you for tuning in. Hope to see you back tomorrow and every day for more Everyday AI. Thanks, y'all.

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