Ep 875: RSI Explained: When AI Starts Improving Itself and What It Means (Replay)

Jordan Wilson (0:00)

Welcome to the Everyday AI Podcast. My name is Jordan Wilson, and for the past three and a half years, we put out more than 800 episodes. Yet, one of the most common questions I get, I didn't really have an answer for. Where do I start on the Everyday AI Podcast? And that's why we started the Start Here series. And with fall now back in full swing, the Everyday AI Podcast is going back to school and playing back the entire Start Here series from front to back. We've hit pause on our normal Monday to Friday programming to run back our most popular series ever for the next 30 days. We made the Start Here series for beginners and AI champions alike. So whether you're just trying to get a grasp on large language models or grappling with the best coding harness for multi-agentic workflows, the Start Here series covers it all. Plain language, no jargon, and easy to follow along each day. So make sure to subscribe to the podcast and check back each day for new insights, day by day. The series is a culmination of spending more than 10,000 hours covering generative AI over the past three and a half years. So you don't want to miss a single episode of the Start Here series. Let's get into it.

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-56 sole 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 for 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 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-56 sole model made its Luna model, the smaller variant, just cheaper and more efficient. And then they cut prices by 80 percent. But the full human-free recursive self-improvement hasn't yet arrived. But over the past two months, there's a lot of AI news stories that we're going to be connecting 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 what you're going to learn. You're going to learn why OpenAI was able to cut prices up to 80 percent when everyone predicted that AI costs would continue to climb in 2026 You're going to know more about this eight-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 Wilson and welcome to this thing for you. This is your daily, unedited, unscripted, live stream 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 going to 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 free to four letter acronym in our ever evolving bowl of alphabet AI soup. We are talking RSI.