The most important report in AI right now

Recursive self-improvements has already begun. And this comes with proof from a third-party evaluator that had near unfettered access to internal anthropic systems, researchers, and basically everything else. So this all really started a few months ago when leaders of the top AI labs including Anthropic and OpenAI all called for pacing of the frontier because they were nervous about recursive self-improvement and then eventually super intelligence. And at that time they all said, "Hey, we will allow third-party evaluators to basically have employee like access internally, unfettered access to all of our systems, our latest models, our employees, our chat history, everything." And so the company meter, which is a well- reggarded independent evaluation company, worked with Anthropic, very hands-on, and got nearly full access internally. And so the first results are here and I have to be honest, it is making me a little bit nervous. And so we're going to go over this report together. I'm going to show you some of their findings and what this truly means for the future of AI over really the next 1 to two years. This is the report independent assessment of AI R&D acceleration at anthropic in October 2026. So what they are looking for are signs that AI is improving itself. Signs that AI is accelerating AI research and development. And they found it. Now before we get into the results, I want to point out one thing. I mentioned that Anthropic and OpenAI had said, "Hey, we're going to give employee like access to thirdparty evaluators." It turns out this was not part of that program. And so Anthropic was able to basically approve or deny access to anything. And not only that, even after access was given and meter was able to make some predictions and determinations, Anthropic was able to determine what they published and what they didn't. But even with that, these findings are incredible. And so I am going to show you how they were able to prove that AI research and development is absolutely accelerating strictly because of AI. So basically AI is able to improve itself. It's able to accelerate the speed at which anthropic is researching and developing their latest models. And yes, you probably already guessed this. The models are getting better and they're getting better more quickly than ever. and it is almost entirely attributable to AI help. All right, let's get into this. Most meter staff who worked on this project now believe that as of October 2026, Anthropic is likely improving the capabilities of their models on well specified tasks at around twice the rate they did between March 2024 and October 2025. And so when they're talking about well specified tasks, they're generally talking about the AI R&D process. They're talking about the systems around that. They're talking about writing code to help them with these systems. And they go on to say they would not be surprised if the current rate of growth was closer to 1.3x or 3x the historical rate. And now here's the important part. It's not because Anthropic is hiring more people, getting better AI researchers, having more compute. It's none of that. they were really able to isolate the cause of the acceleration to be AI progress itself. And they even pointed out in late 2025 that's when the latest version of Opus came out and that is when models became incredibly good at writing code like industry changing good at writing code. It happened all around the end of last year. And they say yes, the acceleration at that point was likely due to nonAI factors and those are more compute, more hiring breakthroughs discovered by human researchers. But fast forward to October 2026, we guess that the majority, perhaps about 80% of the current acceleration is due to AI. and they're going to not only prove it, they're also going to explain how it's happening. All right, so they put together this fake chart. And yes, it's fake, but it basically could be real. What we're seeing with this blue line is the improvement in model capability. And if it followed the historical trends, we would continue on this dotted blue line. Okay, so it's pretty linear. Then they measured AECI which is basically Anthropic's internal measure of the capabilities of their models without AI and we see this gray line but when we have that same measure excess growth due to AI we have this orange line and we can just see the inflection point at the end of last year and again it lines up perfectly with what all developers experienced at the same time. their entire job changed in October of last year because of Opus 4.5 and it was just absolutely mind-blowingly good at writing code and not just writing code but really like building entire features and entire pieces of architecture and it has only gotten better since then and if you're writing as much code as Anthropic is writing today which is obvious now based on Meter's research you need better tools to do it and that's why I want to tell tell you about today's sponsor. So, if you watch this channel at all, you know I am all in on AI coding agents. And the really hard part is the code that's almost right. And so that's why today's sponsor Jet Brains built Jet Brains Air and Intelligj Idea. You can run multiple agents at once without having to reexplain your codebase every time. You see which sessions are running, done, or waiting on you. Comment on a line in the diff and it goes straight back to the agents to fix. And because Intelligj Idea already understands every usage, dependency, and type in your code, your agents use fewer tokens because they are taking fewer wrong turns. And then the debugger, test, and profiler are all one click away. So you can make sure nothing breaks before you merge. You have the final say on what ships. Try Jet Brains Air and Intelligj Idea with the link down below. Let them know I sent you. Here's where it gets concerning, and this is why I am nervous. A key concern is that acceleration from the use of AI could start to compound. And they're already seeing that because everything compounds on itself. We have the models getting better, which means they're able to discover how to make themselves better more quickly. They're also figuring out how to utilize their limited compute capacity better than ever. And the human researcher output is being amplified because of all of this. And all of it amplifies each other. That is the intelligence explosion that was described in the situational awareness paper. And this was actually revealed in their S1, the document leading up to their IPO, which is imminent. They showed that their utilization of compute, the data centers, the limited amount of compute they have is actually increasing tremendously. So these models are getting more efficient. And of course, when they're getting more efficient, they can do more research. You can throw more tokens at a problem. You also earn more money, right? You have more revenue, which they've also shown. And it's all this crazy compounding loop. So look at Q4 in 2025. The compute efficiency was 67x. Then Q1 2026 break even. Q2 2026 it is 2x. So again, all of these breakthroughs are primarily being accelerated by artificial intelligence being able to figure out how to make itself better. And don't forget Google's Alpha Evolve paper which came out a while ago in which they basically pointed AI at improving Google's overall hardware architecture which allowed them a 1% improvement in efficiency. And if 1% does not sound like a lot, imagine 1% at Google's scale. That is billions and billions and billions of dollars of savings that AI was able to discover for itself. It's able to now run itself more efficiently. This is what we're seeing with this Meter paper. And Meter even goes as far to say they don't see any other bottlenecks between where we are today and an absolute explosion in artificial intelligence. Recursive self-improvement. Listen to this. We did not identify any clear bottlenecks. Limited inputs that would prevent further growth or steep diminishing returns to further scaling that would get in the way of such compounding acceleration. That is a wild statement. They're basically saying we're off to the races. This is it. There's nothing between us and the singularity next. Because of this, we believe there is a significant risk that further capabilities progress could lead to even faster acceleration over the coming year singular. They go on to say, "Annthropic appears to have made as much progress on benchmarks in the past year as they did between October 2023 and October 2025. But again, I think we're all feeling this. The models are getting so much better. The model releases are getting more frequent. We're all seeing it and they have shown it now. And again, they've isolated it to being because of AI accelerated development. I mean, here's the perfect chart to prove it. This is AECI. Again, this is Anthropic's internal model capability evaluator. And what we see is a really, you know, more or less linear growth line all the way through the end of 2025. And then something happens. And so here we have Opus 4.5, which was the absolute inflection point, and then it just accelerates. Mythos comes out at the early part of this year, and it just continues to accelerate. Again, if it followed the trajectory historically, we would be down here. But no, no, we're up here. And if you project out into the future, this curve is actually getting steeper. That is the acceleration that they're describing. The steepness of the curve, the acceleration itself is accelerating. And again, they really had to take their time and make sure that this acceleration wasn't caused by anything else. The first thing that they point out is that we received convincing evidence that the acceleration was not due to an acceleration in the scale of training compute. Meaning even though Anthropic has been able to acquire much more compute, they have evidence. Although they go on to say that further details on this evidence were redacted by Anthropic, but they have the evidence that shows the acceleration was not due to some explosion in compute capacity because we all know anthropic doesn't have that. They at the beginning of this year were struggling to serve their models. I mean they're much better now. They were able to acquire more compute. They're also, as we talked about, able to squeeze a lot more efficiency out of their models again because of AI R&D. So, this is just all so wild. And not only that, they interviewed employees at Anthropic. And they asked them like, "Hey, are you more productive? And if so, can you estimate how productive you are?" Now, yes, granted, this is like a very casual interview, and it's not scientific, but it's a good signal to have. So, let's read what it says. We have many lines of evidence relevant to AI's contribution to progress in different parts of the model training pipeline, including interviews, self-reported uplift, headcount, lines of code, experiment usage, and qualitative reflections from leadership. So, not only those interviews, but they were actually able to look directly at the model training pipeline, what was happening, how much efficiency they were gaining, how many additional researchers joined the team, how many lines of code were being written, and all of this gave them enough signal to determine yes, this is all because of AI. So, let me actually give you some of the quotes from those interviews. Now, they did not put names on this, which of course to protect the privacy of those researchers. So here are a few quotes could definitely endorse a greater than 2x speed up. My inside view is by far the largest effect is model acceleration. The AI acceleration is larger than all the other effects. Now only bottlenecked on ideas and compute. No longer bottlenecked on code. Now for this one I want to pause for a second. Now only bottlenecked on ideas and compute. But meter showed that the compute wasn't a bottleneck. So this is obviously a little bit of contradiction here, but that's okay. However, bottlenecked on ideas is interesting because this has been long talked about where even though AI is incredibly good at doing research, at writing code, at verifying experiments, the one thing that it really hasn't been great at is discovering a research direction that has a lot of promise. that is possibly still the realm of humans at least for the short term. But I'm going to come back to that because meter shows that there are some hints that these models are actually able to generate meaningful research direction hypothesis. I would definitely endorse greater than 2 years of progress in one year and I don't think four is crazy. So again, they're saying 2 to 4x improvement. And so there's just a bunch of quotes just like this. And here they talk about AI being able to make meaningful contributions to new research directions. Third, we understand that AI is not yet able to make highly significant and fully autonomous contributions to the most important parts of the model training process. However, researchers described significant signs of life implying such contributions could occur soon. And then you start to think about some of the math research and proofs that have come out of OpenAI recently. that is more or less net new knowledge to humanity. And so I do think these models given enough compute can actually just test everything. Like maybe they have some intuition on research direction, but with enough compute, they don't actually need to have much intuition. Like the intuition will help save money, save compute, but ultimately if you could just go out and test everything, like that's the same thing. Why not? And again, they're doubling down on basically not seeing any other bottlenecks between now and RSI. We did not see evidence for significant bottlenecks and algorithmic progress, training compute, experiment compute, or data that will take effect in the next 12 months. Enthropic redacted our analysis of a number of specific bottlenecks. All right, so how did this program actually work? How did Meter get access and what did that access look like? First, they only sent 2 and a half full-time equivalent employees into Anthropic. And this engagement started in mid August, right around the time that the whole pace AI thing started happening. And they wanted to answer three specific questions. One, has the growth rate of Anthropic's internal capabilities accelerated? What share of any acceleration is attributable to AI? And are there important bottlenecks that could slow growth or limit acceleration in the near term? And on all three of those, they got the answer that maybe all of us intuitively knew, but now we have some proof. Yeah, there we're accelerating and it is because of AI. So, Meter had the ability to request access to any information we believed relevant to understanding AI R&D acceleration. However, anthropic could unilaterally approve or reject our requests. In practice, we often negotiated with anthropic staff and compromised towards data that was informative, not extremely sensitive and easy to share. And again, this meter program with Anthropic is not the same thing that Enthropic said they were going to do with giving employeeike access to their systems. So, we're still waiting on that. And they specifically called it out here. Note that neither Meter nor Enthropic intended for this project to involve full employee like access. And specifically this project is not the one described in Enthropic's October 15th update on independent evaluators. Now one part of this program meter was originally going to get access to understanding the compute allocation that enthropic decides that means how much is going towards R&D how much is going towards inference serving claude to all of us and how much is going into whatever else they allocate compute towards however anthropic decided mid-program to revoke that access and every time I hear anthropic is revoking access or something like that I think about their IPO because maybe they were just nervous. Hey, if this information comes out, it might delay our IPO. We've already put out our S1 kind of lining up the dominoes to reaching the IPO. So, it is very interesting to see and to think about what some of those internal conversations may have been like. So, the contracted scope of this project originally included a verification of Enthropics compute allocation, especially the allocation between capabilities and safety. However, Anthropic changed the scope of this project midway to exclude compute verification and instead just focus on AI R&D acceleration. This compute was cancelled before Meter came to any substantive conclusions on compute usage at Enthropic. Anthropic stated this was due to wanting to keep the engagement focused on risk assessment for the time being and that it would revisit compute allocation verification later on. Interpret that how you will. All right. So here are a few things from the appendices that really stood out to me. So one, new internal models. These are the models that we don't know about yet. They are using them to accelerate their own development before they give it to anybody else. Those new internal models make a permanent slope change look more plausible than a one-time jump at Mythos preview. Mythos preview was a massive improvement. But what they are saying is that that massive improvement was not specifically because there's something special about mythos. No, no, it is simply because of that compounding effect of the acceleration of AI R&D. And they even were able to see it with internal unreleased models. Look at this. This is model 3, model 4. Mythos 5 was back here. They have models that we don't even know about yet. Next, Anthropic has actually had to make their benchmarks more difficult because their new models are basically saturating them. They're too good. They're too smart. And they just have to keep making those benchmarks more difficult. And researchers inside Anthropic are saying for the first time, these models are becoming actually useful for autonomous experimentation. And so, what does all of this tell us? I think it tells us what we already felt, which is, yeah, AI is accelerating. Now, what makes me nervous is actually seeing a third-party independent well- reggarded evaluator actually with access to anthropic internals saying, "Yes, this is happening and there's nothing in the way. There is literally no blocker between what is happening now and what artificial super intelligence or recursive self-improvement looks like." And where I start to get especially nervous is when we stop being able to understand AI completely. And we already started to see some signs of that with the research paper that just came out this week, weight space programming, where the frontier AI models are actually able to create new models by writing the weights directly. And I made an entire video about that. and go watch that right