# AI Lurches Toward Recursive Self-Improvement — Transcript (2026-11-03)

https://aidailybrief.ai/e/2026-11-03 · Listen: https://pod.link/1680633614

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[00:00:00] 

Hi friends. Before we get into today's episode, I wanted to share that the second episode of How We Got Here has arrived

The episode is called How We Made the Wrong Things Cheap, and looks at how for 25 years the price of stuff, consumer goods, electronics, et cetera, went down while the cost of housing, healthcare, and all that really important stuff went the other direction



I think in many ways this dichotomy

is at the heart of the economic populism that is increasingly dominating American politics



for those of you who have already listened and subscribed to How We Got Here, a big thank you and for those who haven't yet, this is my invitation



if you like what you get on the AI Daily Brief, I think you'll like this show so go check it out How We Got Here with NLW on any podcast platform, or go to howwegothere.com The three letters on everyone's minds are R, S, and I. a new MIT paper showing how a bigger model can edit the actual weights of a smaller model has people thinking about it And Wall Street investors who are trying to understand where the next leg of growth comes from are also digging deep into what RSI might mean

On this episode, we're looking at a set of stories whose through line is RSI, and more broadly, what comes next for [00:01:00] AI

But it starts with Elon Musk casting doubt on the entire personal agent space

The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. All right, friends, quick announcements before we dive in. First of All right, friends, quick announcements before we dive in. First of all, thank you to today's sponsors, KPMG, Robots and Pencils, CData, and Blitzy To get an ad-free version of the show, go to patreon.com/aidailybrief, or you can subscribe on Apple Podcasts.



And to learn more about sponsoring the show, send us a note at sponsors@aidailybrief.ai.

Two more quick announcements. as you just heard, the second episode of How We Got Here is now out



it's called How We Made the Wrong Things Cheap. And for any of you who are interested in

the economics of the modern American economy, whereby some measures we're doing better than ever, but everyone feels more behind

If you wanna know how that came to be, that episode is for you. You can find it at at howwegotthere.com Or by searching How We Got There wherever you listen to podcasts. The second note

is that I am traveling this week for my anniversary

and [00:02:00] basically coming to you live from the jungle. So if you happen to hear bird sounds or water trickling, or let's just call it more nature than usual, that is why. I hope you enjoy these various nature sounds for the next couple of days. Next week we will be back to normal 

Welcome One of the conversations that has been swirling around quite a bit throughout 2026, but especially over the last couple of months, is what it means

for the AI industry and society more broadly, that AI models are getting closer to being able to improve themselves



On Alphabet's recent earnings call, CEO Sundar Pichai said, " We're at the point where AI is helping us increase our training and launch velocity.

I would expect the next 12 months to see continued acceleration because of RSI."

Now, today's stories are not exactly all about RSI

But there is a through line In how the continued acceleration of AI meets adoption of AI, how that impacts market potentials

and what the state of RSI actually is.



the the first then of our troika of stories



comes [00:03:00] after a seemingly nonchalant but not really at all nonchalant post from Elon Musk

Where he said over the weekend the cost of serving Grok Bot was unsustainably high relative to revenue

The genesis of this had been that over the weekend, Grok Bot users complained that they were starting to hit usage limits that had never been a problem before

Elon was responding to someone's request to increase or reset those usage limits

Now, Elon didn't give any more details It's unclear through which plans users were accessing GrokBot

But for those watching the AI space closely, especially market bears



post hit with the force of a bomb

For some, there was a weird contradiction in Elon's communications over the past couple of weeks

Just a couple of days ago, he had posted, "Saving money for the distant future is pointless with the advent of superintelligent abundance

But as Bruno Lemos and others pointed out just a couple days later,

He's talking about costs being too high

Benjamin De Cracker went a little bit farther back, re-sharing an Elon post from a couple of weeks ago when he said, "Never seen anything grow as fast as GrokBot. [00:04:00] And there is no free tier, which means people are deriving real value in excess of what they pay." DeCracker writes, " "Two Two weeks ago, GrokBot was an incredible success story powering SpaceX AI's revenue growth.

Now it's unsustainable and loses money?"

For some, Elon's weird communications were entirely secondary to the feeling of betrayal around the plans

User Shrek said, " "This This isn't about us not having enough usage or picking the wrong plan. I've been buying the 12-inch pizza from your place forever, and you guys have always given me a normal 12-inch. Then one day I open the box and it's a six-inch. You tell me costs are too high, so from now on the 12-inch is actually six inches, and if I'm still hungry, it's my fault for not choosing the right size.

That's insane. If you needed to cut costs, just tell us ahead of time. If your pizza is good and I still want it, I can just order two or three of these fake 12-inch ones. Or if I'm not full, I can go somewhere else. Either way, that's my call, not yours. but you don't get to keep selling me a normal 12-inch and then suddenly shrink it to six inches with zero notice, not even a heads up so that I can adjust.

don't turn around and say I picked the wrong subscription. The problem is you unilaterally changed what you were delivering without telling anyone."



Others Others thought that [00:05:00] inevitably this was good news for the other personal agents. Alex Michael wrote, "This is a huge win for Dots and Duet and will most likely drive a lot of users right back to them." Noose Research, the makers of Hermes, of course, jumped all over it And shared specific instructions on how GrokBot users can migrate to Hermes

But the most interesting discussion whether the issues that Elon and SpaceX are running into are less about anything having to do with SpaceX and their position in the AI race, and more about the fundamentals of this product category Dax from OpenCode reposted Elon and said, " This is another major reason why we weren't super excited about this shape of product."

By which presumably Dax means personal agents. Continuing, he says, " People don't wanna pay for it, so it has to be a top-of-funnel product that leads to something else. Not sure what the something else could be yet." Now, careful observers will note that this sort of conversation about the cost center that is free AI users on free consumer tiers that will never pay, and how it impacts the ability of the company to monetize more broadly, has been around for a very long time

It [00:06:00] was a major subject of discussion in the fall 2025 vintage of Bubble Debates

Where people were basically concerned that OpenAI's breakaway ChatGPT success could actually be a hindrance because of how much it costs to support all of those free users

For as long as we have been talking about personal agents, especially this year as they've gotten more real, there have been some who have applied those same sort of claims that since consumers aren't willing to pay for this category of product, they're just going to be a cost center for the people offering them, and they're going to have to be made up for in other ways

And that remains an open debate

And certainly market skeptics jumped right to what this might mean

For all the other companies offering these products, which includes very notably Meta, who have been riding high on the fact that Muse's leading place in this category has given them a foothold in the AI race more broadly

Certainly the Bears were very happy to jump all over this as fait accompli that the bubble was done and the game was over. Eric Sky writes, Elon is going to abandon the race. Second warning

Nick Huber

who is all about traditional boring businesses and very skeptical of [00:07:00] many categories of tech startups and certainly AI

got on his high horse and said

This is all of AI right now. The music will stop. Better find your chair. VC money runs out means prices rise dramatically, means users drop off, means valuations plummet



Hedgy Hedgy Markets thinks it's just another sign of the end of the subsidy era. They wrote, Grok users ran out of usage days early this week, and Musk was straight with them about why. This is the end of the subsidy I wrote about in this week's newsletter.

AI companies price their plans below cost to win users, and that only works while investors cover the difference. A full month on a two hundred dollar ChatGPT Pro plan would cost about fourteen thousand dollars at regular API prices, according to one analysis this summer."



now I think by that he's referring to semianalysis

But his note that this was the discussion this summer is worth taking a moment on.

You might remember that we did an AI subsidy era episode back at the end of April

The episode not only argued that the AI subsidy era was coming to an end, but tried to provide a set of actions for businesses to adapt to it

So make no doubt that this is [00:08:00] not some new conversation

And it has been inevitable for quite some time. For whatever reason, we got distracted by other things for a couple months over the summer and stopped talking about this



But now that there's so much bubble analysis once again, and critical analysis with Anthropic getting close to IPO

the story is back because of course the debate about the implications was never resolved

The bear case with these subsidies ending is basically that when AI companies actually have to charge

the correct or natural rate for their product, i.e. charge customers what the tokens actually cost plus a little bit of margin for themselves. not a version of that that is subsidized by someone else like venture capitalists and private equity investors

that people don't value or can't afford to value the intelligence at that natural price enough to continue spending in the way that they have been

And thus it means that all the valuations of all these companies are off



that is a possible outcome, and I think it's completely reasonable for market actors to try to figure out how to price in that risk In fact, in general, I will say here that despite the fact that I am very much not [00:09:00] an AI bear I have no issue at all with the AI bears in the market right now I think it would be much more dangerous if everyone was too enamored of Anthropic and OpenAI to actually ask these critical questions I think that likelihoods of a bubble are decreased in proportion in some ways to the loudness of the bears and the resonance of their message with a broad base of investors Now, I also, of course, think that there's a wide spectrum

of the quality of bearish analysis



And that perhaps listening to the permabears who have gotten it wrong for years and continue to move the goalposts to get it right might not be your best play But overall, I think their voices are an important part of the market segment

And I think it's valuable that investors are taking time to price in the risks that they're talking about.

So all that said I don't particularly think

that the shift to API pricing for everyone actually changes all that much

Basically, I think that the plan users shift into one of two categories

The people for whom

The models that come with more efficiency trade-offs are good enough for their types of uses who stay on their 20 or $30 a month plan because for their use cases it doesn't change all that [00:10:00] much. And then on the other side The people who are getting enough value out of whatever plan they're on, whether it's $20 or $200, that they're willing to actually pay for the intelligence they consume and just treat it

as a cost of doing business

I'm certainly not a super normal example of this

But I have APIs running all over the place in addition to my premium plans, and there's absolutely no doubt that the value that I get out of them is well worth the cost. I do things with those APIs

For yes, a premium cost, but a premium cost that's still far below what the comparative human labor would have cost. Which, by the way, is not a one-to-one replacement because the human labor would have cost too much In both actual money as well as time to recruit and manage those people. So from an economic perspective, this is net new money that I'm spending that I would not have spent before. What I believe that we've seen in 2026, basically since the rise of the agentic era



is that a huge amount of the revenue on an actual percentage basis is coming from power users who are already paying via the API. In fact, I think there's a bit of a weird contrast [00:11:00] between 

two bare arguments sitting next to one another. Is it that when people start paying on the API



people are gonna drop off and stop using AI because they don't get enough value out of it anymore? or is the market too concentrated among a very small number of power users?

Now, of course, the more nuanced in between argument here

Is the one that you've seen people like Chamath Palihapitiya make

that argues that too many of those power users are currently subsidized by VC dollars, and that could present some significant problems. I think that's a much more interesting thing to explore

Still, right now, we don't know

And all that we have is this sort of interpretation



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Certainly it's the case that Elon Musk is not above fanning the flames of panic in the market. After CoreWeave's bonds had a particularly bad day last month, Musk commented, "Yikes," which which got picked up across the financial press

And later in October, he said, " Debt markets have almost entirely shut off access to money for any company that doesn't have substantial revenue independent of AI data centers."



there are more than a few people who think this is some version of 4D chess, with Musk pushing the markets to sell off harder in hopes of making some cheap acquisitions or harming rivals

But right now, we just don't know

What we do know is that there are still some companies who are betting very heavily on personal [00:15:00] agents, even if Elon seems to be pulling back

Olivia Moore from A16Z thinks that when it comes to Meta's Muse, the real story isn't even really consumer. it's Muse for small businesses. She wrote, " Muse for SMBs might be the most underrated story in consumer agents right now. they have near infinite consumer distribution at very low cost, with a base of three point six four billion daily active users across Meta apps.

But even more importantly, more than five hundred million SMBs use Meta for marketing. if Meta gives agent discoverability to that SMB base, it could be a huge boost, especially for local businesses who can hyper-target users with urgent purchase needs. Meta said in their Q3 earnings call and in blog posts prior that they plan to build an ecosystem of connectors for SMBs and Muse.



analytics across your Meta accounts, plus QuickBooks, Canvas, Stripe, et cetera. many of these businesses are already trusting Meta with their growth in ad dollars. I could see Muse becoming almost a nervous system for SMBs that allows Meta to capture more and more of their activity over time."



and interestingly, even as these discussions about the viability of personal agents were swirling, we got [00:16:00] two new benchmarks introduced

that go some way towards measuring the value of these sort of personal agents the semi-analysis benchmark is called GDPX and is focused on economically valuable tasks like chip design, finance, and biology. They found that Opus 5.5 was the most valuable model to drive an agent, scoring twelve percent on their scale at fifty-one dollars per task Gemini 4 Carbon was in second place at 10.9%, but only cost $16 per task.



Chubby on X jumped right at this question of efficiency. he wrote, " "While While performing just 1.1% worse, Gemini 4 is about 4X cheaper than Opus 55. So cost performance-wise, Gemini 4 wins by a lot And one of the obvious questions that clearly the labs are thinking about right now is how to use more efficient models to drive down the cost of personal agents, similar to what has been a focus with coding agents across this year

As we'll see later, there's all sorts of buzz around new training techniques as well that lead some to think that it's possible that we'll see stronger, smaller models in the coming year that make personal agents a lot more cost-effective

OpenAI's new benchmark, [00:17:00] meanwhile, called LifeVal, sets out to measure the value of personal agents in everyday life writes OpenAI, for the more than one billion people who currently use ChatGPT every week

We want to build AI that can help with the practical challenges of everyday life, like finding childcare, accessing government services

Creating a budget, preparing for school, modifying recipes, ordering groceries, designing workout plans, and more

OpenAI scoring shows a steady increase in capability from GPT 5.2 to GPT 6 Astra, rising from 49.3% success to 80.6% success. More importantly, OpenAI is now measuring and hill climbing against these everyday tasks With free users in mind. As we've seen with coding, once a capability is measured and becomes a target for reinforcement learning, it dramatically improves and becomes more cost-effective

Frasser X commented, " "Forget Forget all the flashy AI benchmarks. Life foul is arguably the most important benchmark for 99% of AI users. This is what actually matters."

Simon Smith wrote, " Feels like this is the kind of post you publish right before releasing a personal [00:18:00] agent to the general public. Question for me is whether ChatGPT gets better at being a personal agent before Muse gets widespread voice mode. ChatGPT's voice mode is awesome, but Muse is the better personal agent.

And yes, I use dots at work and try them for personal use, so I know what they're capable of and still prefer Muse."

Now let's actually move over to the story

That with just a glance at the AI community seems to be the biggest one of the day

As you'll see It potentially has some implications for all of these cost questions around personal agents. The TLDR is that MIT researchers have published groundbreaking research that could change the way that model training works. led by PhD student Rakiya Tekahi, the researchers found that GPT-6 Astra was capable of reading and directly adjusting the weights of a small model to improve its performance



The experiment used Qwen 3.5 0.8B and targeted improvements in reasoning. Astra changed only zero point zero one six percent of the weights, and yet was able to improve Qwen's scores on math benchmarks by six X and instruction following by three X. [00:19:00] In their abstract, the researchers wrote, " We find that frontier LLMs are surprisingly good at natively reading and writing neural network weights, well past what we'd expect from humans or conventional algorithms.

This might be a big deal for both optimization and interpretability. Just by looking at the weights, GPT-6 Astra can tell what a network was trained to do, and without any environment feedback, it has no trouble writing from scratch small game-playing networks that outplay networks trained with PPO for a hundred million steps."

PPO or proximal policy optimization, by the way, is a standard reinforcement learning algorithm in use by the major labs the research is worth reading in full or having Claude or ChatGPT read and talking to it about, and includes multiple surprising results



but when it comes to altering Qwen's weights, the process was extremely straightforward. Takahii wrote, " We simply asked Astra to make Qwen 3.508b better at math. Astra saw Qwen get stuck in reasoning loops, which is a known issue that Qwen's own model card warns about.



with a budget of 50 forward passes on practice problems, it located [00:20:00] the cause and edited it out." This used no gradients or training Fixing these loops by editing just 0.016% of weights boosted other capabilities beyond math too

People were incredibly excited about this result. Yu-Chen Jin wrote

This is cool as heck. Imagine using a large LLM to directly edit a smaller model's weights and dramatically improve its capabilities. It's like gene editing for neural networks, even cooler than distillation

Developer Nick Dobos wrote, " Astra can handwrite new weights from scratch that outperform other AI training techniques? What? They buried the lead. Improving math via a looping bug fix is very cool, but debugging and fixing weights is quite limited versus generating brand-new weights. If this works, is training large AI models even needed anymore?

Is this true RSI?"

And And for those like Professor Ethan Mollick who said, "This seems really cool, but let's wait for some replicated results," well, it didn't take long for Alex Krensel, a senior researcher at Google, to replicate the results. Alex noted, " "This This is a new step change emerging capability that's appeared in just the latest set of models."

[00:21:00] Now he did continue, " Some very important caveats to understand these results. Note that the models being edited here are very small. The breakout model in their post in my experiment is twenty thousand four hundred and eighty-six parameters. frontier models are more than a trillion parameters."

The way that I'm thinking about it is this. There's a whole body of work of interpretability research from the 2010s a la look at model, this neuron lights up when image contains dog, so this is the dog neuron. This seems akin to that, but scaled beyond human capability We've done this by hand for smaller models, then we built tools to help this for slightly bigger models.

What we're seeing is the reasoning capabilities are enabling the models to do this by hand for much larger but still relatively small models



Krenstil added that the obvious next step in the research is to test self-improvement at runtime, which he believes won't be feasible for current models but could work within a few generations

As you might expect whenever you have big bombastic claims like this, there's gonna be a pretty heated debate about whether the results were overhyped Chandan Singh from Voyage AI commented, for anyone questioning whether this post is overhyped, I think they are [00:22:00] actually underselling the approach.

We did similar experiments and found that Claude models can write entire functioning neural net weights from scratch."

On the other side were people, however,

like Wei Ajou from Microsoft Research who said, " The findings are pretty interesting indeed, but I think the headline is kind of overhyped. The six X improvement on math came from fixing a loop bug in the original model, not from improving general math capability."



And this was the main critique



That this is less about a new model training technique and more akin to a bug fix in an existing model

And yet for others, this was clear evidence that recursive self-improvement is not only a possibility in the future, but now has a clear mechanism that's understood and has been demonstrated in an academic paper. Tenebris wrote, " I think you should strongly expect something like this to happen in the next year and for it to be one of the driving factors of RSI and potentially quite steep RSI. Interpretability is incredibly dual use. Models that are good at optimization writ large are good at optimizing themselves."



Now, RSI was also the main point of discussion around an update of Anthropic's S1 that they [00:23:00] released on Monday morning



AI news aggregator Andrew Curran wrote



new new language added on Anthropic's S1A this morning says that internal estimates indicate they may imminently meet the RSI danger threshold of dramatically accelerated progress attributable to AI

As much progress in one year as two years of the 2018 to 2024 baseline

They added that they are considering updates to their responsible scaling policy because they quote, " Do not anticipate being able to safely use Claude to fully autonomously design and build successive generations of frontier models without meaningful human supervision or understanding."

Basically, the new changes will be aimed at keeping humans in control of frontier model development rather than handing research over to the model entirely.



certainly it is the case that financial professionals are taking note of RSI as one of the most important factors for the IPO. The Wall Street Journal published a chart from Anthropic showing that AI is now leading twenty-six percent of research. Their headline read: " The smartest people in AI believe this is the world's most important chart.

AI is starting to build better AI. This self-improvement is speeding up research and development [00:24:00] and freaking everyone out." The headline seemed to reference Anthropic co-founder Jack Clark, who said that RSI, quote, " Might be the most important technical trend in the world."

Anthropic first published this chart, by the way, back in September based on August numbers, and it was boosted by former AI czar David Sacks over the weekend

Still with the AI narrative beginning to sour, or at least having more questions as we've been exploring, it's notable that Wall Street investors are paying attention to the possibility that RSI leads to faster advances in model capability.

As Sachs commented, that was back in August, so who knows what they're at now being in October?

Now one thing i find interesting about that discussion is the presumption so deeply embedded that no one feels the need to say it, that the key to greater adoption is improved models

The evidence that we have for that is that true step changes have unlocked new capabilities that have led to massively more adoption in the past

The most notable and important of these, of course, was the Opus 4.5 jump that led to the full agenda coding era that we've been living in ever since.

In the absence though, 

of [00:25:00] some entirely new capability coming online

I don't think the evidence suggests that the key to improved adoption is solely model capability



I think ultimately when it comes to whether these business models work or not

It's more or less a question of how much intelligence enterprises consume

They are at the moment consuming a vanishingly small portion of what they could

And it's not because the model capabilities aren't high enough. It's because inertia is real, corporate systems are lumbering, and adoption is hard

Now, I think you could write a bear case or a bull case based on that

The bear case is that even with all this power, we're not using it The bull case is

No, we're just not using it yet

Regardless

Whatever investors want to tell themselves about RSI and its impact on adoption is fine



I'm just not sure model improvement has quite such a straight line to increased revenue as we might intuitively think

What I will say is that at least some of those investors are starting to ask What happens when we have RSI Not just how close to it are we

It feels like we're still stuck, broadly speaking, [00:26:00] Is RSI here yet or isn't it?" discussion rather than



what it means when it arrives. Right now, the only group offering any sort of vision for the world of RSI is the AI safetyists who are terrified of it and think it should be illegal

We may not agree But at least they're actually articulating what they think the world is going to look like after that if and after RSI comes

It strikes me

That like the AGI term before it, we may be getting so distracted with this three-letter acronym



that we're spending more attention on trying to define it

and articulating how close it is to being here, then understanding what it will mean when it arrives

In any case, it continues to be a fascinating time in AI. But that is gonna do it for today's AI Daily Brief. Appreciate you listening or watching, as always. And until next time, peace.​