Chapter 1: What are we scaling in AI progress?
I'm confused why some people have super short timelines, yet at the same time are bullish on scaling up reinforcement learning atop LLMs. If we're actually close to a human-like learner, then this whole approach of training on verifiable outcomes is doomed. Now, currently the labs are trying to bake in a bunch of skills into these models through mid-training.
Chapter 2: How does human labor influence AI development?
There's an entire supply chain of companies that are building RL environments, which teach the model how to navigate a web browser or use Excel to build financial models. Now, either of these models will soon learn on the job in a self-directed way, which will make all this freebaking pointless, or they won't, which means that AGI is not imminent.
Humans don't have to go through the special training phase where they need to rehearse every single piece of software that they might ever need to use on the job.
Chapter 3: What challenges are posed by economic diffusion lag in AI?
Barron Milledge made an interesting point about this in a recent blog post he wrote.
He writes, quote, When we see frontier models improving at various benchmarks, we should think not just about the increased scale and the clever ML research ideas, but the billions of dollars that are paid to PhDs, MDs, and other experts to write questions and provide example answers and reasoning targeting these precise capabilities.
Chapter 4: Why is goal-post shifting considered justified in AI discussions?
You can see this tension most vividly in robotics.
Chapter 5: What role does reinforcement learning play in AI scaling?
In some fundamental sense, robotics is an algorithms problem, not a hardware or data problem. With very little training, a human can learn how to teleoperate current hardware to do useful work. So if we actually had a human-like learner, robotics would be, in large part, a solved problem.
But the fact that we don't have such a learner makes it necessary to go out into a thousand different homes and practice a million times on how to pick up dishes or fold laundry. Now, one common argument I've heard from the people who think we're going to have a takeoff within the next five years is that we have to do all this kludgy RL in service of building a superhuman AI researcher.
And then the million copies of this automated ILLIA can go figure out how to solve robust and efficient learning from experience. This just gives me the vibes of that old joke, we're losing money on every sale, but we'll make it up in volume.
Somehow this automated researcher is going to figure out the algorithm for AGI, which is a problem that humans have been banging their head against for the better half of a century, while not having the basic learning capabilities that children have. I find this super implausible.
Besides, even if that's what you believe, it doesn't describe how the labs are approaching reinforcement learning from verifiable reward. You don't need to prebake in a consultant skill at crafting PowerPoint slides in order to automate ILIA.
So clearly, the lab's actions hint at a worldview where these models will continue to fare poorly at generalization and on-the-job learning, thus making it necessary to build in the skills that we hope will be economically useful beforehand into these models.
Another counterargument you can make is that even if the model could learn these skills on the job, it is just so much more efficient to build in these skills once during trading rather than again and again for each user and each company. And look, it makes a ton of sense to just bake in fluency with common tools like browsers and terminals.
And indeed, one of the key advantages that AGIs will have is this greater capacity to share knowledge across copies. But people are really underrating how much company and context-specific skills are required to do most jobs. And there just isn't currently a robust, efficient way for AIs to pick up these skills. I was recently at a dinner with an AI researcher and a biologist.
And it turned out the biologist had long timelines. And so we were asking about why she had these long timelines. And then she said, one part of work recently in the lab has involved looking at slides and deciding if the dot in that slide is actually a macrophage or just looks like a macrophage.
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Chapter 6: How does the concept of continual learning impact AI capabilities?
I think people are using this Cope to gloss over the fact that these models just lack the capabilities that are necessary for broad economic value. If these models actually were like humans on a server, they'd diffuse incredibly quickly. In fact, they'd be so much easier to integrate and onboard than a normal human employee is.
They could read your entire Slack and drive within minutes, and they could immediately distill all the skills that your other AI employees have. Plus, the hiring market for humans is very much like a lemons market, where it's hard to tell who the good people are beforehand, and then obviously hiring somebody who turns out to be bad is very costly.
This is just not a dynamic that you would have to face or worry about if you're just spinning up another instance of a vetted HEI model. So for these reasons, I expect it's going to be much easier to diffuse AI labor into firms than it is to hire a person. And companies hire people all the time.
If the capabilities were actually at HEI level, people would be willing to spend trillions of dollars a year buying tokens that these models produce. knowledge workers across the world cumulatively earn tens of trillions of dollars a year in wages. And the reason that labs are orders of magnitude off this figure right now is that the models are nowhere near as capable as human knowledge workers.
Now, you might be like, look, how can the standard have suddenly become labs have to earn tens of trillions of dollars of revenue a year, right? Like, until recently, people were saying, can these models reason? Do these models have common sense? Are they just doing pattern recognition? And obviously, AI bulls are right to criticize AI bears for repeatedly moving these goalposts.
And this is very often fair. It's easy to underestimate the progress that AI has made over the last decade. But some amount of GoPro shifting is actually justified. If you showed me Gemini 3 in 2020, I would have been certain that it could automate half of knowledge work. And so we keep solving what we thought were the sufficient bottlenecks to AGI.
We have models that have general understanding, they have few-shot learning, they have reasoning, and yet we still don't have AGI. So what is a rational response to observing this? I think it's totally reasonable to look at this and say, oh, actually, there's much more to intelligence and labor than I previously realized.
And while we're really close and in many ways have surpassed what I would have previously defined as AGI in the past, the fact that model companies are not making the trillions of dollars in revenue that would be implied by AGI clearly reveals that my previous definition of AGI was too narrow. And I expect this to keep happening into the future.
I expect that by 2030, the labs will have made significant progress on my hobby horse of continual learning, and the models will be earning hundreds of billions of dollars in revenue a year. But they won't have automated all knowledge work. And I'll be like, look, we made a lot of progress, but we haven't hit AGI yet. We also need these other capabilities.
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