Dwarkesh Patel

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.

And the AI researcher, as you might anticipate, responded, look, image classification is a textbook deep learning problem.

This is death center in the kind of thing that we could train these models to do.

And I thought this is a very interesting exchange because it illustrated a key crux between me and the people who expect transformative economic impact within the next few years.

Human workers are valuable precisely because we don't need to build in these schleppy training bloops for every single small part of their job.