Dwarkesh Patel

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.

It's not net productive to build a custom training pipeline to identify what macrophages look like, given the specific way that this lab prepares slides, and then another training loop for the next lab-specific microtask, and so on.

What you actually need is an AI that can learn from semantic feedback or from self-directed experience, and then generalize the way a human does.

Every day, you have to do 100 things that require judgment, situational awareness, and skills and context that are learned on the job.

These tasks differ not just across different people, but even from one day to the next for the same person.

It is not possible to automate even a single job by just baking in a predefined set of skills, let alone all the jobs.

In fact, I think people are really underestimating how big a deal actual AGI will be because they are just imagining more of this current regime.