Dario Amodei

We are thus at risk of a situation where, instead of affecting people with specific skills or in specific professions, who can adapt by retraining, AI is affecting people with certain intrinsic cognitive properties, namely lower intellectual ability, which is harder to change.

It is not clear where these people will go or what they will do, and I am concerned that they could form an unemployed or very low-wage underclass.

To be clear, things somewhat like this have happened before.

For example, computers and the internet are believed by some economists to represent skill-biased technological change.

But this skill-biasing was both not as extreme as what I expect to see with AI, and is believed to have contributed to an increase in wage inequality, so it is not exactly a reassuring precedent.

ability to fill in the gaps.

The way human jobs often adjust in the face of new technology is that there are many aspects to the job, and the new technology, even if it appears to directly replace humans, often has gaps in it.

If someone invents a machine to make widgets, humans may still have to load raw material into the machine.

Even if that takes only 1% as much effort as making the widgets manually, human workers can simply make 100x more widgets.

But AI, in addition to being a rapidly advancing technology, is also a rapidly adapting technology.

During every model release, AI companies carefully measure what the model is good at and what it isn't, and customers also provide such information after the launch.

Weaknesses can be addressed by collecting tasks that embody the current gap and training on them for the next model.

Early in generative AI, users noticed that AI systems had certain weaknesses, such as AI image models generating hands with the wrong number of fingers, and many assumed these weaknesses were inherent to the technology.

If they were, it would limit job disruption.

But pretty much every such weakness gets addressed quickly, often within just a few months.

It's worth addressing common points of skepticism.

First, there is the argument that economic diffusion will be slow, such that even if the underlying technology is capable of doing most human labor, the actual application of it across the economy may be much slower, for example in industries that are far from the AI industry and slow to adopt.

Slow diffusion of technology is definitely real.

I talk to people from a wide variety of enterprises, and there are places where the adoption of AI will take years.

That's why my prediction for 50% of entry-level white-collar jobs being disrupted is 1 to 5 years, even though I suspect we'll have powerful AI which would be, technologically speaking, enough to do most or all jobs, not just entry-level, in much less than 5 years.