Dario Amodei

In the next step, some parts of the job of farming could be done entirely by machines, for example with the invention of the threshing machine or seed drill.

In this phase, humans did a lower and lower fraction of the job, but the work they did complete became more and more leveraged because it is complementary to the work of machines, and their productivity continued to rise.

As described by Jevons' paradox, the wages of farmers and perhaps even the number of farmers continue to increase.

Even when 90% of the job is being done by machines, humans can simply do 10x more of the 10% they still do, producing 10x as much output for the same amount of labor.

Eventually, machines do everything or almost everything, as with modern combine harvesters, tractors, and other equipment.

At this point farming as a form of human employment really does go into steep decline, and this potentially causes serious disruption in the short term, but because farming is just one of many useful activities that humans are able to do, people eventually switch to other jobs, such as operating factory machines.

This is true even though farming accounted for a huge proportion of employment ex ante.

250 years ago, 90% of Americans lived on farms.

In Europe, 50-60% of employment was agricultural.

Now those percentages are in the low single digits in those places, because workers switched to industrial jobs, and later, knowledge work jobs.

The economy can do what previously required most of the labor force with only 1-2% of it, freeing up the rest of the labor force to build an ever more advanced industrial society.

There's no fixed lump of labor, just an ever-expanding ability to do more and more with less and less.

People's wages rise in line with the GDP exponential and the economy maintains full employment once disruptions in the short term have passed.

It's possible things will go roughly the same way with AI, but I would bet pretty strongly against it.

Here are some reasons I think AI is likely to be different.

Speed

the pace of progress in AI is much faster than for previous technological revolutions.

For example, in the last two years, AI models went from barely being able to complete a single line of code to writing all or almost all of the code for some people, including engineers at Anthropic.

Soon, they may do the entire task of a software engineer end-to-end.

It is hard for people to adapt to this pace of change, both to the changes in how a given job works and in the need to switch to new jobs.