Capital-Friendly Automation AI vs Labor-Friendly Augmentation AI

Discussion

Daron Acemoglu:

"The main thesis of Power and Progress is that technology does, to some extent, what we want it to do. It does not have a preordained destiny that will take us in one direction or another. We have a lot of agency, a lot of choice in shaping the future of technology, and different futures correspond to different winners and losers, different benefits, different costs, different productivities.

We tried to make that point by going into history, showing how critical periods during our recent history, like the last 1,000 years, have led to sometimes big technological breakthroughs but with huge losers, and sometimes those forces have been reversed, and gains from technological betterment have been shared more equitably. That message, I think, is more relevant today than ever. AI is a particularly versatile technology. It provides so many different futures for us.

The narrative that there is a determined natural future of AI, and we are all going there whether we want it or not — and, ultimately, we’re all going to become incredibly more prosperous out of that — is just simplistic. Fighting against that narrative, I think, is very important today because that narrative lulls us into a sense of helplessness and sense of complacence that could be quite costly. On the other hand, of course, in 2021, 2022, when we were writing, it was impossible to foresee how rapid some of the advances in generative AI would be. But those advances haven’t really changed the basic trade-offs and the basic messages that we wanted to convey in the book.

I talked at the high level about different directions of AI. What are they? I think, simplifying it, you have a couple of poles that are pulling in different directions. I would single out — in the production process — automation, which is the dream of most AI models today, especially under the banner of artificial general intelligence (AGI), which aims for large language models or other generative AI tools to reach levels of capabilities comparable to the best workers across a very wide range of domains. The reason why that is viewed as attractive is that just like previous rounds of software that improved cognition in different domains, that can then be used for automating tasks. So AGI is very tightly interwoven with the automation agenda. Automation is great. It gets rid of some routine tasks, some boring tasks.

When it’s applied in the physical domain, such as with cranes or robots, it could remove the most dangerous tasks from the human work schedule, but automation also doesn’t benefit workers by itself. It takes away tasks from workers. It is beneficial to capital and capital owners and not so much for workers in general.

So at the other pole, we have things that are complementary to humans, meaning that technology enables humans to do more things or better things or completely new things. These new things [are] what I refer to as new tasks. So if you look at people around you, many of the occupations you’ll see involve things that could not even be imagined 50 or 60 years ago. As a journalist, you’re going to be making videocasts and podcasts and [using] technologies for research that require completely different skills than somebody 60 years ago going to the library and sifting through books. Those are some aspects of new tasks. So are many of the physical occupations in manufacturing that involve much more technical work. Those have generally been very good for productivity and for worker wages and employment.

That’s one dimension in which the future of technology could have very different effects depending on whether we go [in] the automation or the new task direction. I would also like to add, whether we use technology for information centralization or decentralization is also important in that many of the early hopes about computers were centered on decentralization. People could [do things] in their garages that IBM as a centralized organization couldn’t do. Personal computers enabled that to some extent, not anywhere comparable to the hopes of pioneers of computing in the ’60s and the ’70s.

But today, we are going in the opposite direction. Large language models are information centralization tools. They collect all of the information. They aim to collect all of the information of humanity ultimately, and then centralize that and process that in a centralized manner that then gives you answers. So there’s less for the decentralized human mind and human participation to do.

Centralization and automation are two different poles, but they are complementary."

(https://sloanreview.mit.edu/audio/ai-is-not-improving-productivity-nobel-laureate-daron-acemoglu/)