✨⏩ My chat (+transcript) with ... economist Robin Hanson on AI, innovation, and economic reality

In this episode of Faster, Please! — The Podcast, I talk with economist Robin Hanson about a) how much technological change our society will undergo in the foreseeable future, b) what form we want that change to take, and c) how much we can ever reasonably predict.

Hanson is an associate professor of economics at George Mason University. He was formerly a research associate at the Future of Humanity Institute at Oxford, and is the author of the Overcoming Bias Substack. In addition, he is the author of the 2017 book, The Elephant in the Brain: Hidden Motives in Everyday Life, as well as the 2016 book, The Age of Em: Work, Love, and Life When Robots Rule the Earth.

In This Episode

* Innovation is clumpy (1:21)

* A history of AI advancement (3:25)

* The tendency to control new tech (9:28)

* The fallibility of forecasts (11:52)

* The risks of fertility-rate decline (14:54)

* Window of opportunity for space (18:49)

* Public prediction markets (21:22)

* A culture of calculated risk (23:39)

Below is a lightly edited transcript of our conversation

Innovation is Clumpy (1:21)

Do you think that the tech advances of recent years — obviously in AI, and what we're seeing with reusable rockets, or CRISPR, or different energy advances, fusion, perhaps, even Ozempic — do you think that the collective cluster of these technologies has put humanity on a different path than perhaps it was on 10 years ago?

. . . most people don't notice just how much stuff is changing behind the scenes in order for the economy to double every 15 or 20 years.

That’s a pretty big standard. As you know, the world has been growing exponentially for a very long time, and new technologies have been appearing for a very long time, and the economy doubles roughly every 15 or 20 years, and that can't happen without a whole lot of technological change, so most people don't notice just how much stuff is changing behind the scenes in order for the economy to double every 15 or 20 years. So to say that we're going more than that is really a high standard here. I don't think it meets that standard. Maybe the standard it meets is to say people were worried about maybe a stagnation or slowdown a decade or two ago, and I think this might weaken your concerns about that. I think you might say, well, we're still on target.

Innovation's clumpy. It doesn't just out an entirely smooth . . . There are some lumpy ones once in a while, lumpier innovations than usual, and those boost higher than expected, sometimes lower than expected sometimes, and maybe in the last ten years we've had a higher-than-expected clump. The main thing that does is make you not doubt as much as you did when you had the lower-than-expected clump in the previous 10 years or 20 years because people had seen this long-term history and they thought, “Lately we're not seeing so much. I wonder if this is done. I wonder if we're running out.” I think the last 10 years tells you: well, no, we're kind of still on target. We're still having big important advances, as we have for two centuries.

A history of AI advancement (3:25)

People who are especially enthusiastic about the recent advances with AI, would you tell them their baseline should probably be informed by economic history rather than science fiction?

[Y]es, if you're young, and you haven't seen the world for decades, you might well believe that we are almost there, we're just about to automate everything — but we're not.

By technical history! We have 70-odd years of history of AI. I was an AI researcher full-time from ’84 to ’93. If you look at the long sweep of AI history, we've had some pretty big advances. We couldn't be where we are now without a lot of pretty big advances all along the way. You just think about the very first digital computer in 1950 or something and all the things we've seen, we have made large advances — and they haven't been completely smooth, they've come in a bit of clumps.

I was enticed into the field in 1984 because of a recent set of clumps then, and for a century, roughly every 30 years, we've had a burst of concern about automation and AI, and we've had big concern in the sense people said, “Are we almost there? Are we about to have pretty much all jobs automated?” They said that in the 1930s, they said it in the 1960s — there was a presidential commission in the 1960s: “What if all the jobs get automated?” I jumped in in the