The AI state of play: My interview with Ramez Naam

My guest this episode is Ramez Naam. Ramez has spent years thinking, writing, and investing around technological progress in energy and artificial intelligence. He is the founder and managing partner of Planetary VC, where he invests in emerging technologies across areas including clean energy, electrification, and AI. He is also the author of The Infinite Resource, More Than Human, and the Nexus science-fiction trilogy.

Today’s conversation will focus on the pace of AI progress, the roadblocks that could stand in the way, and what all of this could mean for long-term economic growth.

In this episode

* The state of AI growth (00:38)

* Roadblocks to an intelligence explosion (06:11)

* AI in the short term (13:34)

* AI’s economic impact (19:00)

* Balancing AI safety and exploration (22:04)

* What will power the AI economy? (31:04)

The state of AI growth (00:38)

AI capabilities are likely to be jagged for the foreseeable future. I believe in superintelligence, I believe it’s already here. We already have superintelligence playing Go. We’re crossing into superintelligence in formal math. That doesn’t mean we’ll have super capable persuaders, politicians or city planners.

Pethokoukis: Where are we right now? As we’re recording this, OpenAI just released all these mathematical proofs and people are being wowed by that. Wherever you think we are at in the world of AI progress, does that result change your opinion?

Naam: It’s amazing. It’s very impressive. I’m not sure I thought we would be here right now. Math is a very special domain. I think about the different types of domains that AI is good or bad at in terms of how highly verifiable they are. And AI first got really good at things like chess and then the game of Go, and now parts of math, formal math, because these have some interesting characteristics. You can generate an unlimited amount of data, and AI is very data hungry. You can get to a very firm yes or no with one-hundred percent accuracy. You can verify it as absolutely correct or not correct.

But like human persuasion, politics, policy, and so on you can’t. You can do all of that at the speed of compute. You’re limited only by the speed of your computing to figure out if you won the game of Go or if the math proof is correct, which is not how it is in biological research or new material science, which are messy real world. So math is a domain that is pretty far over on the highly verifiable, symbolic, perfect training data, absolutely correct yes or no. All of it happens in software. Where something like curing aging is messy.

We don’t have the training data at all. We’re going to need new instruments, new science, you need experiments, and your lag to verify anything is years, decades. And so that’s something that AI is not going to be as inherently good at, in my view.

I made you leap over my first question, which is sort of where we are. There are certainly people who think that, by the end of 2027, there will be no debate that everything is changing in a profound, rapid way that changes all aspects of life. And then there are those who say “that doesn’t seem to be happening right now.” The world is a messy place.

The world of carbon based life forms is a messy place. And whatever the capabilities in the lab of this technology, how it emerges into the real world is going to be a much slower and uneven process. On those two poles, where are you?

Yeah, I mean I’d say look, I’m an AI optimist and I’m also an AI realist. And I think there’s two separate issues we’re dealing with in your question and the one before that. One is speed of diffusion, right? And if you look at expectations of how much impact AI will have on the economy, and you ask people in AI labs, some of them say it’s going to take us to 20 percent annual growth. I mean there’s one chart out there that says it’ll get us to 100 percent annual growth. Then you ask economists

Move the decimal point over.

Yeah, just get rid of it even.

You ask economists and the consensus to you is that, yeah, maybe it’s half a point of annual GDP growth. The economists have just seen too many past technologies run into friction in being absorbed into real life work. The diffusion rate, the dissemination rate, and the bottlenecking on the long pull on the last thing that can be automated.

So in computer science we talk about something called Amdahl’s law, which is usually in terms of performance optimization. And if you have a piece of code and you can take 90 percent of it and speed it up dramatically, well the 10 percent you can’t speed up becomes the bottleneck. And sometimes that means you get very little actual speed up if it’s the thing that you’re waiting on.

So in things that AI can do entirely on its own, I think we should expect incredible breakthroughs and incredible speed of progress. In things where the task is messy, as you said, and a human still needs to be involved where