Arvind Narayanan

speaker

255 appearances 3 recordings 3 series first heard Aug 2024 last heard 25 Jan

Arvind Narayanan’s voice in public audio — every appearance, attributed to the second.

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Recordings per month over the last 12 months — 1 in all, peaking in Jan 2026 with 1.

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The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch · 20VC: AI Scaling Myths: More Compute is not the Answer | The Core Bottlenecks in AI Today: Data, Algorithms and Compute | The Future of Models: Open vs Closed, Small vs Large with Arvind Narayanan, Professor of Computer Science @ Princeton · 28 Aug 2024 podcast

So let's talk for a second about what AGI is. Different people mean different things by it and so often talk past each other. The definition that we consider most relevant is AI that is capable of automating most economically valuable tasks. By this definition, you know, of automating most economically valuable tasks, if we did have AGI, that would truly be a profound thing in our society.

So now for the CEO predictions, I think one thing that's helpful to keep in mind is that there have been these predictions of imminent AGI since the earliest days of AI for more than a half century. Alan Turing. When the first computers were built or about to be built, people thought, you know, the two main things we need for AI are hardware and software. We've done the hard part, the hardware.

Now there's just one thing left, the easy part, the software. But of course, now we know how hard that is. So I think historically what we've seen, it's kind of like climbing a mountain. Wherever you are, it looks like there's just kind of one step to go. But when you climb up a little bit further, the complexity reveals itself. And so we've seen that over and over and over again.

Now it's like, oh, you know, we just need to make these bigger and bigger models. So you have some silly projections based on that. But soon the limitations of that started becoming apparent. And now the next layer of complexity reveals itself. So that's my view. I wouldn't put too much stock into these overconfident predictions from CEOs.

I certainly think the balance is possible. To some extent, every big company does this.

That's fair. And I think, you know, it would take a discipline from a management to be able to pull it off in a way that one part of the company doesn't distract another too much. And we've seen this happen with OpenAI, which is the folks focused on superintelligence didn't feel very welcome at the company.

And there has been an exodus of very prominent people and Anthropic has picked up a lot of them. So it seems like we're seeing a split emerging where OpenAI is more focused on products and Anthropic is more focused on superintelligence. While I can see the practical reasons why that is happening, I don't think it's impossible to have disciplines management that focuses on both objectives.

In the past, they didn't have this balance. They were so enamored by this prospect of creating AGI that they didn't think there was a need to build products at all. And the craziest example for me is when OpenAI put out ChatGPT, there was no mobile app for six months. And the Android app took even longer than that.

You know, there was this assumption that ChatGPT was just going to be this kind of really demo to show off the capabilities of the models. And OpenAI was, you know, in the business of building these models and third party developers would take the API and put it into products. But really, AGI was coming so quickly, even the notion of productization seemed obsolete.

This was, you know, I'm not trying to put words in anyone's mouth, but this was kind of a coherent, but in my view, incorrect philosophy that I think a lot of AI developers had. And I think that has changed quite a bit now. And I think that's a good thing. So if they had to pick one, I think they should pick building products.

But it certainly doesn't make sense for a company to be just an AGI company and not try to build products, not try to build something that people want. And just assuming that AI is going to be so general, that it's just going to, you know, do everything that people want, and that the company doesn't actually need to make products.

So I don't know is the short answer. But at the same time, you know, we've been in this kind of historically interesting period where a lot of progress has come from building bigger and bigger models that need not continue in the future. It might. Or what might happen is that the models themselves get commoditized and a lot of the interesting development happens in a layer above the models.

We're starting to see a lot of that happen now with AI agents. And if that's the case, great ideas could come from anywhere, right? It could come from a two-person startup. It could come from an academic lab. And my hope is that we will transition to that kind of mode of progress in AI development relatively soon.

I think that's a very serious possibility. And I think this is actually one area where regulators should be paying attention. You know, what does this mean for market concentration, antitrust, and so forth. And I've been gratified that these are topics that, at least in my experience, US regulators are considering.

And I believe in the UK, the CMA, the Competition and Markets Authority as well, and certainly in the EU. So yeah, in many jurisdictions, now that I think about it, this is something that regulators have been worried about.

So in a sense, AI regulation is a misnomer. Let me give you an example from just this morning. The FTC has been worried about the Federal Trade Commission in the US, which is an antitrust and consumer protection authority, has been worried about people writing fake reviews for their products. And this has, of course, been a problem for many years. It's become a lot easier to do that with AI.

So now someone who thinks about this in terms of AI regulation might say, oh, you know, regulators have to ensure that AI companies don't allow their products to be used for generating fake reviews. And I think this is a losing proposition. Like how would an AI model know whether something is a fake review or a real review, right? It just depends on who's writing the review.

But instead, that's not the approach that the FTC took. They recognized correctly that it's a problem whether AI is generating the fake review or people are. So what they actually banned is fake reviews. And so what is often thought of as AI regulation is better understood as regulating certain harmful activities, whether or not AI is used as a tool for doing those harmful activities.

80% of what gets called AI regulation is better seen this way.

I broadly agree with that. I will add a couple of additions to that. One is there are many kinds of harms, which we already know about and are quite serious.

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