Mark Blyth

The control valve becomes unemployment. And it's not my unemployment. And it's not your unemployment. It's their unemployment. Right. That is the stakes of them being wrong if, in fact, they're wrong. Exactly.

The control valve becomes unemployment. And it's not my unemployment. And it's not your unemployment. It's their unemployment. Right. That is the stakes of them being wrong if, in fact, they're wrong. Exactly.

The control valve becomes unemployment. And it's not my unemployment. And it's not your unemployment. It's their unemployment. Right. That is the stakes of them being wrong if, in fact, they're wrong. Exactly.

Well, when you spend the past year writing a book on it, you're like, do you think you know one or two things about that? If I don't, I'm in trouble.

Well, when you spend the past year writing a book on it, you're like, do you think you know one or two things about that? If I don't, I'm in trouble.

Well, when you spend the past year writing a book on it, you're like, do you think you know one or two things about that? If I don't, I'm in trouble.

This is 20VC with me, Harry Stebbings, and we're sitting down today with one of my favorite writers in AI. He's been a big proponent in the belief that despite what many people think, increasing the amount of compute from this point will be unlikely to increase model performance significantly moving forward.

I'm thrilled to welcome Arvind Narayanan, Professor of Computer Science at Princeton and the Director of the Center for Information Technology Policy. This is an incredible discussion that goes very deep on the bottlenecks in AI today, and you can watch it on YouTube by searching for 20VC.

You have now arrived at your destination. Arvind, I am so excited for this, dude. I was telling you just now, I am one of your biggest fans on the Substack newsletter. I can't wait for the book. So thank you so much for joining me today. Thank you. I really appreciate that.

Now, I want to get pretty much straight into it, but for those that don't read the substat, which they should do, can you just provide a 60-second intro, some context on why you're so well-versed to speak on the topics that we are today?

I would just love to start because you have done some work in the cryptocurrency field, done a lot of work in the crypto field. I'd just love to start before we dive in deep on infrastructure. How does the AI hype today compare to Bitcoin hype? How is it the same and how is it different?

I do want to ask, and we'll start with kind of the hardest question of all, but it's the most important, and you've written about this, and I loved your piece.

The kind of core question that everyone's asking right now is, does more compute equal an increased level of performance, or have we reached a point where it is misaligned and more compute will not create that significant spike in performance? Kevin Scott at Microsoft says, absolutely, we have a lot more room to run.

Why are you skeptical, and have we gotten to a stage of diminishing returns on compute?

MARK BLYTH, Can we just take them one by one there? There was a lot of great things that I just want to unpack. You said there about kind of potentially the shortage of data being the bottleneck to performance.

A lot of people say, well, there's a lot of data that we haven't mined yet, which the obvious example that many have suggested is kind of YouTube, which has obviously, I think, 150 billion hours of video. And then secondarily to that, synthetic data, the creation of artificial data that isn't in existence yet. To what extent are those effective pushbacks?

What about the creation of new data that doesn't exist yet?

While we're on utility value of data, when we look at effectiveness of agents, I've had Alex Wang at Scale.ai on the show, and he said the hardest thing about building effective agents is most of the work that one does in an organization, you don't actually codify down in data. You remember when you were at school and it says, show your thinking or show your work.

You don't do that in an organization. You draw on the whiteboard, you map it out, and then you put down what you think in the document. The whiteboard is often not correlated in a data source. To what extent do we have the data of showing your work for models, agents to actually do in a modern enterprise?

To what extent do you think enterprises today are willing to let passive AI products into their enterprises to observe, to learn, to test? And is there really that willingness, do you think?