Mark Blyth

You said about smaller models. Help me just understand again. I'm sorry. The show is very successful, Arvind, because I think I asked the questions that everyone asked, but they're too afraid to actually admit they don't know the answers to. Why are we seeing this trend towards smaller models?

And why do we think that is the most likely outcome in the model landscape to have a world of many smaller models?

Will Moore's law not mean cost goes down dramatically in actually a relatively short three to five year period?

Where does it become a barrier and where does it not?

So we have smaller models, and they're effective, as we said, because of cost, and they're popular because of cost. What does that do to the requirements in terms of compute?

When we think about the alignment in compute and models, we had David Kahn from Sequoia on the show and he said that you would never train a frontier model on the same data center twice. Meaning that essentially there is now a misalignment in the development speed of models and that is much faster than the development speed of new hardware and compute. How do you think about that?

So we are releasing new models so fast that computers are unable to keep up with them. And as a result, you won't want to train your new model on old H100 hardware that is 18 months old. You need continuously the newest hardware for every single new frontier model.

Speaking of that commoditization, the thing that I'm interested by there is kind of the benchmarking or the determination that they are suddenly commoditized or kind of equal performance. You said before LLM evaluation is a minefield. Help me understand why is LLM evaluation a minefield?

We mentioned that, you know, some of the early use cases in terms of passing the bar, some real kind of wild applications in terms of how models are applied. I do just want to kind of move a layer deeper to the companies building the products and the leaders leading those companies. You've got Zach and Demis who are saying that AGI is further out than we think.

And then you have Sam Altman and you have Dario and Elon in some cases saying it's sooner than we think. What are your reflections and analysis on company leader predictions on AGI?

Is it possible to have a dual strategy of chasing AGI and superintelligence, as OpenAI very clearly are, and creating valuable products at the same time that can be used in everyday use? Or is that balance actually mutually exclusive?

If I push you, if you think about your priority, your priority at OpenAI is, say, achieving superintelligence and AGI. Their best researchers, their best developers, the core of their budgets will go to that. When you have dual priorities, one takes the priority. And so there is that conflict.

What did you mean when you said to me that AI companies should pivot from creating gods to building products?

Do you think it's even possible for companies to compete in any level of AGI pursuit? When you look at the players and the cash that they're willing to spend, you know, Zuck has committed $50 billion over the next three years. When you look at how much OpenAI has raised over the last three years and they carry on that run rate, it's something crazy like that.

It'd still be $38 billion short of a Zuck spend over a three-year period. Can you create AGI-like products or God-like products unless you are Google, Amazon, Apple, or Facebook?

With the commoditization of those models and the appreciation that value can be built on top of them, does that not go back to what I said, though, which is really there is three to four core models which are financed by cash cow cloud businesses. You know, the obvious says Amazon, there's Google. And then for Facebook, there's obviously Instagram and News Feed.

And there are three large model providers which sit as the foundational model there. And then every bit of value is built on top of them.

If you were, as you said at the beginning about kind of your work on policy, you have US regulators and European regulators, what would you put forward as the most proactive and effective policy for US and European regulation around AI and models?

When I had Ethan Mollick on from Wharton, he was like, you know, the best thing to do actually is like a allow and watch a policy. He had a much more academic approach to it in terms of naming than that with, you know, wonderful principle from some ancient learning professor.

But he said essentially we should let everything flourish and then regulate from there rather than proactively regulate ahead of time, not knowing outcomes. Does that ring true to you?