Aidan Gomez Unfiltered on AGI, China & AI's Biggest Risk

**Aidan Gomez** (0:00)
A very small pool of these large tech players are becoming a single point of failure for the entire Democratic law. That is not a resilient system, right? That is a single point of failure. And the first thing you learn as a computer scientist or engineer is don't build a single point of failure.

**Eric Newcomer** (0:14)
Are we at AGI today?

**Aidan Gomez** (0:17)
I mean, in many respects, yes. In reality, I think it's actually quite expensive to adopt open source models.

**Eric Newcomer** (0:24)
I'm Eric Newcomer, author of the Newcomer Substack. Let's get into it.
You are co-author on Attention Is All You Need. Take stock of where we are in this moment with foundation models, what we're capable of, and then I'll sort of follow up with where you think we're going. But are you surprised from the moment of writing that paper to where we are today that we've gotten as far as we have in such a short time?

**Aidan Gomez** (0:56)
I don't think anyone on the Transformer paper had any idea what was coming. We built it for Translate, like Google Translate. So it's a very scoped small problem to be solving.
It's a huge shock. I think an even bigger shock for me, given that it's been nearly 10 years now since that paper, is the fact that we're still using the Transformer. As a scientist, you hope you build something, but then you're not married to it. You want someone to come and build something on top of that, that is much better, it blows it out of the water, and that is progress.
There has been a ton of progress, but it's hilarious how the Transformer has been such a great artist in its ability to steal the good ideas from every other architecture, like when SSMs took off, this was like an architecture that would let you have a massively larger context window. And everyone was saying the Transformer is dead, we're going to shift over to SSMs, and then the Transformer just copied those ideas, integrated it into the architecture, and we still call it the Transformer. So I don't know if we'll ever move on.

**Eric Newcomer** (2:06)
So how much more room does it have to run? How much does the next great leap depend on a new idea? I mean, we had, I guess, reasoning models were a big leap outside of that mode, or how do you think about what it's going to take for models to continue to make giant leaps over the next couple of years?

**Aidan Gomez** (2:24)
Yeah, it's exactly that. It's like reasoning is still running inside the platform of a Transformer. So I think people move up the stack, and that's where they invest their energy in innovating. So in the same way that the fundamental training algorithms, the train models haven't changed in much longer than 10 years, they're still the same thing. It might be the case that the Transformer is just the platform, and we're going to continue to build on top of that for a very long time, and it would take something pretty monumental to shift us past it.
But I still hope there's room for progress.

**Eric Newcomer** (3:02)
Are we at AGI today?

**Aidan Gomez** (3:04)
I mean, in many respects, yes. I think definitely.
It's a general artificial intelligence, and it seems like whatever problem we point it towards, we can basically exceed human capabilities in that problem.

**Eric Newcomer** (3:21)
So token maxing has become the buzzword of the moment, but it reflects a real issue, which is companies wanted to incentivize their employees to use AI and get the most out of it. So they said, run wild, we'll have leaderboards, we'll track how much you're spending. Now, I think we've seen people like Uber and some other companies say, oh, maybe we should track what's going on because we're going to blow through our budgets pretty quickly. You work with a bunch of enterprises. What's your read on?
Are the inside tech companies ahead of this trend? And there are lots of old guard companies that still need to have the token maxing failure before they pull back? Or where are we in the arc of spending for spending sake on using AI?

**Aidan Gomez** (4:07)
Well, I think a lot of that exuberance came out of coding and those models. You see this typically in technology cycles. There's an exuberant adoption phase where the CFO tells the CFO, no spend caps, just adopt, adopt, adopt. And it goes into excess. And then there's a correction period. And that comes from the enterprise itself constraining spend. But then also from innovation and compression and efficiency within the modeling companies. So I think we just did this big ramp up to a much larger tier of model. You'll now see one of these cycles of compressing back down.