"AGI has arrived" and the argument over what AGI even means

Ah, interesting. Are you a humanist or a speciesist? The same conversation that Elon had with Larry Page.

All right, I'm going to move us on. Uh, our next series of stories here focus on the accelerating conversation around AGI that we're having and that the world is having. So Jensen Huang posted this week that OpenAI trained GPT-6 Astra on more than 100,000 Nvidia Gracewell — Blackwell GPUs, uh, and posted three magic words: AGI has arrived. Congratulations to OpenAI.

So Jensen is calling it uh in Q3 of 2026. Uh, remember on September 1st, we reported Sam Altman expects AGI internally by the end of 2026. And of course, Alex, you've been saying that we've had AGI for what, four years now?

Three years now. And no — since summer of 2020 at the latest.

Okay. All right. And Salim, you've been asking the question, what the heck is AGI anyway?

Why are we even talking about this, right? It's a semantic argument.

Meanwhile, there's economic capability running rampant. If an AI can perform 70 or 80 or 90% of economically valuable cognitive tasks, whether you call it AGI or not becomes completely irrelevant.

I'll just remind people that at last count there were 14 different definitions of AGI and we have no idea what the hell we're talking about, because it's not artificial, it's not really general, and it's not really intelligence. Apart from that, everything is fine. I mean, for God's sakes, I think the more important question is, what scarcities are we now making abundant? Let's just focus on that.

Emad, what do you make of Jensen's proclamation?

I mean, again, for his — his one is a very functionalist one, and it's actually good intelligence, shall we say. I mean, it's clear that Astra is kind of at that level. As to ASI and [unclear], well, maybe we're getting there the next day or two. Um, it is a question of kind of these things. I think you also notice there's 100,000 chips that was trained on. That's a billion dollar training run roughly over 2 months. And the next one is 400,000 chips with those of Vera Rubins. So it's an order of magnitude more compute will be used for the next lot.

Interesting.

If it needs to be used at all, which we'll get to in a bit.

And Dave, what do you make of it? Is this uh — is this just marketing on Jensen's behalf or does he really believe it?

Uh, no, of course he believes it and I think it's real. There's going to be some capability leap. We don't quite know — the chinchilla laws, you know, don't necessarily hold up at larger and larger scales — but some capability leap that in my opinion is only a good thing. Uh, as long as it's contained and kept inside the big labs. Um, but I think that uh, you know, the more GPUs you throw at these all the way up until today, the more GPUs you throw at these training runs, the more spectacular the resulting model has been. Why would that end? I don't think it will end. I think it just gets bigger, smarter, better, more helpful, solves diseases, cures problems. You just have to really be thoughtful about how to contain it. But, but you know, this is Jensen's wheelhouse, too. I think inference is moving off of Nvidia inevitably, but training is not. And so to the extent that we keep getting improvements, it keeps driving Nvidia stock up and up and up.