Karpathy’s AI gap, free Adobe clones, and AI’s effect on persistence

Reid Hoffman (0:00)

Software is definitely not over, that's hyperbole, but the key thing to track is, it's now much cheaper to make software. Even when you get to, not just the, how does AI do encoding, change all knowledge, even if you just say, software becoming cheaper, what does that mean?

Aria Finger (0:16)

George Danzig in 1939, he was a mathematician in class. He came to class late, and there was two math problems on the board, and he wrote them down in his notebook, and you know, he went home and solved them, and then came back into his teacher and said like, I'm so sorry I was late on the homework. And his professor was like, homework? Those were two of the famous unsolved math equations. He didn't realize he shouldn't persist. If we can reverse engineer with AI, a way to increase people's persistence, how many more mathematicians would we have solving these unsolved problems?

Reid Hoffman (0:45)

AI is a cognitive exoskeleton. Part of exoskeleton, you realize you can do a lot more. Like, oh my gosh, you can do these things. Wow, I can jump faster, I can run faster. And so I'm now more excited and interesting in doing it.

Aria Finger (0:57)

Morning, Reid. Great to be here with you.

Reid Hoffman (0:59)

Good morning.

Aria Finger (1:01)

All right, let's jump right in with something that Andrej Karpathy posted last week. And his sort of verdict was that the AI adoption gap has widened into a sharp funnel. So the way he sees it, there's 6 billion people on the planet who have barely touched LLMs. There's 1 to 2 billion people who have seen sort of a free tier chat bot. There's another 20 million professionals that we're talking about a lot, specifically in math and code. And they're watching agents complete in hours, what used to take weeks.

But then there's roughly 5,000 people with internal frontier access. And they're seeing something else entirely. They're seeing autonomous agents running cyber operations, advancing mathematics, and they're doing years of expert work. And so his phrase for this last group was, the vertigo of the curve going vertical.

And it's something felt by almost nobody. It's felt just by these 5,000 people. And so is this just a normal way that technology moves and eventually the market will catch up and people will get to this sort of group of 5,000? Or is something structurally different about AI? And so this gap will take a lot longer to narrow or maybe never narrow at all?

Reid Hoffman (2:18)

So narrowing gaps is kind of a little bit unclear.

What I do think will happen is in terms of, and Karpathy is always super smart in terms of how he looks at these things, that the whole funnel, people will up these levels. So almost like he is kind of describing a pyramid. And what it is actually people will be going through the pyramid. Now whether or not a gap will be narrowing, I think is a unclear question.

What I do think is the thing is that most people have very little substantive experience of the 8 billion people, of what AI adds in their life. And that's one of the reasons why I was like, look, I think part of how we should help people understand about how AI can be very humanity positive, society positive, is to do the medical assistant, and the tutor, and the legal assistant, and the government services, and all of these things in order to get out there. And I think it's one of the things that any government that wants to help its people into the AI age should be doing with vigor and strength. And I've been recommending this to other governments over the years. And because they're so slow at trying to figure out technological deployment for their citizenry, for their society, for industries, that I'm just writing about it now, so that people who actually want to go do something, can talk to me about it. Now, that being said, I think that the most notable thing about Karpathy's statement is not, is there a narrowing of this gap? But as much as a couple of things. One, the fact that the folks who are working at these frontier models are seeing something that is very cutting edge. And I think this is one of the things that, like I'm very strongly in favor of Open AIs having released its mathematical proofs and so forth. And I did see the various reactions of some mathematicians on this.

And we can spend a little bit of time debugging what is right and what is simply Luddite-ism in the mathematicians' reactions. But it's to show these internal models are doing really amazing things. Like the things you see when you use ChatGBT or Claude or Gemini or Co-Pilot from the outside, and from the kind of everyday users are not actually in fact, the cutting edge of what's actually doable and possible now. And I think that's one thing to really note because, and part of that is like, part of the lens of that was OpenAI saying, but we still have amongst the most leading models in the world. Look, we can do all this mathematics stuff. Let's just release a bunch of the results, which is not surprising when you go, there were some mathematicians were like, well, I was working on this for 10 years and now AI has solved it.