Software's 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 question of how does AI in coding change all knowledge work — even if you just say software becoming cheaper, what does that mean?
George Dantzig in 1939 — he was a mathematician in class. He came to class late, and there were two math problems on the board. He wrote them down in his notebook and, you know, he went home and solved them, and then came back in to his teacher and said, like, 'Oh, 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?
AI is a cognitive exoskeleton. Part of the exoskeleton is realizing 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 interested in doing it.
Morning, Reid. Great to be here with you.
All right, let's jump right in with something that Andrej Karpathy posted last week. His sort of verdict was that the AI adoption gap has widened into a sharp funnel. So the way he sees it, there are 6 billion people on the planet who have barely touched LLMs. There are 1 to 2 billion people who have seen sort of a free tier chatbot. There are another 20 million professionals that we're talking about a lot, but 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 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, you know, 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?
So, narrowing gaps is kind of a little bit unclear. What I do think will happen is, in terms of — and Karpathy's always super smart in terms of how he looks at these things — that the whole funnel, like people will move up these levels. It's almost like he's kind of describing a pyramid, and what it is, is actually people will be going through the pyramid. Now, whether or not a gap will be narrowing, I think is an unclear question.
What I do think is that most people have very little substantive experience of — you know, 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 how AI can be very humanity positive, society positive, is, you know, 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, you know, I've been recommending this to a number of governments over the years, and because they're so slow at trying to figure out technological deployment for their citizenry, for their society, for, you know, kind of 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 I'm very strongly in favor of OpenAI's having released its mathematical proofs and so forth. And then I did see the various reactions of some mathematicians on this.
And, you know, we can spend a little bit of time debugging what is right and what is simply Ludditism 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 ChatGPT or Claude or Gemini or Copilot 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 part of that is — you know, part of the lens of that was OpenAI saying, 'Look, you know, 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, you know, is not surprising when you go — there were some mathematicians who were like, 'Well, I was working on this for 10 years and now AI has solved it.' Well, it's like, 'Well, look, I was walking to work and now there's cars.' You know, it's kind of like, 'Okay, you know, that's part of what happens, part of what the adoption is.' And sure, you can say that the walking to work is part of the journey — I get fitness and I get time to think. And then this driving a car thing is kind of barbaric. But actually there's all kinds of utilities and new expanses of driving a car, not to say you shouldn't still walk sometimes.
So anyway, that's one comment from the Karpathy thing. The other is that there's this kind of levels that go through it, and that most people's exposure to AI — part of the reason why they have skepticism, data center skepticism, is that you're telling me that my job may be threatened, transformed, lost, etc. But what I'm seeing from my own use of AI is like an enhanced search. And that's not a good trade for me, right? Like, search works pretty well. I'm happy to have an enhanced search, but I don't want to have a transformed job for that. And it's actually most people aren't yet at the point where they're seeing that their work is substantively changed in ways that make them go, 'Oh my god, this is really amazing.' And that's why they also don't have the exposure to go, 'Well, where is the place that I would still be working? Where is the stuff that I would be doing?' Like, if you have, you know, AI doing all of this work, where's the place for me? Now, those of us who are using it still see huge roles for using AI and huge roles for us in decisioning and meta-cognition, a bunch of other things. And so I think it's also getting through these levels of the pyramid going up to the major thing. And then, of course, the idea is to get as many people into the most productive and useful things for their lives, for their work, for their societies, for doing it. Now, back to the kind of closing of the gap: if the top is asymptoting, then the gap will close. But the top may still be increasing, so it may actually be the gap is widening even as the water raises all of the boats potentially a lot. So that's the reason why I was uncertain on that particular part of that.
Let's stick with power users today. So, what are power users doing with AI? Actually, I was talking to my husband last week and he told me about this story that a developer named Brandon Thomas, motivated by Adobe's cancellation fees — he's a power user — he used Claude's Opus 5.5 to build seven free open-source replacements for Adobe's Creative Cloud: Photoshop, Illustrator, Premiere, and four more. And he built it from scratch; there was no Adobe code touched. And so he called this a clean room re-implementation. His comment was that software was over, especially because it's not as if he was using discarded code or things — he was just using Claude, and he recreated this thing that people complain about a lot, and he said, 'Here's the open-source version.'
And so this is new and different because something similar had been happening in gaming for a while, where AI had made decompilation cheap enough to be a hobby, and so modders were reverse engineering binaries, porting console exclusives to PCs, they were reviving abandoned titles. And so all of these things are happening where people are essentially recreating software using AI. And so AI is really collapsing the cost of replicating what took these large teams years to build. And so, is software over? Is it hyperbole? Like, what is going to be this next generation when someone can just vibe code Adobe Photoshop in, you know, a few days, a few weeks, a few afternoons?
Software's definitely not over. That's hyperbole. But the key thing to track is it's now much cheaper to make software. So previously it was — and all kinds of things change from that. Even when you get to not just the question of how does AI in coding change all knowledge work, which is an interesting one — even if you just say software becoming cheaper, what does that mean?
Well, for example, part of the reason why there was a difference between open source projects and commercial projects was because essentially software was so expensive to build that only a limited set of things that were within the cost envelope could be built and deployed within an open source environment. Because part of it is software doesn't just live — it's not like you build it once and you're done. It has to have security updates, it has to have functional updates, it has to have new OSs, it has to have new integrations, you know. And so once a piece of software is deployed, it starts aging right away, and in the modern accelerated environment it ages even quicker. And so that meant very limited room for open source.
Now, you make it much cheaper; it's easier for individuals to do, it's easier to create open source, it's easier to upgrade it, it's easier to maintain it. And so that means software is massively more plentiful, and that means that your moat of 'we pay a thousand engineers to be maintaining a piece of software, and that's why you buy our software' goes down a lot. And it doesn't go down to zero, but it goes down a lot. And that means the usual — the margins, which cause the investment in the business and cause revenue and all the rest — become more challenging. And that's part of the reason why people were talking about SaaS apocalypse. Now, part of the reason I was, in the earlier thing, saying not SaaS apocalypse is I think it's a tale of two software companies — you know, tale of two cities — which is those that are really adopting AI and figuring out what does AI mean now for how they're providing the services, what their moats are.
You know, for example, because software is plentiful, it could now mean that as opposed to one piece of software that is given to everybody and everyone collectively dislikes — but it's a little bit like Churchill on democracy: it's the worst except for everything else. Now it actually can be much more personalized given all the AI, and maybe software becomes more of a platform in terms of how it operates, and then AI builds on top of it, and people still want the depth of the platform elements of it, and you can still have things. And so I think that's one possible path, and there's a number of paths by which people will still be paying for software. My belief is you actually can even still get to kind of good business margins on software; it's just not what it was before because software is a lot cheaper.
Now, one part of it that I find to be very interesting is the asymptoting to very low and still not zero for software. Not just because, you know, someone who is a really good developer might create a whole bunch of open source stuff and then it might be out there, but actually how you deploy and package and trust an open source package. Now there's going to be 40 open source versions of, you know, Creative Cloud, and so which ones do you use? They'll have different versions and all the rest. But it's also, of course, a question of what happens now that software is so easily constructible. You know, does everyone have their own software running the house, for example? I mean, there's just all these different expansions. That was meant to be deliberately somewhat trivial, but this is a range of possibilities, and that's one of the things that's been exciting me about this.
And it's also one of the things that suggests that part of what people frequently think entrepreneurship is, is 'I have a eureka, I have a new product idea.' And, you know, that's great and important, but then obviously it's 'Eureka, I have a new way of doing product distribution, go-to-market,' which is frequently more important, especially when there's plentiful products — which is, I think, part of what the internet revolution, the mobile revolution brought on. And then there's 'Eureka, I have new business model ideas,' which is the most rare, but when it does hit, it's huge, à la Google AdWords, etc., etc. And so I think this software going to, you know, kind of asymptoting to extremely cheap, I think creates a wide range of additional — not just software products and ranges and new what it could be, but new go-to-market and new business models. And it's across this whole spectrum that I think is really interesting. So the 'software is over' — the thing that's specifically wrong about that is actually software is amplified in importance and is changing across all these things. It's just the software game has massively transformed. And that's what I think is most interesting about creating the future.
And what do you think this does to IP? Like, do IP laws just become unenforceable? Do people try to enforce things even more aggressively because of where we're at? Like, how does that change things?
So, IP law is a human construction in order to create the incentive to create investment in IP that ultimately benefits, you know, humanity and society.
