0:00Software's definitely not over. That's hyperbole, but the key thing to track is it's now much cheaper to make software.
0:08Even when you get to not just the how does AI do in coding change all knowledge work. Even if you just say software becoming cheaper, what does that mean?
0:16George 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, "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?
0:45AI is a cognitive exoskeleton. Part of the exoskeleton is 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 interested in doing it.
0:57Morning, Reid. Great to be here with you.
1:01All right, let's jump right in with something that Andre 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 chatbot.
1:22There's 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 they're doing years of expert work. And so, his phrase for this last group was
1:50the 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 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.
2:18So, narrowing gaps is kind of a little bit unclear. Um what I do think will happen is, in terms of, you know, and Karpathy's always super smart in terms of how he looks at these things, that the whole funnel, like like the like people will up these levels. It's almost like he's kind of describing a pyramid.
2:40And 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.
2:48Um what I do think is the thing 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, you know, help people understand about how AI can be very humanity positive, society positive is, you know, do the medical assistant and the the tutor and the legal assistant and the government
3:16services and all of these things in order to get out there. And I think it's one of the thing 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 um you know, kind of for industries, that I'm just writing about it now. So, that people who
3:46actually want to go do something can talk to me about it. Now, that being said, I think that the the most notable thing about Karpathy statement is not um is there a narrowing of this gap but as much as the um a couple of things. One, the fact that the the folks who are working at these frontier models are seeing something that is very cutting-edge. And
4:15I think this is one of the things that like I'm I'm very strongly in favor of Open AI's having released its mathematical uh proofs and so forth. And then I did see the various reactions of math of some mathematicians on this.
4:29Um and you know, I I'll you know, we can spend a little bit of time debugging what is right and what is simply Ludditism in the mathematicians you know, reactions. But um it's to show these [clears throat] internal models are doing really amazing things. Like the things you see when you use Chat GPT or Claude or Gemini or Copilot from the outside uh and from the kind of everyday
4:57users are not actually in fact the cutting edge of what's actually doable and possible now. And that I think that's one thing to really note because and and part of that is is like you know, part of the lens of that was Open AI 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." Um which you know, um is not surprising when you go I there were some mathematicians were like, "Well, I was working on this for 10
5:27years and now AI has solved it." Uh well, it's like, "Well, look, I was I was uh you know, 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 uh 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 ta-da. And then this driving a car thing is kind of barbaric. But, actually there there's all kinds of utilities and new expanses of driving car, not to say you shouldn't still walk
5:56sometimes. So, anyway, that's one comment from the car path thing. The other is that um 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 on data center skepticism is they 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.
6:26And that's not a good trade for me. Right? Like, search works pretty well.
6:31I'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 a ways that 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?
6:51Where is the stuff that I would be doing?" Like, if you have you know, um you know, AI doing all of this work, where's the place for me?
7:01Now, those of us who are using it still see huge roles for using AI and huge roles for us in decisioning uh and meta-cognition, a bunch of other things. And so, I think it's 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.
7:24Now, 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 like uncertain on that particular part of that.
7:45Let's stick with power users today. So, what are power users doing with AI?
7:51Actually, 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, so Photoshop, Illustrator, Premiere, and four and he built it from scratch. There was no Adobe code touched. And so, he called this a clean room re-implementation.
8:19His comment was that software was over, especially because it's not as if he was using discarded code or things from He was just using Claude and he recreated this thing that people do 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
8:47PCs, they were reviving abandoned titles. And so, all of these things are happening where people are essentially recreating software using AI.
8:57And 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, you know, a few afternoons?
9:16Software's definitely not over. That's that's hyperbole. But, the key thing to track is it's now much cheaper to make software. So, previously, it and and all kinds of things change from that up. Like, it's Like, even when you get to not just the how does AI do in coding trick change all knowledge work which is an interesting even if you just say software becoming cheaper what does that mean? Well, for
9:46example, part of the reason why there was a difference between open source projects and commercial projects was because essentially like software was so expensive to build that only 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 is like you built once and you're done it has to have security updates it has to have functional updates it has to have new OS's it has to have new
10:14integrations you know and and so it doesn't it doesn't it like 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 that very limited rule room for over 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 to maintain it and so that means software is massively more plentiful and that means that your
10:44moat 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's not goes down to zero but it goes down a lot and that means the usual cuz you know 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 SAS apocalypse now part of the reason I was like in the earlier thing was saying not SAS apocalypse is I
11:14think it's a tale of two software companies you know tale of two cities which is you know 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 um 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 everybody kind of most like everyone collectively dislikes it, but it's a little bit like the Churchill on democracy. It's it's it's it's it's it's the worst except for
11:43everything else. Now, it actually can be much more personalized given all the AI and maybe like 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 and you can still have things.
11:58And so I think that you know, 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 like kind of good business margins on software. It's just not what it was before because software is a lot cheaper. Now, you know, one part of it that I find to be very interesting about the the the the the you know, the asymptoting to very low and still not zero for for software not just because, you
12:28know, someone who is a really good developer might, you know, create a whole bunch of open source stuff and then it might be out there, but actually how you deploy and package and trust and open source package now there's going to be 40 open source versions of, you know, uh, Creative Cloud and so like which ones do you use? They'll have different versions and all the rest like there's just that, but it's also of course question of what happens now that software is so easily constructible. You know, does everyone have their own
12:57software running the house for example?
12:59I mean just There's just all these different expansions. That was meant to be deliberately somewhat trivial, but like this is 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, you know, part part of what people frequently think entrepreneurship is is I have a eureka, I have a new product idea.
13:20Um, 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 when especially there's plentiful products, um which is I think, you know, part of, you know, kind of the internet revolution, the mobile revolution bringing on. And then there's the "Eureka, I have new business model ideas," um which is the most rare, but when it does hit, it's huge, ala Google AdWords, etc., etc. And so So, I think this
13:49software going to, you know, kind of, uh asymptoting to extremely cheap, I think creates a wide range of additional, you know, kind of, um not just software products and ranges and new what it could be, but new go-to-market and new business models.
14:07And and it's across this whole spectrum that I think is really interesting. So, the the software is over is 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 like massively transformed. And that's what I think is most interesting about creating the future.
14:31And what do you think this does to IP?
14:33Like, do IP laws just become unenforceable? Do people try to enforce things even more aggressively because of where we're at? Like, how how does that change things?
14:42So, 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.
14:53And, you know, the the simple definition, which I think will probably need to change, is we essentially give you a monopoly on something, um as a first constructor. And, you know, that's uh I think still the right thing to do in pharma, um because it's so expensive and so important to do.
15:11You know, part of the reason why, you know, AI for Monash and, you know, it's it it's it's creating, you know, kind of monopoly drugs, you know, kind of as as a way it's operating. And so, I think there's still some places, but I think there's like like like it'd be interesting to to think about like how would you from first principles reinvent, you know, IP law.
15:32Now, we've always known that software IP law is amongst the most bizarre. It's like the well, I have an idea that you can click on something and with one click you can buy it. And that should be patentable. And you're like, what?
15:45[laughter] Like like Totally.
15:47as a cuz it wasn't like it wasn't that it cost a billion dollars of risky investment in order to do this, which is part of what you're trying to incent in order to make happen. And it doesn't mean that there shouldn't be any software IP.
16:02There's definitely obviously, you know, one of the places where there's also IP is brand and other kinds of things which is important cuz it's like it's my identity. I'm I'm building in trust and reliability and and all of these these features. So, I should have my brand.
16:15But then the question of like, well, maybe certain kinds of intellectual property construction are, you know, rare and risky and tricky enough that we need to kind of award some protection to doing it.
16:29And so, but I think that what it does definitely mean is that the current IP laws around software, which were already pretty mostly, you know, kind of frail and broken, are now even more so from a like like I I trying to patent the action of the software.
16:50Um versus other things. And it's unclear to me which parts of the actions of the software should actually in fact be patentable. Um in a new thing. I haven't really given much thought partially because we're terrible at at at rethinking from first principles. Like, you know, what should IP law generally speaking be in this case?
17:11And it ends up mostly becoming a political fight where, you know, like for example, software companies say, "Of course one click for purchase should be patentable because that adds value to us and you know, this is a little bit like you know, one of the things that I was learning about recently is um for a number of years uh you know, kind of started in Obama and wiped out by DOJ um there was a uh you know, the IRS sending the tax return
17:40to people saying, "Hey, this is a tax return that of all the data we have on you. Tell us if there's anything if there's anything you want to change or anything that's incorrect here. But it means that you don't have to go hire you know, HR Block or TurboTax or all the rest in order to submit it and it's much easier and da da da.
17:58And you know, easier for the person, easier for the government, easier all around. And you know, DOJ got rid of this maybe because of industry pressure um and so therefore is costing the entire society and and all these people more. You you want to have that kind of function and feature. And that's part of the reason why you have to be much more careful about like which things you're actually creating IP protection around because that's the kind of uh of um of you know, kind of uh free to all
18:27society is the kind of thing you want.
18:30Yeah, I mean it's interesting when you bring up brand as it relates to IP because I think some people could argue that brand's going to matter less because then you can vibe code something over the weekend. Who cares about brand?
18:39But I think other people and I'm I'm probably in this camp for the moment are going to argue that brand matters more because the only thing that's going to matter is trust. And so when you're using AI or when you're deciding what to do, sort of the trust of that brand is going to be huge for security, for fidelity, for sort of all the things that you want. Um and so I think people will aggressively um protect their IP as it relates to brand. Um but let's go back to the cognitive space. You again mentioned Tudor and I think a lot of the criticism that people have about AI
19:09they talk about cognitive offloading. They say, "Oh, you know, you're not going to remember the things that you need to know." And people always say, "Well, you know, the calculator was just like that, too. You don't need to, you know, multiply two-digit numbers in your head anymore cuz you have a calculator in front of you."
19:23But, I want to ask about a different kind of cognitive debt. So, recently, a randomized trial of 1,200 people, and this was out of CMU, Oxford, MIT, and UCLA, they found that just 10 minutes of AI use eroded people's persistence. So, once the AI was removed, people abandoned hard problems twice as often.
19:43And the authors called this cognitive debt. And so, this is essentially like grit, persistence. It's like, I am going to try to solve that problem as hard as I can. Well, now that I've used AI, I am so used to sort of getting a quick fix or a quick answer, or this is what people could posit, that I'm going to quit more quickly.
20:01And a lot of people have been talking to this, but this study presented at COLM this week is the first to establish causation, not just correlation. And so, this is, if you use AI, you will be less likely to be persistent. And so, AI capable in so many ways, but perhaps it also makes you less persistent. Like, what do you think, you know, obviously, we need a lot more studies, but what do you think of that trade-off? Does that concern you that you could be more capable, but sort
20:32Well, as a macro, um, and that my answer will completely not surprise you, or probably many of our listeners, which is, look, there might be a challenge here. And if there is a challenge, it's just a question of how we shape AI. So, part of the reason why it's worth doing this is say, well, actually, in fact, we should shape AI to be increasing the capability of persistence. So, like, for example, you know, how do I learn things, you know, in kind of better ways, and so on. Then, I get it that they were contrasting the,
21:00you know, I'm given a hard problem without AI, and will I stay on it? And if I have a tool, it's kind of like like for example when I only had a bicycle, I might bicycle an hour in a town and now that I have a car, maybe I'm not going to bicycle so much cuz I'm like I'm either like and if I don't have my car, I'm just not going to go cuz I'll wait for the car or with car very fixed or something else and that's not necessarily a bad function. Um but you what you do want is you want persistence
21:29all uh ice it is so like for example I don't have I have an amazing tool, the tool's not there. I wait to work to have the amazing tool to work on it. That's not a problem of resistance. A problem of resistance is I just am not really exercising my brain. I'm not trying to get better. I'm not trying to do it and so you want that persistence in the use of the tool and generally you're like to be kept high and I think AI can shape it. The other interesting question about the abandoning hard
21:57problems is part of it what intelligence is is making a you know, kind of and this is the where prediction is important is like, well, do I think I can solve this problem? So for example, you could spend 10 years like working on a hard problem and getting nowhere and obviously maybe there was a chance you could get somewhere and you should have spent it and it was just a acceptable risk, but part of what we learn in wisdom and in judgment is well, which problem should we actually in fact
22:26you know, kind of can persist on, which should we put down and come back to later, which should we consult someone else on, which we say, well, that's one's probably just beyond me or beyond us and I'll I'll see if someone else gets there.
22:39And I think that the um you know, and that kind of gets back to look, AI is a cognitive exoskeleton and actually in fact having that exoskeleton is actually in fact generally really good for us and part of exoskeleton you realize you can do a lot more and part of it is to like you know, what I think you know, mine and yours and all of our team has gotten into is like, "Oh my gosh, you can do these things. Wow, I can jump faster. I can run faster. I can, you know, da da da."
23:07Mentally. And so, I'm now more excited and interested in doing it. It's part of the thing about, like, AI being a really good tutor, is I now want to learn a bunch of things that I wouldn't I wouldn't I would otherwise would have gone, "Ah, going and learning that thing would have been too hard to do." Cuz it's like, "Oh, you just buy a book, and you work your way through the book, and you have enough time, and you don't really have someone to talk to about it, and da da da." And all of a sudden, it's, you know, personalized, and you have an infinitely patient tutor to talk to about it, and so on. So, it's not
23:36that I don't think there's anything like they may well have discovered a real issue. Oh, and then the last point for this is this is for it's a little bit like saying, "Hey, a person has never seen a TV. Now they've seen a TV, and they're a little bit mesmerized by it."
23:52Right? And it's like, "Okay. So, that that's a a known thing." Now, the question is, obviously, as it gets to as people grow up with it, do there is what is their persistence going to be? And that's the most important thing. And keeping persistence high mentally learning is a good feature, and that's a feature we should be paying attention to. How do we shape the AI to make it happen?
24:17It's funny. This is reminding me, and I forget why I heard it recently. It was the it was a UC Berkeley guy, I think George Dantzig, in 1939. He was a mathematician in class, and I I mean, it's apocryphal, but it's so fantastic.
24:30He 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 in to his teacher and said, like, "Ugh, so sorry I was late on the homework. You know, these seemed harder than usual." And his professor was like, "Homework? Those were two of the famous unsolved math equations that I just wrote on the board. You solved two of them." Because he didn't realize he shouldn't persist. He just thought this was the homework. Of course, this is expected of me and so if you know people say like if we can reverse
25:00engineer with AI a way to increase people's persistence like how you know how many more mathematicians would we have solving these unsolved problems. So to your point we just need to be able to tune it so that we can get sort of the persistence and grit that is the optimal amount because there can also be too much. We know people who who run through walls in a in a bad way. So Yeah or keep trying to run through walls and never get through it. [laughter] Right and never actually run through them.
25:26Read always a pleasure. Thank you so much.
25:28Yep, likewise. Great to see you.
25:30Possible is produced by Pallet Media.
25:32It's hosted by Ari Finger and me, Reid Hoffman. Our show runner is Shawn Young. Possible is produced by Tanacity Los, Katie Sanders, Spencer Stromore, Imo Zu, Aman Suri, Danny Garrison, Trent Barbosa, and Tafadzwa Nemarundwe.
25:47Special thanks to Surya Yalamanchili, Saida Sabieva, Ian Ellis, Greg Biato, Parth Patel, and Ben Rellis.
