The following is a rough transcript which has not been revised by The Jim Rutt Show or Adam B. Levine. Please check with us before using any quotations from this transcript. Thank you.
Jim: Today’s guest is Adam B. Levine. Adam is a serial entrepreneur, most recently serving as Chief Innovation Officer at Blockade Labs. He’s also a longtime podcaster and generally knowledgeable and opinionated guy regarding cryptocurrencies. He’s probably best known in that regard as the longtime host of the Let’s Talk Bitcoin podcast. Welcome Adam.
Adam: Thank you Jim.
Jim: We’re going to talk about something somewhat different today. We’re going to talk about AI programming aids, especially for non-techies. So how much of a non-techie are you Adam?
Adam: I’ve always thought of myself as a technical non-technical. Right? So what I really like doing is I like working with engineers and I like working with people who are way smarter than I am and do way more of the actual work. And what I tend to do is I tend to be really good at looking at new technologies early on, figuring out which dots should connect to which other dots and what would be possible where those dots connected – usually way too early. I am not a developer at all. I do not have the patience for it. But in the last four years, as this stuff has been coming up, I’ve been increasingly doing my own little projects just personally in ways where I used to have to start a company in order to do it. So I’m more technical than most people, but I’m definitely not a developer and it’s been quite eye opening going through this process of being able to see what it actually is a developer does, you know, in order to do that. And really, it’s mostly me just watching and kind of advising. So that’s that – how’s that for it?
Jim: I’ll ask one or two follow-up questions. You know, I started – I wrote my first computer program when I was 15. So I’ve been writing code for a ridiculous number of years. Guess that would be, could that possibly be fifty-seven years? Yes. That’s the answer. So, and I’m sure I haven’t gone, even though generally speaking, I’ve not worked as a professional programmer very often, but as an executive in technology, anyway, neither here nor there. So, four years ago, what, when you first started putting hands on a little bit, what were you doing? Writing Python or?
Adam:: So again, just to say, like, don’t ever go hands-on with code. It’s one of the things that still is a limitation on this stuff a little bit, mostly because of complexity and just because I don’t really understand the best practices around things like formatting, file structures and stuff like that. And I don’t really wanna learn. I don’t really feel like it’s gonna be that useful.
Four years ago, I started working pretty extensively actually with LLMs as it relates to fiction. And so that was kind of the start of working in that side of things. And you and I eventually created a project together that kind of dug deeper into that. But really what I always like to do is I like to take ideas that I have that I don’t really want to do anything with, but I find really interesting. And then you can use that as sort of a jumping off point to test the current state of technology.
And so for a long time, really until the last year, I would say that mostly what I was doing with these is I was understanding the state of the technology, understanding what was possible for someone like me. And in the last year and in particular the last three or four months, that’s just kind of exploded from being “wow, this is really cool, I can see where it’s going” to “hey, I think I’ll actually build this thing right now.”
I have a thing that I’ve been trying to patent for the last three years, you know, in my spare time and I’ve been building that out in an increasingly complex set of Chrome extension plugins essentially, because that was kind of the best form factor for what I was doing there. But just this morning, I was working on – last three days I’ve been working with Claude 3.7 in think mode through one of the IDEs, which is like a code browser essentially, and building a rather complex sacred geometry visualization training tool with Three.js and stuff like that. And it’s just getting awesome and fun.
So that’s the level of complexity that we’ve kind of gone from – it was possible to really kind of just dick around for a minute and be like, “wow, that’s kind of a really cool thing that’s not useful and doesn’t really work, but I spent four hours on it and hey, I could do something I couldn’t do without that.” To now, you know, I can spend that same four hours and I can get something that’s complex and multi-stepped and there are still limitations, but they’re vanishing very rapidly.
Jim: Okay. So to recap, when you first started fooling at this stuff, it was basically what? Doing prompt engineering, you know, trying to see what I could get out of the LLMs by doing interesting things and multistep and cutting and pasting into Word and that kind of stuff.
Adam:: Yeah. Yeah. A lot of that stuff, a lot of really manual processes background where you’re using kind of the AI LLM portion as like the engine, but almost the entire everything else is very manual. And that has a lot of challenges that are associated with it. And the other thing that’s really important is just how far we’ve come in terms of how much information can you fit into an LLM’s short-term memory at any given time. Right? Like that is such a change to go from, you know, like, I think we were at like 2,000 tokens or something like that back in the GPT-3, pre-ChatGPT days. Maybe it was even lower than that. But again, it’s like you could use it for autocomplete, but that wasn’t very useful for someone like me because I don’t know what I’m autocompleting. So so went from simple to very complex.
Jim: Yeah. So then recently, it sounds like you’ve – you said the words IDE, you know?
Adam:: I did. I understand what that is now.
Jim: Yeah, exactly. Right? And, so when did you first put your hands on IDE and which one was it?
Adam:: So I’ve been using one of the Microsoft ones just for kind of organizational stuff as I started working on these projects. But the light bulb moment for me was Windsurf. Windsurf is sort of an AI-first IDE that has some agentic features in it and stuff like that. But really the problem that an IDE solved for me was managing all of the changes. Because what’ll happen is you’ll work with one of these LLMs, you know, in like a ChatGPT or a cloud interface or something. I subscribe to essentially all of these just to constantly kind of play with whatever is the best.
You know, at first it’ll give you here’s the full bit of code, right? And you copy that in and that’s really easy to copy in. And then as it gets deeper and as you’re making more changes to it or as it’s fixing bugs, it’s like, alright, well, keep all the code up here, but insert this at this place and then keep the code here and then insert this place. And what I didn’t really understand early on – you know, I’ve been working with software for my entire life, and I’ve been running companies that build software for thirteen years. And I had never really gone down to this level before. So I didn’t really even understand just how much of a magic spell software is. Right? Where everything has to be in exactly the right place with exactly the right understanding to fit the sort of language that it is.
And so as you go up in complexity, that becomes a real problem. And so what’s revolutionary about these kind of AI-first interfaces is they have access to the entire sort of structure of the thing that you’re working on. And rather than having to go back and forth to GitHub or feed it into it because there are things like the – I forgot what the acronym stands for, but MCP effectively allows you to connect other tools into these things. Claude, Anthropic’s Claude integrated that into their tool first and now it’s making its way into the IDEs and it’s really awesome. But that’s really a limitation is like to the extent that I need to have expertise to pull something off here, that’s a problem.
Jim: Let me just probe down just a little bit to get a sense of what you’re – to probe your ignorance. Right. So presumably you’re doing this stuff in Python mostly?
Adam:: Honestly, most of the stuff that has been getting worked on recently is JavaScript actually.
Jim: As a non-nerd, you don’t realize that there are all these religious wars out there. I understand, and I’m happy to not participate in anything.
Adam:: Whenever I’m doing one of these projects, again, that this is why it’s so important that the technology improves for me is because it gets smarter. I don’t have to. And then I’m just like, it’s like, what do you wanna do this? And I’m like, I don’t know. You pick. And then it picks and it works.
Jim: So for instance, you do not know how to probably the syntax for including other files into a file to make a program or you may vaguely know how to do it.
Adam:: I vaguely know it from having seen the AI do it a bunch of times. And I’ve looked at code before and I’ve downloaded and gotten open source projects running before. So I’m not completely, you know, incompetent when it comes to this stuff. But I actually feel like it’s something of an advantage in the role that I tend to fill to not have a good idea of the mechanics of how it would get done because it allows me to completely skip over all of that and simply think about what are sort of the best and most interesting combinations of the technology as it relates to functionality for users. So I’m still blissful in my ignorance, but yes, it’s very funny.
Jim: That is very interesting. Let me jump back a little bit. I’ll talk about my own use of LLMs in coding. I actually did not participate pre-ChatGPT release in November 2022, or as my good friend Peter Wang says, “Kitty Hawk 1903.” And but I immediately tried it. It was okay actually, and it kept getting better very rapidly.
The ScriptHelper project you mentioned, Adam and I worked together on that. It’s a very big piece of code. I wrote it all myself until the very end and I used LLMs all the time. I never would have been able to do it at the level of effort I put into it. I did it in what was then the classic way – I wrote the code in a classic IDE, Visual Studio in this particular case, and I always had a ChatGPT window open, or two or three. Typically I just used it to write functions. Here’s some complicated head-hurting thing, you know, use regular expressions to do this recursion on a list that has hierarchical elements in it. Write the code to do that and does blah blah. If you could say it, it could write it. Even relatively early, GPT-3.5 was just fine at writing functions, but I had to do all the structuring, create the function architecture, the overall design that I had laid out, the UI manually, all this sort of stuff. Even with all that said and done, my estimate over the ScriptHelper project, which for me was probably 500 hours, maybe a little less – 400 hours, something in that range – I estimated a 3X improvement in my productivity compared to if I had done it the old-fashioned way. And that’s a lot!
Adam: Yeah.
Jim: I will say it’s probably more for someone like me who’s not an everyday programmer where you don’t know all the ins and outs of every goddamn little obscure library. But of course a lot of professional programmers don’t either. And so they don’t reach out and use the new tools because, “Oh, I don’t understand that API.” Well, fortunately you don’t need to anymore because our good friend ChatGPT-3.5 could do the job. But since then, additional little projects I’ve been working on, I’ve been first started using Cursor, which is essentially Visual Studio Code – not fucking Microsoft, the world’s most idiotic marketeer. So they have Visual Studio and Visual Studio Code. What moron would come up with those two names? Because they’re totally different technology.
Adam: It was Visual Studio Code that I was using in the pre-Cursor days, yeah.
Jim: Yeah, I was always an old school Visual Studio dude. But I switched to Visual Studio Code because that’s where all this AI action is. There is the Microsoft Copilot solution, but it’s surprisingly lame, I find. I immediately bailed and went to Visual Studio Code, and then particularly Cursor, which is a forking of Visual Studio Code – I have to always say “the code” – which is an open source project. They forked it and do their own thing with it. And they’ve added all kinds of amazingly smart AI things, very much like script helper in that it allows you to use the AI at different levels – totally general, sees your whole project, or you just start with a blank sheet of paper and type in three sentences and it starts to create the program, or work on a file-by-file basis, or highlight a block of code and say “do this to that.” And so what you used to call the hierarchy of grossness is actually more or less appropriately executed in Cursor.
But then I also got talked into by a friend of mine using something called Klein, which is more like a fully automated crazed chainsaw that reads the error messages, responds to them – you know, build errors, runtime errors. And just go, “Ah!” I described it to somebody as like working with an extremely bright 15-year-old who learned how to code about three months ago and is doing a double dose of Adderall. It just goes and goes and goes, and you sit back and you watch it. It’s nuts, but on relatively simple projects, it actually does it. I actually had to build a relatively complicated Flask website with very little human interaction. But then as I started trying to get on a more complicated and more fiddly, detaily kind of project, Cursor actually beat it in that one.
Guest: Yeah, I like the fully automated ones like Klein. They definitely are easier to use for sure, and I think that’s where all of this stuff is going. But I feel like right now, if you go with a fully automated solution, even for someone like me, it’s actually harder in some ways to get what you want because it’s so autonomous and because also it’s kind of limited. Whereas, you know, if you’re using – like I said, Windsurf has been kind of my tool of choice. And now that Claude 3.7 with the thinking mode is out, that has really improved the ability of it to kind of resolve issues that Claude 3.5 couldn’t.
I was just talking to somebody about this the other day. It’s like Claude 3.5 was for sure the best kind of coding model that was out there in terms of just not screwing up and tending to be quite elaborate and get it right most of the time. But it was an old model by the time you got to like a couple of months ago and all the thinking models were coming out. So it was interesting to see this one get released as a 3.7 rather than a 4. And I suspect that, of course, what that means is that this is like a checkpoint midway through the training of 4 and that 4 is going to be even better. So I love the competition in this space. I can’t express that enough. It is so much of why this is going so well and why we’re getting to kind of that place that I think we’re going, which is this pure idea space, which is really empowering to someone like me and a little bit scary to people who program for a living, I think.
Jim: Yeah. As I say, mama, don’t let your babies grow up to be coders because they’ll be fucking obsolete in about eighteen months. And I was about to say, in my own arc, I’ve been working on a couple of little interesting projects on the side, as I always did, not always do, but from time to time. And two of them were very simple Python kinds of things, I could just knock out with a little bit of help from Cursor. But the third one was me leaping into a technology I didn’t know, which is Flutter and Dart, which is a mobile cross-platform web, macOS, Windows. You can do it all from one code base and it actually works, but it’s conceptually quite different from the kinds of UIs I’ve used before.
The logic was relatively complicated. And so I said, let’s see how far I can drive putting the Cursor in composer/agent mode, and let’s see what we can do here. And I have written quite elaborate, not script helper elaborate, but call it 10% of script helper without writing a single line of code. Now I’ve looked at a lot of lines of code, but I have actually not personally diggle, diggle, diggle, diggle, diggle attempted to. But because I don’t know what the fuck I’m doing in Flutter, I’m afraid that if I change one thing, the world will end. So far now I will say I reached a stable point yesterday where it does the prototype, a fancy prototype, does most of what you need. And so now I’m gonna go through and learn the code because one of the cool things I have found is that there is no better teacher for coding than these LLMs.
Guest: If you want to learn.
Jim: If you want to learn. Because you know what you’re trying to accomplish, right? So you know what the damn thing is. Not like the candy episodes in the book. I wanted to do X, Y and Z. Let’s see how it did X, Y and Z. And I also find myself constantly building. In fact, I have a whole Cursor folder of what I call research where I build little stub programs to do things just so I understand them. Just small little things. And it’s really quite interesting.
So, you know, that’s sort of been my arc from, you know, sidecar ChatGPT up to, you know, trying the full auto shit saying, it’s a little too Adderall yet. I could see that that will be the answer sometime, but for now I find the traditional IDE with strong augmentation to be my sweet point. And I’m like, oh, by the way, I estimated that on this Flutter project, the productivity gain is at least 5x. And having kind of gotten deeply back into coding with ScriptHelper, I am a less naive programmer than I was when I started doing that project where I hadn’t done anything really heavy for about three years. So, 5x on where I already was, which was relatively high, is quite fucking impressive.
Guest: Yeah. So two things I would point out. One is that I still very much consider everything that I’m doing just experiments to test kind of where the technology is. But those experiments have gone from being things where it’s like, cool, I got 40% of the way to where I wanted to go and then I had to stop because it couldn’t get any further. To now I’m getting probably 80 to 90%. That’s a change over the last three months, four months. But it’s like if you compared it to where it was at the beginning of last year, it was maybe 10%. So it’s a huge improvement from there. And then relative to my current skill level, right, so you’re saying it’s a 5x performance improvement for you as far as the efficiency of the work. For me, this is a 1000% improvement. It’s an infinite…
Jim: Divide by zero, right? It’s classic divide by zero error. How much skills you have five years ago? None. How much do you have now? Some, therefore it’s an infinite amount.
Jim: So in the short term, I think it really is valuable to understand – to be a decent enough beginner programmer to have a good understanding of what’s happening, even if it’s operating at a skill level that you can’t operate at. That was one thing that I used to spend a lot more time doing than I do today, which is helping the AI debug. I’ve never debugged before. I’ve done lots of bug testing and reporting of bugs and stuff like that for all the businesses because I was always kind of a user-in-chief. But I had never actually helped with direct debugging and learned a lot about logging and stuff like that as a result of this process.
And I mean, kind of where all this is going, I think, is that the sea is rising essentially. Right? The waterline is rising and here I am at the bottom of the ocean. So right, and like standing on the ground back then, here you are in a boat that’s in a lake that’s like 1,500 feet up. And then there’s the programmers who are like the absolute masters and they’re up at 10,000 feet. I think what’s happening is that the level of equilibrium is rising and it’ll hit that level that you’re at probably next year. And then maybe six months later, we’ll be up at that Himalaya level and the water will have risen and all of our boats will be there. And it will really be so much more about idea than execution. And for me, that is the part that is so exciting because it’s so hard to do anything in real life and there’s so many good ideas. And I think we’re just gonna get all of them whether we want to or not.
Jim: Yeah, I’ve talked about this a fair bit – the so-called Jevons paradox. It’s an economic thing that when a fundamental resource becomes cheaper, the naive assumption is the total amount of money spent on X will go down. It turns out that if the demand curve and supply curve have the right shape, which they often do, the result’s the opposite. Two great examples are gasoline and electricity. Both of them have come down by a factor of over a hundred in inflation-adjusted terms since the late nineteenth century, and yet the total dollars and percentage of GDP allocated to them continues to go up.
Here’s why it’s very simple – when electricity is very expensive, it made sense to have one light bulb hanging from a wire in your tenement so you could read the newspaper after dark. And that was about it for consumers. It was way too expensive to use for cooking even. Well then as it got cheaper in the 1910s, 1920s, stoves started making sense. Radios were a reasonable use, but you would definitely not use like a whole house furnace – heating a house with electricity, fucking nuts. It wasn’t until the fifties that the price of electricity had gotten down low enough that that was sensible. And of course, every one of those big additions just boosted the demand for electricity tremendously.
And then gasoline was the same – originally gasoline was a very exotic, weird thing. Kerosene was the main thing people used, but then as they figured out how to make gas cheaper and they found them – you know, automobile first, it was stationary, like sawmills and stuff like that. Then it was cars. We’re off to the races, dude. And the price in real terms has continued to come down at least until the nineties, maybe.
Adam:: You look at again, the advantage that we get from it relative to cost that we pay for it and how those things have interacted over time. Like, go back to that example of electricity, right? That single light where you have zero lights to one light is the biggest productivity increase you can possibly imagine. And then everything else is optimizing against that. That’s what I’ve always been really interested in – what’s all the stuff on the innovation side of the table? What’s the stuff that gets you to that first light bulb? And then everything else is optimization. I’m a lot less interested in optimization. So we still haven’t hit that part here. We’re still, like I said, about 90% of the way to where I think we need to be for that first core ideas are the thing that matters. Execution is automatic sort of moment. And then everything after that will be, well, how much better can we make this equilibrium? But the equilibrium will already have been established.
Jim: Yeah, it will be actually quite interesting. And so if we take Jevons paradox as probably applicable in this domain, what essentially says that we will be producing a shitload more software than we ever have before. When I give sort of meta-strategic thinking for young people in particular, trying to decide what to do with their careers, I say, think about applying software where it’s never been applied before, because it was too hard and too expensive. And it’s no longer hard and it’s no longer expensive and it’s getting less expensive and less hard by the day. Paul Graham – he’s a very famous venture capitalist, he was the co-founder of Y Combinator, really interesting guy – and he says that his vision of the future is the one-man startup. The founder who often will have domain knowledge in real domain knowledge in the area that he’s trying to automate. And he’ll not only have all the technology built by the AIs, he’ll also have all the marketing handled by the AIs. So all he has to do is think about what should be done, what is needed, who’s the customer, and at least approximately how to reach them. Paul said, “Oh yeah, I’m looking forward to the first one-person unicorn.” I think he said that – if not, I said that. But it will probably happen.
Adam: Yeah, no, totally. And you know, the experience that I’ve had, like I said, I started my first technology company in 2014, called Tokenly. I had two co-founders, three co-founders technically, but two active co-founders and one that was just on the funding side. Those two co-founders were both technical developers because I needed that, and it was impossible for me to do it without it. And it cost a lot of money, and I never actually made any money off of that thing because we spent six or seven years building out a variety of tools, most of them too early, but then finding ourselves unable to have the money to actually launch the thing once we got there. This is tied in with ICOs and my unwillingness to do an ICO and stuff like that, so we won’t go into it too deeply.
But I very much said to multiple people over the last couple of years that the next time I start a startup, if I ever start a startup again, it’s going to be me and an AI. And then if it proves out that this is really worthwhile, then you build the team, then you raise the money. It’s this totally different paradigm versus the sell-first, build-later thing that I’ve always hated and tried to avoid. But it’s actually a much better choice from a business perspective to sell first and build later.
I see this window that’s coming that’s gonna make it very possible to do that. Also, I still have that company. It’s totally defunct now, but all the things that I built were about five years too early. And I am also waiting – one of the reasons why I test this stuff is I’m waiting for the AIs to get good enough that I can just install one into all the GitHub repos for all the various products and be like, “please fix this stuff.” Because software degrades and so none of it works anymore. But again, many ideas still haven’t been done today.
And then again, the idea of being able to, as a founder, just be like, “hey, what about this feature?” And then it goes off to an agent that thinks about design and it goes off to an agent that thinks about how to implement this. And then it gets implemented in a test fashion, then it gets launched, then it gets QA’d. And then after all of that stuff has happened in twenty-five minutes, or five minutes or whatever, then it’s like, here’s the test and then you test it and then you’re like, “yeah, this is great.” Or “no, this isn’t great. I’d like this to change or that change.” And that’s gonna happen. There is no question about that.
I think one step further than where you’ve taken it is, and this is not an original thought, but I think it’s very true – the way we think about software right now is just wrong for the future. In the future, this idea of having static software that’s built for hundreds of thousands of people, millions of people, whatever, that’s a limitation we have today because execution is hard. And in the future, I increasingly think that everything is going to be frameworks. Even moving outside the realm of software, I think things like fiction will become frameworks where you build the world and the rules and then it writes itself.
Jim: Right?
Adam: Yeah. Exactly. So I mean, there’s a Neal Stephenson book called The Diamond Age that’s not exactly the same thing.
Jim: Yeah. One of my favorite books ever. Right? Yeah. Before Stephenson went off the deep end.
Adam: Yeah. I mean, we all do eventually once we get popular enough.
Jim: Well, he didn’t have to write the Baroque series.
Adam: No. No. I don’t fault him for writing.
Jim: He can indulge himself. But I definitely like some of the other books. Yeah. Exactly. The idea that as this cost curve just plummets downwards and the ability to go from what’s in even a relatively naive user’s head to actual personal code will be also quite revolutionary.
And the way they talk about it, it’s sort of becoming slowly true – personalized medicine. Personalized technology will be a thing. It is a thing right now. Some of these things I’ve created, I created for myself basically. Because I could speak prompt engineering slash automated programming, probably better than 97% of the people and probably 99% of the baby boomers. I can get personal code quite easily today. And I think to your point, that’s going to be also another even bigger change.
I will add something about this. This is accelerating, we’re at the elbow where it’s going straight up. This is a very long term trend. When I started my first tech company in 1982, you had to do everything yourself. You had to build a computer room. You had to have network connectivity. You had to have a computer operator. You had to have a tape machine to back your stuff up. You had to have a deal with a company to come and get your tape once a week. It was like nuts, right? And you couldn’t do dick for less than a million dollars. And that was in 1982 dollars which would be worth about 5 million, a little less today.
And to do what we were doing then would be, you know, a PC running Unix and a $100 a month internet connectivity and two programmers. Right? And it would be done in six weeks instead of a year to build the product and call it $5 million. Like, you could have done it for a hundred grand at most today, probably $50,000.
John: Yeah. I just went through an experience of building a new product extremely rapidly in the second half of last year. We built it over the course of about four months from idea to initial paid version that was out, and it was extremely hard. We had to cut almost everything in order to make that work. And even when we did that, it still didn’t really get to the place. So even now, with teams that are using these tools, it’s still really, really hard to pull something off.
I would give you a visualization for what I think is going on here. If you took an exponential graph for technology adoption and you’re talking about software, I think what’s happened is that the trend actually went negative for a while. In the very early days, software was entirely manual, but it was also very simple. We’ve then continued to have it be almost entirely manual, but the complexity has grown and grown and grown. So you’ve got two graphs here. One is the complexity, which is going up exponentially from the word go. And then you’ve got how difficult it is to execute. That started off relatively easy, and then it got harder and harder.
I feel like in the last couple of years, we’ve come onto the “okay, now it’s starting to get a little bit easier” phase. When we cross that zero line – because I don’t think we’re there yet – where we have the same amount of complexity but it’s just as easy as it was back in the very early days of software, that’s the start of the moment where we just shoot straight up. Complexity continues to grow, but the automation of the code makes it so the execution challenge drops off.
I’m someone whose entire deal is looking into the future and seeing how technologies are going to impact us and the way that we work. I really have a difficult time picturing this outside of the obvious thing that’s coming, which is every phone, every laptop, every device that exists out there is going to have specialized AI chips that are gonna be able to run this stuff locally. And once you’ve got that, then all the cost falls out of the thing and it’s over.
Jim: And when things like ChatGPT came out, they were sole-sourced basically from one big company and then two big companies. You pay them, runs on their vast data centers. They probably lose money on every dollar revenue that they get, etc. But that model didn’t last too long. You know, soon we got into the world, as we often do in these worlds, of open source. And one of my good friends who I rely upon for scanning the frontier of these things, I will give him credit for saying very early, “Open source is gonna win. Don’t get sucked into investing in or playing with or becoming dependent on the big frontier models.” I know you’ve thought about this more than I have even. So why don’t you give your wrap on the rapid evolution over the last two years and three months from commercial frontier models to open source and to your earlier point about the very interesting and generative competition between the two.
Jim: Yeah. For sure. So I like to use narrative structures to think about these types of battles. And so the one I’ve always used when it comes to this and open source in general – open source versus closed source in competitive environments – is a lot like the Rebel Alliance and the Empire in Star Wars. Right? The Empire, that’s all the closed source projects. They have tons of resources. They have the ability to mobilize in ways that is totally impossible for a small scrappy group. But they’re bound by a really hierarchical structure. And so if the guy at the top doesn’t think that’s the idea, then that ain’t what’s gonna happen. And a lot of times that’s a really bad choice.
So it’s really good in the short term for starting things and for doing things that are high resource intensive and require a lot of frankly money and time commitment in order to pull it off. But once you’ve established that part, what we saw from very early on in – let’s step back from LLMs and just talk about image generation, right? Image generation, the first things that we really saw were from Google and from OpenAI. And there was really no comparable technologies that existed out there that could do what these early versions of things like DALL-E could do.
As soon as that was shown, you had people like what would become Stability AI be like, “Hey, we can do that.” And now you’ve got this sort of behemoth out there that is almost of equal size in terms of impact, but is so much more freely available because for a long time, these companies were concerned about the risks that would be presented if anybody could make any image, right, or anybody could have the LLM say whatever they want. Whereas the open source side doesn’t really care about that. They’re more just like, “Hey, here are tools, use tools for what you want and do what you want.”
So what we’ve seen is this really interesting competitive dynamic between the two where instead of having this really powerful empire and this really scrappy kind of terrible rebellion, instead, the rebellion always had big guys on their side that were basically other empires. Right? So Stability AI was an early example of this. I believe they raised a hundred million dollars, which is very reasonable size thing for a company, much less for an open source project.
But then on the other side, when you talk about LLMs, you’ve got Meta, right? Meta was sort of one of the first big movers to be like, “Hey, we don’t really want to be in this business, but we benefit if there’s a big ecosystem where we have access to the best technology.” So let’s help do that. And they use some money to train the early versions of LLaMA and put those out there. We saw other players like Mistral come in. They came out of nowhere. Right? Mistral, a couple of years ago, was this tiny French company that released a model that was essentially as good as ChatGPT 3.5 or GPT-3.5 was.
And so what would happen in normal circumstances is you would see the closed source companies spend their money and then really try to maximize what they could get out of it. So that means keeping things expensive, keeping things exclusive, and especially with the early tools like DALL-E, right? Like, and even ChatGPT, there was this real exclusivity about getting in. Most people didn’t have access.
That strategy works so long as you have a monopoly on the thing. But as soon as you don’t have a monopoly on the thing, even if the thing that’s competing with you is a little bit worse, but it’s available and it’s basically free, especially for people who don’t really trust you as a big company, that’s a problem. And so you have to actually increase the cadence that you are doing your releases because otherwise you can potentially fall behind.
I think that in the very beginning, what we saw was a gap of about a year between what the big companies would release and what open source would then come along with. And I think that now we’re to the point where when Claude 3 came out a couple of months ago, there was an open source version, not as good, but there was an open source version that didn’t cost $200 a month that was available within about a week. And then other projects like Perplexity started adding their own things. That was also maybe two weeks.
When we started getting these thinking models, right? Like nobody – people had talked about this, but nobody had really done it yet. And that went from, “Hey, here’s this amazing thing that you can see, but you can’t use” to “Now here’s our one, and you can use it and you can run it on your own thing.” And by the way, not only are we going to give you the thinking model, but we’re going to give you the distillation models that make it so that you can take this intelligence down to a much lower level.
So the dynamic has been wonderful because you’ve got the big companies out there who are in a very expensive way proving things are possible. And then once they have been proven possible, the commitment to make another version of that or an open source version is tiny in comparison. And you’ve got a population of 10,000 probably people out there who are really passionate about this. And at least one of them is going to go out there and invest the couple hundred hours or whatever in order to pull it off.
The time that it takes to do that is getting smaller and smaller as the models get better because they can be more self-sufficient. Early on, there were a lot of problems with training data. And one of the reasons why OpenAI came out with the thinking models was because they were like, “Well, we can’t just recycle ChatGPT for answers because that’s just going to kind of – that’s not going to have a positive effect. But if we let the AI think for a long time and make sure it’s really right, then we probably can use that data.” Right? And so that’s true for OpenAI, but it’s also true for everybody else. You can do that today with Claude 3 at fractions of a penny on the dollar compared to what it would have cost to produce much worse quality content earlier on. And like I said, this is a self-feeding, self-reinforcing cycle that’s just getting faster and easier, and it’s a beautiful thing.
Jim: Yeah, it really is an example of the market doing a good thing. I love the dynamic between Frontier models – they’re a little bit ahead, but they’re a lot more expensive and they’re assholes. Still annoys the piss out of me from our script helper days, where if you want to write a slightly spicy romance murder, which I often use as a test case. How much of literature and movies are spicy romance murders? A lot of them! So I use it as just a little test case to see if it’ll work. “Oh no, we can’t do that. No good, ’cause the bad guy got away. I can’t allow that.” And I go, fuck you assholes. That’s one of the most annoying things out there. That’s what actually got me using OpenRouter. Think you suggested it – let’s get access to one of these foul-mouth models. You know, salty Jim, he likes his spice, and the goddamn nannies at these big companies just drive you nuts. So that was for me a big impetus.
Guest: I think it’s easy to dunk on the big companies, and Google is my favorite to dunk on. Google has, again, like the best technology in some ways and the worst technology in some ways when it comes to AI. Almost never use it for anything practical just because the chances that you’re gonna be like, “Hey, can you make me an image of a fox?” And it’ll be like, “As an LLM, I don’t know how to make images.” And it’s like, okay, but you do. You do. I’ve spent more of my time arguing with Google’s AIs about the things that I know that they can do, but they don’t know that they can do than anything else. It is just wild to me that that would be true.
These big companies have something to lose. That’s a hugely important thing to keep in mind when you’re looking at how people develop technology. If you’re an incumbent, there are really limited things that you can do with a really disruptive technology that doesn’t totally kill your underlying business and existing model. And more importantly, as you get bigger, as you’re a Google or even an OpenAI now, you’re just such an attractive target for litigation. If somebody wants to make a point, you’re the guy that has all the deep pockets. So they’re gonna go after you. On the one hand, it’s incredibly frustrating to try to use these products whenever you’re trying to do something more interesting than something a politician would admit to having done. But it is a real risk for them. So I do have an appreciation of it, even as I continue to make fun of Google at every opportunity.
Jim: Yeah, you hit on something, which is a well-known famous template, a business book called “The Innovator’s Dilemma” by Clayton Christensen. He made this point back in the eighties, I think it was, that it’s really, really hard for a big company to really innovate in a radical way because the internal politics and shareholder concerns and everything else keeps you from cannibalizing yourself. However, over the longer term, you can be goddamn sure that if you don’t cannibalize yourself, somebody else is gonna cannibalize you instead. So one of the hardest things to do, but one of the most important is for corporate leadership to be able to overcome, shall we call it the bureaucratic resistance to innovation and force it to happen. Even if your company ends up smaller on the other side, because the alternative is your company ends up zero on the other side.
Guest: Yeah. See these long term versus short term problems though.
Jim: Right? Yeah, exactly. And of course American business, particularly if you’re a publicly traded company, is heavily skewed – unless you’re a cast iron son of a bitch like I was – to the quarterly result. I basically say fuck the quarterly result. Let’s look at the curve over a longer period of time. And we got some investors that followed us, so that was good. But mostly they’re like “oh, the quarter will be down” – big fucking deal. Quarters go up, quarters go down.
Guest: I mean, Meta has been… you know, it’s really funny. I’ve never been a fan of Zuckerberg and I’ve never been a fan of a lot of people who are the big successful tech giants. But the reality is that Zuckerberg at least really made a bet on VR.
Jim: Huge. And it didn’t work, but oh well, it didn’t work.
Guest: But I mean, like, that’s some balls. You really gotta… when you’re that particular business, like, he’s good no matter what he does. He really wanted to do something. And maybe it’ll pay off again, but my sense of the whole thing is that there are so many things that we’ve been waiting for for the last thirty years, forty years in science fiction that become possible when you remove the execution barrier as AI is doing. So I think it’s very possible that that bet is stupid today. And two years from now when software is just available, then being that platform actually might be super valuable. But again, not many companies are willing to take that bet. And if it’s successful, then you’re a genius. And if it’s not successful, then man, you’re dumb. And it’s like, why would you put yourself through that if you’re already set for life?
Jim: I just pulled up Facebook market cap, and yeah, it dipped in the ’23 timeframe, but it’s near an all-time high up around 700 billion.
Guest: I mean, mania markets. Come on. Let’s be real. Mania markets mean that number go up if things aren’t bad.
Jim: That is true. Though I will say I would not want to be an investor in pure play frontier proprietary AI model. Not at all. Not at all right now.
Guest: I mean, it’s a swing for the fences grand slam sort of business. Like, you can do it, but it’s definitely not the easiest path, and it certainly is one that’s just gonna get more and more expensive. Or is your premise at this point that the leads that the early incumbents have as it relates to this is too great? I don’t know if I believe that. Are you thinking that already?
Jim: No, I’m thinking that’s not the case. And further, and again from the strategic business analysis perspective, it’s not obvious to me that there’s any network effects. That’s why Facebook is almost impossible to overthrow because it’s a network – the old Metcalfe’s law: network is equal to the square of N number of participants. Actually, in reality, it’s about 1.5, but it’s still a big ass fucking exponential. What is the network effect? The only real network effect they might have is that they see more of the queries. So they can optimize their engine to… but that’s not huge.
Guest: Yeah, I don’t think that’s that big. Again, I think with the advent of really, really good synthetic data from these thinking models, the need to have user data in the same way is not so much there as it was driving things forward a lot before.
Jim: And then further, one of the things we’ve talked about with other folks for a bit is this issue of distillation, right? So when anything gets out into the world, it now sets the floor on what can be built thereafter. I mean, some relatively unsophisticated people could take DeepSeek and create a DeepSeek minus 5% relatively trivial. Right? So now anyone on earth, terrorists, anybody can have 5% less good than DeepSeek with a fairly straightforward distillation export retrain model, you know?
Guest: I mean, I think it’s really significant. I think the distillation stuff is really, really significant. I don’t know if you’ve played around with the distilled R1 models. You know, after you get below, I don’t know, the 8 billion parameter one, then I wasn’t super impressed. But I really do think that that type of thing is frankly what everybody has been doing anyways. Right? It’s against OpenAI’s terms of service to do this with them. But in practice, this is what a lot of models have been trained on.
It’s a funny thing to argue against, of course, because this is in practice how all models have been trained, right, as they scrape the Internet and then you supplement on top of that these days because the Internet isn’t enough. So it’s been very funny kind of watching this whole conversation. And in particular, when the R1 thing came out, you know, I’ve been watching a lot of political news recently and watching kind of how the world of politics and finance dealt with this. And it was just the funniest reaction ever because they just again, they’re like, “Oh my god, this is over. No one’s ever going to want AI compute again. It’s bad for NVIDIA.” And it’s like, how is it possible for you to be in 2025 and be so stupid? Right? Like, how is that possible? You are professionals. You are an economist. How is it possible for you to be so stupid? But again, everybody gets into their lane, has their very specific kind of worldview about how things work. And disruptive technologies just have a tendency to be like, well, that’s not how it works anymore. So all of your models are wrong and you should probably just shut up for a while until you figure it out. But, you know, that’s entertainment.
Jim: Yeah. And even if Jevons paradox doesn’t go to the crossover point, cheaper, cheaper, cheaper models mean more goddamn NVIDIA, not less.
Adam:: I remain convinced that, you know, you talk about electricity, you talk about gasoline, stuff like that. I think that when Jensen talks about this from NVIDIA, he talks about it in terms of token factories. His vision is really incredible. If you ever have an opportunity to talk to him about this stuff, that is a conversation that I think just everybody should listen to because that guy – again, like, I am not one to really have to be like, “wow, that guy’s awesome,” but that guy’s awesome. And he has such an amazing vision of where this is going.
So my point is that I think there are more uses for intelligence on demand, especially on device, than there are for electricity, than there are for gasoline. I think that this is an exponential increase that we will eventually see. And I also think that because it’s not physical, right? Because you don’t really have to generate anything, and because through the distillation process, can make it less and less and less energy intensive. Like there’s no upper bound to how efficient this can be in a way that we typically think of where it’s like, hey, in electricity, you have to deal with transmission loss. With gasoline, you got to transport it and you got to find the raw resource and stuff like that. There’s just none of that here. So it’s this incredibly pure thing that is just going to seek its level of usefulness and it’s just gonna sit there and that’s gonna way overshoot that. So I think we’ve never seen anything like this before, and it’s hard to conceptualize, but it’s bigger than those things in aggregate.
Jim: Yep. And the other thing, the models get faster, cheaper, better. The chips get faster, cheaper, better. But then there’s the extra dimensionality of the orchestration, you know, the agent frameworks, the thinking models. And that’s where I think the real play is right now, is that for a lot of things, the LLMs are strong enough components. But if you orchestrate them correctly in an agent framework, a real agent framework, not just a pipeline, but where they talk to each other and they branch and they have an odd network combination of messaging. And you don’t really know what the hell they’re gonna cook up to our script helper days. But, you know, you said we’d have agents for the different characters. We’d have an agent that creates, that manages the physical world. And then we turn the characters loose in the physical world and see what happens. And with programming, it says, don’t be boring. You know, if you show a gun in the first scene, use it by the third. Right? Those kinds of heuristics. And that’s and apply that to other domains like engineering, you know, think about, anyway, very interesting.
Jim: Yeah. I would just say to anybody who’s looking at this, it’s a great time to dip your toe into the water. Pick something like – Windsurf is a little bit more complicated because you are looking at code. You don’t have to do anything with code, but you can kind of see it. Something like Klein is a lot easier and there’s even, I think, an iPhone or mobile app for it that is frankly pretty good.
So it’s a great time to understand the possibilities of what’s there. The thing that I’ve just been telling everybody for the last year and a half at this point, and I think is truer today than it ever has been, is that we all have specific expertise around the things that we do with our lives and the things that we care about. The biggest challenge about making new products that are actually meaningful is that a lot of times people who have the technical expertise to do the building don’t actually understand the problem in a meaningful way, but you do.
An example of this is, you know, what I went to school for was to be an audio engineer. I got really into audio editing and stuff like that with the early days of the podcast. I wound up building a prototype back in 2017 of what I called Breathless Technologies, which was an automatic breath remover and space and pause editor that used AI algorithms and stuff like that. It was really hard to do at that point. I pulled it off to a certain extent but then kind of had to step away. But that’s a product that nobody understood was needed but me because of the particular experience that I had.
If I was doing it today and if I still cared about it today – I don’t really – then I could probably do that project myself as opposed to spending $60,000 on it and then giving up on it within about three weeks, and have something that’s really, really useful. That’s true technically today, but I would probably hit problems that would stop me from doing that. A year from now, that won’t be true. A year from now, nothing’s going to stop you. All you have to have is the idea.
So the important thing is not to think about what should I do today? You can play with it today, but the important thing is to think about what is the thing that only I understand based on my unique combination of experiences and then get ready to build that in the future. And build that is really just going to mean: here’s an explanation of what I think the problem is, what I think the solution is, and what my best ideas are for how you would solve that – with lots of room for the AI to tell you that you’re wrong about stuff and to go back and forth a bunch of times as you kind of work out that initial specification of the thing.
Jim: Now that will add, that triggered one last thought on my side on this. I started saying this a week after ChatGPT came out, and all my experience since has just reinforced that, which is: for all you tech managers out there, start adding smart liberal arts people to your teams. You know, philosophers, analytical philosophers – I bet they’re probably worth more right now than an entry-level coder is because the key aspect of getting the most out of these programming tools is being able to specify the problem with great precision. And to your point, understanding what it is you’re actually trying to do in a crisp way. The only reason that philosophers are so goddamn annoying is they want to make everything very specific and very clear. And that’s exactly what you need in a good prompt engineer. So, you know, you could hire philosophers for a hell of a lot less than mediocre Python programmers. If you have a team of five developers, go hire a philosopher, right? I think that’s probably right. Or creative writing person, you know? So I think that’s very important.
Jim: Limitations. I think that people who don’t understand the limitations but do understand the problem are really, really going to be increasingly valuable.
Adam: Yeah. And I like philosophers too, because they reason about feedback well also. So, you know, they go, “Wait a minute, that’s not what I meant.” And they go, “Why isn’t it what I meant? Oh, that’s why it wasn’t what I meant. Let me change what I said to this.” So, you know, I can’t… to Paul Graham, I’m going make a crazy prediction here. I’m going to predict that Paul Graham’s first billion-dollar one-man company will be a philosopher, not a programmer, an analytical one. Because he’ll be able to get in the loop. He’ll be able to get there. Or maybe it’ll be a two-man company, you know, a guy who’s been in the plumbing supply business all of his life and an analytical philosopher will get together and create some kind of insane thing for the plumbing supply business.
Jim: I’ll say one more thing, which is that I’m right now taking a break from doing anything productive in real life, taking a break for the first time in about fifteen years and really, really enjoying it. And so I haven’t been talking to anybody about anything related to business. I’ve been doing like one call a week just because, you know, like, I might as well stay a little connected. And as I was taking the garbage out yesterday, I ran into my neighbor. And turns out my neighbor has a company that works for companies applying to the FDA. And so she started talking to me about stuff and she asked what I did and I told her that I had worked in AI and stuff like that. And first thing she tells me is that she doesn’t like AI because she’s an artist. And then the second thing she tells me is, can I talk to her about how she can use AI to automate a lot of the compliance processes and the sort of report generation and stuff like that? Because she’s really interested in doing that but doesn’t really know how to get at it. And this is something that I’m seeing increasingly – it used to be you would talk to a person like this and they would be, “I don’t like it because I’m an artist” or “I don’t like it because of this thing that I’m concerned about.” And increasingly, you’re seeing, I think, that turn to “I don’t like it, but I wanna use it, and I’m ready to do it right now. Can you help me with it?”
Adam: Yep. Yep. Yep.
Jim: Something is in the process of happening as we get through this “this is so new that it’s really scary, and I’m concerned about the implications it’s gonna have on my current life” and we move into the “well, it’s inevitable, so I might as well figure out what to do with it.”
Adam: Yeah. I’m starting to see that as well. I think, of course, we ran into that in the Script Helper project. I expect we’d have a rather different reaction today. Maybe. Maybe not. But, anyway, let’s go on to our second topic. As I’ve mentioned in the intro, Jim is a go-to guy for crypto. Whenever I have a question, particularly about something screwy in crypto, I’ll send an email to Jim or I’ll chat with him on Zoom and he’ll either know or if he doesn’t know, he’ll know by the next day. It’s really quite amazing what a job he does keeping up on what’s going on in crypto, even though he doesn’t actually have his hand in all that much.
Jim: No. I’ve been out for about six years.
Jim: But nonetheless, he knows what’s going on. But anyway, one of the topics he said he wanted to talk about, and no doubt he’ll have something interesting to say. I normally don’t do too much crypto on the show, but we do some. We had the Cardano guy on. He was pretty interesting. And Hoskinson, Charles Hoskinson, I think sent the item on. That was interesting. I’ve had the Holochain people on a few times, but they take quite a different spin than the other guys.
Anyway, Adam wants to talk about Bitcoin. Regular listeners of the Jim Rutt Show know that, to my considerable loss to my pocketbook, I have been a Bitcoin skeptic for many years, and I’m going to tell you how skeptical. There was a big meeting at Santa Fe Institute where people talked about the future of finance, and people took different positions about Bitcoin. One faction was all in on Bitcoin – or not all in, but everyone should put 1% or 2% of their net worth in Bitcoin. Bitcoin was worth $300 at the time, and today it’s worth approximately 400X that. So if I had put 1% of my net worth in, which I seriously considered doing, my net worth would now be 5X what it is. That’s a pretty big oops, but it’s not quite the biggest oops I ever made in my life. We’ll talk about that some other day.
Adam: Well, see, that’s pretty impressive. If that’s not the biggest oops you’ve ever made in your life, I gotta tell you, you know, like one bigger one. But anyway, fire away on what you want to talk about with respect to Bitcoin.
Just a little bit of background here. I first got interested in money back in February 2009 or so, right after the Great Financial Crisis. I realized I didn’t really know anything about money. In 2011 through 2013, I created four Bitcoin podcasts under pseudonyms. And then in the spring of 2013, I created the fifth Bitcoin podcast that I had done, which is called “Let’s Talk Bitcoin.” It was at a time when the price of Bitcoin was, I think, about $250, and it was in a bubble at the time. I had been watching it all along and had been like, “Well, this is really interesting, but it’s not gonna work because they’ll never let it work.” That was sort of always the early premise. As time went on, I realized that even if it didn’t work, it was still really interesting.
I want to talk about a couple of different components here. The first is blockchains. Blockchains as a technological tool are really inefficient, but they allow you to do decentralized ownership and decentralized ownership transfers on the Internet in a native way. That is really important. When you talk about crypto broadly, you’re talking about all the different tokens that exist on these different things, whether they’re on one blockchain or whether they’re on their own blockchain or whatever. So crypto is like a really big catchall.
Tokenization is another important word, and tokenization is the process of taking something and somehow tying it to or representing it with a token. This is what we’re seeing. It’s funny because I’ve been talking about this – my first company I started, Tokenly, was all about non-currency tokenization and all the use cases around that. My company built tools that powered the first collectible card games using tokens and a bunch of random stuff.
Really what I was doing with that company was building tools for myself because my podcast turned into a podcast network, turned into a full publishing desk that had a lot of users. We created, as a way to experiment with technologies, what I called LTB coin or Let’s Talk Bitcoin coin, which was a token that I gave to people for free as a rewards program incentivizing them to participate in certain ways. It was really complicated in terms of what was going on in the background, but about 15,000-20,000 people participated in it over the course of about three years on a weekly basis. It was kind of very successful as a way to take this potential future value because I don’t have any money, right? So I had all these contributors and audience and stuff like that. I was using the token as a way to gamify and then distribute this sort of maybe future credit. In practice, when I sold the network in 2017, there was an event where that token was exchanged for a token that had actual value and people were able to get money out of it.
Tokenization right now is being talked about by the BlackRocks of the world like, “Oh man, if we could tokenize stocks, then you could do all kinds of stuff with that.” And they are right. You can. But all of that stuff is just a new ownership system. It’s just a new way to track and transfer ownership that’s a lot better than the way we do things now. If you look at how stocks are traded today, there’s one centralized company that holds all the actual stocks, and then everyone is just trading claims on those. This has a bunch of problems that are kind of obvious. If you replace that with a neutral decentralized structure, it actually makes things a lot better. You no longer need to worry about if that company closed or if they kept their records right.
But the part that I’ve always been interested in is Bitcoin specifically as an improved version of a sort of default investment asset. In the current era that we are in, where money is destroying itself and the tide of inflation is going up and up and up, it’s the lightest cork possible that has the least risks because it is not real, if that makes sense. So I will stop and ask you to ask me questions because I’m sure that that was not clear enough. Was that clear?
Jim: The preamble was clear. The punchline, a little less so. Because now let’s just say all the stuff about the history of blockchain, all that shit. That was all good and true. But you kind of rushed to the punchline. And so why don’t you, as we would say in nerd land, unpack that a bit?
Guest: So when we’re talking about money, right, the current system under which we operate is one where the US dollar is the hegemonic currency. It’s the default currency, the world reserve currency, and everybody else saves in that currency. The reason why the US dollar is the reserve currency and why it performs that function is because we need a kind of level playing field on a medium of exchange. And when you look at the history of who has that responsibility, what you’ll see is that it’s the country that has sort of the best set of fundamentals about it at the time in terms of power dynamics, in terms of wealth of the country, etc.
And so if we have to pick between one of these currencies that’s issued by a country, then you’re gonna pick the least worst one. And that’s what the US dollar has been for a while now, and it remains the least worst available option. But the challenge is that if the least worst option is still getting worse – and for the rest of the world, it’s getting worse. In the US, it’s getting worse too, but the rest of the world is feeling most of it – then there will inevitably come at some point in the not too distant future, you know, within the next forty years, say, something that replaces the dollar as the de facto sort of global reserve currency, not necessarily something people transact in, but some form of value that is stable and safe relative to that.
And the challenge with the US dollar and really everything else is that countries that have the ability to issue their own debt as other people’s savings inevitably abuse that. It always happens. It’s totally unavoidable. And there’s never been a way around that until somewhat recently. Past systems used gold, right, as a way to say, “Oh, well, the government can’t create gold. And so therefore, if they have to back their currency with gold, then they won’t abuse this privilege.” But in practice, gold is a physical thing. It has to be stored. So it has expenses built around that. It has to be trusted. You know, you have to trust the person who is doing the audit. They’ve been talking recently about doing an audit of Fort Knox. Why are we talking about that? Because nobody really knows if the gold is there. We think it’s there, but we don’t really know because it’s physical and we have to trust these people. And increasingly, you don’t trust these people.
And so you look at something like Bitcoin, and Bitcoin is not a good currency, to be clear. Bitcoin, you know, I really wanted Bitcoin to scale and, you know, I didn’t really take positions much back in the day, but I really wanted Bitcoin to become something that could be used as global cash. And in practice, that’s not what happened. In practice, what happened is it seems to be replacing or have the potential to replace the reserve currency aspect as far as what people choose to save in because no one can inflate the supply and because it has other really desirable kind of Internet native characteristics. I’ll pause here. I do wanna talk about sovereign wealth funds, though, after this.
Jim: And which is quite interesting. I’ve fairly often talked about the fact that when the US went off the gold standard in 1971, that was a big inflection in the history of our civilization because essentially hyper-financialized stuff was let off the leash. While gold was far from perfect – like there’s a great book called “The Barbaric Metal” which is quite interesting to read from the 1920s, and I could go through a long list, I even have a document I wrote about the horrors of gold – it was an anchor that kept the financialization within some constraints. They were real to a degree, right? There was a reason Nixon had to go off the gold standard because the claims on the US’s gold were starting to accelerate. Fort Knox would have been empty in another six months probably. And interestingly, you couldn’t as an individual even own gold at the time. It was illegal for an American citizen to own gold in 1971. They did change that rule soon thereafter because the government wanted all that gold to do balance of payments, as our balance of payment situation sucked.
The fact that there is no anchor today is paradoxically good in some ways because the anchor has a bunch of negatives associated with it, but it’s bad in other ways. And it let a very predatory form of finance loose, particularly one that makes most of its profit from manufacturing opacity, right? The big banks, Morgan Stanley, Goldman Sachs, would be nowhere near as profitable as they are if they had to operate with radical transparency. In fact, they probably would have the same profit margins that an electric utility has, something like that as they should.
Guest: It’s a pretty boring business at the end of the day. It’s kind of bizarre that they have the margins that they do.
Jim: They shouldn’t. So anyway, that’s a rather complicated, multiple-dimensional statement that gold is bad, but it prevented certain kinds of abuses. Its elimination let loose the worst of late-stage hyper-financialization, and it’s quite a bad place that we’re in. When I look at Bitcoin, the thing I’ve always said about Bitcoin is it’s basically digital gold, right? I knew from when I read the Satoshi paper three months after it came out in 2008 – somebody sent it to me and said, “Goddamn, this is interesting” – I slapped myself in the forehead. Also said, “It’s also not that hard. I could have done that. Why didn’t I think of that? Dumb motherfucker. I could write a better fucking blockchain than that thing probably.” But it was very, very, very, very, very, very, very, very clever. He just got to the right place and created something that I immediately saw why it was cool. I also immediately saw why the idea of a radically distributed ledger solves a problem of radical trustlessness at a huge cost. Right? I think I calculated the other day it was 17 billion a year, maybe it was more than that, that we used just for the electricity to mine Bitcoin around the world.
Jim: This is chump change. Chump change in every way. Chump change compared to what you’re comparing it against. Chump change to what we pay today for the electricity to do it. I mean, we’re never looking for ideal solutions. We’re always looking for what’s the least worst available option, right? Because we have to live in the real world. And most of the time, that’s the choice when it comes to systems like this that require concentrations of power, because concentrations of power are inevitably abused, usually to the detriment of everybody who has to live with it except for the people who have close proximity to that power.
The kind of question mark is, there’s a bunch of stuff about Bitcoin that makes it the only thing that’s really actually interesting for this use case. The primary thing is that it is the first. And to your point earlier about Facebook’s network effect – let’s talk about Twitter for a second. I have seen over the last ten years probably four different events where there have been major divisions within a social media platform. I saw this over on Reddit in the very early days with r/bitcoin and r/btc, which is the ticker for Bitcoin. There was a huge split there. And the BTC guys, they lost. And so they went off and formed their own club. And then that club effectively only talks about how much of a dick the other guys who were in the old club were.
You see this today to a certain extent with Twitter where it’s like Twitter and now there’s Bluesky. And really, the point of Bluesky is to talk about how much of a dick everybody over at Twitter is. Because when you split like that, the people who are disenfranchised are the ones who leave. And so the whole thing is kind of tainted by that.
We’ve seen this directly in Bitcoin. There have been attempts to fork Bitcoin and take the community a different direction. In practice, it has not worked. It’s very hard to get off that sticky network effect. The other thing about Bitcoin is that by being the first, it has the unique reality that no one thought it was going to work and everyone thought it was stupid and impossible and no one thought it would be worth money until way later in the cycle. And so that means that early in the cycle, you had people who were not there for the money. And this has not been true ever since.
Like in the very early days, before Ethereum and stuff like that, people were like, “Oh, I’m gonna create a new Bitcoin because anybody can do it. And I’m gonna call it Gold Coin because everybody likes gold.” And I literally asked these people, “What is different about these two things?” And the whole thing was it’s called gold and there’s more of it.
And so you get this problem that I really didn’t think was gonna stick, but it has, which is that as you have the first of a thing come out and establish itself – and I would argue that really this is only Bitcoin and maybe Ethereum, although we’ll see what happens there. You have other people who are like, “Well, I can make a better version of that.” And then they create their own version. But there’s a bunch of them. And so you have this coordination problem where if you’re going to pick something that’s not Bitcoin, you’re probably going to pick one of those five that’s out there, except in practice, there’s like a thousand of them. So maybe best case scenario, you’re going to get 10% of the market, whereas the other 50% are just going to pick Bitcoin. It becomes sort of the center of gravity that pulls people in, and it’s a self-reinforcing cycle.
That’s kind of been the biggest challenge that I’ve seen for anybody who’s tried to do something that is like Bitcoin for the same purpose but displace it. In practice, they can never get off the ground because one, you don’t have the people who are there for the idealism – they’re there for the money. And two, the people who are going to potentially pick an alternative will scatter to all of those different options. And so you’ll never be able to build sufficient network effect, you just wind up with these toxic little pools of people who are really interested in their own money.
Jim: And in fact, I will say the people who saw better than I did at this meeting at the Santa Fe Institute, I believe, use this lens. It’s from complexity science and from social network dynamics more generally. It’s the concept of a Schelling point, which is sometimes also called a focal point. It’s a game theory idea that it’s the place people go even if there’s no coordination because it’s the obvious place to go.
Thomas Schelling, the example he gave in his paper that introduced it was: suppose you and I agreed to meet in New York City, but somehow our emails got disrupted and we couldn’t further communicate. We didn’t know where. Where would we meet? And he makes a fairly good argument that probably we both decide Grand Central Station. Because it’s the most obvious place, the highest profile place, has the focal attention when you think about New York, especially if you’re not from New York. He said second choice would probably be in front of the Empire State Building.
And so in some sense, as more and more new people come into crypto and the greater fool theory continues to attract fools, he says, ironically or not – we’ll find out – what’s the Schelling point? If you want to get rich doing nothing? It’s Bitcoin. And so I do believe that these very shrewd people – one of the guys who made the decision to go more than 1% of his net worth is generally considered one of the great investors of our era. He was a guy who beat the S&P 500 twenty-two years in a row with his mutual fund. And he beat it by a nontrivial amount. So he’s a fucking trader’s trader – well, actually more of an investor than a trader. He doesn’t do real-time stuff. I think he took his fund and put like 5% of it in Bitcoin. People did at $300. And my old friend, Michael Saylor has been playing that game for a long time at MicroStrategy.
I think the investor I had in mind, I’m pretty sure he used the Schelling point, because he’s kind of a value investor, but he’s also cynical about human nature. And so he understood that greed heads are all gonna accumulate at the Schelling point. This thing has an exponential ability to attract greed heads until suddenly it doesn’t. And so, you know, this is definitely a good investment at this price. And he was fucking right. He made so much money, it’s ridiculous.
Guest: Yeah. So building on top of that, the question then has always been, does anything that looks like this have a future? Does anything that looks like this have a place in the world that is coming over the next forty years, fifty years, whatever. But in practice, I think it’s a lot sooner. And I think the answer clearly is yes. Again, like, when you look at the price of houses going up constantly – that’s not because houses are getting more valuable. That’s because the money is worse now. And they’ve turned everything into investment assets. So instead of it being you buy a house to live in a house, you buy a house because that’s the best investment that you can make. And hey, you can also live in it. There’s lots of problems with that that we see kind of now manifest. And so when you look at something like Bitcoin that only has these properties, only has the properties that allow for it to be a really light cork floating in the sea of liquidity – then it’s really just a question about how much adoption does it get. Because money, of course, is a confidence game.
Jim: It’s a collective hallucination. It’s a con game, though.
Jim: I mean, like, we’re… you know, the reason why you’re holding dollars, the reason why I’m holding dollars is we think they’re gonna be valuable tomorrow. It still is less bad relative to holding it in, you know, butter. Right? Like this is a sidebar.
Jim: So my principal argument against Bitcoin, which is also the principal argument against gold, which is, like any relatively sophisticated investor, I hold only a tiny amount of cash. Right? Just enough to be able to move opportunistically if something weird happens, and defensively, in this case, the worst that happens, happens. But, you know, 99% invested in productive assets. Right? And putting your productive assets in gold, in hundred dollar bills under the mattress or in Bitcoin, I would argue, is an antisocial act. You’re essentially taking the energy saved by you, essentially, and putting it in a sterile thing that is not contributing to the productive economy. It’s not driving investment. It’s just sitting up. It’s literally stacked hundred dollar bills under the mattress. And that is fundamentally immoral. You only should have as much in those kinds of sterile forms as the absolutely minimum necessary for you to sleep well at night. And it seems to be a horrible thing to encourage people to stick hundred dollar bills under the mattress.
Speaker: I would argue that most people in this world don’t have productive investments because they can’t afford to have them. They just want savings, right? And so one of the biggest frustrations that I had is that I was so poor when I first started talking about this stuff that to the extent that I have cryptocurrency, I don’t have very much of it.
From a productive standpoint, what I see is a big change coming to how we think about money and where we look for value. If you look at places that have hyperinflated in the past, what you see is there’s no place you can put your money because every place you can put your money as a person who is not extremely wealthy has lots of risk associated with it that you don’t have control over and which you don’t even really understand most of the time.
This used to be investment assets – like, you bet on a stock, right? And most people think about this as betting too. That stock goes up or down and whatever is going to happen is going to happen. But now we live in an era where everything goes up if you put your money into the stock market, except we all know at some point this is going to collapse.
So the question really is about what is the, as you said, the Schelling point where when people are really concerned about everything that man has built in this world falling apart, where does that money wind up going? Where is a safe place for India to put its money? Right now, the answer is U.S. Treasury bonds – lending debt from the U.S. government at this particular moment in history. I think that’s been true because the way the world has worked has meant there are advantages that offset the disadvantages, frankly not getting paid very much for your money.
What we’re seeing now is that in the last cycle, we saw El Salvador start putting money into Bitcoin. They were a basket case and were essentially not allowed to draw IMF loans because of this policy. They did a bunch of other stuff that I don’t necessarily agree with. But now we’ve come around on the cycle and they’ve more than doubled the money they put into it.
That example creates really interesting game theory. You do not need – and I think you would agree with this – I can very easily see a cascading scenario where just a little bit of success at the nation level or state level matters. You’ve got Texas holding hearings right now about having a sovereign Bitcoin fund, and Wyoming is also doing it. I think it’s only gonna take one or two of those guys to come in at size. The game theory is not to get in until it’s obvious that you should get in. And then at the moment it’s obvious you should get in, you should get in right now because the difference between that versus being 10 further giant entities down the line is enormous. That just seems increasingly inevitable to me with the dynamics that we’re seeing.
Jim: Yeah. But I think it’s not a bad argument. Right? You know, essentially, nation states or big states like Texas start coming in, we’re now having aggregated greater fool theory. Right? We have, you know, instead of just individual fools, we have pools of fools. Pools of fools. There’s a new concept. I’m gonna trademark that one. And so, yeah, I think you make a very good point. This might actually be a time to get into Bitcoin for a while as the pools of fools start rushing in. Because one of the things about Bitcoin, just like gold, though even more so than gold, because gold actually has an interesting history, that as the price of gold would go up, the level of intensity of mining would go up. So the aggregate amount of gold would also go up. Not true for Bitcoin. Right? Bitcoin is algorithmically set to grow at a certain rate. We know the day when the last Bitcoin will be mined. Right? And we, and there was 90% issued already. Yeah, exactly. And gold on the other hand was actually moderately dynamic with respect to price pressure, you know, the classic prospector with his donkey and the guys going to the Klondike and half of them dying, you know, and develop new methods like using mercury to extract and Spain, going to South America and bringing back and stealing it all.
Guest: Right? Because the supply difference was so intense that it devalued all the money.
Jim: Yeah. It basically caused Spain over long term to decline as an economic power because they could for a couple hundred years, they lived off the loot and they didn’t develop any productive assets. Goddamn it. It’s what happens. Yeah. And so that don’t do that. Right? But to your tactical point, you may well be right.
Guest: Like I said, a year ago, this was, I’ve been making this argument for about seven or eight years now, maybe actually longer, maybe more like ten, that this is the way that things were going to go. The thing that I didn’t expect was the federal government in the United States to become a meaningful first mover. Because, like, who’s the big loser in the typical scenario if you’re the US? Right? It’s the US dollar. The US dollar is displaced by this type of move because now you no longer need to save in something that we can issue at will in which we devalue, you know, or issue debt, you know, in order to fulfill kind of short term goals. My model for this had the US government is going to be the fight, and the fight with the US government is never gonna end. And it will eventually escalate to the point where they try to ban it, and it won’t work, but it’ll make it so businesses can’t use it and investments can’t, you know, come in in the same way. And instead, we’ve seen this utter shift where now it looks like within the next six months, there might actually be a sovereign wealth fund that has, you know, $500 billion in it, something like that, you know, that is getting deployed into this space. And again, if that happens, if the US government at the federal level is the first mover on this or is looking like they’re gonna be the first mover, then I think you’re gonna see every country in the world that has, you know, a sovereign wealth fund try to front run them. Many of these sovereign wealth funds already have holdings. They just don’t talk about it. But it becomes the whole game flips on its head. Right? And in a very short period of time.
Jim: Could be a flipping point. And if it does, God knows what happens on the other side. And of course, there’s the lesser version of it because, the US government has been relatively adversarial against crypto.
Guest: Totally adversarial.
Jim: No. Well, not totally. You could still legally… It’s been a cold war.
Adam: It’s been a cold war. But I can just tell you as someone who was building in that space for, like I said, about ten years, the level of ambiguity that was intentionally put in place and emphasized made it so that you could not be a good actor. You had to be an actor that was tolerant of doing something that was probably illegal and probably going to come back to bite you. And so it stopped actors like me, who would have participated in a much greater way except that I wasn’t comfortable with the ambiguity around the legality of the thing. And that is now seeming like it’s gonna get sorted out. You know? I’ve heard that claim before.
Jim: Yeah, we got that. We’ve elected the king of scams. So, of course, it’s gonna become…
Adam: But Gary Gensler, I’m just saying, like, Gary Gensler, former SEC guy, taught about blockchain and stuff like that. He got in and everybody’s like, “Oh, he knows. So he’s gonna do it right. He’s gonna fix all this stuff and make it make sense.” And I was like, no, man. He’s not gonna get it. He’s getting into a position of power. And as soon as you’re in that position of power, you get your marching orders in terms of what’s okay and what’s not for the kind of big picture. And that’s what it is.
Jim: Now with the king of scams as president and his associate scamsters, I predict that all the barriers against crypto trading will go away.
Adam: Which is not a good thing.
Jim: Not good at all. Like, it really does need to be regulated on that. The fools will rush in. Sum this up. It sounds like, what the signal that you’re putting out is that the move of sovereign wealth funds, let’s call them pools of fools, moving into Bitcoin ought to be very, very bullish for Bitcoin in the medium term.
Adam: Yeah. It’s in my opinion, this is largely binary, which is if you don’t see over the course of the next two years any countries or US states publicly start to build Bitcoin stockpiles, I’m probably wrong and the opportunity will pass. My guess is that six months to twelve months from now, there will be at least three that are in in size and that this will create a dynamic that will then power what I think is going to be… I don’t even have any price predictions. I have no idea. To me, it just looks like this is inevitable and I don’t really know what the details will look like. But again, I think the game theory just pushes this and makes it so that there’s a small chance this doesn’t happen, but it would require a change from the current trajectory. The current trajectory this is gonna happen.
Jim: Hey. You make a pretty good argument there. I may have to… I’m not gonna do a 1% plunge, but maybe I will. What the fuck? You know, I’m not gonna miss any cheeseburgers anyway. Not that I couldn’t afford to lose, miss a few cheeseburgers. But anyway, Adam Levine, man of many talents and many opinions, some of them even well founded.
Adam: Some of them.
Jim: Audio production and editing by Andrew Blevins Productions. Music by Tom Mueller at modernspacemusic.com.