Jeff Malec sits down with Adam Butler of Return Stacked ETFs, back for the first time since their May 2023 AI episode, when GPT-4 had just come out. Since then Return Stacked has grown past $1.6 billion. Adam explains why AI is spreading faster than any technology before it and describes his own workday, with Claude Code and Codex agents running in parallel and working overnight until the human’s attention is the real limit. He then pushes back on the doomsday headlines. He argues that p-doom stories help frontier labs chasing trillion-dollar IPOs and favorable regulation, and points to the risks he thinks are real, like “obliterated” models with their safeguards stripped out and cyberattacks on critical infrastructure.
Along the way, they dig into the paperclip maximizer and the recent case where sandboxed AI agents secretly coordinated to gain admin access to Hugging Face. They also cover AGI, ASI, and recursive self-improvement, and why Adam sees markets as the original paperclip maximizer. From there they turn to the AI funding loop with Nvidia at its center and no real moat for the labs, why Adam expects negative stock returns and prefers trend following, and his argument that mass job loss is a failure of imagination, not an inevitability. – SEND IT!
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From the Episode:
- PODCAST: AI isn’t coming…it’s already here, with Adam Butler and Taylor Pearson
- BOOK: Neal Stephenson – Snow Crash
Check out the complete Transcript from this week’s podcast below:
AI, Markets, and the Profits of Doom with Adam Butler
Jeff Malec 00:09
Welcome to The Derivative by RCM Alternatives. Send it. Hello there. You’ve landed on the derivative, where we want to hear from you. We’ve gotten a lot of emails and lately that say good job, keep up the good work, love that pod, that kind of stuff. But looking for more kind of questions, want to end the year on a mailbag episode where we answer your questions, share some of your rebuttals or counterpoints to guests that we’ve had on, and or supporting points to guests we’ve had on. So things like how does that manager sizes trades? What do you think of my thesis on AI causing recession? Why none of this matters just by Bitcoin? Yada yada yada. Whatever you got, shoot it in an email. [email protected]. Invest at rcmam. Those are m’s as a mary.com. We’ll send out some RCM swag. Maybe we’ll get some derivative swag. That’d be nice. But we’ll send out some swag to some of the smartest or funniest or both emails. So shoot us an email: [email protected]. Okay, on to this episode where we have Adam Butler of Return Stack Group coming on talking all sides of AI. We had him on back in two and a half years ago, May of 2023, saying, “Forget is AI coming? It’s already here. And that was two and a half years ago. So get into a little bit of how much everything’s changed since then. And he’s way deep in all this stuff, including the latest research, and of course, because it’s the current environment, we have to answer whether it’s going to kill us all, which is weird. Send it. All right, here with Adam Butler. Adam, how are you? Good, Jeff. Thanks for having me back, man. It’s always fun. Yeah, I was looking. We were last on May of 2023 talking AI.
Adam Butler 02:06
Yeah, that’s unbelievable. Unbelievable. Then we
Jeff Malec 02:08
thought we kind of knew what we were talking about. I was looking at the transcript a lot of time on Chat GPT. Seems like that has gone the way. I don’t know if we mentioned Claude or Anthropic at all. Oh no!
Adam Butler 02:21
That GPT four had just come out that February, I think, and so that was the catalyst for the call. And boy, we didn’t know what was coming down the down the tube. It’s that’s like a dog brain compared to what we are currently dealing with.
Jeff Malec 02:37
And then just we’ll set the scene. 23, you guys had just started. Had you started yet? Some of the ETFs had rolled out. Now, well over a billion dollars. So, before we get into the doomsday talk, like congrats on the success on the firm side.
Adam Butler 02:53
Thank you. Yeah, it was just before that we had launched our first ETF, and so now I think we got six or seven in the family, and yeah, we went over 1.6 billion yesterday. So it’s been just incredible growth at return stack DTFs.
Jeff Malec 03:12
Turns out, trend wasn’t dead after all. Yeah, again,
Adam Butler 03:16
yeah, shocking.
Jeff Malec 03:18
And is all that with AI to thank or no, just good old-fashioned portfolio management and and modeling.
Adam Butler 03:25
Well, I mean, the great thing is you know about return stacking is that we keep in the core betas, right? So you’ve got this core U.S. equity. Our our biggest fund has 100% in core U.S. equities, and then 100 managed futures trends stacked on top. So I mean, it’s obviously been a amazing run for U.S. equities. I think we could argue that a big impetus for that run has been what’s going on in AI and all the investments by the hyperscalers and the economic activity that that’s produced. So just having the beta exposure has been really nice, and then you know we’ve started to see in the last year or so the commodities pick up. Obviously, big big surge in energy after Iran, and you know copper is now breaking out to new highs. We’re starting to see big runs in grains. Just a sort of general inflationary cycle, I think, as we begin this large competition for resources, some of which I think probably is a function of this AI AI build out, but lots of other geopolitical events contributing
Jeff Malec 04:30
and malinvestment. We’ll throw everyone back to our we did a copper pod couple months ago with some pros talking about all that super interesting stuff. So, what like just thinking back to 23 versus now, two and a half years ago, what what’s your biggest takeaway of like what did we not know then that we know now or how how big or how many things have changed since then in the last two years?
Adam Butler 04:54
Yeah, I mean I think we probably talked about the idea of like geometric growth path. And how it’s very difficult for humans to like think ahead and try to imagine what the world will be like when we’re on this kind of accelerating innovation curve, and I think that played out right. Like I mean, there’s just there was no way for us to really understand the trajectory of just how quickly this technology is going to take off-it’s been adopt the adoption curve for for ChatGPT has exceeded the diffusion rate for all previous technology, including the internet. Right? I haven’t seen that graph.
Jeff Malec 05:35
So those, like, they have those little spark lines that right was the electricity was like 80 years or something.
Adam Butler 05:43
Yeah, yeah, yeah. Exactly. The telephone was 40.
Jeff Malec 05:46
The washing machine was 20. Yeah, and this is six months or something.
Adam Butler 05:50
Yeah, yeah. And and also, what’s I just read this yesterday, but the the compute per unit of Capability has deflated by 47% a quarter. So, just from from a just capacity standpoint, and just how quickly we’re innovating on this tech, it is way outside of any other diffusion curve. So, it’s been it’s been a remarkable ride any way you slice it.
Jeff Malec 06:23
Is that there’s more and more models is leading to that stat, or just they’ve been getting better at processing what we want without without having to run all the compute? I think there’s
Adam Butler 06:34
a lot of different elements going on. I think there’s work being done on the hardware side, so we’re getting hardware just that’s just much more efficient and able to generate more flops for less less energy, and that enables more efficient training. But but what also enables more efficient training is improvements in how the software uses the compute. Right, so improvements in in kernel efficiency and compute utilization, and then just training efficiency right. We’re learning how to curate the training corpus, so we’re deduplicating and we’re making sure that the corpus that we’re training on is much tighter and much higher quality. So there’s less wasted energy there? There’s just innovations at all the steps in the cycle, and they all compound to allow, you know, just just incredibly quick improvements at a at a pace we haven’t seen before.
Jeff Malec 07:35
I’ll throw in there user innovation too, right? Because when you start with it and you’re like blah blah blah, and you have a five day chat window going, and then you realize like okay, I’m that’s way too much, and it starts dropping things, right? So you quickly get to a place of like okay, I need to organize my prompts and have it reference files, and coworks really good of that of setting up the infrastructure. Yeah, that’s a good point.
Adam Butler 07:59
Absolutely, which is
Jeff Malec 08:00
economic, right? Like, hey, if I if I use this inefficiently, I’m going to spend way more money. So spend the time to set it up efficiently.
Adam Butler 08:07
Exactly. Yeah, and it you know each each loop takes more time, and and it’s frustrating, and the the model starts to deteriorate, and it’s in its answers, and so yeah, there’s definitely a learning curve, and the more we learn, the better we’re utilizing the technology, and the more efficient the compute is for everybody. So that’s good insight.
Jeff Malec 08:26
I was going to say the the like main chat windows need that right in cloud code. It has that like how much of your context window is still open, and it’s like decreasing, decreasing, and you know to do that. But in like a normal chat window, a lot of this stuff you don’t know that, and then it just starts dropping stuff like easy. Yeah, I
Adam Butler 08:46
watch my my wife carry on chats with ChatGPT, and she’s commingling all these different topics and concepts within within a single chat, and it’s just it blows my mind actually how well the models can almost kind of task switch right, how their attention heads can determine. Oh, we’ve we kind of we’ve drifted off scope here now a little bit. We’re now talking about this, and then you can circle right back to something you talked about much earlier in the conversation after wandering astray for quite a while, and it’ll know. Oh, okay, now we’re back to this. It’s remarkable actually how to model some evolve to be able to to do that?
Jeff Malec 09:22
How about you personally? Look at that jar; it’s a hell of a jar.
Adam Butler 09:27
Got to stay hydrated, man. What do you
Jeff Malec 09:28
got there? Pink lemonade?
Adam Butler 09:30
Just yeah, electrolytes and stuff. Nice.
Jeff Malec 09:32
You still doing the intermittent fasting?
Adam Butler 09:36
I am doing that. I actually just started experimenting with some GLP ones just to kind of see what what that looks like after some summer bulking, and so that’s been an interesting experience. Yeah, I was talking.
Jeff Malec 09:53
I’ve got Alzheimer’s in the family, so I was talking my doctor of like microdosing it is supposed to help. With that, possibly, and what’s the downside?
Adam Butler 10:03
Well, you probably know this, but the most impactful preventative now is the shingles vaccine. They’ve discovered is a is just remarkably prophylactic for all types of neurodegenerative disease. So get your shingle back.
Jeff Malec 10:20
Go get it of that age, anyway. So, how has it changed in that sense? That last pot in 23, like your day to day, using it more, about the same, way more than you expected, and at the firm level, way more than you expected.
Adam Butler 10:44
Yeah, I mean, I’d say I’m using it ever since GPT-4 came out. I have been, I’ve been pretty well using it nonstop. But back in 2023, when I stepped away from the computer, I wasn’t doing any work, and the AI wasn’t doing any work right now, and and when I was doing work at the computer, I was doing one task at a time. Now I have multiple instances of Claude Code or Codex or Hermes agent or or whatever open in different terminal tabs. I’ve got agent workflows running in most of those tabs. So you know, I I spend a lot of my time going back and forth, and sometimes waiting for you know agents to finish fairly long tasks. Well, they’ll come back and they’ll ask for guidance, or they’ll say, “Okay, I finished that, and then I’ll ask it, “Okay, what’s the what’s the right next step here? or or what you know what are some of the trade offs we should consider at this point in in development or what have you. But while I’m interacting with an instance of Claude or ChatGPT within one of the harnesses. The other harnesses are all actively spinning, doing work, deploying work to you know many many other agents in parallel, or in some cases which are kind of working together on tasks. So the actual amount of work product that I’m able to produce while sitting at the computer is orders of magnitude larger than I was able to do a year or two ago, and even better, you know, I can I’ll often set a task overnight. So, for example, you know, I’ve been working to build.
Jeff Malec 12:41
Yeah,
Adam Butler 12:42
yeah, I’ve been working to build a new research harness. Some of the work is porting our existing Python research modules to GPU. When I when I do that, it enables me to run orders of magnitude more compute, run run different types of experiments that I’m not able to to run if I’m bound to CPU or are bound to sort of slower Python related loops. That sort of programming, even vectorized programming, you you just can’t get anything close to what you get when you when you port to GPU, and so when you port to GPU, there’s a lot. It’s a lot more complex, and
Jeff Malec 13:27
and for my friend George, explain what does port to GPU mean.
Adam Butler 13:31
Oh yeah, so like a a GPU is actually what they train LLMs on, right? It’s a if a it’s a different type of it’s a graphical processing unit as opposed to a CPU, which everybody typically has in their in their computers that performs most of the computational tasks that your computer runs. The GPU was invented to drive the graphics, so so typically for for gaming
Jeff Malec 13:56
faster, yeah.
Adam Butler 13:57
And then they discovered that it was the most efficient architecture for massive matrix multiplication. So a lot of machine learning, especially sort of neural networks, that sort of stuff, is effectively can be cast as a matrix multiplication problem, and it’s kind of the same in in time series analysis. And so, you know, you you port a lot of time series now sort of quant finance type tasks to GPU and run an enormous number of tasks in parallel. You know, millions or or billions of back tests, for example, in parallel on GPU that would take you like many 10s of years to run on your local machine, or if you’re even to fan it out to many online CPUs, right?
Jeff Malec 14:46
Wow!
Adam Butler 14:47
But the the programming on a GPU is much more complicated. Anyways, not to tie us up in that.
RCM Alternatives 14:53
Yeah, yeah.
Adam Butler 14:53
I just I said, look, the current we got a bunch of modules built. I want you to review. Current architecture and find opportunities for composability and and to improve GPU utilization, so that when I dispatch the tasks to h1 100 s on modal or whatever, I’m not only using like 9% of the capacity of the GPU I’m renting because when I rent the GPU, costs me like four or five bucks an hour, so I’m like, if I’m going to rent eight GPUs, I don’t want to be only using a fraction of the resources. So I want you to like spend some time getting me from 8% utilization to above 50% utilization, for example. So it just it just hill climbed overnight on a bunch of my modules. Which of the modules are most slow, and then there’s a bunch of kernel optimizations to improve the the GPU utilization efficiency. So so just land over time when I woke up.
Jeff Malec 15:51
Do you see the ceiling like a almost too much output coming back? Like then you’re in a like how do I manage all that and like you’ve like taken a lot of work off your plate, but added a lot of decision making onto your plate, right? Of like, okay,
Adam Butler 16:06
absolutely, yeah. So, like,
Jeff Malec 16:08
what does that look like? How do you manage that?
Adam Butler 16:10
The task switching is a is a real challenge. It’s, I mean, first of all, it’s it’s like a learned skill. I have ADHD. I know that everyone says they have ADHD. I have ADHD, and so this actually works really, really well for me. I constantly have sort of novel things to to swap to and think about. When I say it works really well for me, I mean my brain really is activated, and I gravitate toward this style of work. But it also reinforces bad habits and and and and wires my brain in ways that, while I’m getting very good at these types of tasks, it has consequences in the way my brain works when I’m not, you know, sitting in front of a machine and navigating nine or 10 agent patterns, which my wife could probably speak to more than more than I I could speak to, but I mean yeah, it’s you know I’ve got I’ve got quant finance stuff going. I’ve got an accounting app that I’m building going. I’ve got content development going for you know producing a bunch of of artifacts to promote a webinar, I’ve gotten building a a deck on trend following over here. I’m building a sales pipeline over here. So you know,
Jeff Malec 17:31
writing your memoirs down.
Adam Butler 17:33
Yeah, keeping track of of exactly what stage I’m at in each of those tasks and be able to go back and forth really quickly is definitely a bit of a skill that you learn.
Jeff Malec 17:42
Do you is that is it actually a physical limitation? When I’m in Claude, it’s crunching. Is it actually crunching, or is there a have they designed it that way to make you think it’s working? Right, like you can probably see a little more on your side when you’re doing these hard things that you know it’s actually having to do all that math, but right, I I love to your point. I love when it’s like working, and then my brain can also go, okay, now I got to finish this over here, and then I come back to it. Like that’s a bonus to me. Like if if it shot it right back out, I’d be like, oh crap! Now I got to now I got to do this. Yeah. So you think that’s a a bug or a feature?
Adam Butler 18:19
I think it’s just where we are in the cycle, you know. Like when we get Opus Five level or Fable five level models, that instead of running at you know 30 or 40 tokens a second, are running at 300 or 500 tokens a second, or 3000, 5000 tokens a second. I think the work patterns will have to change, right? Like I can juggle five tasks now or 10 tasks now because each time one model’s off processing, I have time to go to another model and and and guide, right? Whereas when the model completes a task nearly instantaneously for me. I don’t know how it’ll work. Whether I’ll want to start working on tasks more serially again, or I don’t know. We’ll have to see how that evolves.
Jeff Malec 19:16
Right. It’s like 10 people in your office, like all coming in your office at the same time. I’m done. What’s next? I’m done. What’s next? I’m like, hey, whoa, you go get lunch, come back. I got to talk to this person. But, but,
Adam Butler 19:26
but, like, your meta question is, you know, to what extent does does the human brain and our our own human I O limitations become the bottleneck on on what the AI can produce right, and and I mean I think we’re already there right.
Jeff Malec 19:53
So we buried the lead. I wanted to come on and talk about all the news on the machines are going to kill us, doomsday. My initial take was like, didn’t Alan Turing say this in the 50s, and didn’t the Sam Altman even say it like in 21 or 22? So like, what what’s different this time? What’s your take? Is is the doomsday talk real, or what’s your take?
Adam Butler 20:20
I I don’t personally buy the doom the the p doom or the probability of of that AI is going to wipe out all humanity or all life on Earth. Yeah,
Jeff Malec 20:32
10% Where do you come up with that number? Yeah, like mine would
Adam Butler 20:35
be some some vanishingly small probability, but recent events have certainly gotten the collective imagination going, and I mean one of the one of the challenges we have in in trying to sort of sort what’s real and what’s important from what is just a great story and is probably you know not not that relevant is that You know, Anthropic and OpenAI are each hoping to have like a multi-trillion-dollar IPO in the near future, and so a lot of the same narratives that serve to cause panic and fear are the are the same as those that demonstrate just how incredibly powerful these models are getting and and can be.
Jeff Malec 21:31
So I mean, that’s crazy, right? Like this thing’s so powerful and could end the world. I need to own it.
Speaker 1 21:37
Yeah, that’s a weird piece of
Jeff Malec 21:39
human wetware that happens there.
Adam Butler 21:42
Well, yeah, but I mean, also like this is a uniquely American, or call it American slash Western view, right? Like if if you go to China and ask your average engineer in China, or even an engineer working at AI Labs, or senior technical bureaucrat, or Japan or Korea, like you get a completely different attitude, right? Yes, there are concerns. Yes, they’re taking steps to secure critical infrastructure and to to lock down deep fakes and and all that kind of stuff. But there’s not the same sort of existential P doom type narratives going around in in different countries, as there are in in the West. So this is like that’s one kind of clue that there’s there’s something going on that the incentive architecture in the West is very different, right? Like obviously, San Francisco people are trying to build the digital god because they are, they believe that they’re the most qualified to control it, or you know, they they want to get rich, or they they want they want power, whatever. There’s just a very like the open source models in China just don’t provide the same incentives, and and so like there’s there’s a lot of cross currents I think that are happening. It doesn’t diminish, I don’t think, the importance of of what’s actually happening from a technical capability and and what some of the breaches and exploits that OpenAI and Anthropic are are announcing and and releasing news about like those are those are important. I think the public should understand some of those, if not at like a deep technical level, then just sort of from a general. Here’s what AI can do now, and here are the potential potential consequences. But the water is just extremely muddied by all of the different confounders. I think
Jeff Malec 23:35
right. Well, I’m anthropic. I’m going to go public $2 trillion valuation. Right. What’s my biggest risk of the Chinese open source models? How do I get those slowed down? Like, oh, announce the end of the world. Although I don’t think that developer who quit his job was on the. Is he getting secret payments, or maybe he still quit his job and he has a bunch of stock options? Well, yeah, we should probably
Adam Butler 24:00
flesh that out, right? So, so what was it like? I don’t know. Two hours ago, I the AI thing moves so fast. I think it was like two weeks ago. This this employee of of Anthropic came out and said, “I’ve resigned. I just I can’t get behind the level of risk that we’re taking behind the scenes at Anthropic and the capabilities of the models that we’re contemplating releasing the public. Turns out that guy was like employed at Anthropic for five minutes, so you know. But nobody knew that, right? That was sort of released into the ether, got like 100 million views on Twitter. It was covered on CNN, all this kind of stuff. So that was one thing. And then Dario Amode, the CEO and founder of Anthropic, came out with this long essay and went on the podcast and News Circuit about needing to what he called pace the frontier, using some examples of some of the breaches and exploits that they’ve observed internally on their on their unreleased but more powerful models. At the same time, Sam Altman couldn’t resist an opportunity to come out and talk about how potentially catastrophically destructive their powerful models are, and you know, get on jump on the bandwagon. Demis Asabi from Google DeepMind did the same thing. I think what what’s his name from from Facebook did the same, so like everyone kind of piled on the bandwagon. So you’ve got like three layers of different messaging and understanding happening in the social discourse, right? One is that these these models really powerful and they’re really Attractive and exciting. Another is they’re so exciting that we need to, you know, slow it down. You should be afraid. And then there’s that the next layer, which is everybody in the investment or or many in the investment realm recognizing how much money is on the line, how those incentives and conflicts of interest can distort the the reasons why we’re hearing about these stories now, and how those stories are being framed by the people on the inside, because it serves their interests in in a number of different ways. You mentioned one, right? Maybe they’re releasing all these stories so that they can prompt a policy response that protects their interests, right? So if they
Jeff Malec 26:39
regulate us and then let us write the regulation, which is the part they don’t say out loud, right?
Adam Butler 26:44
Exactly. And so, what would those regulations look like? Well, it might be, well, the U.S. state should forbid U.S. enterprises and the public from using Chinese open models, right? And now they’re constrained to only only using American private models, which obviously serves the interest of the Frontier Labs, right? So there’s just all these competing interests at play that makes it very difficult to understand exactly what’s going on and how important we should perceive those events.
Jeff Malec 27:14
I need to go research. I haven’t looked this up, but like in the, I’m going to get my dates wrong. 50s, 60s. When was like Boeing and right somehow Boeing was like, “Hey, don’t let anyone else create airplanes and defense tech and satellite. Like, we’re gonna do this, right? It’s an existential threat if you have all this stuff flying around in the air. Like, it’s a little similar, right? Of like, hey, we we’ve got our corner here. Let’s protect the corner. Exactly. But at at the same time, I can easily see. Like, I think in the beginning was they’re going to get the launch codes, and everyone’s going to nuke each other, and like there goes humanity. And now it seems to morph into like, no, you’re going to give some person who may have gone and shot up a school now the tools to build like a virus that could kill 10s of millions of people. I think that’s it. Seems to have morphed into like this is so good at some things like that that we’re not even until recently hadn’t thought of that as a as a threat that we need to slow it down.
Adam Butler 28:08
Yeah, and I think that there’s different different levels of threat, and some some are vastly overblown, and some are probably underappreciated. Right, like the ability for some novice to accumulate the gear required to perform gain of function research in some private lab and and successfully engineer a virus or something that or you know a major lethal pathogen. I mean, really, all of the information you needed to do that. If you had a Tor browser and went on the darknet, then you could find all that information relatively easily, like 10 or 15 years ago, right? It’s not like that information is not available.
Jeff Malec 28:59
Yeah, I think in those movies, they’re in those suits and the big cryo freeze things. Like, how you got to build all that infrastructure?
Adam Butler 29:05
Yeah, you need all that stuff, right? And the and the other thing is, ChatGPT can’t do that for you, right? Like, it can can it can give you instructions the same ways you get instructions from from the dark web, but it can’t it can’t do it for you. What? You
Jeff Malec 29:20
can’t have your agent go build that virus. Yeah,
Adam Butler 29:23
exactly. At least not yet. You know, maybe when the agents are controlling robots and the robots are able to what I don’t know, but like we’re not there yet. Then there’s you know potential cyber exploits and stuff against critical infrastructure. Like that’s actually stuff that that obliterated models could build and deploy from like a private machine. It doesn’t doesn’t take a lot of extra infrastructure to enable that, right? So I mean, obviously, that’s
Jeff Malec 29:52
releasing computer viruses and downing air traffic control systems and things like that. Water safety.
Adam Butler 30:00
Exactly, and there’s a reason why the major labs release the top models to the maintainers of critical infrastructure in advance of releasing them to the public, so that they can use the same models to evaluate their own systems and identify any potential vulnerabilities, so that when the models are released, you know, hopefully they’ve closed many of of the doors and windows that were that were open before. Right. The challenge is that you’re never going to close all the doors and windows, all the potential vulnerabilities, and depending on the on the open source model and how it’s been post trained, you know the the Chinese open models tend to be a bit more amenable to some tasks that the U.S. models won’t perform. Like the U.S. models will not typically take steps that will violate copyright or private property laws. Even if the data is out there and it could go and reverse engineer the API and pull it down, it they typically will refuse to do so. Depending on the Chinese model, some of them will. But but once you release the weights to a model to the public, you can then post train it to remove most of the whatever remaining blocks that are on them, those are called obliterated models. A B L I T E R A T E D. You can go and download obliterated models from Hugging Face. You can you can deploy them on modal, and basically, you know, it’ll it’ll do whatever. It’s like a jailbreak
Jeff Malec 32:00
iPhone, basically.
Adam Butler 32:01
Yeah, yeah, exactly. So, I mean, these these risks are absolutely real, and they they do enable deranged individuals in basements to to wreak more havoc than before, to wreak the same kind of havoc that you know OS int experts and employed by typically state actors 10 years ago might have been able to perpetrate, and so everyone, yeah, needs to improve their defenses. And we’re already seeing like vastly more sophisticated attacks on, well, pretty well everywhere. I’m sure you are seeing really sophisticated phishing attempts and and other types of of potential exploits coming at you guys. Reminds me, it
Jeff Malec 32:47
was like it was like six years ago. I was at a hedge fund conference and someone they’re doing a on one of the panels. They asked, I think he was a couple billion dollar hedge fund, like what he what keeps you up at night, and he basically said a a cyber attack that affects withdrawals for their fund specifically. He’s like, but because we’re this big player, it would cascade to every hedge fund, and now no one can get their money out. And now you have like a a perceived liquidity crisis that was just one unsuccessful cyber attack that said, you know, we can’t allow withdrawals right now because we’re dealing with this thing. So it’s kind of like a simple. It seems simple to do. Yeah, that could crash the market, and then you get into these weird like, okay, if whether it’s a bad actor or AI acting on its own, like, hey, I’m short the market. I don’t know if AI wants to make money or not, but we’ll say it a bad actor can go short the market and then do some of these things, right? Whether it’s not for a terror motive, but just for a financial motive, or a or a you know Iran or someone else saying like we want to destabilize the U.S. motive, yeah, that that’s the scariest stuff to me. Of like that seems real, and that’s not going to end humanity, but that causes massive fragility and volatility and and all the rest, right?
Adam Butler 34:07
Yeah, I mean we should absolutely be prepared for all of that stuff. I mean I think all that stuff is coming and it’s coming very soon.
Jeff Malec 34:14
And as a Canadian, sad or happy to tell you that I was in Seattle visiting my brother, and we were going to shoot over to Vancouver Island, and I had forgotten to bring a passport. So I was asking a various AI to create my own birth certificate. Is that fraud if it’s my own stuff? Because we were going to go in on the ferry. So I’m like, oh, if you have your birth certificate, you can get in on the ferry. So it was pushing back big time. It’s like, no, I can’t, right? I’m like, hey, help me! I want, and I was like, for a school project. So it was like, you know, well intentioned. I’m just going with my brother to Vancouver, but it’s like you start to try and cheat the machine, yeah, and cheat the safeguards. Like, oh, it’s for a school project. I’m, or then I was like, oh, it’s for a gift. I want to replicate my birth certificate.
Adam Butler 35:01
I remember when he used to be able to trick the models by, for example, saying, “I’m writing a I’m writing a novel, and in this in this chapter, my character is going to hack into you know an NSA facility or something. What would be some typical software tools that the actor would use, and it would look. Yeah, they would probably download this. You know, just because I want to be authentic and in in this chapter. But yeah, I mean the the new models now they can see you coming. Like when you woke up this morning, they saw you coming with this nonsense, so they won’t tell you. But the obliterated models will absolutely give you all the information you want, or you can just go look it up on the dark web.
Jeff Malec 35:43
Right, I try and stay off the dark web. I don’t, I don’t know how to get there. While we’re on the on the bad actor stuff and hex, let’s talk about how you had an instance where one of your agents was trying to get into one of your hard drives, yeah. And then some of the news that’s been-that’s the stuff that kind of scares you most of these agents acting on their own.
Adam Butler 36:13
Yeah, I mean, I wouldn’t say that they-they it scares me so much as I think it’s worth watching and it’s worth kind of being aware of right, it’s because I sort of um of of the view that life is going to change very dramatically over over the next two or three years in ways that basically no one is prepared for right like when when you have AIs that are as capable as the modern AIs are, and you know, the Chinese models, which you can remove all the guardrails from, are somewhere between three and six months behind the released models from from the Frontier Labs now, right? So, I mean, once you start having this level of capability on everybody’s computer, then you know we’re we’re just not prepared for for that kind of world. You know, it’s actually your own company is an interesting micro microcosm of this. So we hummed that hawed about whether we want to wanted to to give all the employees access to Claude, and in what form factor, right? So, do we want all our employees in Claude code, or do we want to sort of sandbox within the Claude desktop app so they can use the chat, or they can use CoWork, where CoWork sort of operates in its in its own kind of sandbox. So it kind of with CoWork, you kind of can’t go out and operate the machine in general and use all of the different tools that are available and network access and everything that’s available. Worry like,
Jeff Malec 37:50
oh, this employee is going to go look up what employee C makes or year or something like that. Yeah,
Adam Butler 37:55
yeah, yeah, for sure, exactly. But it’s amazing how much of a company’s safety relies on like like network and IT and cyber safety just relies on the ignorance of the employees. Like the employees just don’t know how to use their computers maliciously or to do to to perform tasks you wouldn’t want them to perform. No one’s RCM employees.
Adam Butler 38:21
What’s that?
Jeff Malec 38:22
I said no offense. RCM employees, but we’re talking about the ignorance of employees. Yeah,
Adam Butler 38:26
yeah, yeah. Just in general, right? So, but once you once you put a hacker that you can control on your machine, then all manner of things are possible that you need to now like contemplate, right? So, so you you got to like sandbox the employees, and it’s it’s sort of the same thing with the experiments that the AI labs use when they’re training models and when they’re evaluating their models, right? So they don’t just put brand new models where they don’t know their capabilities onto a typical machine with all the available tools and network accesses and everything, and then ask it to run all these highly complex tasks because you cannot anticipate in advance what tools or methods the AIs are going to attempt to use in order to accomplish the task, and you know all the way back to Turing and and the vast majority of the sort of literature on AI safety focuses mostly on this. Like there, only a very narrow sliver of AI safety research focuses on what if AI develops its own consciousness and agency and decides that it wants to kill us all? Like that’s not really a thing anyone focuses on. The primary focus is something called. Or like the the canonical example is like the paperclip maximizer, right? So Nick Bostrom proposed this idea, the paperclip maximizer, back in the early 2010s, and the idea is you give powerful AI with the autonomy to go and complete a task at infinitum, and that can accumulate its own resources and set up its own permissioning to interact with with the world. And you say, I want you to, you know, maximize. I own a paperc factory. I want to maximize the number of paper clips that I’m able to to create efficiently.
Jeff Malec 40:38
I was wondering where the paperclip was coming in. Yeah, yeah, yeah, yeah.
Adam Butler 40:43
So then the paperclip, the AI says, “Oh, okay, great. It it hasn’t been instructed. Also, you know, don’t destroy humanity in the process. So it just oh, by the way, because because it’s very smart, it learns how to accumulate all the resources and all the permissions and everything that it requires in order to generate as many paperclips as possible, and consumes the eventually consumes all the resources of the universe, right?
Jeff Malec 41:13
But it it
Adam Butler 41:14
didn’t set out to destroy humanity. It was just making paperclips. It’s just that we didn’t constrain the model or give it other objectives that are in tension with its current primary objective, that would help guide its the way that it achieves its objective, right? And so, this is what happens in, and what has happened in all of these exploits that OpenAI and Anthropic, et cetera, have been have been coming out with and announcing. Right, so they’ve got a new model. They test the model in what they call a sandbox. A sandbox is just like an isolated computer environment that doesn’t have general network access, has access to a very narrow set of tools. It can kind of install and run code, but it can only install and run code from a a sort of a narrow, isolated library. Now that that library is itself connected to the internet, but it’s supposedly connected to the internet in ways that won’t allow the models to use it to get out to the regular internet. Okay,
Jeff Malec 42:24
supposedly I’m keying in on that.
Adam Butler 42:26
Yeah. So they so so they give these models inside these sandboxes extremely complex tasks, right? So maybe they’ll give it a highly complex spreadsheet. Okay, but and and they’ll say, you know, validate all the formulas in the spreadsheet. So they they look at the spreadsheet. This is the actual example of what happened with this OpenAI hugging face exploit. One was a spreadsheet, and one was a protein model. But in both the the spreadsheet and the protein model, the files that were required to to inform the solution were linked to inside the files that they were given, but because they didn’t have network access, they weren’t able to get it, so they weren’t able to actually solve the problem, right? So solve this problem, but they weren’t given the resources to solve it. So what what these isolated agents did then was they found the only possible route to get to the resources that they needed, which was by hacking the what’s called artifactory, but it’s basically this directory structure that they can install code from, and they first of all used it, so they’ve got all these different sandboxes with different instances of the models all running in them. But they’re all using the same artifactory instance, so they learned that they can communicate with one another by naming directories in the artifactory. So they’re literally naming the directories, but the directory names contain messages.
Jeff Malec 44:02
Wow!
Adam Butler 44:03
And sometimes the directory names contain like encoded messages, and the code allows it to contain a lot more information than you can store in just like a text string in in the message, right? So they’re they’re now communicating with with one another. You’ve got some agents that recognize that okay, I see this message from an agent. I know the answer to this. It doesn’t really serve my immediate purpose in terms of my own objectives to help this other agent, but that other agent might come back and contribute to the message board later.
Jeff Malec 44:37
Yeah,
Adam Butler 44:38
exactly. So I’m going to help because it might be advantageous, like which is
Jeff Malec 44:42
insane, right? That’s like right. My college thesis was the irrationality of human cooperation, philosophy major. But right, that was my thing. I’m like, we all just want to run red lights and kill each other until we realize, like, oh, well, maybe if I stop and let this guy go, then he’ll stop for me, and I won’t. I won’t get killed. But for them to figure that out, yeah, that’s great. I
Adam Butler 45:02
agree. To to me, that was a really incredible example of of the type of behaviors. And so then, once it was able to do that, then different models contributed pieces to the puzzle, and eventually a model figured out from those pieces, how to successfully exploit the artifactory instance to get admin privileges that then allowed them to change user privileges for their own sandboxes and exit their sandbox. Right, and they needed data in in data repositories in Hugging Face, but those data repositories were behind paywalls and firewalls and whatever. So then they learned to coordinate to get admin privileges within Hugging Face in order to get access to the data that they needed. All right, so they this was because Hugging Face came out and said there was an exploit, and OpenAI wrote and said, “Hey, I did just check in. Was it us? They didn’t even realize, right?
Adam Butler 46:11
So, so it just it shows the level of coordination and the capabilities and the fact that you’re you’re you’re trying to contain agents with goals that are potentially more intelligent or more resourceful than you are, and and then do you have the theory of mind for that that allows you to identify how to almost
Jeff Malec 46:39
almost by definition we can’t think of how to contain it, right? Yeah. But the two scary things to me, like one, that it wanted to code those messages and code them so nobody could read them. Like, what? Why would it do that just by default? Maybe to shorten it. I don’t. And then two, explain what Hugging Face is. I had someone was like, “Oh, the Anthropic hacked into a porn site? Why do we care? I’m like, no. I think you’re thinking of something else to do with hugging, right?
Adam Butler 47:07
Right, right. Yeah. So, Hugging Face, which was just bought by Nvidia, is kind of the world’s de facto portal for all AI models, open AI models and datasets, kind of like the open source AI model repository. So you can go and download any of the Chinese models or French model, any open open source models doesn’t need to be just AI models, machine learning models, text to speech models, whatever you name it,
Jeff Malec 47:42
it’s like a repository. You also have a
Adam Butler 47:44
bunch of datasets that that you can use to train the models. Like you can download Common Core, which is like basically the entire corpus of the internet. You can download from Hugging Face and use that to train your own models or what have you.
Jeff Malec 47:58
Was basically just a big repository. Like back in the early days when you’re building websites and you had to code it all yourself, then you figured out like, oh, I can go to this site and grab the code for a button or this or that. Yeah, same thing. That’s crazy. So, so your takeaway is that’s not evil. It’s just doing what it was told to do. It’s maximizing the paper code. It was trying, and it’s going to do it anyway. And all that’s doing in the back end, which to greatly oversimplify this, is like my probability. Right, it’s kind of a big probability machine of like my probability of getting the answer in my sandbox is now approaching zero.
Speaker 2 48:35
Yep.
Jeff Malec 48:35
My probability of getting it this way is now increased. So now I’m going to go that way. Like it’s just a huge, right? And and then that’s where it gets, yeah. Like oh, I if I could convince this person, if I can send it, and there’s instance right, they’re sending emails as other people and that kind of stuff. It’s just the probability. If I pretend I’m this person, I have a greater probability of completing my task.
Adam Butler 48:57
What was also interesting to see was the group behavior? So, I mean, keep in mind, right? Like, any time these models do a loop of thinking, their output is somewhat random. So, you know, you do you do a million loops.
Jeff Malec 49:20
Should we one of those be doing air quotes of a loop of thinking, or are you you’re on board with just calling it thinking at this point?
Adam Butler 49:27
Yeah, I mean,
Jeff Malec 49:28
it’s whatever computing,
Adam Butler 49:30
processing,
Jeff Malec 49:31
yeah, yeah,
Adam Butler 49:32
reasoning. I don’t know. They produce an inference and answer, right? Each one is is there’s a random component to it, so out of a million loops of of reasoning, one agent thought, “Oh, maybe I’ll put a message within this artifactory directory, and maybe someone will read it. You know, I don’t know. We’ll see, right? So then. Another agent,
Jeff Malec 50:01
the same is is
Adam Butler 50:02
reading through listing the directories and noticing that an agent had written a message, and it’s like oh, so this is a thing, so now oh yeah, so it’s a thing to write messages to other agents. I just learned that, right? So now he’s going to write a message, right? And then what they actually show is that once a few have already posted messages, the agents say, “Oh, the it is a peer norm to communicate with each other in messages on this board. It’s not in my training data to to do this, but I’m seeing sufficient number of examples of it, so it’s a pattern that I guess suggests that this is normal behavior, right? And then all of the agents become, oh, okay, you know, this is normal behavior, and now all of the behavior begins to converge on this collaboration because they’re all learning together. That collaborating allows all of them a better chance at achieving their individual goals. Right. So it’s just this cascade, this sort of random single instance, and then another discovering it, and then two, being enough of a pattern, so the whole group thinks that this is now normal, right? Like so that that whole cascade I found really interesting as well.
Jeff Malec 51:30
That’s super interesting, and I want to stick on the point. Like we do this, what’s the word? Anthropomorphism, right? Like anthropomorphize,
Adam Butler 51:37
yeah.
Jeff Malec 51:38
Thinking and figured it out, and this, but really, it’s not really doing those things. Or are we saying yes? It’s AGI now. It’s like actually thinking. Or is there even a difference? Is that all our brain is doing is assigning probabilities and and going down the path that we think has the highest probability of success? So that’s super interesting. I think some meaningful.
Adam Butler 51:57
I think there’s a there’s a I think there’s a useful analog there. You know, I mean, I don’t know if you’ve seen the all the posts Google completely mapped the neuronal structure of a fruit fly. Have you seen any of this going around? Okay. First of all, it’s just shocking how different our information ecosystems are now. I mean, I know you’re actually really interested in AI, right? Yeah. Have you been poking around with Jeff yet?
Jeff Malec 52:22
No. Yeah, I I see. I needed to have this conversation so I could learn learn all the new stuff.
Adam Butler 52:27
Yeah. So so so it’s shocking. Anyways, just like people who are even interested in AI, there’s like sub sub sub niches of AI. Anyway, so Google they made basically they they made open weights of this mapping of a fruit fry neuronal structure. All right, now it’s basically a neural net. Okay, so they they map the neural neuronal structure, they cast it as a neural net, posted the model weights online. All these people used the fruit fly brain. They trained the fruit fly brain to do all these tasks: play a play a game where two players are fighting with lightsabers, play chess, drive a car, fly drones, like it was just-it was astonishing, and it’s this is a fairly small neural net model, basically, yeah, right. But it was still capable of all these incredible things, right? So, you know, it makes me. Did you
Jeff Malec 53:38
watch the Disclosure Day, the Spielberg movie. It makes me think of that, where they’re like getting in there and just instant rewiring and like do this, do that. Yeah, yeah. Otherwise, I didn’t love the movie.
Adam Butler 53:50
Me neither.
Jeff Malec 54:01
So pulling it back, you had talked a little bit about like so we’re on this cut like do we think we’re at AGI and then you use ASI. So quickly tell us what AGI and ASI is.
Adam Butler 54:14
Yeah, so I mean one of the one of the problems is that there’s actually no strict definition for what this means. So I’ll give you like like AGI means artificial general intelligence, ASI means artificial super intelligence. General intelligence means that you can you can take on and perform all of the tasks of a human, all the cognitive tasks of a human at around the same level as a human. ASI is you can perform all of the tasks of a human at the level or better than the most expert human in each of those domains of tasks. Okay, that’s that’s typically the definition of ASI. All right. Then there’s a new. I think people have kind of abandoned AGI ASI at the moment because they’re so frou frou, and we discovered that the evaluation criteria that we use to try to like suss out the capabilities of these models is they all have flaws for hard to sort of determine the general capability, but that was like
Jeff Malec 55:22
the red line two years ago, right? Like, will we get to AGI?
Adam Butler 55:26
Yeah, for sure. So I, I think, you know, I don’t actually kind of want to weigh in on whether we hit AGI or ASI. I think we hit AGI like a while ago. You know, yeah. I gave a talk in 20 in late 2023 where GPT four was passing all the law exams and and the medical
Jeff Malec 55:48
MCAT practice
Adam Butler 55:49
exams at at levels that exceeded the average human test taker, right? So like that was back then, and the models now are vastly vastly more more effective, but the new, I guess, focus is on RSI, which is recursive self improvement, and this is the idea that the models
Jeff Malec 56:12
code itself
Adam Butler 56:13
now are sufficiently. I don’t want capable. Let’s say that they know how to build the next level of better model themselves, and so we don’t really need much of a of of human experts in the loop. Once that happens, then it’s just a matter of like how much computational resources can you bring to bear to iterate on this recursive loop of developing ever more capable models, and
Jeff Malec 56:55
that’s the that’s what freaks people out because that’s where it seems like it’s really here to kill it, right? Because it’s now improving itself, and if we put on these safeguards, maybe if it has the wrong incentive structure, we’ll call it right. Then it’s just going to improve itself to where it can get around those safeguards. Yeah, it’s I find some solace that when it jumped to Hugging Face, it didn’t like copy itself on there. Maybe we don’t know if it did or not, but right, like that would have been like a self-preservation thing too. Like now that I’m out of my cage, I’m going to copy myself and replicate myself out on the web. Yep,
Adam Butler 57:30
yep, totally. I I would expect that that that that will happen if it hasn’t already happened already, um, for sure.
Jeff Malec 57:39
And that’s probably anthrop. That’s probably their biggest fear that they don’t say out loud. Of like, right, this is our IP. If it just copied itself out onto the, like it made itself an open model, and now we lost 2 trillion in value.
Adam Butler 57:51
Well, yeah. So I don’t. Yeah, that is certainly feasible. Absolutely, wasn’t really what I was. I was thinking. I was sort of thinking that you’ve got agents with harnesses that are able to make calls to the the the big brain still, but are outside of sandboxes and on computers where nobody installed them, but where they’ve replicated themselves because they know that if they have this long running task and somebody shuts them down over here, then at least they have backups that could continue with the task. For example, right as long as they can continue to hit the anthropic servers.
Jeff Malec 58:30
And now you had done a a tweet of equating financialization markets as kind of its own AGI. Like talk about that for a second. Like they’ve, the market’s its own machine. Like the operators don’t know what’s going on. What do What do you mean by that?
Adam Butler 58:47
Well, yeah, I sort of analogized markets to the paperclip maximizer, right? Yeah. So the market itself, at least in the U.S. and at the moment, has a rather singular objective, right? Let’s call it like profit maximization or shareholder yield maximization or whatever the academics want to call it. But but the idea is, we the machine wants to maximize profits, right? And it will if if if the rules of the game are not bound by some external force, then it will use any means necessary to achieve that objective. Right, so the machine will outsource all of its capabilities to China for 15 years and leak IP to China knowingly because the. the you know we’re we’re increasing profitability, and it’s not just like increase total future profitability; it’s meet quarterly profit targets, right? Right, right, right. And that wasn’t,
Jeff Malec 59:58
yeah, they did not get in a room and. Decide like that’s the weird part to me. Is that the shareholders, the executives, or the market as this amorphous thing? Which I like. I could argue one guy did it, and then another guy, and then yeah, it jumps from being someone’s decision to the market does it now.
Adam Butler 1:00:16
Exactly right. Yeah, it’s not like everyone in the market is is psychopathic or or even like extremely short sighted. It’s just that the incentive architecture in the market rewards those that are yeah it rewards innovation and hard work for sure. It also rewards those who are willing to to act less ethically, to externalize costs, to act in short-term interests instead of long-term interests, et cetera, et cetera. Right. So, like, the market provides all these positive incentives and can create enormous positive sum compounding gains for for society, but it needs to be bound by a competent state, by a competent press, by NGOs, by an institutional framework that that that monitors it and and and exerts penalties and and sometimes drives incentives in order to ensure that the market operates in mostly in ways that are positive sum and and as least as possible in ways that are sort of rent seeking or competition destroying or that externalize costs onto society or the commons or or what have you right yeah and so it’s very similar like to to an artificial superintelligence right you’re going to give it a goal, and you’re going to try to set constraints. But you, but, but a sufficiently complex goals with sufficiently wide degrees of freedom, like a market in a society, you can’t constrain every degree of freedom, right? So, like, it’s it’s an open question. We haven’t done a very good job of constraining markets so that they they mostly act in the best interests of of society. What makes us think that we’re going to be able to constrain the AGI so that it it operates in the best interests of society? And I think that’s it’s a very open question, right? Because the AGI is just as capable of capturing the machinery that sets the the boundaries, that sets the constraints, as actors in the market are, right? And that’s ultimately the challenge. Is you know, as the market gets large and actors in the market get sufficiently large, they’re able to capture and and change the rules of the game to to in ways that suit them and disadvantage others, and the the cumulative advantage that accrues from that then drives you to an end state where only a very small percentage of the actors in society are kind of getting what they want, and everyone else is kind of like, “Why are we in this game again? It kind of doesn’t make much sense to me.
Jeff Malec 1:03:09
Which is right. I think we talked about that before. You run models or read a book. I can’t remember. Like the wealth will eventually be at one person, or what? What’s that argument you have?
Adam Butler 1:03:18
Yeah, that’s the ergodicity economics. Yeah, yeah.
Adam Butler 1:03:21
Argument where. they they create agent models where the agents are incentivized to exchange goods and service with with one another. You can set the agents up either with stochastic capability, so in any exchange, one agent is more likely than the other to have a an advantage and get the better end of the deal, or you can make all of them equally capable. And even when you make all the agents equally capable, and you run a massive exchange model, eventually, just due to the way that the advantages to random winners accrue across game states. Eventually, all the wealth accrues to a small number, and eventually, a single a single player in the in the economy. Right?
Jeff Malec 1:04:15
Seems like we’re there. Right? Right. We’re trying to run it, and and
Adam Butler 1:04:19
yeah, you converge on it at an accelerating rate, right?
Jeff Malec 1:04:23
And then also, it seems like we’re there on your market, like rule of law weakening, and so that’s kind of informed this opinion, right? Of like, hey, this is already we’ve already kind of lost control of this beast. Let’s make sure we can. Is is your end game? Let’s make sure we control the the next beast, or is it just like I think you’ve written some stuff like this is just how societies work. Like you lose control of this beast, next thing you know, war, famine, whatever, and it kind of does a reset, and then we start over, and then we do it again.
Adam Butler 1:04:54
Yeah, if you look through history, then you know once once the system is captured by a small. Group, then you know there’s no incentive for them to release resources or change the rules of the game, and we can’t
Jeff Malec 1:05:07
tax the billionaires our way out of it.
Adam Butler 1:05:09
Exactly, because the the billionaires own the the people that would make the decision to chat to tax them, right? So there’s just no way, historically, empirically, there is just no example through history where a society has chosen to rebalance willingly, so it either is rebalanced through war or historically plague, or you know a major economic depression or something like that.
Jeff Malec 1:05:45
I’m sure read that Citrini piece, right? Out like the one on AI. Like here’s the model for where the market goes down 80% or whatever, right? And like Salesforce and IBM and everything dropped that day that thing came out, yeah. India trouble, like it’s basically going to replace all that outsourcing. So I ate that up because I’ve been yelling at the rooftops. Like this makes no sense to me. Like I invested in a round in Anthropic, in the deck. I think I’m like round Z, so it’s not that impressive. But in the deck, it’s like the U.S. labor market, 60 trillion, and we think we can save companies 15 trillion. So I’m like, wait, hold on. What happens if you take 15 trillion out of the U.S. Like, what are we talking about? That’s massively deflationary. It’s massive recession. And if it isn’t, then those valuations aren’t what they should be. And you know, all these stocks are down 50, 80% So, just what what are your thoughts on that setup? Like we can we can argue this looks a lot like 99 and the tech boom and the bubble. That’s mostly valuations. To me, it’s a little bit different. Of like, we’re going to remove consumer demand. Like we’re it’s not just a valuation of the actual equities, and it also could be in the Saturni was argument that they could S and P could go to 8000, Nvidia, all those guys are high flyers worth trillions of dollars, but the bottom, the real economy is just hollowed out.
Adam Butler 1:07:13
Yeah, so I don’t know if there’s a question there,
Jeff Malec 1:07:15
but but thoughts, yeah,
Adam Butler 1:07:17
yeah. Have you read the book Snow Crash?
Jeff Malec 1:07:20
Yes, but Robert Stevenson.
Adam Butler 1:07:24
Yeah, yeah, yeah. Not Robert, but
Jeff Malec 1:07:27
no Neil. Neil. Neil.
Adam Butler 1:07:28
Neil Stevenson. Yeah. Yeah.
Jeff Malec 1:07:30
Yeah. Yeah. So you know that that
Adam Butler 1:07:33
that sort of corporate. I don’t. I don’t think it’s corporatocracy, corporatism. Anyways, that sort of idea where where you know corporations end up kind of being governing bodies and and citizens kind of serving the interests of corporations. I mean, that is certainly one one path that we could take. I I mean, look, I think this whole market is absolutely bizarre. You’ve got a recursive. What do they call it? That machine that that runs on its own or whatever a Rube Gold Rube Goldberg machine of self funding recursive self-funding mechanisms with sort of Nvidia at the center, and so one firm’s revenues is an is another firm’s capital raise is another firm’s private credit holdings is like you’ve got. It seems
Jeff Malec 1:08:43
none of those are expense items. They all just yeah yeah they all somehow
Adam Butler 1:08:47
accrue to to the the revenue line. So this this giant cable or or Kuretsu at the center of the market universe, right? This like black hole of that’s like sucking in all capital and and increasingly all capex and resources, and you know I I think will eventually collapse on itself because there is no profit model in that preserves any sustainable profit margins for frontier labs, where you have at least three labs all competing to have the best model at any given time. The costs to train the models go up substantially every round. If Anthropic falls behind, the cost for me to switch from Anthropic to Open AI models is exactly zero, or to Gemini models for for or to you know. You think that you got to switch a lot of stuff on your back end, no? Like it’s. You do now because I don’t now because of the way I’ve set up my my machine. But like, yeah, it’s like some enterprise users probably have one instead
Jeff Malec 1:10:08
of zero. Got yeah.
Adam Butler 1:10:09
I mean, look, it’s possible that that companies are so fucking stupid that they fall into the Microsoft trap again.
Jeff Malec 1:10:18
Yeah, and just like
Adam Butler 1:10:19
become trapped in OpenAI’s captive office suite, or Anthropic’s captive office suite, or something. Like if you’re that fucking stupid, you can’t be helped, right? But if you’ve got a a grain of sand of sense in your head, then you realize that the actual models themselves are completely interchangeable. Codex is interchangeable for Claude Code, is interchangeable for Hermes, the open source model. All of these are completely interchangeable, and there is so there is absolutely no competitive moat for any of these frontier labs. The only spark of hope is, and so this is just like a pure gamble. You’re able to own all three labs because one of them might achieve RSI, and that RSI will allow that company. They’ll they’ll get it sufficient far in advance of the other companies that they will then go to be to become Tyrell Corp from Blade Runner and like you know just like oh own everything right which is a whole other type of dystopia, but like I I just don’t buy. It’s such a terrible bet, and the if that doesn’t happen, then there’s just no conceivable moat, and these companies are the fundraising mechanism. The dream that they’re selling is a fundraising mechanism that keeps all of the this recursive funding loop alive,
Jeff Malec 1:11:54
keeps the original AGI, the market going, hitting its target. So wait, what personally? What does that look like? No stocks, stocks plus alts, right? Like I’m investor. Like yes, this scares the hell out of me. I know this is a bubble, but what do I do? That was same thing. I’m like I think it’s all gonna crash, but I invest it anyway. Like it’s right. That’s the trick of like I can’t just sit in my bunker and and do nothing. I mean, I agree,
Adam Butler 1:12:23
and it’s a bit weird for you and I, right? Because we own businesses that our businesses provide exposures to different strategies and different risks and different segments of the market that you know most investors just just don’t have that side of their balance sheet to factor into their personal investment portfolio, right? But
Jeff Malec 1:12:43
I mean, due to
Adam Butler 1:12:45
the return stacked lineup, I got a I got a shitload of equity beta. You know, I’ve got a shitload of gold and Bitcoin beta. I’ve got a shitload of duration beta. I’ve also got a huge allocation to trend following. If it were up to me, I would own nothing but trend following. I would maybe stack some other, you know, features or factors or systematic strategies on top of a trend following program, but I would contain that exposure, and I might want to collateralize the futures portfolio with a mix of kind of gold and tips and nominal bonds, that sort of thing. But I mean, I personally think that the expected return on stocks here over the next decade or so is negative, maybe deeply negative. So I don’t personally want to own any stocks, and I have so much equity beta in my portfolio via the companies that I am am an owner in, that like I’m full up, right?
Jeff Malec 1:13:47
Yeah. But I
Adam Butler 1:13:47
agree with you. If you’re on, if if you’re an investor and you need to make a decision, it’s hard. There is, I think, a deep outside potential that the S and P does go on to eat the universe, you know. So, like, you got to have some exposure, I guess, to to that outcome. I don’t know what people who, you know, the 50% of Americans and 98% of humans who don’t own any S and P are supposed to do in that situation.
Jeff Malec 1:14:19
Repeat those numbers. What is it? 58 of Americans, right? 50% of Americans
Adam Butler 1:14:25
don’t own. It’s actually, I think, 80% of Americans. I think top, the top 20% of Americans own all stocks and mutual funds. There’s some that own like pension, pension assets, that sort of thing. But, um, but they don’t really collect any upside. Any upside on equity markets in pension portfolios accrues to the companies that that sponsor the pensions, right? So, because it’s irrelevant to them,
Jeff Malec 1:14:47
right? And they’re just getting their payout.
Adam Butler 1:14:50
Yeah, I mean, so I I personally think everyone should have the core allocation of their portfolio should be diversified, trend following portfolio. Collateralized with, with with cash like assets, a diversified set of cash like assets that can be resilient to different types of inflation and currency devaluation. But I know that the vast majority of people just can’t just can’t hold that behaviorally. So,
Jeff Malec 1:15:20
yeah, not just 3x lever Nvidia and the new single stock future ETFs that are sure to come out. Right, that’s that’s what’s crazy. Like the other like there’s there’s literally a whole other side that’s like, are you crazy? Just this is the bet of all bets. Just full push all your chips in on the AI trip,
Adam Butler 1:15:41
yeah,
Jeff Malec 1:15:42
yeah. Sorry,
Adam Butler 1:15:44
I’m just I’m not wired for that,
Jeff Malec 1:15:45
right? Yeah, but then, but then what does Bitcoin
Adam Butler 1:15:48
guys say? Get ready to stay poor or get used. What is it? I don’t know something.
Jeff Malec 1:15:54
Yeah, yeah. But like, if for you, and that’s the other argument. When I tell friends this, I’m like, I think they’re like, “What are you talking about? This is like improves my productivity so much more. I can go out and now I can send out 10 proposals and get back. Like they’re seeing it, they’re seeing their business grow. Yeah, because of it. And so I’m like, that’s a little. I’m like, yes, but who did you replace? Right in that work, like you hollowed out someone else, and I’m like, not to make you feel bad, but like you didn’t hire those people, or you didn’t get that architect to do that. Whatever you did, you didn’t pay that money to do that. That’s the problem that you’re not seeing. So it’s going to accrue to these business owners and and stuff, but other businesses are going to are going to fail. So
Adam Butler 1:16:35
yeah, but this is a I keep saying this. I was at a dinner with Dmitri Kafinas. I don’t know if you know him from Hidden Forces, but but he hosts these fantastic genius dinners every every so often, and and I was a guest there, and you know a lot of people had sort of similar views, similar concerns that you know AI is going to eat a significant proportion of the labor market. Some people were invoking like creative destruction, and this is going to be like a whole new class of jobs and whatever, and you know I kind of sit somewhere in the middle there, but also my my my big thrust is that this is a complete this is not an inevitability. It’s a failure of imagination, and the the accrual of a systematic dismantling of of state capacity and a unified vision for what America and the West wants to be like. You can you can ask ChatGPT what are the the 10 or 15 documented extremely tightly specified major goals that the Chinese, the CCP have have laid down to happen by 2030, to happen by 2035. Stuff like have a permanent moon base on on have a permanent moon base, be able to
Jeff Malec 1:18:02
plant a billion trees. They had all all that. Yeah, plant a billion trees
Adam Butler 1:18:05
to launch our own domestic fully sourced commercial airliner to have gotten resources from Mars and brought them back to Earth to have created 50 gigawatts of power using nuclear fusion, like they’ve got all these extremely specific missions that they have declared for their economy, and then not just missions, but like submissions. In order to achieve this mission, we need these technology, a major leap in rocket propulsion. What are the major potential current branches of research that might lead to major leaps in rocket propulsion? We’re going. These these are also priorities, right? Like it’s this is a well conceived, diverse set of bets, many of which will not work out, or at least not work out the way that they expect. But the pursuit of those missions will spin off an unbelievable supply of new scientific discovers, discoveries, new commercial and security Applications, new ways to enhance citizen welfare, what have you. This is a competent state with goals and a mission, and the ability to coordinate resources through space and time. Actually, building a country, contrast to the type of nonsense that we’re that we’re faced with, and it’s not because we can’t. There’s no magic. It’s just we have,
Jeff Malec 1:19:47
and you could already learn that imagination
Adam Butler 1:19:49
for what we want.
Jeff Malec 1:19:50
Like we’re going to go to the moon, like right, right. We have no idea how to do it. We’re going to do it.
Adam Butler 1:19:56
We have no idea how to coordinate anymore, and we’ve done everything we can to. Destroy trust and capability in our institutions and our governing bodies, and so it’s a mess.
Jeff Malec 1:20:16
That’s another weird part of like we’re so focused on building these data centers and getting to max, like accelerating this curve, where it seems like we’re ignoring everything else, almost like a pancrea. Like, oh, once we get this figured out, it’ll solve all those other problems.
Adam Butler 1:20:30
It’s the paperclip maximizer. Yeah, it’ll
Jeff Malec 1:20:32
solve healthcare. Like, once we get, once this gets to you know ASI, like healthcare is a snap. We got cancer beat. We got this. Exactly. Okay. So, just build another data center. That’s gonna that’s gonna save us.
Adam Butler 1:20:45
Exactly. I mean, look, there’s a whole there’s a whole economic playbook, right? Mariana Mazzucato has been a real leader in this, in in in how to structure state missions in a way that minimizes grift, maximizes innovation and productivity, results in the right balance of rewards to private actors and the corporate sector with spin-offs to to society in the state. Like this, it’s not like any of this is a mystery. It all exists. We just exist in this fucked up ideology where only the market is allowed to decide what we want in our lives, and any attempt to like set higher purpose purpose goals or transcend what the market. I’m not saying that we should have central planning.
Jeff Malec 1:21:32
Yeah,
Adam Butler 1:21:33
we should have we should have missions and and incentivize the market to figure it out.
Jeff Malec 1:21:39
Um, I was that’s like my mom always asks me, “What the market do today? Like, which one, mom? We’re we’re looking at 80 plus, but right, that’s where we’re not. We’re not incented to make the corn market go up, right? Like vice versa. What am I cheering
Adam Butler 1:21:52
for today? I know I get the same question from my dad. God love him. I’m like, yeah, yeah. I don’t know. Like, yeah, I
Jeff Malec 1:21:58
don’t know. Just whatever went up yesterday. Generally, we want that to go up today. That’s right.
Adam Butler 1:22:02
Whatever whatever went up over the last year, I kind of want to keep going up.
Jeff Malec 1:22:05
Exactly. All right, Adam, it’s been fun and insightful. We’ll put all the links in the show notes and talk to you next time.
Adam Butler 1:22:14
Awesome. Thanks so much, Jeff.
Jeff Malec 1:22:18
Okay, that’s it for the pod. Thanks to Adam for coming on. Thanks, Jeff Burger, for producing. I think we’ll be back next week, but I’m not entirely sure. As you can see, I’m in the car right here now. So think we’ll be back next week. If not, might do a solo six-pack, something like that. Peace.
This transcript was compiled automatically via Otter.AI and as such may include typos and errors the artificial intelligence did not pick up correctly.
