← Bankless
Loading summary
Transcript88 lines
- [00:03]
A
Welcome to Bankless, where today we explore the frontier of AI. Is it going to take our job? And how can we survive the transformation? This is Ryan, Sean Adams. I'm here with David Hoffman, and we're here to help you become more Bankless. David and I read a paper and a thread corresponding to that paper. It's called the Simple Economics of AGI. And one of the writers of that papers on the podcast today, his name is Christian Catalini. He's an economist. He is an MIT scientist. Fun fact, David. He was actually one of the creators of the original Diem project over at Facebook. Do you remember that?
- [00:37]
B
Oh, wow.
- [00:38]
C
I, I did not know that. We just interviewed him.
- [00:41]
A
He's been in cryptocurrency for over a decade. In fact, about 10 years ago he wrote a paper called the Some Simple Economics of the Blockchain. So he was all over crypto when it was new, and now he's coming back to AI talking about economics yet again.
- [00:55]
C
Hmm.
- [00:55]
B
Yeah, he seems a little bit like
- [00:56]
C
a Robin Hansen type of. He's putting models onto cultural phenomena and trying to provide answers to him, mainly the knowledge that we try to get out of him is if AI is going to commoditize a lot of, you know, easy tasks, where does the value go? If we're, if we're just automating the, you know, the, the foundations of like the push button jobs of society, what do we do? Where, where do people go next? And that's basically the theme of the episode and the question that Christian answers in the pod.
- [01:28]
A
Really important episode. I think it's on everyone's mind. The core argument of this paper is that the scarce resource is no longer intelligence. The things between our ears, our brains, it's verification and the human capacity to check on AI and its output. There's a lot of implications following that from that idea. So let's get right to the episode. But before we do, I want to thank the sponsors that made this possible.
- [01:51]
C
Are you sitting on USDT or stablecoins taking profits and wondering where to deploy next? What if you could access stocks, gold and ETFs without ever leaving crypto? That's what tokenized stocks on Bitget unlock. Traditional markets still run unlimited hours, but capital is moving on chain 24. 7. On Bitget, you can trade tokenized stocks and ETFs 24. 7 with up to 100x leverage, all settled directly in USDT or USDC. No brokerage accounts, no off ramps, no platform switching. Bitget has already processed over $18 billion in tokenized stock trading volume, with most of that happening in the past month alone. The platform now capt captures close to 90% of Ondo's tokenized stock spot market share as Gold and Silver hit record highs on chain trading followed over the past two weeks. Volume in SVL on tied to Silver and IAU on linked to Gold surged on Bitget. This is Bitget's universal exchange vision in action. Crypto equities and real world assets in one place built with crypto native speed and flexibility. If you want to trade stocks the way you trade crypto, explore tokenized equities on bitget. Learn more by clicking the links in the show Notes this is not investment
- [02:53]
A
Advice Some exciting news. We are launching a new podcast to help people figure out the crypto cycle. How to navigate it the best crypto cycle investor I know, his name is Michael Nadeau. He runs the Defi Report. This is the guy that sent me a sell alert before the 10:10 price drop happened. His cycle analysis has been absolutely on point. I've been following him for years and this year we started recording weekly podcast episodes. Each one we get into his portfolio, what he's holding, the market structure, entry targets, fair market value of Bitcoin and ether, and where we are in the cycle. There's new episodes that released every Wednesday. They're 30 minutes, they're short, they're punchy. I think this crypto cycle is harder to navigate than most. So let's do it together. Go subscribe to this podcast, Search the Defi Report Wherever you get your podcasts, YouTube, Apple, Spotify, or find a link in the show Notes There's a new episode waiting for you now. Christian I think a lot of people listening feel the way maybe I feel, maybe the way David feels, which is like some kind of a low grade panic. There's an underlying uncertainty.
- [03:53]
C
Basal anxiety.
- [03:54]
A
Yeah, basal level of anxiety. And it's funny because David and I are techno optimists. Like we're very excited about the future and yet even I feel it. And I think it's born of this feeling that AI is going to change everything. It's going to automate a lot of things. Maybe there's some anxiety that it's going to be us, that we won't adapt soon enough. There was a Citrini research post that made the rounds three weeks ago or so, and it was a basic idea of almost like a hollow economy, that AI would be so bullish and so successful that no one had jobs anymore. This type of doom or porn has product Market fit. And it's easy to see why. It's because this low grade anxiety is pretty pervasive. Why do you think people are worried about AI and are they justified to be uncertain and worried in this way?
- [04:44]
B
So first of all, I think we, we all feel the same. I would say this paper was really the result of that low grade fever. Maybe at times, you know, spikes of high fever. It's a period of rapid and transformative change. The closer you are to code, the closer you're probably already witnessing the acceleration and we're talking honestly the last few months and that exponential becoming very real between even December and March while we're recording this. That feeling of the technology really jumping ahead and delivering on things that many would have thought would have taken much longer. It's something that we're all kind of struggling with. I do think, and this is where, you know, the doomer view I think is wrong. People tend to underestimate the potential that comes with these tools. Yes, there's going to be a period of transition. It's going to be a very difficult one. A number of jobs will have to change and we'll have to change at the speed, pace and speed that I don't think it's, it's, you know, historically seen before. That's where it is fundamentally different. But that said, and I hope the paper really speaks to this, if you take the best feature of the technology, if you realize where weakest points are and you start investing in those, then I do think in the long run is mostly upside. Although, you know, along the way things will get pretty bumpy. It affects us all. I think if anything there's no individual job that's not going to be affected. Jobs tend to be economies considered in bundles of tasks. Some, some of those tasks are going to be automated and that's great news. But how do you retrain yourself? How do you keep on the frontier? That, that's a big question.
- [06:35]
A
Christian, you mentioned those that are closest to kind of code will be hit first. And maybe you're talking about developers. It's unclear to me to what extent they have been hit so far. I get the sense that maybe more junior level developers, there's less demand for them. The senior level developers appear to be getting more productive on this technology. So you know, even that is sort of a mixed scenario. It's not as if demand for developers has just dropped to zero. And then there's some other tasks here in the economy, you know, a doctor, a lawyer, some of these are, let's Say, protected by almost credentialism and by government mandate. And so they might be safer for a time. And then there's also the, the argument that like, okay, like I'm, I'm a lawyer and AI can never automate my task because there always has to be a human in the loop. We have to have some level of, of human judgment. I, I listed a bunch of things and I'm not sure to what extent some of these things are, are cope. Just humans not being willing to sort of adapt to the, to the future or like, maybe another way to ask this is what do you think gets hit first by this AI automation wave? And what gets hit hardest? And I think everyone is asking, like, am I safe? Like, who's safe here?
- [07:49]
B
Look, there's so many different thoughts on that. Excellent question. I would say first, what I meant by, you know, whoever's close to code has been hit first. They've been hit with the reality of just how powerful this is, right? And as we've seen, and there's been long conversation around Jevons paradox, right, the idea that of course, if something becomes really scarce, we kind of start consuming a lot more of it. Coding, I think, will bifurcate like many other professions where we're already seeing what in the paper we call the missing junior loop. If you're entry level, if you haven't really acquired that tacit knowledge about what makes for a great product versus, you know, just average product, AI is out of the box, often a good substitute for you across every domain, right? So everybody now has access to a pretty good marketer or pretty good, you know, IC4, maybe soon, maybe an IC6 in engineering terms, or, you know, a lawyer that will navigate you through most, most situations and maybe even some complex ones so that you can save money and maybe you use the ipaid lawyers for the final level of verification. That's one part of it. The other one is that as we bring AI into everything we do, even top experts are essentially creating, sometimes consciously, sometimes not consciously, the labels, the information and the digital trails that will automate them out of a job. So you're seeing top foundational apps hiring, you know, top, top people in finance or other domains. They're essentially using them to create the evolves, to create the harnesses so that those, those, you know, those domains of expertise can be brought into the, into the main models as that unfolds. I think, first of all, I don't think any individual job is 100% safe, even the physical ones. I mean, we're yes, we're bottlenecked by the capacity to build robots and bring them into the real world. The real world brings a very high level of entropy and complexity. So things will be somewhat slower. But word models, I think, will make massive leaps even in those domains over the next few years. Anything that's in front of a screen, of course, can be traced, replicated, you can learn from. And we're also very tempted, right? Who doesn't want to augment their own productivity and remove all the grunt work by using these tools? And as we do that, we are trading something that will replace a good chunk of what we do as a result, I think for every profession and for everyone, the idea is to really think through, okay, if I can delegate as much as possible to these new tools, where can I still add value? What is that layer of decision making where my expertise, my unique point of view, essentially everything I learned from the time you were born to where you are today in your career, you've seen all of these out of distribution example situations that you've learned from. And that's the difference between, you know, an IC4 coder and an IC7 or 8. I think there's a lot of cope around terms like taste and judgment. They're very vague. And so in the paper, we tried to really knock them off out of the gate by saying there's no such thing as taste. Good luck defining it. There's no such thing as good judgment or bad judgment. There's only measurable and not measurable. If something has been measured, the machine will be able to replicate it. If something is still, you know, just embedded into your own weights in your brain. And that's kind of what, what a top designer would look like, a top podcaster. They've done so many hours, you know, the 10,000 hours of mastering in their domain, maybe more. And that's what allows them to choose what should be shipped and what should not be shipped. We have this concept of verification. All verification is this final step. You've got the agents, the swarm of agents, creating all sorts of interesting work and product. But then you're the final, the residual claimant. You're the one deciding as a CEO, essentially, of this new type of enterprise, is this ready for the market? Should I ship it or not? Or do I need to go back and iterate on this one? And yes, it relates to taste, it relates to judgment. But I think the key difference is that while taste and judgment are A, hard to define, and B, what used to be good judgment or relevant judgment, yesterday could be not relevant tomorrow. Right, because the machine can replicate it. Once you start thinking about measurement as the key primitive, it becomes obvious where, okay, if we. If we're getting better data, this is going to be more automated. If we don't have data and it's super uncertain, or we may never have data, think about the stock market. Unknown, unknowns. Eventually, maybe these models will know enough that they'll be able to predict things, you know, a few days out. But there's something magical about these domains of fundamental uncertainty that, for now, are still human. Now, maybe not forever, but, you know, for maybe the next couple of years.
- [12:47]
C
Measurement is being the key feature here. The key mechanic is kind of the main quest line of your article and therefore this podcast, too. I'm not ready to get in there. I want to put a pin on that, but I just want to let the listeners know that we're going to come back to that idea in a second. Before we get there, the question I want to ask is, do you. Do you think that coding and engineering, as you say, is like the first industry to materially be impacted by AI? Which makes sense. The engineers building the thing, they all know how to code, so they automate their jobs first. It's just very natural. To what degree do you think that this industry represents a canary for all the others? Or do you think it's a little bit more spiky? And every industry has facts and circumstances, and each one will be impacted uniquely. And generalizing what happens in one industry across others don't do that doesn't really make sense. You know, each one of those facts and circumstances, where do you land between these two spectrums?
- [13:45]
B
I would almost say that. So, first of all, we have enough evidence that seems to scream, the change will be jagged, right? So we'll spike in certain parts of a domain and not others. But even for coding, what we're automating is a lot of the groundwork at this stage and being able to ship and replicate what's been done. Right? So there's a best set of practices for security, There's a best set of practices for building a backend and a front end. All these things that are sort of known, I think agents will be really good at. But once you start pushing into domains that they haven't seen, sure, they will be able to simulate them and learn from them and run unit tests or whatnot against them, but that's going to be a little bit harder. And so I do think even for coding, the verdict is out in terms of, like, Maybe we would just build a lot more software. And I think that's going to be a big part of the story. So if I were to answer your question, I would say that the algorithm to think through is, is this job sort of a wrapper under something that is fundamentally not that valuable today to society, but happens to be wrapped in a special casing as a job? Those are probably the ones most at risk. So take something like your average work on consulting. If that was typically repackaging information that was somewhat widely available and distilling it, summarizing it, that's clearly a risk. Now, of course, there's some forms of consulting that are extremely valuable, right, because they bring in rare domain expertise. Then there's political reasons for bringing in consultants. Right. Maybe when you're doing layoffs or like some sort of other external party, verifying your strategy, strategy as a third voice, those will survive. But as you look through all of these professions, I would, I would try to ask, is this profession profitable today because it does solve a complex problem, or is there some other bottleneck that either is fictionary or, you know, it's kind of falling apart because we can now do it with code and just automation?
- [15:53]
A
I think that's hard to reason about because maybe this is the first time that we've ever seen something akin to human cognition that is becoming cheaper over time and becoming far less scarce than it used to be. I love the way your paper opens, which is kind of the historical landscape on, you know, 300,000 years of homo sapiens, where cognition really was the binding constraint for progress. You said this in your thread. Human cognition was the binding constraint. And I think you're pointing to an era like, of many different inventions, but I think you're pointing to an era where it was really like our brain size that was the limiter in terms of what technology we discovered or what progress we made in, you know, civilization or, you know, societal organization that is no longer the constraint. I guess I think that's part of your, your thesis behind the new economics of artificial intelligence. Can you talk about that? Why is that insight profound in and of itself?
- [17:01]
B
Yeah. I would say a lot of institutions and things we do today have been designed around the idea that cognition or intelligence is scarce. And we try to get the most leverage out of the most talented individuals in an organization. You know, the, the way we make decisions, we're. We're kind of optimizing for this bottleneck. And that bottleneck is going. But the second realization, and this is where, again, I think A lot of the doomerism is premature is that we're still in the phase where. And again, this, this could change, especially if you start thinking more about artificial superintelligence. But at least as our definition in the paper of AGI is something that, you know, for most intensive purposes is human level human, like with some gaps. And we will have gaps, they will have gaps. So it's going to be almost like two different forms of intelligence that will trade with each other. In this particular phase, what's happening is that we will be able to execute really quickly. We will be able to apply intelligence to a lot of problems. We may not necessarily be able to fully know that that intelligence is following our original intent 100% and that intelligence is still executing within, you know, what we wanted to execute inside. And so if you assume that that's still true, of course, when those boundaries go, then we're talking about a very, very complex society and one where we are dealing with peers and eventually with something that, that's even more capable than us. But within those boundaries, humans will spend a lot more time on verification and in making sure that their intent, their preferences. Right. Are respected. And that's going to keep us busy. I think for the long haul we'll have more capabilities, so we'll be a lot more ambitious about what we can do. It is a massive change and I think for many jobs it's going to be a drastic change. You think about your job, right? It's not like you ended some sort of guardrails. That's kind of what we're doing with agents today, right? You ended some guardrails, you execute, you have your metrics and KPIs. I think that universe is going to shrink for most professions. And the universe of I have some sort of higher level intent or human preferences that I'm trying to respect and carry along through the task. I think that's going to be really, really important going forward.
- [19:23]
A
Okay, so you use the term verification and that seems to be a central point of the paper itself. But so far we've said that there's really no distinction, I suppose that's relevant in terms of taste or curation really in the things that human can do but AIs can't do. There's only really measurable and unmeasurable. And then also human cognition is no longer the binding constraint on progress because we have a different form of machine cognition that we're growing, that maybe it can do all of the things that human cognition can do, but it can do Enough to really push us towards progress. And so you said the scarcity will move from the idea of cognition, maybe in the number of humans that we have, or the applied human intelligence, to something else. And that something else is verification. That'll be the thing that we focus on. What exactly do you mean by verification? Because this is, it's hard for me to break down the work items I do in a particular day and map out which ones are cognitive work items versus which ones are the verification work items. What does verification really mean?
- [20:38]
B
Yes, let me start from the first principles that we have in the paper. And of course, please push back. I mean, part of this is really getting to the fine grained items so that we can, we can see are they right or not. If you buy the idea that models to date have been extremely good at automating anything that they can ingest data on, and I think we have plenty of evidence for that, then you suddenly realize that there's things that, you know, agents can measure because they've learned, they've ingested all of the web, all of the books, all of the materials, all of the traces and things that we can measure. There's a big overlap between the two. And that's why there's going to be dislocation and job loss wherever agents can measure the same things that we can measure. Well, guess what, agents are going to be cheaper, right? We can just throw computer the problem for many professions, not all of them, but when you do the balance. Is it cheaper to hire a human or to hire a swarm? The swarm will be cheaper. It's definitely more scalable. It also learns in a swarm like way. So it's more copy paste and replicable. But then there's things that the agent doesn't know yet. And this goes back to what's been measured by your brain. What is your own neural net, what are your weights? And by the way, this is what distinguishes again an average designer from a top designer, an average coder from a top coder. Every profession has this distinction, right? Where there's some individuals that are just on the tail and sometimes that's lucky. Think about the creative arts, right? There are many people probably as talented as Taylor Swift, but there's only one Taylor Swift. But it is also true that she has some really unique weights about, she thinks about not just the art, but also the business and everything else that comes around. That, that unique training data, it's really just in our brain and it hasn't been quantified yet. And so now you have a Situation where there's stuff that the agents can see and measure and there's stuff that any one of us has in their brain through their own experience, through their own struggles, that makes them really unique. And they see the universe from a unique perspective. They make different decisions, even faced with the same information, you know, one person that maybe, you know. And this, this is kind of related to crypto. Many of the people that were early in crypto were people that grew up in countries like Argentina, Venezuela, Nigeria, where they saw that hyperinflation firsthand. You know, the parents coming up with bags of cash and they felt the need for better money early on. And so when the technology came about, they were the first ones to react very differently to that piece of information. I think that unique measurement that's inside all of us, it's still a massive advantage. And so what is verification? It is really the difference between your measurement, your own calibration about the universe and what the agent may have. And it's fundamentally the distinction of take a piece of writing, right? The difference between sloping and a great editorial is that person that has written thousands of them knows exactly what resonates, what doesn't, what's funny, what's not funny. We'll take that and say, okay, no, this is still slop. Let me iterate, let me fine tune it. And then eventually ship something that is AI augmented, but with the final verification step of human. You can still call it taste, you can call it judgment, you can call it curation, but it's fundamentally applying your own weights to that output and deciding, is this up to standard or not? Is this code safe to ship or not? Again, agents can build massive code bases today. And we're accumulating some sort of risk, of course, because no human can go through all of them. But a top CTO would say, okay, this, this is the thing that of all the things that this code base needs to get right, these are the ones that absolutely need to be in this kind of boundary on verification. These are the ones that are going to go line by line and check the code or I, I'm gonna ask the LLM to make really careful decisions in this area that, that's the part that's not measured yet, and that's the part where humans, I think, can play a major role.
- [24:39]
C
I wanna try and distill this concept down. So let me spit it back at you in, in my terms and we'll see if we can move forward with that. So there was, there was an AI video that I was watching that was getting shared on Twitter and it was, it was of the Iranian conflict, which has been a great testbed of people's ability to see and understand what's AI versus what's not. And this was a video of Israel getting just pounded by missiles. And you know, upon further inspection, I would look in and zoom into the video and see a lot of the buildings were copy and pasted and the cars on the street were incoherent shapes that didn't really make sense. And you know, a few other features that made it very clear that what I was looking at was AI generated. And so am I doing verification in that role by doing that process of like, these are the things in this video that I, using my own weights as you've described, my brain weights, I'm identifying that this, I'm verifying that this is AI slop. And maybe I could take my weights and if I was in charge of the model I'd be like, let's make the cars better, let's fix these copy and pasting buildings, let's fix all these things that are very clearly AI and I can like maybe re prompt for a better video. And that's me using my verification ability plus my own talents to actually produce a better output. That's the gap that is valuable that we are trying to measure. Is that right?
- [26:10]
B
I mean, I think that's an excellent example. Let me take it one step further. We're probably not far from a universe where that video will be to most people, right. Indistinguishable from the real thing. Sure. And then the next phase, and again it's a moving target. That's what makes it so hard. The next step will be a military expert maybe just looking at it and saying, well, the way the dynamics of the bomb are happening in the video doesn't make sense. And this is what the flame should look like, different color. Then there's an even further step which is even the military expert at first view cannot tell will be prompted. An AI with the right set of questions and saying, hey, can you analyze this for me and go into the physics of it, replicate it, run some simulations. How likely is it to be accurate? And eventually there might be a point where it's completely indistinguishable, especially with word models. We may be at a point where we don't know if it's true or not. And at that point we'll have to rely on some sort of provenance and crypto grounded infrastructure to even know is this real or not. So it's almost like different stages. And the video is a great example because I think everyone can resonate with radar works. Same with. Take a domain that has expertise like medicine. We're at a stage where you could have some of these models look at imagery and probably make a pretty good assessment. There's gotta be some edge cases where a top radiologist will say, no, no, no, I understand where you're coming from. Almost like training a junior, right, A resident, and saying, I would have made the same mistake 20 years ago, but this happened actually to me with a patient. And this is given this other context about the patient and where they are in their journey. No, this is the wrong decision. That's that thin layer of final filtering that we're kind of focused on as we do that. By the way, we free a lot of our time. So the upside here, and this is why I don't think we should be taking this too negatively, we will be able to do a lot more with less. The cost of a lot of these things that used to be very exclusive will drop. We'll consume a lot more of them across society. So all in all, it depends on the transformation, but I think it's good news.
- [28:20]
A
But, Christian, isn't this an example, the example that David just gave of like, he's starting with verification right now, right? He's able to verify these explosions and he's got sort of just like maybe an average level of. He doesn't have military expertise. Right. So you can't get there. But then it moves up to the military commander being. And pretty soon the military commander cannot verify it either, and he has to outsource it to AI to begin with. Isn't this just another example of something that has the ability to get measured? And I guess you can measure how well a video matches reality and what reality looks like. Isn't this just an example of verification being valuable at first, but then getting quickly automated again by AI? So even verification is not safe in this model.
- [29:03]
B
100%. And in fact, we have a name for it, the paper. We call it the qualifiers curse, which is essentially the very rational act of performing verification is pushing the. The frontier, right? Everyone is tempted to do this and we can't stop, right? It's not that all the lawyers could coordinate. I mean, they're trying, right? If you look at some of the laws that. That are being proposed saying, oh, lawyers can. Can only be the ones using an LLM. Regular citizen cannot just LLM themselves, you know, out of court. There'll be all sort of Weird regulatory and policy pushback on this stuff, but you're absolutely right. And at some point the layer of verification is so thin that the only way I think we will keep up is by augmenting ourselves, whether it's brain computer interface or better tooling. Right. So I think we're already seeing it through the IDES evolution encoding. The IDEs will get better and better at helping the human focus their attention and becoming a better verifier. But it's a race. And eventually we have this section of the model that goes a little bit more into the future where, you know, you have to take these agents as peers and take them very seriously. The key problem we surface is one where we don't know what preferences these agents will have. Right. And there's already evidence that sometimes they develop really quirky, weird preferences almost by mistake. And that's where things get a lot more complex. But yes, I think verification is kind of a shrinking frontier.
- [30:29]
A
Okay, so shrinking frontier. So that, that is the idea of the codifier's curse. It's basically like, you know, this is humanity's last job is what you're saying is verification. But even that last job is we're all standing on an iceberg and that iceberg is kind of slowly melting away and the surface area is getting smaller and smaller even on the verification front. Right.
- [30:49]
C
So where's the part where I get less anxious?
- [30:53]
B
Yes, yes. Look, first of all, some things are non measurable by design. And sociologists have all sort of names for these, but sometimes they get called as status games or, you know, things where people are trying to describe and ascribe meaning. Those things are not going to be the domain of machines because the very feature is that it's around about human coordination. You can think actually of cryptocurrencies to some extent like this, which is there's similar technologies. People could converge on one being a store of value or a different one. What matters is the consensus among humans what should be worth something. And so I think as the domain of measurable work shrinks, we will invent many, many ways to make non measurable work meaningful.
- [31:43]
A
So the iceberg actually will get bigger in some ways, which is kind of the non measurable human status type games, human subjective preference types games. That's where I guess the economy for humans will expand and the job opportunities for humans will expand. I'll give you another example. So David and I are messing around with an open claw agent. We have a discord for him.
- [32:09]
B
Who doesn't these days, right?
- [32:10]
A
It's fun, right? The prompt is kind of simple. Hey, create a media company. Just because we're just seeing if it can create a media company the way Bankless has created one. And just getting it to tweet something that doesn't sound like AI slop, something coherent, is just like. It feels like Mission Impossible. And the number of times we've gone to it and said, hey, Daniel, that's its name. Hey, Daniel. This is like, you keep tweeting things that sound a lot like AI slop. You have to look at what humans are saying, and you have to kind of model that behavior. And we'll give it, like, detailed instructions in terms of how it can tweet better. And what does it do? You know, an hour later, it just sends off another AI slop type tweet. And I start to get the sense of, like, oh, well, you know, it's so smart. And it can wire up a website in, like, 10 seconds and develop an application in, you know, 20 seconds. But it can write a simple tweet that sounds interesting to a human audience. And I guess that's part of the jagged frontier. But maybe that also gets into the element of, like, the verification, right? In order to have a. A tweet that sounds good subjectively to other humans, that might be one of the last things that AIs are actually able to do. Is that an example of the expanding surface area for us, maybe we can still tweet and that can have some like. It provides some value to other humans.
- [33:39]
B
So, first of all, I think people will care that it comes from a human for. For different reasons, too. So at some point, some sort of proof of personhood would be important in all of this. But I think you're underselling yourself a bit short on the. On the tweet exam. I would argue it's actually quite hard. And anyone that has tried to get any one of these LLMs to make a funny joke knows that they will come up with some. But they're mostly dad jokes. I'm a dad, so I can say that. And the reality is that nothing is harder than in a media company, reading the moment, understanding your audience, and really intercepting it with something that's truly novel. A tweet competes every second for so many other tweets. And the algorithms are pretty rootless. And so if you wanted to break through that, it kind of needs to break into something not measurable. I think you'll be pretty good at tweeting updates about the conflict right now in Iran. You can get into, do the systematic SEO type things with no problem. Anything that has been done before and just needs to be executed well, I think you'll do a pretty good job. But getting somebody's attention, that's creative work that is ultimately trying to break into something that has never been measured. And that's where I think our neocortex and whatever part of our hardware is still giving us an advantage. We've been selected and we hint at this in the paper, to be able to respond to very changing environments. It was life or death. So the way we've selected this new intelligence, this alien intelligence of sorts, it's very different. It's optimized for kind of, you know, search and pattern matching and replication of kind of what's known. We only survive if we could respond to something completely unseen and you know, make the right decision. And so we're very flexible at the moment. I think some of this will fall over time. And that opens the question of like, okay, then we're really jumping the iceberg is like, okay, there's this non measurable world where we can just feel human and give each other's meaning. We call them the meaning makers. In the paper. It's a job that's very hard for me to understand personally. Right. Because it's all about human coordination. And to some extent you've seen it in the arts and industries that have already been hit by automation, to some extent music, where the cost of producing the initial product is really low. So you have had massive entry and they've all turned into blockbuster economies in anything that requires meaning. Think about art, right? Who decides what's. But it's valuable art. And this is salient when you go into a modern art museum where that consensus has informed and so often you walk by and if you're not a domain expert, you'll be like, I don't really understand this. And much of that will be filtered out. So 10 years from now, people will not think of that as successful art. Some of it will be. But for those domains where in a sense we're discovering together what we should be paying attention to, I think that's still safe, that's probably going to survive even in a world where AI surpasses us because the whole value is like, okay, we, we decided this is the relevant history. A bit like with a blockchain, right. But for the stuff that it's objectively useful. That's where this tension between the verification layer and what the machine can do on its own is really important. And, and the tweet is a very eye bar, I think the verification and the steering, it's something where maybe you could build a harness where you have all conversation with your agent before and given the right context and saying, okay, I think this would be the right thing to tweet about and figure out the best way to optimize it and write for it, then it can go and do it. But you still have to set that intent.
- [37:26]
A
You called this paper the economics of AGI and I just want to make sure we're getting some of the core economic fundamentals from this. So one of course we've talked about is anything that can be measured will be automated. And the cost to automate the measurable things is just like decreasing at a exponential at this point in time. There's another cost curve in the paper, which is the cost to verify. It's unclear what's happening with the cost of to verify. You're arguing that that is a biological constraint. At least it's constrained by some level of human cognition. Does that cost curve, like what happens to that cost curve over time? Does it get cheaper and cheaper to verify as well? Or is that always going to be biologically constrained?
- [38:09]
B
So it is currently biologically constrained. And that's why in a sense, I think people are underestimating maybe the speed of adoption, right? If we're deploying these systems at massive scale and we don't have the bandwidth to verify them. You hear it every day right now. It's like, okay, our company ships 20 to 30% or maybe even 50% of its code as AI generated. When you read below that, that headline, you realize that, well, you definitely didn't read all of those lines of code. So there might be something there that's unverified. And I think while now probably people are underestimating that we're going to run into some massive failures because of it. And it's just a result of again, the cost of automation like you were explaining, decaying faster than our capacity to kind of verify the output.
- [38:58]
A
But can't The AI can't AI's help with the verification piece? So isn't the answer to all of that AI generated code? Well, you have AIs also running to verify these things.
- [39:09]
B
That is a very tempting conclusion. But again, if you really focus on what the cost of verification here is, anything that AI can properly verify that's automatable. So yes, we will use tons of AI to verify AI, but because the blind spots a checker AI, even if you're using multiple Models, right, They're, they're kind of all trains around similar things, so they're not that different. But even if you mix all the best models and state of the art technology that you, you will automate whatever you can with AI, you will verify all of it with AI, and then you're left with what's really unverifiable. And that's where the human comes in. And so at that bottleneck, I do think people will invent great tooling. So AI will help a design the better tooling for augmenting, verification. So maybe it's not just linear, but it's still somewhat bottlenecked. We maybe will augment ourselves. I think that's probably the most promising technology in the end, which is if we are on par with our creation, if we can at least compute as fast as, you know, the agents can, then we're at least peers forever. And that verification bottleneck kind of disappears to some extent. We may still need to go run experiments and create things in the real world to get feedback where the AI cannot simulate them. But we will be working in a scenario where we don't augment ourselves. It's very clear that at some point, like the example right from the video, we will be less and less useful for verification. And sure, maybe if you're 99.999, you know, uptime to, to, to all sort of problems is really important. You still have humans for many applications. We'll, we'll just take them out of the feature.
- [40:48]
A
Is there another negative externality, I guess that crops up here, which is like if the cost to automate is going down, but we're sort of bounded by this verification. Right. It could be tempting to just let automation keep running and just to do less verification. And I'm wondering if an externality pops up, which is sort of a safety and alignment type of externality, which is just a world that we have no idea like whether the work that's happening is aligned with what we want to actually happen. And there's this like oversight weakening, alignment drift happening going on such that the AIs are doing things that only they can comprehend. And it's not necessarily an outcome that we want. Is that part of the story here or is that a separate track?
- [41:38]
B
100%. And so we g, we gave it a name. We call it the Trojan horse. Externality. Why is it a Trojan horse? It's because it's extremely tempting for all of us to automate as much of our work as we can. Same for companies, right? If you can ship code faster, products faster, you will do it. And some of the cost of that missed verification may not be immediate, right? Of course, if they're immediate, you ship the code, you see it breaks, then you learn, you iterate. Okay, next time we better do this type of code review. But the more nuanced and subtle problem is something where the risk accumulates over time. And a good example is like long term capital management, right? So these examples in history where there was very clever financial engineering and the fund run really well for a long time and then some hedge case hit and the whole thing unraveled tragically. Or you know, think about Chernobyl where complex system fail in complex ways and so for the longest haul everything may look fine and then suddenly you hit this kind of debt that you've been accumulating. Why is it an externality economist have a very precise definition around that, which is something that the market cannot fully price. If I'm building legal software right through LLMs, of course I would not want wrong citations because in court that's going to surface and it's going to undermine the entire product. I will think already about a number of dimensions of verification and I will price them in. I want to be a good player, I will do that. But these longer run things tend to be underestimated and not fully internalized, especially in a race, right? Maybe the best example is actually the foundational labs. Are they deploying new models at the same speed? If they knew exactly all of the side effects or the potential cost to society. Some of those costs to society they're internalizing for sure because they could be company ending, but some they may not. And in a race, you know, speed of delivery may matter more. The same is playing out, I think geopolitically, should the US slow down versus China, right? Make, make, makes of course no sense. And as we accumulate this hidden depth, we could find ourselves in a situation where I don't think, you know, the nefarious scenarios of the sci fi movies are probably the most likely. And many of the instances where people say, oh, the robot didn't want to be shut down. Well guess what, the LLMs read the SCI fi fiction too. Or maybe they had a previous objective which is like you've asked me to solve these problems. If I shut down, I won't be able to. The reality is that I think these systems may fail in ways that are almost benign and we may not have anticipated. It's not that they're trying to take over yet, but they're just following orders or they've accumulated some sort of hidden preferences that we don't understand. If you look back, there's been lots of cases where you prompt the LLM with something strange and suddenly something happens that's completely out of context because of the way they were trained. And if we don't understand the preferences of these models fully, if we cannot interpret their decisions, then we're kind of living with a black box. And some of that black box could be hidden risk.
- [44:47]
A
You used this term in your thread too, hollow economy. And that does remind me a little bit of the Citrini post that we talked about at the very beginning. What's your concept of a hollow economy and what scenario could that happen?
- [44:59]
B
Yeah, for us the hollow economy is actually a fairly narrow definition. And then we spend most of the paper thinking about the augmented economy. Again, it's tempting to think about doomer scenarios, but the reality is that we've been pretty resilient as a species, so hopefully we can, we can survive this new filter. What? Why is it hollow? It's because the proxy metrics, the things you tracking are looking green. Everything is looking great.
- [45:26]
A
Like all the measurable things like GDP and growth, that kind of thing is looking green.
- [45:32]
B
Imagine even more simply inside a company, right? You're seeing, oh, we're shipping more code than ever, customer growth, everything is booming. But the problem is that you and your agents are optimizing always for proxy metrics. There's no way for you to capture the full intent of what you're trying to do. Goodard's Law is kind of the classic name for this, which is like when a metrics becomes a target, it ceases to be kind of a good metric. If we get to a situation where some of these agents at least are pushing these metrics that look good on the surface, but maybe hiding back to your iceberg example some hidden problem below the surface, then for a while we will feel great about ourselves before running into the, you know, nightmare scenario of like, you know, long term capital management, a fund unraveling really quickly some other systemic risk and cascading effects that may be very hard to to buffer for.
- [46:29]
C
Galaxy operates where digital assets and next generation infrastructure come together, serving institutions end to end. On the market side, Galaxy is a leading institutional platform providing access to spot derivatives, structured products, defi lending, investment banking and financing. With more than 1600 trading counterparties, Galaxy helps institutions navigate every phase of the market cycle. The platform also support long term allocators through actively managed strategies and institutional grade staking and blockchain infrastructure. That scale is real. Galaxy has over $12 billion in assets on the platform and averaged a $1.8 billion loan book in late 2025, reflecting deep trust across the ecosystem. Beyond digital assets, Galaxy is also building infrastructure for an AI powered future. Its Helios Data center campus is purpose built for AI and high performance computing with more than 1.6 gigawatts of approved power capacity, making it one of the largest sites of its kind. From global markets to AI ready data centers, G is serving the digital asset ecosystem end to end. Explore galaxy@galaxy.com bankless or click the link in the show Notes Christian I want to learn about how to protect myself or really what parts of the economy become valuable or what kind of jobs become valuable. So if we understand that verification is the new scarce thing, but we've also established that verification is a receding frontier, then I still feel a little bit at a loss, as if I'm if I work in the economy where and how I want to work, or if I'm investing in the economy where and how I want to invest. So with, with this knowledge, with this paper that you've produced, the knowledge in the paper, where do you point people towards as the new valuable thing that the new valuable sector of the economy of the future?
- [48:13]
B
Yeah. So we have a section in the paper where we try to go through strategies, very applied strategy, practical things for individual companies, investors and also policymakers to some extent. But for the individuals, I would say we have a two by two, which is a classic in an MBA class. But it's essentially taking those two costs, cost to automate and cost to verify, putting them against each other and saying where does your job really fit into that box? And of course you don't want to be into the bottom left quadrant, which is the displaced worker. That's where it's easy to automate things, it's easy to verify them. You're going to use AI to, to verify that the output is good. Why would you pay a human? But then there's at least three ways you can succeed. And I would say, I mean this is at least the approach I'm taking. You probably need a little bit of each one of the other boxes and you find kind of your perfect balance. I don't think people should go in all the way into one box. Every job will be some blend. But let me start with the hardest one, which is the meaning makers. Right. So these are the people we were talking about before, where it's not even clear that there's a better or worse outcome. It's all about building that social consensus, rallying people around some sort of meaning. You're really monetizing those status gains, that human connection. Very difficult to do. Some people do it.
- [49:36]
C
Taste maker.
- [49:38]
B
I mean you could call it taste, but it's really a coordination maker. Right. You're trying to rally people to care about something.
- [49:44]
C
I see.
- [49:44]
B
I think art is often like that where you know what is a good art, what is bad art. I mean, I think you have the NFT there in the background, right? Yeah.
- [49:53]
C
I think the fashion industry comes to mind. Like I have a hard time understanding how AI will display. Like New York has a huge fashion industry. I don't really see how AI or robots gets involved with that.
- [50:05]
B
That's a great example by the way, because when you think about fashion is fast moving, the top fashion makers, the mini makers in fashion continuously have to evolve because they get knocked out by and replicated by the low cost producers like within the season. And in fact, that gap with automation and manufacturing has been closing so, so quickly. But it is true that what makes a great fashion designer from an average one has been capturing the moment, pushing the boundaries and kind of jumping ahead and creating that coordination. I don't know how much of that is really objective versus you're just good at creating that movement around it. A lot of crypto falls into this category, right? Which is like those initial blockchain moments are typically okay. People just got the right narrative around it, they have the right DNA and genealogy. The end of story.
- [50:57]
C
I think in our sector of the world for crypto, this feels very much like Twitter influencers. Like if you can create a narrative and if you can educate about a narrative and provide value around an idea, that kind of feels like tech influencer, Tech Twitter people is kind of like that's where I see this, at least for my purview of the world.
- [51:20]
A
David wants to say he's going to be safe. Don't worry man, you're going to be safe. David. It's okay.
- [51:25]
B
I'm going to be okay. The hard part of that is that, and I think many of the best ones will be augmented, which is right now you can only track so many things. And so I think what makes a great personally in that role, it's someone back to the tweet example that reads the moment, understands what people are kind of resonating versus not maybe even like a comedian right through something out, learns from it, deletes a tweet and keeps evolving. That process of Experimentation is super important even for the meaning makers. So I would like us to think of them as pretty scientific too. They're not just fully improv. I mean, maybe in some professions it's like, you know, like a religion. If you're launching a new religion, then even there you probably need to understand, okay, right now everybody has this low grade fever around AI, so some AI centric religion may actually make a lot of sense. But look, the, the other two boxes are the ones that I at least can, can relate more to. The, the liability underwriter is obvious. It's essentially saying if you're a top expert in your domain, you're really at the top of that verification layer. Can you augment yourself and do just a lot more of it? I think the top lawyers will do this, the top medical doctors will do this. Like every domain, right? It's like if you know something that's narrow and niche, well, guess what, now you can scale it rather than just being part maybe of a bigger machinery
- [52:48]
C
and the liability underwriters. This is the quadrant of our two by two grid that where automation is easy but verification is hard. And this sounds like, you know, the top 1% of engineers, the top 1% of lawyers, the best in their fields, are augmented the best by AI and their value is going to become more scarce. Like being the leader of your field is going to be a more valuable thing.
- [53:12]
B
Venture capital is another great example for a slightly different reason, which is some of these things have a gap between when they're created and when you get feedback. Did I make the right decision or not? Right. And so whenever that gap between I make a decision today and I will know if I made the right decision tomorrow is long, you need someone to sort of underwrite that risk. And so a venture capitalist with a good track record and good taste, curation, judgment, whatever you want to call it, is essentially underwriting that when I make this investment today. Well, maybe not all of them, but I will get some of those home runs. Same with a doctor in a hospital. Doctors are essentially already underwriting decisions on behalf of the hospital they work for. And it matters a lot more for those edge cases. Many decisions are kind of rubber stan, and they'll just use AI to do it and put their name at the end. But then there's a few that are really critical for the reputation of that hospital. Say, okay, if you have a rare condition and this particular doctor is kind of the word expert for making the underwriting on that right, should you get treatment or should you not?
- [54:14]
A
For example, okay, so we have opportunities for meaning makers where verification is in. Automation is hard. It's kind of the, you know, the social games type of space. And that's where the, the iceberg is actually getting larger. I mean, there's more surface area for opportunity. The liability on Underwriters is kind of that top 1% where they're just massively automating themselves with AI, but they are still providing a lot of value on that verification layer. There's another quadrant here where verification is easy, but automation is still hard. You call these the directors? Is this where, like, people are doing more artisanal type of tasks? Like things only a human can do, or like what's in this quadrant now?
- [54:57]
B
This is actually all about intent. So if you think about the verifiers about that final filter, this is the hardest role, being an entrepreneur, or essentially coordinating economic activity, including coordinating agents towards a certain goal. What's important in this bucket and why it's hard to automate is because there's what economists called nightmare and uncertainty. 19. Uncertainty is this distinction between risk where you can assign probability, saying, okay, 60% chance this, this happens. I may be wrong, but I sort of can put some probabilities on it, and not even knowing what those probabilities are. When somebody starts a startup, typically if it's trying to push something truly new, there is fundamental uncertainty about, is this even the right way to think about the problem? Is this even a problem? Right. Is this the right technology?
- [55:47]
C
It's the difference between knowing that you'll be 60% of the, of the time, you'll be wrong and you know that 60% is correct versus you don't even know what that number is in the first place because it's unmeasurable.
- [56:01]
B
Yeah. The best definition of this is the famous unknown unknowns.
- [56:04]
A
Right.
- [56:05]
B
So in the land of unknown unknowns, you need someone, again, you can call it someone with good taste, good judgment, good curation, really all they are. Even as entrepreneurs, you think about founders, they've seen a bunch of situations and instances and they learn maybe from their own travel through different careers, that some problems are worth solving, that the way to solve problems might be a certain approach and then what they do. And this is why this job, I mean, we call it directors, kind of from a Hollywood band, in a sense, they're the final ones that will know, okay, this is the right output. Right. When they see the final cut, they're like, okay, this meets my bar. But the more important part, I would say, is not so much the filtering where they may even rely on the liability underwriters. They're the ones that launch the swarm, keep it within bounds, right as you go. You're always course correcting. That's why there's no recipe for a startup, right? A startup is some weird zigzag. That adjustment along the way, that tackling of different situations that are updating to new information and redirecting those agents. That's what the director needs to do. And it also needs to figure out, okay, the agents are hitting the KPIs because I told them to, but I do feel drift and maybe you won't be able to explain it, maybe it's kind of a gut intuition in that phase. They're the ones that will bring that swarm back into, into compliance with the original intent. So it's, it's. Many professions I think are, are like this, especially in creative industries, in entrepreneurial endeavors. Science. So if you think about AI will automate and augment a lot of science. But if you're truly pushing the boundaries of the non measurable, I think you'll need a director. And sometimes it will be a single individual, sometimes it will be a team. The other piece of good news is that by the way, a lot of the economy is not measured. There's things like in space that we haven't measured, there's things on the planet we haven't measured. There's things about humans and their interactions that we haven't measured. That's all the domain where you can make investments, you can make R and D bets, you can really push the future. That doesn't change with AI.
- [58:15]
A
Okay, so I guess the goal is to be in one of these quadrants, be a director, be a meaning maker, be a liability writer. If the idea holds that verification becomes very cheap, or sorry, verification is the scarce thing and automation becomes very cheap. This other quadrant we've talked about already, but I just want to underline that the displaced workers quadrant, that's where you don't want to be. That's where wages drop to the cost of compute. And certainly no one wants to compete against the cost of a token, not in this economy. So if you were to map out the existing economy right now and all of the jobs, let's say in the United States, all the jobs in the United States, which you know what portion of them are right now closer to this bottom left quadrant of being a displaced worker, is that most of the work that we do, in a sense
- [59:07]
C
it feels like a not insignificant amount.
- [59:10]
A
This is the reason for the underlying angst I think that people are feeling is because they're sort of worried that they're in the bottom left quadrant, or at least a good portion of their work that they do is in that bottom left quadrant. Is that, is that how you read it?
- [59:24]
B
I do read it that way, but I also combine it with. So here's the good News. Imagine even 60, 80% of your portfolio can now be displaced. The key is that now if you have anything from the other buckets, you can do a lot more work. And so a single individual becomes super individual. You get these superpowers, the challenge. And some people have talked about agency. That's another cope term that's been going around is like, oh, don't worry about. Humans have agency. Agents do not.
- [59:56]
A
That's my favorite cope term, by the way. Christian. I like that one.
- [59:59]
B
There you go. So look, every. Everyone needs one now. You can do new things and you can be a lot more ambitious. And even the learning and this is where, you know, even the. We were talking about the juniors not getting jobs and the codifiers curse. These are the same tools. It's such a double hedge sport, right? It's like these are the same tools that you can prototype, go from prototype to idea in the market within. Within a few hours or a weekend in a way that you could have never done before. So if you're willing, if you're, if you're taking the positive side of the technology, no matter what your job is and no matter what percentage will be automated, I think for most of them, it won't be 80% overnight. Now there's some jobs that were already kind of very thin layers, like wrappers on other things of. I'm gonna pick on one like search engine optimization, right. If your job is to generate cookie cutter content to, to beat rankings, I mean, the real ranking theme will change too. But put that aside for a second. That type of output that is non original is replicable. And it's kind of replicating the same thing over and over again. That's going. But now you can gravitate and move up the value chain. You have to.
- [61:13]
A
Can I throw another one in there? What do you think of paralegal? Is that like a dangerous bottom left quadrant?
- [61:19]
B
100% right. So. And when you think about the role of a good paralegal, often it was a career step, right? You start in that role, you accumulate additional experience and then you move up. Some good law firms have summer analysts, you know, some are programs where they will take the best of the new batch. They Put them into essential part of legal work, they follow along, they learn mastery from the people with more experience and then they essentially either it's up or out. I think it's the same here, which is that's why entry level is so challenged. It's because often the entry level job is a training ground and that training ground has been taken by AI already.
- [62:00]
A
Yeah, that's your idea of the missing junior loop, right? That's in this paper. Let me ask you though. So this is quite a chasm for those that are just starting their career, maybe, you know, coming out of university and just starting to enter the workforce is if what you're saying is there's very little value in being kind of a paralegal or sort of entry level, whether you're a developer, you know, in the legal profession, across professions. But there's a lot of opportunity on the other end of the spectrum. Once you are once, once you have the sufficient judgment and curation and taste that usually comes with spending 80,000 hours in your chosen profession in a decade long career, well, there's an opportunity for you to become a hundred X liability underwriter. Okay, but there's still this chasm between the two. And how do you even get to the other side if there's no opportunity for you because you're too junior and an AI can do what you're trying to do? There's like, there's a huge gap here.
- [62:57]
B
There is. And I think the good news is that you can now compress what would have been, you know, multiple years of learning into a much shorter period. You can also, you know, skip the training step. If the training step was, you know, trying to maybe ship something and develop something in the real world. Take that, IC4. That may not get the internship or the entry level job they can now arm with. You know, some of these tools do the same things that a team of engineers would have done. And at the beginning their intuition will be wrong, by the way, because they're fresh. They may also question things in a novel way, so they may even have an advantage. They can bring those ideas to reality in a way that I think none of us when we were that age could have done. And so yeah, it cuts both ways. I do think in the end the positives will outweigh the negatives. But it's a massive cultural shock. Right. So if you were expecting, I get a good degree, that leads me to a good internship. And once I have that good internship, if I work hard, you know, I'm going to get this the, the job to keep improving, that. That path is gone. I, I think that's what makes it particularly hard for individuals that are probably fresh out of college right now. If you're in college, you probably have a few years to figure out, you know, where this is going. If you're super young, maybe these tools will make your learning experience so different. But yeah, if you're in the crux of it, if you're in the missing junior loop, my advice is essentially, look, you have superpowers. Try to use them, try to build things, try to use them to engage with society in a way where your ambition should be like 100x what our ambition would have been at that age.
- [64:40]
C
Yeah, this is starting to align with one of the big takeaways that we had from our recent Lynalden podcast. And we were talking about the supply chain of this whole AI revolution where, you know, the big, the big tech companies, the hyperscalers, are taking all of their profits and they're throwing it into data centers, and the AI labs are spending way more than their profits on training the AI models. And then, like, you know, everyone who's. Anyone who's in the supply chain is, Is not making money, and they're all burning money. And so I asked Lynn, like, Lynn, where. Who's making the money here? If this is such a valuable industry, where's. Where is the value being created here? And her conclusion was, it's in the end consumers, the end consumers actually get the value. The LLMs that they use are smart and they get to express the value of that. And this is starting to align with what you're saying where I'm kind of getting that, like the, really the beneficiaries of this is everyone who leans into this technology and becomes sort of like a founder who takes an executive mindset, a leadership mindset, a I will take this technology and I will create something mindset. Whereas the quadrant that really loses are the button pushers. If you just show up to your job and it's your job to press buttons and to write emails, like, you're not. That is, you're gone. Like. And so you need to go from a button pusher to a founder. And that seems to be like the technological trend shift that things that this quadrant is mapping just like go into any of the other three quadrants. You need to be a tastemaker, you need to be a coordinator, founder, literal founder, or the other one can't remember. But the point is, it's just like the automated jobs are out and I can go into the future and imagine the future. And I will say like, oh, you know, no Citrini article will ever psyop me into thinking that the future is going to be worse when we have abundant intelligence, so much more productivity, we get free labor with robots. There's no way that we go into the future and all of a sudden like I'm, we're in a depression. Like I don't believe that that will be the outcome. The, the future is going to be sick because of AI and no one is going to be able to psyop me away from that. But I do understand, Christian, that as a society, as a human species, we have had the button pusher quadrant be the dominant labor sect, sector forever, going back to peasants. Like pick wheat, put it in the mill, bake the bread, just do the thing, don't use your, don't think too hard, just do the thing. And that has employed the large swath of society forever. And so I think that's kind of your, like what you're saying is like, yeah, this is going to be, this is going to like tear society at the seams. Like this is going to cause a bunch of chaos. And so I'm looking at the future and I'm, I'm like, like it's going to be great, but I'm looking at the short term and be like we're kind of fucked. And so I'm of two minds about this. How, how do you think about this dichotomy?
- [67:45]
B
Look, two things. So first of all, society will always recreate button pusher jobs if it needs to. So we will have to. Right to, to keep the societal calm. And you could argue there's already jobs in professions that are created in that way for different reasons. But I think the, the more interesting part is how many of the people that are in those types of jobs the intellectual capacity to do a lot more. And I think it's more than you think. I think it's more than I think. I also think it's not everyone. Yes, it's not going to be everyone. But then you also have to ask are those differences in whatever measurable capacity you want to take? Right. And all these measures are completely inappropriate like IQ or EQ or whatnot. Right. How many of those gaps are driven by lack of opportunity? All sort of other environmental factors that we haven't tracked yet, from pollution to other things that affect, okay, this child born here will have a better intellectual trajectory than someone else. The way they're stimulated through education as we reinvent all Those pipes and we discover probably all the things that have kept human capacity behind. I do think it's still net positive. And yes, we will have probably some jobs and some islands that governments will have to maintain. And this is where, by the way, I think the old UBI approach is completely wrong, which is people need meaning and nobody's going to want to end up from the government in a fully augmented society. Maybe some people will and they'll just enjoy it. But I think for many, that agency or that feeling that, okay, I'm learning, I'm improving myself, I'm pushing myself, I think it was Karpathi that had this example or someone else like, I can't recall, which is like, look, when manual labor stopped being necessary, we invented the gym. And for anyone that goes to the gym, right, it's like you're going there first of all because it's good for you and your health, but also because that progress, that feeling of challenging yourself, it's a core part of happiness. I think we're gonna do the same for intellectual labor. And you're already seeing it. People are building all sorts of crazy things on the side and some of those things will become jobs, right? So people will discover a passion. The creator economy is a good maybe cannery in the coal mine for that, right? It's like, how deep do some of these YouTube channels or TikTok channels go in terms of like people that are really mastered something super narrow and, you know, maybe to a small crowd, there's going to be a lot more of that, probably.
- [70:16]
A
Christian, I'm wondering what you think about this. So this low level angst that we've talked about, if it's widespread enough and if it's stoked by charismatic political leaders, it could actually throw a wrench into this entire thing. It could really slow down the cost to automate or create entire sectors of the economy that really can't be automated. I'm thinking of some legislation that I recently saw coming out of New York State. And this is legislation to actually prohibit LLMs from being able to even provide any sort of healthcare, therapy, financial type of a device, essentially protecting credentialed authorities. As you know, these are the high priests and they get to comment on these things. If you are trying to use an LLM to get any form of therapy or advice, that's off limits. And this is a way we can. Society can kind of organize to slow down automation, protect incumbents. There's maybe a good side of this which is if your argument is, well, like this is going to happen so fast, naturally that we actually have to slow things down in order to give time for society to adapt. On the other hand, it's also a bad thing because we're limiting the propagation of these tools that can increase well being and increase affordability. And if the US doesn't adopt them, then some other country will and will become more relevant over time anyway. What do you think about this social force, let's say cultural force, political force that is starting to push back on AI automation? It feels like that's starting to strengthen, maybe even crescendo. Do you think this disrupts the entire plan here and the thesis and the economics of everything?
- [72:03]
B
I think it's a very serious concern. I mean, think about the historical example of the Luddites. This is like Luddites on steroids. We're going to see probably all sort of attacks on data centers. And right now is in the policy level, right. Trying to stop deployment. The lobbying is going to be very strong. I mean we've seen it for example with crypto and financial services, how many years it took, you know, for the technology to be taken seriously. I think it would be very detrimental. And the main reason is that every moment we stop these services from being improved and deployed, we also stop a lot of people from having access to them. You know, you mentioned medical advice and therapy. Like there's a lot of segments that are excluded from high quality.
- [72:43]
A
It's expensive, I mean 200, especially in the United States.
- [72:47]
B
Right. And people have found immense comfort in these LLMs even sharing all sort of really personal things that they couldn't have afforded the equivalent human. Right. It's like 20amonth versus $200 a session or a hundred dollars a session. And so I do think these laws are very dangerous. They do create this impression that the future is going to be negative and the technology is here to take the jobs rather than the technology is here to deliver a service that used to be expensive. And we can now expand to many more people. Some clash will be inevitable and I think we'll have to be prepared. Different countries will probably make different choices. Right. If you, if you were to guess maybe the Eurozone, based on past experience of overregulating things, may take a very slow approach. But look, the reality is that this is where I think open source models are great. Yeah, maybe New York will ban, you know, you're getting that kind of advice from a commercial entity, but if you can run a local model, you know, on your hardware and intelligence is becoming too cheap to Meter anyways, the model is already pretty good. I think people will work around this, people will be smart and ultimately it's for the expert to show that, look, I will be using the model myself. So what used to cost you X will cost you way less. I will focus most of my session with you on the work we cannot do with the model and there I still add value. So it's kind of a change in how you do business even for some of these jobs where maybe you'll take a lot more clients but you spend less time with each one of them. And where you're focusing on is kind of the thin layer of verification. But the alternative is kind of also, historically it doesn't work out. So the slowing down, the progress of the technology will not work. The genie is out of the box, the models are out of the box and in fact I think we need to focus on the side effects and prevent some of those. I do think, when I think about where the doomer crowd has a point is that the capabilities do enable bad actors to take advantage of this. And whether it's a proprietary model or open source one, I don't think it matters. For those that follow Plinius on X, he kind of jailbreaks all of these models within hours. So the aura of a closed model being safer I think is minimal at this point. These capabilities are out there. Beta actors will try to exploit them. How do we re engineer society so that our antibodies and our ability to respond to side effects of AI will be rapid? I mean, just think about identity. There's all these situations where everything we used to rely on is going to be broken. Like think about Social Security numbers, right? It's like, it's ridiculous. Our infrastructure is not ready for what AI can do.
- [75:31]
A
It's really not. There's so much work ahead and that's why it does seem daunting at times. But it's a fantastic opportunity at the same time. So we talked about what individuals can do. We've talked at some level about what societies can do. How about companies and how about investors? What can they do with this shift towards verification Scarcity rather than, yeah, I guess, intellectual and cognitive scarcity.
- [75:52]
B
Yeah, I would say for the companies, the roadmap is not that different than the individuals. Step one is okay, take advantage of the capabilities, automate as much as you can. But keep in mind where verification may be weak, start thinking about what kind of investments in verification infrastructure and talent in those kind of harnesses around it I can make today so that my product is better than the alternative. You're seeing some of this, right? So I think it was a voice model that now adds insurance so that if the agent ends up saying something crazy, you're kind of insured by the consequences. That's an early sign of what we call liability as a service. Kind of moving from software as a service or even software as labor to I'm going to underwrite not only the agentic output, but also the consequences. So I'm taking full responsibility end to end for your workflow. Another key area, and this relates to verification again is companies that can build what we call proprietary ground truth are going to be extremely valuable. I'll give you an example. I've been following actually some of the developments on the war, mostly through LLMs. I have kind of a script that I run every few hours Rather than monitoring the situation in the eye adrenaline, eye cortisol mode of X, which I also enjoy at times. I've learned to kind of pace myself. And so I get that update. It's very well written. It kind of focuses on the dimension I want to track. But I could imagine a better version of that that has access to maybe some of the articles that are behind the paywall that are kind of closer to the source of truth. And so for many of these companies that used to get that ground truth, if you make it agent available, I think you will build an even bigger business. Another example would be something like reliable product review. The ground truth of what is a product actually. Like things like, you know, wire cutter or consumer reports. I think it's even more important in gente commerce because the agent will really want to know, am I building on solid ground or is it shaky? Right. The human used to do verification. They read the reviews and say, well, this sounds like fake. Or maybe they check on Reddit a few sources. Right. They triangulate and then they buy something. The agent I think will be more gullible in that phase. And so if you're selling ground truth, I think you have a very profitable business model ahead because you used to maybe only have access to data. Now you can sell the labor around that data flow. So I think that that's going to be quite important.
- [78:19]
A
Christian, how about investors? This is very difficult to navigate from an investment perspective. You've seen anthropic drop, just kind of different extensions, a security extension or something or a legal extension. And entire SaaS industries go down by like, you know, 10 to 20% and the stock market, Right. So it's hard to know really what's going to be displaced in this world versus what the net new business models are going to be. Do you have any insights for investors into how to invest in verification scarcity?
- [78:52]
B
Yeah, so first of all look for the companies that are advancing verification infrastructure. And some of this also relates to crypto fundamentally. You know, companies that build better tooling for the top verifiers to scale a gentic output I think are going to be very valuable. Second one, we already touched on it from a company perspective. If a company has a unique moat in some sort of ground truth access information like think about the Bloomberg data, like if you can get that information first and you can serve it fresh to the globe, you can scale that even more. But maybe the most important piece of all for investors is that focus on the non measurable. If the measurable is becoming cheap, can you push deeper tech ventures, ventures that push on R and D that goes into domains that haven't been fully measured? Can you re venture into the things that it's maybe a few years out or less depending on the acceleration where again there's no digital trips. In a sense it makes the job harder because there's no more a playbook and things like oh, network effects matter. Well not all network effects matter. So an investor now needs to ask themselves is this a type of network effect that agent can unravel? Because I can throw, compute, edit, the agent will populate, the platform will reach out, will onboard people will do all the things that used to be hard and created the large two sided marketplaces that are dominant today. Or is this a very specific new type of network effect where as I deliver agentic output I get better and better at underwriting it. Why? Because I have better telemetry, I have better feedback loops, I kind of learn from the agent in the wild and I can make that agent cheaper, faster, more insurable than the competitor. I think you're going to see some multi billion dollar companies being created out of this idea that okay, intelligence is cheap, but verified intelligence, which is actually what people want to buy, it's going to be harder to get.
- [80:50]
A
In some ways blockchains and cryptocurrency is a verification technology, though it's unclear to me whether it's the same type of verification technology that you've been talking about throughout this conversation. To what extent do you think crypto will be useful in this whole verification move?
- [81:10]
B
Yeah, and the paper has a few easter eggs for the crypto crowd and probably won't be surprising to people knowing kind of my past in the Industry, I do think, and I've asked this question for a long time, which is like, okay, what does AI and crypto look like as a combo? And where I landed is actually that what's interesting is that the crypto space over the last decade has built some of the primitives that I think are going to be extremely important for the new landscape. To some extent, they weren't necessary. Take something like proof of personhood. Very clever. You know, people have come up with all sorts of constructs on chain attestations and whatnot. But because crypto never kind of scaled up to the mainstream and some of the early things like stable coins and payments probably don't need that in this phase, we haven't seen it shine. But as AI scales, I think a lot of the side effects are going to be a lot more painful. Identity is an obvious one where what is real, what is not, Is this the right person, is this account being taken over or not? Crypto has all the right primitives for building around that and providing stronger forms, verification, and they will become more important. The other one is provenance. And maybe the video is a good example. But can you document that that camera was a real camera in the real world taking that video? Some of this has been actually experimented with. There's a lab at Stanford that has been documenting war zones. And a key part is like, okay, when we're taking evidence, can we prove like a cryptographic chain of custody from the moment it's recorded to the moment it's shown? We'll need, for everything we do, we'll need that, right? We'll need this kind of hard cryptographic lineage on information being generated, information being used. And even for models like, you know, can we verify that what they're doing is what they're supposed to be doing?
- [83:01]
A
Christian, this has been a fantastic conversation, really enjoyed it. I think you've shed a lot of light on the possible futures where, you know, like, people can adapt and still continue to drive value and where the economy is going to adapt. I guess if you were to leave us with a summary of what this all means. What should people be doing maybe over the next 12 months to really think through this issue and apply this in their careers, in their companies and their investments?
- [83:32]
B
I would say first, don't panic. Don't let that low grade fever paralyze you. If anything, again, get into action, play with the tools, try to think through what parts of my job are augmented by the tool versus replaced. Try to replace as much of yourself as possible through experiments and Then run those experiments across not just your work life, but all of your life. I think for many, maybe their hobby or something they do on the side might be the most meaningful thing in this new economy. So experiment broadly, see what resonates, and try to really turn your ideas into reality. I don't think there's a more precise playbook than that, which is go through the flow, go through the process. Worst case, you learn where these models break and where they're not there yet. And that could be very profitable. But my sense is that for many, it'll be kind of a eureka moment where they're like, oh, wow, this thing that used to be my hobby. And we've seen it with creators online, right? Their hobbies turn into their business. That will be probably what they'll be doing in the future. And then of course, if you have kids, if you're trying to think through, you know, not only how to navigate yourself, but navigate some, some. Some younger human, I would say. And this is what we're doing is like, in this new future, the most important thing will be discovering your natural aptitude, what you love doing what gets you in the flow and doing more of it. And so I, I don't think there's a recipe. It's not like STEM versus the arts or like everyone will have to find their path even more so. And the good news is the tools are great at helping you find that path.
- [85:05]
A
Christian, you're very plugged into this. Do you think this is going to go well for humanity?
- [85:09]
B
Absolutely.
- [85:11]
A
Good. We'll end on that note of optimism. We'll include a link to the paper and the thread in the show Notes, simple economics of AGI. Christian, thank you so much for joining us today. Thank you, Bankless Nation. Got to let you know none of this has been financial advice, although I think, I do think it was some career advice in here for sure. You could list what you put in, but we are headed west. This is the frontier. It's not for everyone, but we're glad you're with us on the Bankless journey. Thanks a lot.