AI Just Gave You Superpowers — Now What?

19:47Robert Hackett:those people are creating the evals and the training that will eventually displace their peers. So this verification layer is a really important one. I think many people will thrive in it. It's one that really rewards almost like hyperspecialization, right? So if you're the one person that really can deliver that final unlock, again, your leverage is massive. So that's one category, the verifier.

20:10Christian Catalini:That's the one that you have called the codifier's curse.

20:13Robert Hackett:So the codifier's curse is what we describe as the mechanic where if you're a top verifier, you need to keep moving up the stack, right? Because the technology gets better and better. And so you need to keep adding value at that thin, thin layer so that you're always one step ahead of the machine, so to speak. The director I already mentioned, right, is essentially someone that really drives the intent. Entrepreneurs are directors, right? So they see some future, they imagine some path for getting there. And then, of course, startups are continuous drifting and realignment of the object along the way, right?

20:48Robert Hackett:There's many jobs that are director types, including, of course, in media, right? In movie production. That's where we stole the title from. And then there's going to be jobs that I think we need to recognize are easy to automate, are easy to verify. And those jobs are gone or soon to be gone. And I think society hasn't really grappled with some of those effects and there's going to be a massive need for retraining and really pushing people further up the knowledge frontier on that. But when you look at those jobs, we're going to use also AI to verify AI. So one of the things that sometimes people misunderstand from the paper is that we talk about human verification as the last step.

21:27Robert Hackett:But in many cases, AI will verify AI. So there's going to be a whole series of steps before it really gets to the final human that may be or may not be needed depending on the job. And then we have a category that was the hardest one to qualify. I mean, we call them like the meaning makers. So imagine settings where actually it's all about, and here, again, my past in crypto shows, it's all about consensus. These are individuals that are really good at understanding trends, societal changes, or things society cares about that require everybody to coordinate around something. Art is like that, you know, crypto networks to some extent are like that.

22:09Robert Hackett:And these meaning makers are essentially not, they're not in the land of what's measurable. You know, we could land on one equilibrium or another, it doesn't really matter. But they're really good at creating that social coordination around some sort of outcome. These are not necessarily, by the way, the jobs that sometimes people say require human touch. I do think people severely overestimate, you know, how important that human touch is. You hear it for jobs like, you know, a therapist or even elderly or child care. Yes, I think people will have all sorts of concerns initially, but nobody's really accounting for the drastic reduction in cost, right?

22:46Robert Hackett:So if it's 100x, 1 ,000x cheaper, and some people may even feel it's more private, people will rapidly shift. In fact, we already know, right? People are using all of the LLMs aggressively for all sorts of questions that would be very intimate or personal. That said, of course, there's going to be jobs where human-made or made by a human will be a very important label. And crypto will play a role here because soon we're going to lose the nature of that identity without some strong cryptography behind it. But that human-made will be valuable just because of the scarcity that's inherent in the fact that it's human-made.

23:24Robert Hackett:So not because it's better, it's just knowing that a human dedicated their scarce time and attention to deliver that experience, that culture, whatever it is. I think those things will still be important.

23:36Christian Catalini:So you brought up cryptography and crypto. What is the place for crypto then in this world? It's a really important one. It would seem to be complimentary, but how so and in exactly what ways?

23:48Robert Hackett:Yeah, when we started this journey, I mean, many before us had already said, look, LLMs and AI is kind of probabilistic, crypto is deterministic. You know, think about a smart contract, putting the guardrails on an agent or being able to give an agent the ability to buy and sell resources. All these things resonated, but I do think there's an even more profound complementarity between AI and crypto. And maybe the reason why it's not so salient in the economy today is because we haven't seen the side effects yet, but issues around, think about identity or provenance of digital information. I think we're about to enter very uncharted territory in the next few months.

24:28Robert Hackett:As these capabilities become truly amazing, every digital platform will have to really wrestle with the idea that what used to be a human contribution, whether it's a post or an image or anything else that's been done, it's now potentially an agent. You know, those bots sometimes come on a delegation of a human. So you need to treat them completely differently. As that unfolds, I think society will have to drastically reimagine its identity stack, the way it really certifies things, the way it thinks about, is this true or not? what is the kind of the chain of custody of this digital item until the way it reaches me.

25:05Robert Hackett:And so, yes, I do think crypto probably is going to shine in all of this. And everything that's been built over the last decade, it's going to be a lot more foundational. Back to verification. When you have underlying information on a blockchain, verification is cheap. It's more reliable. You can trust it. And so in a land where trust is going to be increasingly scarce, Yes, I do think crypto primitives will finally truly shine across a number of applications.

25:31Eddy Lazzarin:Yeah, one way I'd put that, Robert, that idea is that the cost of automation is declining very rapidly. And the cost of verification, in this broad sense we've talked about, I think it is declining, but it is declining not as quickly. And that creates a gap, right? And that gap is an interesting thing. There's many ways to describe that gap. Some may describe that gap as an opportunity. That's kind of what Christian is saying for human laborers is that if there's this bottleneck, there's this gap in measurability because of humans general adaptability and experience and generality. Humans are probably able to specialize to the verification component faster than we can get the machines to.

26:11Eddy Lazzarin:And there's some interesting sort of deep challenges that make handling verification hard for machines in the short term. In the long term, I don't know that that's I don't think that that's a permanent thing. But in the short term, that is definitely the case. Cryptography and blockchains are a verification tool. Provenance is, you know, just a chain of cryptographic evidence, right, that something, you know, traversed some path between specific hands or it underwent some series of transformations that we can be sure of. And that gives a signal about what we're looking at. It just makes different categories of verification easier.

26:46Eddy Lazzarin:So anything that makes verification easier is going to be a part of solving that gap, trying to close that gap. And that gap is a kind of systematic inefficiency in what the thing is trying to do. A really interesting frame that the paper puts out is splitting things in terms of measurable and non-measurable tasks, measurable and non-measurable labor. I wanted to ask Christian, is measurability basically just cost of verification? Like, is there more to it than that? Like, do you think of measurability as the essential cost of verification? The idea, just to say measurable, non-measurable tasks, a measurable task is one that I'm understanding as having a low verification cost, such that you can kind of put the measurement components into the existing loop, right?

27:32Eddy Lazzarin:You don't need to do a lot of additional work in order to figure out that it was done properly or that it's working or that it's fitting or that it's compatible or that it's bug-free, so on and so on. Whereas non-measurable tasks seem like they're either in this complex domain or you were just saying, Christian, like consensus domains where there's not really a concept of right or wrong, but there is a concept of consensus that's important to reach just to proceed, just to organize future tasks. What do you think? Is that how you think about measurability?

27:59Robert Hackett:So I would say you're absolutely correct about the bifurcation. And I think that's an interesting one for society, right? Because to some extent, some things are not measurable. And even if we had perfect measurement, we probably wouldn't improve on them because they're social constructs. Some people call them status games, right? Where it's like, okay, we're coordinating on this piece of art being important because it reflects some sort of meaning to that society, to that culture, to that growth. But to the automation question, I would say the latter category is probably the most important, which is, again, there's probably a distinction between what's measured outside of a human brain versus inside.

28:39Robert Hackett:What is it that a single individual has recorded through their own experience? And of course, as we start carrying devices that were video and, you know, capture all sort of rich information, that barrier will come down. But right now, I think what makes a seasoned engineer different than even a machine that has read all the code is that they've struggled through all those moments. They've learned some out-of-distribution examples that they will be in the data for the machine, but they don't know how to weight them. And so our neural net has been trained in a very unique way. And so I do think the distinction is essentially the reason why verification may matter for this category versus not.

29:22Robert Hackett:Is it something that you've measured that's unique or is it something that the machines can also measure? And of course, as we feed better, better data, that shrinks and that's why we need to move more into the unknown.

29:34Christian Catalini:Do you have solidly defined examples of things that you think are at least right now unmeasurable and safe because of that?

29:43Robert Hackett:I think across pretty much every profession, right? You're seeing this in law, you're seeing this in engineering, you're seeing this in strategy. there's components of it where the machines are really good at average, or I would say even above average. They've ingested the right materials, they've seen enough examples, and then there's the final verification layer, which is all about the exceptional. You know, the recombination that pushes the boundary a little bit forward. And you see it also in domains like the arts, right? So some of the greatest artists are really good at capturing a sentiment that hasn't been fully expressed in data yet or by society.

30:22Robert Hackett:I mean, that layer of applying your own expertise, your own accumulated experience across your life for that decision, it's still human across all of those professions. It's almost like a universal meta skill, I would say.

30:37Christian Catalini:So if we're going to make this concrete for people, I just got back from Paris a few weeks ago and went to the Musée d 'Orsay, looked at all the Impressionist artwork there. And it's funny to me now that, you know, France claims the Impressionists as their beloved artistic movement that they presented to the world when actually they faced just persecution and were completely rejected by the Academy for so long. and now they're celebrated. But they might be perhaps an example at that time of their unique combination of the way that they saw the world and expressed it through color and shape. Now, I'm not saying that that is safe from AI today.

31:22Christian Catalini:I'm not an artist, so I'm not going to make claims. But that is maybe a historical example people could latch onto about people whose unique experiences and perhaps refined taste enabled them to transcend. Maybe another example could be like the Michael Burry's of the world during the big short, you know, the financial global financial crisis calling the big short, you know, when everybody else thought that everything in the economy was humming along just perfectly wonderfully. And the few who kind of saw that that risk that other people overlooked.

31:55Eddy Lazzarin:Well, I'd say the first example, the Impressionist example, is closer to, I think, what Christian was getting at with like, maybe there's a little bit of a regime change in the consensus, but there's not necessarily some underlying new information that they had. It's not like they had some secret knowledge, basically, or some secret proprietary understanding of what art was good. The consensus changed. And the whole idea of consensus, there's like a rabbit hole we could go down where, like, take consensus in like a software engineering sense about like specific coding standards to enable interoperability, right?

32:31Eddy Lazzarin:It's not this approach or that approach. They're different. There's some degree of mutual exclusivity. exclusivity, you kind of got to pick one, which someone's just got to decide, right? And if everybody aligns on this one standard or this other one, just one of them, then we're more efficient. If you consider a future market where there's a bunch of machines as peers with the humans, then there is a concept of consensus that spans both groups, right? You can actually have like kind of a machine consensus and a human consensus about a specific software engineering approach or technical approach, then it starts getting really murky.

33:06Eddy Lazzarin:Like why would the human being necessarily have an advantage in consensus construction? In fact, the machine might because it could like automatically pull like every other model or like create some incentive scheme among models that they could decide is rational instantly. In other words, there's ways you could imagine that machines could find ways to coordinate faster. So this idea of consensus formation being uniquely in the domain of the human, I don't think is necessarily permanently true, even though it is today, because most laborers and most tasks are obviously remain coordinated by people.

33:36Eddy Lazzarin:The second example, the Michael Burry style example, that's more of a proprietary information where just the market has not incorporated some information or some incentive scheme. It makes it hard to actually act on that information or something. And they arrange facts, they arrange their positions, their capital and things to exploit that, that error. And even that domain, it seems hard to imagine why a human would have a monopoly.

34:04Robert Hackett:Yeah, look, if you push it to the limit, I think we all know that it goes to full kind of equivalency, right? Their peers. And then, yeah, I mean, unless we reinvent ourselves, and I think technology will be a piece of this. We're already seeing all sorts of experiments, right? With brain-human interfaces. It will be more powerful than us. I think with the impressionist it's also important to remember that in a sense that was a response to photography automating right of what's considered art and so if you could paint perfectly real looking paintings now that's glorified right now suddenly the photography will be way way way better and I think we're witnessing a lot of that and so people were moving in the meaning making space it's like how do we respond what is still the nature of being an artist and completely agree with Eddie I mean, with the big short example, and this is why I love, you know, biographies.

35:01Robert Hackett:When you think about some of the most influential people in history, put good and bad. There's something about their entire trajectory, the experiences that really put those weights in their model, right? In that net that are unique. They've just lived life through a set of experiences that calibrated them completely differently than others. And so given the same amount of information, the response is very different. So maybe eventually we will train models that will bring back that diversity, that unique, you know, biased opinion about reality.

35:34Eddy Lazzarin:Could we talk just a little bit about the Trojan horse? We haven't kind of gone in the dimension of the negative externalities of extremely low automation costs. You know, we've talked about the risks to human laborers, and there's so much more to say to that. But maybe outside of that, like for the productive benefits toward the economy, like what are the risks to the economy of low automation costs?

35:55Robert Hackett:Yeah, I think we're seeing glimpses of it. When companies today say that X percent of their code is now generated by machines, that's amazing. And it's a sign of growing productivity and I think the release cycles are shortening. But at the same time, because we already know that it's humanly impossible to review all of that code, there's a good chance that it may carry some technical debt of different types. We've all been tempted to ask requests to an LLM skim through it, and ship it as our own without full verification, because the models are getting better. But whether it's a wrong sentence or wrong line of code or some sort of zero date that is now part of your code base, I think we're going to see more of that.

36:41Robert Hackett:And what the model says about this is that essentially it's perfectly rational to ship code or to ship writings or any sort of AI-generated work that will contain some potential error because you can't verify the full thing. And if you scale it up to the entire society, that means that we're probably accumulating some degree of systemic risk as we accelerate through. Hopefully we can develop better verification tooling, better technology to really go back and look what we may have released. But in the immediate term, I think companies face this tension where if you think about the long run sustainability, even for a startup, right?

37:19Robert Hackett:Investing today in better tooling for verification, including some of the cryptographic primitives that we were talking about is expensive. It may slow you down. The benefits of that are all in the future and the rush to ship and to grow might be really strong. So I think we're going to see probably two set of founders. Founders that think about that second long-term liability and will build things in the right way. We're seeing glimpses of this kind of liability as software model. 11 Labs recently insured their audio agent, right? So saying, sure, deployed in production, you're also ensuring that there's some weird side effect of the agent making a bad decision.

37:58Robert Hackett:I think we're going to see a lot more of that. Alex Rampel has written extensively around this concept of Libra as software. As we deploy these agents as workers, that issue of liability and insurance, I think is going to become increasingly important. It's not probably the most glamorous topic, but as you think through, you know, to Eddie's point, what will be happening in the world, I think we're going to see a lot of systemic failures. There's a good example historically, right? So if you think about long-term capital management making lots of really smart investment bets until the old fund collapsed.

38:31Christian Catalini:This is the quant hedge fund that tried to use computational models to beat the market.

38:36Robert Hackett:Yeah, I mean, there's many of these instances where humans jump ahead of the technology that they don't fully understand. And then, you know, yeah, we have some major side effects.

38:45Eddy Lazzarin:Yeah, I think this is such an interesting idea because if what was happening in the production of software before or anything or any other service in the economy, if a lot of it has been the result of direct human work, then you can sort of take for granted that people have been observing and quality checking many, many, many steps. Now, I'm not trying to say that until today, there have never been errors or flaws, right? Hardly, right? But there is a limit to how severe those have gotten in specific cases that we may not fully appreciate because there's always kind of been somebody touching every step along the way.

39:22Eddy Lazzarin:But as things become more and more automated and as things become higher and higher stakes and more valuable, then the liability radically, radically increases. Now, of course, the benefits are radically increasing, right? Which is why we're tolerating that. But the ability to supervise and limit and understand the boundaries of risk have to expand. And so the idea of bringing in like an insurance type thing where you actually put a dollar value on the risk that things fail might be an important component in managing an entire enterprise because you just have to take for granted that it cannot be supervised.

39:58Eddy Lazzarin:and you want to delegate the responsibility of quantifying that risk and understanding what's going wrong to a specialist. It's basically a demand for a type of specialization, which always emerges whenever there's some new massive surplus with some big trade-off people specialized to handle the negative side of that trade-off. So I think that it's very interesting that even the process of producing software might develop a new financial dimension that it lacked before, right? And this kind of smells good to me, like as an idea, because everything is getting this financial component. And I don't mean this in some cynical sort of money bags, crazy way.

40:41Eddy Lazzarin:I just mean that the tools of financialization allow us to handle more complexity and increasing abstraction in the economy. Like that's what financialization is sort of for. So it kind of feels on trend to me. It feels right.

40:55Robert Hackett:Yeah, and, you know, back to crypto, So to some extent, everything we've been building over the last decade or so has been advanced in the frontier, how we can measure and weight risk, can go out of DeFi and the evolution within it and prediction markets. All those primitives are suddenly kind of critical, right? So if you're deploying software, if you have these agents, a stack that allows those agents to see better signals. I'll give you a very simple example. I was talking to a founder that's building in the agentic commerce and payment space. And he made this really interesting observation that when he switched from a traditional legacy payment system to just having payments over a stable coin, the system behaved more reliable.

41:42Robert Hackett:And the reason was that the signals were all on chain. The agent had a much better understanding of what was happening. It wasn't just hitting a dead API. we were seeing the whole context of those actions

41:53Christian Catalini:and I think there's going to be a lot more of that. Christian, you're saying there was more out in the open for the agent to be able to see and to have full complete context and understanding of what was actually going on with given transactions. Correct. Whereas in the legacy model, that stuff is hidden behind various companies, intermediated, left and right.

42:13Robert Hackett:We have all these data silos, right? And on an on-chain native transaction flow, a lot more is surfaced to all the participants. And of course, there's privacy requirements for some of these things, so it really depends on the flow. But another interesting part of this, and this relates to Eddie's point on insurance and liability, people say sometimes that, oh, network effects are going to be a sustainable mode in the AI era. I think that the reality is going to be a bit more nuanced. In fact, AI agents and autonomous systems are really good at breaking down a lot of the modes that have made two-sided marketplaces very, very defensible.

42:53Robert Hackett:Just the cost of bootstrapping these things and a lot of the grunt work that goes into seeding two sides of a market is coming down. But there's a different type of network effects that I think is going to become even more important. We call it verification-grade network effect. It probably needs a better name. But the idea is that if you have key proprietary data that you're generating as part of what you're doing, and if that data allows you to scale verification out of the hands of humans and into the hands of machines more and more, you will inevitably be able to underwrite risk better, make better decisions, and deliver a product at a lower cost that's safer.

43:32Robert Hackett:And that kind of mode, I think it's going to be very persistent in this phase. So when you look at the incumbents versus startups, the incumbents that have a whole database of failure, Think about a decade of information about how some of these flows could fail. Extremely valuable. And in general, startups that will center their attention on, is it true that every time we do an interaction, we automate a system, we bring in a top expert, a top engineer to make a decision, we're learning from it and we're kind of creating a positive feedback cycle around verification? I think those companies are going to be extremely successful.

44:06Eddy Lazzarin:Yeah. More evidence for the idea that proprietary data, you know and the data the data that an organization can keep inside and specialize from might be one of the most defensible things i have a direction i'd love to take it is that in the paper there's this concept of the the hollow economy and the augmented economy right this sort of like possible split could you unpack those and what do you see as like the key factors that distinguish them because i like this framing i think this is a really good really interesting framing and resonates but isn't it true that in some sense like the hollowing out forces like the undermining, self-reinforcing feedback loops, like in the codifier's curse or in the missing junior loop problem, right?

44:48Eddy Lazzarin:Aren't these also sort of the national side effects of just being able to automate something and find efficiency?

44:53Robert Hackett:Yeah, so we start with the hollow economy. You've already hinted at some of the dynamics, but the first one, I think, is already top of mind. It's happening, I think, in the labor market. There's early evidence of this. And tech companies will realize that they can do a lot more with less. And of course, they're going to start with below average or average performers because AI is already there and younger performers because now the senior one can already scale 100x or 10x depending on the task. So that's one of the forces driving changes. The second one we already hinted at is the codifier's curse.

45:27Robert Hackett:As an expert trains, you know, makes decisions, they essentially create those labels. Those labels can be used in the future to do the same decisions without the expert. And last, there's this concept of alignment drift. And without getting too much into the model itself, the punchline of that is that it's going to be important to think about alignment not as a one-shot, you know, we train the model, it's aligned, we're good. I think it's actually, I'm sure, as this definition of raising a child, where you're course-correcting and continuously kind of providing feedback along the way. If you take those three dynamics together and you combine them with the idea that the incentives for deploying unverified AI, if it can get the job done, are super high because maybe I get productivity today, right?

46:13Robert Hackett:60 % of the code written by machines versus humans. But some of the costs, maybe in the future, we may be racing towards an economy where we're not training our future class of verifiers, right, the juniors. Our top verifiers are progressively becoming slimmer and slimmer. That class is shrinking in size. and we're creating all these potential risk that can lead to what we call hollow. But then we use that actually to carve the path for what we prefer as the end state, which is the augmented economy. Again, I've already mentioned I'm an optimist. I think we're going to land on an augmented economy eventually.

46:50Robert Hackett:The question is like, how fast can we get there? And can we make that transition, which in some cases is going to be painful, as painless as possible for a lot of people that will have to be retrained and adapt. And the augmented economy is the opposite because essentially we realize, okay, juniors are not being trained. But guess what? AI is magical at accelerating mastery. You can find a young individual, discover their real aptitude rather than pushing them through K1, K2, K whatever of standard curricula. You accelerate them so that they can find who they really are, what they truly love, what gets them in the flow.

47:28Robert Hackett:That's at least what we've been thinking about our kids, which is like, who knows what, you know, it's going to be valuable. STEM, not STEM, arts, we don't know. But if you're building on your true talent, you have a much better shot at advancing. And I think AI is going to play a massive role in that. These are wonderful, wonderful tools for learning. We have to build that. I don't think they exist at scale today. Second, if you take the codifier's curse, well, guess what? Those individuals will have to keep retraining and moving up the value chain and discovering, oh, now that I have all this leverage, maybe I can be a director type.

48:00Robert Hackett:Maybe have an agent swarm. Some people have talked a lot about agency being important. I think that really gets at the crux of you need to realize you can be a director. You can do a lot more than you were doing before. And on alignment, I think between a lot of the safety, R &D, everything else that's happening, and better verification tooling, including human augmentation, if we can augment our capabilities, we'll be able to verify much better and be kind of peers. If you put those all together, you're suddenly in a scenario where a lot of things that used to be expensive in life are practically free.

48:36Robert Hackett:Anything that can be measured can be automated. So we'll converge at the cost of compute, right? Maybe even energy. Then you have other things that we're going to invent. Lots of new jobs, lots of new things that people want to entertain themselves, including in the status economy, in the non-measurable economy. Underline a strong verification stat so that we do have ground truth. We're not submerged by fake identities or like, you know, actors trying to essentially civil attack our society. If you put that all together, the future looks pretty good, right? And a lot of the things that I think governments have been trying to do forever are going to be cheap and available, like great education, great healthcare.

49:13Robert Hackett:All these things that used to be, you know, very, very rid of friction, I think we can deliver on. But yeah, we do need to make some investments along the way to make sure that we build that versus, you know, just struggle through the transition and make some crazy decisions like, Like, okay, let's dismantle the data centers. Let's stop everything. It's impossible. It's never going to work.

49:33Christian Catalini:So if you're early in your career or you're just starting out, you should be using these tools to simulate environments that you'll encounter to train yourself up, basically is what you're saying. And if you are later in your career, you need to get a fire under your butt, get some agency to realize that you can do more with less.

49:54Eddy Lazzarin:You know, it's hard to say how long all this lasts until there's another whole set of changes that are hard to predict. But the specialty of the human being is going to be looking at the whole thing and being able to zoom in and zoom out and zoom in and zoom out across an entire endeavor, an entire enterprise, whatever it is, and to know where more attention needs to be paid, more resources need to be paid, how the entire project needs to be shifted. If I was a young person today, starting off my career, yeah, I'd be a little sad that the glory of kind of going into the back room and carefully reading the instruction manual for some assembly language one line at a time and writing a beautiful program that's as efficient as I can imagine it over the whole summer.

50:38Eddy Lazzarin:like yeah that's gone that's a hobby that's something you can do for fun download a fantasy virtual machine and make up a game you know go to github there's tons of cool stuff there that's a hobby now no one's doing that anymore instead i would try to convince my parents to give me some money to harness a huge swarm of computers and see like can i spend five thousand of compute productively. You know, can I make 200 ,000 tokens per hour that are like useful or something like that? Like that's the challenge. Like can I guide a whole swarm of machines to do a thing? We've been talking about a meme sort of in tech world for years now has been the idea of like the one person billion dollar startup, right?

51:23Eddy Lazzarin:Yeah. Is this not how that happens? What we're describing is exactly how that happens, right? Not necessarily it's literally exactly this way, but the skill to control a huge class of machines and data and have this wide view of a thing and constantly be adapting it, that is itself a skill set that has never been developed because that's never made sense to do. If you wanted to have a big project, you've always needed to learn how to marshal many, many, many, many people. That has been the way that you get leverage when labor has been shaped as it has been shaped. Well, that's changing its shape.

52:00Eddy Lazzarin:And so now you should learn how to harness this new thing. There's a new surplus. Learn to exploit it. Like that is the lesson for a young person. It's not that things are over. That's that's just like black pill garbage. Like that's like that's a ridiculous, ridiculous. I cannot condemn it enough. That's people trying to sound smart about being all negative or whatever. Now you've just been told you have superpowers. You've just been told you can have multiple employees for$200 a month. What do you do? And they're a little weird, by the way. The$200 employees are strange. Okay, well, now learn to talk to them.

52:34Robert Hackett:One way to summarize it is essentially, look, the apprenticeship might be dead, but the real work is beginning, right? So what used to be an old phase where kind of doing groundwork or kind of side-by-side, you don't need any of that anymore. If you're passionate about building, even hardware, I think a lot of these domains that used to be technologically harder to tackle for someone, if you have the curiosity, now they're really yours to grab. If I were to classify it, the most positive thing coming out of the model is this idea that the cycles on experimentation are going to compress and people are going to be a lot more able to scale their ideas rapidly into things in the real world.

53:16Christian Catalini:Eddie, are you seeing this in the companies that you're assessing for investments? Yeah, completely. Of course. Absolutely. Fewer employees like than usual for an early stage company? I don't think I've seen a formalization

53:27Eddy Lazzarin:of the number of employees. I mean, I have seen over the years that pattern. Of course, we've seen like, as Christian reminded us, block cutting a bunch of people. Obviously, Elon did that with X and X didn't fall apart, even though everybody said it would, right? There are many such examples. So I think there's a lot of empirical support for that. I haven't seen a formal analysis, but look like Hyperliquid, Uniswap, like many companies in crypto are incredibly valuable despite having had less than 20 employees. We're still having fewer than 20 in some cases. So that just seems true to me, as a matter of fact.

54:03Eddy Lazzarin:There may be a radical example I haven't quite seen, but I've seen glimmers of is a single person or a duo of founders who have been able to go from their idea to a live product that is working and serving customers in a matter of weeks or months. Like I haven't seen many examples of that yet, but that seems like a this year thing, like a happening now thing, not a maybe, well, maybe five years from now. That is like happening, happening now.

54:31Christian Catalini:Eddie, you also mentioned the black pill and how you reject it outright, that it's not all over. There is a path forward. Christian, I want to mention last time I spoke to you was October of last year. and the book you had recommended when I asked you for a book recommendation was If Anybody Builds It, Everybody Dies, which is perhaps along the black pill genre of AI books out there.

54:57Eddy Lazzarin:I also read this book. I'm not going to take too much time in the podcast to disparage it, but I will say that Nick Bostrom, whose book Superintelligence, I feel treats this topic the most carefully and sort of formally and philosophically. It's actually a great read, even though I disagree with elements of it. Even Nick Bostrom has changed his tune on this. He recently had a paper basically analogizing the choice of whether to pursue superintelligence in this sort of broad, extreme automation as not a choice between build a bomb or not, which is how many seem to frame it, like in the book you mentioned.

55:37Eddy Lazzarin:Should we make a bomb that blows us up or not? This sort of stark, obviously good or bad decision. Instead, Bostrom now frames it as a patient who is terminally ill, going to die, but we can choose to perform a risky life-saving surgery. And what he's trying to say is human beings are doomed already. And I don't mean in some cataclysmic way necessarily. I'm not saying he means it that way. Just, you know, we're all mortal. Like we are all going to die. Right. I mean, in just a standard memento mori type way. And if we want to try to treat that and we want to solve that type of problem, we need incredible works.

56:23Eddy Lazzarin:So why not? Why not take the shot? Why not take the shot? I find that very convincing. So in some way, obviously, I'm not I'm not trying to rob him of his nuance. Bostrom's thinking is very, very, very thorough and fascinating. But I think even he, in some senses, flipped a little bit on this equation.

56:39Christian Catalini:Bostrom, who gave us the paperclip thought experiment of a rogue AI that maximizes paperclip output and in the process vacuums up all of the resources in existence to do so.

56:54Robert Hackett:Yeah, I would say that the Trojan horse externality in the paper is definitely inspired by the paperclip analogy, right? It's this idea that there's going to be side effects. And this actually brings me to open source. I do think very much like in crypto, open source is going to play an important role here. The gist is essentially that if you believe that some of the defenses on the proprietary models are easy to circumvent anyways, then the value you get from deployment of open source in society may actually give an early signal of how these systems can be abused and build the countermeasures.

57:29Robert Hackett:What I liked actually about the audio book was the single idea, and I disagree with a lot of the conclusions with most of them, that these models may pick up preferences, and we've seen it in the wild, that are almost like side effects. And some of these might be minor, some of these may be major. And as we deploy them, we may not be aware of those preferences in the system. And I do think going back to what I think it's important to do now is that that verification infrastructure is almost the antibodies for the side effects. Part of it is going to be experimentation on open source. some of it is going to be crypto primitives, some of it is going to be better tooling, to be honest, than we give engineers and everybody else that uses AI to make sure that when they're automating, they still have some oversight and they can steer in a line.

58:18Robert Hackett:When you combine those both together, I think we're drastically reducing the cost of a massive, massive failure.

58:24Eddy Lazzarin:I actually wish we had even more time to talk about this side of it because it's so, so, so interesting. Take what we're talking about, Robert, that if it's possible for only a few people to make a company, then there will be many, many, many, many, many companies. And I don't mean like gig economy, someone doing sort of a simple type of labor that is easily kind of commodified and understood by a larger network. I mean, complex work, maybe potentially lots of complex work. And if that's the case, you need coordination across many of them. And coordination is very complicated. You need reputation, you need identity, you need provenance for types of data, you need provenance for types of payments.

59:03Eddy Lazzarin:We talked about this insurance idea. it gets incredibly complicated. And maybe if moats are harder to form, as Christian was alluding to, some of the things that we thought may be moats may actually be easily dissolved by AI, then there may be fewer majorly large platforms that can sort of coalesce energy to solve these problems, coalesce focus to solve these problems. So what you'll need if you have all these companies, many, many, many complicated challenges, and it's difficult to form specific certain network effects to coalesce solutions to solve them, then you still need networks. So the blockchain networks end up being this very attractive thing because they're credibly neutral.

59:46Eddy Lazzarin:So all the individual agents and actors in the system, I mean, can scrutinize them for their neutrality. No, they're not necessarily being rent collected by using them. And they may want to coordinate around these things for, well, exactly what I was saying, Information sharing, payments, insurance, provenance of data. There's just a lot of things you'd want to do with them. You know, why worry about trying to figure out the exact reputation of the 50 billionth company you've interacted on this thing, when instead you can trust some smart contracts and some verifiable AI models to ensure that the exchange happened the way you expected and payment was tendered as needed.

1:00:25Eddy Lazzarin:Right. So it's almost a little inevitable to me. I feel that blockchains end up being a very, very big part of the story. If there's a lot of complexity, a lot of fragmentation, more verification needed, more financialization of services rendered. I think there's a lot to disentangle there.

1:00:43Robert Hackett:I completely agree. And to some extent, I mean, it really boils down to, do you believe that intelligence, the relevant intelligence, right, for making decisions, for creating productive outcomes, is going to be fully centralized in a supermodel that's AGI and that's the only one, you know, or ASI eventually. That's the only one that gobbles up everything. Or as we've seen to date, right, where the gap is maybe months, right? Even between some of the open stores. And of course there's problems with some of that is being built right now, not respecting intellectual property. But putting that aside, if you believe that intelligence is going to be more distributed, then I think the future you're describing is inevitable, right?

1:01:21Robert Hackett:Because you have all these pockets of relevant intelligence in the economy that will need to transact with each other, will need to trade. And yeah, I think we've been building in crypto the rails and the infrastructure for that for a long time.

1:01:34Christian Catalini:So I think it's going to become a lot more useful. Christian, having done all of this research and investigation, how are you taking the findings into your own work, your own life?

1:01:43Robert Hackett:I would say I already hinted at, you know, with our kids, a big part is, OK, accelerated mastery. They're in the driver's seat, even if they're little. I think that director's role is something we need to train for really early. And I think a lot of the education system is optimized for the opposite. It's optimized for making them, you know, actually perfectly automatable. For me, it's just, you know, pushing myself to rethink every time I start a flow. It's like, okay, this is how I used to do it. And I like that it's highly verified at the end. But do I dare taking a little bit of risk and just automating more of it?

1:02:20Robert Hackett:So it's uncomfortable, right? Because especially if you strive for really good outcomes, Sometimes you're like, well, should I do this or not? But I think it's the only way. And last, I think I've been thinking more about what are the gaps. It seems that AI is creating, like any great new technology, all sort of side effects. And often those are the shovels in the gold rush that are worth building on. And so thinking more about what will society need? What are the things worth building? And yeah, why aren't they here yet? So the classic exercise of like projecting, you know, a few years into the future.

1:02:51Robert Hackett:I mean, at this point, two years is like two or three and working backwards. But honestly, a lot of all. As you see these systems and look, we couldn't have written this paper without all of them. Gemini, HGPT, Grok, Claude, of course, they were great co-authors. At times, you know, they went off the rails and they kept deleting pieces that we needed into it. At some point, we had left some Easter eggs for LLM's reading it. and I was having this conversation with Gemini and Gemini really surfaced the fact that she you know whatever enjoyed the Easter egg and it had a super sassy comment I'll post it when we share the podcast

1:03:35Christian Catalini:Are these the equivalent of prompt injections that you like hid inside of the We didn't leave a few

1:03:41Robert Hackett:but it was kind of a moment where you could see the intelligence it was definitely creative it was really insightful It was one of those defining moments in the writing of the paper. It's like, okay, you feel really like a peer, not like a tool. So fascinating stuff.

1:03:57Christian Catalini:To the extent that you used AI in the creation of all of this great work, I could not have wrapped my head around it without those AI tools as well, which they held my hand and broke down all the concepts for me along the way. So it was useful on the other end too. And I just want to also highlight the fact that you've done all this investigation into the economics of AI and its impact, and you work in crypto. I think that is an interesting testament to where value could be in the future economy that you're still staying in there, right? You're still going to work in this field.

1:04:34Robert Hackett:Again, we said this in many different ways, right? Through the podcast. The two technologies are complementary and if anything, I think we will see really soon. As some things start breaking in society, a system that we used to rely on will not work anymore. Yeah, well, we have the primitives in crypto, so it's going to be quite an exciting time for anyone building in this space.

1:04:53Christian Catalini:All right. Well, anybody who wants to read this paper, it's called Some Simple Economics of AGI. Highly recommend you check it out. There is some alpha in there that could maybe affect your life and what you should do with it. So give it a read and thanks for tuning in. Eddie, Christian, thanks so much for your time. Thank you. My pleasure.

1:05:19Eddy Lazzarin:Thanks for listening to this episode of the A16Z podcast. If you liked this episode, be sure to like, comment, subscribe, leave us a rating or review, and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts, and Spotify. Follow us on X at A16Z and subscribe to our Substack at a16z.substack.com. Thanks again for listening, and I'll see you in the next episode. As a reminder, the content here is for informational purposes only. should not be taken as legal business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund.

1:05:58Eddy Lazzarin:Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details, including a link to our investments, please see A16Z.com forward slash disclosures.

1:06:17Thank you.