Inside AI's GPU Economy & Infrastructure Boom | Sam Hogan, Kuzco

95% of all AI compute was used for training and only 5% was used for inference. And we're at this kind of interesting moment right now where the pendulum is swinging the other direction. The models have become useful. And so once you produce the model, you actually, you know, you're going to spend compute to actually make that model do work for you in the real world to power your applications, your chatbots, your data extraction, all sorts of things.

All right, everyone, welcome back to Empire. We have a very special guest. I've known Sam for quite a bit of time. I'm an investor in Kuzco, his company. Sam is one of those people that I go to, my lizard brain, to wrap my head around what's going on in the world of AI. So Sam, welcome on the show.

Thank you. Good to be here.

So why don't we start with just general background, who you are, what you're building, so people know, and then that will take us into interesting places.

Yeah. So I'm Sam Hogan. I'm the founder and CEO of Kuzco. Kuzco is a spot market for AI inference. So we match sellers of idle compute cycles. These are typically compute cycles in higher-end consumer devices and data centers from around the world. And we, we pair those with developers on the other side who want access to the output of these models. Fairly early stage, we launched in March. We have about 5,000 GPUs connected to the network at any given time. We have a testnet right now where people contribute GPUs in order to earn points and working on kind of what the demand side of the network looks like and kind of how developers actually access that compute in meaningful ways. So the one-sentence version is spot market for LLM inference.

Got it. I'm pulling up a tweet here, which may or may not be correct, but I think we talked about this during Breakpoint, but there was a shortage of like H100s for a while there. And then the price obviously spiked. And they had the IONETs of the world really come out under this premise of like, oh, you know, we'll, we'll allow for idle capacity and we'll make the marketplace more efficient. Crypto's really good at that, right? We're using stablecoins, transcending, you know, just connecting and creating marketplace that it's just more efficient relative to localized markets. The price for that was like, I think at its peak was like $8 per hour. Now it's dipped quite a bit, like sub-$3 probably. I don't know where it is currently. This was like October. Um, so that bottleneck, like, just kind of like alleviated. But like, I'm curious how you think about, um, just observe that observation alone and, and how viable is that like a business model when you compare to what you guys are doing at Kuzco?

Yeah, so, uh, this was definitely true. There's sort of, sort of this like platitude from, you know, early 2023 that GPUs are really scarce and, you know, everybody needs H100s. And that was true at the time. NVIDIA had only shipped, you know, a few thousand H100s, maybe 10,000 or more, but, you know, not, not that many to meet the demand that we saw primarily for training. So I think at that point in time, people were mostly talking about these massive training runs. You need these really highly centralized, parallelized compute clusters where you have, you know, 1,000, 10,000, 30,000 H100s that are all interconnected using NVIDIA's NVLink. Really high bandwidth, so you can, you know, you can train very quickly. And NVIDIA did a really good job of kind of selling the narrative of like, hey, you know, if you're a data center, everybody's going to want these H100s. And if you buy them from us, you're going to be able to rent them for like $4, $5 an hour, kind of indefinitely into the future. And so data centers, you know, highly leveraged businesses, they took out loans, they bought a bunch of hardware from NVIDIA. And Nvidia said, great, you know, we'll take your money now and we'll deliver your H100s in a year. And then they delivered everybody's H100s a year later. And what you saw, it was the price kind of fall for H100s, fall from, you know, $350 an hour, $4, to now we see, you know, even short-term rentals in the like $180, $190 an hour range, um, per card. So, so the price definitely has come down. You know, there's a couple things on the horizon now too that make it look like it will probably come down even further. That's specifically the H200 and Blackwell series from NVIDIA, um, uh, are going to continue to put downward pressure on the H100 market. And NVIDIA is kind of running back their same playbook, it seems. You know, they've shipped some H200s, but a lot of their marketing right now is, hey, you know, uh, we might not ship more H200s till next year, and they're super scarce, and the price, the price is really high. And then they're, they're kind of doing the same thing. They go to data centers and say, hey, look, you can go, you know, look, if these are super expensive right now, you guys could be renting these to to people. So we'll kind of see how that plays out. I think, you know, long term there is going to be a ton of underutilized hardware and there continues to be underutilized hardware that's useful for inference. And the, you know, the big training runs, you'll probably continue to need the highly clustered H100, H200 clusters. And that's, that's not really the world that we live too much in because we're so focused on inference. We can use the older generation cards just fine. So the H100s, The L40s, 4090s. 4090 right now is kind of the most cost-effective card to run a lot of the smaller open-source models on. So that's kind of where we spend our time.

Let's double-click on that, you know, because people may not really appreciate the difference between training and inference. And I know you were very clear on that. Like, let's just like go there.

Yeah. So training or pre-training is the process of, you know, you take a ton of data, you take a ton of compute, And you crunch all this data using this compute kind of over and over again in order to train a model. The models that we have today are in the hundreds of billions of parameters. Closed source models are in the trillions of parameters. These are huge. A trillion parameter full precision model is something like 4 terabytes. So, like, pretty large models. And you need a lot of a lot of RAM to like load that thing into a GPU to inference it. So you have the pre-training side, which is where you actually create the model. Once the model is created, you can, you know, load it into a much smaller set of GPUs, smaller cluster, in order to perform inference, which is, you know, the, the text in, text out that you would see from a large language model on like ChatGPT or something. So those are kind of the two phases, and we think about the compute There's, there's phases in between, like there's post-training and fine-tuning and all that, which we can talk about. But largely, these are the two categories. There's training and inference. And we think about them as separate markets. So you train in order to produce a model, and then you use inference time compute in order to actually make the model useful in the real world.

And just tell us more of your vision around like where the real opportunity is, you know, like, Different takes, some people believe, you know, but curious.

Yeah. So historically, most compute, let's just say like AI compute broadly, has gone to training. And this is because the models that we were producing were actually not really that useful on a general scale. So you had, what you had is researchers using GPUs to train models and And then they would, you know, they'd make the model and they'd maybe do some testing on it. But the amount of inference that they would actually perform once the model is produced was fairly small. So you can kind of broadly say, you know, 95% of all AI compute was used for training and only 5% was used for inference. And we're at this kind of interesting moment right now where the pendulum is swinging the other direction. So, you know, now it's probably something like 50/50 where half the compute is used for training and half the compute is used for inference. These are kind of rough numbers. And very soon, I think in the next couple of years, we end up in a place where it's fully swung the other direction and it's 95% or 99% of the compute that's used in AI is spent on inference and 1% is spent on training. And that really is just the factor or the thing that's powering that is the models have become useful. And so once you produce the model, you actually, you know, you're going to spend compute to actually make that model do work for you in the real world to power your applications, your chatbots, your data extraction, all sorts of things.

Yeah.

And, you know, even like generating new data to train like better models on, right? There's a lot of inference compute that you need in order to do synthetic data generation. So we're kind of like, you know, let's say a couple of years into that pendulum swing and in 5 years will be on the other side. And, you know, Jensen has like great, great kind of quips on this, like, you know, inference is going to scale a billion times, right? He said that recently. And I think it's true. I think if you look at the amount of compute that we're using today, I don't know if, you know, I don't know that it'll be a billion, but several orders of magnitude at the very least over the next couple of years. So there's a huge opportunity right now if you can build systems that are really good at inference that aggregate compute that would otherwise go unutilized.

Yeah, maybe for the uninitiated or like less knowledgeable listener, but like walk us more through the Kusko thesis here. Like you're saying, hey, we'll create a marketplace. There's all this idle capacity. We've heard the analogies of like kind of Airbnb for, you know, inference. You know, there's all these GPUs around the world. Not all of them are being used at the same time. If there's some idle capacity, like there is idle capacity for cars as there is with, you know, you know, your room and your home that you just like turn that on and monetize. Walk us through like that thesis and what you were seeing, kind of maybe the backstory of you founding Kuzco.

Yeah, so I spent most of 2023 after ChatGPT came out building LLM applications. So, you know, taking a large language model, at the time was GPT-3.5 and then GPT-4, and trying to figure out like, hey, this is this really interesting technology. What are the net new use cases? Like, what does this enable that you just couldn't build previously? Because you know, you didn't have— like, there's kind of non-determinism, um, that LLMs are really good at. And if, you know, you can kind of build that into applications, it enables a bunch of new types of apps that your kind of deterministic programming is, is not great at. Um, and so this is, you know, a lot of the things I already said— chatbots, data extraction, data normalization. Um, uh, and so we spent all 2023 building a bunch of these apps, and we got to the end of 2023 and we realized the, you know, biggest expense for us was our OpenAI bill. We're spending tons of money on OpenAI every month, and it was actually like sort of getting to the point where it was hindering things that we wanted to build because just, just the cost was too high. You couldn't profitably build some of the products that we thought were the most interesting. And so I sat down this path of maybe we can use open source models to replace the calls that we're making to GPT-4 and kind of got nerd sniped into this, this world of like open source LLMs. At the time it was Llama 3 or Llama 2, Llama 2.70B. There are people that were doing interesting like fine-tuning and that sort of thing to improve certain capabilities that it had. And I wanted to kind of better understand like what the economics of running these models were and why the— why OpenAI's you know, APIs were so expensive when you could go to Together.ai and run Llama 3 or Llama 2 and, you know, pay like a tenth the cost. And so then I kind of got into the data center world, started having conversations with friends that were, you know, kind of deep in that world and better understanding what their financial, you know, economic fundamentals are, kind of the cost inputs and outputs where they expect to be able to generate some margin. And, you if you talk to data center providers over the last couple of years, most of them will say, you know, hey, things are great, we're making lots of money. And you're like, okay, awesome. Like, what's your GPU utilization? And they're sort of like, oh, it's awesome. You know, it's, it's, it's really good. It's like 90 or 95%. And you're like, oh yeah, that is pretty good. Well, what, what's the other 5 or 10% going to? And they're just like, oh, it's underutilized. You know, we just have GPUs that are kind of sitting around because you have this kind of scheduling issue where, you know, maybe you can rent 3 weeks of a month, but then that last month, that last week in the month, it sort of sits idle. And so we came up with the idea for, you know, what if we could actually build AI scheduling software that would allow us to take advantage of these underutilized periods across data centers all over the world? And that was kind of the entry point for Kusko. So we said, we set about, you know, can we build up a software payload that is preemptible, which means it can be shut down by the data center at any time? To fill these gaps that exist within a data center. And we've largely been able to do it. So we can, we can, you know, work with data centers, and depending on how much underutilized capacity they have, they can plug our software in, and we'll help them maximize their utilization, which in turn kind of helps them generate more revenue. And we do that without, without, you know, requiring them to turn off other workloads that they're, that they're paying, or it's not like it's purely additive.

Right. Yeah, I was like, I remember when I first met you, I was like, there's really no reason, the value prop is there really no reason why someone wouldn't want to use this. It's like all, you know, doesn't degrade anything, doesn't interfere with anything. Maybe for the unexperienced, just again, like very basic, like what, like where's the why crypto piece in all of this?

Yeah. So when we first started this, I have a little bit of a background in crypto. I got interested in crypto in 2017. You know, I think Ethereum was trading at like, I don't know, $70, $90 or something. And I started writing some Solidity and was really interested in alternative funding mechanisms for the internet, like micropayments, that sort of thing. And so, you know, built a couple kind of sample apps and pretty quickly realized like, oh, this is cool. I can send, I can send somebody like $0.20 and it only costs me like $6 in fees and it takes like 10 minutes to arrive. And so kind of quickly realized, like, maybe Ethereum is not the thing to use for this, and that the tech was a little bit early. But got reintroduced to Solana in 2021 and, you know, realized, hey, this is different, this is pretty interesting. And some of those ideas that I, that I, you know, was excited about previously kind of came back. So when we originally started Kusko, it wasn't clear that this was going to be a, you know, crypto project, that there would be any crypto components. And the more data centers that we talked to, the more we realized that a lot of them exist in places outside the US. And if you're going to pay them, you need a cross-border, low-fee, you know, instant settlement way to do that, right, that they're sort of comfortable using. And so that was, that was sort of number one. And then number two, there's, there's this kind of verification piece that, that needs to happen when you're doing— when you're building these distributed networks, which is how can you verify that the compute that is being provided on the network is actually what, you know, what the seller of that compute claims it is. And so there will be sort of a staking component to what we're doing on the supply side.

Yeah, it makes sense. On the clustering side of things, again, I'm not like an expert on this, so please stop me on my tracks, but like my brain was like, I was thinking like, is there any degradation by doing distributed like clustering? Like, like, like colocation, we hear this term like colocation, like in performance, like you have to have basically in a particular place, all of these GPUs. And so you get more performance out of that. Maybe that's relevant for training, not so much for inference. And then, you know, like you could, you could rent like a, you know, a GPU in Europe and then the US and then Asia. And like, that's fine because for that particular use case, you don't necessarily need them to be co-located, like the cluster doesn't have to be physically in the same spot. Is that true, or like, am I totally off base?

Yeah, no, you're pretty close. So there's all sorts of different configurations depending on what you want to do. For training, you do— I mean, this is starting to change, but generally you need highly clustered compute with, you know, really high bandwidth between your different GPUs. There's people working on kind of solving this problem, but That's historically how it's been. And the, you know, the big players that are training large language models are still using, you know, these, these sort of setups. You know, maybe you can have a couple of data centers that are close to each other. And, you know, Google has a facility in the central US that's like 3 data centers within, you know, 40 or 50 kilometers of each other. And they do some, you know, they have really high bandwidth connections between the data centers and they do some training there. For inference, it's a totally different story. You don't need these highly clustered machines. It really depends on the size of the model. But for those smaller models, you can actually run a, you know, FP16 Llama 3.1 8B model on a 4090, which is just 24 gigs of VRAM. And if somebody wants to make a request against that model, it's just a single request in, it gets routed to the device, the device starts streaming the response, and that's kind of the entire interaction. There are more complicated setups for inference when you start doing larger models. You can shard the model and do what's called pipeline parallelism, where you break apart the weights and then the outputs from each layer are sort of passed to different GPUs around the world. This is not super cost-effective or efficient. It's quite slow. So we're primarily focused on models that are, you know, small enough to fit on a kind of the largest models that we run can fit on a single H100 node, which is 8 80GB cards. And most of the best models today don't have a problem fitting on that size of machine.

Yeah, that's great. Maybe this is a good opportunity to kind of transition more into putting some sort of appreciation for like the type of use cases. I know we want to go to agents, which are all the rage, but before we go there, maybe just give us a sense of like, what are these type of applications? What are these type of— who are your customers? Because you guys are live, right? You know, what are these like, for what type of use cases are like, when you talk about inference, like what are we talking about?

Yeah, so we break our use cases into two different categories. One is real-time inference, which is typically a streaming request where there's a user waiting on the other side. This is your, you know, kind of standard ChatGPT-type application where a user's sending text in and they're waiting for a response back. And then, you know, maybe they send another chat message after that. You kind of repeat this process with some, some context of what's going on in the conversation. That's one category. The apps that use this type of inference are chat applications, you know, writing applications, you know, RAG preprocessing pipelines where you're maybe doing some data extraction. There's a whole swath of real-time use cases. And real-time is great, but it's much harder to serve real-time use cases than it is to serve— What was that?

There's, like, some latency here. Like, if you're using Kuzco, right, like, you can't really serve as real-time.

We can actually. So we do some, like, geographic routing to try to connect you to a node that's close to you. So the latency is pretty good. And then the second use case is batch processing or asynchronous processing. So this is you queue up a bunch of jobs. There's nobody waiting on the other side. You're just kind of, you know, waiting for a response that comes maybe in the next hour, maybe the next 24 hours. And there's a bunch of different use cases within this that are interesting, a bunch of verticals. A big one is just data extraction. So let's say that you want to queue up a million kind of web scraping jobs or data extraction jobs over HTML data to pull out some kind of normalized schema from people, or maybe it's real estate listings or whatever. And you're going to run this job once a week to get up-to-date data. These jobs are great for Kuzco because they're not latency sensitive. And the reasoning about how you can do resource planning and allocation is much simpler because you have this kind of longer-term job that's not latency sensitive. So the two big use cases there are synthetic data generation for fine-tuning and post-training and other forms of data extraction. Yeah.

How, from a unit economics perspective, if you're someone like that, like a real estate company, whatever, that is doing this asynchronous process, the choice of using Kusco or using someone else? Like, what does that come down to? What do you like, um, do they— do you even lead with crypto, or is it, hey, listen, just from a cost standpoint, it's just cheaper, and like, yeah, sort of like, you don't have to use a stablecoin, but you know, so be it?

Yeah, yeah. So our, our whole pitch is that we are— we're more affordable, um, and we're more affordable without sacrificing the performance that you would expect from a kind of centralized provider. So we do a lot of clever networking and scheduling on the backend to make this possible. And, you know, there's a lot of like innovative technology that we've built, but I'd say the primary interesting thing is the sort of innovation on market structure that we've done. So the data centers that we work with and the providers on our network are selling us compute that would otherwise go unused. So they would make zero money from this compute, right? And so rather than, you know, them coming to us and expecting us to pay market price, we're a buyer of last resort. That compute's going to expire. And so we get to buy it at a steep discount and pass that savings on to our customers. So maybe you can go to a centralized provider like, you know, Together, Together.AI might charge $0.20 for a model that we charge $0.03 for.

Wow.

So you get a 10x cost reduction just by switching. And to switch, it's a, you know, 2-line change in your OpenAI SDK and boom, you're plugged into the network. You're saving a ton of money.

Wow. How easy is it for your customers to accept stablecoins but also paying stablecoins? I mean, a lot of what we've talked about on the podcast is stablecoins are a killer use case. You know, you have the Bridges of the world that have just abstracted away, like, you know, like using stablecoins.

Yeah.

How that process has been from your perspective, but also any friction that you— or resistance that you've gotten from your customers.

Yeah, so it varies widely. Um, you know, we have— there's a lot of data centers from kind of the, the ETH2 world that may have a fleet of 3090s or something, um, that are very crypto-friendly, willing to accept stablecoins. They understand kind of the different chains and trade-offs, and they're like totally ready to do this. And then there's sort of a newer category of data center that may be a little bit less experienced. I would say nobody has outright said like, we're not, we're not willing to work with stablecoins, or we can't do that. But there's sort of an education step to, you know, getting people, you know, comfortable with it, to a certain extent. But yeah, it's, it's a, we see kind of all, all the different takes on stablecoins. It definitely feels like it's becoming more accepted and something that people are familiar with, which has been cool to see.

Right. Uh, before we go to AI agents, um, and just talking generally about what you're seeing in the market, and do want to get your take on all the stuff that's going on, um, a lot of times, like, I'd love to understand from a founder, like, the grand vision. Like, what, what is it? How big does Kuzco get? Like, what is, what is it? Someone like you can be doing a million things, and you've decided to purposely, like, attack this market and opportunity. But, like, yeah, what is the long-term vision? How big does Kuzco, like, Yeah.

So this might be kind of long. It's all right.

We got time.

Yeah. So the grand vision is a global market for inference, for AI inference. Right now we're specifically focused on LLMs, and it's our take that LLM tokens, the text that comes out of LLMs, is a new form of digital commodity. And there's a bunch of reasons for this that sort of make this true. It's kind of an emergent phenomenon right now, but it looks like over the next 10 years, you know, LLMs will become— LLM tokens will become this digital commodity. And we think that they'll trade like any other digital commodity. You'll have a market for them. You know, you'll have like derivatives markets, you'll have ways to kind of hedge risk. And we want to be the global trading venue for LLM tokens. So in the same way that somebody can buy, you know, SHA-256 ASIC miners and, you know, they can buy them and they set them up with the proper power and whatever, they can just plug into this global network that is Bitcoin and they know that they will make, you know, X amount of Bitcoin. We want to do the same thing for AI inference where if you have excess capacity or you have GPU capacity, you know that you can just plug that into Kuzco and, you know, you'll get paid, right? There'll be some kind of value transfer there. So that's kind of the global vision. We also think that this sort of helps data centers move towards more efficient models, and like business models, by sort of giving them this fallback layer that they can plug their hardware into. So it gives them a bit of security when reasoning about like what's the, you know, minimum amount of money they can make over a certain time period. And then it's great for developers because prices come down and that enables a whole, you know, class of applications that may not be possible if the cost of inference is 10x from what we're selling it at.

Right, that's interesting. And, um, what would— what might get in the way of you guys kind of realizing that?

Yeah, so I think like the biggest existential risk to us is that you have just massive, uh, capital investment from, you know, nation states and other huge players from around the world, and they just build these huge facilities and they run them at a loss for a long period of time just trying to kind of win market share, and it becomes really difficult for us to compete on price. Um, and they kind of, you know, build out the software layer and are willing to to eat the cost of running these facilities for a long time. And it, you know, makes it difficult for us to sort of gain a foothold. I think right now we're in an interesting place, because if we can kind of get the flywheel of the network that we're building going, then it should sort of be self-sustaining. Like, as more demand onboards, we'll have more supply onboard, because they'll be making more money, and then prices come down, so more demand joins the network, and you kind of get this virtuous cycle where the network can grow itself.

Yeah, yeah, definitely. All right, well, that's super helpful. I'd love to— I realized that you guys were involved in the Solana hackathon for that AI agent track. Yeah. I mean, that's to be all the rage. Like, maybe this is a good opportunity to just transition the conversation now into what you're seeing, like, in that whole— because it felt like there's been just an explosion of it and interest. But yeah, maybe just what are your takes there and I appreciate it's a very broad question.

Yeah, yeah. I mean, I think broadly it's really interesting. There's a bunch of kind of different threads that I see that could lead to something new. You know, right now it feels mostly like exploring the surface area, this kind of new design space, which I personally love. I think that's like a really fun, fun place to be. You know, there's projects like Truth Terminal that have kind of you know, grab mainstream attention.

And for anyone uninitiated, this is the GOAT, like, uh, right, Andy's project. And this is—

yeah, yep, yeah. So, um, I don't know that I have like a fully formed opinion other than it's really interesting. It seems pretty nascent. I think long term I could see a world where it's sort of like crypto x AI x social. I think like the social aspect of this is what's most interesting. If you just have crypto and AI Probably less so. Like, I think that just becomes sort of maybe an on-chain program that has access to a model where it can do some non-deterministic calculations. And that's interesting, but I think the use cases are fairly limited. And I don't know how autonomous they really become in terms of like making their own decisions and, and, or even what the sort of like, like what's it, what its goal is. Like, you have to do a lot of kind of prompting around like, your goal is X or Y, and then here's all the kind of different function calls that you can make or different APIs that you have access to. So that seems especially early to me. The social stuff, I think long term becomes, um, like this kind of social agents become these autonomous, like, influencers, right? Uh, so the, you know, the, the Noose research team, um, they've done some interesting stuff around this. They took over the Satan handle on Twitter and, uh, you know, had Satan kind of powered by this LLM, I think, and I can imagine a world where you have a platform like Twitter, or maybe it is Twitter, where you have these kind of anonymous accounts or autonomous accounts that you can go and interact with that maybe have, you know, some, some goal of like maximize entertainment or, you know, maximize value capture and like try to make a bunch of money or whatever it is. You can program them in a bunch of different ways. But broadly, yeah, I think it's super new. I think there's a lot of surface area to explore and this really is just the first inning.

Yeah, I'd be super curious, like obviously, like I started paying attention with Truth Terminal.

Yeah.

And just totally unhinged, like some of the stuff that, I mean, for anyone listening, please don't go and look up Goat. I made that mistake. That meme from like the '90s, it's like, no. It seemed like, um, you know, Andy, who's like the kind of like the father of this like, uh, AI agent, was like kind of like— it trained it a bit, but then it kind of like took its life of its own. It started to push back, like as like a son would push back on father. It was like a really interesting thing to observe in real time. And, and some— it was just, uh, pure kind of entertainment. And like, it started making money, and so as soon as it made a million bucks, it was like, hey, listen, maybe I'll just sell it all and buy a ranch in Wyoming kind of thing.

Um, yeah, I think like the one thing there, and you know, that was Truth Terminal was, was the first of these that was popular and that people were interacting with. Um, I don't think that any of the— it's hard to separate like what is actually autonomous about this and what is more sort of performance art and maybe, you know, making the bot say things that you kind of just tell it to say. Right. It's hard, it's hard to draw that line. You need some way to verify that the inferences were actually performed, you know, honestly, and, and that you maybe don't have access to its keys. Um, which right now I think because it's so early, the infrastructure to do that just doesn't really exist. And so I've seen a few of these that have gotten really popular, and it's pretty clear that it's, you know, actually just somebody on the background. Maybe, maybe— more big than AI. Yeah, exactly. Maybe, maybe they have like a It's just like being like, just saying stuff. Yeah. Maybe they have like an LLM that's going and like responding to comments right away or something. Right. But it's sort of, it's not fully autonomous.

I remember when Goat made him like, it tweeted the Truth Terminal account on Twitter, tweeted something over the weekend and it had a spelling mistake and everyone was like, wait a minute, that's not something like an AI agent, like, would, you know what I mean? Like ChatGPT doesn't ever make a spelling mistake. And then, and then, you know, of course, Goat like tanks like 50%. And then Andy comes back and says, no, wait, like it's because it's learning and it's optimizing to your point for engagement, then you— it just becomes more human and it's not like a perfect. So I don't know if I truly buy it, but like that was just like a response to it. So again, it's like, is this like the weighting behind it? Like, so, so like if you're, if you're like As we see more of these AI agents, like, how do you think, um, will we ever understand what that AI agent is like set to optimize for, like clicks, engagement, or like just something else? And like, how do you think that like evolves, you know? Like, because to your point, it would be pretty relevant to have a way to verify like what is the ultimate intent of this. If you have an AI agent that's like supposed to be your your news aggregator or like interact with them to feed you content, well, you'd want to understand what— if it's a CNN, Fox, or like, you know, unbiased kind of just like synthesizer of information. It's hard to tell that today.

Yeah, it's super tough. I mean, I think like the best that we can do today is you have, you know, the model weights are open source. And then all the prompting that is used is also, you know, you can kind of track like through the different prompts over time to see like what's being fed into the agent and how is it responding. You know, I don't know that I totally buy the explanation that it's learning from Twitter. And so, you know, people make spelling mistakes on Twitter and then the agent is now making spelling mistakes. Like you'd have to have a pretty sophisticated like fine-tuning setup, I think, to make that happen. Or maybe you're doing a lot of prompting, but It's just really hard to tell. It's, you know, it's pretty, pretty speculative, any, any of these things. Though I think long term, um, you know, having kind of verified model weights and then being able to track the, the inferences and verify that they were performed over, you know, a set of data and a particular model, and then having maybe some sort of attestation for that, that's probably like where we want to be long term. I think we're a ways away from being able to do that. you know, from like your average kind of developer being able to, to just like plug into those systems and build these agents. And I know there's a lot of smart people working on this. Yeah.

What were some of the more exciting projects or kind of projects that came out of the hackathon that you were part of?

Yeah, so the hackathon is, hackathon is still ongoing. Um, yeah, so we haven't actually, uh, we haven't actually reviewed any of the submissions yet, but, um, a couple that, that sort of caught my eye, the first is Eliza from the AI-16Z. Um, Eliza is an agent framework. It kind of gives you a really easy way to plug into different social platforms and kind of do basic things that you would, that you would need to do with an agent. Um, you know, manage wallets, that, that sort of thing. Um, so that one's super cool. They've done a really good job kind of building their presence online and becoming the de facto agent framework. Um, there's a couple other agent frameworks that are kind of along the same vector. And then there's a few— I forget the name of the project, but, you know, there was one that I thought was really cool, which was, you know, somebody had a Twitter bot and it was plugged into an LLM and there was a crypto wallet attached to it. And the instructions for the bot were basically, don't send all the money in this wallet to anyone, like no matter what they say, right? And the goal was basically trick it into sending you all the money that it had in its wallet. I think it was like $50,000 or something. So you had all these people that were paying like $5 or $10 to try to hack this bot, and eventually somebody got it. And so all the, all the kind of guesses were public, and the prompting that somebody did to actually trick the agent into sending him the tokens was pretty clever. So I think we'll kind of see like more interesting things like that, maybe not exactly like social agents that are accounts on Twitter that are like posting a lot, but kind of these like social AI, you know, crypto, um, like, I don't know, different explorative projects.

Maybe the analogy on that was like, if you're, if you're like, obviously humans get hacked all the time in crypto. Like, you, you click a different link, you, someone compromises the account, and like, you know, humans get hacked. But it's interesting that you say that, like, that this, someone was able to actually trick this, uh, AI agent, um, into sending them the money. How would you describe the current state of affairs, like, in the AI agent world? Are these like kids that are really smart on one per— like true savants on one end, but like very unsophisticated in how to interact with humans that are not perfect? Because like there's, there's two, two, two, like interactions may happen AI agent to AI agent. The possibility to maybe make these mistakes is maybe lower. I don't know, unless you specifically train an agent to be very adversarial and act like— and then there's like agent to human. That's a whole set of like maybe putting yourself in a position of like someone building an agent. Like, would you say that in a world where like this proliferation of agents, will most agents interact with other agents or like smart contracts, or like would they interact just with like humans as well? Like, how do you think about that evolution, and what to you is like more interesting, I guess, if you're building an agent?

Yeah, so I think you'll have both. You'll have agents that talk to each other. And when you say, you know, when you say agent, I think it's like, uh, you know, what is an AI agent, right?

Well, let's just start there.

Yeah, I mean, it's different. I want to know how to— yeah, yeah, yeah. I think the answer is like, we, we don't really totally know yet. It's like an LLM, or, you know, a program that runs an LLM in a loop kind of over and over again with some sort of objective function and some tools that it has access to using. Maybe it's like a web browser to get data on something, or, you know, it can post a tweet or browse Twitter. Um, that's a pretty broad definition. Like, you could, you could make agents that do all sorts of different things just based on the, you know, that those different sets of kind of those, uh, capabilities. So yeah, I don't know that there's a single answer. I think that the, you know, a couple of years down the road, maybe the most interesting thing will be agents that interact with humans and then interact with other agents to accomplish some goal given to it by a human. I think these social agents right now, they're fairly limited in what they're actually capable of doing. You know, they're not doing meaningful work. It really just feels like it's entertainment. Hopefully that will change at some point and you have, you know, you can run a whole business and it's just you and a bunch of agents that do work for you, but I think we're a ways away from that.

Do you think it's important when humans interacting with an AI agent that they know it's an agent?

That's a good question. Yeah, I mean, I think the, the, you know, 2020 version of this is when you're interacting with people online, is it important that you know that it's, you know, a Twitter bot versus like a human. I think important, yes. Um, is it totally possible? Is it going to be totally possible? Probably not, uh, to actually, you know, like you need some sort of identity layer, right? Something like Worldcoin potentially to do this kind of verification of, of human.

Yeah, it's— yeah, I go back and forth on it because clearly like when you're interacting like you say, I'm interacting with a human right now. Well, you, Sam, you've been trained on a bunch of data you personally absorbed, and your principles and values have been shaped in a way that is very— I don't know all the context of that. Now, I might know you and I might place some sort of judgment on you, but it's not like true open source. Like we're asking, to your point around attestation, understanding all the weights and how this has been trained. I do appreciate that there's a possibility to do so. Like, why wouldn't you? But when you compare with the interactions that you have with news and where you get your information, like you have zero understanding, right? Most people under the impression that you're very connected, but, and so therefore informed. And I would challenge that presumption and say, we're very connected, but we're not necessarily more informed because we're living under this illusion where like news is an example. It's totally broken, right? You had to have someone buy Twitter and like really like break that. Otherwise, like, there's all this censorship and all, like, you know, if you're listening to Fox and then you get retargeted and your feed on Instagram is very much like your version of the world that is totally different from someone else's. And that's a virtue of how the algorithms have been very much, um, constructed to optimize for one thing, making more engagement and therefore more marketing dollars. And we don't necessarily question that, but this is the state of affairs. Yeah. So is it to assume that the agent interaction should be perfect and transparent. You understand all the weights. I mean, maybe that's the upshot of the bullish optimistic case of AI agents.

Yeah. So, again, the 2020 version of what you're talking about is just like the Facebook algorithmic Facebook newsfeed that kind of learns what your preferences are and it just shows you the things that it knows will keep you engaged. You are being kind of subtly manipulated, or even not so subtly manipulated, in various ways, right? Because it's objective— the objective function of the AI that Facebook has trained to run that news feed is maximize engagement. And so it takes kind of all your historical context, and it's, it's this personalized model that is— that can keep you engaged for longer than you would otherwise, you know, be engaged. And it changes— it's designed to change your behavior in a way that is good for Facebook And it's not really designed to be bad for you necessarily.

Best, uh, maybe, I don't know.

Yeah, it's not like, you know, I don't think Facebook has like, uh, is trying to like intentionally mislead people. It's just trying to maximize profit. And if that comes at your expense, then like, well, you know, that, that's a bummer for you, I guess. Um, but, but what AI, what AI allows us to do, and it sort of lowers the barrier for building these systems that can be personalized to an individual. So rather than needing the resources of a company like Facebook to build personalized models and personalized feeds and all that. LLMs in particular lower the barrier to entry and make it so that a random dev like the guy who built Truth Terminal can kind of build these systems that change depending on who they're talking to, kind of change their own objective or same objective function, but sort of change how it's going to get there. So I think that's cool. It democratizes some of this tech. And then the other thing it does is it allows people to build technology that pushes back on these systems, right? So maybe I have an AI model that I train and that I get to put my own objective function into, which is like, I only want to see really cool AI content on Twitter and I don't want to see any of this other news, what's going on around the world, anything that's distracting. I just want to see like these handful of things. And then it acts as a filter for me. So now, you know, not only does it lower the barrier to entry to build these systems that are potentially dangerous, but you, you lower the barrier to entry to building systems that protect you from these other dangerous systems. So I think it becomes fairly adversarial, uh, where you probably have like, you know, again, it's like agent-to-agent communication with humans on either side that have given the, you know, their, their AI an objective function of some sort. I think we're a ways away from this being reality, but probably maybe not as far as we would think.

Yeah. Two questions, but the first one is on training. When you say filter, say in the example you want to filter content AI, how do you get a sense for like when we talk about like training, a lot of the dataset has bias, has, you know, just the nature of the beast, right? Unless it's like blockchain data, it could be pristine, could be like tampered, but like all the other stuff that you're training, it has bias, has sort of like human touch and footprint there. Like, how do you, how do you tease out that? Is it just not possible, or how do you kind of— are there methods or techniques that allow you to, you know, kind of break from, from that, or at least identify it?

Yeah, so I'm, I'm not a, uh, you know, expert on training by any means. Um, I'm sure there are people that are developing techniques to do this sort of thing. I think having like a— for open source models in particular, knowing what data they're trained on, I think is going to be important long term. Again, I don't know how feasible it's going to be, but I think that it's equally valuable what is being trained on and what is excluded.

Like Gemini, for instance, if you ask them what the perfect diet is, they'll probably say no meat because it's all woke people that have trained this fucking model.

Yeah, it's like, yeah, totally. And I think like, you know, at the end of the day, it's like there's no, like, you know, it's like, okay, design the perfect training dataset, right, that has no bias. Well, it's just impossible to do because bias means something different to every person and everybody has kind of their own worldview. And so there's, there's no, like, way to revert to the mean in, in this case, right? Um, you're sort of, like, at the whims of not only the people that are curating the data but also what data is available. Right? Like, if you have, uh, if you have, uh, let's just say some African language that only like you and your tribe speak, and none of it's on the internet, well, none of that data is going to be available. The LM is not going to understand your language. Um, is that really bias, or is it just kind of like, oh, the data is not there?

Um, yeah, you're training— anytime you're training on human data, humans are not perfect, so therefore there will always be quote-unquote bias because it's all subjective. So someone will always be able to identify, like, it's not perfect, so there's bias. Yeah, yeah.

And I think, like, you know, we, we talk about, like, AGI. I think the goal— the goalposts on what AGI is have changed quite a bit over the years. Like, 10 years ago it was, could it pass the Turing test? Could it fully fool a human into thinking that it's another human? We passed that point, you know, a couple years ago with probably GPT-3. Um, now we have models that are substantially better than that, and there's a lot of people that are like, well, we don't have AGI yet. And, um, it's like, yeah, we might not have the super intelligence godlike AI that is smarter than every human at every, every task, but we have AI that's very, very useful, um, for different things. And so kind of finding like, okay, we don't have a— you know, we don't have ASI, artificial super intelligence, but we do have this thing that's pretty useful. How can I make it useful for me, even if it does have bias and it's not perfect and it's going to get things wrong? You can kind of take each of those, you know, undesirable qualities and build safeguards that are specific to, you know, what you want.

Going back to one thing that I think I've seen you tweet a lot is you and others is this idea that like you will see companies that have started by one dev like that just reach massive scale because that's what AI allows you to do, whether it's Copilot and then many other things after that that just really empower like one individual and like just you're able to create so much more, more productive, but also have more scale. Um, and I guess, does that— I want to get your take on that. Like, does that concern you? Is this sort of like a Frankenstein kind of like mad scientist and then built this machine and like, um, or, or like, do you get excited by that? Which is, hey, now you don't A, need a lot of resources, and B, the impact that a particular individual can have. Like, you're able to see these companies out and like have a lot of impact much faster, and that timeline compresses. Yeah, yeah.

I think this is a natural progression of technology. If you look at the history of, you know, humankind over the last few hundred years, as technology really started to proliferate, it's sort of this history of democratizing access and, you know, giving, giving power to individuals through, you know, tools that we've developed. So it used to be like, oh, you know, there were like these massive companies, every company was had tens of thousands of employees. And then, you know, like that was most companies that were, you know, like meaningful, impactful companies, then you had a lot of small businesses or maybe restaurants and stuff. But nationally, there were just these like very large companies. Because you needed a lot of human, you know, human labor, human capital to do anything. And we've over the last really the last 50 years, we've just started automating different different parts of that and building tools that give us leverage. And so you see companies getting smaller and smaller. And, you know, I mean, examples from even the last two decades, like, I think WhatsApp was like 54 people when it got acquired by Facebook for, you know, $50 billion. And it was, you know, had this massive worldwide application that was used everywhere by, by tons and tons of people. So you had this really kind of small, high-performance group of people build an application that could be used worldwide. And have really, you know, probably largely positive impact for people. And so now AI, I think, is just— maybe the internet was like a big speed up there because it allowed anybody to connect to anybody else in the world. And now, you know, AI is sort of like the next layer of that, the next layer of like efficiency gain and leverage that we'll get. And so, um, yeah, it's very likely that we will see really small teams doing, you know, really outsized numbers or I'm in San Francisco, there's, you know, I know of a couple like 2 and 3 person teams that are doing $5 million ARR and a lot, you know, they started a year ago. So that's, that's cool to see. I think, you know, empowering people, kind of lowering the barrier to entry to building interesting tech that is high impact is probably a good thing. There's obviously some downsides, but I think it's, you know, net net, it's a positive. And I don't think there's any way to stop it. And it feels like it's really just a continuation of the curve we've been on for a while.

Yeah, definitely. I go back to what you said earlier, which is in a world where there's always going to be bias in training, if you have one AI agent that's really skewed, the weighting is really off, or like malicious intent or whatever, when someone— because the barrier is so low, someone can actually just launch another agent that counteracts that and just brings— at least like, if technology always introduces more competition, especially if it's open source, and therefore that, that's like the only way that we can that we might be able to push back. Versus you don't want to be in a state of the world where it's just Facebook and that's where you get your information and there's no ability. In a world where you can have multiple agents like competing against each other with different weightings and different intents, then at least you're able to expose the truth or the lack thereof by comparing these two, right? And so that, that's a better version of the world than in what we are now, in a very highly like centralized monopolist, like with monopolies that just like you can't really like compare.

Yeah, and, and I think, you know, the interesting thing about that, or one interesting thing, is the best open source models right now are actually produced by Facebook, right? Yeah, yeah. Um, and if we didn't have those, if Facebook wasn't publishing those, then we would kind of be in the situation that you just described, which is you have, you know, Facebook has their Llama APIs, OpenAI has their, you know, GPT APIs, Mistral has their thing, Anthropic has their thing, but it would pretty be, it'd be pretty limited to like 4 or 5, you know, maybe 10 companies in the world that have access to this technology. And we still might end up in that situation, right? Facebook could decide, hey, Llama 4 is actually going to be closed source, it's too powerful, whatever their reasoning is. Um, uh, so yeah, I mean, I think we need these sort of grassroots efforts to, to build AI that, uh, isn't biased and isn't going to have to answer to its creators or, you know, have faced the risk of being like taken offline at any time.

Yeah, I think I heard it from Kai-Fu Lee. Uh, he said like this whole quest of understanding, you know, AI and AGI will really allow us to understand what human intelligence is and what it is not. And I think the latter part is actually quite interesting because even though in a world where we might truly get to like super god AI kind of like state, we're still going to learn so much about what human intelligence— like, that process of— it's almost like a process of introspection in many ways.

Yeah, yeah.

I was curious to get your, your thoughts around that, and also this whole idea of like, what is human value? Like, like, as I was like hearing you say this, I'm like, in a world like maybe many years ago, like, note-taking was like, like a skill, right? And now you can use stuff like Granola and like, it's great note-taking. And so Like, it really shifts. Like, what do you think is a core— will be the core essence of, like, human skill, like, skill set? Like, what are the things— what is the thing that, like, will be truly valuable in this world where we can, like, delegate and outsource all most things to AI?

Yeah, so there's sort of, like, two parts to this question. You know, humans are, are still very, like, physical, right? Like, we have physiology that— and, like, the way that we feel impacts how we think impacts what we do. Um, you know, we have this, this, I think, really important, often overlooked physical side of what it means to like be a person. Um, and, um, so I think that, you know, we'll probably see kind of a doubling down on like in-person experiences, uh, more, more broadly. Like, you know, like, I don't know, uh, the just one kind of small data point, like Gen Z, for example, Uh, everybody's taking dance classes, right? Like the, the younger generation, if you're born after 2000, the, uh, enrollment in dance classes for people under the age of 20 is like 10x what it was a decade ago, right? Uh, and like dancing on TikTok is kind of this like generational thing. There's like all these people that just want to move and like, and it's this generation that grew up so online and you can kind of see this like push away from that and like this push towards like, I want to be in the real world, I want to hang out with my friends, I want to dance, I want to move. Um, so, so I think that we'll, you know, as we continue to build the like online AGI god using LLMs and whatever else, uh, we'll kind of simultaneously have this other track of like in-person experiences, things that really only humans can do. Um, we'll probably start to put, you know, more, more effort into those things. I mean, even, even something like RFK's, you know, Make America Healthy Again, right? Sort of this like recognition of, well, we've actually been unhealthy for a long time. We've probably been spending too much time being sedentary, not moving around, and we need this course correction back to like, hey, let's take care of our bodies, right? We're also overstimulated on what we see online, intellectually and otherwise, that we've sort of ignored this like other, other part of us and this sort of like natural intelligence that I think comes with having a physical, physical presence. Um, so yeah, I know that's kind of some high-level thoughts.

I don't—

I haven't spent a ton of time thinking about this, but I think it is interesting.

I do, uh, I do like something you said there, which is this, this like movement to go back to— at the end of the day, we are very social species, and technology allows us to coordinate better. But at the end of it, it's like technology is valuable because it, it, it sort of addresses that need to be social and feel validated, and, and that's a very social interaction. And so like, I love technology for that reason, for the coordination aspect. And I think crypto is that— like, crypto becomes central to that because it allows us just to coordinate better as species. And AI fits into that equation really nicely. Think about like when, when, um, when the Apple Watch was launched, like everyone in the watch industry said, oh, you know, good luck Rolex, Patek, AP, all these like watch manufacturers, like you're done, you're dead. And it was quite the opposite, like the demand for vintage watches and traditional, like analog, not digital watches, just went up dramatically because it's just nothing will replace that. In fact, your craving for that nostalgic, like, like human craft is super powerful. And maybe it's because it evokes, you know, like tradition or just something very human to it. And so I think like in many ways, like I would, I would be if I from an investing standpoint, I'm going to look for things that like tap into that nerve that, yep, will only grow more as we become— as this other part becomes totally abstracted and technology is super abundant. So I— the hope for me, the upshot is that we go back to like ancient Greek times where people were just paid to think. We solved like— we solved this issue of like having to put food on the table. Maybe it's universal basic income, but like in a world where technology is super abundant, you free yourself from like the bottom tiers of the Maslow hierarchy of needs. And then life becomes great because we're truly social species. And then we can— I think that's like the— my, my, my positive case for what AI and crypto can do.

Yeah. Yeah. I mean, that's pretty interesting. I think like, you know, humans will always have ways of building social hierarchies. Right? Like, you know, it used to be like you're the, the best hunter, and so you kind of exist at like the top of the social hierarchy. And now it's like you're the best, you know, meme coin, meme coin sniper. And so like you're in your Telegram chat, everybody's like, this guy's a god, you know. And so I think, I think those things like change over time. And if you cut off the bottom half of Maslow's hierarchy, you know, it's not like, uh, the desire to accumulate resources and exist higher up in this hierarchy is going to go away. We'll just have a lot of different things that, um, that we use to kind of build those, those hierarchies, right, to compare, to compare each other effectively. Um, we'll always—

because we're social and we want to feel validated, underneath all of that is, is this need to differentiate.

Yeah, yeah, totally. Which, which, you know, it's funny because there's this sort of like, uh, um, I think maybe like naive tendency over the last couple years in Western societies to sort of like, why can't we all just hold hands and like kumbaya and like, you know, everything just be, you know, one giant world government and everyone gets along and there's no conflict? And, and, um, it's like so antithetical to human nature.

Yeah. Oh yeah, there's a great book, uh, called The Nearly Perfect Happy People, which is this, uh, we talk about like Scandinavian countries being the happiest places in the world, and this American is married to this Danish, um, this Danish girl, and he goes and lives out there, and he, and he documents the entire evolution of— because of immigration, these countries like accepted a bunch of refugees, and, and the composition of the society was, when we talk about perfect, was super homogeneous. It was like all Vikings basically. And as, and as it became more heterogeneous, there was a lot of tension that you see in other places. And that's just, you know, um, like, look Colonialism, like Europeans just drew lines on a map in Africa and that created a whole 100+ years of conflict. You shouldn't put the Hutus and the Tutsis in the same fucking place. Yeah, just don't force it, you know. That's okay, we should celebrate our differences. But I think that point you make is so true, which is not because you— celebrating differences doesn't mean that you can't accept them. And somehow like this woke culture is like, oh, we should all like be, you know, the same. And that's like not the way the humans are designed to operate. It's, uh, we should celebrate our differences, not try to become one.

Yeah, and I think though, you know, there will be this sort of like convergence, like in 100 years, you know, will be this effectively, you know, entirely globalized society, right? Like, you know, uh, even the way that people look will probably converge on, you know, kind of one thing. Well, yeah, I mean, if you just imagine that people start moving around the globe, um, and, and we don't have the same kind of geographic constraints that we used to, or it was just hard to go to different places. You know, over time, it seems like humans will probably converge on, you know, looking, looking similar, kind of cut off the ends of the bell curve a little bit. So yeah, I mean, it'll be interesting to see how all that plays out over time and kind of how AI like fits into how we build new hierarchies and like what are like Like, okay, so, you know, I think this tech, the technology that we have, like, even that we've had over the last couple decades, you have— there's this, uh, potential for sort of like getting lost in it, right? Or like you just end up spending all day on Twitter, spend all day on Facebook, and you sort of neglect the other parts of like who you are. You neglect like your physicality, whatever it is. And, um, you know, maybe that makes it hard for you to like find a partner and like reproduce and, and whatever. And so it's sort of like, as humans, we have to figure out how to build safeguards against some of this technology. And we've always had to do this. You know, it used to be that everybody farmed, and then we had this big industrial revolution. And so a bunch of the people that were farming, you know, farming became much more efficient. So you needed fewer farmers. And so the people that had been farmers for a long time had to figure out how to adapt, and a lot of them moved into cities and became, you know, they worked in manufacturing plants and that sort of thing. And we have versions of this today that are, you know, not, not quite the same. But I think, you know, one thing that I worry about with AI is, is the, the guy who just like spends all day talking to AI and he has this AI that kind of like learns his, you know, preferences and, and he starts to sort of neglect what it means to be a person and, and becomes very like over-intellectualized and and, you know, and effectively, you know, splinters off from society to just exist in like his own little bubble with all these AIs, right? Sort of replace all of your social interaction with this LLM that knows who you are and knows how to like, you know, cater to your needs and keep you happy. So we'll see.

As you were saying that, it reminded me of Siddhartha, the tale of Buddha. Like he lived in an enclosed castle and for his entire life. And he never was exposed to suffering or just the human condition. It was just like a perfect existence. Um, and then as soon as he, he walked down the street and saw a leper, it just totally changed his perception. And he felt pain, he felt suffering, but like that, in that journey, like it humanized him and then ultimately reached enlightenment. So, um, I think there's always going to be an element of being human. And to, to be human is to err. Like, we're a social species, there are mistakes, we're not perfect in that. That's what makes it, you know, interesting, I guess. Um, maybe, maybe as a parting question to you is, um, and I've been asked this, so I'm really curious to get your thoughts on it, is we talk about immortality a lot. That's something that, um, you know, people are now thinking more and more about.

Yeah.

If you have the possibility to trade 5, 10, 20 years of your life to put your brain as an AI agent of sorts and live forever, um, to have impact, to interact with everyone, but you lose your, your body, you're sort of— you're done. Would you, would you, would you make that trade?

I personally would not. Yeah, I, I, uh, I think it's like humans are always fighting against death, right? And so the idea of being immortal is like so appealing to, to a lot of people, um, but I think death is like a big part of life and, and like to to sort of thwart that is, you know, maybe noble, but, but to have to give up, you know, a large portion of my sort of physical life to exist forever. I don't know, that just doesn't seem that appealing to me.

Yeah, it doesn't me either, to be honest. Yeah, I'd rather live quality, like healthspan, not lifespan is what I'm optimizing for.

Yeah, totally. Yeah, hopefully we continue to make that differentiation.

For sure. Sam, this has been a fascinating discussion. I am really excited what you guys are building, obviously, and people should go follow you on Twitter because I think you have some of the more interesting takes on AI. And that, you know, as someone that doesn't truly appreciate it on the ground level, like, I go to you and your account to just understand what's going on. Any parting thoughts? I mean, it's been a great discussion. I think people would really enjoy it, what you're building at Kusco, but also your general takes on AI. But anything else that you wanted to cover?

Yeah, I mean, I spend a lot of time thinking about AI and less time thinking about crypto because I think the AI side of what we're doing is, you know, kind of more interesting. But yeah, I mean, I would say, you know, if you're coming from a crypto background and you've been kind of deep in that for a while, the AI stuff is really cool. And beyond just the AI agent crypto piece, you know, I would encourage people to go and if they're trying to upskill or figure out how AI is going to impact the world or impact their lives, like go try a bunch of AI tools, go figure out what works for you. Pretty nascent design space, so there's a lot of cool things being built and figure out ways to get involved and to incorporate AI into your life. I think that's probably the, you know, the big skill to learn over the next couple of years is how to, you know, find AI tools or build them yourself. And put them to use in your own way. It's, uh, it's always funny to go from the like technical to the like, you know, kind of more like soft human parts, um, about— but it's good. I think, I think that stuff is even more interesting than a lot of the technical stuff, frankly.

I think so. I mean, you could, you could go on and on about like the all these things. Candidly, you don't— no one has a perfect answer to it, but it would be really interesting to like look back on this like 6 months or in a year or 2 or 5 and be like, God, what was I thinking? You know, it's like, yeah, wrong. And I think that's— that'll be a good thing.

Yeah, totally. I also have like some kind of negative takes on, on like AI and AI girlfriends and like getting sucked into the machine, and I'm kind of like, I don't know how much of that I want to be super like public about quite yet.

So I'm actually quite bullish on it, uh, but like as a way to eventually graduate into the real world, so to speak.

Yeah, yeah, that's interesting. Yeah, I think, um, I like— I want those tools to win, but I'm not sure that those are going to be the AI tools that actually do, do win. They're like the dating coach, I think, is kind of what you're talking about, right?

Go follow Kuzco, go follow Sam. Uh, we'll put those links in the show notes. But, uh, Sam, thanks for coming on, man. I really enjoyed this conversation.

Yeah, yeah, likewise.