The Intersection of AI and Crypto: What Worked, What Didn’t, and What’s Next | Roundup

It just takes one or two breakouts like maybe it's some vibe coding thing, maybe it's some prompt thing like who actually knows what it is where a bit of luck and a bit of skill and then the whole AI space is talking about it as well. So it be a breakthrough not just in crypto but also in AI. So there's always that small chance and that's why I think it keeps people interested in the ecosystem.

Hey everyone, quick disclaimer before we get into today's episode. Nothing said on bell curve is a recommendation to buy or sell securities or tokens. This podcast is for informational purposes only, and the views expressed by anyone on the show are solely our opinions, not financial advice. Our guests and I may hold positions in the companies, funds, or projects discussed.

All right, everyone. Welcome back to another episode of El Curve. You got me, Miles, and Zave. Fellas, how's everyone doing?

Doing great. One short week off. Missed you guys less. We got a we had a full team offsite in Costa Rica, so I have a good excuse. But didn't get any Tanner, unfortunately. But yeah, I have a good excuse. What you guys What happened over this last week with the two of you?

I'm in Australia right now, so calling in nice and early for this podcast, which is nice. So appreciate you guys in the afternoon over there on the east side of US. But yeah, here for the summer. So, getting into routine this week and yeah, it's been been nice to be back back home.

I've been freezing in Boston. It's been absolutely miserable, but yeah, no, year's off to a good start. No more dog bite incidents or anything like that. We're getting over it. So, no more. Sorry, buddy.

Well, I think we can start the zero off with a bang on the bell curve side of things by tackling, you know, we hinted at the end of our commercial open source software episode that we're going to get into this intersection of AI and crypto. And, you know, I think you can look at crypto and then eventually AI is the next big iteration of software as an industry.

You know, I think crypto took and ran with open-source and definitely created more opportunities for monetization. And we talked about L1's breaking some really interesting trade-offs and but AI is redefining the game of software at the same time and there are implications from everything like just SAS as an industry and like pricing doing kind of you know fixed recurring revenue versus outcomes based pricing.

There is, you know, people are looking at how like how product works within an organization product and engineering and you know really you it was a very flashy part of the industry going into 2025, this intersection of AI and crypto. And I think the thought was, hey, there is this AI thing that's happening and this crypto thing that's happening. And man, wouldn't it be even more exciting if you smash these things together?

And I'm curious what the retrospective was for the two of you. First of all, maybe we could just start with why were people excited about this intersection outside of potentially token go up, right? Maybe there are some bad reasons why people were excited about this, but what was, you know, what's the retrospective on why this might not have taken off as much as people thought and what was the initial real kernel of excitement for where this made sense in the first place?

Yeah, I'm happy to kick it off. I think in general, like you said, crypto and AI is a massive intersection, and I think speculation maybe took everything away from where it actually was. you had a lot of AI meme coins launch virtuals launched and there was a lot of hype around kind of Agents but back then Agents didn't do a whole lot since then actually virtuals has enabled Agents to do more things and it's quite interesting to follow but outside of that there was only a few successful kind of breakout projects another one was Bit Tensor which we'll touch in this podcast but not a whole lot and there was a lot of venture funding that went into this and particularly on the infrastructure side which I think looking back on maybe in the last 2 years, it's hard to find many successful crypto AI infrastructure and maybe people jump the gun a bit in terms of like where the value might actually be in the crypto AI space.

Like I'm I'm quite bullish on kind of like the agentic layer and the application layer and I think the infra layer in in crypto AI is actually commoditizing quite fast apart from kind of building the guard rails that make us as humans trust what the Agents are doing for us in crypto itself. So we had a bunch of startups working on layer ones kind of like agendic frameworks etc.

But I think one of the main reasons where it failed was really just in terms of the complexity of what we can do with these Agents on chain. I think right now like most of us probably don't use Agents in our day-to-day life and that's a problem because in web 2 many of us are using Agents in different ways sometimes without even knowing it.

So I think crypto has a long way to go in terms of like understanding the demand and then actually being able to serve that demand with frameworks and infrared applications that people actually want to use and pay for. So I think it's just a market misunderstanding exactly where the demand is coming from and speculating on where it might be and actually realizing in hindsight that it wasn't really there in the first place.

Like I think there's just so much noise in that like first generation of of the crypto AI intersection. And my like I don't know read from from a distance to be honest with you was like okay we see this AI is finally kind of here. It's being led by huge centralized research labs.

And I think it was like a a fleeting moment where it was like, okay, can you really trust these infrolabs? Like, do we should we have a decentralized version of this, right? That's, you know, accessible to everybody, open source, like, you know, running on open source like models. And you get like some, you know, like sort of trust minimized guarantees by what you get back, right?

And the problem was like the like rate of acceleration and progress from the centralized orgs was so much you know larger than people kind of expected. And so if you looked at it from afar and you're like okay you know I'm going to ask like an LLM you know a question. Obviously the one that's a centralized or is going to give you like the fastest answer, right? Rather than having a bunch of like folks all run that same question and give you back something like that is they all agree on, right?

And they're going to improve the models faster, right? And crypto will always lag. And so like the just like decentralized version of let's say like LLMs or LLM training just didn't really play out. But I think like we've kind of turned a corner here where there's I see it as like three categories that are interesting.

You know one is more like using crypto like incentives and coordination mech mechanisms to bootstrap a supply side that can you know of of like AI inputs right that you can then build a product with that's like the Bit Tensor side of things the other is just adding like additional trust assumptions or like trust guarantees to AI and then the Third one is just AI like Agents that can move money on chain, right?

So we'll cover all of this stuff, but yeah, I think there's like it's becoming clear where this stuff where it's actually valuable. And this is not like if you talk about crypto and AI, it's not just one thing. It's many many things. So yeah, I think just add my piece here too.

I think this is a little bit of a tale as old as time when it comes to I think we're over there was a lot of infrastructure that needed to get built out and you know we we'll talk about how effective something that we say often is crypto is amazing at coordinating incentives and that's true in some not all instances and I think if we look at the last couple of years the levels that needed to get built out first to make AI usable just in this chatbot that early instantiation that we currently have were really like like close to the metal like you know FPN heavy that type of stuff.

So like if you were to break down the AI value chain and everything that needed to work, there's the compute kind of like the atoms and silicon layer. There's the infrastructure layer, so like data centers, you know, getting these massive clusters of GPUs and making them network faster. There's like the model development layer which is actually tinkering with them like these individ like these foundation models.

There's the inference and then real- time kind of intelligence layer and then finally like tooling Agents and middleware and those needed to kind of proceed in chronological order. And I think we're going to, you know, unpack maybe the inference layer, but I think what's clear over the last couple years is that the first four value layers there were just not ever meant to be coordinated by crypto.

And I don't think there's a lot of great evidence yet like we'll talk about this with DPIN that crypto is capable of coordinating that in a decentralized incentive based way better than a highly function highly functional centralized organization. And I think you know so there's just what is the right coordination mechanism for all of these resources and when it's super fixed asset heavy I just don't think crypto is well suited to do that and then also in order for these things that where crypto might be a really interesting use case and fit like Agents we needed to build out all this other stuff first and also I don't think we're giving enough credit to like crypto being s I don't know if you guys remember there was a whole narrative around AI is private or open AI is private but you can trade X, you know, this little AI shitcoin. That was, you know, I think that was the prevailing reason why people were actually excited about it more so than the actual tech.

I would pause it to all of you guys. Yeah. And I think might just to briefly add on to what you were saying, if you look at the kind of AI stack, you know, starting from the bottom, you have kind of compute networks and you have kind of training and inference on top of that, more like agent platforms, etc. on top of that making use of the infrastructure itself and then you know apps on top of those Agents and I would say maybe for us in in crypto like for me it makes a lot of sense that Agents will use crypto rails crypto rails are 247 they're permissionless they're much faster than what we have they're open they're not siloed there's a lot of properties that crypto gives that in my opinion Agents will need long term but in terms of where crypto can compete with AI I think the other layers like you were saying Mike just then the comput the training, the inference, etc. It's very, very hard to compete with centralized incumbents that have billions of dollars they're throwing at this.

And then crypto kind of copies centralized incumbents and they try and bring it to crypto, but it just doesn't work the same as what we have outside of crypto. And so then really as in terms of opportunities and where crypto can push AI forward and where the intersection actually is I would probably make the argument that I do think it's actually at the application layer at that agentic layer using crypto rails and giving users a better UX and they can get even in the centralized landscape and I think that is really where Emperor could have been built around and is still being built right now but that is kind of a more emerging area in the crypto and AI space that we'll touch on in this podcast I'm sure that I I personally think is the most interesting where crypto can actually compete.

I have one thing that I rarely ever hear people talking about and there's a little it's related to what we're doing at Blockworks in very early stages, but I actually I I it's been very difficult to put these words into a category, so I haven't I haven't heard anyone talk about this, but I mean Miles, I think this is going to come up when you talk about kind of guard rails and provenence for data for crypto.

But one, Ribbit put out a piece and I'll link it in the show notes here, where they connected the idea of tokenization as we understand it in crypto with tokenizing. Tokenization as it exists within literally tokenizing words and phrases for LLMs to understand. And the connection in their mind, which I thought was beautifully succinct and really powerful, was the idea of making the real world machine readable.

And that's a very simple concept, but it's a super powerful concept in my opinion. Because when you make essentially everything, and in the case of crypto, that's mostly financial data machine readable and very easy to interact with. You kind of completely redefine what software means and workflows that you build.

So I mean literally if you just think about the the process of what does it mean to create like a piece of fintech or financial software today you would kind of go with this very historical product approach and you look and you'd create a user you know problem and then you'd create this big PRD you'd build software and you kind of think of engineering on the back end versus the way that this might work in the future is like one beautiful thing about crypto data at least in different virtual like VMs is that it's standardized And if if there are Agents that are actually building software in real time, you might actually not start with or worry too much about that because the user might end up building a lot of custom things. It might be all about how you structure this standardized data.

It's a it's a super unique thing that only exists in crypto that your core critical company data everything about your costs your revenues there is offchain data so there's asterisks to this but huge component of that the whole history of your business could be machine readable and you could easily ask an LLM to build workflows on top of it I mean it is the it sounds esoteric but the idea would just be like if you think about this from the user perspective is instead of having one piece of software ware with a ton of seats. You pay to have your data standardized, versioned, authenticated, structured in such a way that an LLM could easily query it, come up with customized workflows, and answer any question that you'd have in the whole history of the org.

It's like it's a totally different way of I think the idea there is personalization. you're you're going to get much higher degree of personalization and time like quick decision-m once Agents are taking these actions on the raw data that's been structured in a certain way. So I think the way that we build products and especially SAS is going to get completely reinvented and it's going to happen on crypto rails first because this very unique property of data but also because crypto is mostly startups and you don't have this incredible tech debt of massive you know multinational orgs which are you know have like their Oracle onrem software and like all these different licenses and they're going to have to untangle that and it's going to take forever.

So yeah, I think crypto foreshadows the future in a number of ways. And I think just by virtue of it's a young industry and has this really unique property around what the data looks like that you're going to see what the future of organizations look like here first before anywhere else.

I think Mike, I actually might throw a question at you here which the audience might be interested to hear about. I mean outside of crypto they talk about AI and you know how Agents are going to sort of completely overtake SAS and you know to some extent block works is a SAS company. Are you thinking, you know, as a crypto company, are you thinking about using AI to replace some of your more software as a service or like how do you see it disrupting Blockworks right now? Like the entire AI category if you have to throw it back on on that?

I think where the space is generally going to move over a long period of time is like outcomes based pricing. And it's like in this rivet piece I thought they did a really good exa like it's a high level framework but I think it's a useful one is thinking of businesses as token factories. You have a certain number of like you know you essentially are requesting a specific output and there's a certain amount of computation that happens before it outputs something.

So, I think that it's going to be so the cost is going to end up being so variable in this new model that you won't be able to do standard pricing the same way you were doing within SAS. And this like per seat model where you kind of lock everything in and try to jam people into like too much structure I think is going to break pretty soon.

So yeah, I kind of agree with all these trends that you hear about that I think it'll take longer like we're still have SAS products with subscription revenue and I think that that's going to it'll take a couple years for that to really change. The other thing too is I think buyers are very used to paying in a subscription form and like you kind of need big companies to innovate on this first. it doesn't make sense to move on things like SAS pricing before like a big incumbent like a Google or an Apple or something like that moves first just as a strategy.

So and to double click on that though like what would that mean? That would mean like okay now the majority of people are not coming to Blockworks directly. Maybe they're asking an Agent to go do write like a pull data for a research report or pull data that informs like actions on their behalf. And now you have to like change your product to be like, you know, have like agentbased interfaces, right?

And I think one of the big pain points right now is everybody having to give the API keys to their Agents that they pay for, right? And that's where X42 will like I think come in pretty interestingly like down the road is just moving over to like usage based models. So it's either usage or outcome based like I totally agree.

Yeah, outcome is really tough to me. I think everyone's going to want to go towards outcomebased. The problem is quantifying outcome based is really difficult and it that actually is based on a company taking the time to define what an outcome looks like. Right? So this is actually one of the big geniuses of Google is they were the one not only did they create the best search engine that ever you know like advertising engine that's ever existed but they also taught marketers how to do attribution.

So, not only did they create the best thing, but they took the time to put together you know a framework for how to do attribution for marketers and then they educated everyone. So, they wrote the rubric, right? They built the machine, then they wrote the rubric and they don't get quite enough credit for writing the rubric. And it's it's difficult to measure something like an outcome, right? Like let's say you are building I don't know some sort of marketing module or like marketing piece of software where you're trying to acquire customers.

Attribution is super difficult there. How do you measure an outcome? you you need you know it software providers will eventually define a benchmark or create a methodology for but it takes time to educate your customer there I think it's you know the way I could see it developing is like it's really what you pay for like the outcome maybe that you want you're willing to pay more for it and maybe it's better defined and then you pay a certain amount for that and then if it's not the outcome that you define maybe you pay a bit less for that because the Agent can't execute on what you want.

I think Mike you also touched on a few interesting points around like relevancy and so if We move into this world where Agents are the ones that are actually paying for maybe something like Blockworks, how can a company like Blockworks make sure that when an Agent is scanning the internet that it is pulling from Blockworks data over maybe some other peers in the space and like do you have to pay for that to get in front of the Agents? Is there some sponsoring ads going on there? Like it's unclear as well in the AI space right now what this even looks like and how things will become relevant.

We're kind of going through that Google moment all over again, but in this time for AI. And so I do think in terms of crypto and AI and opportunities like this is potentially one that even centralized crypto companies, incumbents like maybe someone like Blockworks can get very right and really actually become like a behemoth in the space by understanding like how to get in front of the Agents at the right time to get their data in the hands of these Agents because Agents are hungry for data at the end of the day.

So like I do think that the crypto AI landscape is still new. Mike, you said before it could take many years before maybe you switch over to a more agentic kind of software. And I think that's really where we are in crypto and AI overall where there's been a lot of experimentation, but realistically, I think we're probably still two or three years away from understanding exactly where it's going to hit the hardest.

Why don't we break down some of the major categories that people have been excited in or excited about in the past? And why don't we start with this idea of coordinating compute? Because this was like we talked about this a little bit at the top of the episode, but I think the very first the very first application that people were interested in was render aos Jensen these sorts of protocols where you're it's kind of like filecoin but for you know GPUs and the idea here being you know instead of centralized training you could maybe do the painting a broad brush like some of these categories moved up into inference and but just for painting with a broad brush purposes decentralized training and physical infrastructure was a huge one.

And then you had some you had some other uh you had some other protocols like move one layer above the stack there and get to inference. So this was this is a little bit where I is currently playing kind of not 100% but kind of you also had like ritual if you remember that that that protocol and yeah there are some other there are some other pretty interesting one like some one that flies a little bit under the radar is like hyperbolic is an example of this but and then Bit Tensor was this kind of you know incentivized marketplace for bringing you know inference inference models to bear.

So can we start with the first category here of just decentralized compute I'm just to not bury the lead this one doesn't seem like it worked super well. So what what went wrong here do you think in this category or how how much how how much do we like this idea of you know bring like leveraging incentives in a decentralized way to like to improve AI outcomes?

I mean I think uh yeah there's it's like some very very important nuance like we were just talking before the show started you know you're not like the the product experience of asking you know openai or claude you know a question and getting it like a response back is always going to be you know you asking like you know and a validator set of 150 like machines to do like the same task check each other and then give you back some result, right? And so you're not going to like pick the shittier product like product for ideological reasons.

And I think where like I think Bit Tensor has found its niche is it's the like amount of money and time like and resources that would go into standing up a new AI based service that is not built on one of the big incumbent labs, right? Is like in getting more and more and more expensive, right? In the same way that like you know it's the classic deep end like sort of thesis like if you were to try to stand up a new cell network around the world that would be a herculean task that would require you know maybe definitely billions maybe tens of billions of dollars.

And so where Bit Tentress kind of found its niche is like, well, okay, you don't want to be totally dependent and get on on like one of these big models that could just rug you at any point in time or lock you. And you don't want to go out and have to raise like a billion dollars to build like a defendable, you know, business with a with a like the sufficient amount of resources that you would need to actually like stand it out, right?

And so we've just got a big pool of operators, you know, people running machines kind of waiting around for jobs. They get paid, you know, some financial incentive of like the Bit Tensor token, right, to go do a job for a particular service. And that isn't just like doing, you know, running models. That's also like, you know, collecting, you know, data, right? Or like labeling data. It's doing like testing. It's it's a bunch of different stuff and so like you know Z you've dug into it more than I have but it does feel like you know Bidens kind of stood up like this general purpose you know uh coordination layer that if you want to build a like a new AI company today and you don't want to go raise ton and of like an enormous amount of money this is kind of like an accelerator right for your supply side but that does not mean that the product that you're operating has to be decentralized itself, right? The product you can build a product in totally centralized way.

And you're just kind of, you know, paying for these resources to this open supply side and that's seems like it could be viable. But yeah, what's working, what's not working, Dave? Like is how different from the ones that fail. Maybe just to touch on two. So like a Bit Tensor versus like on a cache here. I think a cashache you know massive props to that team known them for many years and have a lot of respect for them.

The hard part with comput networks is that it's kind of a commodity compute itself and so like what's stopping the next person creating the next decentralized compute network and how you going to compete on costs and like what is the infra that you actually own underlying that is it tokenized like who's the actual customer etc. And also is it cheaper for like longstanding workflows which are needed continuously instead of like having to set it up per workflow.

On the other side you have like Bit Tensor which I think you know Mike was asking earlier in this podcast like is coordination a thing to do incentives work? I think to the most part it doesn't actually work. I would make the argument actually a bit tenser. It does work and it's actually something that I think should be studied still and it's something that I am studying right now in terms of like I didn't spend enough time thinking about like why it exists or why it's still working and why there are so many builders still going there.

Realistically, I think what Bit Tensor does well and maybe better than just standard compute networks that are maybe more commoditizing is it incentivizes miners to provide very very cheap compute using their token and they kind of play these token crypto games to get what they want and then as a result people who actually want to pay for this who have the model for example that need to train they're willing to go to Bit Tensor because it's probably a lot cheaper. I haven't done the maths here, but I expect it to be a lot cheaper than other comput networks because it's subsidized by the Bit Tensor token and then they kind of speculate on then the appreciation of that token and it's a classic crypto circle.

The question is if the token doesn't appreciate is Bitensor still as interesting and does a coordination work in itself? That's like TBD. However, just to finish quickly, I do think there are some subnets in the Bit Tense ecosystem that are showing very early signs of some level of PMF. And I think their target market is more like indie AI developers and there's nothing stopping indie AI developers now in this day and age with the amount of tools they have and it just takes one or two ideas with enough compute and and training etc to create a product.

The next question is can you productize it and make it feel like it's not a crypto bitensor AI app. that super difficult and maybe that's actually where the bottleneck is right now and the constraint in that ecosystem but that's how I see it. I could I maybe take the other side of why I actually don't think this is I I I am starting to maybe as a as a hot take. I do incentives work at all?

I'm actually not really sure like maybe in a very very select number of cases, but I think I'm starting to get on the train of incentives don't really work. And the reason why is the best thing that you could there's so much reflexivity in crypto. The very best thing that you could do for adoption of your product is to make your token go up. And as long as you're dumping out tons of incentives, even if you actually get more usage, your token will go down and therefore people will not use your product.

So you it's you're I think this is why there's why there's so much confusion behind the like my my this is one part of the reason there's confusion around this but you know my my metrics are actually improving but my token price is going down. It's like yeah because you're paying a ton of money for that for that activity right you're you're just paying for that activity. So you know I I think it does come down to the maths here.

And yeah, I think there's we people should just start looking at this stuff as though it's actually cash compensation because it is. So, I don't know. The Bitcoin people would push back like that is that's worked out pretty well and it's because there's like a cult sort of, you know, belief around this token. and like what it represents and I think you know Bit Tensor folks are like I mean they're they're into Bit Tensor like there are entire funds just dedicated to stacking TOAO and you know I think it's it's super interesting and I think like a lot of us faded it because it was mixed in with all these like VC backed you know typical like turned out to be kind of scammy or like at least low you know short-term plays that the original crypto AI like narrative.

And you know, Bit Tensor is not VC backed. It's kind of got like a little bit of hyperlquid, you know, sort of lore to it along with like um, you know, obviously trying to play off the the Bitcoin history. And yeah, I mean that you have to like I agree with you. Unless there are people that are actually just not selling the reward tokens, you know, immediately or like, you know, they're selling maybe a little bit of it, but yeah.

By the way, I don't know what like Bit Tensor's chart looks like. It's probably not amazing. But it's actually a lot better than most other crypto projects, I would say, in the last 12 months. So like they've held pretty steady and that's why I think it's interesting because it hasn't been negatively impacted by the current cycle.

And I do agree Mike like you know I think we're both aligned that demand matters again more than anything and you shouldn't just incentivize with tokens on the supply side and get people doing certain things without anyone actually wanting to use it in the first place. We've seen that fail many times. The only time I think it works in crypto is actually if you subsidize enough and you have a goal to achieve and that goal is PMF and actually getting people paying for this stuff and like actually earning you know whatever revenues, fees, etc.

Bitensor has this subnet ecosystem that you know I think it's like 128 subnets that are allowed to go live there at least for now. I think they're working on trying to increase that, but if they manage to get 128 subnets, it's kind of like throwing darts on a dart board where like you just need one or two to stick and then probably it gets talked about in other AI circles. And I spoke about this with my intern yesterday. Shout out to Spencer. He's been doing some research for me and there is one I can't remember the name like right now, but there's one that Subnet that did better than Claude Code for in terms of like performance and what you can do and that's like very cheap to use and was very cheap to build.

And there it just takes one or two breakouts like maybe it's some vibe coding thing, maybe it's some like prompt thing like who actually knows what it is where a bit of luck and a bit of skill and then the whole AI space is talking about it as well. So it be a breakthrough not just in crypto but also in AI. So there's always that small chance and that's why I think it keeps people interested in the ecosystem.

But is so that that is kind of like the the thesis here, right? that they can offer because their their supply side like their capex is being subsidized by Bit Tensor, right? they should be able to build products much much cheaper which means I could sell it for much cheaper and so like that is the thesis in my like what it seems like is like you get these you know potentially more success in like the long tail of like services right but they are going to be cheaper than you know a company that had to dole out like $2 billion of training costs before they could even release the product right and the question is like is that cheaper going to come at the cost of like quality latency like reliability because it is, you know, an open supply side without SLAs's and all of that good stuff.

And it sounds like, you know, there are a couple projects out there that are like giving uh building really performant, you know, as you just mentioned, save like products. And they were able to sell it much cheaper than their, you know, centralizing like counterparts or compet competitors. So that that to me is a thesis and like uh maybe I'm missing something, but it's interesting and it's worth paying attention to. If they can win demand, right, because it's cheaper.

I wonder if again you know this reminds me of a little bit is when crypton natives were trying to sell Ethereum they used all these words like credible neutrality and you know you had a gajillion ways of saying we have the most validators at the lowest cost. I mean like all these buzzwords and all this stuff and now that you actually have financial institutions building it's unclear that they actually care about that.

But what they do care about is one property which is that it's 24/7. Like that seems to be the the killer app, right? And it's unruggable and all this other stuff. I don't want to trivialize any of that, but I think that's you know when you hear people from the like wow this stuff just happens 24/7 and that that seems to be a huge component of it.

And uh you know I wonder with some of this you know with the Bit Tensor stuff I wonder actually if the killer feature is that it's just close to being on chain and the proximity of it these models existing on chain already. And um you know I think a lot of crypto