Will Stablecoins Own Machine-To-Machine Payments? Ft. Christian Catalini

[0:00] The part that I do find intriguing though is machine to machine. And in crypto, it's funny, right? We we had all the right ideas at all at the wrong time. If you look back at, you know, when Balagi was building 21, he had this whole idea of like machines transacting for resources, paying humans to do tasks. Um, nothing came out because it was so early. But when I look at X42, you know, Cloudflare and many others coming around the idea that okay, the web is broken. How do we monetize data? Is there a better model there versus you
[0:30] know agents going around and capturing resources data you know activity whatever might be needed to deliver a service [music] or product that's where I do think a lot of the crypto infrastructure will be quite useful welcome to tokenized the show focused on stable [music] coins and the institutional adoption of tokenized real world assets my name is Simon Taylor I'm your host author at fintech brain food and head of market data tempo and joining me as always when we're talking about all things agentic commerce is bam Aizi, [music] CEO and founder of Mesh. How you doing,
[1:01] B? >> I'm doing great. I'm super excited for this episode. >> Yeah, we're going to go deep because joining us this week is none other than Christian Cassellini, the founder of the MIT Cryptonomics Lab, the co-founder of Lightspark, the co-creator of Libra. Yes, that Libra, and a former head economist at Meta. So, how you doing, Christian? >> I'm doing great. Excited for the conversation together. Yeah, likewise. Before we get into that conversation, I got to remind viewers and listeners that views and opinions of our contributors
[1:32] today are their own and might not reflect those of companies they represent. And please don't take anything we say as tax, legal, or financial advice. And of course, got to remind you that this episode is sponsored by Mesh. This episode is brought to you by Mesh, the first global crypto payments network connecting over 300 wallets, exchanges, and payments platforms. Mesh enables anyone to pay and get paid instantly anywhere in any asset. Mesh makes digital transactions
[2:04] seamless, secure, and universal, fueling the next era of aentic commerce. Learn more at meshpay.com/ai. Okay, Christian, let's get started. I mean, I read out the CV there and some of the jobs, but it might be the most interesting CV in all of finance. like MIT professor, co-creator of Libra, economist at Meta. What do you learn on that journey? Um, you know, like what are you picking up along the way and you know, give me give me the potted history
[2:34] here? >> Yeah, I mean, you know, they say the famous quote goes, you can only connect the dots uh looking backwards. I I'm not at that stage. So when I look back it just looks like a game of ping pong from academia to big tech to startup um to you know now really trying to figure out what what's we're doing next. I would say every piece of the journey um shaped me in different ways. I did love my years at MIT. I mean it's it's an amazing environment uh being next to researchers and and and trying really to get on the frontier with with a lot of
[3:06] really talented individuals around you starting with with our students uh many of which you know ended up going into crypto early on. Uh big tech was probably the one stop that was less of a fit. Um I I I find large organizations to be complicated and and political and and sometimes slow although you know Libra was very different at the time. I do love building. So uh the startup part is is probably the one that I will get back to at the right time. Right now it's just a very strange time to be thinking about what to build because on
[3:37] one side it's probably the best time ever and many have said this right the tools what you can even do as as as a small group of individuals is like 100x what was possible before on the other side you know when I uh decided to go part-time on on my startup light spark I felt like okay with more time I will be finally able to grasp what's happening on the frontier and every day that passes I feel like the closer I get to it the more it's just rapidly advancing ing. It's It's literally crazy how quickly things are moving.
[4:07] >> You feel the same about that stuff, Bam. I'm sure. >> Yeah, it's Everything is moving 10x faster. >> Kind of nuts, isn't it? And the fact that you held on uh to all of those jobs and all those times and still stay curious, Christian, is is I think what's uh what's fascinating me. You write a lot on Forbes and you've been writing a lot about Aentic Commerce lately. Um where did that passion for Agentic commerce come from? Like what what gave you the bug for Agentic Commerce? >> I I think I got the AI bug uh about a
[4:40] year and a half ago and it was most an existential bug. So not very original, right? The low-grade fever that many of us have felt and you know economists when when they when they do get that kind of fever cheat and the way we cheat is very simple. Um, I did the same when I got passionate about crypto back in 2013, which is like here you have a fuzzy object coming your way, lots of potential, lots of, you know, intriguing opportunities, but how do you make sense of it? And, you know, economists tend to try to boil things down to cost. If you
[5:11] can look at that fuzzy thing and really identify the one or two fundamental costs that are driving it, then suddenly you're like, okay, I may be, you know, wrong for a good part of it, but directionally I I kind of feel where this might be going. Uh, we did that exercise back in 2013 with crypto. I think we did get probably 80% of that right. And and of course the the 20% is the one that matters. Um, as as you realize once you're building uh with AI was the same. you know, if you want to look at your time, if you want to build
[5:41] something next, like what's worth still building? Why should you, you know, leave leave the AI labs to do or the fastmoving fintex to do versus what can a small startup do? It was a journey really guided by by that personal personal question. Also, I I have kids and so as many with kids, we're all like wondering, okay, what should they be learning? What should we nudge them towards, right? Everything that that was known before is now on the table again. And um a gent commerce is almost like the intersection of that that
[6:11] existential fear and what I've learned I guess along the journey within crypto payments stable coins and digital assets. So it's obvious to me that we're going into a future where the same way most traffic on the web today is agentic. Most commerce most interactions will be agentic in some shape or another. and and really trying to tease out, you know, what's the what's the hype and what's actually the ground truth of why is this interesting. I mean, S, you you've written way more than me on this and I love your decomposition of the different protocols
[6:42] and stacks. Um, there's just so much fluidity in how different players are coming at this, which makes it really fascinating. Um and then look when it comes to a gentic commerce for me it's almost like the the the old historical lesson that every payment revolution is a revolution in allocating risk uh when you think about the card network right the the airdrop in Fresno that Bank of America did uh on the merchant and the consumer side then later on eBay uh trusted sellers with PayPal right online
[7:13] commerce kind of slowly becoming safer and comfortable we need the same for Agentic which is like who's going to take the liability when your agent goes out and and does things. Um hopefully it's not going to be like the agent from OpenAI that you know decided that knocking on Hugging's face door was the best thing to do. Um but we we were missing a piece there. So the missing piece is the part that that I find fascinating. >> Yeah. Um so Kristen, I wonder based on what you're seeing in the market, what
[7:43] is the role of agentic commerce in the world of AI? What does agentic commerce mean? Um, and I know that you have a controversial view on that end like what we have today is not agentic commerce in a true sense. Um, why is that? Why why that ma what that definition matters and maybe you can talk a little bit more about your point of view there. Yeah, I would say probably something that's I I don't know a contrarian but definitely something that will not make some
[8:13] startups happy is that I do think that a big chunk of agent commerce uh it's going to be actually boring iteration of what already exists. Uh that's where you know fintex like stripe and and others um the credit card companies uh anyone that really touches money today on behalf of a consumer is going to do great. uh it could be a neo bank, it could be a traditional institutions, uh you know, once they get their act together, but like the idea that if I want to book a travel hotel or something, it's going to be some some
[8:43] weird crypto rail and stable coin. No. Right. So that that's not what what consumers expect. And so I I I think that chunk of aentic commerce, which is essentially doing something we're already sort of doing today with a lot more friction and time, I think it's going to go the way of the incumbents. Um distribution there is just going to play such an important role. And also the UIUX of of crypto is ne is never, you know, up to par on day one. It can get close, but it's it's it's not really a substitute. The part that I do find intriguing though is machine to machine.
[9:14] And yeah, >> in crypto, it's funny, right? We we had all the right ideas at all at the wrong time. If you look back at, you know, when Balagi was building 21, he had this whole idea of like machines transacting for resources, paying humans to do tasks. Um, nothing came up because it was so early. But when I look at X42, you know, Cloudflare and many others coming around the idea that okay, the web is broken, how do we monetize data? Is there a better model there versus, you know, agents going around and capturing resources, data, you know,
[9:44] activity, whatever might be needed to deliver a service or product. That's where I do think a lot of the crypto infrastructure will be quite useful. So final bifurcation between kind of the boring volume that we already know I think is going to go the way of the traditional systems the new stuff the wide space that that's where I think you know crypto rails and stable coins can play an important role. >> Where where does that tipping point come for machines buying stuff cuz X42 has largely been a memecoin story like most
[10:14] things. >> Yes. you know, and and but but I I I have seen a few dashboards now that are showing some signs of life. Um we've had the guys from AXTP on the show before and uh they're sort of seeing, you know, real volume of buyers, not just like uh sellers or, you know, people doing buyers and sellers that are really the same people and circular stuff, but like consistently buying from multiple merchants. But that's a sort of a skills library that exists in that some people
[10:46] know about that they can install inside their harness for some of the time like it's a fraction of a fraction of a fraction of the users of codeex cloud code and cursor. So where does this start to get any sort of traction on the machine to machine site? >> Yeah, I think I think it's extremely early days. Uh a lot of people I think are also ignoring that the business model doesn't make sense, right? Uh one of the reasons why we never got microp payments for humans has nothing to do with the tax stack. Uh I think it was in
[11:18] 2022 when we released this idea that you could stream money at a fraction of a fraction of a cent uh on lightning on Bitcoin of old systems. Um and I I was having long conversations with our engineers that thought, okay, this is super cool. And I'm like, yeah, humans love microp paying for stuff. Imagine you're watching a movie and every second you're reminded is this good enough to continue or not. Um, a lot of that changes for machines. I do think the thing that makes me bullish about eventually a market for information is
[11:48] that if you think about what makes AI capable today is a combination of two things. Of course, we have the base models and all the progress happening there. But then in the end and you're hearing this also from like Sati Nadella when he's arguing about you know a broader ecosystem around intelligence more distributed intelligence what you need for that agent to be useful is some sort of proprietary data tacid knowledge information that's either in in one of our brains right any expert in a domain has calibrated weights through experience that are different than
[12:18] what's in the public domain and it's in the models or historical track record that's why I think you know the likes of Stripe and and even and the card networks have been able to train these new AI models. RAMP has been doing a lot of progress on this. Um once you do have that rich data I I think you can build fundamentally new services and that telemetry I think is going to be extremely useful also because when you think about it when we can measure something not only we can actually automate it ironically you can't automate something that that you're not
[12:48] able to measure and we can go deeper into that but like the second part is if you're really able to automate something end to end like a full work stream the work of an actual person which is kind of the old promise around here. Um then you know what you're missing is the ability to also underwrite that. Um we talk about agent commerce but there's many aspects where I think people will want to delegate to an agent almost as if it was like a digital worker but that agent needs to be liable for it. That agent needs to be able to underwrite the
[13:18] risk that comes with all of those actions. And so when you put that all together, I do think that whether it's X42 or many of the other competing protocols, I mean there's there there's fuzziness, right, in where the the industry will land, something that's programmable, it's interoperable by design, it's neutral, it's cryptonative, I think will do very well for these new markets for information. So I I have no idea what the killer app might be, but there's going to be something there probably important. >> You're going for the auditability benefit. uh at the thing I'm reminded of a company at the moment called um the AI
[13:51] underwriting company AIU uh really interesting group uh who were heavily involved in uh as consultants putting together the insurance liability framework that sat around uh self-driving cars because self-driving cars have been better than people for a while but they fundamentally didn't start to get any uh approvals anywhere around the world from any regulator until there was an insurance framework that sort of sat around it. So liability is such a crucial point. Bam. Bam. I
[14:22] think you had a question as well. >> Yeah, my my question uh for Kristen was around um like the like his view on the micro payments. Do you think that's the killer app for agent e-commerce? Do you see an like I know that you don't accept an agent paying with credit card to a shop as an agentic commerce quote unquote um maybe you can explain that more but also do you think microtransaction would be the wedge that
[14:52] agentic commerce is going to grow there and then slowly replace the credit card and traditional card networks on regular purchases. I I would say look on the consumer side uh again the consumer product is the one that's going to decide how that is experienced and I don't think you want to change human behavior beyond the okay this is my goal go and do it um I think we're going to see a lot of that right but your Amazon your PayPal you know pick your wallet uh we'll we'll we'll do
[15:23] a pretty good job at it on the machine to machine I think what's missing is is the market right so to size point. I think we're seeing some volume, but I I've yet to see even on the web monetization problem, people have been banging their head there forever, right? If you if you look back in crypto, I think it was the Brave browser that had designed this whole three-way economy between advertisers, users, um, and and and essentially the ecosystem to reward you for your attention and give you access to content on a microp payment
[15:53] basis. I think a lot of that is really challenged because look there's a reason why digital goods get bundled right your Spotify subscription your newsletter subscription all these things take advantage of the fact that the marginal cost of replicating the digital information is practically zero so once you've created it you just want to have as much distribution as you can without undermining your own demand and microp payments don't do that right so if I can read the one article that I may care about you know once a week in the Wall Street Journal good luck subscribing to
[16:23] the IoT tier subscription that they're able to price at least part of the demand curve on. Um so I I I I do think that even on monetizing the web, the key is going to be who can get the ecosystem right. Um I think some companies that are trying right parallels there's a few of these that are essentially saying let's get the publishers to buy in so your agent can access better data. Nobody has cracked that. And I think part of it might also have to be that for an existing publisher is very hard to play under the new rules. So maybe it has to be a whole new industry category
[16:54] that gets designed around this new you know economics and new mechanism serving agents rather than you know eyebolts. But this is like prei right after AI there was like big controversy around like open AI and and anthropic and all these um trendy names. They were like parsing information for free now and they package it and sell it as tokens on the other side. So do you think at some point um AI push will change that economy model in a way that anyone who
[17:25] has any piece of information on the web can monetize it by selling it to these machines. I I do think and you're seeing I mean right now it happens with players like Mker and others that have created essentially a cottage industry of like can we get new proprietary data for the AI labs to train on. That's kind of the state. They hire expert they have you write eels that that's how it's working. I do think eventually we may have something better and you're right I mean if all my content gets absorbed and you know redistributed without attribution the the old trade-off was like okay free
[17:56] to access but I get distribution and then maybe I monetize through some other you know uh business if that's it's that's taken by the model then of course why would you play that's what essentially I think Microsoft Palunteer even Salesforce many have come out saying we don't want a world where the big labs kind of absorb it all >> well and also it's It's a it's an ROI um question. So, as um as a content creator, um I'm fairly wellsightable now inside most of the LLMs. There's
[18:26] probably I don't know good million words of mine on the internet, something like that. Um I I spam you all ridiculously. And frankly, that's all upside. Like I I give away the content mostly for free and I sell ancillary services around it. And that's far more profitable than a micro payment. So ultimately it it it's that return on investment for my time uh of the association to the brand that I think a lot of people want. And for some
[18:56] products that works, for others it doesn't. I I wrote a piece called the intention economy though where um that you're you're doing something different. When an agent arrives, it's it's usually given some sort of mandate or it has a mission and it's trying to get to that mission as effectively and as cheaply as it can. And if you look at Parallel and Exa and you know the the sort of the data serving startups that have done really well so far, they are able to do better search quality with lower token costs than um the traditional tool
[19:28] calling inside LLMs. And so I suspect that at some point as we go down this road of like we don't have enough compute, um we don't have enough money to pay for the tokens to the big labs and there's just this real focus on efficiency. That push for token efficiency pushes the agents to be much more token efficient and buy from whomever in the marketplace is token efficient. And so there's this marketplace effect that that starts to emerge there. I don't know if that's five years or 500 years away, but it's
[20:00] you you can follow the chain of first principles logic to something like that, Christian. And interested in your thoughts on like this idea of agents having intents versus and mandates and and how that fits together and then and then I'm going to throw the sword at you. I'm going to say stable coins and and see how you react. >> Yeah, look, I I think you're spot on. the intent economy is is quite interesting also because it's one that has big repercussions for some of the big historical web aggregators from Google to others right and it's really
[20:30] unclear how disruptive it might be uh in the end again distribution is quite powerful and so if I have the entry point on distribution and I can best serve your intent uh will we have an economy where everybody has their decentralized agent probably not right the entry point is going to be one of the big big companies that they already know uh but I I think you're hinting at at a really interesting tension which is like intent also means um you know absence of steering and continuous verification. I don't think we're at the level where you know you can delegate
[21:01] these agents to do some complex things and and trust them. You you can still do fairly simple things. Um but as we're seeing uh they're probabilistic systems and so whoever can to your point about efficiency efficiency is going to be a mix of like lowest cost best ground truth which delivers lower cost right because I can point you to actually what you need and also can I ensure that you know we're not going off the guardrails. So there's going to be a whole fintech wave um since we're getting getting into stable coins around building the best
[21:32] rails on payments on financial services around these agents. I I think that uh economist's equation for uh services that win when agents are the buyer is your next Forbes piece because that is is kind of again a set of principles that you can base yourself in that that's really powerful. So yeah, I did mention the sword. where where does stable coins come into this or you know is is is Bam just um sort of like cluing
[22:02] stable coins into every conversation because he's great at it, you know, like because he's having so much success with mesh and and all of the demand that stable coins have latently. Are we are we trying to cluge agents into this and actually credit cards the better answer for them? >> Look, I mean I I have strong opinions of this. Um, I do think stable coins are having in quite a moment and it it's probably the second product in in the crypto ecosystem to to show loud product market fit. If you think of, you know, Bitcoin as this new type of, you know,
[22:32] digital asset that people uh care about, what's fascinating to me right now is almost like the battle on the horizon between stable coins, tokenized deposits, any other sort of cash and cash equivalent being tokenized and what that means for the economy. I I think there's massive upside there and look whether it's touching a gentommerce or not I think there's so much to be done already on crossber money movement treasury operations and just streamlining stuff that should not have friction uh that should keep us busy for for a few years even before the agents
[23:02] come. I think I believe that Kristen is right but also like even the the new economy without the agents requires a new form of payment uh network. Um I think it's time for an upgrade no matter agent existed or not. I think Asians are I would say gas on the fire or like maybe that's not a right analogy but I mean like they help or facilitate or the catalyst for stable coin to be adopted faster but even without agents we need a
[23:33] new form of payment networks that is global is fast it's cheap and can settle immediately and instantly uh and I think what we had is what we have today is like 70 year old technology running on mainframe and I think it's time to upgrade those systems. >> Yeah. And they're domestic by nature and agents are not. Um they're an internet native uh technology. Well, um before we get on to part two and we unpack some more of this, we're just going to take a quick pause while we hear from our
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[24:33] coin linked cards are in hyperrowth, as our good friend Kai likes to say. That is Visa, of course, the global leader in payments and of course sponsor of this show. You can find out more at visa.com/crypto. Thank you to our sponsors. All right. So, you alluded a little bit to the economics of AI, Christian, there, but like what gets commoditized most and what's going to like stay super super valuable cuz I think this is like the eternal debate in AI circles and
[25:05] everybody sort of, you know, um uses the word taste and like common sense and we reach for these ephemeral concepts that AI seems weirdly bad at and yet you can't quite put your finger on like how do you put an economic value on that stuff? I think it's really difficult. >> Yes. Look, um part of my journey into this actually was the reaction to everyone talking about taste, curation, agency, judgment as the answer on how we're going to make it out on the other side. Mostly because I felt like no
[25:36] that's that's not going to help us. So what what's going to help us? Um so we wrote a piece on this back in 2025 that had a super simple idea. you know after talking to a bunch of computer scientists looking on the frontier the idea was like if it can be measured it will be automated and and now this has made it into the mainstream but back then you know people again we're coming out with with all sort of wonky theories about what I can and cannot do if you truly believe that as long as we have digital traces the right data recorded uh for any type of task that you can end
[26:08] it over to AI and automate it um then the next conclusion is that okay this thing is going to get very abundant So the ability to throw intelligence and execution at problems that we measure it's it's going through the roof and what's the compliment right um economists don't like you know ideas where oh suddenly everything is abundant uh there's there's always some new bottleneck some new friction that captures you know ironically all of the value and when it comes to measurable uh and verifiable things the complement to
[26:38] that is what's not measurable and what's not you know verifiable through a machine so we landed actual >> yeah human verification being being the bottleneck and it's even broader than evils I think eels are a great example of that but it really boils down to you know think about any profession what the model can do and what the human that's steering the model giving feedback doing all the rounds of back and forth over you know codeex or claw code is doing they're essentially using the weights in their brain to look at the output and
[27:08] and realize okay is this ready is this up to par can I ship it can I trust it or not they're essentially using tacid knowledge, information that they've learned and acquired over their entire career uh and you know the sum of all their errors, all their mistakes or or all the stuff that they've seen doesn't work and applying it to to what the agent did. That's that's the collaboration. And of course, you know, look, the bad news is that agents are getting more and more capable. So that frontier of where do you need the human to come in and do verification is is
[27:38] perennially shrinking. uh but it it's really you know as simple as can I have a digital trace of what's in size brain or bamb's brain when they're doing their job in this domain and that's why you know even tools like cloud tag or you know computer use are kind of dangerous right because they're allowing these companies to build those digital traces that track your decisions your reversions your you know up votes and down votes uh that that's your test of knowledge right and the more of that
[28:08] makes it into the machine the more you know the next round will automate it. >> I wonder if you can create like a a DNA fingerprint or of like somebody's tacet knowledge and and how many data points you would need on them to build a model of how how their decision-m process kind of works because uh generally like uh humans have this uh really interesting set of idio uh synrasies. they are unique and those unique idiosyncrasies
[28:39] persist and what you need is the ability to measure those idiosyncrasies over a persistent period of time and to define the pattern and when they emerge. So to your point, if I'm just watching everything you're doing on your digital devices, I can start to build a model of how this brain is interacting with the with the environment it finds and and maybe reverse engineer those to to to your point. So, so what does that mean like outside the the individual human for for startups for incumbents? Are
[29:09] there actual mutes left here? Like the or is is everybody just going to be praying to the mountain of uh Mount Dario um and the new gods? >> So, good news there. I I I don't think actually uh if you read the economic implications of the bitter lesson, they actually work against the big labs. Um I I think it's just unfolding now. It's going to take some time. Uh but it's very natural to you know to go in a circle on modes. Uh to some extent you may start by looking at the models and
[29:39] say okay the models are at commoditizing entire categories whether it's design, finance, accounting. Um then you see okay but you know seven months later 6 months later four months later we get an open weight model from China or somewhere else does 90% of the job. All the token volume shifts you know in quantity to that. That doesn't mean there's no demand for state-of-the-art right? If you're doing cyber, if you're doing any sort of like sensitive job or patentable matter, you pay a premium, right? And and many of us will pay a premium for for the very best intelligence for the most hard task. But
[30:11] if the if the commoditization is happening right after almost like in fashion, right, where the designer brand knocks throws something out and then it gets knocked down, you know, by by the copycats, then routing feels suddenly very interesting from a mode perspective. is like okay companies like open router super powerful right if I control that data play if I'm the one arbitrating between all these different models that's the aggregator right of the new era only to realize that okay what what what's happening right now is like every every app is building their
[30:42] own router right ramp release one versel and so suddenly the app layer looks very interesting and saying okay routing is going to get commoditized by the apps now you're at the app layer look lots of good reason for think the app layer is usually where it stops apps distribution is is typically a mode. Um, but then you see the labs again coming in and saying, "Oh, with enough traces, we can do the the work of the app. You don't even need to leave, you know, your your your codeex or your claw code." Um, so it's a dog biting its own tail. And I' I I come
[31:14] to this conclusion for now. I may revisit it soon, but I do think there's there's there's still a few modes. Um, and it really boils down to what makes the models really capable. having that ground truth. So if you have access to unique information about the universe and and by the way the reason why people obsess about agency is because agency is a powerful way to extract information out of the universe. You have to go out you have to have that friction as you know s you know with regulators for example when you're talking about stable coins or tokenized deposits and all of
[31:44] this that's information. Oh, that's some friction. All right, >> that's a lot of friction, right? That's ground truth that now if you do your next AI move is super useful. The second one, I think back to the entropy in everybody's brain. Uh, yes, probably we are very predictable, right? So, I was passionate about crypto because it's decentralizing, democratizing, all this stuff. Uh on the eye side, I'm passionate about open weights because I do see a lot of those similarities from like not your keys, not your money to not your intelligence, not your weights, not not not your intelligence. But then
[32:15] again, humans capture entropy all the time. So yes, maybe we're predictable, but I I like to to believe that we're also updating our weights over time, and that's a big part of where what we are. Uh for those of you that have watched Westward, there's that scene where she's going through the library and picks a book and and every book is a person, right? fully predictable by by the machines. Let's hope that's not not true. But those weights in people's brain, I do think are a comparative and and competitive advantage. So, um that's a mode you're seeing it already with the
[32:46] labs. They wouldn't be fighting for talent if that wasn't true. And so, the labs are a bit of a canery in the coal mine. Um and then and and again, I'm an economist so I I have nothing to contribute here. But there's many domains where the fact that they're not measurable, it's a feature, right? There's there's things that are all about social consensus. Um, think about art. It doesn't matter that one piece of art is better than the other. Does it capture does it become a focal point for a bunch of individuals? Do we all converge? Um, those things will continue being important with or without AI. Um,
[33:18] now I'm a bit more bearish on things like, oh, people care about human touch. Yeah, they do. But if the product is 100x less expensive, they will reconsider. And anyone that has experienced a Whimo or FSD does typically not regret the experience of a of an Uber or a Lyft. It's just a simpler experience end to end. It's a lot more predictable. So what modes persist? What should startup care about? I I would say look for friction in the real world. What whether it's atoms or people or regulators, just increase that
[33:50] flywheel because if you have information that nobody else has, that is an accelerant for AI. hire some of the best people in the world for the job you're trying to do. The ones with the weirdest weights in their brain. Um, and then yeah, I mean, you just have to to execute execute like everybody else. And then the the speed is is nuts. So based on what you just said, how does it how come that this is good for like new businesses and startups and founders because like the if if there is no mode and the
[34:20] mode is talent entropic and Google Microsoft they have most amount of capital to hire those talents. Plus in last 20 years that AI or 30 years that AI existed like everyone knew like models are commodity or will become commodity soon and whomever that has proprietary data can train their model better and they can they can act better and respond better then they are ahead of the game. So and again like Microsoft
[34:50] and Google and Apple of the world they have all the data that they need compared to new startups. How come that this like open open weight might like open up a little bit of uh or level the playing field a little bit? But overall, the data is a king, right? Isn't it? Look on the talent. First of all, you'd be surprised how many talented people would rather take a job in a startup with with high talent density and people that you know care about the same things that they do. So meaning is as important
[35:20] as as the the big check. Um so that that's on talent. on data. I do think incumbents have a massive advantage with AI and in fact many slowmoving incumbents including some of the GIBs. I think they're gonna be a lot more dangerous. Right? So if you think about the gap between a neo bank and a GIB, it used to be massive because one had an IT department, the other one had actually a tech software company. AI closes some of that gap if you want to if you want to do the right moves. Uh but incumbents tend to have the right data for the past. And so I do still think that
[35:51] opinionated founders that do go out on a limb that sometimes look that limb might be five to 10 years. Um right now I feel like at least on X there's this like topic of the of the day everybody converges like toddler soccer where everybody's talking about one thing and then 3 days later it's the next thing and the mode discussion is exactly the same. >> Welcome to the internet. It's toddler soccer all over isn't it? It's like boom whatever it is. Oh, South Korea's gone because the chip makers, the memory makers aren't doing anything. Like,
[36:22] well, yeah, completely. Uh, humanity is one big game of toddler soccer on X. That is, that is absolutely the case. I I'm so interested in this point about the quality of data versus the uh depth of it because incumbents have a lot of it, but most of them can't access it. Um, and the ones that can um might like the talent to do something useful with it. There are a few notable exceptions, but you know, there lots of data. Curser just put out their latest um model, I
[36:52] think version three of whatever it's called. Um, and they have more tokens about how engineers create code than pretty much anybody on the planet. So, a weaker model can outperform Fable 5 on codegen and soul 5.6 on codegen because it's rldled within an inch of its life for that one specific task. And there's this move towards smaller models. And so you kind of get that that interesting direction. Then you get the other direction of like the not so tacet
[37:22] knowledge that you said that Merkore and others are trying to build in with with the specialist eval. So it's going to be interesting to do that. And and as as you think about financial services, who has like a real upside opportunity here? I've got a couple of favorites, but who's grasped that nettle of AI and done a good job in finance that you've seen? Um, and you know, is is finance just waiting to be cooked like uh the legal profession or is there something else going on there?
[37:52] >> No, I mean when you look at legal, I would say it's going to be much harder for some of the top law firms uh to embrace for impact. Uh actually, one of my co-founder, the Lightsburg used to work for one of the top US firms. So we've been talking about this quite quite a bit. Um but it's difficult right and there's all sort of restriction on data usage privilege like it's not simple for an actual law firm to to go ahead on you know with a with a legal lab uh you know legal AI lab trying to replicate the work. Um I do think there
[38:24] you know at the top end of the distribution probably it's going to look like a hybrid but the bottom is just going to go the way of the big models. Um I I do think fintech has been different right? So fintech probably because very tuned to data for some of the fintexs I mean you could not be profitable if you didn't have good good ML right forget AI right and some of the more recent methods in LLMs um fraud has always been adversarial uh in crypto I think same same kind of feeling um AI is definitely more adversarial agents are
[38:55] going to be adversarial so look I I think it's obvious that Stripe has been you know training really interesting models on the payment side um the card networks probably are trying their own, although of course they're larger, slower moving machines. They're now founderled. So that that creates a gap. Uh then companies like RAMP, right? So, if I were to think through which companies that become almost like foundation AI labs for fintech, uh they they've done an amazing job at releasing tools and and playing into that middle
[39:25] arena of like, okay, we're not entropic, we're not open AI, but we know a thing or two that the CEO and the chief accounting officer of a company will care about. Um I do think we're going to see a lot more than that. That is the vision of like, you know, own your intelligence. If you're a company with domain expertise in X, you will fine-tune, you know, your own extension of a model, thinking machine recently released an interesting early uh result with Bridgewater, right? Where they essentially show that their base model, which is essentially a distilled version
[39:55] of Kimmy um plus uh Bridgewwater's, you know, extension to that can really outperform the expensive models. So to your point about efficiency, that that solves two problems at once, right? One is cost. I'm a lot less interested in cost than control. If I'm a VC firm, a law firm, you know, a financial services firm, the last thing I want is to send my traces back to Entropic and OpenAI and now they're promising they're not training on your outputs and inputs. They don't need to. Everything around it, unless it's DDR, there's so much
[40:27] signal leaking. That's why, you know, cloud tag feels like a frozen horse to me. Why would you like >> Beware into your >> Beware FDES that that come from uh labs? It's it's uh Wow, this this horsey looks really nice. That's just outside our castle walls. >> We spend resources. Yeah. >> What a beautiful What a beautiful horsey. Let's Let's let it in. Oh, no way. There were Spartans hiding inside this horse. Who would have thought that's the FDE model from the labs? Like
[40:58] we're ready. we we're ready to leave you the, you know, the dispose of the war. We're not going to invade your industry. Take the horse. That that's exactly know now that for those of you that have caught up with Odyssey, uh yeah, Christopher, >> if you've seen Odyssey, you get it now. Like 100%. Uh it's not just a weird little metaphor in a book anymore. But the the thing that I've observed as I speak to boards and um CEOs of the incumbents quite a bit, I get asked about AI is they're smart to that. They're absolutely smart to that. and they'd rather have their employees in
[41:30] revolt about how bad their internal tooling is compared to the outside world than risk giving away the secret source that they have because they are very very protective of that. So really interested as as we round out here in terms of like where the opportunity is for builders like especially where stable coins meet AI where payments meet AI agentic commerce where are you spending your time and energy? I mean I'm spending my time and energy across too many different things. Uh that's the phase I'm in. Uh so probably not that
[42:01] useful for for the people listening. Um but what I would say is like look this is a period this is essentially the advice that I would give my my my daughter right which is like now is the time to realize two things. First of all we all have superpowers through these tools. So no dumerism no regret like this is this is wonderful. Uh approach it with with with a positive attitude. And second is like what is the one thing that your innate talent your obsession your your history makes you truly unique at um in a period where there's infinite
[42:34] change the modes that you know one week ago we could have discussed about oh the labs are going to eat everything and now we're here debating oh will open weights crash you know the entire IPO market for open eye and entropic it's it's pointless to to I think you know narrow down those modes too to too closely I would say just double down on something that you enjoy doing that you get in the flow, find that friction with the universe. Uh agencies just literally you going out and learning that's stuff that the models haven't seen. So if you're worried about the models, just think
[43:04] about the models are sitting in a data center, right? Or in many data centers. You can actually go out and and and do things that that's probably the the main recipe for success. It sorry if it's too generic, but you know, >> no, go collect new data from the universe like my 2-year-old does. Uh oh, I wonder if I can stick this in. Yeah, it's that's the best way to learn 100%. Look, um we are closing in on time here, but I I want to make sure that um there wasn't anything that you wanted to say that on the agent commerce topic that we haven't covered today. >> One thing Simon
[43:34] >> Yeah, go ahead. Bam. >> Can I ask? [laughter] >> So, you mentioned ramp, but you skipped over stripe and open router. What do you think about that? >> Oh, I I I I think it's a great idea uh for stripe. Um, look, whether you build routing inhouse or you acquire it, um, Stripe is going to have to do it. Um, it's important to have it. I think it's going to be table stakes. Uh, I think the open router team has been honestly, uh, top in this domain. And if there's anyone that can turn routing into a real data play, which is really where routing
[44:05] becomes sustainable, it's probably done. So, yeah, you look, Stripe has has a really good taste in acquisition. So I will I will use the word taste uh for this particular instance. >> Well, if you make uh your money growing the GDP of the internet and then the take rate on the GDP of the internet, then maybe you can have a take rate on another part of the GDP of the internet. It's it's fascinating strategy. Uh it it really is a very exciting time to be watching this market, isn't it, Christian? And I'm so uh glad we got to
[44:37] do this conversation. If people want to learn more about you and all of the things you're up to, where do they go to do that? >> I can be easily distilled at uh Catalin X. That's probably where all of the information flows uh for free. >> Go grab that API and do your distillation attacks. Um Bam, how about you? >> Yeah, you can you can find me Bam as easy mesh on Telegram on X and also Mesh Pay on X on LinkedIn. You'll find me at sytail on all of the socials, screaming
[45:07] into the void at fintechbrainfood.com and of course at tempo.xyz where we just b dropped a banger of a rapport on uh creating additional liquidity in markets. Uh if you haven't already, uh go ahead and subscribe to this podcast cuz then you'll find a lot more of this show with conversations like this and leave us a review or a comment as well. That's how you say thank you and it means a lot if you would go ahead and do that. I know everybody says that at the end of a show, but it actually does work. I can see it in the data. When you go do that thing, it really helps us.
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