20VC: Will OpenRouter Sell for $10BN to Stripe? | Why Chinese Open Models Are Beating America—and What Happens Next | Why Enterprises Are More Fearful of Anthropic and OpenAI Than China | Is the Routing Layer Becoming a Commodity with Alex Atallah

22:48Harry Stebbings:Alex Karp said on CNBC in his rather wonderfully energetic way that companies are terrified of working with frontier model providers. Do you think they are? I haven't seen what he talked about there when I talked to our customers, but there was definitely like a little, there was some skittishness that the, particularly when, when Claude Design came out around Figma and that part I did see. And I do think that there are like real concerns. Like Figma is very different. But if like a startup is only building a like go to market wrapper around intelligence, like, hey, we are we're a company that kind of like brings AI to this market and does so by like doing the right integrations and like customizing the system prompt.

23:40You're going to be fine if the model labs don't care about that market, which there will be many markets like that. But the model labs have several incentives to go after you eventually. One is getting multiple teams within companies they do care about to be dependent on them. This is my theory behind why like Claude Design was strategic. While it's not like a massive amount of revenue for Anthropic, like probably not a significant amount of revenue, it does get the design team to really care about Anthropic models.

24:14another team that really wants to stick to Anthropic. So that team strategy can make you compete with the model labs. And so I think companies like that, that find themselves like, oh, we're building a product for a team that has now become strategic for the model labs, for companies they actually care about, that's where I see probably the most near-term threat.

24:33Harry Stebbings:Do you think Claude Design will have a meaningful impact on the Figma business? I speak to many founders today who are bluntly switching from Figma to Claude Design, and it's cannibalizing their Figma usage. Do you think that will happen? So I saw a lot of designers try out Claude Design, including our own. But so far, I haven't heard of the repeat story. I don't know. Honestly, like, I have not talked to very many designers about this. I certainly haven't heard a lot of chatter about Claude Design. And, like, if you just look at the numbers for Figma, they're quite good. Like, they had a very, very incredible earnings.

25:11Harry Stebbings:This is why you don't want to be public, dude. You see great numbers. Figma, down. I'm like, poor Dylan. Like, what? Yeah, that was crazy. Do you know what I mean? It's like, really? Come on. We were talking about the different models that we have on offer and whether companies are willing to work with frontier models. The rate of model development feels immense. Do you think we will see the same rate of model development continue over the next year, two years, three years? Frontier model development or general model? General model, both frontier and open. Yeah. Just because, I mean, every single day there's two, three, four new models.

25:50In July, we launched 70 models, about one model every 10 hours. There's some agent labs starting, too, that are all going to kind of like probably make models eventually. Like Jeff Dean is starting an agent lab right now from Google. the companies that are known for making agents have an incentive to create their own model, a very clear incentive to create their own models and distribute it through the agent. And we haven't even seen the start of that. Sorry, we've seen the start of it, but we haven't seen it really pick up. Like Cognition has a model, Cursor has a model. Does Lovable have a model yet?

26:25I don't think so. Not publicly. Yeah. So the agent labs are going to, I think, develop models. This pressure from both the GPU makers like NVIDIA to create more competition in the space and create more diversity in the space, plus us, plus investors who just want to try new things that all could improve intelligence in some neurodivergent way. I think those are strong incentives. I think that they're enough to incentivize more founders to make Neolabs. And if American open-weight models pick up in Steam, then it gives these Neolabs a base to train on. That's not Chinese, which will then probably create more American Neolabs.

27:13Harry Stebbings:Do you think we should be concerned by the rate and quality of Chinese open models? We should. We're behind. America is very, very behind still. Still, I think things are picking up. I think we have poolside, we have thinking machines, we have RC. Do you feel a sense of responsibility for that? And what I mean by that is you are a routing business, and you could route a company to a Chinese model that, who knows, people are worried about backdoors, CCP involvement. You could be the deliverer of that to those models. Do you feel a sense of responsibility for that? So we do feel a responsibility to have safe access for all these models.

27:58Like customer trust is like our paramount goal. If one of these models is unsafe to use, generally considered unsafe, we pull it from the platform. If there's like a way to use it in an unsafe way, I mean, there's a way to use all the models in an unsafe way. And then we believe in using technology to make it safe and to work with the model labs themselves to figure out how they're doing it on their side so that we can be state of the art or better. We spend an enormous amount of time making sure that our practices match what the best things that we're seeing coming out of the labs or better. because we're a way of like exploring all the models and finding them for the first time.

28:40We're a good focal point for deploying safety measures across your whole company. For example, we have prompt injection protection. You can just turn it on and immediately flag prompts that look like prompt injection that's trying to happen. We have PII redaction. We have like a couple different things that you can automatically just turn on with a click and get an added safety layer on top of all of your inference. And we build that so that enterprises feel like they can safely deploy new models and that their employees can try them out. I think of the models a little bit like the internet. You can't just ban the internet at your company because there's some bad things on the internet.

29:21You can create guardrails, and you should. You need to use AI to build the best possible guardrails that you can.

29:29Harry Stebbings:I'm with you, but do you think you actually know what's going on within Moonshot or Alibaba with Quan? And these are incredibly secretive organizations in the depths of China. Can't pretend I know what's going on inside of them. As a U.S. company, we're going to follow the best practices of what happens in the U.S. to make sure that we're not doing something irresponsible. What do you think U.S. companies are more nervous of, frontier models or Chinese models? I think they're more nervous about frontier models usually, Part because there's just like much more confusion around the data policy about what's like actually happening to the props that I'm sending and where they're being stored and how they're being looked at.

30:10And you can't run them on your own machine or in a provider of your choice. And so that just immediately creates all of this uncertainty in a lot of enterprises. And it's uncertainty that they can also pattern match. It's very similar to running on their own infra versus running in their VPC and knowing who can see the data. How extraordinary is that, though?

30:33Harry Stebbings:They're more nervous of US companies headquartered in Silicon Valley where you can see and touch and feel the headquarters and the leaders. It's just like, what a strange world to be in. Yeah, it is very strange, especially with the frontier models having the biggest cyber posture right now. What do you make of every company kind of posturing, ha ha, we hacked someone? First you had OpenAI, then you had Anthropic, and then you had Zark coming out. I don't want to miss the party. We did too. Yeah, well, I think they have to talk about it. The right thing to do is to reveal when there's been a cyber incident involving your model, covering it up doesn't work.

31:10It's not going to work in the long term. And it certainly looks like they're all bragging about it. But really, if you were in their position and something happened with one of the models and you had to make the choice about whether to publish it or not, I think the right thing to do is to publish it, regardless of how people are going to spin it. So I doubt that they're actually thinking of the felony bench or whatever it's called.

31:37Harry Stebbings:How significant was the latest Kimi model, which got so much attention? Was it as significant as everyone thought? It's quite good. It's not cyber capable in the same way the frontier models are. And long horizon tasks, I think it's still a bit behind the frontier models. But GLM 5.2 was a really big, big step for open weight models. Kimi was kind of like moonshot getting up to that step. That's a little bit how I see it. And Kimi's also a very good writer. Like the voice and tone are both pretty good. Whereas like some of the frontier models have like voice degradation that happens when they get better at coding, especially.

32:24And I was like, oh my God, like I can't read this output anymore. The output sounds like three of the four arguments you made are right. And one is a turning point. You know, and here's the rub. Like it's just, it's sometimes just impossible to read what they're saying. and this stuff is fixable. But Kimi, I think, has always had pretty interesting writing.

32:47Harry Stebbings:In 12 months, will the chasm between US open source and Chinese open source be bigger or smaller than it is today? My fear is that it will be bigger because when you have DeepSeek, it becomes a national champion in China. And I mean, Xi Jinping is going, this is our AI horse. I will concentrate all of my money and efforts behind this and I will supplement this ecosystem to the end. This is the winner. And then when you see another moonshot come out, suddenly all regulation gets moved aside. All policy gets pushed aside. All funding becomes available. Everything is allowed. You are free to run.

33:24Harry Stebbings:And these guys are unabridged in their ability to do whatever they want to get to the end goal. Whereas OpenAI and Anthropic and all the other providers in the US, especially open source, fuck, you got to try raising billions of dollars for a US open source model. Bit tough, actually. not impossible at all, but tougher, business model questionable, AI research is super expensive, and you're competing against open and anathropic. I think the comparative landscapes they sit in mean that the Chinese open source providers are just inherently advantaged, sadly. They have very, very good researchers, and I think Americans underestimate that a lot.

34:00I do think they're going to be concerned about the cyber posture of their models, and they do seem very concerned about censoring the models and censoring the information that the models can provide to people. So while today people complain about American models censoring more due to cyber, I'm not sure that's always going to hold. And as the Chinese models grow in importance for China, what are they going to do? Are they going to drop the Great Firewall? Are they going to give up on putting the firewall around the models? I don't know that much about China, but it does seem kind of strange that they don't seem to care more, that the models are.

34:41I've never seen anyone do a profile of what you can do with DeepSeek that you can't do with the internet in China that's available to you within the border, what information you can access. I've never seen anyone do a real deep dive. How far past the firewall does DeepSeek go? If the firewall matters to China, if it's going to matter in 10 years, something's going to change.

35:04Harry Stebbings:Well, what's interesting is obviously the abilities of the Chinese models outside of China is immense. The abilities of the Chinese models inside China is actually relatively limited. The guardrails. The guardrails are incredibly stringent and prohibitive. It's ironic that they are incredibly superior to us. Shit domestically. Terrible. Interesting. I literally just had my dear friend Jason Lampkin, who runs SAS, to come back and be like, couldn't figure out what time Starbucks opened on DeepSeek. Like, wasn't on offer. Would say, like, not allowed. Wild. Wow. Very basic, rudimentary requests.

35:35Harry Stebbings:We're speaking about all of these different models. And the thing I think is, what about loyalty? And you have this incredible seat in the ecosystem where you can see everything. Do we see any developer loyalty today with models? Honestly, we do see some. We try to make switching costs close to zero so that when new models come out, people can try them out really easily. But we also measure retention and churn from all the models. We share this data with model labs, too, when they ask for it so they can know, like, oh, you know, for my model that just came out, which models drove traffic to it?

36:14And, like, for those users, like, when they leave, which models are they leaving to? And we'll, like, make this more and more available to the world soon. And we do notice in the churn data, there are developers who kind of like continuously stick to models, even when there are better models out there, better models for their use cases. I think it's a combination of like a couple probably root factors. One is my app works and I don't want to break it. You know, if the support bot starts saying something weird that I didn't expect, why add more headache? I've already done all this optimization and like I've already put all these guardrails around it.

36:50Another is new models are not necessarily going to make your pricing better. In fact, in general, what happens is that the current models, like price goes down over time. And especially when new advancements in the labs happen, you'll see like intelligence jump, but like the price curve like also jumps and then we'll start going down over time. So it's not necessarily the most like price effective thing to do to like shift over to the newest model, even for open weights. The third reason, it's just fundamental trust in the outputs. If I'm using a model to do my work and I like the way it talks, I probably have some eval, like a personal eval.

37:33A lot of people have these personal evals that are just these random tests that they give the models. If the random test doesn't look really good on the new model, they'll just be like, good, I liked Kimi K2.6 anyway.

37:45Harry Stebbings:People thought before that memory would be the retentive mechanism. While OpenAI has all of my previous prompts, it knows that I live in London, I do podcasting, and that will make it a better model for me moving forward. Is memory no longer a retentive mechanism? Memory is really interesting. I've always thought it is a retentive mechanism, and the question is where it lives. Is it going to live with the model? Is it going to live with the inference provider? Is it going to live with the app? Is it going to live with the infrastructure provider, the router? My guess is that all of those layers are going to try to own memory in different ways.

38:24And there are going to be advantages to sticking your memory in each layer. You know, if you stick it with the app, then the memory has like the most app related context and is model agnostic. If you stick it with the model, the memory might perform the best on personalized benchmarks and perhaps have the best ultimate intelligence. And I think the model labs are going to work on memory. And then the ultimate thing might be like, is there a good combination? Can I use memory in the model and memory at the infrastructure layer or the app layer at the same time? Is that going to confuse the model?

39:00We don't know yet. I do think that it's impossible for one layer to capture all valuable memory because the apps own so much important context that the model labs don't have. And the model labs, in order to get this to work, they'll have to incentivize the apps to give them that context.

39:17Harry Stebbings:Speaking of the apps and the model set, claw code, cursor, bundle, model, and harness, is the router absorbed into the agent framework before it ever has the chance to be independent when you have the agent and the harness together? The harnesses are pretty interesting because in our early days, one of our early bets was that most apps were underestimating the desire for users to choose the model. Most apps in the very early days, in like 2023 and 2024, it wasn't even clear which model was being used under the hood. They were like, oh, people are not going to care about that. They just want AI.

39:55And one of our strong convictions then was that, no, people are going to want to use particular models. They're going to care about who they're talking to. It's like, I want to know which employees I'm talking to when I'm trying to solve a problem. And models will be kind of like that. And that has played out. In Notion, you can choose the model that you talk to, even though you would think an app like that might want to obscure it completely. A similar thing happened with harnesses, where particularly with developers, they started to build an affinity to different harnesses. And that's because it's a user experience.

40:31So I think that is my favorite argument for why harnesses are going to stick around. Not that they're being bundled with the models, because in fact, as models get better, they get more resourceful. and the junk that gets thrown in the system prompt just becomes a handicap. Anthropic, I think, published a good article about this where they showed that, like, oh, we got rid of stuff from the system prompt and suddenly fewer contradictions showed up later on with user prompts and the model performed better. And we're seeing a lot of the harnesses right now are, like, deleting code in order to perform better with the latest frontier models.

41:07That, I don't think, means that harnesses are bad. In fact, I think we'll see more harnesses come up in the future because it's a way of building a user experience on top of models. It's a way for developers who are not model labs to own a user relationship, and that is just going to be incredibly valuable for the economy to have that layer. I'm going to get killed for this.

41:30Harry Stebbings:What's the difference between a harness and an app? It feels like it's word wank of everyone talking about harnesses and harnesses. I'm like, is that not an app? Hello? The nice thing about the harnesses compared to the apps is that they're more composable. I can have a harness call another harness. I can have a harness spin up another harness in a sandbox in the cloud. Because I don't know what APIs did for apps. Yes, but it's much more reliable and deterministic and sort of easy for users to grok with a harness because the harnesses are Unix-based. They all have, and the models are so well trained on Unix, on bash commands.

42:09Whereas if I'm telling a harness to go orchestrate an app in the cloud, it's going to be like, oh boy, does this app... How do you log into this app? Do I need your password? Do I need to fire up a virtual browser? It's going to be pretty slow. I'll figure it out. Okay, I fired up a browser and now I need your password and I'm going to try to find the input where to put it in. And apparently there's probably an API in this app somewhere. I need to look up the docs to figure it out. and, okay, now I've got the API. There's so many unknown unknowns when you're composing around an app. Very, very, very, very few unknown unknowns when you're composing around a harness.

42:49So I think it just gives developers more flexibility and flexibility that they can inspect. Like API calls, you're just seeing a whole bunch of code flying around the screen. A harness, oh, I can jump into the harness and look at what's going on and talk in English about it. So it's much more user-friendly.

43:07Harry Stebbings:We've seen Meta and Muse really be a focus for Zuck. We've seen Alex Wang front and center much more. Were you impressed by what Meta delivered with Muse? They've been doing a good job, yeah. I mean, it takes a while to set up a whole new model lab from scratch and ensure a lot of organizational debt to deal with. Do you think they will be a serious challenger? I do. I think they have the resources. I think there's some competitive things they can do around the model that helps people in ways that the model labs are not as interested in doing. Like just having like a social network and like a focus on people.

43:47You know, it's like something for the brand that maybe Grok and like SpaceX AI have it too. But they do need to find their niche. Like, I think people don't quite know what to do with MuseSpark yet, like when to use it or when to go for it or what its, like, core advantages. They just released a coding harness. They're trying to be, like, a generally capable model right now. I expect that in the future they're going to be like, look, we are way better at this thing. And that's going to be a really important moment for them.

44:17Harry Stebbings:Fascinating. I was impressed by it, actually. Do you know what I use now? Maybe plug in one of our mutual friends, but Anastasios and Arena. and it's so weird so i'll put my prompt in arena and then obviously it comes back with a load of different model options yeah and you know i come back with i use one the other day pergamum pergamum yeah it was like kimmy and pergamum and they offers you four different options and it takes me to models that i would never have used before and actually muses come up a couple of times have been pretty impressive but i love that in terms of this like discovery mechanism to models that i would never have used i would never get a kimmy honestly dude i just could fucking chat gpt it's really interesting it basically that goes to the point of the model there just becoming a utility layer what do you mean by that well actually i have no loyalty to them i have no affiliation with brand i go to arena and i want to see what you got for me show me the results i don't care if it's kimmy or muse or claude or sonnet or do you know what i mean and actually i just want to see the options you got and i'll pick the best from there i'd rather run four in parallel.

45:19Harry Stebbings:Do you buy this whole, we're going to have one frontier model run four open models? And the frontier model might be 160 IQ points and the open models might be 120 IQ points, but that will be a model infrastructure or structure that we'll work with. Totally think that that is a great architecture that everybody needs to explore. We've been helping lots of developers do this. You have sub-agents, we have a sub-agent server tool that we like tune to be really, really good at using models generally. And then you have an orchestrator model that calls out to the subagents when it wants particular tasks to get done.

45:57And these subagents are just very, very low cost, and they're focused on deterministic tasks. This is what open weight models are generally really good at compared to frontier models. When you have a deterministic task where you know the shape of the output, but you know the type of problem that you're working on, and it's a type of problem that has been solved, like classifying some text, for example, then you should definitely use a low-cost model from OpenRouter and then have the orchestrator model read the results and then go and continue working on the unknown, non-deterministic task that it was set out to do.

46:33Harry Stebbings:I want to create an open American ecosystem, yeah? One of the more amazing open American models. and I make you head of this program, what would you do to encourage, incentivize the open US ecosystem to compete more vociferously with the Chinese? I think I would spend time talking to the current American labs a little bit more to figure out what distilling the Chinese models looks like for them and how effective it is. You can probably get pretty far distilling the Chinese models. The nice thing about the open weight models and the Chinese models is that they allow distillation, and they allow most of them.

47:16And that means that you can take the outputs of these models to do reinforcement learning on top of the model that you're building. You know, this is just like a very important and common practice in AI that all labs do. And also when you distill, you see the output. So you can like inspect them to make sure that they're aligned. So if there's anything about the open weight models that you're worried about not being aligned with like the voice or constitution of the model you're creating, you have a much better shot at catching it when you're doing these RL rollouts. The other thing I would try to figure out is the compute question.

47:54Compute is just a huge advantage that I think we still have relative to China. And these Neolabs need a shot. And there needs to be an easier way to get compute to the right talent in all countries. But especially if we're trying to create a competitive American Neolab system. NVIDIA has been doing a good job of this. But there's Google, there's TPUs, there's Tranium from Amazon. I would work with all of the hardware companies and also the Neo chips to help with compute.

48:30Harry Stebbings:I don't think we will have that compute advantage for long. I think you see DeepSeek and ByteDance both aggressively pursuing their own chips now. The export controls mean that they have to. And this is the number one problem for Xi Jinping in his race to win the AI war. If they build a bridge in four weeks, I think they'll manage a chip in six months. Yeah. Like, staying ahead on the chip war is critical for America. Is distillation wrong? I mean, distillation is a technique to build models. But people view it with cynicism and shade. Well, they're just distilled models. It's a technique to build models.

49:06The closed-weight model labs distill models, too. Like, Sonnet is a partially distilled version of Opus. And this is how you make smaller models out of bigger models. You know, it's an important way to just teach your model new things when you find like something useful in the ecosystem. We do think that labs have a right to say it's not allowed in their terms of service. A company can like cut off access to someone who is trying to build a competitive model. If you're just trying to build like a smaller model that's like really focused on doing one specific thing that's not competitive, most of the frontier labs don't prohibit that to my knowledge.

49:45But there are going to be markets for companies that allow it and companies that don't. And we make sure that we help both companies uphold their terms of service.

49:54Harry Stebbings:I have to ask you one question before we do a quick fire round. I'm going to get killed if I don't ask it. There are reports that you are selling to Stripe for$10 billion. Is that going to happen? I can't comment. But whatever happens, we're going to execute on the vision. What we're doing is critical for the ecosystem. And we believe for safe access to AI where one monopoly doesn't take over, where we have a vibrant ecosystem of models that everyone can explore. And when new providers and new server tools and new inference-adjacent tech comes online, there's a really easy way to discover it and connect it with all of your existing AI.

50:36Harry Stebbings:I was thinking in these situations, my response would be like, well, I own 22 % of the company,$10 billion,$2.2 billion. dollars. Now I'm a venture capitalist. But is it hard not to think like that? I don't really think about it. Do you not? I don't spend a lot personally. What I think about when I what I do with like personal capital, I really want to help people work on problems that are not that just don't lend themselves very well to venture capital. They're sort of falling in this gray area of problems that people need to solve but are really tough to fund because they don't come with a business model attached.

51:16And I think they're very cool things to do now in the nonprofit space because you can use AI to review way more data than you ever could before. I'm not quite ready to talk about it publicly yet, but I do want to do something that helps researchers work on those problems and get grants to do it.

51:35Harry Stebbings:One really cool example I think of this is David Fialcow, who's one of the founders of General Catalyst, who basically finds incredible stories that won't get funded for movies and funds them to shine a light on them because he thinks they're very important. So like The Dissident, which obviously told the story of Khashoggi and Khashoggi being, you know. And then, you know, Icarus, which is the story of the Russian doping. And these were films that would not get funded had it not been for his funding because they are politically sensitive, charged, and he's like, I'm going to enable the stories of these forbidden tales.

52:11Yeah.

52:11Harry Stebbings:It's kind of like that. I love that stuff. He's great. He's fucking awesome. Anyway, are you ready for a quick fire round? Sure. Okay. What is the most underrated model on OpenRouter today? Ooh, good one. I mean, first, like poolside's models are great. I'd probably like my fire round answer, like new American lab, building interesting coding models, that are small, highly effective, and they're building a lot of useful tools for accessing them. Good team. 70 % of Neo Labs will die in the next three years. Agree or disagree? Disagree. 70 seems very high. Of Neo Labs, there aren't that many Neo Labs.

52:50If getting acquired by one of the model labs counts as die, I do think there'll probably be some potential consolidation. But if you include the consolidation, I'd say 50.

53:03Harry Stebbings:Do you think Dario should be less negative and more positive as a voice in AI? I think it's important to have somebody who is very paranoid about the future and how things are going to shake up. And I personally appreciate Anthropik's paranoia. Obviously, there are areas where I want other model labs to not feel like they're just being pushed off the table. But I'm a big believer in neurodiversity. And like Anthropic is a part of the neurodiversity map that really matters. And if no one is being extremely paranoid, then no one is like offering that voice. And so I appreciate that they're doing it.

53:45Harry Stebbings:What's the craziest thing that you see in your seat on top of everyone's usage that you don't think people talk about enough? I mean, a lot of companies are obviously worried about cost management and freaking out about the amount of inference they're spending. and they don't know how to think about it. It's like a whole new way of doing business and thinking about your OPEX. The old way of thinking about how much you give your employees, you give them a salary and you kind of forget about it. Someone knows what everyone's making, but it's a static number that gets readjusted on a quarterly basis maybe after performance reviews.

54:22Really, your employees all cost totally dynamic, different amounts now. I think a lot of companies are putting it on them to do routing. And I think in the future, there's a good chance that it will get pushed downwards to the employee level. Your employees should figure out which tools and models to use that are best for their tasks. And then we should figure out how much you're costing due to the choices that you make as an employee. Your cost as an employee is going to be a dynamic number. And it's going to be dependent on how much that employee is effectively using expensive and cheap models to do their job.

55:01I advise companies to kind of still do their normal management work, have their managers kind of assess how effective and productive employees are, but also line it up with how much their employees cost and then kind of come up with a quadrant of celebration. Like these employees are doing a good job and they're pretty price effective or cost effective. and then a quadrant of concern. These employees are kind of maybe doing a so-so job and whoa, they are not cost-effective at all. Their AI psychosis is off the charts. And then you address the quadrant of concern. So I don't think people talk about like basically how you think of like employee cost in the age of AI and that it's really, it should be a dynamic number and not a static thing that like only a few people know about and it's gone.

55:46Harry Stebbings:Wonderful, but can you imagine going to someone, oh, I'm sorry, you were worth 100 grand last month. Now you're worth 50. I think it would make planning. They're in control of how much they cost. That's the great thing. Like all employees are in control of how much they cost and can like influence that. Now you get to think like, okay, how good am I as an employee and how efficient am I being as well? Final one. When you look at the landscape today, there are so many things to be excited about. What are you singly most excited about? Two things come to mind. One is rare disease research, which I think is one of those things that has been intelligence bottlenecked or really just the inference bottlenecked.

56:28It involves trying out lots of ideas and seeing if they work. The other is crowdsourcing productive urban life improvements. For example, imagine if someone was curious about finding every lead pipe in America or every lead pipe in the UK and had an approach to it, but they really need to make it mature and stress test it. Now you can use AI to do that. And we just might solve some weird problems that everyone's just kind of given up on because you need a crazy idea to come from somewhere. Brilliant ideas are sort of evenly distributed all over the world. They can come from anywhere. And now you just give them leverage to actually work.

57:12So I'm excited about sort of very like broad kind of urban or rural quality of life improvements

57:19Harry Stebbings:that we'll be able to make. Alex, dude, I've wanted to do this one for a while. I'm so glad we could do it in person as well. I was worried that we were going to have to do it remote. It is so much nicer to do it in person. You've been fantastic. So thank you so much for doing it with me. Likewise. This was great. But before we leave you today, founders face a different set of challenges at every stage of growth. For Sid Shait, co-founder and CEO of Dematrix, JP Morgan delivered the guidance and expertise to help navigate what came next. He credits JP Morgan's high-touch approach with supporting Dematrix as it grew and expanded internationally.

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