"The CEO Must Be the Chief AI Officer"

25:35I mean, actually, as an applications engineer on top of the LLMs, that's actually a huge blind spot. And that's what Mercore and a lot of the out-of-data companies are doing. Like a lot of the jobs for them is to say, well, where are the mind spots for LLMs? And it's funny, like I think a lot of the data labeling companies right now trying to understand the pitfalls in the models. But the problem is in order to do that, you have to be an expert to know what the gaps are in the answers. But the problem as a founder, when you're looking for an idea, is you know nothing about it. So there is a curse of knowledge and a curse of not even knowing what the bounds of knowledge is, which I think can make you believe that you understand something that you actually – you in order to model actually understand.

26:17Can I confess something weird about like after creating G-Brain? Now I do use AI in a different way where now that I have a retrieval system that is actually usable, if I have a problem or question about anything. Like, for instance, I was trying to work on a really, really, like, the last humanized prompt. And, you know, a lot of that stuff probably isn't in distribution yet. There's a whole Wikipedia article about, like, you know, characteristics of AI writing. But, you know, now I can just go tell it. Like, go spend a day. Like, deep research literally every single paper, article. Like, read everything.

26:56Put it into my Git repo. And then I'll be able to retrieve it. and summarize it into something that actually is usable. And so it's sort of like filling in the things that are out of distribution. Like I can sort of like pack it with whatever context. And it's like you can do that with anything. It's like if you're interested in, you know, running a restaurant, literally you could have – you could go and read like 500 books about like every – the top 500 books about what it's like to run a restaurant. And you would have like the compendium of all information about it. Yeah, and I think a lot of what – like for example, Like one of the things that we do at Brax now is building this customer world model is a similar idea where we're trying to get every single touch point that the customer has of us.

27:40Like literally like what – how many times they click a button to the dashboard all the way to what they tell someone on an email or what they say on the phone or they send a call and ingest that and consolidate it. Okay, what should this customer need next from us? What should this customer be thinking about? Like what are the issues that they will face but haven't faced? And again, it's just a distribution problem. This is actually an answer to one of the questions, which is like, will there be jobs or whatever? It's like, as long as there are limits on RAM, actually, like there will be. So I don't know.

28:13I mean, it's kind of an interesting one, right? I think so. Literally, you can't have a model that has enough parameters that could like have everything that you could possibly need in distribution. Like there aren't enough atoms in the universe, right? It's like a modeling problem. I think we forget that the world models in which the models are trained, like there is something that the designers of the models influence the way the model actually behaves in the end. So, you know, one of the things that we spend a lot of time thinking is like how to make LLMs work for people that look very different from us.

28:44How to make LLMs work for like the average finance person in the U.S. that if you're talking about an answer and the model defaults to AI CapEx as a default category for like, for example, that's a really funny example. I was playing with AI for accounting categorization. And then the first example of an example of an, it's just like writing prose. And an example is like AI CapEx. And I'm like, oh, why is it AI CapEx? The first example it comes up with because the people that are building the models fucking only think about AI CapEx, right? So there are things like that that I think is kind of interesting to think about, that the mental models of the models, I think, are out of the box, are more biased than we may give them credit for.

29:28I mean, speaking of AI CapEx, like earlier you were saying, you know, we're so early still. I don't know. The funniest thing about AI to me is how often I find myself thinking crypto maxims. Yes. This is the worst the models will ever be. Yes. My favorite now is telling people who hate AI coding, like have fun coding at 1x speed. Exactly. Exactly. I was telling a friend about, you know, how to be, you know, long inference that basically the thesis is that there's going to be a lot more inference than people think. And people are expecting a lot of inference if you just look at public markets and, you know, semi-supply chain, all that.

30:04People are saying like 10 ,000x. Yeah. But the underwriting, which is kind of funny, is I think there's one image, 2 ,500 dots. Each dot is 3.2 million people on the planet. and basically, you know, 84 % of the world never used AI. 16 % have used at least once a free chat bot. Then 0.3%, which is, I guess, six or seven squares, paid 20 bucks a month for AI. And one box out of the 2 ,500 actually use agents in whatever capacity. So that's the argument to be long inference. And I think it's just starting out. And I think a funny thing on this is I think it will be the biggest expense in a company easily.

30:50And yes, there's a lot of margin in tokens right now, but people always want to be the bleeding edge. But even token costs decrease by 10x, they're going to have 10x more usage, so it will be still a large cost. And we're spending a lot of time thinking how to help companies actually manage token spend on Brex. we ended up building our internal version of this. We call it MagPi, where the idea is you can effectively, you know, every dollar of token spent in the company, you can attribute to a product we have to customers, an internal tool that we use to serve, or an internal employee, and understand model usage, et cetera.

31:26And we're now figuring out how to build analytics on what are we trying to do with the tokens to start to get a sense of ROI. But anyway, it's a fascinating topic that I think has a lot of early, early, early work compared to what it will be one day. Can you share any of the data that you've gotten from Brexit about just like what token spend is like in the economy? It's increasing. No, look, I think two things are surprising. One is, I think, to your point earlier on how do we how do we look at token maxing? I do think there's such a thing as how much cost boundaries you create internally dictate token consumption, obviously.

32:04But to me, I think what's the most fascinating is when you look into the sort of 10-mile radius we're in now, and maybe you include New York, tons of token consumption. And you could probably argue, and we've seen the data, also faster revenue growth. I think what's really interesting is the gap between anyone in these two 10-mile radius and everything else. And this is like not small companies. You look into like very large companies with very large budgets and that could be token maxing. And the economic thing for them to do would be to token max. and they spend like, I don't know, 10 ,000 a month.

32:46And you're like, you should probably be spending 10 times more or 20 times more or 100 times more. That's still surprising. And I think the reason is, again, sort of similar to the point in the beginning. Like we did this exercise two and a half years ago where I sat down with a lot of the engineering product leaders in the company and we had this question, which was if we started Brex again in 2024, the answer would be even more different now. What would we do differently? And it turns out like everything. and we start going down this route and it's like it's kind of maddening because you're like okay we have this like completely old way of like even thinking about the fabric of the company and and the way we build the product and the way we build our processes internally the first best answer is yes we wish we had started now second best answer is like let's go do something about it and change that we do things right and i think a lot of our approach in terms of like adopting i has also been, you know, how do you pause and say, okay, like there is a discontinuity in the, not just in how we solve the problem, but on what the definition of the problem actually even is and sort of take a step back and rethink it.

33:52And, you know, like there's like millions of examples of that, but, you know, one example, which is kind of funny is, you know, we're redesigning our KYC process. Like whenever we onboard a customer, we have to do all these checks to KYC, the customer. And KYC historically is something that you can automate like 80 % of it, 20 % is manual. And of course, the original impetus for anyone is that it's with an agent that does it. Yes, we can go do that. But what we decided to do is actually say, let's redesign the entire process end to end. And then what we redesigned is the entire onboarding process.

34:21And when you redesign the entire onboarding process, what you realize is there's a very important thing that happens in the beginning of the funnel, which is deal qualification. Like, is this customer even remotely qualified to be a Brex customer. But when you have KYC for free, you can KYC a lead versus a customer. So you start to have risk orientation up in your funnel and that changes who you even target because you know who's going to qualify. And the same thing is true for credit to some degree. So now the bounds of the problem have changed. And you can go in and say, and I think a lot of, including a lot of our competitors had this approach of saying, oh, I have this entire old process.

34:55Let me go and like latch on AI on top of it or latch on AI on top of our product. And I think the biggest discontinuities in a positive way that we've had were when we said, hey, let's keep this old way here, put it in a corner, and how would we design it if we started the company today from scratch? And then just doing that. It takes a little bit of founder energy to do that, but I think it's the only thing we've seen working to really sort of inflect.

35:20Pedro Franceschi:I think that reminds me a lot about this. It's sort of way back. I don't know if you ever try to compile ARC distributions of Linux. the culture within power uses of our clinics versus ubuntu is very different very different i think that ubuntu people kind of feel more like people that try chat gpt stuff kind of just works out of the box there's some stuff that you can get up and running there's still not a lot of people that use linux by the way which i think it feels where ai is but with arc you're like super hardcore and i think that's what open claw and hermes feel like because you have to really customize it to your own use case, maintain your skills, have all the markdowns.

36:02Pedro Franceschi:And if you get it working, you can build something awesome. One of the most impressive things I've seen people build with Arc is actually, I don't know if you know, Valve, the Steam engine, the operating system that makes it feel like a Nintendo Switch is actually built on top of Arc. Oh, interesting. They customize all the drivers, over-the-air updates. It works with all consoles. It works with all sorts of hardware out of the box. but they super duper customized it. And I think this is kind of what's happening. If you get your open claw to work really well for you, you could kind of build your own custom Nintendo Switch for whatever you need to do.

36:40Yeah, I always have this thing that I tell people, which is funny, which is, think about your time two years ago. Like, I feel like you're working a lot more now than two years ago, right? And probably the same for everybody here. So then the argument is, what about the productivity? Where's the productivity? Right? And I was talking to a very large public company this week. And she was telling me that we see all this token consumption. And, you know, we're trying to measure, like, product velocity. And we're seeing, like, more lines of code pushed. So, yes, maybe that's the way to measure the ROI.

37:13But is it really there because people are spending so much on tokens? And I think this analysis, like, yes, of course, I think having a sense on ROI on tokens is important. But I think it misses the point that you're standing in the timeline of history and it's six months after electricity was invented. Like thinking about – imagine someone saying in like, I don't know, the 1800s, like, oh, my electricity bill is so high now. Like, gosh, let's use a little less. Let's push this steam engine to come like maybe 20 years later because the cost savings – like, yes, of course. Like don't bankrupt your company on tokens.

37:52It's actually a perfect analogy because I don't know if you know this, but when electricity was first invented, it didn't work very well. And the ROI was actually bad. And so if shortly after the invention of electricity, some accountants had done this analysis, they would have been like, this electricity thing is like, is it never going to be a thing? The ROI sucks. Why do people stick to it? And it wasn't the cost savings. It was just because people were curious about it. And I think the point of like why, you know, like I was yesterday until 2 a.m. playing with slash workflows and Opus 4.8 and all that is because I think I'll be doing the exact same thing if I wasn't making any money because you just see the possibilities and you see what you can do to technology.

38:30And that just drives people to behave differently. And I think that to me is the ultimate litmus test. And it's a good separator. And sure, if tokens are so expensive, they're going to be, I think, over the fullness of time, probably free. If you project this, I don't know, 100 years online, almost compared to what electricity. Now we don't think of electricity costs in our day-to-day, unless we were in a data center. But I think there's something similar for sure. We talked to a lot of founders of later stage companies who wish that their companies could be as AI-pilled as possible. And you run this big company now with all of these employees.

39:10And that's only the Brex side. There's also the Capital One side. I'm curious what you've done to bring the rest of the company along with you on this journey and if you have advice for other people, for other CEOs. There's a lot to do. I think the CEO needs to be the chief AI officer. It's not an engineering team thing. It's not like a product team thing. It's like you have to understand the bounds of the technology better than anyone. I would argue that unless you really experience the limits of the technology every day, I think it's really hard to even understand what it can possibly do. Oh, you know why?

39:43It's because nobody can say no to the CEO except the board, and the board won't be in the weeds per se. That is 100 % true. When you go think about the whole example of KYC that we were saying, the KYC team would never think of using the KYC technology to score a lead. The only people that can think about the organization of the system itself is if you have the context of the whole. And to me, like the single most important question that any CEO needs to answer is forget about the competitive landscape. Imagine you could get the state of the technology today and transports to the moment you started your company.

40:22The opportunity was still the same. But just the possibilities of the way to build a company are totally different. How would you do it? And then diff this versus what you have. And then first suffer in silence for a little bit because you will. I mean, I do every day. But then the second thing is, okay, what do you do about it? And how would you do it if you were starting from scratch? You'll be the one figuring out, okay, how do we design our onboarding process or how we design our growth engine and our customer acquisition and the way we talk to users and the way we synthesize the data. And all of that would be redesigned from scratch.

40:54So I think it's almost like you have to sort of refound the very concept of what the company self-identity is and the way the functions and people's sense of success get structured. AI is an umbrella that I think has like three things in the way we talk about it internally. There's product AI, the product we actually ship to customers. There's operational AI, which is things that directly affect our ability to serve customers at scale, like think of customer success, risk, onboarding operations, et cetera. And then there's corporate AI, which is how people work internally. The three agendas matter and they matter in different ways depending on the timing of the company.

41:28And I think people will sometimes sort of pigeonhole themselves in one of the three. But in reality, I think you have to take a step back and be like, you know, the same thing we were talking about earlier. Like, why can't you solve everything with AI? At a limit, that's the question. And then sort of start from there and start problem solving around that question. It's a turnaround almost. I think you have to assume that if you're a big, large company that's not an AI native, you're doing a turnaround to some degree. I guess we've been making fun of Foxconn factories for some time. But on the other hand, like if you look at them, they're like this paragon of like very extreme efficiency.

42:02Yeah. But they also are designed to be that, to like create like one thing perfectly back to back to back. And so you have to build a factory like that. And most companies are designed that way, right? I think like processes are designed not to change. There is a certain amount of broken glass required. The question is how – I think it's 10x easier for the CEO to break glass than an executive. And 10x easier for an executive than an employee. So, you know, a lot of times like someone comes to me and says, I'm trying to do this with AI. but someone is saying no because we haven't tested this in this use case or in that thing.

42:36And I'm like, okay, what are you trying to do? Like, do you understand the risks? Do you understand the guardrails? Yes. Okay. It takes me literally 10 seconds to solve that problem. And it would take someone 10 hours to go in into the meetings and escalate and understand, okay, can we do this with AI? Or maybe never. And I think the conclusion is probably never because most people would say, you know what? I'm just going to like build this product in the old way because like, why wouldn't we? It just works. We know it's here. That guy's going to hate me. And then I have to look at that person in the lunch line every day.

43:07And it's like, I want people to be happy and like me. So I'm just not going to do that. And what I tell people is I think the escalation paths need to be desensitized in the system because the company builds antibodies against any sort of disturbance to the social cohesion of the company. Typically gets rejected by the antibodies. And I think making escalations faster and being like, hey, we're going to go try this thing. You know, I understand the risks. Let's take this risk. Because the biggest risk is not taking that. It's just literally missing the opportunity to rethink a problem from what would you do if you started the company today?

43:45On the corporate AI sort of leg of that stool specifically, like, do you buy into sort of like the Jack Dorsey view of every company is essentially trying to like build its own little company AGI? I do, but maybe in a slightly different way. I do think domain specificity matters. So I don't believe in the like, well, I'm going to have like a single company model that has like every piece of data, like in a single, like with no judgment or lens into anything. And the way I think about it more is more sort of the virtual employee analogy, so to speak, which is like, how do I build an agent or virtual employee that is exceptional at understanding everything that matters about this customer?

44:27That is a well-defined problem with clear boundaries of like clear APIs of who depends on the data, who interact with the data. That is self-contained. Then there's another agent that can be, okay, given all the customers that we have and the problems they have, how do I manage my product roadmap? That can be a separate agent, but that builds on top of this customer roadmap. Like a virtual exec team, basically. Exactly. Functional and domain knowledge still matter, right? These things are not going to go away. And I think the way knowledge is structured, I think, is still true, right? That doesn't necessarily change that much.

44:59And you should separate the agent and the systems that are actually emitting code from the system that is talking to customers and the system that is reasoning about the conversations with customers and translating into a product roadmap. These are three separate things. We're kind of like the Tesla for AI. We're like, I don't believe in anything that doesn't have real usage. So it's like, yeah, I built this great model and I'm like, okay, how many people are using it? Is it actually displacing the need to hire a person inside the company? Is it actually displacing the need to, you know, spend literally hours?

45:27Like how many hours is this thing saving? And I think a lot of times people say, well, you know, it's a cool model. And I'm like, yeah, but like, that's not going to cut it, right? Once you have that orientation i think customer role model okay like your for example our client sales team now runs on our customer role model so i know it works i'm actually having lunch of a customer tomorrow and i don't know the state of that account as well as i probably should customer role model answered a question for me and i now have a report with including things that the team didn't know about that came through support tickets and you know an executive that was traveling had an issue at an airport with their car.

46:03Total information awareness. Total information awareness, right? That is a well-defined problem. That is working. I can trust this building block as part of my company model as a whole. And you can have evals on it. Like we know, like, you know, I think a very, we should talk about evals. There's a bunch of learnings on this and how to build evals into the fabric of a company. But anyway, I think it's more of like, you have to decompose the problem a little bit. Yeah, my favorite thing about evals is just running cross-modal evals against each other. So one of the things that we're doing that is related, but I think is really fun, which is how do you have every single human interaction in a company becoming an EVOL when you have an AI agent?

46:45So, for example, we have the onboarding agents doing something. And then you have a team that actually goes in and looks at KYC exceptions that the model can't figure out. How to make that a breaking change? and okay, like this manual interaction will become an eval case. You know, we have an expense agent in Brex. Whenever someone has a conversation with the agent that is the flags, an issue or a bug or something that feels like the conversation didn't go as smoothly, that creates a bug. That bug triggers an agent that's going to go and modify the code base and the prompts and everything to make that eval pass.

47:24And if that doesn't break, Like that engineer is going to go in and figure out how to make that pass. Because the goal at the end, I think, is to make the whole thing a self-learning system, right? And I think a lot of what I see with companies is they spend a lot of time getting an agent working, but never thinking how to make the agent improve every day. And I think that's like always the biggest unlock. You need a dream cycle. You need a dream cycle. And the dream cycle sees everything every night. Exactly. And then it's like, oh, what's going on there? I need to put this over here. Exactly.

47:52What actually happened? Is there a pattern? How do I cause this? So how to bake the dream cycle into the products and into the agents and into things that ship? My favorite thing right now is I'm building like three or four agents for my friends. Oh, interesting. And some of it is like this is a user research for me for Gbrain because it's like I have one. It's working really well. I have 350 ,000 markdown pages in there now. What a crazy – like I thought it was this wild, you know, pie in the sky thing. And it's like it's going to happen in our lifetimes. You know, I remember when Neuralink came out and I used to think about it.

48:27I was like, I don't get it. I was like, yeah, of course I get it conceptually, but why is it a thing? And then now I use AI and you're like, yeah. Yeah, makes sense. Makes sense. I'm the bottleneck. Yeah, yeah.

48:38Pedro Franceschi:Typing is so slow. I don't know if you use a lot of dictation. I use a lot. My most used developer UI right now is like voice memos to OpenClaw. I said this before. It was maybe accidental, but I actually just really love the fact that like telegram works so well with um with audio because it's forced me to just put more stuff like make the agent more intelligent so that you can do more stuff via voice memos because you have to sort of fight the natural instinct as like a traditional developer where you're like oh like i can't quite do this or it doesn't do this i need to go like build more client or more ui or like more functionality for it foxconn yeah yeah exactly it's like no no just let it do what it wants to do give it some context and it'll just think about you know oh like actually what about this?

49:22I think a lot of the work, to your point, is how do you organize the context for the model? And you can use the model to help, but that is the bottleneck for most things. Once you have the context in there, it's actually, you can do some pretty crazy stuff. Like my favorite new feature. I saw your G-Brain LSD. Yeah. LSD, yeah. Lateral synaptic drift. So you just bump the temperature on the search? It's not just that. So you have the vectors, And so if you think about what conventional ideas are, most people give you like, oh, well, this idea with this idea, and it's kind of like in this cone. LSD mode actually says you cannot combine concepts that are within this cone.

50:03They actually must be orthogonal or just seemingly random. And then it'll try randomly hundreds of these combinations. And then it'll rank order them into the ones that are actually the most coherent. yeah and then if you do like a hundred of them actually like the top five tend to be banger tweets you know what's crazy is like i didn't tell afro explicitly to be dry i went i actually had like chat gpt generate like the soul file and it's like based on like everything you know about me all the interactions here like generate like a soul md for like my open call agent and it was so unerringly like accurate about like kind of what i would want from like a an agent and it's like Like, oh, damn, these models know a lot about us.

50:46My open clock got really interesting once I just ingested my 60 gig Google takeout. I mean, you have to write a bunch of haiku code to like only get the emails that are actually real. But, you know, there's like it extracted like 4000 emails out of 60 gigs that actually matter. But like those are like, oh, actually, like a lot of your thinking and the consequential moments of your life. So, Pedro, thank you so much for being with us. I mean, you're by far one of the most AI-pilled, farthest out on the edge, but also very practical CEOs who is playing with this stuff and actually building it yourself.

51:24What would you say to people watching who are founders, who want to be founders? I think that you are sort of the model for the way people should start companies and run them with AI as your Esalen buddy. I really can't stop thinking about the electricity analogy, which is, you know, you're standing, there's a 200-year timeline of human history. There's a point in time where electricity was invented. It sucked in the beginning or six months after that point. What do you do differently? Knowing everything that will be true about electricity, knowing that data centers will consume electricity and even AI, right?

51:58Well, you do a lot of things differently, I think. So I think that's one of just marveling at the possibility of the exact moment in time we're now. I think the second is like, have a post-it on your computer, which is you wake up, whatever problem you have in your life, why can't you solve it with AI? And just like start there. And 80%, yeah, you can use a chatbot, but a 20 % that you can't, figure out why and go build something that makes you solve that problem. Less so because of the immediate usefulness that solving that thing at scale will have, because it gives you a texture and a feel for the possibilities of the technology, which are really hard if you're not playing with it every day.

52:37And maybe the third thing is like, I think it's like just measure your token consumption and how much you're just pushing the limits of the company and starting at the premise of like, okay, why can't it just be one person? Like, why can't it just be me that builds the whole thing? And you're going to probably face a wall of the elements of, you know, what models can and cannot do. But at a limit, I think the question is, you know, how do you spend your time on the things that only you can do as a founder? And these things to me are, number one, which problems are worth solving, and two, and sort of the choice thing we talked about.

53:11And the second thing is, okay, given these choices, what are the limitations of an LLM that they still cannot do? And I have to go in and do those things myself. But almost, you know, to some degree, you're working for the LLM to some point. And if you're in a bigger company, you're in a turnaround to put the LLM as almost a founder and a CEO. and you're almost architecting the entire company around that idea. But I think early on, so much of it is, you know, choosing what matters, talking to customers, injecting the signal that models don't have, and just, you know, rebuilding it the way you would do it in 2026 with electricity being six months old.

53:50Thanks, Pedro. This is awesome. Yeah. Thanks for having me. I appreciate it. Thanks for coming.

54:02You