The Most AI-Pilled CEO We Know

You wake up, whatever problem you have in your life, why can't you solve with AI? And just like start there. I think the CEO needs to be the chief AI officer. Like 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 think a good proxy for how to spend your time is what are things that only you can do and the models cannot do. You have to sort of refound the very concept of what the company's self-identity is.

Welcome back to another episode of The Light Cone. Today, we're joined by Pedro Franceschi, co-founder and CEO of Brex. Pedro started Brex in the YC Winter 17 batch and built it into one of the most important fintech companies of the last decade. He's here today because Brex has gone deeper on AI than almost any enterprise company we know. entire team down a rabbit hole of building on their own. So Pedro, welcome to The Light Cone. Thanks for having me. Excited to be here. Thanks for changing our lives. Yeah. Oh God, that lunch.

I'm like, I think the model company should be sponsoring me for it. The token consumption increase I generate. We supposedly generated on that lunch. That was the precursor of Gbrain, I guess. I was still working on GStack. I was still a 2013 Web 2.0 engineer who time traveled instantly to the AI tools of January 2026. I was, you know, probably... half a million lines of Rails code in, and I could create a G-Stack because of that to, like, help me make a software factory. Yeah. And then after I met you, I realized everything is about freeing the claw.

Free the claw. I knew you were going to say that. Yeah. And then... And give it tokens. Yeah. Well, no, I mean... Let it rip. The craziest thing was realizing, like, what I had gotten wrong that I think actually most people in software are still getting it wrong is they've been treating the LLM... like this very precious thing that's very expensive. And so as a result, you have to literally put the agent inside a Foxconn factory. And it's like, can you imagine? I mean, that's what the half a million lines of Rails code was for me.

It's like, no, no, no, I need to control what the LLM sees because it's about really, really, like, I only want the context from here and let me write all the if statements to make sure, like a Foxconn engineer, you're waking up and if you don't, you're going to get electroshocked. I mean, like, this terrible thing that you do to agents. Yeah. And they want to be, like, at the Esalen Institute, and that's what OpenClaw is. Exactly. And it's funny because I feel like every single good AI product you've used, is an agent loop with tools.

That's it. Like there's no, you try to sort of over-engineer the harness and then do certain things, but at the end of the day, it's, skills to, and a model. There's not really much else. Maybe we start earlier, because one of the things we'd love to kind of get down as a part of lore is how did you... get so AI-pilled and like all the way to the edge. Well, I'll tell you my encounter with LLMs, which was, so I remember in the pandemic, there was someone, someone gave me an API access to GPT-3 and I was playing with it and I was like, okay, this is, this is really cool.

This is, there's, there's something here that could be, could be special. But it was the kind of thing that was like, yeah, it feels like a research project. The kind of thing that Google used to release and you play with it for 10 minutes and you stop. thought, interesting was when you started to see reasoning models and of course tools. But I think everything else was sort of a blip until December. And the way I describe it to my team is like, you know, electricity was invented in December. And I think electricity was Opus 4.5.

And sure, Opus models and, you know, OpenAI models got better and better since then. But to me, that was the tip of the sphere where you could say, yes, like, coding harnesses actually work. And, you know, cloud code existed for probably a year before, but it wasn't that valuable yet. And I remember, you know, during the holiday break, I was playing with it and it was pretty shocking, probably similar reaction that everybody here had. And I think the question becomes, you know, if you sort of, if you think about, you know, you're sort of standing, you know, looking at 200 years of history, and then you imagine you are, we're now in May, you're sort of five or six months after electricity was invented, you know, questioning, you know, what can you do with candles and fire and, you know, like...

Who needs light? Yeah, exactly. It's not so bright. What about these lanterns and what can you do with it? And, you know, the steam engine is like, I don't know, maybe like 20 years away still. But, you know, electricity already exists. That to me was the... sort of the fundamental, uh, uh, light behind it. And I would say, I think since then, OpenClaw was kind of an interesting sort of next step, which is, I think, when we realized that, uh, The reality is good AI products are agentic loops with tools.

And we started doing that in our own product at Brex, but then on the personal side, I started spending a lot of time understanding, okay, what is at the frontier of using OpenClaw? And I think the insight was just, yeah, like markdowns can take you really far, just like configuring and automating a lot of the things in your life. It's kind of funny. I remember I had this experience of like buying a movie ticket entirely in OpenClaw using like a Brex card. It was provisioned through an API. And then I showed it to my team and they were like...

oh, but you can go online and book it in 10 seconds. And I'm like, that's not the point. You're completely missing the point. But anyway, and then I went obviously very deep in this rabbit hole and started spending a lot of time thinking how to change the fabric of the company and the way we build the products. Tell us more about your personal OpenClaw journey. Because before you came for lunch, I had it in store, but I was way too scared to do anything with it. We were all scared. Yeah, don't get me wrong.

We deal with financial services data. We spend a lot of time figuring out how to be mindful of security and protection. vignettes. I think people are... a little bit more risk averse than the technology probably requires them to be, given where the technology is. And when we started using OpenClaw personally, I started doing it in a lot of my own personal setup. Basically what I did in the V1 was I'm going to give it read access to everything and just create like OAuth tokens to my email, to Slack, and to everything to just literally not write.

And I was kind of shocked how far it got me. And then the next question that we spent time on Brex was, okay, like, How do we actually get it to right into our systems? And everybody, including our security team, was, well, we cannot do that for all the reasons that we know. And then basically where I spent, I don't know, probably four weeks of my time was, okay, let's solve the hardest problem, which is security. And we ended up realizing that the only way to actually do something about it was to...

do something in the network layer. And if you treat the agent like the agent has its own wills desires and you know they they go to the aslan institute for for for agents and uh you know they have a foxconn factory instead of foxconn's factory they will try to do things that are network boundary that that could not be the right ones um and we decided to actually just focus on that so a lot of folks were you know and we saw nvidia and others on nimble claw let's build these like open shell forks that have controls over you know what tools the model calls and the reality is Yeah, you can do all that, but you can also just make an HTTP request wrong.

So we focus on that layer, and then we build this thing called Crab Trap, which we open sourced probably about two months ago, which is actually the way we use to secure agents at Braxin production. And the basic premise is you analyze, you HTTP proxy the entire network boundary of an agent. another agent to analyze the traffic and create a policy to let traffic go through or not. And surprisingly or unsurprisingly, because these models are trained on, they're trying hundreds of billions of web documents, HTTP traffic is actually, I would say, probably the way the models reason more so than anything else, because they just literally learn on the web.

So the ability of the model to watch like 1000 requests and make sense of what's happening was way higher than we anticipated. So we actually build that, put that in production of Brex. And after you record the traffic of an agent operating for a day, you can build a pretty good policy. uh that you know sets things that should be automatically approved and for things that you the agent isn't really sure uh you can just use an llm as a judge and the llm determines is this request something that should be approved or not based on the policy for what that agent should be doing so for example we have like a recruiting agent at brex called jim uh we have a policy for jim uh and you know all the traffic goes to that same policy and 98 requests go through automatically two percent use an llm so we sort of got that problem solved to the degree that we got comfortable much more aggressively and sort of freeing the clock on the enterprise, which is really hard inside Capital One.

So I would say if we found a way to experiment with these things and granted, we don't do the most aggressive things with this stuff yet. We don't use it on like, you know, customer data to the degree that we want one day to do. And there's boundaries to how we do it. I don't see any reason why a YC company shouldn't be at the bleeding edge of the stuff. Yeah, I mean, I think your intuition around, like, the proxy at the network level ended up being quite prescient. Like, I think a lot of the stuff that I'm seeing kind of around the OpenClaw ecosystem at the moment, at least, or just agent ecosystem is essentially doing that.

Like, we're seeing that, like, with credentials, credential brokering. Like, Agent Vault is doing a lot of that. I think you had mentioned the first version of Crap Trap included, like, credentials, Vault. Why did you decide not to include that? I think it was just, let's just do one thing really well. I think there's going to be a lot of solutions that do that. You could do credential brokering and other tools already. But the LM as a judge was for us the determining capability to say – do you trust us in production or not?

In our security team at Brex, very rigorous and very good at what they do. for a long time were, well, you know, not really. So getting them to a, yes, we actually believe this is enough, was a big unlock for us. And look, I always say this, we're not in the business of building HTTP proxies. We are in the business of being the bleeding edge of what we can do with AI. And to get to the bleeding edge required us to build this proxy. That's why we did it. Hopefully someone's going to build a YC company.

Hopefully we're just going to build a better version and we're just going to go use it. But at the end of the day, That's the journey that took us to being at the bleeding edge in a way. How much was you pushing this forward? How much resistance did you get internally? How did you get everyone AI peeled? I think there was a lot of excitement about it. But the way I describe AI adoption inside most companies is... I think there's like... Sort of. Three tiers. There's tier number one, which is your token maxers, like your engineers that are pushing a bunch of code and typically living inside coding harnesses.

And those are sort of well known. We know who those are. Then you have the sort of average engineer. that is building a few things, but not sort of token maxed to the same degree and probably I don't know. a tenth of the productivity. And then you have like the entire rest of the company. And the entire rest of the company typically is interacting with AI in what I call like Google search mode way, which is a chatbot with a few MCPs or G Suite equivalent. Yeah, you have a few tools from Google, but at the end of the day, it's really just like a search.

And I think that our thesis was if you think about the value that AI creates for AI, like a token maxer, for example, a lot of the value comes from the harness. And the thesis was how to actually build an equivalent harness for other teams that are non-technical. And our whole sort of thinking behind it was like, that's a lot of what OpenClaw you know, created, which is this ability that you can self-bootstrap a lot of the capabilities of the agent by the way you edit your skills and markdowns and sort of set up the environment around the agent.

And how far can we get this ability to, for the agent to self-bootstrap a capability without anyone actually going and coding it by hand? So the analogy we use internally for, I would say, the sort of the company-wide adoption of AI is we don't believe in the, want is, in my opinion, is really a way of saying, okay, this is actually a virtual employee almost that has, you know, it's on Slack, it has an email, I can actually invite it to a meeting, can join a meeting, take notes. And you're trying to replicate that as much as possible.

So how do you build the infrastructure to support that kind of use case? And I think the harnesses will look a little different and probably more like open claw than a coding model. Jared and I just did this this week for the first time where we installed AquaVoice and then you open Telegram with the claw, or actually we have it in Slack now. And then basically it was like, Me and Jared and like three engineers and someone from the events team. And we're trying to put together, how do we put together 60 dinners with 20 people each of attendees from startup school with 21 partners and visiting partners at YC?

Sounds like a great problem. And then we just basically started talking about it. And then I picked that up and I pressed enter. And then, you know, our claw just started doing it. None of us opened clawed code. It sort of built a bunch of markdown. It did the analysis. Yeah, people forget that Cloud Code is a magic. It's just literally a harness around the same models you can use in an API, right? So I think that's the unlock of – and by the way, there's a few things that Cloud Code is doing that I think are really cool.

Oh, they're amazing. And yeah, it's just a harness. And Cloud can use Cloud Code. Exactly. And Codex, right? It really prefers to use Codex these days. Exactly. It really does. Everything, actually. I don't know why. Exactly. But ACP helps in that. Yeah, ACP is good. Why do you think the adoption of token maxing hasn't really taken off? The thing that we've... found it very curious working with a lot of startups early on is, A lot of founders are very shy about burning tokens. I think you really get to experience this when you really go all the way.

Gary mentioned this point, which is tokens are expensive. And I think there are... I'm in a fortunate position to be able to spend on tokens, but I would say, I keep trying to picture myself, imagine if I was like 14. or 12 when I started coding for real and I had the technology we have now, I would be token maxing in the cheapest way possible. And there are people doing that. You know, you look at the Chinese models, for example, like they're, they're pretty decent. There's a huge, um, uh, hobbyist community where they, you know, build a gaming rig, but then they try to build like local LLM.

And then that actually is like totally reasonable way to do it. A hundred percent. A hundred percent. I have a friend that, that, that has the exact same setup. He has his like little and the first time I went there I was like wow heating is on here it's really warm and he's like no no it's my GPUs and I was like great like you know power efficiency all the way through it's funny because at Brax and we should talk about managing token costs and spend management for tokens, which is a topic we're spending a bunch of cycles on now.

I think the cost part is one. But even if you take the cost part aside, You know, the first symptom is a lot more people should be complaining about the max plan. limits. And you know, I, you see what's the percentage of Twitter that probably complains about it, like 0.1%. So, so I think, I think we're probably still early to me. There's this like, the AI pill... Test. in my opinion, is... Whatever problem shows up in your life, do you default to AI first or not? It's like, of course, mechanically you can do it, But there's a point that it becomes like second nature and then your whole like – brain gets rewired and you cannot think in a different way.

And there's the whole topic about AI dependency, human-machine interaction. There's all these things that we can talk about and put in the corner. It still sort of surprises me how many people you go talk to about a problem. And I'm It's so cheap to intimately understand the bounds of this problem now. Like, why haven't you done that yet? And come in with, like, a much more digested view on the problem. And I think the second thing is, like... I think if you have the luxury of building a company now, the fabric of the company from day one can be built in such a different way that I think – If I were to start a company today, I would say, Okay, the premise is why can't it be just me?

Like, and then you start from there and your token consumption is probably going to be a lot higher than if you said, well, I'm going to have like three people or five people or seven people. And I think the fundamental constraint isn't as much, in my opinion, like, oh, like AI as a cost savings or I'm going to be more efficient. I think the unlock is like the fabric of the company just looks very different when the boundaries become type systems, interfaces. agents talking to each other versus people. And I think people are still didn't fully grasp by, okay, what does it mean to build code with new agents and the new technologies we have?

I think that's well understood. But... how to live in a world where intelligence is on a tap. And your default answer is... let me actually solve this problem with AI first, even if you feel suboptimal. And then from there saying, okay, how do I actually make it optimal? Because I think for the majority of problems, there is a way to solve it with AI that is probably better. And your job is to figure that out. even if it's going to take you more time because that will compound. YC startup school is back.

We're hand selecting the most promising builders in the world and flying them out to San Francisco for July 25th and 26th to discuss the cutting edge of tech and startups. Apply now for your spot. When you started Brexit, I mean, it's well-known. You're like MVP, had no web URI, which is all terminal, super scrappy. Yeah, today it would have because no one needs HTML. It's just testing more. Was it actually still the right approach, just have a really simple MVP and test that anymore? 100%. Would you have like a way more fully featured?

So I have this controversial view, which maybe you all will disagree, which is like, I actually think if I look into a pattern of companies that succeed. I think there's a... really interesting pattern, which is minimal surface area. And the problem is with AI, I think you see like look at Stripe, for example, Stripe earlier days was like literally an API. Brax in the early days, no UI, just like literally a terminal. You look at Airbnb is like the website was a form. And the form was just like literally where you inputted what you needed.

And then someone somehow went there and figured out how to actually make the booking happen. Like DoorDash in the early days, similar, right? Like it was just like literally – so the surface area was so small. with the customer. And so much of the intelligence and the bandwidth of the founders were spent nailing this one single interaction pattern. And I think the risk with AI is that the agency behind choice, goes away. So you have this lack of discipline on what matters to solve. And I think people tend to believe that I can just experiment a lot of things.

And that's absolutely true. But that doesn't preclude you from actually choosing what matters. I always tell people, I think if you can't minimize your surface area, and solve the problem with a very clear set of boundaries, you haven't found the right problem to solve. And I think that's... And you can, of course... find how to compress the problem into a smaller surface area using AI. And that's really valuable. But I don't think you should use it as an excuse to not do that, which I think is, well, I can just build so many other things.

But, you know, I always tell this to people, intelligence is compression. So when someone comes to pitch me an idea in the company, I'm like, It has to fit in a napkin. Like, where do you fit in a napkin? What's your napkin? And then someone comes with this, and I'm like, I don't know where you buy napkins, but the ones in my house are not this size. How about the step before it, then, even? Actually, a lot of the pivot advice I give founders during the batch comes from... you talking about how you found the brex idea and if i like the approximate view i remember is that you thought about it as like two week cycles and like you're either in like exploration or exploitation mode and you're like trying a bunch of things but then you want to like hone down like would you still use that pattern now 100 i think one of the most one of the hardest things of building a company is talking to customers and and and and not not just having the conversation how to extract the sort of unspoken signal from these conversations and i think to me the the Can AI solve this lens?

Like whatever problem shows up in your life, can AI go solve that? And you think about like building a successful company. Like why can't you prompt your way into that? And the reason is very simple is because there's signal that the models weren't trained on. And the signal is when you go talk to a person and they tell you about a problem they have, They're not going to tell – they're not going to give you the answer shaped – into a prompt that you can put into an LLM and that LLM is going to go and output the product that's going to win and be a billion dollar company.

They're going to tell you a very sort of local optimum answer based on their worldviews and their constraints and the way they see things. And I think a lot of the job. is the job now is to have the wisdom to choose what you want. And because before the wisdom was not just to choose, was to choose and know how to execute it. The execution is out, right? The execution is gone and the model is going to do that better. The wisdom to choose is still, I think, the missing bottleneck.

And to me, that all comes from which signals are not in the models. So say like pre-AI, you had personal bandwidth to explore like three ideas in parallel. You're saying like now in AI world, you'd still do three in parallel? Would you like 30 and let the models try and... Let's pick a broader universe of things to do sort of an early initial exploration. But to me, the lens is, okay, why can't AI solve it? And which signal is not in the model? And I think the signal is typically the customer.

And then when you go talk to the customer, I think I wouldn't paralyze that probably. I would be, okay, let's try to get into headspace of this person. And I think there's like, it's so easy. And we did a lot of exploration with like synthetic customers and building customer world models and things like that. And those are really valuable. Once you know a lot about the customer, but when you don't know enough yet, I think there's this like, Very basic thing, which is even at Brex, for example, one of the hardest things for us as a company was we initially sold to founders.

We were founders. We knew about ourselves. We knew about our problems. And then as the company got bigger, we were selling to finance teams. And finance teams are different. So building that mental model of like what's the value system, like of course, you can eventually make the model represent that and have that worldview. But there's an intangible. That I think is where a lot of the alpha still comes from. And I think to me is like the, I think a good proxy for how to spend your time is what are things that only you can do?

And even in the company of one, what are things that only you can do the models cannot do? And that to me is like one of them. I think that's so on point. I think a lot of founders like you that successfully navigated Pivot have this loop. people are able to simulate what the other person is thinking and what the other and others. Theory of mind. Exactly. And I think the founders that get that and have the empathy to figure out what the customer is not verbalizing is what is make the, I think Gary says this, make the implicit explicit.

100%. of what are all those desires. 100%. And they're very subtle signs a lot of time because they're murmurs as founders go through them and figure out the insights that, oh, is this really a thing? But how do you know when to poke for it? Exactly. And the problem with relying on models and right now, which is, I'm still very optimistic that there's still a lot of job through founders. Oh, definitely. Is you don't even know what the right incantation or set of prompts to ask the model because they're there, you don't even know what to ask.

Exactly. There's like another meta layer. of like, you know, which question is the university answer for kind of and of course these these are these are generalities right but but I think what I've seen is you have to remember LMs are not magic. LMs are trained on a very specific corpse of information, optimizing for a very specific set of benchmarks and outcomes. And I think the biggest pitfall of LMs is... you have no sense of how much training data the model has seen for the exact thing that you're asking it.

So imagine if like every time you ask an algorithm a question, It gave you like, yeah, like the sampling frequency of this in my data set was... I don't know, X and on this other answer was 0.00001X. You would trust it's very different, right? The distribution is so different. Oh, I would pay for that. That's a great startup idea. Exactly. So we should do that. We need to do a model that does that. Yeah, 100%. I would pay for it. It's fascinating because then anything that's out of distribution, you just go and fill that in.

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 other 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 is the founder when you're looking for an idea is you know nothing about it so so the so there there is a there's a curse of knowledge and in a curse of not even knowing what the bounds of knowledge are is uh which i think can can can make you believe that you understand something that you actually you and our model actually understand can i confess something weird about like um after creating g brain now um i i do use ai in a different way where um now that i have a retrieval system that is actually usable if i have a problem or question about anything uh like for instance i was trying to work on a really really like the last uh humanized prompt and uh you know a lot of that stuff probably isn't in distribution yet there's a whole wikipedia wikipedia article about of AI writing, but Now I can just go tell it.

Go spend a day. Deep research literally every single paper, article. Read everything. Put 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. I can sort of like pack it with whatever context. And it's like you can do that with anything. 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 like 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 say on a call.

and ingest that and consolidate a 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. I 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 LMS work. for people that look very different from us. How to Make Helens Work for, like, the... average finance person in the US that if you're talking about an answer and, you know, the model defaults to like AI CapEx as a finance, as a default category for like, Like, for example, that's a really funny example.

I was playing with AI for accounting categorization. And the first example of an example of an expert, 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.

I mean, speaking of AI CapEx, like earlier, you're 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 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 semi-supply chain, all that. People 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, 84% of the world never then 0.3 percent which is i guess six or seven squares uh pay 20 bucks a month for ai and one box out of the 2500 actually use agents in whatever capacity so And 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 the, you know, it will be the biggest expense in a company, like easily. Right. And 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, you're going to have 10x more usage. So it will be a still 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, you know, model usage, et cetera. And we're now figuring out how to do the 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, you know, I think to your point earlier on 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. But to me, I think what's the most fascinating is when you look into... the sort of 10-hour 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 Bitcoin. Anyone in these two 10 mile radius and everything else. And this is like not small companies that 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. And 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, you know, a lot of the engineering product leaders in the company. And we had this question, which is if we started Brexit again in 2024, I would have to do this. The answer would be even more different now. what would we do differently? And it turns out like everything. And it started going down this route and it's like, it's kind of maddening because they're like, okay, we have this like, completely old way of like, even thinking about the fabric of the company and the way we build a 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 let's go do something about it and change the way we do things, right? And I think a lot of our approach in terms of like adopting AI has also been 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. And, you know, like there's, there's like millions of examples of that, but One example, which is kind of funny, is we're redesigning our KYC process.

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. 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 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 let me go and like latch on ai on top of it or lash on ai on top of our product and i think the the biggest is continuity is in a positive way that we've had, or when we said, hey, Let's keep this old way here.

keep putting it in a corner and like 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... It's the only thing we've seen working to really sort of inflect. 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. Mm-hmm. the culture within power users of our clinics versus Ubuntu is very different.

Very different. I think that Ubuntu people kind of feel more like people that try ChatGPT. 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. And if you get it working, you can build something awesome.

One of the most impressive thing 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 customize 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.

Yeah, I always have this thing that I tell people, which is funny, which is, Think about your time two years ago. I feel like you're working a lot more now than two years ago. Right. And probably 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 the soaking consumption and we're trying to measure product velocity and we're seeing more lines of code pushed. So, yes, maybe that's the way to measure the ROI, but 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 And it's six months after electricity was invented. Like thinking about it. Imagine someone saying in like, I don't know, 18, the 1800s, like, oh, my electricity bill is so high now. Like, gosh, let's use a little less. Let's keep this push the 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.

It's actually a perfect analogy because I don't know if you knew 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 of 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, 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 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 and 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, but unless we were in a data center. But but I think there's something there's something similar for sure. We talked to a lot of people. founders of later stage companies who wish that their companies could be like as ai pilled as possible and you run this like big company now with all of these employees and that's only the breck side there's also the like how to once i'm curious what you've done to like bring the rest of the company along with you on this journey and if you have advice for other people well other there's a lot to do i think the ceo needs to be the chief ai officer like it's not a engineering thing is like you have to understand the bounds of the technology better than anyone i would argue that unless you unless you really experience the limits of technology every day, I think it's really hard to even understand what it can possibly do.

Oh, you know why? It'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 Bye. you know, the whole example of KYC that we were saying. The KYC team would never think of using the KYC technology to score a lead. So, 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 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 transport to the moment you started your company. The 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, OK, 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, OK, 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 you synthesize the data? And all of that would be would be redesigned from scratch. So I think it's like it's almost like you have to sort of refound the very concept of what the company's 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, 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, etc. 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. And 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. Yeah. 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... I think it's 10x easier for the CEO to break last than an executive. And 10x easier for an executive than an employee. So a lot of times 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.

And 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 build this product in the old way because why wouldn't we? It just works. We know it's here.

Well, that guy's going to hate me and then I have to look at that person in the lunch line every day and 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 like desensitized in the system because the company builds antibodies against any sort of you know disturbance to the social cohesion of the company typically gets like rejected by the antibodies and i think making escalations faster and being like hey we're gonna 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? On 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 and I do but but maybe in a slightly different way I do think domain specificity matters So I don't believe in the like, oh, 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.

So, and the way I think about it more is like, it's more the sort of the... the 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. That is a well-defined problem with clear boundaries, of like clear APIs, of who depends on the data, who interacts 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 world.

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. And 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 for AI, we're like, I don't believe in anything that doesn't have real usage.

So it's like, yeah, I build 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, like 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, that's not going to cut it, right? Once you have that orientation, I think, Customer role model. Okay, like, 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 the 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. Total 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 Evol. There's a bunch of learnings on this and how to build Evol into the fabric of the company. But 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 I, I, I, it is related, but I think it's really fun, which is, How do you have...

every single human interaction in the company becoming an EVOL when you have an AI agent. So 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. And if that doesn't break, then an engineer is going to go in and figure out how to make that ass. Because the goal at the end, I think, is to make the whole thing a self-reported. 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. What 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 to 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 agents. 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. I 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 ball rack. Yeah, yeah.

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 open claw i said this before like it was maybe accidental but i actually just really love the fact that like telegram works so well with um 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 Just let it do what it wants to do.

Give it some context. And it'll just think about, you know, oh, actually, what about this? I think a lot of the work, to your point, is the... how do you organize the context for the model and and you can use the model to help but but that is that is that is the that is the bottleneck for for most things once you have the context in there it's actually you can do some pretty crazy stuff like uh my favorite new feature i saw your lsd yeah yeah lateral synaptic drift so you just bump the temperature on the search it's not just that so you have the vectors right yeah and so you know if you think about what conventional ideas are It's like kind of like in this cone.

LSD mode actually says you cannot combine concepts that are within this cone. They actually must be orthogonal or just like seemingly random. And then it'll try like, you know, randomly hundreds of these combinations. Yeah. 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 Alfred explicitly to be dry. gpt generate like the sole file and just like based on like everything you know about me all the interactions here like generate like a sole 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 i was like oh damn these models know a lot about us my open clock got really interesting once i i just ingested to like only get the emails that are actually real.

But you know, there's like, it extracted like 4,000 emails out of like 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-peeled, farthest out on the edge, but also very practical CEOs who is playing with this stuff and actually building it yourself. 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'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 and they will consume electricity and even AI, right? Well, 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 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.

And maybe the third thing is like, I think it's 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, how do you spend your time on 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. And 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 going to turn around 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. Thanks, Pedro. This is awesome. Yeah. Thanks for having me. I appreciate it.