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you wake up whatever problem you have in your life why can't you solve it with AI and just like start there I think the CEO needs to be the chief AI officer like it's not a engineering team thing it's not like a product team thing it's like you have to understand the bounds of a technology better than anyone I think a good proxy for how to spend your time is what are things that only you can do the models cannot do you have to sort of refound the very concept of the what the company selfidentity is welcome back to another episode of the Liteco. Today we're joined by Pedro Franchesci, co-founder and CEO of Brex.
Pedro started Brexy Winter 17 batch and built it into one of the most important fintech companies of the last decade. He's here today because B has gone deeper on AI than almost any enterprise company we know. And Pedro's own AI setup is so compelling that when he came to YC for lunch, it sent our 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 the token consumption increase I generate we you know supposedly
generated on that lunch. That was the precursor of G Brain. I guess I was still working on GStack. I was still a 2013 web 2.0 engineer who timeraveled instantly to the AI tools of January 2026. I was, you know, probably half a million lines of Rails code in and create a GStack 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 uh you they've been treating the LLM like this very precious thing that's very expensive. Yeah. And so as a result, you have to literally put the agent inside a Foxcon factory. And it's like can you imagine like 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 like I only want the context like from here and let me write like all the if statements to make sure like, you know, like a Foxcon engineer, you're waking up at 6:00 a.m. and you
know, if you don't, you're going to get electroshocked. I mean, like it's just like this terrible thing that you do to agents. Yeah. And they want to be like at the Eselin Institute and that's what OpenClaw is. Exactly. Exact. 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 overengineer the harness and then do certain things, but at the end of the day it's skills tools and a model like there's not really much else.
Maybe we start earlier because one of the things we'd love to kind of, you know, get down as a part of lore is like how did you get so AI pill and like all the way to the edge? Well, I'll tell you my encounter with LMS, which was so so I remember in the pandemic, um there was someone gave me an API access to GPT3 and uh and I was playing with it and I was like, okay, this is really cool. This is there's there's something here that 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. Uh chat came out and I think everybody was sort of interested in it. where I think
it got interesting was uh when you started to see reasoning models and of course tools but I think everything else was sort of a blip until December um and the way I describe it to my team is like you know electricity was invented in December uh and I think electricity was open 4.5 and sure open models and you know openi models got better and better since then but to me that was the tip of the spear where you could say yes like coding harnesses actually work and you know cloud code existed for probably a year before uh 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 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 and most people are still playing with candles and you know questioning you know what can you do with candles and fire and you know like who needs light so bright what about all these lanterns and what can you do with it and uh 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 light behind it and I would say I think since then open cloud was kind of a interesting sort of next step
which is I think when we realized that uh you know The reality is good AI products are agentic loops with tools. Uh and we started doing this in our own product at Rex but then on the personal side I started spending a lot of time understanding okay what is at the frontier of uh using openclaw and I think the insight was just um 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 uh and then I showed it to my team and they were like, "Oh, but like you can go online and like book it in 10
seconds." And I'm like, "That's not the point. You're missing you're completely missing the point." Uh but anyway, and then I went obviously very deep into this rabbit hole and uh started spending a lot of time thinking how to change the fabric of the company and the way we build the products and tell us more about the your personal open claw journey cuz I before you came for lunch I had it like in store but I was like way too scared to do anything with it. We were all scared. Yeah, don't get me wrong like we deal with financial services data. We spend a lot of time figuring out how to be mindful of security and protection. But and yet uh I think people are a little bit more riskaverse than the technology probably
requires them to be given where the technology is. And when we started using open cloud 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 ow 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. Um, 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 in Google security team was well we cannot do that for all the reasons that we know and then basically where I spend I don't know probably four weeks of my time was okay let's solve the hardest
problem which is security uh and we ended up realizing that the only way to actually do something about it was to do something in the network layer. Uh and if you treat the agent like you know the agent has its own wills desires and you know they go to the ALM institute for agents and uh you know they have instead of Foxcon's factory they will try to do things at a network boundary 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 Nemo claw let's build these like open shell pro 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 uh probably about two months ago which is actually the way we use to uh secure agents at Brax in production. And the basic premise is you analyze you HTTP proxy uh the entire network boundary of an agent. And the idea is um when a request goes through that becomes auditable and you basically can uh use 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 you know 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 a thousand requests and make sense of what's happening was way higher than we anticipated. So, so we actually build that uh put that in production at Rex and after you record a 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 LM 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 BRS 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 2% use an LM so we sort of got that problem solved to a degree that we got comfortable experimenting much more aggressively and sort of freeing the clock on the enterprise which is really hard inside uh capital one so I would 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 there's boundaries to how we
do Uh 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 precient. Like I think a lot of the stuff that I'm seeing kind of around the open claw 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 uh credentials vault. Why did you decide not to include that? I think it was just let's just do one thing really well. Um and
you know at the end of the day 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? And our security team at BR very rigorous and very good at what they do for a long time were well you know not really. Uh to getting them to a yes we actually believe this is enough uh was a big unlock for us. And look, I always say this like we're not in the business of building HT proxies. We are in the business of being at the bleeding edge of what it 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 going to build a better version and we're just going to go use it. But at the end of the day, um that's the journey that took us to just sort of being at the bleeding edge in that way. And how much was you like sort of pushing this forward and like how much resistance did you get internally and just how did you like I mean AI build 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 uh that is building a few things but you know not sort of token maxer 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 um or a G Suite equivalent like 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 uh where our thesis was if you think about the value that AI creates for 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 nontechnical 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. Uh 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 companywide adoption of AI is we don't believe in the yes like give people a few MCPS and let them go because I think what people really want
is uh 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 harness because we will look a little different probably more like open claw than a coding model. Jared and I just did this week for the first time where we installed Aqua voice 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 our someone from the events team and we're trying to put together um how do we put together 60 dinners with uh 20 people each of attendees from startup school with 21 uh 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 then I pressed enter and then you know our claw just started doing it. None of us opened cloud code like it just sort of built a bunch of markdown. It did the analysis and yeah people forget that cloud code isn't magic. It's just literally a harness around the same models we 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 yet, uh, it's just a harness. Uh, and claw can use cloud code. Exactly. And codeex, right? It really prefers to use codeex these days. Exactly. It really does everything actually. I don't know why, you know. Exactly. But ACP helps on that. So, yeah. Yeah. ACP is good. Why do you think the adoption of uh token maxing hasn't really taken off? The thing that we 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 you know 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 is like totally reasonable way to do it.
100% 100%. I have a friend that has the exact same setup. He has his like little GPU farm in his house and uh and first time I went there, I was like, "Wow, heating's on here. It's really hot in here and he's like no it's my GPUs and I was like great like uh you know power efficiency all the way through. It's funny because at Brax and we should talk about managing token cost and spend management for tokens which is a topic we're spending much of cycles on now. I think that 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 how what's the percentage of Twitter that probably complains about it like.1%.
Mhm. So, so I think people are probably still early. To me, there's this like the AI pill test in my opinion 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. Yeah, 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 like 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 I 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 opin opinion uh like uh oh like a 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 uh and I think people were still didn't fully grasp like okay what does it mean to build code with new agents like and the new technologies we have. I think that's like 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 Brex, I mean like it's well known like your like MVP like had no web UI, right? It was just like all terminal like super scrappy. today would have because no one needs HTML. It's just test anymore. Like was it actually still the right approach just have a really simple MVP and test that anyone work or would you have like a way more fully featured? So I have this controversial view which maybe you all will disagree uh
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. Stribe early days was like literally an API. Braxton early days no UI just like literally a terminal. Um you look at Airbnb is like the website was a form and the form was just like literally where you put it what you needed and then someone somehow went there and figured out how to actually make the booking happen. Like Door Dash 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 band the
sort 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 like I think if you don't 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 like 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 great ideas 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 I like actually a lot of the pivot advice I give founders during the batch comes from um 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 not just having the conversation but how to extract the sort of unspoken signal from these conversations and I think to me 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 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 LM and that LM 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's 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 do like 30 and let the models try and the way I would probably approach it is is like let's let's pick a broader universe of things to do sort of a early initial exploration but to me the lens is okay why can't AI solve it and like 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 in the headsp space 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 role 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 like one of the hardest things for us as a company was we initially sold to founders we are 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 world view. But there's 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 and 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. Basically, there's this uh there's this book others in mind from psychology that has to do with people that have
very good emotional connection with people are able to simulate what the other person is thinking and what the others and others 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 uh make the I think Gary says this make the implicit explicit 100%. of what are all those desires and they're very subtle signs a lot of time because they're murmurss 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 and the problem with um relying on models and right now which is
I'm still very optimistic that there's still a lot of job founders definitely is you don't even know what the right incantation or set of prompts to ask the model because they're they're you don't even know what to ask exactly there's like another meta layer yes it's it's the whole like Elon thing of like you know which question is the universe the answer for kind of and of course these are generalities right but I think what I've seen is you have to remember that LM are not magic like LM 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 asked an LM a question it gave you like yeah like I 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 is very different, right? The distribution is so different. Oh, I would pay for that. That's a great startup idea. Exactly. Someone should do that. We need to do a model that does that. Yeah, 100%. I would pay for it. Yeah. Well, because it's fascinating because then like anything that's out of distribution, you just go and like fill
that in for use. I mean, actually, as an applications engineer on top of the LLMs, that's actually a huge like blind spot. And that's what Merkore and a lot of the other data companies are doing. like a lot of the jobs for them is to say well where what are the blind 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 the so there is a there's a curse of knowledge and a curse of not even knowing what the bounds of
knowledge are is uh which I think can make you believe that you understand something that you actually you in order to model actually understand. Can I confess something weird about like um after creating Gbrain now um 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 like the last uh humanized prompt and uh you know a lot of that stuff probably isn't in distribution yet.
Wikipedia article about it like go spend a day like deep research literally every single paper article like 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 that's sort of like filling in% um the things that are out of distribution like I can sort of like pack it with whatever context and it's You can do that with anything is 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 with us like literally like what how many times they click a button on 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. 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 uh RAM Mhm. actually like there will be so I don't know I mean that's kind of an interesting one right like 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 I think we forget that the world models in which the models are train 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. uh how to make elements 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 for example that's a really funny example like I was playing with AI for accounting categorization and like the first example of like an example of an like it's just like writing pros 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 [__] only think about AI capex, right? So, so
there are things like that I think is like are 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 were saying like you know we're we're so early still. I don't know the funniest thing about AI to me is uh how often I find myself thinking um crypto maxims. Yes. This is the worst the models will ever be. Yes. My favorite now is uh telling people who hate AI coding like have fun coding at 1x speed.
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-upply chain all that people are saying like 10,000x. Yeah. But the underwriting which is kind of funny is like I think there's one image 2500 dots each dot is a 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 chatbot then 0.3% 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 that's the argument to be long inference. Uh and uh 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 and yes there's a lot of margin in tokens right now but people always want to be at 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. Um, 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 Magpie where the idea is you can effectively, you know, every dollar of
token spend in the company, you can attribute to a product we have to customers, an internal tool that we use to serve uh or an internal employee uh and understand model usage, etc. and we're now figuring out how to build analytics on what are we trying to do with the tokens um to start to get a sense of ROI. Uh but anyway, it's a fascinating topic that I think has a lot of like 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 and uh it's increasing.
No, look, I think two things are surprising. one is um 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 milei radius we're in now uh and maybe you include New York um tons of token consumption and you could probably argue and we see in the data also faster revenue growth. I think what's really interesting is the gap between anyone in these two 10 milei radius and everything else. And this is like not small companies like 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 at 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 Brex again in 2024 the answer would be even more different now what would we do differently and turns out like everything and we start 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 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 the way 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 think it um and you know like there's 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 on board 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 uh and of course the original impetus for anyone is let's build 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. And when you redesign the entire on boarding 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 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 let 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 when we said hey let's keep this old way here keep put 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 It's the only thing we've seen working to really sort of inflect. I think that reminds me a lot about this is sort of uh way back. I don't know if you ever try to compile Arc distributions of Linux. Mhm. The culture within power uses of Arc Linux versus Ubuntu is very different. Mhm. Very different. I think the 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, and if you get it working, you can build something awesome. One of the mo most impressive thing I've seen people build with Arc is actually I don't know if you know uh Valve the Steam engine, the operating system that runs that makes it feel like a Nintendo Switch is actually built on top of Arc.
Oh, interesting. They customized all the drivers, overtheair updates. It works with all consoles. It work 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. Yeah. I always have this thing that I tell people, which is funny, 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 same for everybody here. So then the argument is what about the
productivity? Where's the productivity? Right? And uh and I was talking to this CF very large public company uh this week and she was telling me that we see all the token consumption and you know we're trying to measure like like product velocity and we're seeing like more lines of code pushed. So, 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 the I think 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 6 months after electricity was invented. like thinking about like 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 let's 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 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 of accountants had done this analysis they would have been like, "Oh, this electricity thing is like isn't never going to be a thing. The ROI sucks.
Why did people stick to it?" And it wasn't the cost savings. It was just because people were curious about it. And I think it's I think the point of like why, you know, like I was yesterday until 2 a.m. playing with SLworkflows and Opus 4.8 made and all that is because I think I would be doing the exact same thing if I wasn't making any money because you just see the possibilities and you see what it can do to technology and that just drives people to behave differently and I think uh that to me is the ultimate litmus test and it's a good separator and sure tokens are so expensive they're going to be I think over the fullness of time probably free uh if you project is I don't know 100 years down the line almost compared to
what electricity now we don't think of electricity costs in our day-to-day days but unless you're in a data center uh 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 like as AI pill as possible and you run this like big company now with all of these employees and that's only the bre side there's also the capital one side 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 There's a lot to do. I think the CEO needs to be the chief AI officer.
Like it's not a engineering team thing. It's not like a product team thing. It's like you have to understand the bounds of a technology better than anyone. I would argue that unless you really experience the limits of a 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 like you know the whole example of KYC that we were saying like 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
and to me like the single most important question that any co 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 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 on boarding 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 so I think it's like it's almost like a you have to sort of refound the very concept of the what the company selfidentity is and the way the functions and people's sense of success get structured AI is a is an umbrella that I think has like three things the way we talk about it internally Okay. Uh 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, on boarding, operations, etc. And 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. Um and I think people will sometimes sort of pigeon hole 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're talking about earlier. Like why can't you solve everything with AI? Like at a limit, that's the question. And then sort of start from there and sort of problem solve 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 AI native, you're doing a turnaround uh to some degree. I guess we've been making fun of Foxcon 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 uh are designed to be that to like create like one thing perfectly 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 like I think it's 10x easier for the co to break glass than an executive. Oh 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 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 guard rails? 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 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 soft this product in the old way because like why wouldn't we it just works we know it's here that's 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 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 going to go try this thing you know I understand the risks let's let's take this risk because the biggest risk is not taking that is just literally missing the opportunity to rethink a problem from what would you do if you started a company today on the corporate AI sort of leg of that
store specifically like do you buy into sort of like the Jack Dorsey view of every company's 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 like 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 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. That is a well- definfined problem with clear boundaries with like
clear APIs of who people who depends on that data, who interact with that 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 road map that can be a separate agent but that builds on top of this customer world like a virtual exec team basically uh 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 still is still true right that doesn't necessarily change that much and you should separate the agent that 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 of customers and translating into product road map. Bas 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 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 a 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 gonna cut it, right? Once you have that orientation, I think customer model, okay, like your, for example, our client
sales team now runs on our customer world model. So I know it works. I'm 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 in 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 card all these things awareness total information awareness right that is a well- definfined problem that is working I can trust this building block as part of my company model as a whole and you can have evolves on it like we know like you know you I think a very we
should talk about evolves there's a bunch of learnings on this uh and how to build evolves into the fabric of the company. But anyway, I think it's more of like you have to decompose the problem a little bit. Yeah. My favorite thing about EVELs is uh just running crossmodal evals against each other. So, one of the things that we're doing that I is related, but I think it's really fun, which is how do you have every single human interaction in the company becoming an evolve when you have an AI agent. So for example, we have the on boarding 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 evolve case. You know we have an expense agent in Brex whenever someone has a conversation with the agent that is that 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 in and modify the codebase and the prompts and everything to make that evolve pass. And if that doesn't break, then engineer is gonna 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 re a self-learning system, right? And I think the 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. a dream cycle sees everything every night and then it's like oh what's going on there I need to put this over here what actually happened is there a pattern how do I recause this so how to bake the dream cycle into the products and into the agent into the things you 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 h it's going to happen in our lifetimes. You know, I remember when um when Neuralink came out and I used to think about it. I was like I don't get it. Like 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. I'm the bottleneck." Yeah. Typing is so slow. I don't know if you use a lot of adaptation. I use a lot. My most used developer UI right now is like voice memos to open cloth. 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 more functionality for it. Foxcon. Yeah. Yeah, exactly. 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?
I think a lot of the work to your point is the 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 uh my favorite new feature. I saw you brain LSD. Yeah, LSD. Yeah. lateral synactic drift. So you just bump the temperature on the search. It's not just that. So you have the vectors, right? And so you know if you think about what conventional ideas are like most people give you like oh well
this idea with this idea and 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 uh orthogonal or just like rand like feeling seemingly random. And then it'll try like, you know, randomly hundreds of these combinations. Yeah. And then it'll rank order them into uh 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 a I didn't tell Alfred Express to be dry. I went I actually had like chat GPT generate like the soul file. It was just like based on like everything you know about me, all
the interactions here like generate like a soul MD for like my open core agent. and it was so unairringly 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 uh I just ingested my 60 gig um Google takeout. I mean, you have to write a bunch of um haiku code to like only get the emails that are actually real. But, you know, there's like extract like 4,000 emails out of 60 gigs that actually matter. But like those are like oh actually like a lot of your thinking and you know 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 pill farthest out on the edge but also very practical CEOs who is you know playing with this stuff and actually building it yourself. What would you say to people watching who are founders who want to be founders? you know, I think that you are sort of the model for the way people should start companies and run them. Um, with AI as your SLN buddy, I really can't stop thinking about the electricity analogy, which is, you know, you're standing there's a 200year 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 would be true about electricity. Uh, knowing that data centers 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. Um I think the second is like have a boastit 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 the 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 but 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 like just measure your token consumption and how much you're just pushing the limits of the company and starting with the premise of like okay why can't it just be one person like why can't it just be me that to that builds the whole thing and you're going to probably face a wall of the uh the elements of you know what models can and cannot do but at a limit I think the question is uh 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 for solving and two uh and the sort of the choice thing we talked about and the second thing is okay given that the given these choices what are the limitations of an LLM uh that they still cannot do and I have to go in and do that those things myself but almost uh you know to some degree you're working for the LM to some point uh and if you're in a bigger company you're in a turnaround to put the LM as almost the founder and the CEO and you're you're you're almost architecting the entire company around that idea but I early on. Uh so much of it is, you know, choosing what matters, talking to customers, injecting the
signal that the models don't have, and just, you know, rebuilding it the way you would do it in 2026 with electricity being 6 months old. Thanks, Pedro. This is awesome. Yeah. Thanks for having me. I appreciate it. Thanks for coming.
