"The CEO Must Be the Chief AI Officer"

Brex co-founder and CEO Pedro Franceschi believes most people still underestimate how much AI will change the way companies are built. AI isn't just another tool, it's a new foundation for building products, teams, and companies. In this episode of Lightcone, Pedro shares why he thinks we're only months into a platform shift as significant as the invention of electricity, how AI has changed the way he works, and why every founder should be "token maxing" to understand the limits of the technology firsthand. He explains why the CEO needs to be the chief AI officer, how Brex is rebuilding itself around AI, and why founders should rethink what's possible when intelligence is available on demand.

• 0:00Speaker 00

  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 what the company's self-identity is.

• 0:32Speaker 01

  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. 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.

• 1:06Speaker 00

  Thanks for having me. Excited to be here.

• 1:08Speaker 02

  Thanks for changing our lives.

• 1:09Speaker 00

  Oh, God, that lunch, I'm like, I think the model company should be sponsoring me for it. The token consumption increase I generated, we supposedly generated on that lunch. That was the precursor to G-Brain, I guess.

• 1:23Speaker 01

  I was still working on G-Stack. 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.

• 1:42Speaker 00

  Yeah.

• 1:42Speaker 01

  And then after I met you, I realized everything is about freeing the claw.

• 1:47Speaker 02

  Free the claw, I knew you were gonna say that.

• 1:49Speaker 01

  Yeah, and then...

• 1:50Speaker 00

  And give it tokens.

• 1:51Speaker 01

  Yeah. Well, no, I mean...

• 1:52Speaker 00

  Let it rip.

• 1:53Speaker 01

  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.

• 2:07Speaker 00

  Yeah.

• 2:08Speaker 01

  And so as a result, you have to literally put the agent inside a Foxconn factory.

• 2:14Speaker 01

  And it's like, 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, I need to control what the LLM sees because it's about really, 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 Foxconn engineer, you're waking up at 6 a.m. And, you know, if you don't, you're gonna get electroshocked. I mean, like, it's just like this terrible thing that you do to agents.

• 2:41Speaker 00

  Yeah.

• 2:42Speaker 01

  And they want to be like at the Esalen Institute, and that's what OpenClaw is.

• 2:45Speaker 00

  Exactly, 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, tools and a model. Like, there's not really much else.

• 3:03Speaker 01

  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-pilled and like all the way to the edge?

• 3:14Speaker 00

  Well, I'll tell you my encounter with LLMs, which was, so, 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're like, you play with it for 10 minutes and you stop. ChatGPT came out and I think everybody was sort of interested in it. Where I think it got interesting was when you started to see reasoning models and of course tools. But, but I think everything else was sort of a blip until December. And the way I describe it to, to my team is like, you know, electricity was invented in December. And I think electricity was Opus 4.5.

• 3:58Speaker 00

  And sure, Opus models and, you know, OpenAI models got, got better and better since then. But to me, that was the, the, the, the tip of the spear where you could say, yes, like, Coding harnesses actually work and you know Claude code existed for probably a year before, but it wasn't that that valuable yet. And I remember, you know, during the holiday break, I was playing with it and it was was pretty shocking, probably similar reaction that 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 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.

• 4:42Speaker 01

  Who needs light?

• 4:43Speaker 00

  Yeah, exactly.

• 4:44Speaker 01

  Bright.

• 4:44Speaker 00

  With all these lanterns and what can you do with it? And 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 the the fundamental uh uh light behind it. And I would say, I think since then, OpenClaw was kind of a interesting sort of next step, which is, I think, when we realized that, you know, the the reality is good AI products are agentic loops with tools. Uh, and we started doing this in our own product at Brex, but but then on a 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.

• 5:27Speaker 00

  Just like configuring and automating a lot of the things in your life. It's kind of funny. I remember I had this this experience of like buying a movie ticket entirely in OpenClaw, using like a Brex card that 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.

• 5:56Speaker 02

  Tell us more about your personal OpenClaw journey, because I, before you came to lunch, I had it like in store, but I was like way too scared to do anything with it. We were all scared.

• 6:05Speaker 00

  Yeah, don't get me wrong. Like, we we we deal with financial services data. We spend a lot of time figuring out how to how to be mindful of security and protection, but and yet, uh, I think people are. A little bit more risk averse than than the technology probably requires them to be, given where the technology is. And and when we when we started using OpenClaw personally, I started doing it a lot of my own personal setup. Basically, what I did in the the V one was I'm going to give it read access to everything and just create like OLOF 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 we spent time on Brex was, OK, like, how do we actually get it to write into our systems?

• 6:46Speaker 00

  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, OK, 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, you know, the agent has its own wills, desires, and, you know, they they they go to the Aslan Institute for for for for agents. And, you know,

• 7:16Speaker 01

  Instead of Foxconn's factory.

• 7:17Speaker 00

  Instead of Foxconn's factory. They will try to do things that are network boundary that that could not be the right ones. 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 forks that have controls over, you know, tools, the model calls. And the reality is, yeah, you can do all of that, but you can also just make an HTTP request wrong. So we focus on that layer, and then we build this thing called Crabtrap, which we open sourced probably about two months ago, which is actually the way we use to to secure agents at Brex in production. And the the basic premise is you analyze, you you HTTP proxy the entire network boundary of an agent.

• 8:01Speaker 00

  And the idea is when a request goes through, that becomes auditable. And you basically can 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,

• 8:19Speaker 00

  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 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 in production at Brex 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 LM 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 Brex called Jim. Uh, we have a policy for Jim.

• 9:03Speaker 00

  Uh, and you know, all the traffic goes to that same policy and ninety eight percent requests go through automatically, two percent using LM. So we sort of got that problem solved to the degree that we got comfortable experimenting much more aggressively and sort of freeing the clot on the enterprise, which is really hard inside Capital One. So I would I would say if we found a way to experiment with these things and 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 and there's there's boundaries to how we do it. Uh, I don't see any reason why a Y C company shouldn't be at the bleeding edge of the stuff.

• 9:38Speaker 02

  Yeah, I mean, I think your intuition around like the 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 open crawl ecosystem at the moment, at least, or just agent ecosystem is essentially doing that. Like we're seeing that with 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 do you decide not to include that?

• 10:01Speaker 00

  I think it was just, let's just do one thing really well. Um, and and and you know, at the end of the day, I think there's going to be a lot of solutions that do that. You 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 this in production or not? In our security team at Brex, very rigorous and very good at what they do. For a long time, we're, 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 H C proxies. We are in the business of being 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 Y C company. Hopefully we're just going to build a better version and we're just going to go use it.

• 10:46Speaker 00

  But at the end of the day, uh, that's the journey that took us to just sort of being at at the bleeding edge in a way.

• 10:52Speaker 02

  And how much was was you like sort of pushing this forward and they, how much resistance did you get internally and just how did you?

• 10:59Speaker 00

  I mean, we got it. 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 and and typically living inside coding harnesses. And and those are sort of well known. We know who those are. Then you have the the sort of average engineer, uh, that is building a few things, but you know, not 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 M C Ps.

• 11:44Speaker 00

  Um, or a G suite equivalent that you, yeah, you have a few tools from Google, but at the end of the day, it's really just like a a search. And I think the where our thesis was, if you think about the value that AI creates for like a 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 Open Claw, you know, created, which is this ability that you can self boot strap a lot of the capabilities of the agent by the way you edit your skills and markdowns and and and sort of set up the the environment around the agent.

• 12:24Speaker 00

  Uh, and how far can we get this ability to for the agent to self bootstrap a capability without anyone actually going in coding by hand? So the analogy we use internally for. I would say the sort of the the company-wide adoption of AI is we we don't believe in the yes, like give people few MCPs and let them go. Because I think what people really want is, in my opinion, is really a way of saying, okay, this is actually a virtual employee almost that has, you know, this on Slack, it has an email, I can actually invite it to a meeting, can join a meeting, take notes. And 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 will will look a little different and probably more like open Claude than a code model.

• 13:04Speaker 01

  Jared and I just did this 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, how do we put together 60 dinners with 20 people each of attendees from startup school?

• 13:29Speaker 00

  Nice.

• 13:30Speaker 01

  With 21 partners and visiting partners at Y C.

• 13:33Speaker 00

  Sounds like a great problem.

• 13:34Speaker 01

  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 Claude code, like it just sort of built a bunch of markdown, it did the analysis and.

• 13:48Speaker 00

  Yeah, people people forget that Claude code isn't magic. It's just it's just literally a harness around the same models we you can use in an API, right? So so I think that's the the unlock of. And by the way, there's a few things that Claude is doing that I think are really cool.

• 14:01Speaker 01

  Oh, they're amazing.

• 14:01Speaker 00

  And yet, it's just it's just a harness.

• 14:05Speaker 01

  And and Claw can use Claude code.

• 14:06Speaker 00

  Exactly.

• 14:07Speaker 01

  And Codex, right? It really prefers to use Codex these days.

• 14:10Speaker 00

  Exactly.

• 14:12Speaker 01

  I don't know why.

• 14:14Speaker 00

  Exactly. But A C P helps on that, so.

• 14:17Speaker 01

  Yeah, A C P is good.

• 14:18Speaker 03

  Why do you think the adoption of token maxing hasn't really taken off? The thing that we've found 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.

• 14:35Speaker 00

  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 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.

• 14:59Speaker 01

  There's a huge hobbyist community where they, you know, build a gaming rig. But then they try to build like local L M.

• 15:08Speaker 00

  Yeah.

• 15:08Speaker 01

  And then that actually is like totally reasonable way to do it.

• 15:11Speaker 00

  A hundred percent, a hundred percent. I have a friend that, that, that has the exact same setup. He has this like little GPU farm in his house. And, uh, and first time I went there, I was like, wow, heating is on here. It's really.

• 15:21Speaker 01

  It's really hot in here.

• 15:22Speaker 00

  And he's like, no, no, it's my GPUs.

• 15:25Speaker 00

  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 costs and spend management for tokens, which is a topic we're spending a bunch of cycles on now. I think that the, the cost part is one. But, but even, 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?

• 16:06Speaker 00

  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, and, and there's the whole topic about AI dependency, human machine interaction. Yeah, there, there's all these things that we can, 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 I think 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.

• 16:49Speaker 00

  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 and I think the the fundamental constraint isn't as much, in my opinion. Uh, like, uh, uh, oh, like A I 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 and I I think people are still didn't fully grasp by, okay, what does it mean to build?

• 17:33Speaker 00

  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 the tap? And your default answer is let me actually solve this problem with A I 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 A I 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.

• 18:04Speaker 04

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• 18:19Speaker 02

  When you started Brex, I mean, like, it's well known, like, you're like M V P, like, had no web UI, right? It was just like all in terminal, like, super scrappy.

• 18:27Speaker 00

  Today would have because.

• 18:28Speaker 02

  Yeah, that's what I'm.

• 18:29Speaker 00

  No one needs H T M L, it's just test anymore.

• 18:31Speaker 02

  Like, was it actually still the right approach? Just have a really simple M V P and test that anyone worked or would would you have like a way more fully featured?

• 18:37Speaker 00

  So I I 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. Stripe early days was like literally an API. Brex in the early days, no UI, just like literally a terminal. You look at Airbnb, it's like the website was a form. And the form was just like literally where you input it, 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, so the the surface area was so small with the customer.

• 19:19Speaker 00

  And so much of the 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 so you have this, you have this, this, this, 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 but that doesn't preclude you from actually choosing what matters. I I always tell people, like, I think if you don't, if you can't minimize your surface area and 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,

• 20:03Speaker 00

  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 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, where I just 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 the size.

• 20:28Speaker 02

  How about the step before it, then, even? I'd like, actually, a lot of the pivot advice I give founders during the badge 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?

• 20:49Speaker 00

  A hundred percent. 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, but how to extract the sort of unspoken signal from these conversations. And I think to me, the Can AI solve this lens like 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 and the reasons are simple is because there's signal that the models were in train dock. And the signal is when you go talk to person and they tell you about a problem they have. They're not going to tell you, 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.

• 21:31Speaker 00

  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 and I think a lot of the job is the 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 the missing bottleneck. And to me, that all comes from which signals are not in the models.

• 22:02Speaker 02

  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 thirty and let the models try and?

• 22:13Speaker 00

  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 signals are not in the model? And I think the signal is typically the customer. And and then and then when you go talk to the customer, I think I wouldn't parallelize that probably. I would be, okay, let's try to get into headspace of this person. And and and I think there's like, it's so easy. And and we we did a lot of exploration with like synthetic customers and building customer world models and things like that. And 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.

• 22:56Speaker 00

  We initially sold to founders, we're 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 so building that mental model of like, what's the value system? Like, of course, you can eventually make the model represent that and and have that worldview. But but there's there's an intangible that I think is is where a lot of the alpha still comes from. And I think to me is like the 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.

• 23:30Speaker 03

  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, theory in mind.

• 23:51Speaker 00

  Exactly.

• 23:51Speaker 03

  And I think the founders that get that and have the empathy to figure out what the customer is not verbalizing is the, what is, uh, make the, I think Gary says this, make the implicit explicit.

• 24:03Speaker 00

  A hundred percent.

• 24:04Speaker 03

  Of what are all those desires?

• 24:06Speaker 00

  A hundred percent.

• 24:06Speaker 03

  And they're very subtle signs a lot of time because they're murmurs. As founders go through them and figure out the insights, like, oh, is this really a thing? But how do you know when to poke for it?

• 24:15Speaker 00

  Exactly.

• 24:16Speaker 03

  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 for founders.

• 24:23Speaker 01

  Definitely.

• 24:24Speaker 03

  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.

• 24:30Speaker 00

  Exactly.

• 24:30Speaker 03

  There's like another meta layer.

• 24:32Speaker 00

  Yes, it's, it's the whole like Elon thing of like, you know, which question is the universe to answer for kind of? And of course, these, these are, these are generalities, right? But, but I, I think what I've seen is you, you have to remember L M's are not magic. Like L M's are trained on a very specific corpus of information, optimizing for a very specific set of benchmarks and outcomes. And I think the biggest pitfall of L M's 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, imagine if like every time you're asking an L M a question, it gave you like, yeah, like I, the, the, the, the sampling frequency of this in my data set was,

• 25:13Speaker 00

  I don't know, X and on this other answer was zero point zero zero zero zero one X. You would trust is very different, right? The distribution is so different.

• 25:20Speaker 01

  Oh, I would pay for that. That's a, that's a great startup idea.

• 25:23Speaker 00

  Exactly. Someone should do that.

• 25:24Speaker 01

  We need to do a model that does that.

• 25:26Speaker 00

  Yeah. A hundred percent. I would, I would pay for it.

• 25:29Speaker 01

  Yeah. Well, because it's fascinating because then like anything that's out of distribution, you just go and like fill that in.

• 25:34Speaker 00

  Exactly.

• 25:34Speaker 01

  I mean, actually, as an applications engineer on top of the LLMs, that's actually a huge like blind spot.

• 25:41Speaker 00

  And that's what Merckor 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 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 is a there is a curse of knowledge and and a curse of not even knowing what the bounds of knowledge is, which I think can can can make you believe that you understand something that you actually, you in order to model actually understand.

• 26:17Speaker 01

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

• 27:01Speaker 01

  and summarize it into something that actually is usable. And so it's sort of like filling in

• 27:06Speaker 00

  A hundred percent.

• 27:07Speaker 01

  the things that are out of distribution. Like, I can sort of like pack it with whatever context. And it's like, you can do that with anything. It's like, if you're interested in, you know, running a restaurant, literally, you could have, you could go and read like 500 books about like every, the top 500 books about what it's like to run a restaurant. And you would have like the compendium of all information about it.

• 27:27Speaker 00

  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 send a call, and ingest that inconsolable data. 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.

• 28:01Speaker 01

  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, that's kind of an interesting one, right?

• 28:15Speaker 00

  I think so.

• 28:16Speaker 01

  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.

• 28:26Speaker 00

  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, 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 LMS work for like, the average finance person in the US? That, if we're talking about an answer and, you know, the model defaults to like, AI CapEx as the finance, as a default category for like, like, for example, that's a really funny example. Like, I was playing with AI for accounting categorization. And then like, the first example of like, an example of an, it's just like writing prose. And an example is like, AI CapEx.

• 29:10Speaker 00

  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, so there are things like that, that I think is like, a kind of interesting to think about, that the, the mental models of the models, I think, are out of the box, are, are more biased than we may give them credit for.

• 29:28Speaker 01

  I mean, speaking of AI CapEx, like, earlier you're saying, like, you know, we're, 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.

• 29:40Speaker 00

  Yes.

• 29:40Speaker 01

  This is the worst the models will ever be.

• 29:43Speaker 00

  Yes.

• 29:43Speaker 01

  My favorite now is telling people who hate AI coding, like, have fun coding at 1X speed.

• 29:48Speaker 00

  Exactly, exactly. I was telling a friend about, you know, how, how, how to be, you know, long inference that. Basically, the thesis that there's gonna be a lot more inference than people think. And people are expecting a lot of inference if you just look at public markets and, you know, semi-supply chain, all that.

• 30:04Speaker 05

  People are saying like 10,000x.

• 30:06Speaker 00

  Yeah, but but the the underwriting, which is kind of funny, is I think there's one image. 2500 dots, each dot is 3.2 million people on the planet. And basically, you know, 84% of the world never use AI. 16% have used at least once a free chatbots. Then 0.3%, which is, I guess, six or seven squares, paid 20 bucks a month for AI. And one box out of the 2500 actually use agents in in whatever capacity. So that's the that's the argument to be long inference. And I think it's I think it's just it's just starting out. And I think a funny thing on on this is, I think the, you know, it will be the biggest expense in a company, like like easily, right?

• 30:50Speaker 00

  And 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 gonna have 10x more usage, so it will be still a large cost. And we're spending a lot of time thinking how to help companies actually manage token spend. On Brex, we we ended up building our internal version of this, we call it MagBuy, 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, or an internal employee, and understand model, usage, etc. And 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.

• 31:34Speaker 00

  But anyway, it's a fascinating topic that I think has a lot of like early, early, early work compared to what it will be one day.

• 31:41Speaker 05

  Can you share any of the data that you've gotten from Brex about just like what token spend is like in the economy?

• 31:46Speaker 00

  Uh, it's increasing. No, look, I think two things are surprising. One is, um, I think to, to your point earlier on, how do we, how do we look at token maxing? I, 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 ten mile radius we're in now, uh, and maybe you include New York, um, tons of token consumption and, and, and, and you could probably argue, and, and we've seen the data, uh, also faster revenue growth. I think what's really interesting is the gap between anyone in these two ten mile radius and everything else.

• 32:31Speaker 00

  And this is like not small companies, so you look into like very large companies with very large budgets and that, that could be token maxing and, and the economic thing for them to do will 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 a hundred times more. That's still surprising. And, and, and I think the reason is again, sort of similar to the, 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, the engineering product leaders in the company and we had this question, which was if we started Brex again in 2024, the answer would be even more different now. What would we do differently? And turns out like everything. And we started going down this route and it's like, it's kind of maddening because they're like, okay, we, we have this like

• 33:16Speaker 00

  completely old way of like even thinking about the fabric of the company and, and the way we build the product and the way we build our processes internally. The first best answer is yes, we wish we had started now. Second best answer is like, let's go do something about it and change the way we do things, right? And I think a lot of our approach in terms of like adopting AI has also been, you know, how do you, 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. Um, and, 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.

• 34:01Speaker 00

  Like whenever we onboard a customer, we have to do all these checks to KYC the customer. And KYC historically is something that you can automate, like 80% of it, 20% is manual. Uh, 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. And when you redesign the entire onboarding process, what you realize is there's a very important thing that happens in the beginning of the funnel, which is deal qualification, like is this customer even remotely qualified to be a Brex customer? But when you have KYC for free, you can KYC a lead versus a customer. So you start to have risk orientation up in your funnel and that changes who you even target.

• 34:43Speaker 00

  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 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 discontinuities in a positive way that we've had were 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 to really sort of inflect.

• 35:20Speaker 03

  I think that reminds me a lot about this is sort of way back. I don't know if you ever tried to compile ARC distributions of Linux. The culture within power users of ARC Linux versus Ubuntu is very different.

• 35:35Speaker 00

  Very different.

• 35:35Speaker 03

  I think the Ubuntu people kind of feel more like people that try chat GPT. Stuff kind of just works out of the box or 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 OpenClaw 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 things I've seen people build with ARC is actually, I don't know if you know Valve, the Steam engine. The operating system that runs, that makes it feel like a Nintendo Switch is actually built on top of ARC.

• 36:18Speaker 00

  Oh, interesting.

• 36:19Speaker 03

  They customize all the drivers, over the air updates. It works with all consoles, it work 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 OpenClaw to work really well for you, you could kind of build your own custom Nintendo Switch for whatever you need to do.

• 36:40Speaker 00

  Yeah, I I always have this thing that I tell people, which is, which is funny, which is.

• 36:45Speaker 00

  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, oh, but what about the productivity? Where's the productivity? Right? And, uh, and I was talking to this CFO of a very large public company, uh, this week, and she was telling me that we see all the token consumption and, and, 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, but, but is it really there because people are spending so much on tokens? And, and I think the, I, I, 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

• 37:26Speaker 00

  misses the point that you're standing in the timeline of history and it's six months after electricity was invented. Like thinking about like, i- i- imagine someone saying in, 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 this steam engine to come like maybe 20 years later because the cost savings are like, like, yes, of course, like, I'm, like, don't bankrupt your company on tokens.

• 37:52Speaker 05

  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, this electricity thing is like, isn't ever going to be a thing. The ROI sucks.

• 38:07Speaker 00

  Why do people stick, stick to it? And, and it wasn't, it wasn't the cost savings. It was just because people were curious about it. And I think it's, I, I think the point of like, why, you know, like I was yesterday until 2am playing with slash workflows and Opus 4.8 and, and all that is because I think I'd 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, and I think, uh, that to me is the, the ultimate litmus test and, and it's a good separator. And, and sure, if tokens are so expensive, they're going to be, I think, over the fullness of time, probably free. Uh, if you project this, I don't know, a hundred years on the line, almost compared to what electricity, now we don't think of electricity costs in our

• 38:51Speaker 00

  day to days, but unless you're in a data center. Uh, but, but I think there's something, there's something similar for sure.

• 38:57Speaker 05

  We talked to a lot of 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 Brex side. There's also the like how it wants to. 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.

• 39:20Speaker 00

  Well, 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 unless you really experience the the the limits of the technology every day, I think it's really hard to even understand what it can possibly do.

• 39:42Speaker 01

  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.

• 39:49Speaker 00

  That is a hundred percent true. When you go think about like, you know, the whole, 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 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 transports to the moment you started your company. The opportunity was still the same, but 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?

• 40:31Speaker 00

  And then first suffer in silence for a little bit because you will. I mean, I do every day. But then the second thing is, okay, what do you do about it? And how would you do it if you were starting from scratch? You'll be the one figuring out, okay, how do we design our onboarding process? Or how we design our 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 so I think it's like, it's almost like you have to sort of refound. The very concept of what the the company self-identity is and the way the functions and and and people's sense of success gets structured. AI is a is an umbrella that I think has like three things, and the way we talk about it internally. There's product AI, the product we actually ship to customers.

• 41:13Speaker 00

  There's operational AI, which is things that directly affect our ability to serve customers at scale. Like, think of customer success, risk, onboarding operations, et cetera. And then there's corporate AI, which is how people work internally. The three agendas matter, and they matter in different ways depending on the timing of the company. And 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 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 and then sort of start from there and start 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 to some degree.

• 41:54Speaker 01

  I guess we've been making fun of Foxconn factories for some time. But on the other hand, like, if you look at them, they're like this paragon of like very extreme efficiency.

• 42:02Speaker 00

  Yeah.

• 42:02Speaker 01

  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.

• 42:11Speaker 00

  And most companies are designed that way, right? I think like processes are designed not to change.

• 42:15Speaker 01

  Yep.

• 42:15Speaker 00

  There is a certain amount of broken glass required. The question is, how, like, I think it's 10x easier for the CEO to break glass than an executive.

• 42:24Speaker 01

  Oh.

• 42:24Speaker 00

  And 10x easier for an executive than an employee. So, you know, a lot of times, like, someone comes to me and says, I'm trying to do this with AI, but someone is saying no because we haven't tested this in this use case or in that thing. 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 gonna like build this soft this product in the old way because like, why wouldn't we? It just works. We know it's here.

• 43:02Speaker 01

  Well, that guy's gonna 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 gonna do that.

• 43:11Speaker 00

  And what I tell people is I 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 gonna go try this thing. You know, I I understand the risks. Let's let's take this risk because the biggest risk is not taking that. It is is just literally missing the opportunity to rethink a problem from what would you do if you started the company today?

• 43:45Speaker 02

  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?

• 43:57Speaker 00

  I I do, but but maybe in a slightly different way. I do think domain specificity matters. So like, I I don't believe in the like, well, I'm gonna 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 a and and the way I think about it more is like, it's more the sort of the 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 people who need who depends on that data, who interacted that data, that that is self-contained. Then there's another another agent that can be, okay, given all the customers that we have and the problems they

• 44:40Speaker 00

  have, how do I manage my product roadmap? That can be a separate agent, but that that builds on top of this customer world.

• 44:46Speaker 02

  Like a virtual exec team, basically.

• 44:48Speaker 00

  Uh, e- exactly. Functional and domain knowledge still matter, right? These things are not gonna go away. And I think the 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 product roadmap. Basically, three separate things. We're kind of like a 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.

• 45:32Speaker 00

  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 you're, for example, our client sale scene now runs on our customer role model. So I know it works. I'm actually having lunch with a customer tomorrow. And I don't know the state of that account as well as I probably should. Customer role model answered a question for me. And I now have a report with, including things that the team didn't know about that came through support tickets and, you know, an executive that was traveling had an issue at an airport with their car.

• 46:03Speaker 01

  Total information awareness.

• 46:04Speaker 00

  Total information awareness, right? That is a well-defined problem. That is working. I can trust this building block as part of my company model as a whole. And you can have evals on it, like, we know, like, you know, I think a very, we should talk about evals. There's a bunch of learnings on this and how to build evals into the fabric of a company. But, but anyway, I, I think it's more of like, you have to decompose the problem a little bit.

• 46:26Speaker 01

  Yeah, my favorite thing about evals is just running cross-modal evals against each other.

• 46:31Speaker 00

  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 eval? When you have any agents? So for example, we have the onboarding, 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.

• 46:55Speaker 00

  How to make that a breaking change? And, and, okay, like this, this manual interaction will become an eval case. You know, we have an expansion in Brex. Whenever someone has a conversation with the agents, that is the flags in issue or a bug or, or, or, 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 engineer is going to go in and figure out how to make that pass. Because the goal at the end, I think, is to make the whole thing a self, a self-learning system, right? And I think the, a lot of what I see with, with companies is they spend a lot of time getting

• 47:38Speaker 00

  an agent working, but never thinking how to make the agent improve every day. And I think that's like always the biggest unlock.

• 47:44Speaker 01

  You need a dream cycle.

• 47:45Speaker 00

  You need a dream cycle.

• 47:46Speaker 01

  See everything every night.

• 47:48Speaker 00

  Exactly.

• 47:48Speaker 01

  And then it's like, oh, what's going on there? I need to put this over here.

• 47:51Speaker 00

  Exactly.

• 47:52Speaker 01

  What actually happened? Is there a pattern? How do I cause this?

• 47:55Speaker 00

  So how to, how to, how to bake the dream cycle? Into the products and into the agents and into the things to ship?

• 48:00Speaker 01

  My favorite thing right now is I'm building like three or four agents for my friends.

• 48:05Speaker 00

  Oh, interesting.

• 48:05Speaker 01

  And some of it is like, this is a user research for me for G Brain. Cause 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 hap, it's gonna happen in our lifetimes.

• 48:22Speaker 00

  You know, I remember when, um, when Neuralink came out. And I used to think about it, I was like, I don't, I don't get it. Like, I was like, yeah, of course I get it conceptually, but, but why is it a thing? And then now I use AI and you're like, yeah.

• 48:34Speaker 01

  Yeah, makes sense.

• 48:35Speaker 00

  Makes sense.

• 48:35Speaker 01

  Yeah.

• 48:36Speaker 00

  I'm the ball rack.

• 48:37Speaker 01

  Yeah, yeah.

• 48:38Speaker 03

  Typing is so slow. I don't know if you use a lot of dictation?

• 48:41Speaker 00

  I, I, I use a lot. My most used developer, developer UI right now is like. Voice memos to open call.

• 48:47Speaker 02

  I've said this before, like it was maybe accidental, but I actually just really love the fact that like Telegram works so well with.

• 48:54Speaker 00

  With audio.

• 48:55Speaker 02

  Cause 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. Cause 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.

• 49:13Speaker 00

  Foxconn.

• 49:14Speaker 02

  Yeah, exactly.

• 49:15Speaker 00

  Free default.

• 49:15Speaker 01

  No, just let it do what it wants to do. Give it some context and it'll just think about, you know, oh, like actually what about this?

• 49:22Speaker 00

  I think a lot of the, 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.

• 49:34Speaker 01

  Once you have the context in there, it's actually, you can do some pretty crazy stuff like my favorite new feature.

• 49:38Speaker 00

  I saw your brain LSD.

• 49:40Speaker 01

  Yeah.

• 49:42Speaker 01

  Lateral synaptic drift.

• 49:44Speaker 00

  So you, you just bumped the temperature on the search?

• 49:46Speaker 01

  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 orthogonal or just like rant, like feeling seemingly random. And then it'll try like, you know, randomly hundreds of these combinations.

• 50:12Speaker 00

  Yeah.

• 50:13Speaker 01

  And then it'll rank order them into the ones that are actually the most coherent.

• 50:17Speaker 00

  Yeah.

• 50:17Speaker 01

  And then if you do like a hundred of them, actually like the top five tend to be banger tweets.

• 50:21Speaker 02

  You know what's crazy is like, I didn't tell Alfred explicitly to be dry. I went, I actually had like chat GPT generate like the soul file. And it's like, based on like everything you know about me, all the interactions here, like generate like a soul MD for like my open call agent. And it was so unerringly like accurate about like kind of what I would want from like a, an agent. And I was like, oh damn, these models know a lot about us.

• 50:46Speaker 01

  My open clock got really interesting once I just ingested my 60 gig Google takeout. I mean, you have to write a bunch of haiku code to like only get the emails that are actually real, but you know, there's like 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 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-pilled, 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?

• 51:29Speaker 01

  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.

• 51:37Speaker 00

  Yeah, I really can't stop thinking about the electricity analogy, which is, you know, you're standing, there's a 200 year timeline of human history. There's a point in time where electricity was invented, it sucked in the beginning or six months after that point. What do you do differently? Knowing everything that will be true about electricity, knowing that data centers when 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 right now. I think the second is like have a post-it on your computer, which is you wake up, whatever problem you have in your life, why can't you solve it with AI? And just like start there. And 80%, yeah, you can use a chatbot, but a 20% that you can't, figure out why and

• 52:22Speaker 00

  go build something that makes you solve that problem. Less so because of the, 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 like just measure your token consumption and how much you're just pushing the limits of the company and starting at the premise of like, okay, why can't it just be one person? Like, why can't it just be me that builds the whole thing? And you're going to probably face a wall of the elements of, you know, what models can and cannot do. But at a limit, I think the question is, you know, how do you spend your time on the things that only you can do as a founder? And these things to me are

• 53:06Speaker 00

  Number one, which problems are worth solving? And two, uh, and 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 LM? Uh, that they still cannot do, and I have to go in and do that, those, those things myself. But almost, uh, you know, to some degree, you're working for the LM to some point. Uh, and, and, 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, you're almost architecting the entire company around that idea. But I think early on, uh, so much of it is, you know, choosing what matters, talking to customers, injecting the signal that models don't have and just, you know, rebuilding it the way you would do it in 2026 with electricity being six months old.

• 53:50Speaker 01

  Thanks, Pedro. This is awesome.

• 53:52Speaker 00

  Yeah. Thanks for having me. Appreciate it. Thanks for coming.