Kavak's Playbook for Rebuilding a Company Around AI

1. 0:00 – 1:03

   Intro

   1. AAAlejandro Maza Ayala0:00

      I'm investing more today in tokens than in knowledge workers. We could build superhuman agents. This means that by every dimension that matters, our agents would outperform the best human we had ever hired.

   2. ASAngela Strange0:13

      The most ambitious companies listening to this will decide to follow suit, which is you decided to build an agent per customer.

   3. AAAlejandro Maza Ayala0:20

      Yes. Every day, between 100 and 200,000 agents get instantiated specifically for this customer with its own virtual machine.

   4. GVGabriel Vasquez0:29

      There's a lot of people worried about how the organizations of the future are gonna look like and the role that humans are gonna play.

   5. AAAlejandro Maza Ayala0:36

      If you haven't faced fear before, you haven't felt it, then you haven't tried AI. We launched a program inside Kavak that's called the Jedi Academy. From the CEO to, like, AI engineers to mechanics, we train everyone, and after six weeks, they launch state-of-the-art agents to production.

   6. ASAngela Strange0:53

      What advice do you have to future founders or first-time founders that might be listening?

   7. AAAlejandro Maza Ayala0:57

      What works right now is-

2. 1:03 – 2:23

   Machine Learning Before Transformers

   1. GVGabriel Vasquez1:03

      Welcome back to the a16z podcast. Uh, today we have Ale Maza, the head of AI at Kavak. We're gonna discuss today the transformation that Ale led within Kavak to turn it into an AI-native company. Thank you, Ale, for being with us today.

   2. AAAlejandro Maza Ayala1:15

      Thanks for having me.

   3. GVGabriel Vasquez1:16

      Before starting at Kavak, uh, you were running a company called Oppy Analytics.

   4. AAAlejandro Maza Ayala1:21

      That's right.

   5. GVGabriel Vasquez1:22

      And you were very much into AI before ChatGPT.

   6. AAAlejandro Maza Ayala1:26

      Yes.

   7. GVGabriel Vasquez1:26

      You wanna tell us a little bit about that journey?

   8. AAAlejandro Maza Ayala1:28

      Yes, yes, of course. Well, we called it machine learning back then. It was a different family of, of algorithms. And, and we founded a company with this very, like, like ambitious vision there that, that new machine learning models would be so powerful that they could solve any complex problem. This was pre-Transformers, right? This was, like, 2013. So we started building the company that way, and I think we were, like, 10 years ahead of time. Uh, but we built a great company. We served, like, Fortune 500 companies, uh, around, like, risk algorithms, logistics, forecasting, marketing. Um, but, like, really the, the, the power of what Transformers and then, like, the ChatGPT moment, uh, when it arrived, made things, like, very clearly that, that we could now build a whole new,

3. 2:23 – 4:59

   What Kavak Does & the Agent-Per-Customer Architecture

   1. AAAlejandro Maza Ayala2:23

      uh, company and, and, and way of, of, of building companies. Um, and we joined Kavak to encourage us to build that.

   2. ASAngela Strange2:34

      Amazing. All right. So we're gonna spend the bulk of this podcast talking about exactly how you've agentified Kavak. But maybe just to start, what does Kavak do, and what is your role there?

   3. AAAlejandro Maza Ayala2:43

      Kavak, uh, started out as a used case-- as a used-car marketplace. So we buy cars, we refurbish them, and then we sell them and finance them. But to do that, we also had to build a fintech and a logistics company and a Carfax, and, like, basically all the infrastructure for this to work didn't exist in, in LatAm, so we had to build everything vertically so we could s-serve our customers the right way.

   4. ASAngela Strange3:11

      I wanna sort of start with the framing of what the architecture looks like.

   5. AAAlejandro Maza Ayala3:14

      Yeah.

   6. ASAngela Strange3:14

      So a consumer comes in and says, "I wanna sell my car." Like, how many agents do they touch? Like, what's the harness look like? Like, ground us in how you design this.

   7. AAAlejandro Maza Ayala3:22

      Right. So, so, so we bet the company in transforming to a company run by agents. The questions we ask ourselves is, how would we build Kavak in 2035 with fabled 10 or, or, or GPT le- 10-level intelligence? And actually, that company looks very different than, than what we had built or what we had back then. So when a customer comes in right now, um, agent will get spawned specifically for this customer with its own virtual machine. It will remember years of interaction of this customers with, with Kavak, what they visited in the webpage or a call they had two years ago, remember everything, like, in its memory, come up with a strategy, and set a long-term goal to maximize the lifetime value of this customer and do whatever it takes to make the customer happy and convert them into, like, all our different products, um, like, across time. And this is a completely new and groundbreaking architecture at scale, uh, I think, because, like, people are still building multi-agent system with, with experts, and, and we realized to bet that long-running agents with hard goals, not just workflows, uh, could, could maximize our, our customers', uh, satisfaction and obviously their, their lifetime value.

   8. ASAngela Strange4:44

      Awesome. Okay, so we're gonna jump to the nuances-

   9. AAAlejandro Maza Ayala4:47

      Yeah

   10. ASAngela Strange4:47

       ... of that. But may-maybe versus many companies that say, "Hey, we wanna be agentic," and they try some workflows-

   11. AAAlejandro Maza Ayala4:54

       Yes

   12. ASAngela Strange4:54

       ... you guys took the just rip, like, we had to make this work.

   13. AAAlejandro Maza Ayala4:58

       Yeah.

   14. ASAngela Strange4:58

       You had to downsize

4. 4:59 – 10:49

   Three Bets: Redesign the Company, Build Superhuman Agents, Change the Metrics

   1. ASAngela Strange4:59

      dramatically.

   2. AAAlejandro Maza Ayala5:00

      Right.

   3. ASAngela Strange5:00

      It didn't work for a year.

   4. AAAlejandro Maza Ayala5:02

      Right.

   5. ASAngela Strange5:03

      So do you wanna talk through-- Obviously, you had to tune a lot of things to make that work.

   6. AAAlejandro Maza Ayala5:07

      Totally.

   7. ASAngela Strange5:07

      Like, describe the harness at that time and, like, what models you were using and sort of specifically.

   8. AAAlejandro Maza Ayala5:12

      Totally.

   9. ASAngela Strange5:12

      Yeah.

   10. AAAlejandro Maza Ayala5:12

       So, so there, there were, like, three main decisions that, that we had to make. The first, and this is where, where I think many companies are stuck right now, is the first instinct is, "Okay, let's adopt AI." And you, you basically leave your structure as it is and just give ChatGPT or Claude to, to your team, and then there's no efficiencies. Your customers have the same problems, and, and nothing happens, right? And so, so you need to redesign your whole company around the agents and around the future capabilities. And this means really, like, rebuilding most of your APIs, rebuilding your system so the agents can use them to, to perform.

   11. ASAngela Strange5:52

       Mm-hmm.

   12. AAAlejandro Maza Ayala5:53

       Then you need to start generating the data and the feedback loops to fine-tune these agents. The only way to really make them work Is if you teach them. And how do you teach them? You, you put them out in the open, you, you put them in front of customers, you get that data, you get those evals, and then you train your, your, your agents. And this is the second bet that we made, that, that we could build superhuman agents. This means that by every dimension that matters, like conversion, lifetime value, uh, customer experience, our agents would outperform the best human we had, we had ever hired, and we put them in front of the hardest problems.

   13. SPSpeaker6:31

       Mm.

   14. AAAlejandro Maza Ayala6:31

       Um, and finally, you, you start to change how you measure the success of the company. Kavak was a transactional company. We used to measure how many cars we bought, how many cars we sold, how many brakes we, we needed to, to, brake pads we needed to buy. Uh, and we moved to a relational company, where now I have 10 million customers in my database, and I have agents assigned to most of them with the task of maximizing their lifetime value. Now, we're selling cars and, and, and personal loans and very high-ticket items, so just activating 1% of this customer base-

   15. SPSpeaker7:09

       Mm

   16. AAAlejandro Maza Ayala7:09

       ... it's, like, hundreds of millions of, of dollars, uh, if we do it the right way. So, so it's a bet that made sense for us because of our industry, because of the ticket, and because at the end of the day, customers need to build trust with, with the company because they're buying a used car. And the way to build trust is to, to know them and to, and to plan and, and nurture a long-term relationship.

   17. SPSpeaker7:35

       Yeah.

   18. GVGabriel Vasquez7:35

       Ale, I just wanted to double-click on something. You know, evals over agent demos.

   19. AAAlejandro Maza Ayala7:40

       Yeah.

   20. GVGabriel Vasquez7:40

       Um, you probably get pitched a lot of, you know, agents and, you know, it's, it's never been easier to build things like before. But, um, one of the questions is, like, how do you, how do you guys go about evaluating this? Because not everybody tests them across 90% of their customer interactions to see if they're really working, and, you know, you guys, uh-

   21. AAAlejandro Maza Ayala7:58

       Yeah

   22. GVGabriel Vasquez7:58

       ... I believe it's, uh, about 98% of the interactions or something-

   23. AAAlejandro Maza Ayala8:02

       Yes

   24. GVGabriel Vasquez8:02

       ... like that are now handled by agents.

   25. AAAlejandro Maza Ayala8:04

       Yes, totally. So, so, so to give you a sense of the scale, um, 90-- like, 96% of all interactions, uh, are handled by agents, so, so no humans there. Um, like, 95% of all transactions are completely handled by, by agents. Obviously, you meet a human when you pick up your car, like, there's someone physically there to give you the keys, but the rest of the, of the, of the experience of the journey is handled by, by an agent. Every day, between 100,000 and 200,000 agents get instantiated in a day. They wake up, they work, sometimes for three minutes, sometimes for eight hours, sometimes for three days, and they, like, set an alarm clock for their ner- next task, and to go back to sleep. So, the scale of this i- is just, it's just amazing, and, and it's working. Now, how do you get this to work at scale? And the answer you, you mentioned it, is, is evals. Like, I like to move extremely fast, but in order to move fast, you need to have brakes, right? Imagine a car. Uh, you, you'll hit on the gas just if you have the right brakes. And AI is super powerful, and I've seen many companies get this wrong because they try to go slow because they, they don't have the right brakes. So, so I thought about it the other way around. Like, how fast can we go? Well, it depends on the quality of our evals. So, a good rule of thumb here is we spend about the same amount of time, engineer time, tokens, and, and, and money on building the evals than building the agents. And this is how you get better-

   26. SPSpeaker9:45

       Yeah

   27. AAAlejandro Maza Ayala9:45

       ... and better and better, not, not letting evals as an afterthought. So, what do we measure? First and foremost, like, the, the, the results for the business. Like, if my customer is happy, they'll buy a car, they'll, they'll get their loan approved, uh, they'll sell a car to us, and, and that's the, like, first check. Like, did it convert? And that's where most things break. Like, I, I see companies, like, measuring number of calls or minutes during the call or, or some, like, superficial KPIs that give you some information, but that doesn't really work. Like, the important thing is, did this customer convert? Is it bringing value to the customer? And is the customer happy to re-engage with us after a while? And once you get those evals connected, then it's just optimizing the right agentic architecture and giving the agent skills to, to scale this and, and cater to millions of customers.

   28. GVGabriel Vasquez10:42

       It's really, really amazing. And, you know, related to this is, like, okay, so you create the right evals. You know it's working.

5. 10:49 – 14:23

   Agents That Sell: 2.1x Better Conversion Than Humans

   1. GVGabriel Vasquez10:50

      You know, some people s- some companies still feel a little bit risk-averse in putting them in front of-

   2. AAAlejandro Maza Ayala10:54

      Yeah

   3. GVGabriel Vasquez10:55

      ... of, of the customers and being able to perform-

   4. AAAlejandro Maza Ayala10:58

      Mm-hmm

   5. GVGabriel Vasquez10:59

      ... the highest leverage tasks, which, in your case, would be selling. Do, do your-

   6. AAAlejandro Maza Ayala11:03

      Right

   7. GVGabriel Vasquez11:03

      ... agents really sell to customers?

   8. AAAlejandro Maza Ayala11:04

      Yes. So, so we never built customer support or customer service agents. We, we, we built, like, sales agents. It's extremely hard to sell a car in, in Latin America. So, imagine someone wanting to buy a car. They can choose, like, like amongst, like, 20,000 SKUs. Then they need to pick, like, financing and go through the financing, well, process, insurance and, and coverage, and then they're probably trading in their car, so, so we need to quote that car. So, it's a process that if someone does it, or, or the way Kavak did it back in, in, in 2020, 2021, was you need to be extremely good at 15 different things and have 15 different experts in 15 different teams. And usually, the person would go and speak with the expert in financing, the expert in ca- car advisory, the expert in buying, the expert in insurance, and they'll build a package and buy a car. That's extremely hard to do. But, like, the first thing we did was, okay, can we get an agent to be better than the expert in each of these things?

   9. ASAngela Strange12:11

      Mm-hmm.

   10. AAAlejandro Maza Ayala12:12

       And then put it together and have, like, a mega expert that's an expert in insurance, financing, et cetera, and that's who we put in front of the customer. So the experience for the customer is amazing. We tripled, uh, NPS and customer satisfaction score by putting the, the agent in front of the, of the customer, and it, at first it converted, like, 50% more than our human team, and now it's converting over that, like, 2.1, uh, X more. So it's a completely different company.

   11. ASAngela Strange12:45

       So your agents are better sellers.

   12. AAAlejandro Maza Ayala12:47

       Totally better. And, and, and you get this, right? Because they're experts, and they're infinitely patient, and they know all your history, and they, they, they can plan for the long term, and they never get tired. So-- And if they make a mistake, they learn it, and the next day, not just them, but the other 200,000 agents will have learned from that mistake. So that's the feedback loop that we engaged, and, and that's showing in the growth and results and satisfaction of our customers.

   13. ASAngela Strange13:16

       Yeah. One of the, well, one of, two of the very cool things I think about Kavak is I think, I think the world has gotten comfortable with AI can do customer service.

   14. AAAlejandro Maza Ayala13:25

       Mm-hmm.

   15. ASAngela Strange13:25

       It's still very hard to do well. But, you know, as Gabe said, there's still a view that, well, customers aren't gonna wanna buy expensive things from AI.

   16. AAAlejandro Maza Ayala13:33

       Right. Right.

   17. ASAngela Strange13:33

       And you are proving them wrong.

   18. AAAlejandro Maza Ayala13:34

       Yes.

   19. ASAngela Strange13:35

       The next layer on that is, well, you're not actually gonna be able to do regulated financial services-

   20. AAAlejandro Maza Ayala13:41

       Yes

   21. ASAngela Strange13:41

       ... end-to-end with AI.

   22. AAAlejandro Maza Ayala13:43

       Yes.

   23. ASAngela Strange13:43

       But if you walk through what you're doing, you are underwriting a thin-

   24. AAAlejandro Maza Ayala13:46

       Yes

   25. ASAngela Strange13:46

       ... or no-file customer.

   26. AAAlejandro Maza Ayala13:48

       Yes.

   27. ASAngela Strange13:48

       Pricing them correctly.

   28. AAAlejandro Maza Ayala13:49

       Yes.

   29. ASAngela Strange13:50

       Doing servicing. So, so maybe talk through how did you, how did you write the evals to get comfortable with that? And then versus, I don't know, going to a bank branch or, or even a fintech, sort of how, how is that experience-

   30. AAAlejandro Maza Ayala14:03

       Yes. So-
6. 14:23 – 16:13

   Car Loans Approved in Three Minutes

   1. AAAlejandro Maza Ayala14:24

      whi- which is, like, pretty cool because we have all this data around the customer and the car. And if the customer can't pay for the car anymore, they'll just return it to us, and we can give them a cheaper car, and, and then the- they pay, uh, uh, a smaller amount each month, and they, like, get out of the water, which is amazing about the-

   2. ASAngela Strange14:43

      Mm-hmm

   3. AAAlejandro Maza Ayala14:43

      ... the vertical integration of, of the business. But, but then, like, when we started launching other financial products, we, we realized that this is a very important decision for the customer, right? Like, like, y- they usually take three to four months to make their, make up their mind in buying a car and, and, and, and getting a loan or getting a personal loan, like a large personal loan that, that we also, um, do. So if you get to know your customer throughout this process and make the, the, the process easy for them, then just your conversion and retention metrics start going through the roof. It's not just the transaction, it's understanding each customer personally-

   4. ASAngela Strange15:26

      Mm-hmm

   5. AAAlejandro Maza Ayala15:26

      ... and get them to, to convert when they're ready with a very deep personalization of the interest rate, the, the risk, the max amount of the loan, in a way that makes sense for the portfolio as a whole, obviously, but that's optimized to the risk level and, and probably the other offers that the customer is, is getting.

   6. ASAngela Strange15:50

      And then maybe give us, just to be, um, you know, evals are always a very hot topic. You kind of led with that. What is, uh, like, what is an example of maybe a, a hard-to-design area for evals, or one where you had to spend extra amount of time? What's just given the fact that-

   7. AAAlejandro Maza Ayala16:03

      Yes

   8. ASAngela Strange16:03

      ... like, there's real money PII at risk.

   9. AAAlejandro Maza Ayala16:06

      Right.

   10. ASAngela Strange16:06

       Yeah.

   11. AAAlejandro Maza Ayala16:07

       So when we decided to, to, to, to redesign the company around AI,

7. 16:13 – 20:13

   The AI CEO Experiment: 1.5x Profits in Six Weeks

   1. AAAlejandro Maza Ayala16:13

      you ask the que- the question, okay, is, is AI going to be able to do this job, like even the CEO job or, or jobs where the leadership is? And the answer, honestly, is probably yes. Like in 2035, with a rate of improvement, it will be able to do. So we said, okay, let, let's try it now. Let, let's try and build an AI CEO. So we carved out a, a city in Mexico. It's, it's Cuernavaca. And we put, like, an agent in one of our harnesses as a CEO, and it starts learning, and it starts making decisions and evaluating on those decisions. And it's only been running for, for six weeks now.

   2. ASAngela Strange16:53

      Yeah.

   3. AAAlejandro Maza Ayala16:54

      The goal of the first month was to double the, the, the profits of, of Cuernavaca. It didn't reach it, but it was 1.5x, like 50% more profits-

   4. ASAngela Strange17:05

      Wow.

   5. AAAlejandro Maza Ayala17:05

      Just by managing-

   6. ASAngela Strange17:06

      That's amazing. Wow

   7. AAAlejandro Maza Ayala17:07

      ... the, the city, which is, it's crazy, right? It's, it's amazing. And, and it's, it's a CEO. Like, people were s- like, that was the last job AI was supposed to take. And no, it isn't, really. And how did this happen? And, and it's like, uh, a very smart person, like, like Fields Medal level smart, like going into every single number, every single customer, making the perfect forecast, and going to micromanage every single things that needs to be executed every day to reach a plan. So he'll literally send messages to all the physical workers in, in Cuernavaca with their plans for the day and ask them to send voice notes back to, to know their, their progress. So customer satisfaction grew. Uh, we got a better inventory.

   8. ASAngela Strange17:58

      Wow.

   9. AAAlejandro Maza Ayala17:58

      We rotated better. Better financing penetration. Like, every KPI started to, to improve. Um, so it's super cool. It's super exciting. Now- What are the jobs where, where we think, uh, we're still, like, training and hiring humans? Those are related to the physical world.

   10. ASAngela Strange18:17

       Yeah.

   11. AAAlejandro Maza Ayala18:18

       So when we talk about mechanics, Kavak has around, I think in Mexico, around 100, uh, mechanics. There's lots of dexterity and, and senses that's super hard to, to substitute. So there, we also build these agents with the exact same harness that's scaling, and the, the mechanics have the sidekick. Um, I was telling you guys earlier, it's like the, the movie Ratatouille, like the, the mouse that's actually a chef collaborating with a, with a human. It's kind of like that. So it's a sidekick, we call it El Mike, and it tells them how to inspect a car and gives them tips and, and shows them the way to, to do it. And the quality of inspections, again, went through the roof. We're inspecting faster, we're repairing faster, it's cheaper, but most importantly, we're delivering higher quality cars. Um, warranties came down around, like, 20, 26% since we, since we launched, and customer satisfaction, again, went up. So i- it's about this, like, how would you design your organization from scratch, uh, with, with, with abundant super intelligence that's, that's cheap, and just go build it.

   12. GVGabriel Vasquez19:37

       Nali, this, this is a good segue to a key topic right now in Silicon Valley where, you know, there's a lot of people worried about how the organizations of the future are gonna look like, um, and the, the role that humans are gonna play-

   13. AAAlejandro Maza Ayala19:51

       Yes

   14. GVGabriel Vasquez19:51

       ... in this. And I think you touched a little bit on that, so would love to hear, yeah, like, how you guys are thinking about that and-

   15. AAAlejandro Maza Ayala19:57

       Yes

   16. GVGabriel Vasquez19:58

       ... and the organizations. Yeah.

   17. AAAlejandro Maza Ayala20:01

       Totally. So, um, we, we took that question very seriously three years ago. And the truth is that everyone's job will change. So--

8. 20:13 – 28:44

   The Jedi Academy: Training Mechanics to Ship Agents

   1. AAAlejandro Maza Ayala20:14

      And what we were doing a couple of years ago will probably be, be, be performed better by an AI agent, right? So what does this mean? We need to train everyone. So, so we launched a program inside Kavak that's called the Jedi Academy, where anyone from Kavak, like from the CEO-

   2. GVGabriel Vasquez20:34

      We love this

   3. AAAlejandro Maza Ayala20:35

      ... to-- Yeah, and it says something, like, from the CEO to, like, AI engineers to mechanics-

   4. GVGabriel Vasquez20:42

      Mm-hmm

   5. AAAlejandro Maza Ayala20:42

      ... like, going to the academy. It's super hard. Like, I, I, I've, like, led, like led them myself.

   6. GVGabriel Vasquez20:49

      You, you designed the program.

   7. AAAlejandro Maza Ayala20:50

      I designed the program.

   8. GVGabriel Vasquez20:51

      Uh, but constantly you-

   9. AAAlejandro Maza Ayala20:52

      Constantly.

   10. GVGabriel Vasquez20:53

       Yeah.

   11. AAAlejandro Maza Ayala20:53

       Because you, you, you need, you need to be upgrading the, the program because everything's changing so fast. And there, there's-- Like, you can't send these people, like, outside to Stanford to, to learn this because, like, it's new stuff, right?

   12. ASAngela Strange21:05

       Yeah.

   13. AAAlejandro Maza Ayala21:05

       So we train everyone, and after six weeks, they launch state-of-the-art, uh, agents, AI agents to production. And it's mechanics and, and finance guys and engineers, like, everyone can do it. And what this generated is maybe this person won't become an AI engineer. Some of them have, but they, they know how to collaborate with this new technology, right? So the way we looked about it was, guys, there, there's no way back. Like, this is the way Kavak is going. This is the way the company will look like. These are the changes for the engineering team, the finance team, the product team. Like, this is what's going to change. You have the choice to, like, train and, and get the skills to perform in this new reality, in this new world, um, or maybe leave Kavak if this is not for you.

   14. ASAngela Strange22:00

       Mm-hmm.

   15. AAAlejandro Maza Ayala22:00

       But this is the way we're going. And it worked great. Like, like, we, we, we strengthened the culture. Everyone was super excited. Um, p- people really know how to build these agentic systems. And then if you look at Kavak now, any process, it's really a collaboration of agents and humans, and sometimes, like, agents are the bosses or, of, of humans, and sometimes humans are designing the agents. But I think we were-- we managed to really build this and, and change this. Uh, and it's through this idea that we need to be learning every day, and things will continue to change, and the only way to, to continue being relevant is to upgrade your skills, uh, every month or every couple of months.

   16. ASAngela Strange22:46

       But you do have, or did have, you know, thousands of people. Now agents do most things.

   17. AAAlejandro Maza Ayala22:53

       Yes.

   18. ASAngela Strange22:53

       So, like, what is the org structure of Kavak? Like, does the middle management concept even exist anymore? Like, what does your org look like?

   19. AAAlejandro Maza Ayala23:00

       Right. So the way it looks like now is very flat teams, very senior teams, super empowered. If you look at a team, you'll have engineering, AI, like, operations, like, everything, and they're either building the agents, working for the agents, or being in the physical world in front of the customer.

   20. ASAngela Strange23:22

       Yep.

   21. AAAlejandro Maza Ayala23:22

       Like, most of our organization looks like that. So, so it's really built around, um, a- around the idea of how organizations will look like in the future and around AI and really harnessing this, this new technology. Obviously, this required lots of retraining because-

   22. ASAngela Strange23:38

       Mm-hmm

   23. AAAlejandro Maza Ayala23:38

       ... in 2023 or 2022, no one was building agents, no one was helping agents on, or taking orders from, from agents, and the way you cater to the physical world or the customers was in a different way than if an agent's telling you what to do or helping you make your job, uh, better.

   24. ASAngela Strange23:56

       Yep.

   25. AAAlejandro Maza Ayala23:57

       Um, so it's a completely different structure than, than we had just two years ago.

   26. ASAngela Strange24:01

       Yeah. Explain, um, we talked about this before, what working for the agents look like. Like, I think-

   27. AAAlejandro Maza Ayala24:06

       Right

   28. ASAngela Strange24:06

       ... the way you described it was you had an agentic system, and then sometimes when it fails, it's like, oh, that's kicked out to kind of a human queue.

   29. AAAlejandro Maza Ayala24:11

       Right.

   30. ASAngela Strange24:11

       But then that's lost.
9. 28:44 – 32:52

   Destroying Two Years of Work: From Multi-Agent Graphs to One Agent Per Customer

   1. AAAlejandro Maza Ayala28:44

      Uh, like if I could advise everyone, don't build agentic workflows, to graphs or, or functions or, or objectives, and we built that. These are multi-agent systems that can perform a whole function for a complex goals. Like the ones I told you that to sell a car, you need to do financing, purchasing, like recommendations, uh, et cetera. And we had thousands, like tens of thousands of these agents working at scale running the business back in December. But then Opus 4.5 came out, and I realized, like, this isn't the right paradigm anymore. Like, the, the intelligence now doesn't need, like, the graph and the multi-agent latticework and harness because it will constraint this level of intelligence. So we decided to, like, destroy everything we had been building for, for two years that was working, that brought us to profitability, that brought us amazing growth, and start over with a harness that we thought would be robust and scalable and, and leverage recursive self-improvement.

   2. ASAngela Strange29:51

      Mm-hmm.

   3. AAAlejandro Maza Ayala29:51

      Or new models, more intelligent models coming out every month. So the way this looks like, it's a virtual machine with an agent, with access to memory and evals and a CLI where they can access every tool and every API in my company, and a long-term goal. And I instantiate hundreds of thousands of these each day with long-term goals, like maximizing the, the lifetime value.

   4. ASAngela Strange30:17

      Yeah.

   5. GVGabriel Vasquez30:17

      The self-improving organization.

   6. AAAlejandro Maza Ayala30:19

      Exactly.

   7. ASAngela Strange30:19

      Yeah.

   8. AAAlejandro Maza Ayala30:19

      The self-improving organization. And I think people are super obsessed with, with RSI kind of- Now, and this will improve the, the, um, the models. But if you look at it this way, economic value in humanity f- for the past 4,000 years has been delivered by organizations, not, not by individuals. So what you want to self-improve and to engage in that loop is the organization that can deliver more economic value, right? So that's the loop that I think companies will start to, to focus on, because if you get that loop working, and it's an organization that is really self-improving and harnessing the, the newer models and the better intelligence that we're getting every couple of days now, uh, then, like, you hit the exponential, not just in intelligence, but in the value, uh, that you can generate as a company. So, so that's real exciting. That's what we're working on.

   9. GVGabriel Vasquez31:16

      You mentioned that because of all the challenges in adopting AI, you saw the biggest opportunity on net new companies being formed, working in this new way and then disrupting markets. Like, you wanna talk a little bit about that?

   10. AAAlejandro Maza Ayala31:29

       Yes. Um, th- there's this concept in economics about creative destruction from, from, from Joseph Schumpeter, and what it says is that the, the, the way innovation hits the economy isn't by companies adopting the, the, the new technology, but by companies remaining the way they, they were, and incumbents with the new technology destroying the old companies. So this destroys value in the short term in the economy, but in the long term, it's better for everyone because this new, more efficient, more effective, uh, companies will provide better products and services for, for the economy as a whole. And this has happened in the past, like, industrial revolutions, and this has always happened. And this is a great opportunity for entrepreneurs and, and people today because it's hard to adopt AI deeply. Uh, it's really hard for a CEO today, especially of a large company or public company, to go and say, "Hey, like, I'm betting everything on AI. Uh, the company has to look this way. I'll, like, destroy and rebuild everything I've been building for the past 40 years to become an AI native company." Like, how many CEOs will, will, will do that in a company at scale? So while they, they adopt,

10. 32:52 – 34:45

    Creative Destruction & Ford's Factory: Why Adoption Isn't Enough

    1. AAAlejandro Maza Ayala32:52

       new companies can be formed that are built around the, the, the strengths of, of AI and, and take over and, and, and, and bring new products and services to, to, to the masses. And, um, this has happened before. Like, this happened with electricity. This is a story I always tell, tell my team. The, the, the technologies for Ford's production line were developed in 1879 and 1881. Edison started commercializing electricity in, in New York and then London, and he invented a, a dynamo that was extremely efficient. So you could have built Ford's Factory 40 years before Ford. The technology was there, everything was there. But the way people adopted electricity and, and Ford's dynamo was, "Okay, I'm gonna leave my factory, like four floors, shafts and belts, and just change my coal engine for an electric engine." And this will bring you benefits, yes, but like 6% efficiency. What needed to be done was, like, to destroy that factory, build it in a flat s- surface, not in the center of New York, but in Connecticut or New Jersey, and redesign your whole factory around small dynamos and, and electricity. And then you get, like, the 3X, uh, improvement in productivity that, that, like, powered the US during the 20th century. And the same happened again with the computer, and the same is happening again today. People want to adopt it-

    2. GVGabriel Vasquez34:24

       Mm-hmm

    3. AAAlejandro Maza Ayala34:25

       ... but they're not willing to redesign the whole company, and they just adopt it superficially. And in the end, that'll give you a 6% or a 10% improvement, not a 10X improvement. And it's like the innovator's dilemma at an industrial, uh, scale again.

11. 34:45 – 36:16

    Advice for Founders: The Most Exciting Time in Human History

    1. ASAngela Strange34:45

       I think you've just made an amazing case for any future founders out there-

    2. AAAlejandro Maza Ayala34:49

       [laughs]

    3. ASAngela Strange34:49

       ... that it's time to build.

    4. AAAlejandro Maza Ayala34:50

       It's time to build, for sure.

    5. ASAngela Strange34:51

       Um, and, and maybe a, a great, a great place to, to end this, you know, you've built and scaled your own company. You've now turned Kavak fully agentic. Like, what advice do you have to future founders or first-time founders that might be listening?

    6. AAAlejandro Maza Ayala35:04

       So this is the most exciting time in human history. I, I believe that. Like, like, we're living in the most exciting time in human history, and it's the most exciting time to, to be a founder because it's the first time that anyone has access to the most powerful tools and intelligence in the world, like, for almost for free or for $20 a month. So, so literally there, the democratization of the tools for people to build has never been, uh, this way in human history.

    7. ASAngela Strange35:38

       Mm-hmm.

    8. AAAlejandro Maza Ayala35:38

       And there's so much problems to be solved and a new reality to be built around this, this new paradigm. So say, like, just go for it, but go for it deep. Like, imagine what the future around AI will look like.

    9. ASAngela Strange35:53

       Mm-hmm.

    10. AAAlejandro Maza Ayala35:55

        It's just a, a-- it's not even an exponential. Just, just map a trend. It's linear. If things keeps getting, like, AI keeps getting better at a linear scale, and just build for that and, and you'll come up with, with wonderful ideas that will, like, bring a lot of value to the world.

    11. ASAngela Strange36:13

        Amazing. Ale, thank you for joining us.

    12. AAAlejandro Maza Ayala36:15

        Thank you. Thanks for having me.

    13. GVGabriel Vasquez36:16

        Thank you so much, Ale.

Episode duration: 36:31