Kavak's Playbook for Rebuilding a Company Around AI

Angela Strange and Gabriel Vasquez are joined by Alejandro Maza Ayala, Chief Product & AI Officer at Kavak, to unpack how the Latin American used-car marketplace rebuilt itself around AI agents, with 96% of customer interactions and 95% of transactions now handled by agents. Alejandro explains why Kavak decided that simply giving employees AI tools wasn't enough, and instead redesigned the company's systems, teams, and customer experience around agents. They discuss why Kavak spends as much engineering effort on evals as it does building agents, how its AI sellers outperform its human teams, and an experiment where an AI "CEO" increased profits in one city by 50% in its first month. The conversation also explores what happens to organizational structure when agents do most of the work, why Kavak trains everyone from executives to mechanics to build with AI, and Alejandro's argument that companies looking for incremental AI adoption may be missing the larger opportunity: redesigning the organization itself. Timestamps: 00:00 - Intro 01:03 - Machine Learning Before Transformers 02:23 - What Kavak Does & the Agent-Per-Customer Architecture 04:59 - Three Bets: Redesign the Company, Build Superhuman Agents, Change the Metrics 10:49 - Agents That Sell: 2.1x Better Conversion Than Humans 14:23 - Car Loans Approved in Three Minutes 16:13 - The AI CEO Experiment: 1.5x Profits in Six Weeks 20:13 - The Jedi Academy: Training Mechanics to Ship Agents 28:44 - Destroying Two Years of Work: From Multi-Agent Graphs to One Agent Per Customer 32:52 - Creative Destruction & Ford's Factory: Why Adoption Isn't Enough 34:45 - Advice for Founders: The Most Exciting Time in Human History Resources: Follow Alejandro Maza Ayala on X: https://x.com/alehandromz Follow Angela Strange on X: https://x.com/astrange Follow Gabriel Vasquez on X: https://x.com/GEVS94 Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Show on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Show on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711 Follow our host: https://x.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see http://a16z.com/disclosures.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 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.

• 0:15Angela Strange?

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

• 0:22Speaker 00

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

• 0:32Speaker 02

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

• 0:39Speaker 00

  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 AI engineers to mechanics, we train everyone. And after six weeks, they launch state of the art agents to production.

• 1:00Angela Strange?

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

• 1:05Speaker 00

  What works right now is

• 1:06Erik Torenberg?

  Most companies are asking how to add AI to the organization. Kavak asked a much more radical question. What would we build if we were starting the company from scratch with AI? Angela Strange and Gabriel Vasquez sit down with Kavak's chief product and AI officer, Alejandro Ayala, to unpack what happened when the company bet on rebuilding itself around agents. Today, hundreds of thousands of agents can be instantiated each day, handling everything from selling and financing cars to maintaining long-term customer relationships. They discuss why Kavak tore down an agent architecture that was already working to start again, how evals became the foundation for moving faster, and what happens when agents don't just work for humans, but humans sometimes work for agents.

• 1:53Speaker 02

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

• 2:05Speaker 00

  Thanks for having me.

• 2:06Speaker 02

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

• 2:11Speaker 00

  That's right.

• 2:12Speaker 02

  And you were very much into AI before ChatGPT. Want to tell us a little bit about that journey?

• 2:18Speaker 00

  Yes, yes, of course. Well, we called it machine learning back then. It was a different family of algorithms. And we founded a company with this very ambitious vision there 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. But we built a great company. We served 14, 500 companies around like risk algorithms, logistics, forecasting, marketing. But really, the power of what Transformers and then the ChatGPT moment, when it arrived, make things like very clearly

• 3:02Speaker 00

  that we could now build a whole new company and way of building companies. And we joined Kavak and Carlos to build it.

• 3:12Angela Strange?

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

• 3:22Speaker 00

  Kavak 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 Carfax and like basically all the infrastructure for this to work didn't exist in Latam. So we had to build everything vertically so we could serve our customers the right way.

• 3:48Angela Strange?

  I'm going to sort of start with the framing of what the architecture looks like. So a consumer comes in and says, I want to sell my car. Like, how many agents do they touch? What's the harness look like? Ground us in how you design this.

• 3:59Speaker 00

  Right. 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 Fable 10 or GPT 10 level intelligence? And actually, that company looks very different than what we had built or what we had back then. So when a customer comes in right now, agent will get spawned specifically for this customer with its own virtual machine. It will remember years of interaction of these customers with Kavak, what they visited in the webpage or the call they had two years ago, remember everything in its memory, come up with a strategy and set a long-term goal

• 4:42Speaker 00

  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 across time. And this is a completely new and groundbreaking architecture at scale, I think, because people are still building multi-agent system with experts. And we realized to bet that long-running agents with hard goals, not just workflows, could maximize our customers' satisfaction and obviously their lifetime value.

• 5:15Angela Strange?

  Okay, so we're going to jump to the nuances of that. But maybe versus many companies that say, hey, we want to be agentic, and they try some workflows.

• 5:23Speaker 00

  Yes.

• 5:24Angela Strange?

  You guys took the just rip, we had to make this work. You had to downsize dramatically. It didn't work for a year.

• 5:32Speaker 00

  Right.

• 5:32Angela Strange?

  So you want to talk through, obviously, you had to tune a lot of things to make that work. Like, describe the harness at that time and what models you were using and sort of specifically.

• 5:42Speaker 00

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

• 6:24Speaker 00

  The only way to really make them work is if you teach them. And how do you teach them? You put them out in the open, you put them in front of customers, you get that data, you get those evals, and then you train your agents. And this is the second bet that we made, that we could build superhuman agents. This means that by every dimension that matters, like conversion, lifetime value, customer experience, our agents would outperform the best human we had ever hired and would put them in front of the hardest problems. And finally, 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 brake pads we needed to buy.

• 7:07Speaker 00

  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 personal loans and very high-ticket items. So just activating 1% of this customer base, it's hundreds of millions of dollars if we do it the right way. 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 a company because they're buying a used car. And the way to build trust is to know them and to plan and nurture a long-term relationship.

• 7:50Speaker 02

  Ale, I just wanted to double click on something. Eval's over agent demos?

• 7:55Speaker 00

  Yeah.

• 7:55Speaker 02

  You probably get pitched a lot of agents and it's never been easier to build things like before. But one of the questions is like, how do you guys go about evaluating this? Because not everybody tests them across 90% of the customer interactions to see if they're really working. And you guys, I believe it's about 98% of the interactions or something

• 8:13Speaker 00

  Yes.

• 8:13Speaker 02

  like that are now handled by agents.

• 8:15Speaker 00

  Yes, totally. So to give you a sense of the scale, like 96% of all interactions are handled by agents. So no humans there. 95% of all transactions are completely handled by agents. Obviously, you meet a human when you pick up your car, like there's someone physically there to give you their keys. But the rest of the experience of the journey is handled by an agent. Every day between 100 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 set an alarm clock for their next task and go back to sleep. So the scale of this is just amazing and it's working. Now, how do you get this to work at scale?

• 9:01Speaker 00

  And the answer you mentioned is evals. I like to move extremely fast. But you know, to move fast, you need to have brakes, right? Imagine a car, 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 don't have the right brakes. So I thought about 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 money on building the evals than building the agents. And this is how you get better and better and better, not letting evals as an afterthought.

• 9:46Speaker 00

  So what do we measure?

• 9:49Speaker 00

  First and foremost, the results for the business. Like, if my customer is happy, they'll buy a car, they'll get their loan approved, they'll sell a car to us, and that's the first check. Did it convert? And that's where most things break. I see companies like measuring number of calls or minutes during the call or some superficial KPIs that give you some information, but that doesn't really work. The important thing is Did this customer convert? Is it bringing value to the customer? And is the customer happy to reengage 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 scale this

• 10:34Speaker 00

  and cater to millions of customers.

• 10:37Speaker 02

  It's really, really amazing. And related to this is like, okay, so you create the right evals, you know, it's working. You know, some people, some companies still feel a little bit risk averse and putting them in front of of the customers and being able to perform the highest leverage tasks, which in your case would be selling. Do your agents really sell to customers?

• 10:59Speaker 00

  Yes, so we never built customer support or customer service agents. We built like sales agents. It's extremely hard to sell a car in Latin America. So imagine someone wanted to buy a car. They can choose like amongst like 20,000 SKUs. Then they need to pick like financing and go through the financing process, insurance and coverage. And then they're probably trading in their car. So we need to quote that car. So it's a process that if someone does it, or the way Kavak did it back in 2020, 2021, you need to be extremely good at 15 different things and have 15 different experts in 15 different teams.

• 11:43Speaker 00

  And usually the person would go and speak with the expert in financing, the expert in 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? 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 NPS and customer satisfaction score by putting the agent in front of the customer.

• 12:26Speaker 00

  And at first it converted like 50% more than our human team. And now it's converting over that like 2.1X more. So it's a completely different company.

• 12:40Angela Strange?

  So your agents are better sellers.

• 12:41Speaker 00

  Totally better. And you get this, right? Because they're experts and they're infinitely patient and they know all your history. And 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 a feedback loop that we engaged. And that's showing in the growth and results and satisfaction of our customers.

• 13:11Angela Strange?

  Yeah. One of the, well, 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. It's still very hard to do well. But you know, as Gabe said, there's still a view that, well, customers aren't going to want to buy expensive things from AI. And you are proving them wrong.

• 13:29Speaker 00

  Yes.

• 13:30Angela Strange?

  The next layer on that is, well, you're not actually going to be able to do regulated financial services end to end with AI. But if you walk through what you're doing, you are underwriting a thin or no-file customer, pricing them correctly, doing servicing. 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 even a fintech, sort of how, how was that experience?

• 13:58Speaker 00

  Yes.

• 13:58Angela Strange?

  That much better.

• 13:59Speaker 00

  So the first financial product that we launched was a car loan. And usually in Mexico and in some emerging markets, it'll get like two months or more to get a car loan approved. We usually approve it in under three minutes, 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 then they're pay a smaller amount each month and they like get out of the water, which is amazing about the vertical integration of the business. But then like, when we started launching other financial products,

• 14:44Speaker 00

  we realized that this is a very important decision for the customer, right? Like, they usually take three to four months to make up their mind in buying a car and getting a loan or getting a personal loan, like a large personal loan that we also do. So if you get to know your customer throughout this process and make 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 and get them to convert when they're ready with a very deep personalization of the interest rate,

• 15:29Speaker 00

  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 probably the other offers that the customer is getting.

• 15:44Angela Strange?

  And then maybe give us just be, you know, evals are always a very hot topic. You kind of led with that. What is a, like, what is an example of maybe 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 like there's real money PII at risk?

• 16:01Speaker 00

  Yeah. So when we decided to redesign the company around AI, you asked the question, okay, is AI going to be able to do this job, like even the CEO job 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's try it now. Let's try and build an AI CEO. So we carved out a city in Mexico. 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.

• 16:45Speaker 00

  And it's only been running for six weeks now. The goal of the first month was to double the profits of Cuernavaca. It didn't reach it, but it was 1.5X, like 50% more profits, just by managing the city, which is, it's crazy, right? It's amazing. And it's a CEO, like people were like, that was the last job AI was supposed to take. And no, it isn't really. And how did this happen? And it's like a very smart person, like fields metal level smart, like going into every single number, every single customer, making the perfect forecast and going

• 17:29Speaker 00

  to micromanage every single thing that needs to be executed every day to reach a plan. So he'll literally send messages to all the physical workers in Cuernavaca with their plans for the day and ask them to send voice notes back to know their progress. So customer satisfaction grew. We got a better inventory. We rotated better, better financing penetration. Like every KPI started to improve. And so it's super cool, it's super exciting. Now, what are the jobs where we think we're still like training and hiring humans? Those are related to the physical world. So when we talk about mechanics,

• 18:14Speaker 00

  Kavak has around, I think in Mexico, around 800 mechanics. There's lots of dexterity and senses that's super hard to substitute. So there, we also build these agents with the exact same harness that scaling. And the mechanics have the sidekick. I was telling you guys earlier, it's like the movie Ratatouille, like the mouse that's actually a chef collaborating 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 shows them the way to do it. And the quality of inspections, again, went through the roof.

• 18:59Speaker 00

  We're inspecting faster, we're repairing faster. It's cheaper, but most importantly, we're delivering higher quality cars. Warranties came down around like 20, 26% since we launched. And customer satisfaction again went up. So it's about this, like how would you design your organization from scratch with abundant super intelligence that's cheap? And just go build that.

• 19:32Speaker 02

  Now, this is a good segue to a key topic right now. It's like a valley where, you know, there's a lot of people worried about how the organizations of the future are going to look like. And the role that humans are going to play.

• 19:45Speaker 00

  Yes.

• 19:46Speaker 02

  And I think you touched a little bit on that. So we'd love to hear, yeah, like how you guys are thinking about that.

• 19:51Speaker 00

  Yes.

• 19:52Speaker 02

  And the organizations, yeah.

• 19:56Speaker 00

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

• 20:09Speaker 00

  And what we were doing a couple of years ago will probably be performed better by an AI agent. Right? So what does this mean? We need to train everyone. So we launched a program inside Quebec that's called the Jedi Academy, where anyone from Quebec, like from the CEO to... Yeah, and it sounds like from the CEO to like AI engineers to mechanics, like going to the academy. It's super hard. Like I led myself.

• 20:43Speaker 02

  You designed the program.

• 20:45Speaker 00

  I designed the program.

• 20:46Speaker 02

  But constantly.

• 20:47Speaker 00

  Constantly, because you need to be upgrading the program because everything's changing so fast. And there's, like, you can't send these people like outside to Stanford to learn this because like it's new stuff, right? So we train everyone and after six weeks, they launch state of the art

• 21:07Speaker 00

  agents, AI agents to production. And it's mechanics 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's no way back. Like, this is the way Quebec 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 get the skills to perform in this new reality, in this new world.

• 21:52Speaker 00

  And or maybe leave Quebec if this is not for you, but this is where we're going. And it worked great. Like, we strengthened the culture and we were super excited.

• 22:04Speaker 00

  People really know how to build these agentic systems. And then if you look at Quebec now, any process, it's really a collaboration of agents and humans. And sometimes like agents are the bosses of humans and sometimes humans are designing the agents. But I think we managed to really build this and change this. 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 continue being relevant is to upgrade your skills every month or every couple of months.

• 22:41Angela Strange?

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

• 22:47Speaker 00

  Yes.

• 22:48Angela Strange?

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

• 22:55Speaker 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.

• 23:16Angela Strange?

  Yep.

• 23:17Speaker 00

  Like, most of our organization looks like that. So it's really built around the idea of how organizations will look like in the future and around AI and really harnessing this new technology. Obviously, this required lots of retraining because in 2023 or 2022, no one was building agents. No one was helping agents or taking orders 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 better. And so it's a completely different structure than we had just two years ago.

• 23:56Angela Strange?

  Yeah. Explain, we talked about this before, what working for the agents look like. Like, I think the way you described it was a agentic system. And then sometimes when it fails, it's like, oh, that's kicked out to kind of a human queue.

• 24:06Speaker 00

  Right.

• 24:06Angela Strange?

  But then that's lost. And so how have you brought that together?

• 24:08Speaker 00

  So like, we see human in the loops and most of these agentic systems in production right now, like large scale agentic systems. Usually, if an agent hits a wall or can't perform anymore, it'll like send this case or this customer to a tier two support and forget about it. That doesn't really work because you don't close the loops. So you don't generate the data to train the agent to do this better. What works right now is we have an agent that's obsessed with each of the customers, like millions of this. They have access to every single API, every single skill, like, and we have agents building those, humans building those skills for them.

• 24:50Speaker 00

  And then if an agent hits a wall or cancels something, it'll call this API saying, I need help. And on the other side, it's not an agent or software, it's a human helping them out. But if you map this out in an org chart, it's really human teams that have an agent. I'm getting better results. It's super clear. Like, it makes sense.

• 25:13Angela Strange?

  That's actually a perfect segue. I know you get lots of leaders at larger institutions inbounding to you. So maybe this will save you many phone calls. But I think rationally, many leaders of companies intuitively understand this. It is very hard still to deploy AI through the organization. Like, the models are good enough. You know that. It's an org problem. It's a psychology problem. Like, what advice do you have or what have you seen?

• 25:39Speaker 00

  I think it's two things. The first is it has to be top down because of this. Like, if you just get adoption, it won't go anywhere because it's hard to generate this taste or strategy for people to bottom up, decide what to build and whatnot, and come up with something that works for the company. So the transformation has to be top down and leaders need to adopt. And leaders have to have a very clear plan on what to build. I've seen so many companies, it's just like, oh, like, we're doing a hackathon. People are coming up with use cases. We're sponsoring some of these use cases. That doesn't work. It's like, be very clear on what the company will look like in three or five years.

• 26:24Speaker 00

  And then start building that and be like very vertical in guiding your troops towards that. Like, an army doesn't really work if everyone comes up with ideas on the strategy and tactics and goes to the battlefield and like does whatever they want. Like, you need a very clear strategy. And that's what we need now. It's a like transformation stage. The second one is you need to measure what really matters. And it's evals, but it's also the right evals. So I see a lot of companies spending now huge amounts and they say, okay, I got adoption. I'm just spending like hundreds of millions of dollars in tokens now. What about that? Like, there's quality in the tokens. So have a framework here that's also useful.

• 27:05Speaker 00

  Like, level tier three tokens, the most valuable, are these agents where you can

• 27:13Speaker 00

  get the ROI of each specific token. And I can do that now. That's great news for me. Because I'm growing and because I know the ROI of each token, because it goes to agents that are performing the job of the organization, right? These are the best tokens. Tier two tokens are things that you can measure indirectly. Do I see deaths in the in the code base? And I can evaluate the value of these tokens at least indirectly and then push those to productions. Tier one, when most companies are, is people are just using plug code or chat GPT or cowork or whatever. What happened with those? I have no idea. So it's not just about adoption. It's really about having a very clear vision

• 27:56Speaker 00

  and then measuring that each token you spend is bringing you those benefits and just iterate, iterate, iterate from there.

• 28:03Angela Strange?

  And I want to, and we touched on this a little bit, but I think it's worth a dive as maybe the most ambitious companies listening to this will decide to follow suit, which is you decided to build an agent per customer.

• 28:16Speaker 00

  Yes.

• 28:16Angela Strange?

  Versus per task. And then discovered along the way that each one of those agents needs its own sort of micro virtual machine. So maybe kind of walk us through

• 28:27Speaker 00

  Yes.

• 28:27Angela Strange?

  those decisions at architecture.

• 28:28Speaker 00

  And I think we're seeing these results now, but it was a really risky bet. Because people usually go from workflows.

• 28:39Speaker 00

  Like if I could advise everyone, don't build agentic workflows to graphs or functions 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, 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 intelligence now doesn't need like the graph and the multi-agent

• 29:20Speaker 00

  lattice work and harness, because it will constrain 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 leverage recursive software 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 the CLI where they can access every tool and every API in my company and the long-term goal.

• 30:04Speaker 00

  And I instantiate hundreds of thousands of these each day with long-term goals, like maximizing the life and value. Yeah.

• 30:12Speaker 02

  The self-improving organization.

• 30:14Speaker 00

  Exactly, the self-improving organization. And I think people are super obsessed with, with our site now. And this will improve the, the models. But if you look at it this way, economic value in humanity for the past 4,000 years has been delivered by organization, 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 sort of 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.

• 31:01Speaker 00

  Then like you hit the exponential, not just in intelligence, but in the value that you can generate as a company. So, so that's really exciting. That's what we're working on.

• 31:12Speaker 02

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

• 31:25Speaker 00

  Yes.

• 31:27Speaker 00

  There, 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 is sent by companies adopting the, the new technology, but by companies remaining the way they, they were and incumbents with a 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 companies will provide better products and services for, for the economies of. And this has happened in the past, like industrial revolutions, and this has always happened.

• 32:09Speaker 00

  And it's a great opportunity for entrepreneurs and then people today. Because it's hard to adopt AI deeply. 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. 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, new companies can be formed that are built around the, the, the strengths of, of AI

• 32:54Speaker 00

  and, and take over and, and, and bring new products and services to, to, to the masses. And, and 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 New York and then London. And he invented 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,

• 33:36Speaker 00

  I'm going to leave my factory like four floors, shafts and belts. I 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 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 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.

• 34:19Speaker 00

  People want to adopt it, but they're not willing to redesign the whole company. And they just adopted 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 scale again.

• 34:41Angela Strange?

  I think you've just made an amazing case for any future founders out there that it's time to build.

• 34:46Speaker 00

  It's time to build, for sure.

• 34:47Angela Strange?

  And maybe a great, a great place to, to end is, 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?

• 34:59Speaker 00

  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

• 35:12Speaker 00

  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 this way in human history. 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. It's just a, it's not even an exponential, just, just map a trend. It's linear.

• 35:56Speaker 00

  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.

• 36:08Angela Strange?

  Amazing. Ali, thank you for joining us.

• 36:10Speaker 00

  Thank you. Thanks for having me.

• 36:14Erik Torenberg?

  Thanks for listening to this episode of the A16Z Podcast. If you liked this episode, be sure to like, comment, subscribe, leave us a rating or review and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts and Spotify. Follow us on X at A16Z and subscribe to our Substack at A16Z.substack.com. Thanks again for listening and I'll see you in the next episode.

• 36:40Erik Torenberg?

  As a reminder, the content here is for informational purposes only, should not be taken as legal, business, tax or investment advice, or be used to evaluate any investment or security and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details, including a link to our investments, please see A16Z.com/disclosures.