The Self-Improving Company | Kavak's AI Playbook - The a16z Show

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[00:01]
Alejandro Ayala
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.
[00:16]
Angela Strange
The most ambitious companies listening to this will decide to follow suit, which is. You decided to build an agent per customer?
[00:23]
Alejandro Ayala
Yes. Every day between 100 and 200,000 agents get instantiated specifically for this customer with its own virtual machine.
[00:33]
Gabriel Vasquez
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.
[00:40]
Alejandro Ayala
If you haven't faced fear before, you haven't felt it, then you haven't tried AI. We launched a program inside Quebec 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.
[01:01]
Angela Strange
What advice do you have to future founders or first time founders that might be listening?
[01:05]
Alejandro Ayala
What works right now is most companies
[01:07]
Podcast Host / Narrator
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 Kovac'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 to maintaining long term customer relationships. They discuss why Kovac 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.
[01:54]
Gabriel Vasquez
Welcome back to the ACC podcast. Today we have Ale Mahassa, the head of AI at Kabak. We're going to discuss today the transformation that Ali led within Kabak to turn into an AI native company. Thank you Ali, for being with us today.
[02:06]
Alejandro Ayala
Thanks for having me.
[02:07]
Gabriel Vasquez
Before starting at Kabak, you were running a company called Oppie Analytics.
[02:11]
Alejandro Ayala
That's right.
[02:12]
Gabriel Vasquez
And you were very much into AI before ChatGPT. You want to tell us a little
[02:17]
Alejandro Ayala
bit about that journey? 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 problem, 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 Fortune 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 that we could now build a whole new company and way of building companies. And we joined Kavak to and Carlos to build that.
[03:13]
Angela Strange
Amazing. All right, so we're going to spend the bulk of this podcast talking about exactly how you've identified kvak. But maybe just to start, what does KVAC do and what is your role there?
[03:22]
Alejandro Ayala
KVAC started out as a use 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 the 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.
[03:49]
Angela Strange
I want 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 and how you design this.
[04:00]
Alejandro Ayala
Right. So we bet the company. In transforming to a company run by agents, the questions we ask ourselves is how would we build Caback 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 Kabak, what they visited in the webpage or a call they had two years ago, remember everything 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 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.
[05:15]
Angela 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. You guys took the just rip. We had to make this work. You had to downsize dramatically. It didn't work for a year.
[05:32]
Alejandro Ayala
Right.
[05:33]
Angela Strange
So do 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, yeah.
[05:43]
Alejandro Ayala
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 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 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 we put them in front of the hardest problems. And finally you start to change how you measure the success of the company. Caddac 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. 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.
[07:51]
Gabriel Vasquez
Ale I just wanted to double click on something evils over agent demos. Yeah, 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, is about 98% of the interactions or something like that are now handled by agents.
[08:15]
Alejandro Ayala
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 a hundred and two hundred thousand 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 the next task and to 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? And the answer you mentioned it is evals. I like to move extremely fast, but in order 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 bricks. 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 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. So what do we measure? 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 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 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 scale this and cater to millions of customers.
[10:37]
Gabriel Vasquez
It's really, really amazing. And related to this is like, okay, so you create the right evals. No, it's working. You know, some people, some companies still feel a little bit risk Averse and putting them in front 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?
[11:00]
Alejandro Ayala
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 wanting to buy a car. They can choose amongst 20,000 SKUs, then they need to pick 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 Kaival did it back 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 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. 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.
[12:40]
Angela Strange
Your agents are better sellers, totally better.
[12:43]
Alejandro Ayala
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 that's showing in the growth and results and satisfaction of our customers.
[13:11]
Angela Strange
Yeah, one of the two of the very cool things I think about Kovac is 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.
[13:30]
Alejandro Ayala
Yes.
[13:30]
Angela Strange
The next layer of on that as 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.
[13:43]
Alejandro Ayala
Yes.
[13:43]
Angela Strange
Pricing them correctly, doing servicing. So maybe talk through 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 is that experience?
[13:58]
Alejandro Ayala
Yes, so much better. 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 pay a smaller amount each month and they get out of the water. Which is amazing about the vertical integration of the business. But then when we started launching other financial products, we realized that this is a very important decision for the customer. Right. They usually take three to four months to make up their mind. And 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, 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
[15:44]
Angela Strange
is getting and then maybe give us just to be. Evals are always a very hot topic. You kind of led with that. What is an example of maybe a hard to design area for evals or one where you had to spend extra amount of time with just given the fact that there's real money PII at risk.
[16:02]
Alejandro Ayala
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. 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 crazy, right? It's amazing. And it's the CEO 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 very smart person fields, metal 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 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. 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, Karak 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's 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 Mic. 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. 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, since we launched. And customer satisfaction again went up. So it's about this, like, how would you design your organization from scratch with, with, with abundant superintelligence that's. That's cheap. And just go build that.
[19:32]
Gabriel Vasquez
Now 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 going to look like and the role that humans are going to play in this. And I think you touched a little bit on that. So we'd love to hear how you guys are thinking about that.
[19:52]
Alejandro Ayala
Yes.
[19:53]
Gabriel Vasquez
And the organizations.
[19:55]
Alejandro Ayala
Yeah, totally. So we took that question very seriously three years ago. And the truth is that everyone's job will change. So. 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 tells them like from the CEO to like AI engineers to mechanics, like going to the academy, it's super hard. Like I, I, I've, I led them myself.
[20:44]
Gabriel Vasquez
You designed the program?
[20:45]
Alejandro Ayala
I designed the program. But constantly, 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, to learn this because like it's new stuff. Right. So we train everyone and after six weeks they launch state of the art 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 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 Kabak 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, or maybe leave Kadak if this is not for you. But this is the way we're going. And it worked great. We strengthened the culture. Everyone's super excited. People really know how to build these agentic systems. And then if you look at Kadak now, any process, it's really a collaboration of agents and humans. And sometimes 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.
[22:41]
Angela Strange
But you do have or did have thousands of people Now. Agents do most things. So what is the org structure of Kavak? Does the middleman management concept even exist anymore? Like, what does your org look like?
[22:55]
Alejandro Ayala
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. 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.
[23:52]
Angela Strange
Yeah.
[23:52]
Alejandro Ayala
And so it's a completely different structure than we had just two years ago.
[23:56]
Angela 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 eugenic system. And then sometimes when it fails, it's like, oh, that's kicked out to kind of a human queue.
[24:06]
Alejandro Ayala
Right.
[24:07]
Angela Strange
But then that's lost. And so how have you brought that together?
[24:09]
Alejandro Ayala
So, like the. 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 2 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. And we have agents building those humans, building those skills for them. 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.
[25:13]
Angela Strange
That's actually a perfect segue. And 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 their organization. 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?
[25:39]
Alejandro Ayala
I think it's two things. The first is it has to be top down. Because of this. 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 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 does whatever they want. You need a very clear strategy and that's what we need now. It's a 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 hundreds of millions of dollars in tokens now. What about that? There's quality in the tokens. So have a framework here that's also useful. Like Tier 3 tokens, the most valuable are these agents where you can 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 2 tokens are things that you can measure indirectly. Do I see devs in the code base? And I can evaluate the value of these tokens at least indirectly and then push those to Productions. Tier 1, when most companies are is people are just using plug code or chatgpt or cowork or whatever. What happens with those, I have no idea. So it's not just about adoption. It's really about having a very clear vision and then measuring that each token you spend is bringing you those benefits and just iterate, iterate, iterate from there
[28:04]
Angela 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 versus per task and then discovered along the way that each one of those agents needs its own micro virtual machine. So maybe kind of walk us through yes those decisions that architecture and I
[28:30]
Alejandro Ayala
think we're seeing these results now. But it was a really risky bet because people usually go from workflows 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 lattice work and harness because it will constrain this level of intelligence. So we decided to destroy everything we had been building 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 self improvement 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 and I instantiate hundreds of thousands of these each day with long term goals like maximizing the lifetime value.
[30:12]
Gabriel Vasquez
Yeah, the self improving organization.
[30:14]
Alejandro Ayala
Exactly, the self improving organization. And I think people are super obsessed with RSI now and this will improve the models. But if you look at it this way, economic value in humanity for the past 4,000 years has been delivered by organizations, 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. That's the loop that I think companies will start to focus on because if you get that loop working and it's an organization that is really self improving and harnessing the newer models and the better intelligence that we're getting every couple of days now, then you hit the exponential not just in intelligence but in the value that you can generate as a company. So that's really exciting. That's what we're working on.
[31:12]
Gabriel Vasquez
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. You want to talk a little bit about that?
[31:25]
Alejandro Ayala
Yes. There's this concept in economics about creative destruction from Joseph Schumpeter. And what it says is that the way innovation hits the economy isn't by companies adopting the new technology, but by companies remaining the way 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 the economy as a whole. And this has happened in the past industrial revolutions, and this has always happened. And this is a great opportunity for entrepreneurs and 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, 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 adopt, new companies can be formed that are built around the, the strengths of, of AI and take over and bring new products and services to, to the masses. And this has happened before, like this happened with electricity. This is a story I always tell my team. 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 Ford's dynamo was okay, I'm going to 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 surface, not in the center of New York, but in Connecticut or New Jersey. And redesign your whole factory around small dynamos and electricity. And then you get the 3x improvement in productivity that powered the US during the 20th century. And the same happened again with a computer. And the same is happening again today. People want to adopt it, 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 scale.
[34:39]
Angela Strange
Again, I think you've just made an amazing case for any future founders out there that it's time to build.
[34:46]
Alejandro Ayala
It's time to build for sure.
[34:48]
Angela Strange
And maybe a great place to end is you've built and scaled your own company. You've now turned Kabak fully agentic. What advice do you have to future founders or first time founders that might
[34:59]
Alejandro Ayala
be listening so this is the most exciting time in human history. 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 literally 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 new paradigm. So say like just go for it, but go for it deep. Like imagine what the future around AI will look like. Just it's not even an exponential. Just map a trend is linear if things keeps getting like AI keeps getting better at linear scale and just build for that and you'll come up with wonderful ideas that will bring a lot of value to the world.
[36:09]
Angela Strange
Amazing ale. Thank you for joining us.
[36:11]
Alejandro Ayala
Thank you. Thanks for having me.
[36:15]
Podcast Host / Narrator
Thanks for listening. Listening to this episode of the A16Z podcast. If you like 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 X16Z and subscribe to our substack@a16z.substack.com thanks again for listening and I'll see you in the next episode. 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 Sam.

Summary6 min read

The a16z Show: How Kavak Rebuilt Itself Around AI Agents | Alejandro Maza Ayala Date: August 10, 2026

Episode Overview

In this episode, Angela Strange and Gabriel Vasquez of Andreessen Horowitz (a16z) sit down with Alejandro Maza Ayala, Chief Product and AI Officer at Kavak, a leading Latin American used car marketplace. The conversation explores how Kavak undertook a radical transformation to make AI agents—not humans—the core operators across its entire business stack. Alejandro details how Kavak rebuilt technical infrastructure, organizational structure, and company culture to deeply integrate AI agents that not only support but out-perform humans, from sales to finance to executive decision-making. The episode is packed with actionable insights on AI-native strategy and bold advice for founders and leaders aiming to build companies for the AI age.

Key Discussion Points & Insights

1. Kavak’s AI-Driven Transformation: From Human-Centric to Agentic

Timestamps: 00:00–01:53, 03:12–05:15, 05:42–07:50

Rebuilding from First Principles: Kavak reimagined itself by asking, "How would we build this company if we had AGI-level intelligence in 2035?" (05:03)
Agents Per Customer: The company instantiates 100,000–200,000 AI agents daily, each dedicated to a specific customer and equipped with its own virtual machine. Agents recall years of interaction history and optimize for customer lifetime value. (00:22, 03:59)
Full-Stack Automation: Agents handle >96% of all customer interactions and 95% of all transactions, covering complex operations—sales, financing, insurance—with humans only at necessary physical touchpoints (e.g., handing over keys). (08:15)
Metrics Shift: Kavak moved from tracking transactional outputs ("how many cars sold") to maximizing the value of its customer relationships, driven by persistent, learning agents. (05:42)

Notable Quote:
"What would we build if we were starting the company from scratch with AI?" — Podcast Host / Narrator (01:07)

2. Building Superhuman Agents and Rapid Feedback with Evals

Timestamps: 07:50–10:37, 13:29–13:58

Superhuman Performance: The explicit goal was for agents to outperform the best human ever hired on every key business metric (conversion rate, customer experience, lifetime value). (05:42, 08:15)
Eval-Centric Development: Kavak splits engineering resources 50/50 between building agents and building evals (robust evaluation frameworks). Evals focus on direct business impact: conversion, customer happiness, and meaningful engagement, not superficial metrics. (08:15)
Fast Iteration Through ‘Brakes’: Alejandro analogizes you "go as fast as your brakes allow"—rapid experimentation is possible only with strong eval systems. (08:15)
Real Sales Agents, Not Just Customer Support: Unlike common AI deployments, Kavak’s agents are “full spectrum” sellers (handling underwriting, negotiation, insurance, etc.) who convert 2.1x better than human teams and triple customer NPS. (10:59, 12:40)

Notable Quote:
"The only way to really make [agents] work is if you teach them... you put them out in the open, you get those evals, and then you train your agents." — Alejandro Ayala (05:42)

3. Agents as Bosses: Human Roles in the Agentic Organization

Timestamps: 16:01–22:41

AI as CEO: Kavak ran a radical experiment assigning a city’s management (Cuernavaca) to an AI CEO agent. In six weeks, profits increased by 50%, with the agent handling granular decisions and communication. (16:01)
Ratatouille Model for Human Collaboration: In physical roles (e.g., mechanics), humans work with agent “sidekicks” (El Mic) who coach, guide, and optimize work quality. This boosted inspection speed, repair quality, and cut warranty claims 26%. (16:01)
Cultural Shift & Training (‘Jedi Academy’): All employees—from CEO to mechanics—go through intense in-house AI training (6 weeks to production-ready agents), creating a workforce built to collaborate with AI. (19:55, 20:45)

Notable Quote:
"The CEO—supposedly the last job AI would take—and no, it isn’t really." — Alejandro Ayala (16:01)
"It's like the movie Ratatouille... the mouse that's actually a chef collaborating with a human. It's kind of like that." — Alejandro Ayala, on mechanics and AI collaboration (18:10)

4. Org Structure and Future Work: Flat, Empowered, Agent-Human Collaboration

Timestamps: 22:41–25:13

Flattened Teams: The org is now flat and senior, structured around building, operating, and being managed by agents. Managerial "middleman" layers have largely dissolved. (22:55)
Human in the Loop: Instead of passing “stuck” cases to tier-two human support and losing feedback, Kavak’s agents flag humans directly—closing the loop and ensuring agents learn from every edge case. (24:08)
Continuous Upskilling: Regular retraining and skills updates are standard, given the rapid pace of AI evolution.

Notable Quote:
"Sometimes agents are the bosses of humans, and sometimes humans are designing the agents." — Alejandro Ayala (21:30)

5. Advice for Founders and Leaders on AI-First Transformation

Timestamps: 25:13–28:03, 34:47–36:08

Top-Down Transformation: AI integration must be executive-led with a long-term vision. Hackathons and bottom-up use-case farming yield little; only vertical, top-down execution works for true transformation. (25:38)
Measure What Matters: Go beyond adoption; design and track metrics (evals) that truly impact business. Know the ROI of every “token” spent. (25:38)
Creative Destruction as Opportunity: Real breakthroughs come from new companies built AI-first—most incumbents will only achieve incremental gains from superficial adoption. True 10x leaps require rebuilding from scratch. (31:25)
Founder Call-to-Action: The AI tools and intelligence available today are more accessible than ever—founders should imagine radically new models and “go for it deep.” (34:59)

Notable Quote:
"Be very clear on what the company will look like in three or five years and then start building that... It's a transformation stage." — Alejandro Ayala (25:38)
"This is the most exciting time in human history... anyone has access to the most powerful tools and intelligence in the world, like for almost for free." — Alejandro Ayala (34:59)

Memorable Moments & Takeaways

Agents Per Customer, Not Per Task: The breakthrough was not multi-agent workflows, but long-lived, customer-obsessed agents with persistent goals and memory. (03:59, 28:03)
AI CEO Experiment: Six weeks, 50% profit growth—shows AI can take on high-level leadership roles when structured and empowered correctly. (16:01)
Jedi Academy: Internal upskilling program where everyone, including frontline mechanics, learns to build and work with AI agents. (19:55)
Bold Organizational Bet: Kavak twice destroyed and rebuilt its working AI architecture to stay ahead of model progress—sacrificing short-term comfort for long-term agility. (28:30)
Creative Destruction Analogy: Explains why startups—not incumbents—will likely reap the biggest wins from AI; referencing Ford’s adoption of electricity for 3x productivity gains. (31:25)

Important Segment Timestamps

Intro to agent-driven organization: 00:00–01:53
Alejandro’s AI journey & pre-Kavak: 02:06–03:22
How agents interact with customers & system design: 03:59–05:15
Decisions behind Kavak’s transformation: 05:42–07:50
Evals as the core of scaling & safety for agents: 08:15–10:37
Sales agents outperforming humans: 10:59–13:11
End-to-end financial services by agents: 13:29–15:43
AI CEO experiment in Cuernavaca: 16:01–19:31
Jedi Academy & reskilling all employees: 19:55–22:41
Org structure post-transformation: 22:41–24:08
Practical advice for companies & leaders: 25:13–28:03
Rearchitecting for customer-oriented agents: 28:03–31:12
Creative destruction and opportunity for new founders: 31:25–34:39
Alejandro’s closing advice for founders: 34:47–36:08

Conclusion & Call to Action

Alejandro’s story illustrates the potential and the practical realities of going “all-in” on AI—not as incremental improvement, but as a total company reinvention. For founders and leaders, the message is clear: AI is ready to run—and reinvent—entire organizations, but only the boldest will reap the full rewards. Imagine the company you'd build for an AI-defined future, and start building today.

For more details and episodes, visit a16z.com or follow on major podcast platforms.

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Transcript65 lines

[00:01]
Alejandro Ayala
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.
[00:16]
Angela Strange
The most ambitious companies listening to this will decide to follow suit, which is. You decided to build an agent per customer?
[00:23]
Alejandro Ayala
Yes. Every day between 100 and 200,000 agents get instantiated specifically for this customer with its own virtual machine.
[00:33]
Gabriel Vasquez
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.
[00:40]
Alejandro Ayala
If you haven't faced fear before, you haven't felt it, then you haven't tried AI. We launched a program inside Quebec 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.
[01:01]
Angela Strange
What advice do you have to future founders or first time founders that might be listening?
[01:05]
Alejandro Ayala
What works right now is most companies
[01:07]
Podcast Host / Narrator
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 Kovac'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 to maintaining long term customer relationships. They discuss why Kovac 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.
[01:54]
Gabriel Vasquez
Welcome back to the ACC podcast. Today we have Ale Mahassa, the head of AI at Kabak. We're going to discuss today the transformation that Ali led within Kabak to turn into an AI native company. Thank you Ali, for being with us today.
[02:06]
Alejandro Ayala
Thanks for having me.
[02:07]
Gabriel Vasquez
Before starting at Kabak, you were running a company called Oppie Analytics.
[02:11]
Alejandro Ayala
That's right.
[02:12]
Gabriel Vasquez
And you were very much into AI before ChatGPT. You want to tell us a little
[02:17]
Alejandro Ayala
bit about that journey? 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 problem, 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 Fortune 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 that we could now build a whole new company and way of building companies. And we joined Kavak to and Carlos to build that.
[03:13]
Angela Strange
Amazing. All right, so we're going to spend the bulk of this podcast talking about exactly how you've identified kvak. But maybe just to start, what does KVAC do and what is your role there?
[03:22]
Alejandro Ayala
KVAC started out as a use 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 the 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.
[03:49]
Angela Strange
I want 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 and how you design this.
[04:00]
Alejandro Ayala
Right. So we bet the company. In transforming to a company run by agents, the questions we ask ourselves is how would we build Caback 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 Kabak, what they visited in the webpage or a call they had two years ago, remember everything 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 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.
[05:15]
Angela 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. You guys took the just rip. We had to make this work. You had to downsize dramatically. It didn't work for a year.
[05:32]
Alejandro Ayala
Right.
[05:33]
Angela Strange
So do 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, yeah.
[05:43]
Alejandro Ayala
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 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 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 we put them in front of the hardest problems. And finally you start to change how you measure the success of the company. Caddac 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. 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.
[07:51]
Gabriel Vasquez
Ale I just wanted to double click on something evils over agent demos. Yeah, 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, is about 98% of the interactions or something like that are now handled by agents.
[08:15]
Alejandro Ayala
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 a hundred and two hundred thousand 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 the next task and to 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? And the answer you mentioned it is evals. I like to move extremely fast, but in order 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 bricks. 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 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. So what do we measure? 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 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 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 scale this and cater to millions of customers.
[10:37]
Gabriel Vasquez
It's really, really amazing. And related to this is like, okay, so you create the right evals. No, it's working. You know, some people, some companies still feel a little bit risk Averse and putting them in front 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?
[11:00]
Alejandro Ayala
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 wanting to buy a car. They can choose amongst 20,000 SKUs, then they need to pick 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 Kaival did it back 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 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. 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.
[12:40]
Angela Strange
Your agents are better sellers, totally better.
[12:43]
Alejandro Ayala
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 that's showing in the growth and results and satisfaction of our customers.
[13:11]
Angela Strange
Yeah, one of the two of the very cool things I think about Kovac is 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.
[13:30]
Alejandro Ayala
Yes.
[13:30]
Angela Strange
The next layer of on that as 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.
[13:43]
Alejandro Ayala
Yes.
[13:43]
Angela Strange
Pricing them correctly, doing servicing. So maybe talk through 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 is that experience?
[13:58]
Alejandro Ayala
Yes, so much better. 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 pay a smaller amount each month and they get out of the water. Which is amazing about the vertical integration of the business. But then when we started launching other financial products, we realized that this is a very important decision for the customer. Right. They usually take three to four months to make up their mind. And 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, 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
[15:44]
Angela Strange
is getting and then maybe give us just to be. Evals are always a very hot topic. You kind of led with that. What is an example of maybe a hard to design area for evals or one where you had to spend extra amount of time with just given the fact that there's real money PII at risk.
[16:02]
Alejandro Ayala
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. 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 crazy, right? It's amazing. And it's the CEO 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 very smart person fields, metal 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 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. 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, Karak 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's 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 Mic. 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. 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, since we launched. And customer satisfaction again went up. So it's about this, like, how would you design your organization from scratch with, with, with abundant superintelligence that's. That's cheap. And just go build that.
[19:32]
Gabriel Vasquez
Now 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 going to look like and the role that humans are going to play in this. And I think you touched a little bit on that. So we'd love to hear how you guys are thinking about that.
[19:52]
Alejandro Ayala
Yes.
[19:53]
Gabriel Vasquez
And the organizations.
[19:55]
Alejandro Ayala
Yeah, totally. So we took that question very seriously three years ago. And the truth is that everyone's job will change. So. 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 tells them like from the CEO to like AI engineers to mechanics, like going to the academy, it's super hard. Like I, I, I've, I led them myself.
[20:44]
Gabriel Vasquez
You designed the program?
[20:45]
Alejandro Ayala
I designed the program. But constantly, 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, to learn this because like it's new stuff. Right. So we train everyone and after six weeks they launch state of the art 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 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 Kabak 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, or maybe leave Kadak if this is not for you. But this is the way we're going. And it worked great. We strengthened the culture. Everyone's super excited. People really know how to build these agentic systems. And then if you look at Kadak now, any process, it's really a collaboration of agents and humans. And sometimes 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.
[22:41]
Angela Strange
But you do have or did have thousands of people Now. Agents do most things. So what is the org structure of Kavak? Does the middleman management concept even exist anymore? Like, what does your org look like?
[22:55]
Alejandro Ayala
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. 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.
[23:52]
Angela Strange
Yeah.
[23:52]
Alejandro Ayala
And so it's a completely different structure than we had just two years ago.
[23:56]
Angela 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 eugenic system. And then sometimes when it fails, it's like, oh, that's kicked out to kind of a human queue.
[24:06]
Alejandro Ayala
Right.
[24:07]
Angela Strange
But then that's lost. And so how have you brought that together?
[24:09]
Alejandro Ayala
So, like the. 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 2 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. And we have agents building those humans, building those skills for them. 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.
[25:13]
Angela Strange
That's actually a perfect segue. And 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 their organization. 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?
[25:39]
Alejandro Ayala
I think it's two things. The first is it has to be top down. Because of this. 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 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 does whatever they want. You need a very clear strategy and that's what we need now. It's a 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 hundreds of millions of dollars in tokens now. What about that? There's quality in the tokens. So have a framework here that's also useful. Like Tier 3 tokens, the most valuable are these agents where you can 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 2 tokens are things that you can measure indirectly. Do I see devs in the code base? And I can evaluate the value of these tokens at least indirectly and then push those to Productions. Tier 1, when most companies are is people are just using plug code or chatgpt or cowork or whatever. What happens with those, I have no idea. So it's not just about adoption. It's really about having a very clear vision and then measuring that each token you spend is bringing you those benefits and just iterate, iterate, iterate from there
[28:04]
Angela 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 versus per task and then discovered along the way that each one of those agents needs its own micro virtual machine. So maybe kind of walk us through yes those decisions that architecture and I
[28:30]
Alejandro Ayala
think we're seeing these results now. But it was a really risky bet because people usually go from workflows 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 lattice work and harness because it will constrain this level of intelligence. So we decided to destroy everything we had been building 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 self improvement 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 and I instantiate hundreds of thousands of these each day with long term goals like maximizing the lifetime value.
[30:12]
Gabriel Vasquez
Yeah, the self improving organization.
[30:14]
Alejandro Ayala
Exactly, the self improving organization. And I think people are super obsessed with RSI now and this will improve the models. But if you look at it this way, economic value in humanity for the past 4,000 years has been delivered by organizations, 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. That's the loop that I think companies will start to focus on because if you get that loop working and it's an organization that is really self improving and harnessing the newer models and the better intelligence that we're getting every couple of days now, then you hit the exponential not just in intelligence but in the value that you can generate as a company. So that's really exciting. That's what we're working on.
[31:12]
Gabriel Vasquez
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. You want to talk a little bit about that?
[31:25]
Alejandro Ayala
Yes. There's this concept in economics about creative destruction from Joseph Schumpeter. And what it says is that the way innovation hits the economy isn't by companies adopting the new technology, but by companies remaining the way 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 the economy as a whole. And this has happened in the past industrial revolutions, and this has always happened. And this is a great opportunity for entrepreneurs and 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, 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 adopt, new companies can be formed that are built around the, the strengths of, of AI and take over and bring new products and services to, to the masses. And this has happened before, like this happened with electricity. This is a story I always tell my team. 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 Ford's dynamo was okay, I'm going to 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 surface, not in the center of New York, but in Connecticut or New Jersey. And redesign your whole factory around small dynamos and electricity. And then you get the 3x improvement in productivity that powered the US during the 20th century. And the same happened again with a computer. And the same is happening again today. People want to adopt it, 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 scale.
[34:39]
Angela Strange
Again, I think you've just made an amazing case for any future founders out there that it's time to build.
[34:46]
Alejandro Ayala
It's time to build for sure.
[34:48]
Angela Strange
And maybe a great place to end is you've built and scaled your own company. You've now turned Kabak fully agentic. What advice do you have to future founders or first time founders that might
[34:59]
Alejandro Ayala
be listening so this is the most exciting time in human history. 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 literally 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 new paradigm. So say like just go for it, but go for it deep. Like imagine what the future around AI will look like. Just it's not even an exponential. Just map a trend is linear if things keeps getting like AI keeps getting better at linear scale and just build for that and you'll come up with wonderful ideas that will bring a lot of value to the world.
[36:09]
Angela Strange
Amazing ale. Thank you for joining us.
[36:11]
Alejandro Ayala
Thank you. Thanks for having me.
[36:15]
Podcast Host / Narrator
Thanks for listening. Listening to this episode of the A16Z podcast. If you like 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 X16Z and subscribe to our substack@a16z.substack.com thanks again for listening and I'll see you in the next episode. 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 Sam.