✦ AI generated
Ali Massa argues that by prioritizing token investment over human labor, Kavak can build superhuman agents that surpass human performance in critical areas.
Ali Massa: I'm investing more 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.
Kavak spawns a dedicated agent per customer with its own virtual machine and long-term memory, designed to maximize customer lifetime value across all products.
Ali Massa: When a customer comes in right now, um agent will get spawned specifically for this customer with its own virtual machine. It'll remember years of interaction of this customers with with Kavak, what they visited in the web page or call they had 2 years ago. Remember everything like in its memory, come up with a strategy and set a long-term goal to maximize the lifetime value of this customer and do whatever it takes to make the customer happy and convert them into like all our different products like across time. And this is a completely new and groundbreaking architecture at scale, I think, because like people are still building multi-agent system with with experts and and we realized too that long-running agents with hard goals, not just workflows, could could maximize our our customers satisfaction and obviously their their lifetime value.
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Instead of building agent workflows, Kavak instantiates 100,000-200,000 agents daily, each dedicated to one customer with full context and long-term goals.
Ali Massa: So, when a customer comes in right now, um agent will get spawned specifically for this customer with its own virtual machine. It'll remember years of interaction of this customers with with Cabal, what they visited in the web page or call they had 2 years ago. Remember everything like in its memory, come up with a strategy and set a long-term goal to maximize the lifetime value of this customer and do whatever it takes to make the customer happy and convert them into like all our different products like across time. And this is a completely new and groundbreaking architecture at scale, I think, because like people are still building multi-agent system with with experts and and we realized too bad that long-running agents with hard goals, not just workflows, could could maximize our our customers satisfaction and obviously their their lifetime value.
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Each customer gets a dedicated agent that remembers all interactions and works to maximize lifetime value, representing a groundbreaking architecture at scale.
Ali Massa: So, when a customer comes in right now, agent will get spawned specifically for this customer with its own virtual machine. It'll remember years of interaction of this customers with Kavak, what they visited in the web page or call they had 2 years ago. Remember everything like in its memory, come up with a strategy and set a long-term goal to maximize the lifetime value of this customer and do whatever it takes to make the customer happy and convert them into like all our different products like across time. And this is a completely new and groundbreaking architecture at scale, I think, because like people are still building multi-agent system with with experts and and we realized too bad that long-running agents with hard goals, not just workflows, could could maximize our our customers satisfaction and obviously their their lifetime value.
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Massa argues that simply giving teams ChatGPT or Claude without restructuring leads to no real efficiencies; companies must rebuild APIs and systems specifically for agent use.
Ali Massa: So so there there were like three main decisions that that we had to make. The first and this is what where I think many companies are stuck right now is the first instinct is, 'Okay, let's adopt AI.' And you you basically leave your structure as it is and just give ChatGPT or Claude to to your team and then there's no efficiencies. Your customers have the same problems and and nothing happens, right? And and so so you need to redesign your whole company around the agents and around the future capabilities. And this means really like rebuilding most of your APIs, rebuilding your system so the agents can use them to to perform.
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Ali outlines three critical decisions for AI transformation: redesign the whole company around agents, build superhuman agents through live data feedback loops, and change business metrics from transactional to relational.
Ali Massa: So so there there were like three main decisions that that we had to make. The first and this is what where I think many companies are stuck right now is the first instinct is, 'Okay, let's adopt AI.' And you you basically leave your structure as it is and just give ChatGPT or Claude to to your team and then there's no efficiencies. Your customers have the same problems and and nothing happens, right? And so so you need to redesign your whole company around the agents and around the future capabilities. And this means really like rebuilding most of your APIs, rebuilding your system so the agents can use them to to perform. 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. Like, how do you teach them? You you put them out in the open, you you put them in front of customers, you get that data, you get those evals, and then you train your your your agents. And this is the second bet that we made that that we could build superhuman agents. This means that by every dimension that matters, like conversion, lifetime value, uh customer experience, our agents would outperform the best human we had we had ever hired. And we put them in front of the hardest problems. And and finally, you you start to change how you measure the success of the company. Kavak was a transactional company. We used to measure how many cars we bought, how many cars we sold, how many brakes we we needed to to break pads we needed to buy. Uh and we moved to a relational company where now I have 10 million customers in my database, and I have agents assigned to most of them with the task of maximizing their lifetime value.
Massa emphasizes that rigorous Evals are the 'brakes' that allow fast AI deployment, and Kavak invests equally in Evals and agent development.
Ali Massa: Now, how do you get this to work at scale? And the answer you mentioned it is is Evals. Like I like to move extremely fast but in order to move fast you need to have brakes, right? Imagine a car you'll hit on the gas just if you have the right brakes. And AI is super powerful and I've seen many companies get this wrong because they try to go slow because they they don't have the right brakes. So so I thought about it the other way around like how fast can we go? Well, it depends on the quality of our Evals. So a good rule of thumb here is we spend about the same amount of time engineer time tokens and and and money on building the evals, the building the agents. And this is how you get better and better and better. Not not letting evals as an afterthought.
Massa emphasizes that evals act as brakes allowing faster deployment, with the company spending equal resources on evaluation and agent development, measuring actual customer conversion.
Ali Massa: A good rule of thumb here is we spend about the same amount of time engineer time tokens and and and money on building the evals, the building the agents. And this is how you get better and better and better. Not not letting evals as an afterthought. So, what do we measure? First and foremost, like the the the resource for the business. Like, if my customer is happy, they'll buy a car, they'll they'll get their loan approved, uh they'll sell a car to us. And and that's the like first check. Like, did it convert? And that's where most things break. Like, I I see companies like measuring number of calls or minutes during the call or or some like superficial KPIs that give you some information, but that doesn't really work. Like, the important thing is did this customer convert? Is it bringing value to the customer? And is the customer happy to reengage with us after a while?
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Ali emphasizes that evals are the brakes that let you go fast—Kavak spends roughly equal time and resources building evals as building agents, and measures conversion and customer re-engagement rather than superficial KPIs.
Ali Massa: A good rule of thumb here is we spend about the same amount of time engineer time tokens and and and money on building the evals, building the agents. And this is how you get better and better and better. Not not letting evals as an afterthought. So, what do we measure? First and foremost, like the the the metric for the business. Like, if my customer is happy, they'll buy a car, they'll they'll get their loan approved, uh they'll sell a car to us. And and that's the like first check. Like, did it convert? And that's where most things break. Like, I I see companies like measuring number of calls or minutes during the call or or some like superficial KPIs that give you some information, but that doesn't really work. Like, the important thing is did this customer convert? Is it bringing value to the customer? And is the customer happy to reengage with us after a while?
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Kavak built sales agents rather than support agents, combining expertise in financing, insurance, car advisory, and trade-ins into one mega-expert. They convert 2.1x more than humans and tripled customer satisfaction.
Ali Massa: So, so we never built customer support or customer service agents. We we we built like sales agents. It's extremely hard to sell a car in in Latin America. So, imagine someone wanting to buy a car, they can choose like among like 20,000 SKUs. Then they need to pick like financing and go through the financing process, insurance, and and coverage. And then they're probably trading in their car. So, so we need to quote that car. So, it's a process that if someone does it or or the way Kavak did it back in in 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 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, etc. And that's who we put in front of the customer. So, the experience for the customer is amazing. We tripled uh NPS and customer satisfaction score by putting the the agent in front of the of the customer. And it first it converted like 50% more than our human team, and now it's converting over that like 2.1 uh x more.
Kavak built sales agents rather than support agents, and these agents now convert over 2x better than human teams while tripling NPS scores.
Ali Massa: So, we never built customer support or customer service agents. We we we built like sales agents... The experience for the customer is amazing. We tripled uh NPS and customer satisfaction scored by putting the the agent in front of the of the customer. And it first it converted like 50% more than our human team, and now it's converting over that like 2.1 uh x more. So, it's a completely different company. >> agents are better sellers. >> Totally better. And and and you get this, right? Because they're experts and they're infinitely patient and they know all your history and they they they can plan for the long term and they never get tired. So, and if they make a mistake, they learn it and the next day, not just them, but the other 200,000 agents will have learned from that mistake.
Kavak tested an AI 'CEO' for a city, achieving a 50% profit increase by micromanaging daily operations and improving customer satisfaction and inventory rotation.
Ali Massa: So when we decided to to to to redesign the company around the AI you ask the question, okay, is is AI going to be able to do this job, like even the CEO job or or jobs where the leadership is? And the answer honestly is probably yes, like in 2035 with a rate of improvement, it will be able to do so. We said, okay, let's let's try it now. Let's try and build an AI CEO. So we carved out a city in Mexico, it's it's Cuernavaca, and we put like an agent in one of our harnesses as a CEO and it starts learning and it starts making decisions and evaluating on those decisions. And it's only been running for for 6 weeks now. The goal of the first month was to double the the the profits of of Cuernavaca. It didn't reach it, but it was 1.5x, like 50% more profits. Just like managing the the city, which is it's crazy, right? It's it's amazing and and it's it's a CEO, like people were like that was the last job AI was supposed to stick, and no, it isn't, really. And how did this happen? And and it's like very smart person, like like Fields Medal level smart, like going into every single number, every single customer, making the perfect forecast, and going to micromanage every single things that needs to be executed every day to reach a plan. So, customer satisfaction grew, we got a better inventory, we rotated better, better financing penetration, like every KPI started to to improve.
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Kavak's Jedi Academy trains everyone from CEOs to mechanics to launch AI agents, ensuring the entire workforce can collaborate with new technology.
Ali Massa: So, um we we took that question very seriously 3 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 be be performed better by an AI agent. Right? So, what does this mean? We need to train everyone. So, so we launched a program inside Kavak that's called the Jedi Academy where anyone from Kavak like from the CEO to like AI engineers to mechanics like go into the academy. It's super hard. Like I I I've I led I can you you designed the program. And but constantly because you you need to be upgrading the the program because everything's changing so fast. And there there's like you can't send these people like outside to to Stanford to to learn this because like it's new stuff, right? So, we train everyone and after 6 weeks they launch state-of-the-art agents AI agents to production. And it's mechanics and and finance guys and engineers, like everyone can do it. And what this generated is maybe this person won't become an AI engineer, some of them have, but they they know how to collaborate with this new technology, right?
Massa argues that the real opportunity is in creating self-improving organizations that leverage AI, drawing parallels to historical industrial revolutions and creative destruction.
Ali Massa: And I think the the we're seeing these results now, but it was a really risky bet because people usually go from workflows. And like, if I could advise everyone, don't build agent workflows to graphs or or functions or or objectives. And we built that 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, etc. And we had thousands, like tens of thousands of these agents working at scale running the business back in December. But then Opus 4.5 came out. And I realized like this isn't the right paradigm anymore. Like the the intelligence now doesn't need like the graph and the multi-agent 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 and leverage recursive self-improvement. And or new models, more intelligent models coming out every month. So the way this looks like it's it's a virtual machine with an agent with access to memory and evals and a CLI where they can access every tool and every API in my company and the long-term goal. And I instantiate hundreds of thousands of these each day with long-term goals. Like maximizing the the lifetime value. Yeah. The self-improving organization. Exactly. The self-improving organization. And I think people are super obsessed with with RSI now and this will improve the 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 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. Right? So, that's the loop that I think companies will start to to focus on because if you get that loop working and it's an organization that is really self-improving and harnessing the the newer models and the better intelligence that we're getting every couple of days now, then like you hit the exponential not just in intelligence, but in the value that you can generate as a company.
When Opus 4.5 launched, Massa realized their successful multi-agent workflow architecture constrained the intelligence of new models, so they rebuilt from scratch.
Ali Massa: And and I think the the we're seeing these results now, but it was a really risky bet because people usually go from workflows... And we built that that these are multi-agent systems that can perform a whole function for a complex goals... And we had thousands, like tens of thousands of these agents working at scale running the business back in December. But then Opus 4.5 came out. And I realized like this isn't the right paradigm anymore. Like the the intelligence now doesn't need like the graph and the multi-agent 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 and leverage recursive self-improvement.
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When Opus 4.5 shipped, Kavak realized their multi-agent graph architecture constrained the new intelligence, so they destroyed two years of profitable, working systems and rebuilt around a simpler per-customer VM harness designed to leverage recursive self-improvement.
Ali Massa: We had thousands, like tens of thousands of these agents working at scale running the business back in December. But then Opus 4.5 came out. And I realized like this isn't the right paradigm anymore. Like the the intelligence now doesn't need like the graph and the multi-agent 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 and leverage recursive self-improvement and or new models, more intelligent models coming out every month.
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Ali invokes Schumpeter's creative destruction to argue that AI's biggest impact will come from new AI-native companies displacing incumbents, drawing a parallel to how Ford's factory couldn't exist as a mere electrification of the old coal-powered layout.
Ali Massa: There's this concept in economics about creative destruction from from from Joseph Schumpeter. And what it says is that the the the way innovation hits the economy isn't by companies adopting the the the new technology, but by companies remaining the way they they were and incumbents with the new technology destroying the old companies. The technologies for Ford's production line were developed in 1879 and 1881. Edison started commercializing electricity in in New York and then London and he invented a dynamo that was extremely efficient. So, you could have built Ford's factory 40 years before Ford. The technology was there. Everything was there. But the way people adopted electricity and and Ford's dynamo was okay. I'm 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 coal factory around small dynamos and and electricity. And then you get like the 3x uh improvement in productivity. And the same happened again with a computer. And the same is happening again today.
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Massa uses the historical analogy of Ford's factory redesign for electricity to argue that AI adoption requires destroying and rebuilding companies, not just adding tools.
Ali Massa: And what it says is that the the the way innovation hits the economy isn't by companies adopting the the the new technology, but by companies remaining the way they they were and incumbents with the new technology destroying the old companies... The the the technologies for Ford's production line were developed in 1879 and 1881... So, you could have built Ford's factory 40 years before Ford. The technology was there. Everything was there. But the way people adopted electricity and and Ford's dynamo was okay. I'm 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... And redesign your coal factory around small dynamos and and electricity. And then you get like the 3x uh improvement in productivity.
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