YouSaid · the spoken record

Jason Droege

lines on the record: 93
first: 2025-10-09
most recent: 2025-10-09
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“Need to charge you this so that the delivery fee can be that. And then if the delivery fee is that and we charge you this, then we think the consumers will adopt. And that's what you need to get your incremental demand. And then we can pay the driver this. And so you kind of fit this whole puzzle together without totally satisfying. And you, in the case of a marketplace, you're not totally satisfying any individuals like 100% of their needs. What you're satisfying is you're getting a clearing rate for them to participate in the market in the case of a marketplace. So that's like one example.”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“Of every meal to ingredients. And so we came in and we said, we're going to charge you 30% of the bill. And they were like, oh my God, is this group on all over again? This is way too high. Oh my gosh. And we explain the economics to them. And they were like, okay, we'll give it a try, but this is way too high. And they were right. The real number, the real clearing prices aren't 25%. But we weren't that far off. And so when you go to find product market fit or, you know, be close to the customers, it's a combination of like, what's the most valuable thing? Well, in like in a restaurantur's case, give me incremental demand because if you were to take a restaurant location and triple demand based on the same labor, the same ingredients, the same labor, but you're just scaling ingredients, you've got 70 to 80 percent incremental gross margin product. Restaurate tours would hate what we would say this because it doesn't work out exactly like that in reality. Because we had that insight, we had confidence that we could go to market with.”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“Was most interesting, which turned out to be right. Oh, so good for us. Very great, and we couldn't get a restaurant tour to help us understand their unit economics. And they'd say, like, oh, it'd be this percentage or that percentage, or why do you want to know? And then we go to a different restaurateur, and they, and they would kind of explain it, but they were a little suspicious. So, like, why are these Uber guys talking to me about how much my ham costs? And so what we did is we ordered just a bunch of food from these places, and then we got a restaurant supplier to give us like a base catalog. And we just matched up like, how much does the hamway? How much does the cheese weigh? How much does the bread weigh? How many pieces of lettuce were on there? And we tried to actually just compose our own independent view of like, what's the ingredients cost versus what's the labor cost? And then we sort of triangulated like what was our ground truth and then what are we being told by restaurateurs and then what is like the zeitgeist telling us about restaurant economics and if those things like all overlap then we're like okay we have an insight about what to do here and how does this relate to uber eats well what we found as part of this is that roughly a restaurant pays 20 to 30 percent”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“That person needs to trust that, like, you're going to do a good job for them. How do you get them to jump with you on this big project? Well, that's part of the journey of like not just the product, but what do they need to hear from us? What do we need to supply them? What do we need to do to actually unlock the opportunity to implement the product? So I think there's like an incentives alignment baseline. Like I'm a big believer that it's cliche, but show me the incentive and I'll show you the outcome. I think that's absolutely true. And even when customers will tell you things, like I'll give you an example, I've been out of game for a while so I can be open about it. Uber eats. So when we launched Uber Eats, I looked at the business. In terms of being close to the customer, we actually couldn't get a restaurant tour. I knew nothing about this industry. So at Uber, my job is to figure out what other businesses we should get into. And so we looked at a billion businesses and Uber eats, you know, like food delivery was the one that we did.”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“I mean, I probably fall in the category of what you just described, which is maybe part of the hubris you need to start anything new. But yeah, I mean, my, I don't think it's like a clean process. I think my process is I'm constantly questioning every single thing that I'm hearing at the beginning of anything. I don't take what a customer says like literally. And there's been a lot talked about on this topic from like a product management standpoint in terms of like, oh, don't do what they say, do what they mean, and look at the real problems and all the things. I think the way that I look at it that might may be additive to the discussion is I look at the underlying incentives of the customer. And the underlying incentives of customers are not always financial. Sometimes it's ego, sometimes it's career growth, right? If you're selling enterprise software to someone, there's an executive sponsor as an example.”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“Them truly robust enough where an important process can be automated. So I think that's where the hype is right that when you do it, the impact is like, whoa. Like I never would have figured that out myself. And I'm one of the most educated doctors in the world as an example. But the time to get there is just longer than what people are selling.”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“You have a bunch of people. One of the reasons why the POCs have failed. One, one, there's a denominator effect because it's so easy to do, I spun up a project. I spun up a project. I spun up a project. So it's really easy for people to try. So I don't necessarily know that the 95% number, I think, is a bit of clickbait in a way. It tells the right story, but it is a little bit hyperbolic because if you take the efforts that happen in the company where they actually get it, you know, get a quality partner like we are. If you get, or if you do it yourself, if you have engineers who have worked with models before and they put in the time, and I'm talking about like months, not like minutes like you see in these videos to actually get legal approval, policy approval, regulatory approval, change management, like an accuracy that everybody's comfortable with. If you actually do that, you know, these things take six to 12 months to get.”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“A lot of hype out there, and our job is to actually build products that work, that deliver value for our customers and figure out where the rubber hits the road and to get a sophisticated, you know, my healthcare example is one we do other sophisticated workflows, claims management for insurance companies, right? This is a financial decision that's happening, but it's an automatable process. But basically what happens is the POCs get to like 60 or 70% of the way there and the human mind goes, oh, the rest is no big deal. But it's kind of like uptime and data centers where like every nine is like, you know, an order of magnitude investment, you know, in terms of like reliability, backups, et cetera, you know, 1-9 is like basically, you know, a web server in a dorm room like we had at UCLA and then five nines is like this crazy like high bar, but it just seems like a very small movement. So you kind of have a similar dynamic going on here where”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“A lot of the speculation has a wide variance because we're at the beginning of it, people take different trajectories on like how that's going to improve. And so if you take a trajectory of like the most aggressive trajectory, which is like, oh, it's actually going to be quite easy to train on these things. And then it's just a change management exercise in the economy, which by the way, change management exercises are not to be underestimated. There are still people in the world without an email address. And so the adoption curve then becomes like a human and policy issue, not a technological issue. We're not there from the technology standpoint. But I do think in the next two to three years, if I take the bait and have to make a guess is the technology will get to a point where it will push the change management and policy makers to say like, oh, what do we do with this? Because it's getting pretty close. That's probably two or three years away.”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“Look, there's so much talk. I think it depends on how much X or news you consume. So I think it's like what sort of our perspective. The general trend right now is going from models knowing things to models doing things. And we're pushing the boundaries of knowledge, like the benchmarks that we put out and that others put out are showing that the knowledge, you know, the knowledge that these models have is getting it's quite robust. And then the next question becomes, well, what can it do for me? And as soon as you get into that world, that's where the environments we were talking about start to come into play. How do you navigate a Salesforce instance? How do you navigate a healthcare system? How do you navigate even like a weather app on your phone? And how does the agent make decisions for you? We're just getting into the beginning of that. It'll be very interesting to see how quickly that happens. And I think that's where”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“Chiseling that's going on in all of these industries. I mean, if you think about how magical these models are, I mean, they're remarkable that if you've been in technology long enough, it's like, it blows my mind even today that they get the punctuation right consistently. I mean, that sounds like almost daft to say, like at this point in the market, but if you were to go back three years and think about that from a technological standpoint, a lot of things that we think are trivial now are very sophisticated. And it's a combination of, I mean, like the real answer is it's a combination of computational power, model improvement, and data. And all three are getting better at once.”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes, no, yeah, I agree. I mean, like with any of these major tech revolutions, the headlines tell one story and then on the ground, laying broadband means you need to dig up every single road in America to lay it. Like there is the like, yeah, it's as simple as that. Someone's got to take up the road or someone's got to run the undersea cable. Like there's always some operational.”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“What does good look like? A lot of the processes and a lot of the things that people are asking these systems to do and systems for us to build are making judgments on their behalf. And so just like we would ask a human being, hey, what do you think we should do in this scenario? What you're looking for is you're looking for the best recommendation or course of action given the current information.”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“I didn't intentionally use that word, but if you use a probabilistic systems, and so depending upon, yeah, so I can get in some nuance here about the right types of problems that AI is good at solving. So if you have a human process that is like 10 or 20% accurate or 10 or 20% liked, AI is awesome because it can get you if you get to 50, 60, 70, 80% accurate, you're in the money. Like you're in the green, everybody's happy. Now the system then has to know, hey, for the remainder, how do I make sure that humans are involved for the remainder of the decision making? But from a net value add standpoint, the humans are pumped in that scenario. If you have a human process, a workflow that is 98% accurate and you expect an AI system to get you the remainder 2%, not totally there yet. And so when I say...”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, a lot of it's evals. And within enterprise customers and government customers, it's mostly evals because somebody who's got to establish the benchmark for like what good looks like. That's the simple way to think about evals. What does good look like? And do you have a comprehensive set of evals so that the system knows what good looks like? It's as simple as that.”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“Sometimes it's new people. Sometimes within our existing base, we find that existing people have expertise we didn't know about that maybe wasn't useful to a model a year ago, but now is useful. So this is a constant progression of getting more and more data into these models. Yes, we are financially incentivized to believe that humans will always be in the loop, but that's not just a business belief. It is a personal belief. Like these systems need to work for us. And if these systems work for us, then we will need to be on the loop or in the loop on any of the decisions that these systems make. As to the broader point around labor, which I think comes up around white-collar apocalypse and these things that kind of come up, I'm definitely on the more maybe practical side of this, possibly just because of my nature, possibly because I see what's going on on the ground actually in these customers where”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“First off, the history of data labeling is a history of new beginnings, right? Like autonomous vehicles do not need as much data labeling as they did in the past. So the company, I mean, like scale is a company that believes that data will always be important at the point at which you don't need external data, human data in models. I think we've gotten to a level of advancement in the world that is almost like unfathomable because you're effectively saying that like no new human skill and no new human knowledge is important enough to put into these models. That feels like pretty far out there. And so for a business like ours, we're constantly looking at how do you build operations that can constantly find the new needs and then work with the contributor network. We call the experts contributors to unearth that data, to unearth that information.”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“Absolutely. I mean, I mean, understanding what there's this broad narrative, you know, we kind of have two narratives. We have like the AGI, like everything's just going to become AGI. And then there's the skeptics, which is like, you know, hey, this is all bunk. Like this is all, et cetera. And of course, my view is most things are kind of like there's truth in between and some of the extreme parts of the extreme are probably correct. But the reality is that it's very hard to get mission critical use cases in agentic systems where agents are talking to agents to a level of accuracy that is necessary to accomplish a goal. And one of the main issues is that one document, like think about the problem of even understanding a document, like a document that reads the exact same words in company A will have a different meaning and importance in company B. So how do you have a system that”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“System because of the culture, the objectives, the incentives, et cetera. And so we're getting to the point now where we see that digitizing judgment, human judgment, true subject matter deep expertise is becoming a bottleneck, the war I'm blocking for our customers.”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“With the models off the shelf. And actually, the people inside of this healthcare system have to do their own labeling. So we talk about labeling for model builders, but we actually start, we're starting to see the labeling move into enterprises and into governments because you can only get so far with off-the-shelf plus rag plus some fine-tuning based on recorded data. One thing people often miss about these systems is we assume, because you hear these numbers of like, oh, this bank in just 200 petabytes of data a year or whatever, fantastical number. What we miss is, is it the right data? Which of that data is useful to the models? And most of it is not useful. Some of it is, but a lot of what we do when we're talking about knowledge work, when we're talking about making judgment, is human judgment based on synthesizing, like, how would this doctor in this case or how would this banker in this case make this decision? And how would they make decision in the context of their overall enterprise? And that might be different bank to bank healthcare system to health.”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“And so, if you're a doctor, how are you going to read 200 or 300 pages of everything? So what they do is they do the best they can, right? They scan it, they ask a nurse to look at it, they ask maybe a more junior doctor to take a look at this case because they want to treat the patient. Obviously, this is why they became a doctor. And then they go into the room and they talk to the person and they make a diagnosis. Well, we basically built a tool that will read that document for them and point out the top five to 10 things that they should take into consideration either allergies that might not be obvious is one example where we actually picked up on an allergy that a patient has that would not have been obvious from reading the document and and that allergy actually would have had a conflict with the medication that they were going to be prescribed. And so the AI tool basically pulled out this correlation that would have even been hard for a human being to do to make this tool better and better, you get to a certain limit.”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“See more patients, they want to be able to provide better care, and they want to prevent the number of revisits because they want to give the accurate diagnosis day one and what the treatment should be. Well, to do this today without the help of AI, the doctor really needs to read two to three hundred pages of documentation. And it's rolled into one document, but in different formats.”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“Absolutely. I can actually give you an example from. So we have two sides of our business one, we supply data to model builders, we sell the data, and then the other is we actually do solutions. We sell applications and services to healthcare systems, insurance systems, et cetera. I actually think it would paint more colorful picture if I gave you an example of one of those because it involves data, but it involves the use of data, the manipulation of data for a very, very specific goal. And so one example there is we work with a healthcare system and health systems have lots of problems. This particular healthcare system has experts that see very rare cases on a regular basis. So you go there only if no one else can figure out your problem. And there's a huge backlog. So the productivity element to this implementation tier. So there's a huge backlog. They want to be able to.”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“It could be both. So in some cases, it's just the website. And here's an example. And then they design it. In some cases, it needs to be annotated in such a way that's like, I made this decision for this reason or this decision for that reason. Or here's how I would think about it. So it depends on what the model builders are trying to accomplish. And so it can get quite nuanced in terms of what they're trying to train on. So it's not like here's a website and then it's created doing websites. It's like, here's a website, here's why I made this.”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“Pops it out, right? Very simple example, like that needs to be generalizable to any calendar search potentially or potentially any calendar action. And the more generalizable it is, the more valuable the data is. So our job is to provide the most valuable data to model builders that accomplishes the goal of making agencies as useful as possible for their end users.”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“Is each individual task or each individual environment. So if you imagine the world of environments of like software systems, configurations, data types, sizes, user counts, complexities, it's like the permutations are endless. So what you need is you need a strategy that allows a lab to collect data that is generalizable enough across a broad spectrum of use cases so that they don't have to collect 45 trillion combinations of what should the agent do in this particular situation. So sometimes the work and the data is highly generalizable. And by generalizable, I mean you have it accomplished in a simple way the task might be find the most meeting on my calendar for my interview with Lenny. And the agent goes and it looks through all my calendar and then it”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“I can get guidance. All of those things need to be trained. And there's no alchemy to it. You just have to put the AI agent in an environment that represents what a human being would be doing. And you can imagine the number of environments in the world and the number of goals within each environment is enormous. So the question is and the research that we have done over the past year to try to be a good partner to our model builders, our model builder customers, is how generalizable”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“Reinforcement learning is very important. And I think this is a broader comment about the move to environments. There's these things called RL environments that effectively are sandboxes for AI agents to play in to accomplish a goal so that they can learn how to accomplish that goal. We've been doing this for over a year. So for example, you have a Salesforce instance. How does an AI agent navigate that instance? That instance has data that it needs to recognize. It has configurations, Salesforce is a highly configurable product. As configurations, it needs to understand how to navigate. You're asking the agent to do a business process that needs very high reliability. And then the agent needs to know, hey, if I can't accomplish what I'm going to accomplish, or I think if there's a low accuracy of what I'm about to accomplish, how do I pop it up to a human being for feedback?”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“Best ones come from the sort of grassroots and referral networks. And the only way you get that is providing a great experience to these people because these people, like they're doing it partly for money, but they're also doing it because they think that their contribution to the AI models is important and interesting. And many times it solves a problem for them.”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“They are hard to find. You have to have many, many tactics, right? So we get, you know, as you would expect, there's not one way you do it. The largest way is that they refer each other. Because when you are enjoying what you're doing and you are using your expertise to contribute to AI, which is pretty cool. Like if you're a PhD on this pretty specific topic and you're using a model and you're frustrated that, oh, it doesn't interact with me in the way that I want. This is a paid way to have an outlet for that and to make hundreds or thousands of dollars doing that. And so a lot of times I refer to each other. We also have campus programs where we will literally go onto the campus and talk to the professors, talk to the students, you know, ask about who would like to do this type of work. And then, of course, there's the more traditional scaled ways of like LinkedIn and places like that.”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“What the task was 18 months ago. I've been here about 13 months, so I was interviewing and I remember seeing it. You would get a short story and it would say, is this short story better than this short story? And then you would edit it and be like, yeah, it would be better if it was this and you would give some preference.”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“Then you move forward, and the models have gotten better over this period of time. And so the models get better, they need different types of data. So we've constantly been adapting to the type of data that models need to be successful. And so then the Gen AI wave hit, and this went through the moon, you know, or to the moon. And so as part of that, that industry is changing constantly too. I mean, it is correct that when the models came out two or three years ago, I mean, we remember using them, they would hallucinate all the time. They would get basic answers wrong. They didn't know which poem was better, you know, this poem or that poem. And that was the state of labeling a couple years ago. And things have changed quickly. And we've changed with it. And now the state for everyone, and we've been at the forefront of all of this, is expert data labeling, more sophisticated tasks. So to give you a sense of”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, yeah, yeah, totally. I think the current positioning out there from competitors is just bogus. So I'll start with that and then maybe talk a little bit about, I'll explain what I mean by that in a second. But I think it's important to just give 30 seconds on like what the history of scale is and what's the thread going back to 2016. So Alex had this insight in very early days that the important thing to models was data. And I think he was 19 or 20 years old at the time as well. And so he's like, okay, well, what business would I create around this? And the business that he created around it was, okay, let's do labeling for autonomous vehicles. Because if you label the data that they have, the cars do better. And then that wave turned into the computer vision wave, which we have a relationship with the Department of Defense where we do labeling for them. And that was in 2020.”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“Over on the transaction. So scale has about 1100 employees or so now, and we have two major businesses. Each of those businesses has, each of them is hundreds of millions of revenue. So we kind of have two unicorns inside the company today that sustains the business has grown every month since the deal happened, which I've read the reporting is not consistently reported. We haven't talked about it, right? So this is part of getting the word out. And, you know, we're excited to continue to build, deliver data, and do what we did before.”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, so scale is a fully independent company. The transaction was Meta invested a little bit over $14 billion to get 49% of the company non-voting stock didn't take a new board seat. Alex fills the board seat. So the board is the same. The governance is largely the same. There's no preferential access to anything that Meta has. There's no preferential relationship. I mean, we've had a longstanding relationship with Meta on the data side of the business for a long time. And even on some business development related things to maybe work on things in government together, et cetera. And so those might get bigger just as we're closer now, but there's nothing that prevents us from doing things with other parties and they have no access to anything that they wouldn't have had otherwise, like all the privacy is still in place, all the data security still in place that was there before. And in fact, only about 15 people went.”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“And then you settle for a million dollars. And of course, they were just trying to drive us into bankruptcy, drive us out of the market. And these are established companies. So we're like, if these guys don't have a playbook to follow, they just kind of make up numbers, then wow. Like how should we navigate like the rest of our lives?”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, so at the time, so basically, what Scour was it was a multimedia search engine and then peer-to-peer file sharing network. But what it was used for was finding free content. At the time, like the laws around this were pretty ambiguous because we weren't, you know, like mixtapes were legal, but this was like a hyper version of that. But we were eventually sued for a quarter of a trillion dollars. So I guess if you're going to experience something that's potentially as life devastating as that, you know, doing it when you're, I think we were 21 or 22 at the time is the time to do it. But it was just like this like very, you know, cold splash of water about how the real world really works because the MBA and the RAAAAAAAAAAA were the ones who sued us. The entertainment industry sued us or the associations that represent the entertainment industry. And then they settled it for a million dollars. So we're like, wait, you wanted a quarter of a trillion dollars.”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“And we were using our own computers in the dorms to serve up this website and product. And then when we got into financing, like the financing process was fascinating, and this is where the sort of everything is negotiable lesson came from, which is it was Ron Burkel and Mike Govitz were the initial.”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“I mean, there's so many lessons. How they like to pick one. I think that the main lesson is that in business and in startups, everything's negotiable. I think that's like the main thing because we were 19 at the time, 1920 at the time. We built this search engine in a dorm room and we were running it out of the dorm room and our first URL was scoured.cs.ucla.edu. And these things were like not necessarily infractions at the time, but we were just being practical. It was basically a project that we had started. And so we built the search engine. And, you know, people started using it. And we thought we would get in trouble. But it turned out the computer science department was excited about it, even though we had like basically parked a domain on their servers.”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“From an entrepreneurship standpoint, it truly is about what insight do I have? Why am I so lucky to have this insight? Why in a world of a million entrepreneurs who are thinking, who are smart, who are trying everything? Why am I in the position where I likely have an insight that others do not?”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“18 months ago, you would get a short story and it would say, Is this short story better than this short story? And now you're at a point where one task is building an entire website by one of the world's best web developers. Or it is explaining some very nuanced topic on cancer to a model. These tasks now take hours of time and they require PhDs and professionals.”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“The general trend right now is going from models knowing things to models doing things. The next question becomes what can it do for me? How does the agent make decisions for you?”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source
“These things take six to 12 months to get them truly robust enough where an important process can be automated. Like with any of these major tech revolutions, headlines tell one story and then on the ground, laying broadband means you need to dig up every single road in America to lay it. Someone's got to dig up the road or someone's got to run the undersea cable.”
2025-10-09 · Lenny's Podcast · First interview with Scale AI’s CEO: $14B Meta deal, what’s working in enterprise AI, and what frontier labs are building next | Jason Droege · IDENTIFIED FROM THE TRANSCRIPT · source