Scale AI CEO on Meta's $14B deal, scaling Uber Eats to $80B, & what frontier labs are building next

Lenny RachitskyThere's been a lot of talk these days about AI not delivering on the promise that we hear, especially at enterprises.

Jason DroegeThese things take 6 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.

Lenny RachitskyIs there anything you think people don't truly grasp or understand about where AI models are going to be in the next two, three years?

Jason DroegeThe 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?

Lenny RachitskyLet's talk about Scale and this whole world of AI that you're in, you essentially pioneered data labeling, trading data, creating evals for labs.

Jason Droege18 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.

Lenny RachitskyI've talked to a bunch of people that have worked with you over the years, and I heard a lot about just how high of a bar you set for new businesses.

Jason DroegeFrom 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?

Lenny RachitskyToday, my guest is Jason Droege. Jason is the new CEO of Scale AI. This is the first interview that he's done since taking over for Alex Wang after the Meta deal. Alex now leads the super intelligence team at Meta. Prior to Scale, Jason co-founded a company with Travis Kalanick. Before, he started Uber, worked at a couple startups. Most famously, Jason launched and led Uber Eats, which went from an idea that he and his team had to what is now a multi-billion dollar run rate business and one that basically saved Uber during the pandemic when nobody was taking rides. This interview is following a theme that I've been following through a bunch of interviews, which is the evolution of how AI models actually gets smarter. Along with scaling, compute and improving the actual model code, much of the improvements we're seeing in ChatGPT and Claude and every frontier AI model is these labs hiring experts to filling gaps in their knowledge and correcting their understanding of how things work, and basically showing them what good looks like in every domain that consumers are using models.

Scale was the pioneer in this space. They created the category, and in our conversation we talk about what is happening at Scale and just how this deal with Meta worked, what experts like doctors and software engineers are specifically doing to help models get smarter, how the whole market of data labeling and evals and data training has changed from when Scale entered the market to today, and also just how long will we need humans to keep helping AI get smarter. We also get into where Jason sees models going in the next few years because they have such a unique glimpse into the future. We also talk about a ton of really unique and really important product lessons from the course of Jason's career, including a bunch of advice on how to start a new business, both startups and within existing companies, and also a bunch of advice on hiring and leadership and so much more.
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Jason DroegeYeah, thanks for having me. Excited to be here.

Lenny RachitskyAs I was researching your background and prepping for this podcast, I learned a really interesting fun fact about you that I don't think a lot of people know. So Travis Kalanick, he had a startup before Uber. It was called Scour. It was a peer-to-peer file sharing app, and then I think got shut down. You were his co-founder. This was the early part of your career. I'm guessing there are hours of stories we could talk about during this experience. So let me just ask you this one question. What's just a lesson that has stuck with you from that experience that you've taken with you to future places you've worked and built product at?

Jason DroegeI mean, there's so many lessons. I like to pick one. I think that the main lesson is that in business and in startups, everything's negotiable. I think that's the main thing. Because we were 19 at the time, 19, 20 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 scour.cs.ucla.edu. These things were not necessarily in fractions 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 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 basically parked a domain on their servers and we were using our own computers in the dorms to serve up this website and product.

And then, when we got into financing, the financing process was fascinating, and this is where the everything is negotiable lesson came from, which is, it was Ron Burkle and Mike Ovitz, who are the initial investors in the business. We were in LA, so we were at UCLA, so we were not quite wired into the entire Sand Hill Road scene. And as we were doing the deal, the terms kept changing on us. We thought you went and raised money and it was like, "Oh, we'll get a few million dollars at a $5 million valuation." This is back when that was actually a series A valuation. And then over the course of the deal, it was like, "We're doing the deal. We're not doing the deal. Oh, you should give us 50% of the company. Oh, you should give us 75% of the company. Oh, if you want to sign the document today, this person's going to show up for breakfast and if you don't sign today and give us 80% of the company, the person's not going to show up."
It was just completely wild, the things that we saw from day one of what can happen in business, and we thought there was a way to do things, and at a very young age we realized there is no way to do things. There is just the way that you can negotiate your way through the world, which I actually think influenced Travis heavily and then me later heavily at Uber in terms of if you can imagine it and it makes sense and you can align incentives, then it can happen. But there is no way. And to learn that at 19 or 20 years old I think was highly imprinting.

Lenny RachitskyThat is an amazing lesson. What happened to Scour? It got shut down, I think. What happened there?

Jason DroegeWell, yeah, so basically what Scour was 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. And at the time, the laws were on this were pretty ambiguous because we weren't, mix tapes were legal, but this was like a hyperversion 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, 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 this very cold splash of water about how the real world really works, because the MBAA and the RAA 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 $1 million.

So we're like, "Wait, you wanted a quarter of a trillion dollars and then you settle for $1 million." And of course they were just trying to drive us in a 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 make up numbers, then wow, how should we navigate the rest of our lives?"

Lenny RachitskyLet's talk about Scale and this whole world of AI that you're in. This is the first interview that you're doing since taking over CEO at Scale. I'm honored to have you here to talk through this stuff. This is also the first interview you're doing since the whole Meta deal, which is very complicated, confused a lot of people. So I'm just curious to hear the current state of Scale, what people should know. For example, what's your relationship with Meta? What's your relationship with Alex? What is the current state of Scale?

Jason DroegeYeah, 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 working 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. All the privacy still in place, all the data security still in place that was there before.

And in fact, only about 15 people went over in the transaction. So Scale has about 1,100 employees or so now, and we have two major businesses. Each of those businesses, each of them has hundreds of millions of revenue. So we 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, so this is part of getting the word out and we're excited to continue to build, deliver data, and do what we did before.

Lenny RachitskySo the company today, independent, its own company. Alex, just to be clear, he works at Meta now. He's no longer at Scale.

Jason DroegeYeah, that's right. Excuse me, I should have talked about that more.

Lenny RachitskyI think that's really interesting. So basically, it was an investment. Some people left to join Meta, the company continues, you're running the ship. Let's talk about this whole space that you guys essentially pioneered, I don't know best way to call it, data labeling, training data, creating evals for labs. You guys were at this before anyone even knew this was a thing. I know even Scale pivoted into this market from other things. I think there was a bunch of stuff they tried with self-driving cars and all these things, and then it's like, "Oh shit, AI labs need this data."

One of the main stories I've been hearing is, and I've had a bunch of CEOs from this space on the podcast, is that there's been this big shift from the way, from what Scale had pioneered and had been doing for a long time, which is generalists, low-cost labor training. From that to now, labs mostly need experts, lawyers, doctors, engineers doing training, writing evals, things like that. I'm curious just what you're seeing, how that's impacting you guys, where you think things are heading, what people should know about this whole market of data training data.

Jason DroegeYeah, 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 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.

And then, you move forward and the models have gotten better over this period of time. And so, as 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 or to the moon. And so, as part of that, that industry is changing constantly too. So 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, 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 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 ranking to it. It was pretty basic 18 months ago, and you had the rise of some experts, but the models were so far behind that they needed just even the basic stuff they needed. And now, you're at a point where a task is, 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. And these tasks now take hours of time and they require PhDs and professionals.
So to give you a stat to back this up, 80% of the people that we have on our expert network have a bachelor's degree or greater, which is very contrary to some of the positioning that's out there and some of the understanding of this industry. About 15% have a PhD that's greater, and we have PhDs on the network earning significant amounts of money doing labeling, contributing their expertise to these models. So we've been doing expert data labeling ever since the models need it. I mean, this game is keeping in touch with the researchers, knowing what they need, coming up with ideas internally. In some ways, we drove this because we were seeing that the models were not sufficient in more expert ways. And so, we would go to the model builders and say, "Hey, we noticed that this is a problem. If you would like to fix it, this cadre of experts can do that for you." So the counter positioning is out there, but I think that's just what competitors say sometimes. It has nothing to do with reality.

Lenny RachitskyOkay. That was extremely interesting. So what I'm hearing is yes, there has been a big shift to labs need more expert folks involved in training, labeling, writing evals. You guys are very aware of that and have evolved with that. One of the, I don't know, allegations I guess in the market is that it's hard to find these experts. So all these companies have their proprietary network of experts and how they find them. Is there anything you could share about just how you guys go about that because that feels like the hardest part is finding these experts and keeping them from other companies?

Jason DroegeThey are hard to find. You have to have many, many tactics. So we get, 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. 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 they refer each other.

We also have campus programs where we will literally go onto the campus and talk to the professors, talk to the students, ask about who would like to do this type of work. And then, of course, there's the more traditional scaled ways of LinkedIn and places like that. But the best ones come from these grassroots and referral networks. And the only way you get that is providing a great experience to these people, because these people, 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 in many times it solves a problem for them.

Lenny RachitskySo something that I've been seeing on Twitter just this week as I was preparing for this is there's the information headline. This came out and this mirrored something that Brendan from Workhorse said that over time the entire economy is going to move towards just reinforcement learning and everyone's just training AI is basically the jobs that will be left. Thoughts on that? Is that where you think things are going? Is there another perspective?

Jason DroegeReinforcement 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. It has 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 so 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 is each individual task or each individual environment. So if you imagine the world of environments of 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 meeting on my calendar for my interview with Lenny, and the agent goes and it looks through all my calendar and then it pops it out, very simple example. 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 agents as useful as possible for their end users.

Lenny RachitskyI love that you've been sharing these examples of what this stuff is specifically that these people are doing, the data you're providing to labs. So just to mirror back a few of the examples you've shared, one is an engineer building a website, sharing the code essentially with the model. And here's how I would do it. And in that example, is it just like here's the code or is it a recording of them building it? What is the data?

Jason DroegeIt 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.

Lenny RachitskyGot it.

Jason DroegeSo 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 decision, here's why I didn't make this decision, or here's a broken website and here's why it's broken if they're trying to accomplish, I don't know, a debugging tool for a website builder or something like that.

Lenny RachitskyAnd another example you shared is a short story where it's like, here's one short story, here's another I imagine generated by a model. And then it's like, which is better, and then how would you make it better? The other example you just shared is a Salesforce agent where it's like, Hey, book a meeting with a prospect and then teach it how that happens. I love just how concrete these are because it's like, okay, I get it. This is the stuff that these companies do. Is there another maybe one or two examples just to give people a sense of what this data looks like?

Jason DroegeAbsolutely. 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 a 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 there's a productivity element to this implementation tier.

So there's a huge backlog. They want to be able to 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 200 to 300 pages of documentation and it's rolled into one document, but in different formats. 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. 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. They want to treat the patient well, obviously this is why they became a doctor. And then, they go into the room and they talk to the person and then they make a diagnosis.
Well, we basically built a tool that will read that document for them and point out the top 5 to 10 things that they should take into consideration, either allergies that might not be obvious is one example where we actually, we picked up on an allergy that a patient had that would not have been obvious from reading the document and that allergy actually would've 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've even been hard for a human being to do. To make this tool better and better, you get to a certain limit 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 are 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 that 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 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 healthcare 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 that we're unblocking for our customers.

Lenny RachitskyThat's really interesting. It's like the spectrum went from just low skill generous labor to experts to now the specific expert at this one company who needs to do this work, this labeling.

Jason DroegeAbsolutely. I mean understanding what, there's this broad narrative. We have two narratives. We have the AGI, everything is just going to become AGI, and then there's the skeptics, which is like, "Hey, this is all bunk, this is a bubble, 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 probably correct, but the reality is is that it's very hard to get machine 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 a one document, think about the problem of even understanding a document, 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 knows that? So this is all got to be built. So if you're going to make good decisions.

Lenny RachitskyThis is a good segue to this question that is always on people's minds when they look at companies like yours and the other folks in the space is just how long do we need people to be doing this? At what point will AI be smart enough to do it themselves? I know your incentives are to say we'll never run out of people because it's aligned with your growth, but just how should we think about just why do we need people, I don't know, in 10 years? How long do we need these experts telling AI things it doesn't know?

Jason DroegeFirst off, the history of data labeling is a history of new beginnings. Autonomous vehicles do not need as much data labeling as they did in the past. I mean, 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 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. And sometimes it's new people, sometimes within our existing base we find that existing people have expertise that 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. 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 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 supposedly this transformation is going to happen in the next one to two years. And I just think that it might happen. The space is moving super fast, but I don't think it's going to happen.
It is definitely not going to happen in the next year. The idea that it happens in the next two years I think is very far-fetched, but nothing's impossible here. And long-term, I think that if you go back through, I don't know, pessimist archive or whatever, these accounts that post, the radio was invented and then all of this will be eliminated. There will be change, but the change, I think humans are very good at adapting. So I think what we're underestimating in all of the doom and gloom is we believe in human adaptability. We as a company are highly adaptable and I think the history of technology has shown that people are adaptable.

Lenny RachitskyI really like that takeaway. I'm an optimist as well, so I'm always looking for reasons to be optimistic. I want to follow that thread before I get there, something very tactical I want to ask about is evals seems to be coming up a lot, especially with companies in your space. I'm still learning a lot about just what this all is, especially in your market. How much of what you or experts are providing are evals versus other types of data?

Jason DroegeA lot of it's evals, and within enterprise customers and government customers, it's mostly evals because somebody's got to establish the benchmark for 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.

Lenny RachitskySo in the case maybe of the healthcare example you shared, essentially this doctor would be sitting there looking at all these reports, creating evals that are like, this is what this should be discovering in this report, in this record. Is that a way to think about it?

Jason DroegeYeah, that's a very big part of it, which is what does good look like?

Lenny RachitskyAwesome, okay.

Jason DroegeI have to reduce things down to simple terms.

Lenny RachitskyIt's interesting you say good versus correct. Is that a specific term you like to use good versus just this is the correct answer.

Jason DroegeI didn't intentionally use that word, but these are probabilistic systems and so depending upon... Yeah, so I can get into some nuance here about the right types of problems that AI is good at solving. So if you have a human process that is 10 or 20% accurate or 10 or 20% liked, AI is awesome. Because if you get to 50, 60, 70, 80% accurate, you're in the money, 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 remaining 2%, not totally there yet. And so, when I say 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.

Lenny RachitskyTo you, this is so obvious and to people in your market that I think a lot of people think about AI being trained on just here's a bunch of data, check it out, learn everything you can from all of human history and all of written record. But what's wild is basically people are sitting around teaching AI things it doesn't know, filling gaps. That's how AI is getting smarter now. There's no more real data for it to feed on. It's just like, here's what I don't know, or here's what an expert found you're wrong. I'm going to teach you this. And the fact that it scales and that's keeping models improving is so mind-boggling.

Jason DroegeYes. 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. There is the, yeah, it's as simple as that. Someone's got to dig up the road or someone's got to run the undersea cable. There's always some operational chiseling that's going on in all of these industries. I mean, if you think about how magical these models are, they're remarkable that if you've been in technology long enough, it blows my mind even today that they get the punctuation right consistently. I mean, that sounds like almost daft to say 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, the real answer is it's a combination of computational power, model improvement, and data, and all three are getting better at once.

Lenny RachitskyLet's follow that thread. You've been at Scale for a long time, CEO for, you said, 13 months. I feel like you see a lot more about where things are heading because you work with labs on things they haven't even announced yet. You see more than most people, and I know there's only so much you can share about what companies are doing, but just is there anything you think people don't truly grasp or understand about where AI models are going to be in the next two, three years?

Jason DroegeLook, 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 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 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 a lot of the speculation has a wide variance because we're at the beginning of it. People take different trajectories on how that's going to improve. And so, if you take a trajectory of 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's still people in the world without an email address. And so, the adoption curve then becomes 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.

Lenny RachitskyThere's been a lot of talk these days about AI not delivering on the promise that we hear, especially at enterprises. There's this MIT study that just showed that there's all these pilots that people are excited about and then they don't work and companies aren't adopting these tools. There's data showing engineers are not actually as productive with tools. It actually slows them down sometimes. You work with a ton of companies implementing all kinds of AI. What are you seeing on the ground? What kind of gains are you seeing? Do you feel like it's overhyped, underhyped?

Jason DroegeThere's 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, my healthcare example is one, we do other sophisticated workflows, claims management for insurance companies. This is a financial decision that's happening, but it's an automatable process. But basically what happens is the POCs get to 60 or 70% of the way there, and the human mind goes, oh, the rest is no big deal. But it's like uptime in data centers where every nine is an order of magnitude investment in terms of reliability, backups, et cetera. One nine is basically a web server in a dorm room like we had at UCLA, and then five nines is this crazy high bar, but it just seems like a very small movement.

So you have a similar dynamic going on here where you have a bunch of people, one of the reasons why the POCs have failed, one, there's a denominator effect because it's so easy to do, "Hey, 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 a quality partner like we are, or if you do it yourself, if you have engineers who've worked with models before and they put in the time, and I'm talking about months, not like minutes like you see in these videos to actually get legal approval, policy approval, regulatory approval, change managements like an accuracy that everybody's comfortable with. If you actually do that, these things take 6 to 12 months to get 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, I never would've 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.

Lenny RachitskyIt's such a good point that it's not only is it easy to try these things, it's just like everyone's doing it so everyone's feeling FOMO like, "I got to try these things. I got to try all these prototyping tools, Cursor, all these things." Just goes, "Everyone's doing it," and then you just rush into it and it doesn't actually work out.

Jason DroegeEasy to learn, hard to master. That's my summary.

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Okay, let's move on from AI. This could be an endless discussion about AI, but you've got a lot more lessons to teach us. You've helped build Uber Eats, you've had a couple startups in the past. We talked about Scour for a bit. I've talked to a bunch of people that have worked with you over the years, and I got a lot of really interesting insights into the stuff that you're extremely good at. So I'm just going to go through a bunch of these. One is your obsession with being close to customers, talking to customers, and I love this topic because it's something everybody thinks they're great at, and they feel like they completely understand how this is important, why this is important. They all feel like I'm doing this, don't worry about... Everyone else is not doing this, but I am. Talk about just what you think maybe people miss about how this looks when you're doing it well, and just why this is so important.

Jason DroegeI 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 I mean, I don't think it's 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 literally. And there's been a lot talked about on this topic from 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 underlying things. I think the way that I look at it that might 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.

If you're selling enterprise software to someone, there's an executive sponsor as an example, that person needs to trust that 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 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 an incentives alignment baseline. 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, I'll give you an example. I've been out of the 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 was to figure out what other businesses we should get into. And so, we looked at a billion businesses and Uber Eats, food delivery was the one that we thought was most interesting, which turned out to be right so good for us.

Lenny RachitskyVery right.

Jason DroegeAnd 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'd go to a different restaurant tour and they would explain it, but they were a little suspicious of 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 a base catalog, and we just matched up how much does the ham weigh? 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 what's the ingredients cost versus what's the labor cost? And then, we triangulated what was our ground truth, and then what are we being told by restaurant tours, and then what is the site guys telling us about restaurant economics?

And if those things all overlapped, and 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% of every meal to ingredients, and they pay roughly 20 or 30% to labor, and they pay roughly 10% to real estate and a bunch of other, anyway, so goes down the chain. But the important parts is what's the value of incrementality?
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 explained 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 be close to the customers, it's a combination of what's the most valuable thing. Well, in a restaurant tours case, give me incremental demand. Because if you were to take a restaurant location and triple demand based on the same labor but you're just scaling ingredients, you've got a 70, 80% incremental gross margin product.
Restaurant tours would hate when we would say this because it doesn't work out exactly like that in reality. But because we had that insight, we had confidence that we could go to market with, we 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 could pay the driver this. And so, you fit this whole puzzle together without totally satisfying, in the case of a marketplace, you're not totally satisfying any individuals 100% of their needs. What you're satisfying is is you're getting a clearing rate for them to participate in the market in the case of a marketplace. So that's one example.

Lenny RachitskyYeah. I love this example as you almost you figure out how to help them with something they don't even fully themselves know yet. So as you think through their goals for them as if you were them, break down the economics and then here's the solution versus, hey, what can we do for you guys?

Jason DroegeYeah. I mean, if you walked into a restaurant, they would tell you a bunch of things. They would say, "Oh, labor schedule is an issue." They would say, "My rent is an issue." They would say, "All these, my ingredients prices are an issue, that's 20 or 30%." If you could shave off 3% of that, that would be huge. You might then take that and go, "I'm going to go build a business. It's going to save you 10% of your ingredients costs."

Well, but that doesn't actually get into their head on what's truly important day-to-day. That might be important for them on an annual basis, but on a daily basis, what are they doing? They're looking at their numbers, they're looking do people show up. Did I make money yesterday? Am I going to make money tomorrow? So the urgency, I think the biggest thing people miss when they're building new products is the urgency of the buyer part of it. You can build something that provides a lot of value, but if it's not the top thing that the customer is thinking about in their busy days, then you're just going to have a long road to a small town.

Lenny RachitskyThis touches on just the theme I heard a lot about, this idea of independent thinking and how much you value that, and this feels like a really good example of that. Is there anything else along those lines of just why this way of thinking is so critical?

Jason DroegeI think as a founder's job, and I mean I stretched that term because at Uber we had all of the benefits of Uber so I wasn't really a founder. I just started the business there. But there are some elements of founding there is you're looking for alpha in the market. When we started our first company in '97, it wasn't that cool. It might've been cool in Silicon Valley, but it was definitely not cool in LA. Now, it's super cool to start a business. So as a result, everyone's trying everything. So how do you get alpha on that market. If your research is highly influenced by what the world is saying around you, you're not going to have an independent insight. You have to go off and do your own thing.

And this is why from an entrepreneurship standpoint, I have very strong feelings about what the approach to founding a company should be and is probably very particular to me. But it truly is about what insight do I have, because 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? And then, why am I the one to do it?
And the answer might be I'm in this narrow, far-flung place. The other answer might be, I am inherently a contrarian personality type, so I'm just constantly looking for the thing that's true that people don't believe is true, which sometimes worked. But then, the second part of that's super important, which is why do I want to work on this problem for 5 to 10 years? And people get this wrong all the time. They go and talk to a customer and they go, "They have a problem. I'm going to go solve it." And it's just not a great way to start a business. You really have to have this burning desire to constantly be questioning yourself.
The other thing about independent thinking is is that you can't fall in love with your ideas. And I do not proclaim to be the world's greatest thinker for what it's worth, this is what you've been told, but is just part of that is basically throwing away who you are, who you've been, all your ideas for the mission that you're on, which is trying to accomplish something for our customer.

Lenny RachitskyThis is great. I'm glad you went here. This touches on the other theme I heard often about you is just how high of a bar you set for new businesses. And I think this advice is useful both for founders, as you said, and also people starting companies within companies, new business lines. So you've talked about this a bit already, but is there anything more there, just how high that bar needs to be for it to likely work out when you're starting something new?

Jason DroegeLook, if you want to give yourself the best chance, and this isn't always how it works, but if you're in my position 25 plus years in their career, if you want to give yourself the best chance, I think there's two ways that companies end up working out. And the first way, which is probably the most important, quite frankly, is that the founder is just a force of nature over a long duration of time. Because you're going to have to pivot, you have to have that energy to pivot. You have to go years and years and years with it being hard, and that's probably the most important thing.

But the second most important thing is that you can easily educate yourself on what are good business models, what are bad business models, what are good markets, what are bad markets? And even if you're this force of nature, having the knowledge, if you're going to go into a bad market with all your energy, you should at least know, maybe ignorance is bliss because you just throw yourself into it and it just works out with time. But that's not how I would operate, which is marketplaces are good businesses. SaaS, at least historically, we'll see how this changes, but SaaS, historically, great businesses, recurring revenue businesses, sticky businesses, network effect businesses.
And if you look at what the top VCs invest in, yes, there is a lot of portfolio building, but there are similarities in terms of the types of business models that they believe could be worth tens of billions of dollars. And they have network effects, they have lock-in. They are more valuable at scale, a big scale than low scale. So if you just take a filter on a new business, this is what I did at Uber, which is like if you just have a filtering mechanism on a new business, it doesn't take that long to eliminate the bad ideas. And then, of what's left, you can pick, oh, I'm very passionate about this, even though it might have more problems than this other thing that on paper looks better. And then, you have to have passionate about it. But I think people just miss a basic understanding of what businesses even have a chance of being worth $100 billion.

Lenny RachitskySo you launched Uber Eats, you figured out this is the place to go and bet. As an outsider, feels obvious, of course this is going to be a massive success. Of course, food delivery, such a good idea. I know you looked at a ton of ideas in that process. Can you just talk about what you explored and why you ended up picking Uber Eats?

Jason DroegeI am definitely not the smartest person in the room when it comes to figuring these things out. And so, I keep a very, very wide aperture on ideas for as long as I can until I'm like, okay, everything is coalescing. And I think there's a bunch of reasons why you have to keep an open aperture on considering ideas that might seem bad at the start, but you just keep digging and see if you're right that they're bad or you're wrong. So just as a general philosophical principle, I'll start there. We looked at, we did some crazy stuff. I went walking around San Francisco one day and I looked down Market Street and there was a CVS, a 7-Eleven, a CVS, a Walgreens, a 7-Eleven, and I'm like, "How many SKUs could possibly be inside one of these things that people want and couldn't you just put that into a van and you hit the button on the van and the van comes around and you get whatever convenience items you have, and they're convenience items, so why would that be a problem?"

And we launched that in DC. We put 10 of these trucks on the road, we put 250 SKUs in them. And I mean, crickets is an understatement of how bad it was. I mean, we couldn't get an order to save our lives. And what we realized was that we hadn't really done the research on what convenience stores really were. It was if you didn't have cigarettes, you didn't have beer, you didn't have Slurpees, you didn't have these things, for example, you didn't bring people in to sell all the other things. So we didn't know anything about retail. We were clueless. So that's one idea. We looked at grocery, but honestly the unit economics just terrified me of all the pick packing and everything like that. I think Instacart did a remarkably good job at getting the unit economics to a good spot and probably the hardest operational problem you could tackle.
We did generalized delivery, point to point delivery, what's now, I forget what Uber's product is called, but Uber Direct I think it's called, where you have something that needs to go point to point in a city. That was a flop from the beginning because the truth is is how consumers don't really have this need, business sort of have this need, and in 2014 when we were doing this, no one had this need. But we tried 15 versions of all these things before we eventually just said, "Okay, the food delivery thing is just popping off on all signals and we can make the unit economics work. People seem to want it. It's a super cool problem because we can enable independent restaurants with all these tools and allow them to compete with the big guys. We can take the real estate out of the equation. So you can have a real estate location that's non-prime, but if you have prime food, then you get to compete." So we're like, "Oh, this is a very interesting problem and we can really help local economies."

Lenny RachitskyAnd this ended up being, if I remember correctly, this basically saved Uber during COVID. Lyft didn't have something like this. And how big is this business at this point? Anything you share about just how important this turned out to be for Uber?

Jason DroegeYeah, of course. Well, we launched it in December of 2015 in Toronto and within two hours we had done $20,000 for the sales. It was crazy how quickly we saw that it was the right idea and the unit economics were good. And then, four and a half years later, I was at Uber for about six years, but it took us about a year and a half to figure this out. Four and a half years later, it was about $20 billion. So it was 0 to 20 billion in four and a half years, which is pretty good. Uber was very good at scaling things, but competitive market. Others did well. We beat a lot of people. Some people beat us. And then, now I think it's pushing 80 billion, and that's been for another four and a half years since I left. I think COVID turned it from 20, I left right before COVID, total coincidence, 20 to 50 in a year. So I mean, ride-sharing went this and food delivery just went to Pluto.

Lenny RachitskyWhat luck. Well done.

Jason DroegeLuck is part of the game. That's the other thing that's important to realize. Luck is part of the game, so do not begrudge people for luck. This industry is hard. All these things we're doing are really, really hard. Luck is just part of the game.

Lenny RachitskyMaybe speaking that maybe not. One of your colleagues, Stephen Chau, who I am an investor in his new company, he worked with you at Uber Eats for a long time. He told me to ask you about the McDonald's story. I imagine that was just a big milestone, a big moment enough for you guys. So why'd you decide put McDonald's in Uber Eats and there's apparently a story of how you won that deal.

Jason DroegeSo it was interesting, and this just goes to maybe where sometimes ignorance leads you to accidentally the right answer. So we had launched Uber Eats and Uber had a global footprint and we were the only food delivery network with a global footprint excluding China. Everything at Uber needed to be launched globally. That was a very big part of the culture, et cetera, which is a lot of work and you can spread yourself too thin and cause other problems. But in this way it was good. My vision was, okay, let's help the little guy compete with all these chains. They have these systematized food systems and food is what makes a city amazing. And no one talks about the chain restaurant that they visited in Paris. They talk about the local place that they found and let's be part of that. That's who we want to be.

And so, McDonald's actually approached us and they said, "Hey, we'd love to do food delivery with you." And I said, "No." And they're like, "Hold on a second. We have 80 million consumers a day. You don't want to do this together?" I'm like, "It's not really our vibe right now." And so, I pushed them off for four or five months until my team is like, "You're insane. These people are going to put marketing behind it. They really want to do this. They want to lean in." So we actually had, because of that, I think it's hard to correlate these things, we ended up with this exclusive relationship with them, got an insane number of customers of... Chains at this point actually weren't really on food delivery networks because everybody was so worried about the unit economics, because they're so sensitive to the basket size.
And my approach was like, eh, figure it out, which is a very Uber culture thing. Okay, the basket's $17, it's our job to make that work, reduce the radius on the delivery, figure out the economics, maybe mark up some of the food someplace. There's always a way to figure it out. So we did it and then three months later the business just started hockey sticking again at a different level. And my team is just like, "Dude, you were so stubborn on this point," but I think it actually ended up being in net benefit because we got a great deal with them.

Lenny RachitskySo the fact that you pushed him out helped you get a better deal is what I'm hearing. That's amazing.

Jason DroegeYeah, I think that's the story he would be referencing. And then, the onboarding of it was crazy because we basically went global with them in six months, and at this point the business was less than two years old. So activating this, I don't even know, an 80-year-old company that expects processes to be in place and we have two of our office managers in New York managing it. It's just mayhem.

Lenny RachitskyI'm still sad In-N-Out is still not on any of these apps.

Jason DroegeYeah, me too.

Lenny RachitskyI remember someone was hacking it. There's all these ways people found a way around and they're like, "No, no. Okay, you're Postmates. We know we're not going to give you any food."

Jason DroegeYes, love In-N-Out.

Lenny RachitskyYou've touched on this idea of gross margins and margins, how obsessed you are with this. I wanted to spend a little time on here. I've heard just you're obsessed with understanding gross margins before going in on anything. Most founders have no idea what they're doing here. What have you learned about just what people should be paying attention to, what they might be forgetting when they think about just the feasibility of a business?

Jason DroegeYeah, look, it's one filter like many filters. There are certainly businesses that have low gross margins that are great businesses. Costco, Walmart, et cetera. Amazon talks about this all the time of there's companies that increase prices and there's companies at lower prices. But I would say that by and large, high gross margins combined with healthy churn curves are a very healthy sign for the business. I mean, think about it. If I were to sell you something and I can't mark it up a lot, how much value am I adding beyond what's in my hand? And if I'm not adding that much value, then what am I in the business of doing? And I'm in business of adding value. And it's not quite that simple. This is just a litmus test of when someone comes to me and they go, especially in a new business, and we deal with this. I dealt with this at Uber, I've dealt with it everywhere.

Someone comes up with an idea and they go, "We can get into this business and I think we can charge this and it'll get us to a 40% gross margin." And then, my next question is start at a 60% gross margin. Why does that not work? And they go, "Oh, well, the customer..." And immediately, you short circuit to what the real problem is. Oh, the customer has an alternative. Oh, okay, well who's the alternative? Oh, it's some offshoring company. Well, what's their gross margin? Oh, we don't know. You go find out. It's like 20% and they've been around for a long time and they have scaled operations. And you're like, okay, so your gross margin is going to go from 40 to 20 quicker than you think, and you're going to be in a world of hurt unless you do something to differentiate.
So I take gross margin is just a very coarse instrument, not a perfect instrument to think about, am I adding enough value? Am I differentiated? It's not perfect, but it's a very quick short circuit filter to even to see if someone's pitching you an idea, have they thought through this dynamic? Because if the response is gross margin is super low right now, but here's the dynamic I'm going after. And then you're like, "Oh, okay." And sometimes it's like, we'll just make it up with volume and then the gross margin will go negative for a while and you're like, "Wait, this doesn't work."

Lenny RachitskySo what I love about this is just a lens into is my idea good enough if studying, can I keep a high gross margin? Is there a reason why people in this space haven't been able to have a higher margin?

Jason DroegeYeah, exactly. And like I said, it's meant to disqualify just you're doing these large for larger companies and everybody has ideas. And so, it's a way to cut through. Do you understand the machine that is going to need to be in place in two or three years? You might have a 70% gross margin now because the next question is why can't someone else do this? And if you have an answer of like, "Well, they can now, but they can't in two years, if we run really fast." Okay, we might have something. If they can now and they will be able to in two years, you're going to have margin compression.

Lenny RachitskyAlong these lines I was just listening to, I think it was the a16z podcast. Alex Rampell I think was sharing this story about Costco, how as you said, their strategy is actually to keep margins very, very low because all their revenue comes from their membership. So they have something like 50 million members paying 100 bucks a month and that's their entire business. And so, they don't plan and they don't want to make money off the products.

Jason DroegeYeah, that's right. I mean, they're playing a slightly different game, not an expert on Costco, have spent some time with the company, but they use price as a way to get to scale. And so, they're basically saying if we discount, same with Walmart, we will get so much volume that we will just take the air out of the room for all of our competition. And so, then the question of, okay, so if you have a low gross margin today, in two or three years, once you land one of these centers in a market, why won't your margins to get eroded? The answer is because we will have already absorbed all of the demand. You try to go to 8% versus 10% gross margin, which I roughly think is what their gross margin is. That's going to be a really hard business. If you already have a habit with a customer, they have already built their weekly trips around you, you already have relationships with suppliers, you already have general managers that know how to stock inventory, that's not a straightforward exercise. So they're first to scale and then good luck competing with them.

Lenny RachitskyOkay. Just a couple more questions. One is there's this term that I've heard that you often say and believe in is this idea of not losing is a precursor to winning.

Jason DroegeYes, yes.

Lenny RachitskyTalk about that.

Jason DroegeTech is a culture where portfolios are built by investors, and a lot of the narrative is controlled by investors frankly. Founders obviously participate, but this idea that you should just go for it is consensus. Just go for it. Who cares? Well, I don't know, if it's my life and I only have one moment to take a shot, I might want to just not just go for it. I might want to think for a little bit, and I think the best entrepreneurs, I have no data to back this up, but just these are my friend, this is my friend group. I think the best entrepreneurs and the best business owners look at the risk profile of the decisions that they're making and they try to make asymmetrically positive decisions all along the way.

And so, oftentimes I feel like we forget about the risk of a decision, and there's more to unpack there because I actually think taking highly risky decisions and then having it work out is a weird cultural thing too, because then how do you train people to do that? Because it's a very hard thing to take high risk decisions and be right enough because it creates a lot of volatility. But it goes back to my comment about the most important thing in founders, which is just this ability to persevere through. Survival is just part of the game, and most people just give up before they get their timing right, before they get the right insight with the customer before they get the right product in the market. And life can change quickly in tech. You can go from being a dog to being a hero in a very short period of time, but you're on this very, very long journey, but you have to survive for that condition to be met.
And so, then the question is is when you're in a hype cycle, I would argue that we are right now, everyone wants to go for it and then go for it more and then go for it more and go for it more and you don't realize, guys, all of our customers are going to be around in five years. They just want us to solve their problems. We have to be around to solve their problem for them. And so, survival is a precursor to that. So let's not put ourselves in position that could potentially compromise the enterprise along the way. It doesn't mean don't take risks, but think about how you calculate it.

Lenny RachitskyI love how clear it is that this lesson and many of the lessons along these lines have come from just failure and things not working out and things breaking, which is the best outcome.

Jason DroegeIf you ever get on the other side of a high reward, high risk decision, it is so painful because you are just cooked. You are done, and often there's no way out.

Lenny RachitskyIs there a story along those lines that comes to mind or an example of that?

Jason DroegeWell, this is where it is together on why I try to be so I think you can spend a little bit of time thinking upfront to save yourself a lot of pain downstream. I had this business not worth detailing it, but after the bubble burst in 2001, I'm like, "I'm going to self-fund a business. I'm going to build a profitable business. I want to prove that I can do this." And we had started Scour, which had all the things we talked about. And so, what I did is I'm like, I was a golfer and frankly, there was nothing to do in tech.

So I started selling golf clubs on the internet and I was making real money and I might've learned more from this business than any other because I started on eBay and I was 22, and I didn't really understand that my margins would come down because anyone can do this, but I was one of the first ones to do it, so I was making a ton of money and then I built this business and then I just failed to recognize I had a lot of hubris. I was like, "Oh, if I could just buy all the used golf clubs in America, I can be the market maker for prices," and don't people do that?

Lenny RachitskyI love this ambition. That's great.

Jason DroegeAnd it's just like it's madness to actually think about the practicality of that. And so, I just didn't spend the time thinking and then I ended up in this business. The business was profitable, it got to a couple million of revenue, whatever, paid me a dividend for a while, but it was painful the entire way.

Lenny RachitskyI love the spectrum of experiences you've had. You've sold golf clubs, you're helping achieve AGI, you could say. There's also a whole part of your career. We haven't talked about where you built tasers and body cams and drones and all these things. Also, peer-to-peer file sharing before anyone else. Final topic I just want to spend a little time on based on this experience is hiring and building teams, something that I know you have a really strong take on. That I've been hearing a lot on this podcast recently is this idea of it's more important to build the right team than find the most optimal top talent. Talk about that, why that's so interesting and important.

Jason DroegeAs of late, I've developed a more nuanced view of this, which is for certain roles, you absolutely need the right experience in this current market. You see this with researchers, because the market's moving so fast, you don't have time to train up some people, so you actually have to go find people either who have the right relationships with customers that you want to get or you have to, who might not check other boxes but are awesome at that, might not check the classic boxes that I think you're referencing of they're a problem solver, they can grow with the company, they have a high trajectory, et cetera. I would say that's 5% of the roles in the company, but very important whenever speed to market is important.

And then, for interviewing, I just interview for three things and I have to interview across all kinds of expertises, which is hard. I can't be an expert in everything. And so, I reduce it down to just three things, which is like, are you a curious problem solver and can you articulate that verbally? Can you work across people? Are you humble enough to work across and are you a good leader? And if you just do those three things, I think you have a pretty high chance of success, at least in an organization that I'm running, because the world's changing. So you do need people that are adaptable. So all the experience is not necessarily one-to-one relevant.
And then, the working across to your team point, this actually came up at Uber Eats. So when I was building the Uber Eats management team, I'm not sure if this was mentioned to you from that group, but whenever I would hire people, I was trying to compose almost like an organism of strengths and then minimize the conflicts. That management team for the most part outside of some of the operations side, but for the most part, that management team was the same management team from day one when we had nothing to $20 billion. And I just believed that the team, knowing each other's strengths and weaknesses and being able to compensate for each other was more important than the classic advice you get around, "Well, that person hasn't seen this much scale." And you're like, "Well, yeah, but can they learn it?" I learned it. So you do have to kind of believe in people a little bit, which is my job, not necessarily their job. And so, I mean, these are people systems. They're not straightforward rules-based things you can apply.

Lenny RachitskyAnd I especially love this advice because there's all this talk about what skills will matter in this world of AI doing all our jobs, and it feels like these three buckets are maybe the same thing, just are they good at solving problems? Are they good leaders? Can they collaborate well with other people?

Jason DroegeYeah, I don't think that the core rise of humanity, it will change, and I think that these things are pretty core to how humans have been successful for a long time.

Lenny RachitskySpeaking of that, I'm going to take us to a recurring segment of this podcast that I call AI Corner, where I ask folks this question, what's some way that you've found a use for AI in your day-to-day life in your work that makes you more effective, get more done, get better stuff done?

Jason DroegeHonestly, when I came into Scale, so my history was in consumer and I've done some application level stuff with government, and this space is moving so quickly. AI is my, I use it as a tutor. As these new concepts come up, I have a lot of people in the company who can educate me on the nuances of the technicals of all of, excuse me, the technical nature of the data and the products, but they only have so much time. And honestly, there's new concepts coming up all the time and I need to stay on top of it.

So it might sound crazy, but a large percentage of my job is not dealing with the engineering issues related to AI. I'm managing an organization, but I love understanding it. It's one of the most enjoyable, rewarding parts of my job is to learn from all these AI researchers, but they don't always have the time to do it, so I use it as a tutor. I turn on voice mode and talk to it on my way into work. So I think that's probably the most impactful thing that I use it for that's also relevant to this topic.

Lenny RachitskyI do exactly the same thing, especially when I'm prepping for this podcast. What exactly is this? I think about when you say this, I did an interview with the founders of Perplexity a few years ago asking about how they work at Perplexity, and the founders said that before, they were ruled, before they ask a question of anyone on the team, they have to ask AI first. And I was just like, "That's crazy." Now, it's so obvious. But back then, I was like, "That's an insane way of working. I've never heard of this before." Just a sign of how ahead of the curve they were.

Jason DroegeYeah, I think number two would be I'll take internal documents and I'll ask, what's the most important thing in this document? And I'm shocked, and then I'll read it and just double check, but I'm shocked at how good it is at just pulling out. There's so much in organizations that is like, I don't know what you want me to say and I don't know what I need to know, but we each have our own agendas, and so this matching of, and so then there's this huge broadcast problem where it's like, of all of the information you might want to receive, what's actually important to you? And so, I use it a lot for that too.

Lenny RachitskyAmazing. That's a really good tip. I use it for legal documents, just like what do they know about what they're trying to do here for me or against me? Jason, is there anything else you wanted to share or leave listeners with, maybe double down on a point before we get to a very exciting lightning round?

Jason DroegeYeah, absolutely. I mean, I think the really important, the reason why I'm doing this, the reason why want to spend time here outside of wanting to be on the show for a while and being a long-term listener is, our long-time listener, excuse me, is there's a lot of amazing work going on at Scale. The teams are working super hard, we're delivering a ton of value for our customers. The public narrative has not represented the work that the people here are doing and the work that our customers are doing with what we're doing for them. And I just think that deserves the respect and reward that all those people are putting in, and we'd like people to know that.

Lenny RachitskyI appreciate you saying all that. With that, we've reached our very exciting lightning round. We've got five questions for you. You ready?

Jason DroegeYeah, let's go for it.

Lenny RachitskyWhat are two or three books that you find yourself recommending most to other people?

Jason DroegeSome of this is going to sound interesting. The Selfish Gene is one of my favorite books.

Lenny RachitskyLove that book. I don't know if anyone's ever mentioned, it was one of the most influential books for me too. So sorry, keep going.

Jason DroegeYes. I think Selfish Gene. Road Less Traveled, I've read more than once. I mean, it's just one of the classic human psychology book. And then, I think in business, I think Good to Great. It's not the read that you're going to be most excited to enjoy on a vacation, but it's pretty much right, and I think we should take advice from people who have analyzed these business problems before because not a lot's changed, but we keep acting like everything's changed.

Lenny RachitskyWhat's crazy about that book, you look at all the companies they talk about, I haven't read in a while, but just the whole book is about companies that last, I believe, or maybe that's the other book, I don't know. But anyway, all the companies that they talk about, I don't know if they're still around. It's so hard for a business to last a long, long time.

Jason DroegeI would also recommend Thinking Slow and Fast, that's the... Yes.

Lenny RachitskyThinking, Fast and Slow.

Jason DroegeThinking, Fast and Slow. Excuse me, sorry. It's been like a decade since I read it, but just in terms of point there being human biases are very important to understand.

Lenny RachitskyWhat's really crazy to me about that book and Kahneman in general, someone asked them just, how's your life been impacted by learning all these biases humans have? He's like, "Not much. I have the same biases. Knowing them doesn't really help me avoid them."

Jason DroegeSee, I find myself checking myself. Whenever I get super convicted on something now I will be like, okay, what is the list of things that I'm inclined to do to try to catch myself? Because I think we're most inclined to have these bad decisions impulsively, which is what I think the book is largely about. I mean, it's a long book.

Lenny RachitskySo long. Oh, my God. It feels like that's where AI can help us in the future. Just like, "Hey, Jason, are you sure this isn't framing a fact or whatever?"

Jason DroegeYes.

Lenny RachitskyOkay. Next question. Do you have a favorite recent movie or TV show that you've really enjoyed?

Jason DroegeMost of the movies I watch are with my kids, so I wish I had something deep and profound.

Lenny RachitskyNo, kids content also is a very acceptable-

Jason DroegeThe Formula 1 movie I thought was really good. I mean, it's a classic action movie. I don't think it informs anything in AI or business, but it's good to check out from the craziness of tech once in a while.

Lenny RachitskyIs there a product you recently discovered that you really love? Could be an app, could be clothing, could be a kitchen gadget, anything along those lines?

Jason DroegeVO3. Not totally new, but when I was in high school, I wanted to be a screenwriter. I actually grew up in the Bay Area and everybody was an engineer, but I wanted be a screenwriter. And so, I went back and I got the first page of one of my old scripts, which not good scripts, but I got the first page. I took a picture of the script and I fed it to VO3, and I said, "Make this scene," and it got it right.

Lenny RachitskyWow.

Jason DroegeI was shocked. I was just absolutely shocked that you could just take a picture of a script. And so, now I'm thinking about that for how do I use these tools for family videos? Some of the grad tools now with making live images more active, I think are really interesting. I think they need one more step of iteration, but I think those are going to be really emotionally life-changing for people because just a little bit of movement in an image from a grandparent or a relative or whatever you haven't seen in a while, it really does make a big emotional impact on you.

Lenny RachitskyI love that when you play with these tools, you probably can think about, oh, here's the people that help train this thing. Here's the people that helped on the problem that it had.

Jason DroegeI was actually talking to someone who was working on VO3, and I told him the script thing and he goes, "Oh, actually scripts. Yeah, no, the way the data is formatted in a script, that would actually be very good." Because they start with set looks dark interior, this character says it in this raspy voice, and so it gives you all the instructions in the script.

Lenny RachitskyOh, man, just unlocked a whole new business unit right there. Two more questions. One is do you have a favorite life motto that you often think about, find useful in work or in life?

Jason DroegeYeah. The end is never the end. That's my favorite internal saying, and it goes to the comments before about survival being a precursor, surviving being a precursor to thriving. You got to survive before you thrive, which is your brain tells you, and along these entrepreneurial journeys, I think this is most applicable. I mean, this is the hardest journey anyone can go on. If you go on this journey for five years, you are mentally harder than 99.9% of the population. People don't understand the Chinese water torture of having self-doubt and having things go wrong, et cetera.

And so, more tactically, you get this when you're working out like in a day like, "Oh, I'm too tired. I need to stop." But the truth is is you can keep going and the world's going to keep spinning. So I find in the moments where it's just the hardest or you have this hard decision that seems impassable and your body, you're having this visceral reaction to this is impassable, just to remind yourself that I'm going to wake up tomorrow. This isn't the end. There's another end somewhere. I just find that to unlock me to be like, okay, there might not be a perfect solution, there might be an imperfect solution, but it's a solution so let's just keep going.

Lenny RachitskyFinal question. You helped create Uber Eats. I imagine you're still a power user of Uber Eats. You have a favorite restaurant on Uber Eats that maybe people should know about, maybe that you order most from?

Jason DroegeI order a shocking amount of McDonald's actually. Despite my original story, it's the family treat in the house. I would say that that's probably the top thing that we order.

Lenny RachitskyOh, man, I'm worried for your health, but I love, I haven't had McDonald's so long. This is like, maybe I should give it another-

Jason DroegeI mean, more practically we will order mixed greens or tender greens or something like that on a day-to-day basis, but I think that the more notable, surprising thing is is that despite my initial aversion to working with a global chain, it's a good treat once in a while. You just shouldn't have it all the time.

Lenny RachitskyJason, this was incredible. I really appreciate you making time for this. I'm really honored to be the first chat you've had since taking over at Scale. Where can folks find you online if they want to maybe reach out, learn more about what you're, I don't know, maybe join Scale. Where do you want to point people to and how can listeners be useful to you?

Jason DroegeYeah, absolutely. I'm @jdroege, J-D-R-O-E-G-E on X. That's probably the easiest way to follow me, keep up with things and you can shoot me a DM if you like. And so, I think that's how you would keep in touch and, sorry, what was your other question? Sorry.

Lenny RachitskyIf you're hiring, I don't know, where should people go check it out if you are, and then also just-

Jason DroegeAbsolutely. Just go to scale.com, go to our careers page, and we have 250 open roles. To the point about we're in business and we're growing, we're hiring a ton of people. Our data business is growing, our applications and services business is growing like crazy, and so we're going to need a lot of people to help us on that journey.

Lenny RachitskyYou guys just signed some insanely large contracts with the government I was reading.

Jason DroegeTwo $100 million contracts.

Lenny Rachitsky$100 million contracts.

Jason Droege100, yeah. We didn't sign just one. We signed two in one month, so yes, no, our federal business is doing well. Our enterprise business is doing well. Our international government's business is doing well. There's a lot of demand out there.

Lenny RachitskySome salespeople are getting some great commissions. Good job. Jason, thank you so much for being here.

Jason DroegeYeah, thank you. Honor to be a guest here. Super excited to be with you, especially so early in the journey, or at least my journey here leading Scale.

Lenny RachitskyAppreciate it. Thanks for coming. Thanks for joining us. Bye, everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennyspodcast.com. See you in the next episode.

Lenny RachitskyThere's been a lot of talk these days about AI not delivering on the promise that we hear, especially at enterprises.

Jason DroegeThese things take 6 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.

Lenny RachitskyIs there anything you think people don't truly grasp or understand about where AI models are going to be in the next two, three years?

Jason DroegeThe 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?

Lenny RachitskyLet's talk about Scale and this whole world of AI that you're in, you essentially pioneered data labeling, trading data, creating evals for labs.

Jason Droege18 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.

Lenny RachitskyI've talked to a bunch of people that have worked with you over the years, and I heard a lot about just how high of a bar you set for new businesses.

Jason DroegeFrom 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?

Lenny RachitskyToday, my guest is Jason Droege. Jason is the new CEO of Scale AI. This is the first interview that he's done since taking over for Alex Wang after the Meta deal. Alex now leads the super intelligence team at Meta. Prior to Scale, Jason co-founded a company with Travis Kalanick. Before, he started Uber, worked at a couple startups. Most famously, Jason launched and led Uber Eats, which went from an idea that he and his team had to what is now a multi-billion dollar run rate business and one that basically saved Uber during the pandemic when nobody was taking rides. This interview is following a theme that I've been following through a bunch of interviews, which is the evolution of how AI models actually gets smarter. Along with scaling, compute and improving the actual model code, much of the improvements we're seeing in ChatGPT and Claude and every frontier AI model is these labs hiring experts to filling gaps in their knowledge and correcting their understanding of how things work, and basically showing them what good looks like in every domain that consumers are using models.

Scale was the pioneer in this space. They created the category, and in our conversation we talk about what is happening at Scale and just how this deal with Meta worked, what experts like doctors and software engineers are specifically doing to help models get smarter, how the whole market of data labeling and evals and data training has changed from when Scale entered the market to today, and also just how long will we need humans to keep helping AI get smarter. We also get into where Jason sees models going in the next few years because they have such a unique glimpse into the future. We also talk about a ton of really unique and really important product lessons from the course of Jason's career, including a bunch of advice on how to start a new business, both startups and within existing companies, and also a bunch of advice on hiring and leadership and so much more.
That's why Figma built Figma Make. With just a few prompts, you can make any idea or design into a fully functional prototype or app that anyone can iterate on and validate with customers. Figma Make is a different kind of vibe coding tool. Because it's all in Figma, you can use your team's existing design building blocks, making it easy to create outputs that look good and feel real and are connected to how your team builds. Stop spending so much time telling people about your product vision and instead show it to them. Make code-backed prototypes and apps fast with Figma Make. Check it out at figma.com/lenny. Jason, thank you so much for being here and welcome to the podcast.

Jason DroegeYeah, thanks for having me. Excited to be here.

Lenny RachitskyAs I was researching your background and prepping for this podcast, I learned a really interesting fun fact about you that I don't think a lot of people know. So Travis Kalanick, he had a startup before Uber. It was called Scour. It was a peer-to-peer file sharing app, and then I think got shut down. You were his co-founder. This was the early part of your career. I'm guessing there are hours of stories we could talk about during this experience. So let me just ask you this one question. What's just a lesson that has stuck with you from that experience that you've taken with you to future places you've worked and built product at?

Jason DroegeI mean, there's so many lessons. I like to pick one. I think that the main lesson is that in business and in startups, everything's negotiable. I think that's the main thing. Because we were 19 at the time, 19, 20 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 scour.cs.ucla.edu. These things were not necessarily in fractions 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 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 basically parked a domain on their servers and we were using our own computers in the dorms to serve up this website and product.

And then, when we got into financing, the financing process was fascinating, and this is where the everything is negotiable lesson came from, which is, it was Ron Burkle and Mike Ovitz, who are the initial investors in the business. We were in LA, so we were at UCLA, so we were not quite wired into the entire Sand Hill Road scene. And as we were doing the deal, the terms kept changing on us. We thought you went and raised money and it was like, "Oh, we'll get a few million dollars at a $5 million valuation." This is back when that was actually a series A valuation. And then over the course of the deal, it was like, "We're doing the deal. We're not doing the deal. Oh, you should give us 50% of the company. Oh, you should give us 75% of the company. Oh, if you want to sign the document today, this person's going to show up for breakfast and if you don't sign today and give us 80% of the company, the person's not going to show up."
It was just completely wild, the things that we saw from day one of what can happen in business, and we thought there was a way to do things, and at a very young age we realized there is no way to do things. There is just the way that you can negotiate your way through the world, which I actually think influenced Travis heavily and then me later heavily at Uber in terms of if you can imagine it and it makes sense and you can align incentives, then it can happen. But there is no way. And to learn that at 19 or 20 years old I think was highly imprinting.

Lenny RachitskyThat is an amazing lesson. What happened to Scour? It got shut down, I think. What happened there?

Jason DroegeWell, yeah, so basically what Scour was 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. And at the time, the laws were on this were pretty ambiguous because we weren't, mix tapes were legal, but this was like a hyperversion 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, 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 this very cold splash of water about how the real world really works, because the MBAA and the RAA 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 $1 million.

So we're like, "Wait, you wanted a quarter of a trillion dollars and then you settle for $1 million." And of course they were just trying to drive us in a 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 make up numbers, then wow, how should we navigate the rest of our lives?"

Lenny RachitskyLet's talk about Scale and this whole world of AI that you're in. This is the first interview that you're doing since taking over CEO at Scale. I'm honored to have you here to talk through this stuff. This is also the first interview you're doing since the whole Meta deal, which is very complicated, confused a lot of people. So I'm just curious to hear the current state of Scale, what people should know. For example, what's your relationship with Meta? What's your relationship with Alex? What is the current state of Scale?

Jason DroegeYeah, 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 working 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. All the privacy still in place, all the data security still in place that was there before.

And in fact, only about 15 people went over in the transaction. So Scale has about 1,100 employees or so now, and we have two major businesses. Each of those businesses, each of them has hundreds of millions of revenue. So we 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, so this is part of getting the word out and we're excited to continue to build, deliver data, and do what we did before.

Lenny RachitskySo the company today, independent, its own company. Alex, just to be clear, he works at Meta now. He's no longer at Scale.

Jason DroegeYeah, that's right. Excuse me, I should have talked about that more.

Lenny RachitskyI think that's really interesting. So basically, it was an investment. Some people left to join Meta, the company continues, you're running the ship. Let's talk about this whole space that you guys essentially pioneered, I don't know best way to call it, data labeling, training data, creating evals for labs. You guys were at this before anyone even knew this was a thing. I know even Scale pivoted into this market from other things. I think there was a bunch of stuff they tried with self-driving cars and all these things, and then it's like, "Oh shit, AI labs need this data."

One of the main stories I've been hearing is, and I've had a bunch of CEOs from this space on the podcast, is that there's been this big shift from the way, from what Scale had pioneered and had been doing for a long time, which is generalists, low-cost labor training. From that to now, labs mostly need experts, lawyers, doctors, engineers doing training, writing evals, things like that. I'm curious just what you're seeing, how that's impacting you guys, where you think things are heading, what people should know about this whole market of data training data.

Jason DroegeYeah, 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 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.

And then, you move forward and the models have gotten better over this period of time. And so, as 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 or to the moon. And so, as part of that, that industry is changing constantly too. So 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, 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 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 ranking to it. It was pretty basic 18 months ago, and you had the rise of some experts, but the models were so far behind that they needed just even the basic stuff they needed. And now, you're at a point where a task is, 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. And these tasks now take hours of time and they require PhDs and professionals.
So to give you a stat to back this up, 80% of the people that we have on our expert network have a bachelor's degree or greater, which is very contrary to some of the positioning that's out there and some of the understanding of this industry. About 15% have a PhD that's greater, and we have PhDs on the network earning significant amounts of money doing labeling, contributing their expertise to these models. So we've been doing expert data labeling ever since the models need it. I mean, this game is keeping in touch with the researchers, knowing what they need, coming up with ideas internally. In some ways, we drove this because we were seeing that the models were not sufficient in more expert ways. And so, we would go to the model builders and say, "Hey, we noticed that this is a problem. If you would like to fix it, this cadre of experts can do that for you." So the counter positioning is out there, but I think that's just what competitors say sometimes. It has nothing to do with reality.

Lenny RachitskyOkay. That was extremely interesting. So what I'm hearing is yes, there has been a big shift to labs need more expert folks involved in training, labeling, writing evals. You guys are very aware of that and have evolved with that. One of the, I don't know, allegations I guess in the market is that it's hard to find these experts. So all these companies have their proprietary network of experts and how they find them. Is there anything you could share about just how you guys go about that because that feels like the hardest part is finding these experts and keeping them from other companies?

Jason DroegeThey are hard to find. You have to have many, many tactics. So we get, 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. 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 they refer each other.

We also have campus programs where we will literally go onto the campus and talk to the professors, talk to the students, ask about who would like to do this type of work. And then, of course, there's the more traditional scaled ways of LinkedIn and places like that. But the best ones come from these grassroots and referral networks. And the only way you get that is providing a great experience to these people, because these people, 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 in many times it solves a problem for them.

Lenny RachitskySo something that I've been seeing on Twitter just this week as I was preparing for this is there's the information headline. This came out and this mirrored something that Brendan from Workhorse said that over time the entire economy is going to move towards just reinforcement learning and everyone's just training AI is basically the jobs that will be left. Thoughts on that? Is that where you think things are going? Is there another perspective?

Jason DroegeReinforcement 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. It has 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 so 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 is each individual task or each individual environment. So if you imagine the world of environments of 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 meeting on my calendar for my interview with Lenny, and the agent goes and it looks through all my calendar and then it pops it out, very simple example. 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 agents as useful as possible for their end users.

Lenny RachitskyI love that you've been sharing these examples of what this stuff is specifically that these people are doing, the data you're providing to labs. So just to mirror back a few of the examples you've shared, one is an engineer building a website, sharing the code essentially with the model. And here's how I would do it. And in that example, is it just like here's the code or is it a recording of them building it? What is the data?

Jason DroegeIt 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.

Lenny RachitskyGot it.

Jason DroegeSo 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 decision, here's why I didn't make this decision, or here's a broken website and here's why it's broken if they're trying to accomplish, I don't know, a debugging tool for a website builder or something like that.

Lenny RachitskyAnd another example you shared is a short story where it's like, here's one short story, here's another I imagine generated by a model. And then it's like, which is better, and then how would you make it better? The other example you just shared is a Salesforce agent where it's like, Hey, book a meeting with a prospect and then teach it how that happens. I love just how concrete these are because it's like, okay, I get it. This is the stuff that these companies do. Is there another maybe one or two examples just to give people a sense of what this data looks like?

Jason DroegeAbsolutely. 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 a 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 there's a productivity element to this implementation tier.

So there's a huge backlog. They want to be able to 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 200 to 300 pages of documentation and it's rolled into one document, but in different formats. 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. 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. They want to treat the patient well, obviously this is why they became a doctor. And then, they go into the room and they talk to the person and then they make a diagnosis.
Well, we basically built a tool that will read that document for them and point out the top 5 to 10 things that they should take into consideration, either allergies that might not be obvious is one example where we actually, we picked up on an allergy that a patient had that would not have been obvious from reading the document and that allergy actually would've 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've even been hard for a human being to do. To make this tool better and better, you get to a certain limit 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 are 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 that 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 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 healthcare 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 that we're unblocking for our customers.

Lenny RachitskyThat's really interesting. It's like the spectrum went from just low skill generous labor to experts to now the specific expert at this one company who needs to do this work, this labeling.

Jason DroegeAbsolutely. I mean understanding what, there's this broad narrative. We have two narratives. We have the AGI, everything is just going to become AGI, and then there's the skeptics, which is like, "Hey, this is all bunk, this is a bubble, 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 probably correct, but the reality is is that it's very hard to get machine 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 a one document, think about the problem of even understanding a document, 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 knows that? So this is all got to be built. So if you're going to make good decisions.

Lenny RachitskyThis is a good segue to this question that is always on people's minds when they look at companies like yours and the other folks in the space is just how long do we need people to be doing this? At what point will AI be smart enough to do it themselves? I know your incentives are to say we'll never run out of people because it's aligned with your growth, but just how should we think about just why do we need people, I don't know, in 10 years? How long do we need these experts telling AI things it doesn't know?

Jason DroegeFirst off, the history of data labeling is a history of new beginnings. Autonomous vehicles do not need as much data labeling as they did in the past. I mean, 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 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. And sometimes it's new people, sometimes within our existing base we find that existing people have expertise that 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. 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 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 supposedly this transformation is going to happen in the next one to two years. And I just think that it might happen. The space is moving super fast, but I don't think it's going to happen.
It is definitely not going to happen in the next year. The idea that it happens in the next two years I think is very far-fetched, but nothing's impossible here. And long-term, I think that if you go back through, I don't know, pessimist archive or whatever, these accounts that post, the radio was invented and then all of this will be eliminated. There will be change, but the change, I think humans are very good at adapting. So I think what we're underestimating in all of the doom and gloom is we believe in human adaptability. We as a company are highly adaptable and I think the history of technology has shown that people are adaptable.

Lenny RachitskyI really like that takeaway. I'm an optimist as well, so I'm always looking for reasons to be optimistic. I want to follow that thread before I get there, something very tactical I want to ask about is evals seems to be coming up a lot, especially with companies in your space. I'm still learning a lot about just what this all is, especially in your market. How much of what you or experts are providing are evals versus other types of data?

Jason DroegeA lot of it's evals, and within enterprise customers and government customers, it's mostly evals because somebody's got to establish the benchmark for 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.

Lenny RachitskySo in the case maybe of the healthcare example you shared, essentially this doctor would be sitting there looking at all these reports, creating evals that are like, this is what this should be discovering in this report, in this record. Is that a way to think about it?

Jason DroegeYeah, that's a very big part of it, which is what does good look like?

Lenny RachitskyAwesome, okay.

Jason DroegeI have to reduce things down to simple terms.

Lenny RachitskyIt's interesting you say good versus correct. Is that a specific term you like to use good versus just this is the correct answer.

Jason DroegeI didn't intentionally use that word, but these are probabilistic systems and so depending upon... Yeah, so I can get into some nuance here about the right types of problems that AI is good at solving. So if you have a human process that is 10 or 20% accurate or 10 or 20% liked, AI is awesome. Because if you get to 50, 60, 70, 80% accurate, you're in the money, 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 remaining 2%, not totally there yet. And so, when I say 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.

Lenny RachitskyTo you, this is so obvious and to people in your market that I think a lot of people think about AI being trained on just here's a bunch of data, check it out, learn everything you can from all of human history and all of written record. But what's wild is basically people are sitting around teaching AI things it doesn't know, filling gaps. That's how AI is getting smarter now. There's no more real data for it to feed on. It's just like, here's what I don't know, or here's what an expert found you're wrong. I'm going to teach you this. And the fact that it scales and that's keeping models improving is so mind-boggling.

Jason DroegeYes. 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. There is the, yeah, it's as simple as that. Someone's got to dig up the road or someone's got to run the undersea cable. There's always some operational chiseling that's going on in all of these industries. I mean, if you think about how magical these models are, they're remarkable that if you've been in technology long enough, it blows my mind even today that they get the punctuation right consistently. I mean, that sounds like almost daft to say 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, the real answer is it's a combination of computational power, model improvement, and data, and all three are getting better at once.

Lenny RachitskyLet's follow that thread. You've been at Scale for a long time, CEO for, you said, 13 months. I feel like you see a lot more about where things are heading because you work with labs on things they haven't even announced yet. You see more than most people, and I know there's only so much you can share about what companies are doing, but just is there anything you think people don't truly grasp or understand about where AI models are going to be in the next two, three years?

Jason DroegeLook, 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 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 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 a lot of the speculation has a wide variance because we're at the beginning of it. People take different trajectories on how that's going to improve. And so, if you take a trajectory of 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's still people in the world without an email address. And so, the adoption curve then becomes 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.

Lenny RachitskyThere's been a lot of talk these days about AI not delivering on the promise that we hear, especially at enterprises. There's this MIT study that just showed that there's all these pilots that people are excited about and then they don't work and companies aren't adopting these tools. There's data showing engineers are not actually as productive with tools. It actually slows them down sometimes. You work with a ton of companies implementing all kinds of AI. What are you seeing on the ground? What kind of gains are you seeing? Do you feel like it's overhyped, underhyped?

Jason DroegeThere's 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, my healthcare example is one, we do other sophisticated workflows, claims management for insurance companies. This is a financial decision that's happening, but it's an automatable process. But basically what happens is the POCs get to 60 or 70% of the way there, and the human mind goes, oh, the rest is no big deal. But it's like uptime in data centers where every nine is an order of magnitude investment in terms of reliability, backups, et cetera. One nine is basically a web server in a dorm room like we had at UCLA, and then five nines is this crazy high bar, but it just seems like a very small movement.

So you have a similar dynamic going on here where you have a bunch of people, one of the reasons why the POCs have failed, one, there's a denominator effect because it's so easy to do, "Hey, 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 a quality partner like we are, or if you do it yourself, if you have engineers who've worked with models before and they put in the time, and I'm talking about months, not like minutes like you see in these videos to actually get legal approval, policy approval, regulatory approval, change managements like an accuracy that everybody's comfortable with. If you actually do that, these things take 6 to 12 months to get 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, I never would've 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.

Lenny RachitskyIt's such a good point that it's not only is it easy to try these things, it's just like everyone's doing it so everyone's feeling FOMO like, "I got to try these things. I got to try all these prototyping tools, Cursor, all these things." Just goes, "Everyone's doing it," and then you just rush into it and it doesn't actually work out.

Jason DroegeEasy to learn, hard to master. That's my summary.

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Okay, let's move on from AI. This could be an endless discussion about AI, but you've got a lot more lessons to teach us. You've helped build Uber Eats, you've had a couple startups in the past. We talked about Scour for a bit. I've talked to a bunch of people that have worked with you over the years, and I got a lot of really interesting insights into the stuff that you're extremely good at. So I'm just going to go through a bunch of these. One is your obsession with being close to customers, talking to customers, and I love this topic because it's something everybody thinks they're great at, and they feel like they completely understand how this is important, why this is important. They all feel like I'm doing this, don't worry about... Everyone else is not doing this, but I am. Talk about just what you think maybe people miss about how this looks when you're doing it well, and just why this is so important.

Jason DroegeI 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 I mean, I don't think it's 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 literally. And there's been a lot talked about on this topic from 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 underlying things. I think the way that I look at it that might 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.

If you're selling enterprise software to someone, there's an executive sponsor as an example, that person needs to trust that 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 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 an incentives alignment baseline. 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, I'll give you an example. I've been out of the 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 was to figure out what other businesses we should get into. And so, we looked at a billion businesses and Uber Eats, food delivery was the one that we thought was most interesting, which turned out to be right so good for us.

Lenny RachitskyVery right.

Jason DroegeAnd 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'd go to a different restaurant tour and they would explain it, but they were a little suspicious of 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 a base catalog, and we just matched up how much does the ham weigh? 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 what's the ingredients cost versus what's the labor cost? And then, we triangulated what was our ground truth, and then what are we being told by restaurant tours, and then what is the site guys telling us about restaurant economics?

And if those things all overlapped, and 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% of every meal to ingredients, and they pay roughly 20 or 30% to labor, and they pay roughly 10% to real estate and a bunch of other, anyway, so goes down the chain. But the important parts is what's the value of incrementality?
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 explained 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 be close to the customers, it's a combination of what's the most valuable thing. Well, in a restaurant tours case, give me incremental demand. Because if you were to take a restaurant location and triple demand based on the same labor but you're just scaling ingredients, you've got a 70, 80% incremental gross margin product.
Restaurant tours would hate when we would say this because it doesn't work out exactly like that in reality. But because we had that insight, we had confidence that we could go to market with, we 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 could pay the driver this. And so, you fit this whole puzzle together without totally satisfying, in the case of a marketplace, you're not totally satisfying any individuals 100% of their needs. What you're satisfying is is you're getting a clearing rate for them to participate in the market in the case of a marketplace. So that's one example.

Lenny RachitskyYeah. I love this example as you almost you figure out how to help them with something they don't even fully themselves know yet. So as you think through their goals for them as if you were them, break down the economics and then here's the solution versus, hey, what can we do for you guys?

Jason DroegeYeah. I mean, if you walked into a restaurant, they would tell you a bunch of things. They would say, "Oh, labor schedule is an issue." They would say, "My rent is an issue." They would say, "All these, my ingredients prices are an issue, that's 20 or 30%." If you could shave off 3% of that, that would be huge. You might then take that and go, "I'm going to go build a business. It's going to save you 10% of your ingredients costs."

Well, but that doesn't actually get into their head on what's truly important day-to-day. That might be important for them on an annual basis, but on a daily basis, what are they doing? They're looking at their numbers, they're looking do people show up. Did I make money yesterday? Am I going to make money tomorrow? So the urgency, I think the biggest thing people miss when they're building new products is the urgency of the buyer part of it. You can build something that provides a lot of value, but if it's not the top thing that the customer is thinking about in their busy days, then you're just going to have a long road to a small town.

Lenny RachitskyThis touches on just the theme I heard a lot about, this idea of independent thinking and how much you value that, and this feels like a really good example of that. Is there anything else along those lines of just why this way of thinking is so critical?

Jason DroegeI think as a founder's job, and I mean I stretched that term because at Uber we had all of the benefits of Uber so I wasn't really a founder. I just started the business there. But there are some elements of founding there is you're looking for alpha in the market. When we started our first company in '97, it wasn't that cool. It might've been cool in Silicon Valley, but it was definitely not cool in LA. Now, it's super cool to start a business. So as a result, everyone's trying everything. So how do you get alpha on that market. If your research is highly influenced by what the world is saying around you, you're not going to have an independent insight. You have to go off and do your own thing.

And this is why from an entrepreneurship standpoint, I have very strong feelings about what the approach to founding a company should be and is probably very particular to me. But it truly is about what insight do I have, because 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? And then, why am I the one to do it?
And the answer might be I'm in this narrow, far-flung place. The other answer might be, I am inherently a contrarian personality type, so I'm just constantly looking for the thing that's true that people don't believe is true, which sometimes worked. But then, the second part of that's super important, which is why do I want to work on this problem for 5 to 10 years? And people get this wrong all the time. They go and talk to a customer and they go, "They have a problem. I'm going to go solve it." And it's just not a great way to start a business. You really have to have this burning desire to constantly be questioning yourself.
The other thing about independent thinking is is that you can't fall in love with your ideas. And I do not proclaim to be the world's greatest thinker for what it's worth, this is what you've been told, but is just part of that is basically throwing away who you are, who you've been, all your ideas for the mission that you're on, which is trying to accomplish something for our customer.

Lenny RachitskyThis is great. I'm glad you went here. This touches on the other theme I heard often about you is just how high of a bar you set for new businesses. And I think this advice is useful both for founders, as you said, and also people starting companies within companies, new business lines. So you've talked about this a bit already, but is there anything more there, just how high that bar needs to be for it to likely work out when you're starting something new?

Jason DroegeLook, if you want to give yourself the best chance, and this isn't always how it works, but if you're in my position 25 plus years in their career, if you want to give yourself the best chance, I think there's two ways that companies end up working out. And the first way, which is probably the most important, quite frankly, is that the founder is just a force of nature over a long duration of time. Because you're going to have to pivot, you have to have that energy to pivot. You have to go years and years and years with it being hard, and that's probably the most important thing.

But the second most important thing is that you can easily educate yourself on what are good business models, what are bad business models, what are good markets, what are bad markets? And even if you're this force of nature, having the knowledge, if you're going to go into a bad market with all your energy, you should at least know, maybe ignorance is bliss because you just throw yourself into it and it just works out with time. But that's not how I would operate, which is marketplaces are good businesses. SaaS, at least historically, we'll see how this changes, but SaaS, historically, great businesses, recurring revenue businesses, sticky businesses, network effect businesses.
And if you look at what the top VCs invest in, yes, there is a lot of portfolio building, but there are similarities in terms of the types of business models that they believe could be worth tens of billions of dollars. And they have network effects, they have lock-in. They are more valuable at scale, a big scale than low scale. So if you just take a filter on a new business, this is what I did at Uber, which is like if you just have a filtering mechanism on a new business, it doesn't take that long to eliminate the bad ideas. And then, of what's left, you can pick, oh, I'm very passionate about this, even though it might have more problems than this other thing that on paper looks better. And then, you have to have passionate about it. But I think people just miss a basic understanding of what businesses even have a chance of being worth $100 billion.

Lenny RachitskySo you launched Uber Eats, you figured out this is the place to go and bet. As an outsider, feels obvious, of course this is going to be a massive success. Of course, food delivery, such a good idea. I know you looked at a ton of ideas in that process. Can you just talk about what you explored and why you ended up picking Uber Eats?

Jason DroegeI am definitely not the smartest person in the room when it comes to figuring these things out. And so, I keep a very, very wide aperture on ideas for as long as I can until I'm like, okay, everything is coalescing. And I think there's a bunch of reasons why you have to keep an open aperture on considering ideas that might seem bad at the start, but you just keep digging and see if you're right that they're bad or you're wrong. So just as a general philosophical principle, I'll start there. We looked at, we did some crazy stuff. I went walking around San Francisco one day and I looked down Market Street and there was a CVS, a 7-Eleven, a CVS, a Walgreens, a 7-Eleven, and I'm like, "How many SKUs could possibly be inside one of these things that people want and couldn't you just put that into a van and you hit the button on the van and the van comes around and you get whatever convenience items you have, and they're convenience items, so why would that be a problem?"

And we launched that in DC. We put 10 of these trucks on the road, we put 250 SKUs in them. And I mean, crickets is an understatement of how bad it was. I mean, we couldn't get an order to save our lives. And what we realized was that we hadn't really done the research on what convenience stores really were. It was if you didn't have cigarettes, you didn't have beer, you didn't have Slurpees, you didn't have these things, for example, you didn't bring people in to sell all the other things. So we didn't know anything about retail. We were clueless. So that's one idea. We looked at grocery, but honestly the unit economics just terrified me of all the pick packing and everything like that. I think Instacart did a remarkably good job at getting the unit economics to a good spot and probably the hardest operational problem you could tackle.
We did generalized delivery, point to point delivery, what's now, I forget what Uber's product is called, but Uber Direct I think it's called, where you have something that needs to go point to point in a city. That was a flop from the beginning because the truth is is how consumers don't really have this need, business sort of have this need, and in 2014 when we were doing this, no one had this need. But we tried 15 versions of all these things before we eventually just said, "Okay, the food delivery thing is just popping off on all signals and we can make the unit economics work. People seem to want it. It's a super cool problem because we can enable independent restaurants with all these tools and allow them to compete with the big guys. We can take the real estate out of the equation. So you can have a real estate location that's non-prime, but if you have prime food, then you get to compete." So we're like, "Oh, this is a very interesting problem and we can really help local economies."

Lenny RachitskyAnd this ended up being, if I remember correctly, this basically saved Uber during COVID. Lyft didn't have something like this. And how big is this business at this point? Anything you share about just how important this turned out to be for Uber?

Jason DroegeYeah, of course. Well, we launched it in December of 2015 in Toronto and within two hours we had done $20,000 for the sales. It was crazy how quickly we saw that it was the right idea and the unit economics were good. And then, four and a half years later, I was at Uber for about six years, but it took us about a year and a half to figure this out. Four and a half years later, it was about $20 billion. So it was 0 to 20 billion in four and a half years, which is pretty good. Uber was very good at scaling things, but competitive market. Others did well. We beat a lot of people. Some people beat us. And then, now I think it's pushing 80 billion, and that's been for another four and a half years since I left. I think COVID turned it from 20, I left right before COVID, total coincidence, 20 to 50 in a year. So I mean, ride-sharing went this and food delivery just went to Pluto.

Lenny RachitskyWhat luck. Well done.

Jason DroegeLuck is part of the game. That's the other thing that's important to realize. Luck is part of the game, so do not begrudge people for luck. This industry is hard. All these things we're doing are really, really hard. Luck is just part of the game.

Lenny RachitskyMaybe speaking that maybe not. One of your colleagues, Stephen Chau, who I am an investor in his new company, he worked with you at Uber Eats for a long time. He told me to ask you about the McDonald's story. I imagine that was just a big milestone, a big moment enough for you guys. So why'd you decide put McDonald's in Uber Eats and there's apparently a story of how you won that deal.

Jason DroegeSo it was interesting, and this just goes to maybe where sometimes ignorance leads you to accidentally the right answer. So we had launched Uber Eats and Uber had a global footprint and we were the only food delivery network with a global footprint excluding China. Everything at Uber needed to be launched globally. That was a very big part of the culture, et cetera, which is a lot of work and you can spread yourself too thin and cause other problems. But in this way it was good. My vision was, okay, let's help the little guy compete with all these chains. They have these systematized food systems and food is what makes a city amazing. And no one talks about the chain restaurant that they visited in Paris. They talk about the local place that they found and let's be part of that. That's who we want to be.

And so, McDonald's actually approached us and they said, "Hey, we'd love to do food delivery with you." And I said, "No." And they're like, "Hold on a second. We have 80 million consumers a day. You don't want to do this together?" I'm like, "It's not really our vibe right now." And so, I pushed them off for four or five months until my team is like, "You're insane. These people are going to put marketing behind it. They really want to do this. They want to lean in." So we actually had, because of that, I think it's hard to correlate these things, we ended up with this exclusive relationship with them, got an insane number of customers of... Chains at this point actually weren't really on food delivery networks because everybody was so worried about the unit economics, because they're so sensitive to the basket size.
And my approach was like, eh, figure it out, which is a very Uber culture thing. Okay, the basket's $17, it's our job to make that work, reduce the radius on the delivery, figure out the economics, maybe mark up some of the food someplace. There's always a way to figure it out. So we did it and then three months later the business just started hockey sticking again at a different level. And my team is just like, "Dude, you were so stubborn on this point," but I think it actually ended up being in net benefit because we got a great deal with them.

Lenny RachitskySo the fact that you pushed him out helped you get a better deal is what I'm hearing. That's amazing.

Jason DroegeYeah, I think that's the story he would be referencing. And then, the onboarding of it was crazy because we basically went global with them in six months, and at this point the business was less than two years old. So activating this, I don't even know, an 80-year-old company that expects processes to be in place and we have two of our office managers in New York managing it. It's just mayhem.

Lenny RachitskyI'm still sad In-N-Out is still not on any of these apps.

Jason DroegeYeah, me too.

Lenny RachitskyI remember someone was hacking it. There's all these ways people found a way around and they're like, "No, no. Okay, you're Postmates. We know we're not going to give you any food."

Jason DroegeYes, love In-N-Out.

Lenny RachitskyYou've touched on this idea of gross margins and margins, how obsessed you are with this. I wanted to spend a little time on here. I've heard just you're obsessed with understanding gross margins before going in on anything. Most founders have no idea what they're doing here. What have you learned about just what people should be paying attention to, what they might be forgetting when they think about just the feasibility of a business?

Jason DroegeYeah, look, it's one filter like many filters. There are certainly businesses that have low gross margins that are great businesses. Costco, Walmart, et cetera. Amazon talks about this all the time of there's companies that increase prices and there's companies at lower prices. But I would say that by and large, high gross margins combined with healthy churn curves are a very healthy sign for the business. I mean, think about it. If I were to sell you something and I can't mark it up a lot, how much value am I adding beyond what's in my hand? And if I'm not adding that much value, then what am I in the business of doing? And I'm in business of adding value. And it's not quite that simple. This is just a litmus test of when someone comes to me and they go, especially in a new business, and we deal with this. I dealt with this at Uber, I've dealt with it everywhere.

Someone comes up with an idea and they go, "We can get into this business and I think we can charge this and it'll get us to a 40% gross margin." And then, my next question is start at a 60% gross margin. Why does that not work? And they go, "Oh, well, the customer..." And immediately, you short circuit to what the real problem is. Oh, the customer has an alternative. Oh, okay, well who's the alternative? Oh, it's some offshoring company. Well, what's their gross margin? Oh, we don't know. You go find out. It's like 20% and they've been around for a long time and they have scaled operations. And you're like, okay, so your gross margin is going to go from 40 to 20 quicker than you think, and you're going to be in a world of hurt unless you do something to differentiate.
So I take gross margin is just a very coarse instrument, not a perfect instrument to think about, am I adding enough value? Am I differentiated? It's not perfect, but it's a very quick short circuit filter to even to see if someone's pitching you an idea, have they thought through this dynamic? Because if the response is gross margin is super low right now, but here's the dynamic I'm going after. And then you're like, "Oh, okay." And sometimes it's like, we'll just make it up with volume and then the gross margin will go negative for a while and you're like, "Wait, this doesn't work."

Lenny RachitskySo what I love about this is just a lens into is my idea good enough if studying, can I keep a high gross margin? Is there a reason why people in this space haven't been able to have a higher margin?

Jason DroegeYeah, exactly. And like I said, it's meant to disqualify just you're doing these large for larger companies and everybody has ideas. And so, it's a way to cut through. Do you understand the machine that is going to need to be in place in two or three years? You might have a 70% gross margin now because the next question is why can't someone else do this? And if you have an answer of like, "Well, they can now, but they can't in two years, if we run really fast." Okay, we might have something. If they can now and they will be able to in two years, you're going to have margin compression.

Lenny RachitskyAlong these lines I was just listening to, I think it was the a16z podcast. Alex Rampell I think was sharing this story about Costco, how as you said, their strategy is actually to keep margins very, very low because all their revenue comes from their membership. So they have something like 50 million members paying 100 bucks a month and that's their entire business. And so, they don't plan and they don't want to make money off the products.

Jason DroegeYeah, that's right. I mean, they're playing a slightly different game, not an expert on Costco, have spent some time with the company, but they use price as a way to get to scale. And so, they're basically saying if we discount, same with Walmart, we will get so much volume that we will just take the air out of the room for all of our competition. And so, then the question of, okay, so if you have a low gross margin today, in two or three years, once you land one of these centers in a market, why won't your margins to get eroded? The answer is because we will have already absorbed all of the demand. You try to go to 8% versus 10% gross margin, which I roughly think is what their gross margin is. That's going to be a really hard business. If you already have a habit with a customer, they have already built their weekly trips around you, you already have relationships with suppliers, you already have general managers that know how to stock inventory, that's not a straightforward exercise. So they're first to scale and then good luck competing with them.

Lenny RachitskyOkay. Just a couple more questions. One is there's this term that I've heard that you often say and believe in is this idea of not losing is a precursor to winning.

Jason DroegeYes, yes.

Lenny RachitskyTalk about that.

Jason DroegeTech is a culture where portfolios are built by investors, and a lot of the narrative is controlled by investors frankly. Founders obviously participate, but this idea that you should just go for it is consensus. Just go for it. Who cares? Well, I don't know, if it's my life and I only have one moment to take a shot, I might want to just not just go for it. I might want to think for a little bit, and I think the best entrepreneurs, I have no data to back this up, but just these are my friend, this is my friend group. I think the best entrepreneurs and the best business owners look at the risk profile of the decisions that they're making and they try to make asymmetrically positive decisions all along the way.

And so, oftentimes I feel like we forget about the risk of a decision, and there's more to unpack there because I actually think taking highly risky decisions and then having it work out is a weird cultural thing too, because then how do you train people to do that? Because it's a very hard thing to take high risk decisions and be right enough because it creates a lot of volatility. But it goes back to my comment about the most important thing in founders, which is just this ability to persevere through. Survival is just part of the game, and most people just give up before they get their timing right, before they get the right insight with the customer before they get the right product in the market. And life can change quickly in tech. You can go from being a dog to being a hero in a very short period of time, but you're on this very, very long journey, but you have to survive for that condition to be met.
And so, then the question is is when you're in a hype cycle, I would argue that we are right now, everyone wants to go for it and then go for it more and then go for it more and go for it more and you don't realize, guys, all of our customers are going to be around in five years. They just want us to solve their problems. We have to be around to solve their problem for them. And so, survival is a precursor to that. So let's not put ourselves in position that could potentially compromise the enterprise along the way. It doesn't mean don't take risks, but think about how you calculate it.

Lenny RachitskyI love how clear it is that this lesson and many of the lessons along these lines have come from just failure and things not working out and things breaking, which is the best outcome.

Jason DroegeIf you ever get on the other side of a high reward, high risk decision, it is so painful because you are just cooked. You are done, and often there's no way out.

Lenny RachitskyIs there a story along those lines that comes to mind or an example of that?

Jason DroegeWell, this is where it is together on why I try to be so I think you can spend a little bit of time thinking upfront to save yourself a lot of pain downstream. I had this business not worth detailing it, but after the bubble burst in 2001, I'm like, "I'm going to self-fund a business. I'm going to build a profitable business. I want to prove that I can do this." And we had started Scour, which had all the things we talked about. And so, what I did is I'm like, I was a golfer and frankly, there was nothing to do in tech.

So I started selling golf clubs on the internet and I was making real money and I might've learned more from this business than any other because I started on eBay and I was 22, and I didn't really understand that my margins would come down because anyone can do this, but I was one of the first ones to do it, so I was making a ton of money and then I built this business and then I just failed to recognize I had a lot of hubris. I was like, "Oh, if I could just buy all the used golf clubs in America, I can be the market maker for prices," and don't people do that?

Lenny RachitskyI love this ambition. That's great.

Jason DroegeAnd it's just like it's madness to actually think about the practicality of that. And so, I just didn't spend the time thinking and then I ended up in this business. The business was profitable, it got to a couple million of revenue, whatever, paid me a dividend for a while, but it was painful the entire way.

Lenny RachitskyI love the spectrum of experiences you've had. You've sold golf clubs, you're helping achieve AGI, you could say. There's also a whole part of your career. We haven't talked about where you built tasers and body cams and drones and all these things. Also, peer-to-peer file sharing before anyone else. Final topic I just want to spend a little time on based on this experience is hiring and building teams, something that I know you have a really strong take on. That I've been hearing a lot on this podcast recently is this idea of it's more important to build the right team than find the most optimal top talent. Talk about that, why that's so interesting and important.

Jason DroegeAs of late, I've developed a more nuanced view of this, which is for certain roles, you absolutely need the right experience in this current market. You see this with researchers, because the market's moving so fast, you don't have time to train up some people, so you actually have to go find people either who have the right relationships with customers that you want to get or you have to, who might not check other boxes but are awesome at that, might not check the classic boxes that I think you're referencing of they're a problem solver, they can grow with the company, they have a high trajectory, et cetera. I would say that's 5% of the roles in the company, but very important whenever speed to market is important.

And then, for interviewing, I just interview for three things and I have to interview across all kinds of expertises, which is hard. I can't be an expert in everything. And so, I reduce it down to just three things, which is like, are you a curious problem solver and can you articulate that verbally? Can you work across people? Are you humble enough to work across and are you a good leader? And if you just do those three things, I think you have a pretty high chance of success, at least in an organization that I'm running, because the world's changing. So you do need people that are adaptable. So all the experience is not necessarily one-to-one relevant.
And then, the working across to your team point, this actually came up at Uber Eats. So when I was building the Uber Eats management team, I'm not sure if this was mentioned to you from that group, but whenever I would hire people, I was trying to compose almost like an organism of strengths and then minimize the conflicts. That management team for the most part outside of some of the operations side, but for the most part, that management team was the same management team from day one when we had nothing to $20 billion. And I just believed that the team, knowing each other's strengths and weaknesses and being able to compensate for each other was more important than the classic advice you get around, "Well, that person hasn't seen this much scale." And you're like, "Well, yeah, but can they learn it?" I learned it. So you do have to kind of believe in people a little bit, which is my job, not necessarily their job. And so, I mean, these are people systems. They're not straightforward rules-based things you can apply.

Lenny RachitskyAnd I especially love this advice because there's all this talk about what skills will matter in this world of AI doing all our jobs, and it feels like these three buckets are maybe the same thing, just are they good at solving problems? Are they good leaders? Can they collaborate well with other people?

Jason DroegeYeah, I don't think that the core rise of humanity, it will change, and I think that these things are pretty core to how humans have been successful for a long time.

Lenny RachitskySpeaking of that, I'm going to take us to a recurring segment of this podcast that I call AI Corner, where I ask folks this question, what's some way that you've found a use for AI in your day-to-day life in your work that makes you more effective, get more done, get better stuff done?

Jason DroegeHonestly, when I came into Scale, so my history was in consumer and I've done some application level stuff with government, and this space is moving so quickly. AI is my, I use it as a tutor. As these new concepts come up, I have a lot of people in the company who can educate me on the nuances of the technicals of all of, excuse me, the technical nature of the data and the products, but they only have so much time. And honestly, there's new concepts coming up all the time and I need to stay on top of it.

So it might sound crazy, but a large percentage of my job is not dealing with the engineering issues related to AI. I'm managing an organization, but I love understanding it. It's one of the most enjoyable, rewarding parts of my job is to learn from all these AI researchers, but they don't always have the time to do it, so I use it as a tutor. I turn on voice mode and talk to it on my way into work. So I think that's probably the most impactful thing that I use it for that's also relevant to this topic.

Lenny RachitskyI do exactly the same thing, especially when I'm prepping for this podcast. What exactly is this? I think about when you say this, I did an interview with the founders of Perplexity a few years ago asking about how they work at Perplexity, and the founders said that before, they were ruled, before they ask a question of anyone on the team, they have to ask AI first. And I was just like, "That's crazy." Now, it's so obvious. But back then, I was like, "That's an insane way of working. I've never heard of this before." Just a sign of how ahead of the curve they were.

Jason DroegeYeah, I think number two would be I'll take internal documents and I'll ask, what's the most important thing in this document? And I'm shocked, and then I'll read it and just double check, but I'm shocked at how good it is at just pulling out. There's so much in organizations that is like, I don't know what you want me to say and I don't know what I need to know, but we each have our own agendas, and so this matching of, and so then there's this huge broadcast problem where it's like, of all of the information you might want to receive, what's actually important to you? And so, I use it a lot for that too.

Lenny RachitskyAmazing. That's a really good tip. I use it for legal documents, just like what do they know about what they're trying to do here for me or against me? Jason, is there anything else you wanted to share or leave listeners with, maybe double down on a point before we get to a very exciting lightning round?

Jason DroegeYeah, absolutely. I mean, I think the really important, the reason why I'm doing this, the reason why want to spend time here outside of wanting to be on the show for a while and being a long-term listener is, our long-time listener, excuse me, is there's a lot of amazing work going on at Scale. The teams are working super hard, we're delivering a ton of value for our customers. The public narrative has not represented the work that the people here are doing and the work that our customers are doing with what we're doing for them. And I just think that deserves the respect and reward that all those people are putting in, and we'd like people to know that.

Lenny RachitskyI appreciate you saying all that. With that, we've reached our very exciting lightning round. We've got five questions for you. You ready?

Jason DroegeYeah, let's go for it.

Lenny RachitskyWhat are two or three books that you find yourself recommending most to other people?

Jason DroegeSome of this is going to sound interesting. The Selfish Gene is one of my favorite books.

Lenny RachitskyLove that book. I don't know if anyone's ever mentioned, it was one of the most influential books for me too. So sorry, keep going.

Jason DroegeYes. I think Selfish Gene. Road Less Traveled, I've read more than once. I mean, it's just one of the classic human psychology book. And then, I think in business, I think Good to Great. It's not the read that you're going to be most excited to enjoy on a vacation, but it's pretty much right, and I think we should take advice from people who have analyzed these business problems before because not a lot's changed, but we keep acting like everything's changed.

Lenny RachitskyWhat's crazy about that book, you look at all the companies they talk about, I haven't read in a while, but just the whole book is about companies that last, I believe, or maybe that's the other book, I don't know. But anyway, all the companies that they talk about, I don't know if they're still around. It's so hard for a business to last a long, long time.

Jason DroegeI would also recommend Thinking Slow and Fast, that's the... Yes.

Lenny RachitskyThinking, Fast and Slow.

Jason DroegeThinking, Fast and Slow. Excuse me, sorry. It's been like a decade since I read it, but just in terms of point there being human biases are very important to understand.

Lenny RachitskyWhat's really crazy to me about that book and Kahneman in general, someone asked them just, how's your life been impacted by learning all these biases humans have? He's like, "Not much. I have the same biases. Knowing them doesn't really help me avoid them."

Jason DroegeSee, I find myself checking myself. Whenever I get super convicted on something now I will be like, okay, what is the list of things that I'm inclined to do to try to catch myself? Because I think we're most inclined to have these bad decisions impulsively, which is what I think the book is largely about. I mean, it's a long book.

Lenny RachitskySo long. Oh, my God. It feels like that's where AI can help us in the future. Just like, "Hey, Jason, are you sure this isn't framing a fact or whatever?"

Jason DroegeYes.

Lenny RachitskyOkay. Next question. Do you have a favorite recent movie or TV show that you've really enjoyed?

Jason DroegeMost of the movies I watch are with my kids, so I wish I had something deep and profound.

Lenny RachitskyNo, kids content also is a very acceptable-

Jason DroegeThe Formula 1 movie I thought was really good. I mean, it's a classic action movie. I don't think it informs anything in AI or business, but it's good to check out from the craziness of tech once in a while.

Lenny RachitskyIs there a product you recently discovered that you really love? Could be an app, could be clothing, could be a kitchen gadget, anything along those lines?

Jason DroegeVO3. Not totally new, but when I was in high school, I wanted to be a screenwriter. I actually grew up in the Bay Area and everybody was an engineer, but I wanted be a screenwriter. And so, I went back and I got the first page of one of my old scripts, which not good scripts, but I got the first page. I took a picture of the script and I fed it to VO3, and I said, "Make this scene," and it got it right.

Lenny RachitskyWow.

Jason DroegeI was shocked. I was just absolutely shocked that you could just take a picture of a script. And so, now I'm thinking about that for how do I use these tools for family videos? Some of the grad tools now with making live images more active, I think are really interesting. I think they need one more step of iteration, but I think those are going to be really emotionally life-changing for people because just a little bit of movement in an image from a grandparent or a relative or whatever you haven't seen in a while, it really does make a big emotional impact on you.

Lenny RachitskyI love that when you play with these tools, you probably can think about, oh, here's the people that help train this thing. Here's the people that helped on the problem that it had.

Jason DroegeI was actually talking to someone who was working on VO3, and I told him the script thing and he goes, "Oh, actually scripts. Yeah, no, the way the data is formatted in a script, that would actually be very good." Because they start with set looks dark interior, this character says it in this raspy voice, and so it gives you all the instructions in the script.

Lenny RachitskyOh, man, just unlocked a whole new business unit right there. Two more questions. One is do you have a favorite life motto that you often think about, find useful in work or in life?

Jason DroegeYeah. The end is never the end. That's my favorite internal saying, and it goes to the comments before about survival being a precursor, surviving being a precursor to thriving. You got to survive before you thrive, which is your brain tells you, and along these entrepreneurial journeys, I think this is most applicable. I mean, this is the hardest journey anyone can go on. If you go on this journey for five years, you are mentally harder than 99.9% of the population. People don't understand the Chinese water torture of having self-doubt and having things go wrong, et cetera.

And so, more tactically, you get this when you're working out like in a day like, "Oh, I'm too tired. I need to stop." But the truth is is you can keep going and the world's going to keep spinning. So I find in the moments where it's just the hardest or you have this hard decision that seems impassable and your body, you're having this visceral reaction to this is impassable, just to remind yourself that I'm going to wake up tomorrow. This isn't the end. There's another end somewhere. I just find that to unlock me to be like, okay, there might not be a perfect solution, there might be an imperfect solution, but it's a solution so let's just keep going.

Lenny RachitskyFinal question. You helped create Uber Eats. I imagine you're still a power user of Uber Eats. You have a favorite restaurant on Uber Eats that maybe people should know about, maybe that you order most from?

Jason DroegeI order a shocking amount of McDonald's actually. Despite my original story, it's the family treat in the house. I would say that that's probably the top thing that we order.

Lenny RachitskyOh, man, I'm worried for your health, but I love, I haven't had McDonald's so long. This is like, maybe I should give it another-

Jason DroegeI mean, more practically we will order mixed greens or tender greens or something like that on a day-to-day basis, but I think that the more notable, surprising thing is is that despite my initial aversion to working with a global chain, it's a good treat once in a while. You just shouldn't have it all the time.

Lenny RachitskyJason, this was incredible. I really appreciate you making time for this. I'm really honored to be the first chat you've had since taking over at Scale. Where can folks find you online if they want to maybe reach out, learn more about what you're, I don't know, maybe join Scale. Where do you want to point people to and how can listeners be useful to you?

Jason DroegeYeah, absolutely. I'm @jdroege, J-D-R-O-E-G-E on X. That's probably the easiest way to follow me, keep up with things and you can shoot me a DM if you like. And so, I think that's how you would keep in touch and, sorry, what was your other question? Sorry.

Lenny RachitskyIf you're hiring, I don't know, where should people go check it out if you are, and then also just-

Jason DroegeAbsolutely. Just go to scale.com, go to our careers page, and we have 250 open roles. To the point about we're in business and we're growing, we're hiring a ton of people. Our data business is growing, our applications and services business is growing like crazy, and so we're going to need a lot of people to help us on that journey.

Lenny RachitskyYou guys just signed some insanely large contracts with the government I was reading.

Jason DroegeTwo $100 million contracts.

Lenny Rachitsky$100 million contracts.

Jason Droege100, yeah. We didn't sign just one. We signed two in one month, so yes, no, our federal business is doing well. Our enterprise business is doing well. Our international government's business is doing well. There's a lot of demand out there.

Lenny RachitskySome salespeople are getting some great commissions. Good job. Jason, thank you so much for being here.

Jason DroegeYeah, thank you. Honor to be a guest here. Super excited to be with you, especially so early in the journey, or at least my journey here leading Scale.

Lenny RachitskyAppreciate it. Thanks for coming. Thanks for joining us. Bye, everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennyspodcast.com. See you in the next episode.

Lenny Rachitsky这些天大家一直在说，AI 好像没有兑现我们听到的那些承诺，尤其是在企业里。

Jason Droege这类事情要真正稳到能把重要流程自动化，通常得花 6 到 12 个月。就像每一轮重大的技术革命一样，媒体上讲的是一回事，落到地面又是另一回事。铺宽带得把美国每一条路都挖开，总得有人去挖路，或者有人去铺海底光缆。

Lenny Rachitsky你觉得大家对未来两三年的 AI 模型，有什么真正没理解到的地方吗？

Jason Droege现在的大趋势，是模型从“知道东西”走向“做事情”。接下来的问题就变成，它到底能替我做什么？agent 要怎么替你做决策？

Lenny Rachitsky我们来聊聊 Scale，也聊聊你现在身处的这个 AI 世界。你们其实在数据标注、训练数据、给实验室做 eval 这条路上，都算是先行者。

Jason Droege18 个月前，你拿到的还是一篇短故事，然后系统问你：“这个短故事是不是比另一个更好？” 你再编辑一下，说“嗯，如果改成这样会更好”，然后给它一个偏好排序。那时候的任务很基础。但现在，一项任务可能是让全球最顶尖的网页开发者搭一个完整网站，或者让模型解释一个非常细腻的癌症话题。这些任务要花好几个小时，而且需要博士和专业人士来做。

Lenny Rachitsky我这些年和很多跟你共事过的人聊过，听到最多的一点就是：你对新业务的标准非常高。

Jason Droege从创业的角度看，核心真的是：我到底掌握了什么独特洞察？为什么我这么幸运，能拥有这个洞察？在一个有一百万个聪明创业者、大家都在试各种办法的世界里，为什么我会处在一个位置上，让我大概率掌握了别人没有的洞察？

Lenny Rachitsky今天的嘉宾是 Jason Droege。Jason 是 Scale AI 的新任 CEO。这是他接替 Meta 交易后的 Alex Wang 之后，第一次接受采访。Alex 现在负责 Meta 的超级智能团队。在 Scale 之前，Jason 和 Travis Kalanick 一起创过公司；再往前，他参与过 Uber 的早期阶段，还待过几家创业公司。最出名的是，Jason 还创建并带领了 Uber Eats，从他和团队脑子里一个想法，变成了如今几十亿美元年化收入的业务，疫情期间更是几乎成了 Uber 的救命稻草，因为那时候没人打车。今天这次采访，跟我最近几场谈话的主线很一致，就是 AI 模型到底是怎么变聪明的。除了扩算力、改进模型本身，ChatGPT、Claude 以及所有前沿模型之所以进步，很大一部分原因是实验室会请专家来补齐知识空白、纠正它们对世界的理解，说白了，就是在每个消费者会用到的领域里，把“好是什么样”教给它们。

Scale 是这条路上的先行者，也是这个类别的开创者。我们聊了 Scale 现在在发生什么，以及和 Meta 那笔交易到底是怎么回事；医生、软件工程师这类专家具体在做什么来帮模型变聪明；数据标注、eval、数据训练这个市场，从 Scale 刚进入时到今天发生了什么变化；以及未来我们还要多久，才会不再需要人类持续帮 AI 变聪明。我们也聊了 Jason 眼里未来几年模型会往哪走，因为他们确实离未来非常近。还有，Jason 职业生涯里积累了很多很独特、也很重要的产品经验，包括怎么开启一个新业务，不管是创业还是在大公司里做新项目，以及招聘、领导力等等。
（此处为节目赞助信息，已略去。）
Jason，非常感谢你来，欢迎来到播客。

Jason Droege对，感谢邀请，我很兴奋能来。

Lenny Rachitsky我在研究你的背景、准备这期节目时，发现了一个我觉得很多人都不知道的有趣小故事。Travis Kalanick 在 Uber 之前也做过一个创业项目，叫 Scour，是一个点对点文件共享应用，后来我猜是被关掉了。你当时是他的联合创始人。那是你职业生涯很早的一段经历，我猜这里头能聊的故事多到能讲几个小时。我就问你一个问题：那段经历里，有什么教训一直留在你身上，并且带到了你后来每一份工作、每一个产品里？

Jason Droege教训太多了，我挑一个说。我觉得最核心的一点是，在生意和创业里，什么都可以谈。真的，基本都是可以谈的。那时候我们才 19、20 岁，在宿舍里做了这个搜索引擎，最初的地址还是 scour.cs.ucla.edu。当时也没想那么多，就是觉得先把项目做出来。结果搜索引擎真的开始有人用了，我们还以为会惹麻烦，没想到计算机系反而挺兴奋，虽然我们把一个域名挂在了他们服务器上，还用宿舍里的电脑来跑这个网站和产品。

后来轮到融资，这个过程特别有意思，也就是我说“什么都能谈”的来源。最早的投资人是 Ron Burkle 和 Mike Ovitz。我们在洛杉矶，所以虽然在 UCLA，但并没有真正接上整个 Sand Hill Road 那套圈子。我们谈交易的时候，条款一直在变。我们原来以为出去融资就是，“哦，我们能拿几百万美元，估值 500 万。” 那会儿这还真算 A 轮估值。结果谈着谈着就变成，“我们在做交易 / 我们不做了”，“你们得给我们公司 50%”，“不对，得给我们 75%”，“如果你今天签字，早上这个人会来吃早饭；如果你今天不签、不给我们公司 80%，那个人就不会来了。”
我们从第一天起就看到了商业世界里能发生的这些疯狂事情，原来我们以为事情有一套固定做法，结果很年轻的时候就明白了，根本没有什么固定做法。你只能靠谈判，在世界里把路走出来。我觉得这对 Travis 影响很大，后来对我在 Uber 的影响也很大：如果你能把一件事想出来，而且它说得通、激励能对齐，那它就能成。但不存在“唯一正确的做法”。19、20 岁就学到这一点，我觉得印象特别深。
这个教训太棒了。那 Scour 后来怎么了？我记得好像是被关掉了，后来发生了什么？

Jason Droege对，Scour 本质上是一个多媒体搜索引擎，后来又做成了点对点文件共享网络。可它真正被拿来做的，是找免费内容。当时法律其实挺模糊的，因为它不是完全等同于盗版，混音带在那个年代是合法的，但这可以说是混音带的超级版本。后来我们被告了，索赔金额是 2500 亿美元。要是人生里真要经历这种可能把你彻底毁掉的事，我觉得 21、22 岁时经历，反而是最合适的时机。但这也像是一盆冷水，让我们第一次明白现实世界到底怎么运作。起诉我们的是 MPAA 和 RIAA，也就是代表娱乐业的那些协会，后来他们又把案子以 100 万美元和解了。

所以我们就想：“等等，你一开始要 2500 亿美元，最后却 100 万美元和解？” 他们当然只是想把我们逼到破产、赶出市场，而这些又都是成熟的大公司。我们当时就在想：如果这些人都没有现成的 playbook，他们就是直接拍脑袋报数字，那我们往后的人生该怎么走？
我们来聊聊 Scale，聊聊你现在身处的这个 AI 世界。这是你接任 Scale CEO 之后第一次接受采访，能请到你来聊这些，我很荣幸。这也是你在 Meta 交易之后的首次采访，那笔交易很复杂，很多人都看得一头雾水。所以我想听听 Scale 现在的状态，大家应该知道什么。比如你和 Meta 是什么关系？你和 Alex 是什么关系？Scale 现在到底是什么状态？

Jason DroegeScale 现在是一家完全独立的公司。这笔交易里，Meta 投了 140 多亿美元，拿到公司 49% 的非表决权股份，没有拿新的董事席位。董事席位还是 Alex 占着，所以董事会没变，治理结构也基本没变。Meta 没有任何优先访问权，也没有什么特殊待遇。我们和 Meta 在数据业务上合作很多年了，业务开发层面也有长期合作，比如一起做政府相关项目之类。现在我们靠得更近了，这些合作可能会更大一点，但这不妨碍我们继续和别的伙伴合作，Meta 也拿不到本来不该拿到的东西。所有隐私和数据安全机制也都和之前一样。

而且这次交易里真正转过去的人只有大约 15 个。Scale 现在大概有 1100 名员工，我们有两大主营业务，而且每一块业务都有几亿美元收入。也就是说，公司内部现在就有两个独角兽级别的业务。交易之后，业务每个月都在增长，虽然外面的报道并不总是一致。我们之前没怎么出来讲这件事，所以我现在也算是在把情况讲清楚。我们会继续做数据、继续交付，继续做我们一直在做的事。

Lenny Rachitsky所以现在这家公司还是独立的，是它自己的公司。Alex 现在在 Meta，对吧？他已经不在 Scale 了。

Jason Droege对，没错。抱歉，这点我刚才应该说得更清楚。

Lenny Rachitsky我觉得这点很有意思。也就是说，本质上就是一笔投资。有些人离开去 Meta，Scale 继续运转，你来掌舵。那我们聊聊你们基本上开创的这个领域吧，我也不知道最合适叫什么，就是数据标注、训练数据、给实验室做 eval。你们在大家意识到这件事之前，就已经在做了。我知道 Scale 也是从别的方向慢慢转过来的，早年还试过自动驾驶、别的很多东西，然后才发现：“哦，AI 实验室需要这些数据。”

我最近听到的一个主线故事是，我播客里也请过很多这个领域的 CEO，整个市场已经从 Scale 最早开创、长期在做的那种模式，变成了另一种需求：以前更偏 generalist、低成本劳动力训练；现在实验室更多需要的是专家，像律师、医生、工程师这些人来做训练、写 eval 之类。我很好奇你现在看到的是什么，这对你们有什么影响，你觉得这个市场会往哪走，大家又该怎么理解整个数据训练市场。

Jason Droege对，完全同意。我先说一句，外面竞争对手给出的那些叙述基本都是胡扯。所以我先把这点摆出来，等下再解释为什么这么说。不过我觉得先花 30 秒讲清楚 Scale 的历史、以及它从 2016 年起延续下来的那条线，很重要。Alex 很早就意识到，模型最重要的东西是数据。我觉得那时候他也才 19、20 岁。于是他想：那我要围绕这个做什么生意？他做出来的就是，先给自动驾驶车辆做标注，因为你把它们用到的数据标好，车就会更聪明。后来这波又变成了计算机视觉浪潮，我们和国防部也有合作，给他们做标注，那是在 2020 年。

再往后，模型在这几年里一直在进步。模型越强，就越需要不同类型的数据。所以我们一直在适应模型成功所需要的数据类型。然后生成式 AI 浪潮来了，整个行业一下子冲上去了。这个行业本身也在不停变化。两三年前模型刚出来的时候，我们都用过，它们老是胡说八道，基础问题都答错，连这首诗和那首诗哪个更好都不知道。那时候的标注状态就是这样。情况变化得特别快，我们也跟着变了。现在，对所有人来说，尤其我们一直站在最前面的一点，就是更专家化的数据标注、更复杂的任务。
给你一个 18 个月前的任务样子作参照。我来这里大概 13 个月了。我记得面试时看见的任务是：给你两篇短故事，问你“这篇是不是比那篇更好？” 你再编辑一下，说“嗯，如果改成这样会更好”，然后给它一个偏好排序。18 个月前这类任务还很基础。那时候虽然也有一些专家开始加入，但模型落后得太多了，连最基本的东西都需要。可现在，一项任务可能是让全球最顶尖的网页开发者搭一个完整网站，或者让模型解释一个非常细腻的癌症话题。这些任务现在要花好几个小时，而且需要博士和专业人士来做。
给你个数据支撑一下：我们专家网络里 80% 的人至少有本科学历，这和外界的一些说法、以及很多人对这个行业的理解都很不一样。大约 15% 以上的人有博士学位，我们网络里的博士靠做标注、贡献专业知识能赚到相当可观的钱。所以，只要模型需要，我们一直都在做专家数据标注。这个游戏的关键，是持续跟研究人员保持同步，知道他们需要什么，并且在内部想出办法。某种程度上，这也是我们推动出来的，因为我们看到了模型在更专家化的场景里是不够用的。于是我们会去找模型开发者说：“嘿，我们发现这儿有个问题。如果你们想修好它，这些专家群体可以帮你们。” 所以外面那些反向叙述虽然存在，但我觉得那只是竞争对手爱说的话，和现实没什么关系。
好，这段非常有意思。所以我听到的是，没错，实验室对参与训练、标注、写 eval 的专家需求确实大幅上升，而你们也很清楚这件事，并且跟着一起进化了。市场里还有一种说法或者质疑是，这些专家很难找。于是各家公司都有自己专有的专家网络，以及寻找这些人的方式。你能不能分享一下，你们到底是怎么做的？因为感觉最难的就是找到这些专家，并且别让他们被别的公司抢走。

Jason Droege确实很难找。你得有很多很多办法。正如你想的，不可能只有一种做法。最大的来源是专家之间互相推荐，因为当你真心喜欢自己在做的事，而且你是在用自己的专业知识帮 AI 变得更好，这其实很酷。比如你是某个很细分题目的博士，你在用模型的时候，模型没法按你希望的方式和你互动，那这就成了一个有偿的出口，你可以靠它赚几百、几千美元。所以很多时候他们会互相介绍。

我们还有校园项目，会真的跑到校园里，跟教授聊，跟学生聊，问谁愿意做这类工作。当然，也有更传统的渠道，比如 LinkedIn 之类。但最好的来源还是这些草根网络和推荐网络。你能拿到这些资源，靠的只有一点：给这些人非常好的体验。因为他们做这件事，一部分是为了钱，但也因为他们觉得自己对 AI 模型的贡献很重要、很有意思，而且很多时候，这也确实帮他们解决了自己的问题。
我这周在准备这期节目时，刚好看到《The Information》的一篇头条，也和 Brendan from Workhorse 说过的一个观点差不多：长期来看，整个经济都会往强化学习的方向走，最后大家都在训练 AI，剩下的工作基本也就这样了。你怎么看？你觉得事情会往那个方向走吗？还是有别的视角？

Jason Droege强化学习非常重要，我觉得这也是“往环境里走”这个大趋势的一部分。现在有种叫 RL environments 的东西，本质上就是给 AI agent 准备的沙盒，让它们在里面完成目标，从而学会怎么完成目标。我们做这个已经一年多了。比如你有一个 Salesforce 实例，AI agent 怎么在里面操作？这个实例里有它需要识别的数据，也有各种配置。Salesforce 本身就是高度可配置的产品，它得学会怎么导航。你让 agent 去做一个需要很高可靠性的业务流程，同时还得让它知道：“嘿，如果我没法完成，或者我判断自己要做的事情准确性不够高，我怎么把它交给人来反馈，好让我得到指导？”

这些都必须训练，没有什么魔法。你就是得把 AI agent 放进一个真正对应人类工作方式的环境里。你可以想象，世界上环境的数量、每个环境里目标的数量，都是巨大的。所以问题是，我们过去一年做的研究，作为模型开发者客户的好伙伴，核心就是：每个任务、每个环境的泛化能力到底有多强？如果你想象软件系统、配置、数据类型、规模、用户数、复杂度这些环境组合，排列组合几乎是无穷的。你需要的是一种策略，让实验室采集到的数据，能在足够广泛的应用场景里保持足够的泛化，这样他们就不用去收集“这个场景下 agent 应该怎么做”的四万五千亿种组合。
所以有些工作和数据的泛化能力非常强。这里说的“泛化”，意思是你用一种很简单的方式就把事情做成了。比如这个任务可以是：帮我在日历里找一下和 Lenny 面试那天的会议，agent 就去翻我的日历，把它找出来，很简单的例子。这样的任务应该尽可能泛化到任何日历搜索，甚至任何日历操作。泛化程度越高，数据就越有价值。所以我们的工作，就是给模型开发者提供最有价值的数据，帮助他们实现目标，也就是让 agent 尽可能对最终用户有用。
我很喜欢你一直在举这些很具体的例子，告诉大家这些人到底在做什么，也就是你们提供给实验室的数据。顺着你刚才举的几个例子说，其中一个是工程师在搭网站，基本上就是把代码和模型共享，然后告诉模型“我会这样做”。在那个例子里，是只给代码，还是会录他们搭建的过程？这个数据到底是什么？

Lenny Rachitsky明白了。

Jason Droege所以它不是说“给你一个网站，然后系统自己就会做网站”。而是像这样：这是一个网站，这是我为什么这么做的原因，这是我为什么不这么做的原因；或者如果他们想做的是网站构建器的调试工具，那就会给你一个坏掉的网站，再告诉你它为什么坏。

Lenny Rachitsky你刚才还举了另一个例子，是短故事。就是给你一篇故事，再给你另一篇我猜是模型生成的，然后问哪个更好、怎么改得更好。你刚才提到的另一个例子是 Salesforce agent，比如“帮我和一个潜在客户约个会”，然后教它这件事怎么完成。我很喜欢这些例子，因为特别具体，一听就懂。就是，哦，原来这些公司做的是这些东西。还能不能再举一两个例子，让大家更直观地理解这类数据到底长什么样？

Jason Droege当然可以。其实我可以直接拿我们业务里的例子来说。我们业务有两块：一块是给模型开发者提供数据，我们卖数据；另一块是做解决方案，我们给医疗系统、保险系统这些客户卖应用和服务。我觉得如果给你一个后者的例子，画面会更完整，因为它既涉及数据，也涉及对数据的使用、操控，而且目标非常具体。比如我们和一家医疗系统合作。医疗系统的问题很多，这家系统里有些专家经常要看非常罕见的病例。也就是说，只有在别人都搞不清楚你的问题时你才会去那里，所以他们的积压很大。这里面其实就有生产力问题。

积压太多了。他们想多看一些病人，想提供更好的护理，也想减少复诊次数，因为他们希望第一天就给出准确诊断和正确治疗方案。可要在没有 AI 的情况下做到这一点，医生往往得读 200 到 300 页文档，而且这些内容被拼成一个文档，但格式很杂。你想想，如果你是医生，怎么可能把 200、300 页都读完？所以他们只能尽力而为：先扫一遍，再让护士看看，再让更资深一点的医生看一眼这个病例。他们当然是想把病人治好，毕竟他们就是因为这个才当医生。然后他们进房间，和病人聊，再做诊断。
我们基本上做了一个工具，帮他们把这份文档读一遍，并指出最该关注的 5 到 10 个点。比如有些过敏项，单靠肉眼读文档并不明显，我们就曾经识别出一位病人的过敏信息，而这个过敏和即将开出的药物会冲突。也就是说，AI 工具其实抓出了一个人类都未必能轻松发现的关联。要把这个工具做得越来越好，你会碰到一个上限，只靠现成模型不够，医疗系统里的人还得自己做标注。
所以我们虽然一直在说给模型开发者做标注，但现在我们也越来越看到，标注会进入企业和政府，因为只靠现成模型 + RAG + 一点基于历史数据的 fine-tuning，能走到的地方是有限的。很多人对这类系统常有一个误解。比如你会听到那种数字：“哦，这家银行一年处理了 200PB 数据，或者别的夸张数字。” 但我们容易忽略的是，这些数据里哪些真的是对的、哪些是模型真正用得上的？大多数其实没用，只有一部分有用。我们在谈知识工作、在谈做判断时，很多时候判断来自人类：在这个案例里，这个医生会怎么决策？这个银行家会怎么决策？而且还要结合这家企业整体的上下文。这在不同银行、不同医疗系统之间都可能不一样，因为文化、目标、激励都不同。所以现在我们看到，真正把人类判断、专业判断、深度领域知识数字化，已经成了一个瓶颈，而我们正在帮客户把这个瓶颈打通。
这就很有意思了。这个谱系就从低技能的大规模劳动力，走到了专家，再到现在这家公司里那个真正要做这件事的具体专家，要为这份标注负责。

Jason Droege完全对。理解这件事时，外界有两个极端叙事：一种是 AGI 叙事，觉得一切都会直接变成 AGI；另一种是怀疑论，觉得这全是扯淡、泡沫。我自己的看法是，大多数事情的真相都在中间，极端里的某些部分可能是对的，但现实是：在 agentic 系统里，要把 agent 和 agent 之间的协作、以及机器关键流程的准确性，做到能完成任务所需的水平，非常难。这里面的一个核心问题是，哪怕只理解一份文档都不容易。同样的文档，A 公司里的同一句话，在 B 公司里可能意义和重要性完全不同。那你怎么让系统知道这一点？所以这些东西都得被搭建出来。要做出好的决策，就是这么回事。

Lenny Rachitsky这正好引出大家经常会问的那个问题：像你们这样的公司、还有你们这个领域里的人，到底还需要多久？AI 什么时候才会聪明到自己做这些事？我知道你的激励当然会希望“永远都有人需要”，毕竟这和业务增长是对齐的，但我们应该怎么理解这件事？为什么 10 年后还需要人？这些专家到底还要多久，继续告诉 AI 它不知道的东西？

Jason Droege先说结论，数据标注这门生意本来就是不断开启新阶段的历史。自动驾驶现在需要的数据标注，已经没有过去那么多了。Scale 的一个基本信念是，只要模型还需要外部数据、还需要人类数据，数据就永远重要。可如果有一天你真的不再需要这些，那就等于在说：这个世界已经进步到一种几乎难以想象的程度，因为这意味着没有任何新的人的技能、没有任何新的人的知识，值得再被放进这些模型里。我觉得这离得还挺远的。对我们这种业务来说，我们一直都在做的，就是怎么建立一套持续运转的机制，不断找到新需求，再跟我们称之为专家贡献者的网络合作，把这些数据、这些信息挖出来。有时候是新的人；有时候是在我们已有的人里，发现他们其实有我们以前不知道的专长，这些专长可能一年前对模型没用，现在却有用了。

所以这就是一个持续推进、不断把更多数据灌进模型的过程。是的，从商业角度说，我们确实有财务上的激励，希望人类永远留在闭环里；但这不只是商业判断，也是我个人的判断。这些系统必须为我们工作。如果它们要真的为我们工作，那我们就必须对它们做出的任何决策保持在环上，或者至少在环内。至于更大的劳动话题，也就是大家常说的白领大灭绝之类的话题，我自己会更偏现实主义一点。可能是我的性格使然，也可能是因为我真的看到客户现场在发生什么，而外界总说这场转型会在未来一两年发生。我只是觉得，事情也许会发生，但不会像大家想的那么快。
它肯定不会在未来一年发生。两年内发生这个说法，我觉得也很牵强，但也不是完全不可能。长远来看，如果你回头看那些老的悲观档案、那些总在发“无线电发明了，其他一切都会消失”之类说法的账号，你会发现，变化当然会有，但人类适应变化的能力其实很强。我认为我们在所有这种悲观叙事里都低估了人类的适应力。我们公司本身就很能适应变化，我也觉得技术史已经证明了这件事：人本来就很能适应。
这个结论我很喜欢。我自己也是乐观派，所以总是在找理由保持乐观。在继续往下聊之前，我想先问一个很具体的问题：eval 现在似乎越来越常被提起，尤其是在你们这个领域。关于你们或者专家们提供的东西，eval 和其他类型的数据分别占多少？
很多都是 eval。尤其在企业客户和政府客户那里，绝大多数都是 eval，因为总得先定出一个“好”到底长什么样。简单理解 eval 就是：什么算好？你有没有一套足够完整的 eval，让系统知道什么是好？就这么简单。
比如你前面举的医疗例子，本质上就是医生坐在那里看这些报告，给出 eval，也就是定义：这份报告里应该发现什么，这份记录里应该发现什么。可以这么理解吗？
对，这其实是很大一部分工作。核心就是：什么算好？
太棒了。
我得把事情说得尽量简单。
你说“好”而不是“正确”，这挺有意思的。这个“好”是你常用的词，还是只是说“这是正确答案”？
我不是故意选这个词，但这些系统本来就是概率系统，所以具体要看……对，我可以在这里稍微展开一点，讲讲 AI 更擅长解决什么类型的问题。如果一个人类流程只有 10% 或 20% 的准确率、或者只有 10% 或 20% 的人喜欢，那 AI 就特别棒。因为如果它能把准确率提到 50%、60%、70%、80%，那你就赚钱了，大家都开心。接下来系统还得知道：剩下那部分要怎么确保人类继续参与决策？但从净增价值的角度看，这种情况下人类会非常兴奋。
### 中文译文

Lenny Rachitsky好了。

Jason Droege所以我们在做 Uber Eats 的时候，我是从“尽量贴近客户”这个角度去看这门生意的。我们一开始其实根本找不到餐饮商家愿意跟我们聊，那个行业我一点都不懂。那时候我在 Uber 的工作，就是去找我们到底还该做哪些业务。所以我们看了一堆业务，最后觉得 Uber Eats，也就是外卖，是最有意思的那个，事实证明这判断对了，对我们来说是个好选择。

Lenny Rachitsky非常对。

Jason Droege可我们找不到餐馆愿意帮我们理解他们的单元经济。别人会说，“哦，大概是这个比例，或者那个比例吧”，或者“你们为什么要问这个？” 我们去问另一家餐馆，他们会解释，但又会有点怀疑：这些 Uber 的人为什么要来问我汉堡里火腿到底多少钱？于是我们就自己买了很多这些店的食物，再去找餐饮供应商拿基础目录，然后自己去对：火腿多重？奶酪多重？面包多重？上面有几片生菜？我们试着自己拼出一套独立判断，看看原材料成本和人工成本分别是多少。然后我们把自己的判断、餐饮商家告诉我们的信息、以及我们从现场看到的餐饮经济模型放在一起交叉验证。

如果这几样东西都能对上，我们就觉得：好，我们对这件事有一个判断了，那它和 Uber Eats 的关系是什么？后来我们发现，一家餐厅大概会把每顿饭的 20% 到 30% 花在原材料上，另外大概 20% 到 30% 花在人工上，差不多 10% 花在房租和别的杂项上，后面还有一串比例，反正就是这么往下分。关键在于：增量的价值到底是什么？
于是我们就过去说：“我们会收你账单的 30%。” 他们立刻炸了：“天哪，这不是又一个 Groupon 吧？这也太高了吧。” 我们给他们解释了经济模型，他们说：“好吧，我们试试，但这个价格也太高了。” 他们其实也没错，最后真正成交的价格并不是 25%，但我们也没有离得太远。所以当你去找产品市场匹配，或者想真正贴近客户的时候，本质上就是在找：什么是最有价值的东西。对餐饮商家来说，那就是增量需求。如果你能在人工成本基本不变的情况下，把一家餐厅的需求翻三倍，只是原材料跟着放大，那就是一个 70%、80% 增量毛利的生意。
餐饮商家听到这个会很不爽，因为现实并不会完全是这么回事。但正因为我们看到了这个洞察，我们才有信心去市场上说：我们需要收你这么多，这样配送费才能定成那个水平。只要配送费是那个水平、我们收的是这个水平，我们就相信消费者会买单，而这正是你拿到增量需求需要的东西，然后我们就能给骑手付这个价格。于是整套拼图就这么拼起来了。像 marketplace 这种生意，你并不是要 100% 满足某个人的全部需求；你要做的是，让他们愿意以一个合适的成交价进入这个市场。所以这就是一个例子。

Lenny Rachitsky对。我特别喜欢这个例子，因为你几乎是在帮他们解决一个他们自己都没完全想清楚的问题。你不是先问“我们能帮你做什么”，而是先替他们想清楚目标，像他们自己一样把经济账拆开，然后给出解决方案。

Jason Droege对。你走进一家餐厅，他们会告诉你很多问题：人力排班有问题，房租有问题，原材料价格也有问题，这部分大概占 20% 到 30%。如果你能从里头省掉 3%，那就已经很大了。于是你可能会想：“好，我来做个生意，帮你省 10% 的原材料成本。”

但这其实并没有打到他们每天真正关心的点上。那对他们来说，可能年度上很重要，但日常里他们在干嘛？他们在看数字，看看有没有人来，昨天赚没赚钱，明天会不会赚钱。所以我觉得，做新产品时大家最容易忽略的一点，就是买方的紧迫性。你可以做出一个很有价值的东西，但如果它不是客户在忙碌日常里最优先想的那件事，那你就会走一条很长的路，去一个很小的地方。
这也正好碰到我经常听到的另一个主题，就是独立思考，以及你有多看重这个。我觉得这个例子就很能说明问题。还有没有别的类似点，能说明为什么这种思考方式这么关键？
我觉得作为创始人，这本来就是你的工作。当然我这里把“创始人”这个词用得有点宽，因为在 Uber 的时候我们享受了 Uber 的所有资源，所以我其实不算真正意义上的创始人，我只是把业务在那儿启动了。不过创始这件事有个很核心的部分，就是你要在市场里找 alpha。我们 1997 年做第一家公司的时候，这种事一点都不酷。可能在硅谷还行，但在洛杉矶绝对不酷。现在创业超酷，所以大家都在什么都试。那你怎么在这个市场里拿到 alpha？如果你的研究高度受外界声音影响，你就不可能有独立洞察。你得自己出去做自己的事。
所以从创业角度讲，我对怎么创立公司有非常强的想法，而且这些想法大概挺个人化的。但本质上其实就一句话：我到底有什么洞察？为什么我这么幸运，能有这个洞察？在一个有一百万个聪明、爱思考、什么都在试的创业者的世界里，为什么我会刚好处在一个位置上，让我大概率拥有别人没有的洞察？然后，为什么应该由我来做这件事？
答案可能是：我正好站在一个很窄、很偏的地方。另一个答案可能是：我天生就是个逆向性格，所以我总是在找那些“别人不相信，但其实是真的”的东西，这种时候有时确实有效。但第二部分也特别重要：我为什么愿意为这个问题干 5 到 10 年？很多人经常搞错这一点。他们去跟一个客户聊，客户说“我有个问题”，他们就说“好，我去解决”。这其实不是开启一门生意的好方式。你必须真的有一种很强烈的冲动，逼着自己不断质疑自己。
独立思考还有一点，就是你不能爱上自己的点子。说实话，我也不会自称是世界上最厉害的思考者，虽然你们可能听过一些别的说法。这里面的一个部分其实就是，把你自己、你过去那些经历、你所有的想法，统统为了你正在做的使命先放下，使命就是为客户完成某件事。
这个很好，我很高兴你聊到这里。这也碰到了我经常从别人那里听到的另一个主题，就是你给新业务设定的门槛特别高。我觉得这类建议对创始人有用，对公司内部做新业务线的人也一样有用。你前面其实已经讲过一点了，但有没有什么补充？当你开始做新东西的时候，这个门槛到底要高到什么程度，才更有可能做成？
你想给自己尽可能高的成功概率，虽然事情不一定总是这样走。但如果你像我这样，在职业生涯二十五年之后，想把成功概率拉高，我觉得一家公司能跑起来，通常有两条路。第一条，也是坦白说最重要的一条，就是创始人本身得像一股持续很久的自然力量。因为你一定会 pivot，你得有那种能一直 pivot 的能量。你得熬很多很多年，而且一直都很难，这大概是最重要的一点。
但第二重要的点是，你要能很容易地搞清楚什么是好商业模式，什么是坏商业模式，什么是好市场，什么是坏市场。即使你真的是一股自然力量，如果你明知道自己要往一个坏市场冲，你至少得知道那是个坏市场。也许无知是福，你一头扎进去，最后时间久了也许就成了。但我不会这么做。我的想法是：marketplace 是好生意。SaaS，至少从历史上看，是好生意；我们看看未来会不会变，但从历史上说，它们是很棒的生意，是 recurring revenue 的生意，是粘性很强的生意，是网络效应很强的生意。
如果你去看顶级 VC 都在投什么，当然有很多组合拳，但他们相信能值几十亿美元的商业模式，确实有一些共通点：网络效应、锁定效应，而且规模越大越值钱，大规模比小规模更有价值。所以如果你只是给一个新业务做筛选，我在 Uber 就是这么干的：先用一个过滤器去看新业务，很快就能把坏主意淘汰掉。剩下的那些里，你再挑一个你最有热情的，哪怕它纸面上不如另一个看起来好。然后你还得真的对它有热情。但我觉得很多人最缺的，其实是对什么样的业务有机会做到 1000 亿美元规模，缺一个最基本的理解。
所以你创办了 Uber Eats，也判断出这里值得下注。站在外部看，当然会觉得这太显而易见了，外卖怎么可能不成功，当然是个超好的点子。我知道你当时看了很多很多想法。你能不能讲讲，你都探索过什么，最后为什么选了 Uber Eats？
我在判断这类事情上，绝对不是房间里最聪明的人。所以我会尽量把思路的开口开得很大，尽可能久，直到所有东西开始收敛。我觉得你必须保持开放视角，认真考虑那些一开始看起来很烂的点子，一直往下挖，看看到底是你对了，还是你错了。先说这个大的原则。我们当时真的试过一些很疯狂的东西。有一天我在旧金山街上走，看着 Market Street 上一排 CVS、7-Eleven、CVS、Walgreens、7-Eleven，我就在想：这些店里到底有多少 SKU 是大家真正想要的？为什么不能把这些东西塞进一辆货车里，按一下按钮，车就开过来，你想要什么便利店商品就拿什么？反正就是便利品，为什么会有问题？
### 中文译文

Jason Droege于是我们就在 DC 上线了这个想法。我们把 10 辆车拉上路，每辆车放了 250 个 SKU。怎么说呢，完全不夸张地讲，现场冷到不行。我们根本接不到单。后来我们才发现，我们根本没研究过便利店到底是什么。原来如果你没有香烟、没有啤酒、没有 Slurpee 这些东西，举个例子，你就根本带不来那些来买别的东西的人。换句话说，我们对零售一无所知，完全是门外汉。所以这个想法黄了。我们也看过 grocery，不过老实说，那个单元经济把我吓坏了，里面有太多挑拣、打包之类的活儿。我觉得 Instacart 做得非常厉害，把单元经济调到了一个不错的位置，这大概也是你能碰到的最难的运营问题之一。

我们还做过通用配送、点对点配送，也就是现在我一时想不起 Uber 那个产品叫啥了，好像叫 Uber Direct，就是把东西从城市里一个点送到另一个点。那个从一开始就失败了，因为说实话，消费者并不真的有这个需求，企业倒是有一点，但在 2014 年我们做这些的时候，基本没人有这个需求。可我们后来把这些想法试了 15 个版本，最后才说：好吧，外卖这件事的各项信号都在爆，而且单元经济也能跑通。大家显然真的想要它。而且这是个特别酷的问题，因为我们可以给独立餐厅配一整套工具，让他们和大牌竞争。我们还能把房租这个因素踢出去。也就是说，一个地段不算核心的房子，只要你做出来的食物够好，你还是可以竞争。于是我们就觉得：哦，这个问题很有意思，而且我们真的能帮到本地经济。
如果我没记错，这后来基本上在疫情期间救了 Uber。Lyft 没有这样的业务。那这个生意后来到底有多大？你能不能分享一点，这件事最后对 Uber 有多重要？
当然可以。我们在 2015 年 12 月把它在多伦多上线，两个小时内就做了 2 万美元销售额。我们很快就看出这是对的方向，单元经济也很好。四年半之后，我在 Uber 待了大概六年，而我们差不多花了一年半才把这件事想明白。四年半之后，这个业务已经做到 200 亿美元左右了。所以它是四年半从 0 做到 200 亿，这已经很不错了。Uber 擅长把东西规模化，市场也很激烈。别人也做得不错，我们打赢了很多人，但也有人赢了我们。然后现在我觉得它已经冲到 800 亿左右了，而那是在我离开之后又过了四年半。疫情把它从 200 亿推到了 500 亿，一年里就翻了很多，我离开前刚好赶上疫情前，纯属巧合。总之，打车业务那边是这样，外卖业务直接冲上了天。
太幸运了，干得漂亮。
运气也是游戏的一部分。这也是很重要的一点：运气本来就是游戏的一部分，所以别因为别人走了运就去埋怨人家。这个行业很难，我们做的这些事都真的很难。运气就是游戏的一部分。
也许说到这个，顺便问一句。你的同事 Stephen Chau，我是他新公司的投资人，他在 Uber Eats 跟你一起做了很久。他让我问你 McDonald's 那个故事。我猜那对你们来说是个很重要的里程碑，一个足够大的时刻。所以你们当时为什么会把 McDonald's 拉进 Uber Eats？而且好像还有一个你们怎么拿下那个合作的故事。
这事挺有意思，也正好说明有时候无知反而会把你带到对的答案。我们上线 Uber Eats 的时候，Uber 已经有全球布局，而我们是全球范围内唯一一个外卖网络，除了中国。Uber 里的任何东西都得全球上线，这也是公司文化里非常重要的一部分，虽然这意味着工作量很大，也容易把自己铺得太薄，带来别的问题。但在这件事上，它是有帮助的。我的愿景是：好，咱们帮小商家和那些连锁品牌竞争。连锁店有系统化的食物生产体系，而食物正是让一座城市精彩的东西。你去巴黎吃过哪家连锁店，没人会一直讲；大家会讲自己发现的本地小店，我们想成为其中的一部分，这才是我们想做的。
结果 McDonald's 反过来主动找我们，说：“嘿，我们想和你们做外卖。” 我说：“不。” 他们就说：“等一下，我们一天有 8000 万消费者，你不想一起做这个吗？” 我说：“现在这还不太符合我们的气质。” 于是我晾了他们四五个月，直到我团队的人都说：“你疯了吗？这些人会砸营销资源进来，他们是真的想做，是真的愿意押注。” 于是最后，因为这个过程，我觉得虽然很难去精确归因，但我们和他们做成了一个独家合作关系，也拿到了海量用户。连锁品牌当时其实并不太上外卖平台，因为大家都很担心单元经济，毕竟它们对 basket size 太敏感了。
我的思路一直是：那就解决它呗，这很 Uber。好了，basket 是 17 美元，那我们的工作就是把它跑通：缩小配送半径，算清经济账，必要的话在别的地方把食物加点价。办法总是有的。结果我们就这么做了，三个月后业务又开始以另一个层级往上冲。团队当时都说：“兄弟，你在这个点上也太拧了吧。” 但我觉得这最后其实是净收益，因为我们和他们拿到了很好的合作条件。
所以我听下来就是，你把他们往外推，反而帮你拿到了更好的条件？

Jason Droege对，我觉得这就是他会提到的那个故事。后面的上线也很夸张，因为我们基本上在六个月内就和他们做了全球铺开，而那时候这门生意还不到两年大。去激活这么一家我都不知道多少年的公司，人家默认流程都得先搭好，而我们在纽约只有两个 office manager 在管，场面简直乱成一锅粥。

我还是挺遗憾 In-N-Out 到现在也没上这些 App。

Jason Droege我也是。

我记得以前有人在黑它，有一堆绕路的方法，最后他们说：“不不不，你们是 Postmates，我们知道不会给你们任何吃的。”

Jason Droege对，超爱 In-N-Out。

你前面提到过毛利和利润率这个话题，也说你特别痴迷这个。我想在这里多聊一点。我听说你在做任何事之前都特别在意先搞清楚毛利。很多创始人其实根本不知道自己在这方面在干嘛。你学到的最重要的一点是什么？人们在判断一个业务到底能不能做的时候，最容易忽略什么？
对，听着，毛利只是很多过滤器里的一个。确实有些毛利很低的业务也是好业务，比如 Costco、Walmart 之类。Amazon 也经常聊这件事，说有些公司靠涨价赚钱，有些公司靠低价赚钱。但总的来说，高毛利再配上健康的流失曲线，对业务来说是很好的信号。你想啊，如果我卖你一个东西，却没法加太多价，那我到底是在创造多少超出我手上这件东西本身的价值？如果我没加多少价值，那我到底在做什么生意？我做生意就是为了加价值。当然，事情没这么简单。这个只是个快速测试。当有人来找我，尤其是新业务里的人来说这个问题，而我们在 Uber 也一直在碰这个问题，我到处都在碰这个问题。
别人会带着一个想法过来，说：“我们可以做这个业务，我觉得我们能收这个价，然后做到 40% 的毛利。” 我下一句就会问：那你为什么不先从 60% 的毛利开始想？为什么这不行？他们就会说：“哦，客户……” 然后你一下子就会触到真正的问题。哦，客户有替代方案。好吧，那替代方案是谁？哦，是外包公司。那他们的毛利是多少？哦，不知道，那就去查。结果人家是 20%，而且已经做了很久，运营也规模化了。你就会发现：好吧，你的毛利会比你想得更快从 40% 掉到 20%，除非你真的做出差异化，不然你会很惨。
所以我看毛利，更多是把它当成一个粗颗粒工具，不是完美工具，但它能很快帮你判断：我有没有创造足够的价值？我有没有差异化？它不完美，但它非常适合做一个快速筛选器，看看一个人来 pitch 你一个想法的时候，他到底有没有想清楚这套动态。因为如果他一上来就说：“我们现在毛利很低，但我准备走的是另一套逻辑。” 那你就会说：“哦，好。” 有时候他们还会说，我们先靠量把它做起来，毛利先变成负的，过一阵就好了。你这时候就会想：“等等，这不成立。”
我很喜欢你这个视角，它像是在帮我判断：我的点子够不够好，能不能一直保持高毛利？这个领域里的人，是不是有什么原因一直没法把毛利做高？
对，没错。就像我刚才说的，这么做的目的就是筛掉那些大公司里一大堆想法。办法就是快速切掉。你得先看清楚，两三年后这台机器到底需不需要真的存在。你现在可能有 70% 的毛利，但问题是，为什么别人不能也做这个？如果你的答案是：“他们现在能做，但两年后做不了，因为我们跑得够快。” 那我们可能就有东西。可如果他们现在能做，两年后也还能做，那你就会遇到毛利压缩。
顺着这个话题，我刚刚在听 a16z 的播客。Alex Rampell 好像讲了 Costco 的故事。就像你说的，他们的策略其实就是把毛利压得非常低，因为他们的收入全靠会员费。他们大概有 5000 万会员，每个月交 100 美元，这就是他们整个生意。所以他们不打算、也不希望从商品本身赚钱。

Lenny Rachitsky对你来说这太显然了；而在你们这个市场里，大家也会觉得这很自然。但我想很多人以为 AI 只是被喂进去一大堆数据，然后“去吧，学尽所有人类历史和书面记录”。可真正离谱的是，现在实际上是人坐在那里教 AI 它不懂的东西，填空白。AI 就是这么变聪明的。它已经没有什么“真实数据”可以继续吞了。它更像是“这是我不知道的东西”，或者“这是某个专家发现你错了，我来教你”。而且这套东西还能规模化，继续推动模型进步，这真的很不可思议。

Jason Droege对，我也同意。就像每一轮重大的技术革命一样，头条讲的是一回事，真正落地的时候又是另一回事。铺宽带意味着你得把美国每一条路都挖开，总得有人去挖路，或者有人去铺海底光缆。各个行业里总有这些很琐碎、很脏活的操作。你看这些模型有多魔幻吧，它们真的很了不起。你在技术圈待久了，到今天我还是会震惊，它们居然能持续把标点符号都打对。现在听起来这话有点傻，但如果你倒回三年前，从技术角度去看，很多我们现在觉得理所当然的东西，其实都非常复杂。真正的答案是：算力、模型能力和数据，这三样一起在进步。

Lenny Rachitsky顺着这个聊下去。你在 Scale 待了很久，CEO 也做了 13 个月了。因为你会接触实验室那些还没公开的东西，我感觉你看到的东西比大多数人都要多。你也知道你不能透露太多，但还是想问：你觉得大家对未来两三年的 AI 模型，有什么真正没理解到的地方吗？

Jason Droege你看，讨论太多了。我觉得这也取决于你平时看 X 还是看新闻，取决于你接受的是哪种视角。现在的大趋势是，模型正从“知道东西”走向“做事情”。我们在不断扩展知识边界，我们放出来的 benchmark，还有别人放出来的 benchmark，都在表明这些模型掌握的知识已经相当扎实了。接下来问题就变成：它到底能替我做什么？一旦你进入这个世界，前面我们说的那些环境就开始真正发挥作用了。怎么在 Salesforce 实例里操作？怎么在医疗系统里操作？甚至怎么在手机里的天气 App 里操作？agent 又怎么替你做决策？

我们其实才刚刚开始。接下来会发生多快，会非常值得观察。我觉得外界之所以会有很大分歧，就是因为我们还在起点。人们会对这件事怎么提升，走出不同的路径。如果你走的是最激进的路径，也就是“其实训练这些东西很容易，然后经济体里只需要做一轮变革管理”，那你千万别低估变革管理——这可不是小事。
世界上还有人没有邮箱呢。所以 adoption curve 最终就变成了人的问题、政策的问题，而不是技术的问题。单从技术角度看，我们还没到那一步。但我确实认为，未来两三年里，如果你逼我猜，技术会到一个程度，开始逼着变革管理者和政策制定者说：“好吧，这到底该怎么办？这已经离我们很近了。” 大概就是两三年后的事。
这些天大家一直在说，AI 在企业里没有兑现我们听到的那些承诺。MIT 那篇研究刚出来，说很多人兴冲冲做了不少试点，最后都没跑起来，公司也没真正采纳这些工具。还有数据表明，工程师用了这些工具后未必更高效，有时反而更慢。你和很多公司一起落地各种 AI，所以我想听听你在一线看到的是什么。你们看到的收益是什么？你觉得这是被过度炒作了，还是其实大家低估了它？

Jason Droege外面的 hype 非常多，而我们的工作是做出真正能用、能给客户创造价值的产品，看看真正的落点在哪。拿一个更复杂的例子来说，我刚才举的医疗场景就是一个，另外我们还做保险公司的理赔管理这类复杂流程。这是一个会影响财务决策的流程，但它是可以自动化的。问题在于，大多数 POC 做到 60% 或 70% 的时候，人脑就会想：“剩下的应该不算什么大事吧。” 可这就像数据中心的 uptime，每多一个 9，可靠性、备份等等的投资都是数量级地增加。一个 9，基本就像我们在 UCLA 宿舍里搞的那种网页服务器；五个 9，简直是离谱的高门槛，但看上去又好像只多了一点点。

所以这里也有类似的动态。Poc 之所以容易失败，一个原因是分母效应，因为它太容易做了：“我起了一个项目，我又起了一个项目，我又起了一个项目。” 所以人人都很容易试。那个 95% 的数字，我不觉得一定准，某种程度上有点标题党。它说的是对的故事，但也有点夸张，因为如果你真的是在公司里认真做，或者你找到了像我们这样靠谱的合作伙伴，或者你自己做，但公司里真有工程师已经和模型打过交道，而且他们真的投入了时间，我说的是按月算，不是你在视频里看到那种几分钟搞定的程度，等到你真正拿到法律审批、政策审批、监管审批、变更管理，做到一个大家都舒服的准确率，通常得花 6 到 12 个月，重要流程才能真正自动化。
所以我觉得 hype 说对的一点在于：一旦你真的做成了，效果会让你震惊，比如“哇，这东西我自己根本想不到”，哪怕你是世界上受教育程度最高的医生之一。但真正到达那个效果所花的时间，比外面卖的要长得多。
这点太好了。也就是说，不只是这些东西很容易试，关键是人人都在试，所以大家都有 FOMO，觉得“我也得试，我得试这些原型工具，Cursor，所有这些东西。” 大家都在做，然后你就一头冲进去，结果发现并没真的跑通。

Jason Droege容易上手，难于精通。这就是我的总结。

（此处为赞助信息，已略去。）

Lenny Rachitsky好，那我们先从 AI 这个话题往前走。这个话题可以无限聊下去，但你还有很多别的经验可以教我们。你帮着做过 Uber Eats，之前也有过几次创业。我们刚才也聊了 Scour。过去这些年，我也和很多跟你共事过的人聊过，从他们那里听到了很多很有意思的观察，尤其是你特别擅长的那些事情。所以我想把这些逐个拿出来聊。第一件事就是你对贴近客户、和客户聊天的痴迷。我很喜欢这个话题，因为每个人都觉得自己很擅长，大家都觉得自己完全明白这为什么重要、怎么做才重要。他们都觉得“我在做啊，别担心，别人没有做，我有做。” 你觉得大家最容易忽略的、是怎样把这件事做好的？为什么这件事这么重要？

Jason Droege我大概也属于你刚才说的那类人，这可能也是开始做新事物需要的一点点自负。但我不觉得这是一套很整齐的流程。我的做法是，在任何事情刚开始时，我都会不断质疑自己听到的每一件事。我不会把客户说的话逐字当真。关于这个话题，产品管理领域已经讲了很多，比如不要照着客户说的做，而是要去理解他们真正想表达的意思，看真正的问题和底层原因。我觉得我可能能给这个讨论补上一点的是，我会看客户底层的激励。客户的底层激励不一定总是钱。有时候是自尊，有时候是职业成长。

如果你在卖企业软件，举个例子，通常会有一个 executive sponsor。这个人需要相信你会把事情做好。你怎么让他愿意跟你一起跳进这个大项目？这不只是产品本身的旅程，而是他们需要从我们这里听到什么？我们需要给他们什么？我们要做什么，才能真正把落地这个产品的机会打开？所以我认为，激励对齐是基础。我很相信那句老话：show me the incentive and I'll show you the outcome。我觉得这绝对是真的。哪怕客户会直接告诉你很多东西，我给你举个例子。因为我已经离开那个圈子有一段时间了，所以我可以稍微敞开说一下，Uber Eats。
### 中文译文
所以我们推出 Uber Eats 的时候，我是从“离客户近不近”这个角度看这门生意的。我们其实没法去餐厅实地走访，我对这个行业几乎一无所知。那时候在 Uber，我的工作就是判断还该进哪些业务。于是我们把能想到的方向几乎都看了一遍，最后觉得 Uber Eats，也就是外卖，最有意思。事实证明，这个判断是对的，也确实帮了我们很大忙。

Lenny Rachitsky太对了。

Jason Droege我们也没法靠餐厅走访去弄清它们的单元经济。餐厅的人会说，大概是这个比例、那个比例，或者反问你，你为什么想知道？后来我们去别的餐厅，人家倒是愿意讲，但又有点怀疑：为什么这些 Uber 的人要问我火腿到底多少钱？所以我们干脆直接从这些店点了一堆餐，再找餐厅供应商要了一份基础目录，然后对着算：火腿多重？奶酪多重？面包多重？里面有几片生菜？我们试着自己做一版独立判断，看看食材成本和人工成本到底各占多少。然后再把三件事对照起来看：我们的真实判断、餐厅对我们说的，以及一线做餐饮的人告诉我们的餐饮经济。

如果这些东西都对得上，我们就会觉得，行，我们知道该怎么做了，这跟 Uber Eats 的关系是什么？我们发现，一家餐厅大概会把每顿饭 20% 到 30% 的收入花在食材上，20% 到 30% 花在人工上，另有大约 10% 花在房租和其他杂项上，剩下的部分就沿着这条链条往下分。关键是增量价值到底有多大。
所以我们一开始跟商家说，我们要收你账单的 30%。他们的反应就是：天啊，这跟 Groupon 又有什么区别？也太高了吧。我们把这套经济账给他们解释了一遍，他们说，好吧，先试试，但这个比例真的太高。后来事实证明，他们说得没错，真正能成交的价格并不是 25%，但我们离得也没那么远。所以，当你去找产品市场匹配，或者想尽量贴近客户的时候，看的不是单一维度，而是这件事最有价值的部分是什么。就餐厅这个场景来说，给我增量需求。因为如果你在同一家餐厅、同样的人工配置下，把需求翻三倍，只是多配一些食材，那你的增量毛利率就能到 70%、80%。
餐厅的人听我们这么说当然会不高兴，因为现实里并不会完全是那样。但正因为我们抓住了这个洞察，我们才有底气去跟市场说：我们得收你这么多，配送费才能做到那个水平。然后如果配送费是那个水平、我们收的是这个水平，我们就觉得消费者会接受，而你真正需要的就是这部分增量需求；接着我们就能给司机付这个数。这样一来，整个拼图就拼起来了。对一个 marketplace 来说，你并不是让任何一个人 100% 满足，而是给到一个让他们愿意参与市场的成交价。所以这只是一个例子。

Lenny Rachitsky对。我很喜欢这个例子，因为你几乎是在帮他们解决一个他们自己都还没完全意识到的问题。也就是说，你站在他们的角度替他们想，拆解经济模型，然后给出方案，而不是直接问，嘿，我们能帮你们做什么？

Jason Droege对。我的意思是，如果你走进一家餐厅，他们会告诉你一堆问题。他们会说，排班有问题，房租有问题，食材价格也有问题，那大概就是 20% 到 30%。如果你能在这上面抠掉 3%，那就已经很大了。你可能会因此想：那我要去做一个生意，帮你省 10% 的食材成本。可这并没有真正碰到他们日常最在意的东西。那可能在年度层面很重要，但到了每天，他们在看什么？他们在看自己的数字，在看今天有没有人来，在想昨天有没有赚钱，明天能不能赚钱。所以我觉得，做新产品时大家最容易忽略的一点，就是买方的紧迫性。你可以做出一个很有价值的东西，但如果它不是客户忙碌的一天里最先想到的事，那你就还要走很长的路。

Lenny Rachitsky这也让我想到我经常听到的一个主题，就是你特别看重独立思考。这正好是个很好的例子。还有没有别的类似的东西，能说明这种思考方式为什么这么关键？

Jason Droege我觉得作为创始人的工作，我这里说“创始人”其实有点夸张，因为在 Uber，我享受了 Uber 带来的所有好处，所以我并不算真正的创始人，只是在那里把这个业务做起来了。但创始人这件事里有些核心元素，就是你要在市场里找 alpha。我们 1997 年做第一家公司时，这事一点都不酷。也许在硅谷酷，但在洛杉矶绝对不酷。现在，创业很酷，所以大家什么都想试。那你怎么在这个市场里拿到 alpha？如果你的研究高度受周围世界怎么说影响，你就不可能有独立洞察。你必须自己出去做你自己的事。

这也是为什么从创业角度看，我对创办公司的方法有很强的看法，而且可能非常个人化。但说到底，关键就是：我到底掌握了什么洞察？为什么我会这么幸运，手里有这个洞察？在一个有一百万个聪明、努力、什么都在试的创业者的世界里，为什么偏偏是我站在一个位置上，大概率握有别人没有的洞察？又为什么最后是我来做这件事？
答案可能是，我正好处在一个很窄、很偏远的角落。另一个答案可能是，我天生就是个喜欢唱反调的人，所以我总是在找那些别人不信、但其实是真的东西。这个有时候确实有用。不过第二部分也特别重要，那就是：我为什么愿意花 5 到 10 年去做这个问题？很多人会在这件事上犯错。他们去问客户，客户说有个问题，那我就去解决它。这样开始一门生意并不理想。你真的得有一种很强烈的欲望，逼着自己不断质疑自己。
独立思考的另一个关键点是，你不能爱上自己的点子。就事论事，我也没自封是世界上最伟大的思考者，反正你们听到的就是这样，但这里面其实有一部分，就是把自己、过去的自己、所有旧想法都先放下，把它们交给你正在执行的使命，也就是为客户真正做成一件事。

我们就在华盛顿特区把这个想法落了地。我们把 10 辆车拉上路，每辆车放了 250 个 SKU。怎么说呢，完全冷场都不足以形容有多惨。我们几乎接不到单。后来我们才意识到，我们根本没研究过便利店到底是什么。如果你没有香烟、没有啤酒、没有 Slurpee 这些东西，举个例子，你就根本带不来那些来买别的东西的人。所以我们对零售一窍不通，完全是门外汉。这个想法就这么黄了。我们也看过 grocery，不过老实说，挑拣、打包这些环节的单元经济把我吓坏了。我觉得 Instacart 把这类业务的单元经济调得非常不错，而这大概也是你能碰到的最难的运营问题之一。
我们还做过通用配送、点对点配送，也就是 Uber Direct，反正我现在一时想不起它当时叫什么了。就是把东西从城市里的一个点送到另一个点。这个想法从一开始就失败了，因为说实话，消费者并没有这种需求，企业倒是有一点，但在 2014 年我们做这件事的时候，几乎没人有这种需求。不过在最终下结论前，我们把这些方向都试了 15 个版本。后来我们才说，好吧，外卖这件事各方面信号都在爆，而且单元经济也能跑通。大家显然真的想要它。而且这还是个很酷的问题，因为我们可以给独立餐厅配上一整套工具，让他们和大品牌竞争。我们还能把房租这个因素踢出局。也就是说，一个地段不算黄金的门店，只要菜做得够好，照样能竞争。所以我们就觉得，哦，这个问题很有意思，而且我们真的能帮到本地经济。

Lenny Rachitsky如果我没记错，这后来基本上在疫情期间救了 Uber。Lyft 没有这样的业务。那这门生意到现在到底有多大？你能不能分享一点，它后来对 Uber 有多重要？

Jason Droege当然。我们在 2015 年 12 月把它先在多伦多上线，两个小时内就做到 2 万美元销售额。我们很快就看出来，这条路是对的，单元经济也很好。四年半之后，我在 Uber 待了大概六年，而我们花了大约一年半才把这件事想明白。四年半之后，这个业务已经做到大约 200 亿美元了。所以它是从 0 到 200 亿，用了四年半，已经很不错了。Uber 非常擅长把事情规模化，但市场竞争也很激烈。别人也做得不错，我们打赢了很多人，但也有人赢了我们。现在我觉得它已经冲到大约 800 亿了，而那是在我离开之后又过了四年半。我记得疫情把它从 200 亿推到了 500 亿，一年里涨了很多。我是在疫情前刚好离开的，纯属巧合。总之，打车业务是这样，外卖业务则直接飞上天了。

这运气也太好了，干得漂亮。
运气本来就是游戏的一部分。这个也很重要。运气本来就是游戏的一部分，所以别因为别人走运就去埋怨人家。这行业很难，我们做的这些事都真的很难。运气就是游戏的一部分。
也许顺着这个说。你的同事 Stephen Chau，也就是我投的那家新公司的创始人之一，他之前在 Uber Eats 跟你一起干了很久。他让我问你 McDonald's 那个故事。我猜那对你们来说算是个很大的里程碑。所以你们当时为什么会把 McDonald's 拉进 Uber Eats？而且听说你们拿下那笔合作还有个故事。

Jason Droege这事挺有意思，也正好说明有时候，无知反而会把你意外带到正确答案上。我们上线 Uber Eats 的时候，Uber 已经有全球布局，而我们是全球范围内唯一一个外卖网络，除了中国。Uber 里的任何东西都得全球上线，这也是公司文化里很重要的一部分，当然这也意味着工作量很大，容易铺得太薄，带来别的问题。但在这件事上，它是有帮助的。我的愿景是，行，我们来帮小商家和连锁品牌竞争。连锁店有标准化的食物生产体系，而食物正是一座城市精彩的原因。你去巴黎吃过哪家连锁店，没人会一直聊；大家聊的都是自己发现的本地小店。我们想成为这个生态的一部分，这才是我们想做的事。

结果 McDonald's 反过来主动找我们，说，嘿，我们想跟你们做外卖。我说，不。然后他们说，等等，我们一天有 8000 万消费者，你不想一起做吗？我说，现在这还不是我们的气质。于是我把他们晾了四五个月，直到团队里的人都说，你疯了吗？这些人会把营销资源砸进来，他们是真的想做，也是真的愿意押注。后来因为这个过程，我觉得虽然很难精确归因，但我们最后确实拿到了一段独家合作，也拿到了海量用户。连锁品牌那时候其实还不怎么上外卖平台，因为大家都很担心单元经济，毕竟他们对 basket size 太敏感了。
我的思路一直是，行，那就把它做出来啊，这就是 Uber 文化。basket 只有 17 美元，那我们的工作就是让它跑通：缩小配送半径，把经济账算清楚，必要的话在别的地方把食物加点价。办法总是有的。我们就这么做了，三个月后业务又开始以另一个速度往上冲。团队当时都说，老兄，你在这件事上也太拧了吧，但我觉得最后其实是净收益，因为我们拿到了特别好的合作条件。
所以我理解下来，是你把他们往外推，反而帮你拿到了更好的条件？太厉害了。

Jason Droege对，我觉得他大概说的就是这个故事。后面的上线也很夸张，因为我们基本上在六个月内就跟他们做到了全球铺开，而那时候这门生意还不到两年大。去激活一家我都不知道多少年的公司，默认流程本来应该都搭好了，而我们在纽约只有两个 office manager 在管，场面简直乱成一锅粥。

我还是很遗憾，In-N-Out 到现在还没上这些 App。

Jason Droege我也是。

我记得以前有人在想办法绕它，大家找到各种门路，最后他们还是说，不不不，你们是 Postmates，我们知道不会给你们任何吃的。

Jason Droege对，超爱 In-N-Out。

你前面提到过毛利和利润率这个话题，也说你特别痴迷这个。我想在这里多聊一点。我听说你做任何事之前都很在意先搞清楚毛利。很多创始人其实根本不知道自己在这上面干嘛。你学到的最重要的一点是什么？人们在判断一门生意到底能不能做时，最容易忽略什么？

Jason Droege对，听着，毛利只是很多过滤器里的一个。确实有些毛利很低的业务也很棒，比如 Costco、Walmart 这些。Amazon 也总在讲这件事，世界上有两类公司，一类涨价，一类低价。但整体来说，高毛利再加上健康的流失曲线，对业务是个很好的信号。想想看，如果我卖给你一样东西，而且我没法把它加太多价，那我到底比手里这点东西多创造了多少价值？如果我没创造太多价值，那我到底在做什么生意？而我做生意就是为了创造价值。当然事情没这么简单。这里其实只是一个很快的试纸，尤其当有人来找我、尤其是新业务的时候，我会这么看。我在 Uber 的时候就一直这么做，放到别处也一样。

有人拿着一个想法来，说，我们能做这个生意，我觉得可以收这个价格，毛利能做到 40%。我下一句通常就是：那先从 60% 毛利开始，为什么做不到？对方就会说，呃，客户…… 然后你立刻就能顺着这个短路到真正的问题上。哦，客户有替代方案。好，那替代方案是谁？哦，是某家离岸外包公司。那他们的毛利是多少？哦，不知道。去查啊。结果一查，20%，而且人家做了很多年，运营也成熟了。你就会意识到，好，你的毛利会比你想得快得多地从 40 掉到 20，除非你做点差异化，不然会很难受。
所以我觉得毛利只是一个很粗的工具，不是一个完美工具，但它很快，能帮你判断我是不是在创造足够的价值、我有没有差异化。它不是完美的，但它能很快把一些事情筛掉。尤其是别人来 pitch 你一个点子的时候，你可以看看他有没有想清楚这个动态。因为如果对方的回答是：我们现在毛利很低，但我们在追的就是这个动态。那你就会说，好，明白了。有时候他们还会说，我们先靠量补，毛利先负一阵子再说。那你就知道，等一下，这玩意儿不成立。
我喜欢这个视角，因为它能帮我判断：我的点子到底够不够好，如果去研究的话，能不能守住高毛利？这个领域里是不是一直没人能把毛利做得更高？

Jason Droege对，就是这个意思。我刚才说了，这个东西本来就是用来筛掉那些大公司里常见的大想法的。每个人都有点子，所以它是帮你快速穿透表面的工具。你要不要能看明白，未来两三年里必须搭起来的那套机器到底是什么？你现在也许能有 70% 的毛利，但下一个问题就是，为什么别人做不到？如果你的答案是，嗯，他们现在能做，但两年后不行，因为我们跑得足够快。好，那我们可能有戏。如果他们现在能做，两年后也能做，那你的毛利就会被挤压。

顺着这个说，我刚刚听的是 a16z 的播客。Alex Rampell 好像讲了 Costco 的故事。正如你说的，他们的策略其实就是把毛利压得很低，因为他们的收入全靠会员费。他们大概有 5000 万会员，每月付 100 美元，这就是整个生意。所以他们不打算，也不想靠产品赚钱。

Jason Droege对，没错。Costco 玩的是另一套游戏，我不是 Costco 专家，不过我跟这家公司打过一些交道。他们是用价格去换规模。所以他们本质上是在说，像 Walmart 一样，我们只要打折，就能拿到足够多的量，把竞争对手的呼吸空间都挤掉。那问题就来了，好，如果你今天毛利很低，那两三年后，当你在某个市场里站稳了一个据点，为什么毛利不会被侵蚀？答案是，因为我们已经把需求全吃下来了。你想把毛利做成 8% 对 10%，我粗略觉得他们大概就在这个区间，那会是一门非常难做的生意。因为一旦客户已经形成习惯，已经把每周的采购路线围着你来安排，已经和供应商建立了关系，已经有懂得怎么补货的总经理，那就不是一件轻松的事了。所以他们先跑到规模上，之后再来跟他们竞争，基本就自求多福吧。

And we launched that in DC. We put 10 of these trucks on the road, we put 250 SKUs in them. And I mean, crickets is an understatement of how bad it was. I mean, we couldn't get an order to save our lives. And what we realized was that we hadn't really done the research on what convenience stores really were. It was if you didn't have cigarettes, you didn't have beer, you didn't have Slurpees, you didn't have these things, for example, you didn't bring people in to sell all the other things. So we didn't know anything about retail. We were clueless. So that's one idea. We looked at grocery, but honestly the unit economics just terrified me of all the pick packing and everything like that. I think Instacart did a remarkably good job at getting the unit economics to a good spot and probably the hardest operational problem you could tackle.
We did generalized delivery, point to point delivery, what's now, I forget what Uber's product is called, but Uber Direct I think it's called, where you have something that needs to go point to point in a city. That was a flop from the beginning because the truth is is how consumers don't really have this need, business sort of have this need, and in 2014 when we were doing this, no one had this need. But we tried 15 versions of all these things before we eventually just said, "Okay, the food delivery thing is just popping off on all signals and we can make the unit economics work. People seem to want it. It's a super cool problem because we can enable independent restaurants with all these tools and allow them to compete with the big guys. We can take the real estate out of the equation. So you can have a real estate location that's non-prime, but if you have prime food, then you get to compete." So we're like, "Oh, this is a very interesting problem and we can really help local economies."

Lenny RachitskyAnd this ended up being, if I remember correctly, this basically saved Uber during COVID. Lyft didn't have something like this. And how big is this business at this point? Anything you share about just how important this turned out to be for Uber?

Jason DroegeYeah, of course. Well, we launched it in December of 2015 in Toronto and within two hours we had done $20,000 for the sales. It was crazy how quickly we saw that it was the right idea and the unit economics were good. And then, four and a half years later, I was at Uber for about six years, but it took us about a year and a half to figure this out. Four and a half years later, it was about $20 billion. So it was 0 to 20 billion in four and a half years, which is pretty good. Uber was very good at scaling things, but competitive market. Others did well. We beat a lot of people. Some people beat us. And then, now I think it's pushing 80 billion, and that's been for another four and a half years since I left. I think COVID turned it from 20, I left right before COVID, total coincidence, 20 to 50 in a year. So I mean, ride-sharing went this and food delivery just went to Pluto.

Lenny RachitskyWhat luck. Well done.

Jason DroegeLuck is part of the game. That's the other thing that's important to realize. Luck is part of the game, so do not begrudge people for luck. This industry is hard. All these things we're doing are really, really hard. Luck is just part of the game.

Lenny RachitskyMaybe speaking that maybe not. One of your colleagues, Stephen Chau, who I am an investor in his new company, he worked with you at Uber Eats for a long time. He told me to ask you about the McDonald's story. I imagine that was just a big milestone, a big moment enough for you guys. So why'd you decide put McDonald's in Uber Eats and there's apparently a story of how you won that deal.

Jason DroegeSo it was interesting, and this just goes to maybe where sometimes ignorance leads you to accidentally the right answer. So we had launched Uber Eats and Uber had a global footprint and we were the only food delivery network with a global footprint excluding China. Everything at Uber needed to be launched globally. That was a very big part of the culture, et cetera, which is a lot of work and you can spread yourself too thin and cause other problems. But in this way it was good. My vision was, okay, let's help the little guy compete with all these chains. They have these systematized food systems and food is what makes a city amazing. And no one talks about the chain restaurant that they visited in Paris. They talk about the local place that they found and let's be part of that. That's who we want to be.

And so, McDonald's actually approached us and they said, "Hey, we'd love to do food delivery with you." And I said, "No." And they're like, "Hold on a second. We have 80 million consumers a day. You don't want to do this together?" I'm like, "It's not really our vibe right now." And so, I pushed them off for four or five months until my team is like, "You're insane. These people are going to put marketing behind it. They really want to do this. They want to lean in." So we actually had, because of that, I think it's hard to correlate these things, we ended up with this exclusive relationship with them, got an insane number of customers of... Chains at this point actually weren't really on food delivery networks because everybody was so worried about the unit economics, because they're so sensitive to the basket size.
And my approach was like, eh, figure it out, which is a very Uber culture thing. Okay, the basket's $17, it's our job to make that work, reduce the radius on the delivery, figure out the economics, maybe mark up some of the food someplace. There's always a way to figure it out. So we did it and then three months later the business just started hockey sticking again at a different level. And my team is just like, "Dude, you were so stubborn on this point," but I think it actually ended up being in net benefit because we got a great deal with them.

Lenny RachitskySo the fact that you pushed him out helped you get a better deal is what I'm hearing. That's amazing.

Jason DroegeYeah, I think that's the story he would be referencing. And then, the onboarding of it was crazy because we basically went global with them in six months, and at this point the business was less than two years old. So activating this, I don't even know, an 80-year-old company that expects processes to be in place and we have two of our office managers in New York managing it. It's just mayhem.

Lenny RachitskyI'm still sad In-N-Out is still not on any of these apps.

Jason DroegeYeah, me too.

Lenny RachitskyI remember someone was hacking it. There's all these ways people found a way around and they're like, "No, no. Okay, you're Postmates. We know we're not going to give you any food."

Jason DroegeYes, love In-N-Out.

Lenny RachitskyYou've touched on this idea of gross margins and margins, how obsessed you are with this. I wanted to spend a little time on here. I've heard just you're obsessed with understanding gross margins before going in on anything. Most founders have no idea what they're doing here. What have you learned about just what people should be paying attention to, what they might be forgetting when they think about just the feasibility of a business?

Jason DroegeYeah, look, it's one filter like many filters. There are certainly businesses that have low gross margins that are great businesses. Costco, Walmart, et cetera. Amazon talks about this all the time of there's companies that increase prices and there's companies at lower prices. But I would say that by and large, high gross margins combined with healthy churn curves are a very healthy sign for the business. I mean, think about it. If I were to sell you something and I can't mark it up a lot, how much value am I adding beyond what's in my hand? And if I'm not adding that much value, then what am I in the business of doing? And I'm in business of adding value. And it's not quite that simple. This is just a litmus test of when someone comes to me and they go, especially in a new business, and we deal with this. I dealt with this at Uber, I've dealt with it everywhere.

Someone comes up with an idea and they go, "We can get into this business and I think we can charge this and it'll get us to a 40% gross margin." And then, my next question is start at a 60% gross margin. Why does that not work? And they go, "Oh, well, the customer..." And immediately, you short circuit to what the real problem is. Oh, the customer has an alternative. Oh, okay, well who's the alternative? Oh, it's some offshoring company. Well, what's their gross margin? Oh, we don't know. You go find out. It's like 20% and they've been around for a long time and they have scaled operations. And you're like, okay, so your gross margin is going to go from 40 to 20 quicker than you think, and you're going to be in a world of hurt unless you do something to differentiate.
So I take gross margin is just a very coarse instrument, not a perfect instrument to think about, am I adding enough value? Am I differentiated? It's not perfect, but it's a very quick short circuit filter to even to see if someone's pitching you an idea, have they thought through this dynamic? Because if the response is gross margin is super low right now, but here's the dynamic I'm going after. And then you're like, "Oh, okay." And sometimes it's like, we'll just make it up with volume and then the gross margin will go negative for a while and you're like, "Wait, this doesn't work."

Lenny RachitskySo what I love about this is just a lens into is my idea good enough if studying, can I keep a high gross margin? Is there a reason why people in this space haven't been able to have a higher margin?

Jason DroegeYeah, exactly. And like I said, it's meant to disqualify just you're doing these large for larger companies and everybody has ideas. And so, it's a way to cut through. Do you understand the machine that is going to need to be in place in two or three years? You might have a 70% gross margin now because the next question is why can't someone else do this? And if you have an answer of like, "Well, they can now, but they can't in two years, if we run really fast." Okay, we might have something. If they can now and they will be able to in two years, you're going to have margin compression.

Lenny RachitskyAlong these lines I was just listening to, I think it was the a16z podcast. Alex Rampell I think was sharing this story about Costco, how as you said, their strategy is actually to keep margins very, very low because all their revenue comes from their membership. So they have something like 50 million members paying 100 bucks a month and that's their entire business. And so, they don't plan and they don't want to make money off the products.

我们就在华盛顿特区把这个想法落地了。我们把 10 辆车拉上路，每辆车放了 250 个 SKU。怎么说呢，完全冷场都不足以形容有多惨。我们几乎接不到单。后来我们才意识到，我们根本没研究过便利店到底是什么。如果你没有香烟、没有啤酒、没有 Slurpee 这些东西，举个例子，你就根本带不来那些来买别的东西的人。所以我们对零售一窍不通，完全是门外汉。这个想法就这么黄了。我们也看过 grocery，不过老实说，挑拣、打包这些环节的单元经济把我吓坏了。我觉得 Instacart 把这类业务的单元经济调得非常不错，而这大概也是你能碰到的最难的运营问题之一。
我们还做过通用配送、点对点配送，也就是 Uber Direct，反正我现在一时想不起它当时叫什么了。就是把东西从城市里的一个点送到另一个点。这个想法从一开始就失败了，因为说实话，消费者并没有这种需求，企业倒是有一点，但在 2014 年我们做这件事的时候，几乎没人有这种需求。不过在最终下结论前，我们把这些方向都试了 15 个版本。后来我们才说，好吧，外卖这件事各方面信号都在爆，而且单元经济也能跑通。大家显然真的想要它。而且这还是个很酷的问题，因为我们可以给独立餐厅配上一整套工具，让他们和大品牌竞争。我们还能把房租这个因素踢出局。也就是说，一个地段不算黄金的门店，只要菜做得够好，照样能竞争。所以我们就觉得，哦，这个问题很有意思，而且我们真的能帮到本地经济。

Lenny Rachitsky如果我没记错，这后来基本上在疫情期间救了 Uber。Lyft 没有这样的业务。那这门生意到现在到底有多大？你能不能分享一点，它后来对 Uber 有多重要？

Jason Droege当然。我们在 2015 年 12 月把它先在多伦多上线，两个小时内就做到 2 万美元销售额。我们很快就看出来，这条路是对的，单元经济也很好。四年半之后，我在 Uber 待了大概六年，而我们花了大约一年半才把这件事想明白。四年半之后，这个业务已经做到大约 200 亿美元了。所以它是从 0 到 200 亿，用了四年半，已经很不错了。Uber 非常擅长把事情规模化，但市场竞争也很激烈。别人也做得不错，我们打赢了很多人，但也有人赢了我们。现在我觉得它已经冲到大约 800 亿了，而那是在我离开之后又过了四年半。我记得疫情把它从 200 亿推到了 500 亿，一年里涨了很多。我是在疫情前刚好离开的，纯属巧合。总之，打车业务是这样，外卖业务则直接飞上天了。

这运气也太好了，干得漂亮。
运气本来就是游戏的一部分。这个也很重要。运气本来就是游戏的一部分，所以别因为别人走运就去埋怨人家。这行业很难，我们做的这些事都真的很难。运气就是游戏的一部分。
也许顺着这个说。你的同事 Stephen Chau，也就是我投的那家新公司的创始人之一，他之前在 Uber Eats 跟你一起干了很久。他让我问你 McDonald's 那个故事。我猜那对你们来说算是个很大的里程碑。所以你们当时为什么会把 McDonald's 拉进 Uber Eats？而且听说你们拿下那笔合作还有个故事。

Jason Droege这事挺有意思，也正好说明有时候，无知反而会把你意外带到正确答案上。我们上线 Uber Eats 的时候，Uber 已经有全球布局，而我们是全球范围内唯一一个外卖网络，除了中国。Uber 里的任何东西都得全球上线，这也是公司文化里很重要的一部分，当然这也意味着工作量很大，容易铺得太薄，带来别的问题。但在这件事上，它是有帮助的。我的愿景是，行，我们来帮小商家和连锁品牌竞争。连锁店有标准化的食物生产体系，而食物正是一座城市精彩的原因。你去巴黎吃过哪家连锁店，没人会一直聊；大家聊的都是自己发现的本地小店。我们想成为这个生态的一部分，这才是我们想做的事。

结果 McDonald's 反过来主动找我们，说，嘿，我们想跟你们做外卖。我说，不。然后他们说，等等，我们一天有 8000 万消费者，你不想一起做吗？我说，现在这还不是我们的气质。于是我把他们晾了四五个月，直到团队里的人都说，你疯了吗？这些人会把营销资源砸进来，他们是真的想做，也是真的愿意押注。后来因为这个过程，我觉得虽然很难精确归因，但我们最后确实拿到了一段独家合作，也拿到了海量用户。连锁品牌那时候其实还不怎么上外卖平台，因为大家都很担心单元经济，毕竟他们对 basket size 太敏感了。
我的思路一直是，行，那就把它做出来啊，这就是 Uber 文化。订单只有 17 美元，那我们的工作就是让它跑通：缩小配送半径，把经济账算清楚，必要的话在别的地方把食物加点价。办法总是有的。我们就这么做了，三个月后业务又开始以另一个速度往上冲。团队当时都说，老兄，你在这件事上也太拧了吧，但我觉得最后其实是净收益，因为我们拿到了特别好的合作条件。
所以我理解下来，是你把他们往外推，反而帮你拿到了更好的条件？太厉害了。

Jason Droege对，我觉得他大概说的就是这个故事。后面的上线也很夸张，因为我们基本上在六个月内就跟他们做到了全球铺开，而那时候这门生意还不到两年大。去激活一家我都不知道多少年的公司，默认流程本来应该都搭好了，而我们在纽约只有两个 office manager 在管，场面简直乱成一锅粥。

我还是很遗憾，In-N-Out 到现在还没上这些 App。

Jason Droege我也是。

我记得以前有人在想办法绕它，大家找到各种门路，最后他们还是说，不不不，你们是 Postmates，我们知道不会给你们任何吃的。

Jason Droege对，超爱 In-N-Out。

你前面提到过毛利和利润率这个话题，也说你特别痴迷这个。我想在这里多聊一点。我听说你做任何事之前都很在意先搞清楚毛利。很多创始人其实根本不知道自己在这上面干嘛。你学到的最重要的一点是什么？人们在判断一门生意到底能不能做时，最容易忽略什么？

Jason Droege对，听着，毛利只是很多过滤器里的一个。确实有些毛利很低的业务也很棒，比如 Costco、Walmart 这些。Amazon 也总在讲这件事，世界上有两类公司，一类涨价，一类低价。但整体来说，高毛利再加上健康的流失曲线，对业务是个很好的信号。想想看，如果我卖给你一样东西，而且我没法把它加太多价，那我到底比手里这点东西多创造了多少价值？如果我没创造太多价值，那我到底在做什么生意？而我做生意就是为了创造价值。当然事情没这么简单。这里其实只是一个很快的试纸，尤其当有人来找我、尤其是新业务的时候，我会这么看。我在 Uber 的时候就一直这么做，放到别处也一样。

有人拿着一个想法来，说，我们能做这个生意，我觉得可以收这个价格，毛利能做到 40%。我下一句通常就是：那先从 60% 毛利开始，为什么做不到？对方就会说，呃，客户…… 然后你立刻就能顺着这个短路到真正的问题上。哦，客户有替代方案。好，那替代方案是谁？哦，是某家离岸外包公司。那他们的毛利是多少？哦，不知道。去查啊。结果一查，20%，而且人家做了很多年，运营也成熟了。你就会意识到，好，你的毛利会比你想得快得多地从 40 掉到 20，除非你做点差异化，不然会很难受。
所以我觉得毛利只是一个很粗的工具，不是一个完美工具，但它很快，能帮你判断我是不是在创造足够的价值、我有没有差异化。它不是完美的，但它能很快把一些事情筛掉。尤其是别人来 pitch 你一个点子的时候，你可以看看他有没有想清楚这个动态。因为如果对方的回答是：我们现在毛利很低，但我们在追的就是这个动态。那你就会说，好，明白了。有时候他们还会说，我们先靠量补，毛利先负一阵子再说。那你就知道，等一下，这玩意儿不成立。
我喜欢这个视角，因为它能帮我判断：我的点子到底够不够好，如果去研究的话，能不能守住高毛利？这个领域里是不是一直没人能把毛利做得更高？

Jason Droege对，就是这个意思。我刚才说了，这个东西本来就是用来筛掉那些大公司里常见的大想法的。每个人都有点子，所以它是帮你快速穿透表面的工具。你要不要能看明白，未来两三年里必须搭起来的那套机器到底是什么？你现在也许能有 70% 的毛利，但下一个问题就是，为什么别人做不到？如果你的答案是，嗯，他们现在能做，但两年后不行，因为我们跑得足够快。好，那我们可能有戏。如果他们现在能做，两年后也能做，那你的毛利就会被挤压。

顺着这个说，我刚刚听的是 a16z 的播客。Alex Rampell 好像讲了 Costco 的故事。正如你说的，他们的策略其实就是把毛利压得很低，因为他们的收入全靠会员费。他们大概有 5000 万会员，每月付 100 美元，这就是整个生意。所以他们不打算，也不想靠产品赚钱。

Jason Droege对，没错。Costco 玩的是另一套游戏，我不是 Costco 专家，不过我跟这家公司打过一些交道。他们是用价格去换规模。所以他们本质上是在说，像 Walmart 一样，我们只要打折，就能拿到足够多的量，把竞争对手的呼吸空间都挤掉。那问题就来了，好，如果你今天毛利很低，那两三年后，当你在某个市场里站稳了一个据点，为什么毛利不会被侵蚀？答案是，因为我们已经把需求全吃下来了。你想把毛利做成 8% 对 10%，我粗略觉得他们大概就在这个区间，那会是一门非常难做的生意。因为一旦客户已经形成习惯，已经把每周的采购路线围着你来安排，已经和供应商建立了关系，已经有懂得怎么补货的总经理，那就不是一件轻松的事了。所以他们先跑到规模上，之后再来跟他们竞争，基本就自求多福吧。

Lenny RachitskyAmazing. That's a really good tip. I use it for legal documents, just like what do they know about what they're trying to do here for me or against me? Jason, is there anything else you wanted to share or leave listeners with, maybe double down on a point before we get to a very exciting lightning round?

Jason DroegeYeah, absolutely. I mean, I think the really important, the reason why I'm doing this, the reason why want to spend time here outside of wanting to be on the show for a while and being a long-term listener is, our long-time listener, excuse me, is there's a lot of amazing work going on at Scale. The teams are working super hard, we're delivering a ton of value for our customers. The public narrative has not represented the work that the people here are doing and the work that our customers are doing with what we're doing for them. And I just think that deserves the respect and reward that all those people are putting in, and we'd like people to know that.

Lenny RachitskyI appreciate you saying all that. With that, we've reached our very exciting lightning round. We've got five questions for you. You ready?

Jason DroegeYeah, let's go for it.

Lenny RachitskyWhat are two or three books that you find yourself recommending most to other people?

Jason DroegeSome of this is going to sound interesting. The Selfish Gene is one of my favorite books.

Lenny RachitskyLove that book. I don't know if anyone's ever mentioned, it was one of the most influential books for me too. So sorry, keep going.

Jason DroegeYes. I think Selfish Gene. Road Less Traveled, I've read more than once. I mean, it's just one of the classic human psychology book. And then, I think in business, I think Good to Great. It's not the read that you're going to be most excited to enjoy on a vacation, but it's pretty much right, and I think we should take advice from people who have analyzed these business problems before because not a lot's changed, but we keep acting like everything's changed.

Lenny RachitskyWhat's crazy about that book, you look at all the companies they talk about, I haven't read in a while, but just the whole book is about companies that last, I believe, or maybe that's the other book, I don't know. But anyway, all the companies that they talk about, I don't know if they're still around. It's so hard for a business to last a long, long time.

Jason DroegeI would also recommend Thinking Slow and Fast, that's the... Yes.

Lenny RachitskyThinking, Fast and Slow.

Jason DroegeThinking, Fast and Slow. Excuse me, sorry. It's been like a decade since I read it, but just in terms of point there being human biases are very important to understand.

Lenny RachitskyWhat's really crazy to me about that book and Kahneman in general, someone asked them just, how's your life been impacted by learning all these biases humans have? He's like, "Not much. I have the same biases. Knowing them doesn't really help me avoid them."

Jason DroegeSee, I find myself checking myself. Whenever I get super convicted on something now I will be like, okay, what is the list of things that I'm inclined to do to try to catch myself? Because I think we're most inclined to have these bad decisions impulsively, which is what I think the book is largely about. I mean, it's a long book.

Lenny RachitskySo long. Oh, my God. It feels like that's where AI can help us in the future. Just like, "Hey, Jason, are you sure this isn't framing a fact or whatever?"

Jason DroegeYes.

Lenny RachitskyOkay. Next question. Do you have a favorite recent movie or TV show that you've really enjoyed?

Jason DroegeMost of the movies I watch are with my kids, so I wish I had something deep and profound.

Lenny RachitskyNo, kids content also is a very acceptable-

Jason DroegeThe Formula 1 movie I thought was really good. I mean, it's a classic action movie. I don't think it informs anything in AI or business, but it's good to check out from the craziness of tech once in a while.

Lenny RachitskyIs there a product you recently discovered that you really love? Could be an app, could be clothing, could be a kitchen gadget, anything along those lines?

Jason DroegeVO3. Not totally new, but when I was in high school, I wanted to be a screenwriter. I actually grew up in the Bay Area and everybody was an engineer, but I wanted be a screenwriter. And so, I went back and I got the first page of one of my old scripts, which not good scripts, but I got the first page. I took a picture of the script and I fed it to VO3, and I said, "Make this scene," and it got it right.

Lenny RachitskyWow.

Jason DroegeI was shocked. I was just absolutely shocked that you could just take a picture of a script. And so, now I'm thinking about that for how do I use these tools for family videos? Some of the grad tools now with making live images more active, I think are really interesting. I think they need one more step of iteration, but I think those are going to be really emotionally life-changing for people because just a little bit of movement in an image from a grandparent or a relative or whatever you haven't seen in a while, it really does make a big emotional impact on you.

Lenny RachitskyI love that when you play with these tools, you probably can think about, oh, here's the people that help train this thing. Here's the people that helped on the problem that it had.

Jason DroegeI was actually talking to someone who was working on VO3, and I told him the script thing and he goes, "Oh, actually scripts. Yeah, no, the way the data is formatted in a script, that would actually be very good." Because they start with set looks dark interior, this character says it in this raspy voice, and so it gives you all the instructions in the script.

Lenny RachitskyOh, man, just unlocked a whole new business unit right there. Two more questions. One is do you have a favorite life motto that you often think about, find useful in work or in life?

Jason DroegeYeah. The end is never the end. That's my favorite internal saying, and it goes to the comments before about survival being a precursor, surviving being a precursor to thriving. You got to survive before you thrive, which is your brain tells you, and along these entrepreneurial journeys, I think this is most applicable. I mean, this is the hardest journey anyone can go on. If you go on this journey for five years, you are mentally harder than 99.9% of the population. People don't understand the Chinese water torture of having self-doubt and having things go wrong, et cetera.

And so, more tactically, you get this when you're working out like in a day like, "Oh, I'm too tired. I need to stop." But the truth is is you can keep going and the world's going to keep spinning. So I find in the moments where it's just the hardest or you have this hard decision that seems impassable and your body, you're having this visceral reaction to this is impassable, just to remind yourself that I'm going to wake up tomorrow. This isn't the end. There's another end somewhere. I just find that to unlock me to be like, okay, there might not be a perfect solution, there might be an imperfect solution, but it's a solution so let's just keep going.

Lenny RachitskyFinal question. You helped create Uber Eats. I imagine you're still a power user of Uber Eats. You have a favorite restaurant on Uber Eats that maybe people should know about, maybe that you order most from?

Jason DroegeI order a shocking amount of McDonald's actually. Despite my original story, it's the family treat in the house. I would say that that's probably the top thing that we order.

Lenny RachitskyOh, man, I'm worried for your health, but I love, I haven't had McDonald's so long. This is like, maybe I should give it another-

Jason DroegeI mean, more practically we will order mixed greens or tender greens or something like that on a day-to-day basis, but I think that the more notable, surprising thing is is that despite my initial aversion to working with a global chain, it's a good treat once in a while. You just shouldn't have it all the time.

Lenny RachitskyJason, this was incredible. I really appreciate you making time for this. I'm really honored to be the first chat you've had since taking over at Scale. Where can folks find you online if they want to maybe reach out, learn more about what you're, I don't know, maybe join Scale. Where do you want to point people to and how can listeners be useful to you?

Jason DroegeYeah, absolutely. I'm @jdroege, J-D-R-O-E-G-E on X. That's probably the easiest way to follow me, keep up with things and you can shoot me a DM if you like. And so, I think that's how you would keep in touch and, sorry, what was your other question? Sorry.

Lenny RachitskyIf you're hiring, I don't know, where should people go check it out if you are, and then also just-

Jason DroegeAbsolutely. Just go to scale.com, go to our careers page, and we have 250 open roles. To the point about we're in business and we're growing, we're hiring a ton of people. Our data business is growing, our applications and services business is growing like crazy, and so we're going to need a lot of people to help us on that journey.

Lenny RachitskyYou guys just signed some insanely large contracts with the government I was reading.

Jason DroegeTwo $100 million contracts.

Lenny Rachitsky$100 million contracts.

Jason Droege100, yeah. We didn't sign just one. We signed two in one month, so yes, no, our federal business is doing well. Our enterprise business is doing well. Our international government's business is doing well. There's a lot of demand out there.

Lenny RachitskySome salespeople are getting some great commissions. Good job. Jason, thank you so much for being here.

Jason DroegeYeah, thank you. Honor to be a guest here. Super excited to be with you, especially so early in the journey, or at least my journey here leading Scale.

Lenny RachitskyAppreciate it. Thanks for coming. Thanks for joining us. Bye, everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennyspodcast.com. See you in the next episode.

### 中文译文

Lenny Rachitsky太棒了，这真是个很好的提醒。我看法律文件时也会这么想：他们到底想对我做什么，或者想从我这里防住什么？Jason，在我们进入激动人心的 lightning round 之前，你还有什么想补充的，或者想再强调一下的吗？

Jason Droege当然有。说到底，我愿意来聊这件事，除了我一直是这档节目的老听众、也一直想找机会来做客之外，更重要的是，Scale 现在真的在做很多很棒的事。团队非常努力，也在持续给客户创造巨大价值。可外界的叙事并没有真实反映这里的人在做什么，也没有真实反映客户借助我们做出了什么。我觉得这些努力值得被看见，也值得应得的尊重和回报，所以我想把这件事说清楚。

Lenny Rachitsky谢谢你这么说。那我们进入 lightning round，一共五个问题，准备好了吗？

Jason Droege当然，来吧。

Lenny Rachitsky你最常推荐给别人的两三本书是什么？

Jason Droege有几本书可能会让人意外。《自私的基因》是我最喜欢的书之一。

Lenny Rachitsky我也很喜欢这本书。我不确定有没有人提过，但它也是对我影响最大的书之一。继续说。

Jason Droege对。我还会推荐《The Road Less Traveled》，我不止读过一次。这是一本很经典的人类心理学书。商业方面我会推荐《Good to Great》。它不是那种你会在度假时最兴奋拿起来的书，但它的判断大体是对的。我觉得，我们应该多听那些已经认真分析过商业问题的人，因为很多核心问题其实并没有变，但我们总假装一切都变了。

Lenny Rachitsky这本书最疯狂的一点是，你看它写到的那些公司，我已经很久没翻了，但整本书讲的都是“能活很久的公司”，我现在都不确定书里提到的公司是不是还在。让一家公司活得很久，真的太难了。

Jason Droege我还会推荐《Thinking, Fast and Slow》。

Lenny Rachitsky《思考，快与慢》。

Jason Droege对，抱歉，应该这么说。我读那本书也快十年了，但它最重要的一点还是让你理解人类偏差有多关键。

Lenny Rachitsky最让我震撼的是那本书和 Kahneman 本人。有人问过他，学了这么多人类偏差之后，生活有没有变得不一样？他说几乎没有。你知道这些偏差，并不会自动帮你避开它们。

Jason Droege对，我会试着随时检查自己。只要我对某件事特别笃定，我现在就会问自己：我是不是又在那些容易出错的地方上头了？因为我觉得，人最容易冲动地做出坏决定，而这本书讲的很多就是这个问题。当然，它也很长。

Lenny Rachitsky太长了，天啊。感觉这正是 AI 未来能帮到我们的地方。比如说：“Jason，你确定这不是在偷换事实吗？”

Jason Droege对。

Lenny Rachitsky下一个问题。你最近有没有特别喜欢的电影或电视剧？

Jason Droege我看的大多数电影都是和孩子一起看的，所以我真希望自己能说出点更深刻的东西。

Lenny Rachitsky没关系，孩子一起看的内容也完全可以。

Jason Droege我觉得那个 F1 电影拍得挺不错。它就是一部很标准的动作片，不会告诉你什么 AI 或商业道理，但偶尔从科技圈的疯狂里抽离一下，很值得。

Lenny Rachitsky你最近有没有发现什么特别喜欢的产品？可以是 App、衣服、厨房小工具，什么都行。

Jason DroegeVO3。它不算全新，但我高中时一直想当编剧。我是在湾区长大的，周围的人几乎都是工程师，但我想当编剧。后来我找出自己以前一份剧本的第一页，虽然不是什么好剧本，但就是第一页。我拍了张照片，把它喂给 VO3，然后说“把这个场景做出来”，它真的做对了。

Lenny Rachitsky太厉害了。

Jason Droege我当时真的震住了。能直接拍一张剧本照片让它理解，这太不可思议了。现在我开始想，这些工具能不能用在家庭视频上。现在很多生成类工具都在让静态图像变得更有生命力，我觉得这个方向特别有意思。它们可能还差最后一步迭代，但一旦做出来，会非常打动人。哪怕只是给一张祖父母或者很久没见的亲人的照片加一点点动作，情感冲击都很大。

Lenny Rachitsky我很喜欢这种感觉。你在玩这些工具的时候，可能会想到，哦，这就是帮它训练的人，这就是它之前卡住的那个问题。

Jason Droege我其实跟一个做 VO3 的人聊过这个，我提到剧本那件事，他说，哦，对，剧本这种格式确实特别适合，因为它一开始就会写场景、光线、人物语气这些指令，信息量很足。

Lenny Rachitsky天啊，这一下就解锁了一个全新的业务线。还剩两个问题。一个是，你有没有什么常挂在嘴边、在工作或生活里都觉得有用的人生格言？

Jason Droege有。`The end is never the end`。这是我最喜欢的一句内在口号，它和前面说的“先活下来，才谈得上成长”是一个意思。你得先撑过去，才有机会变得更好。创业这条路是最难的路之一。你如果走了五年，心理韧性基本已经超过 99.9% 的人了。很多人根本不理解那种自我怀疑、不断出错带来的折磨。

更现实一点说，你在健身的时候也会遇到这种感觉，觉得“我太累了，得停了”。但事实是，你还能继续，世界也还在转。所以在你最难的时候，或者面对那种看起来过不去的决定时，我会提醒自己：明天我还会醒来，这不是终点。终点总会在别的地方。这个念头常常能把我从卡住的状态里解出来，让我明白：也许没有完美解法，但一定有一个够用的解法，那就先继续往前走。

Lenny Rachitsky最后一个问题。你帮着做出了 Uber Eats，我猜你现在应该还是 Uber Eats 的重度用户。你有没有哪家餐厅是你在 Uber Eats 上最常点、或者你觉得大家应该知道的？

Jason Droege我其实点 McDonald's 的次数惊人。虽然我一开始的故事是那样，但它现在就是我们家里的家庭奖励餐。我觉得这大概是我们最常点的东西。

Lenny Rachitsky天啊，我有点担心你的健康，但我得承认，我也很久没吃 McDonald's 了。也许我该再给它一次机会。

Jason Droege更实际一点说，我们平时还是会点 mixed greens、Tender Greens 之类的健康餐。但最让人意外的一点是，虽然我最开始并不喜欢和全球连锁合作，可偶尔吃一次，确实挺不错。只是不能天天吃。

Lenny RachitskyJason，这次太精彩了。非常感谢你抽时间来，尤其是在你刚接手 Scale 的这个阶段。我很荣幸你把这次聊播客的第一场采访给了我们。如果大家想在线上找到你，或者想了解你在做什么、甚至想加入 Scale，应该去哪里？听众又能怎么帮到你？

Jason Droege当然，我在 X 上是 `@jdroege`，就是 `J-D-R-O-E-G-E`。那应该是最方便关注我的方式，也可以给我发私信。如果你想保持联系，这就是最简单的路径。你刚才后面那个问题是什么？抱歉。

Lenny Rachitsky如果要招聘，大家应该去哪里看？还有就是……

Jason Droege当然，直接去 `scale.com`，看 careers 页面。我们现在有 250 个空缺岗位。我们业务在增长，数据业务在涨，应用和服务业务也在疯狂增长，所以我们需要很多人一起把这条路走下去。

Lenny Rachitsky你们刚刚还签了几笔巨大的政府合同，我之前看到过报道。

Jason Droege两个 1 亿美元的合同。

Lenny Rachitsky1 亿美元的合同。

Jason Droege对，不是一份，是一个月里签了两份。所以是的，联邦业务、企业业务、国际政府业务都挺好，市场需求很大。

Lenny Rachitsky有些销售同事拿到的提成应该挺吓人的。干得漂亮。Jason，非常感谢你今天来。

Jason Droege谢谢你，能来做客是我的荣幸。也很高兴这么早就能和你聊，尤其是在我带着 Scale 走这段新旅程的开始。

Lenny Rachitsky太感谢了。谢谢你来，也谢谢大家收听。如果这期对你有帮助，欢迎在 Apple Podcasts、Spotify 或你常用的播客 App 里订阅，也欢迎给我们评分或留评，这能帮更多人发现这档节目。你也可以在 `lennyspodcast.com` 找到往期节目或了解更多内容。我们下期再见。