Scaling Uber with Thuan Pham (Uber’s first CTO)

When Tuan Pham joined Uber as the company's first CTO in 2013, the company had 40 engineers, the 30,000 rides per day, and the system crashed multiple times per week. He had 5 months before Uber's dissapoint system would hit a brick wall with no way out. Seven years later, he left the CTO of one of the most complex engineering organizations ever built. In today's conversation, we discuss, Tuan's interview with Travis Kalanick for the CTO role, which lasted 30 hours, spread Scaling through chaos, rewriting dispatch before it collapsed, launching China in five months, and the full app arrived known internally as Project Helix. Why Uber ended up with thousands of microservices and hundreds of internal tools? Because existing solutions could not handle Uber's scale at the time, and many more. If you've ever wondered what is like inside the room when a company is growing faster than its systems can handle, and what are ways to get things under control, this episode is for you. As a side note, I've been lucky enough to work at Uber while Tuan was a CTO, and Tuan is a real deal. This episode was presented by Statsig, the Unified Platform for Flags, Analytics, Experiments, and more. Check out the show to learn more about them and our other season sponsors, Sonar and WorkOS. Uber Appryright, which was a crazy project, but before we get into any of that, how did you get started, not just in tech, but in life? You had a pretty rough start. Yeah, I grew up in, I was born in Vietnam, and I was a child, I would say, of the Vietnam War. So in 1975, when I was from the south of Vietnam, my father was tied to the military of the south of the south, and when the country was unified, the South is lost and the North is one, and there were a fair amount of repercussions. People who associated with the southern regime would not have much of an opportunity growing up, an educational opportunity, all these other things. That was, again, the way it was at the time. That's not necessarily true right now, but that was. My mother then made a very bold decision that she wouldn't want her to son growing up with no opportunity. So we had to flee the country, and at the time, there was a massive wave of exodus called the boat people, where people just get onto a rinkety boat, fishing boat, whatever thing they can get their place in and escape the country in the middle of the night. People did not know at the time, and nobody thought about it, but the chance of survival was about less than 50%. About two million people left, about a million people survived the crossing, because these boats are not sea world, and we crossed the ocean, and yeah, but we were the lucky, we were a lucky half, really, but no one thought about it, if we would think too much about it, they probably wouldn't do it. But everyone just like, well, we need to escape, we need to, you know, give ourselves a shot of a better life, and so we did. So we left Vietnam, took many tries, and it depleted the entire, you know, saving of my parents, because it was a scam. People would say, pay up half now, half later, and then the boat never shows up, and finally, on the fourth try, we actually made it, and then we were lucky that we have a really good captain who actually navigates through storms and all that, and we survived even pirates from Thailand. I was around, I think, 11, 12, some of them, and so we crossed that, and we survived three day, four nights of the crossing of the South China Sea to Malaysia. Then we went into Malaysia, we thought we were done, a week later, we got towed back out, and dumped it in Indonesia a few days later, and that's where the government there accepted us in and put it on a deserted island at the time, and we formed a refugee camp there. And then we were waiting to be processed, we got interviewed by all the different countries, and the US gave us a refugee settlement because we were tied to the old regime that were supported by the Americans. So we are very, very thankful to get here, the land of opportunity, and we didn't know any English, we didn't have a penny to our name, we were sponsored by a church. The first set of clothing we got was from the donation closet at the church, and so, but we had to build from the ground up, and so that was how I grew up, and that's how I got here. And I'm from this like, absolutely, not just unconventional, but just extremely hard to start. How did you eventually get your interest into computers into tech? Just like most things in life is by happenstance or luck. I was pretty good in math and science, as most kids in Asia, we were growing up, we learned that, and when we got here, I had a friend in high school who had received a gift from his dad, an IBM PC. That was one of the very first ones. The one with like two floppy- Was it in the 80s or 70s? This was in the 80s, this was in 1982. Yeah, so freshman year, so after school, I would hang out at his place, and he's got a new toy, and so we were writing little basic program and playing game and all that stuff, and we learned how to use word processors and Lodis and Word Stars and all that, and I started coding in basics, and then I just realized that, oh, it comes very natural to me. I can think very algorithmically, and then there's another weird thing I sometimes tell people, I am generally a procrastinator. I don't like to do the same thing twice, so computer programming is perfect for me. You solve the problem one, that's the creative part, after that I get bored, I got to just the next problem, and so writing program was like the perfect fit for me. You do not duplicate your code. Yeah, I don't like duplicate the code, I don't like to do the same thing twice, and so yeah, when you write it and then it execute way fast and you can do it by hand, so that was really wonderful. I just taught myself that, and then I volunteered at a government agency to write code for them after school, and so I did that, and I went in there and I basically stitched together Lodis, Debates 3 with all the scripting languages and automate the entire financial accounting and reduce the workload at the time. Two accountants had to spend about three weeks or so every quarter reconciling everything. I did all that stuff with a push of button and took about three hours for the whole batch to run, and so they were so happy. When I graduated high school, I think they wrote me a really good recommendation letter, and with other things that were going on and the good grades and everything else, I got accepted at MIT, and then I got there and I really learned computer science, like the fundamental computer science. Back then I was just like a kid who just you know, write programs, so. And then during or after MIT, what was your first professional job where you got paid and you were full-time on technology? Yeah, one thing lead to another. I was when I was at MIT, there was a multi-year co-op program with some of the best company, tech company in the world at the time, AT&T, Bell Labs, Xerox PARP, HP Labs, and all these companies, Bell Core, all over the country actually. And so we applied for it, and then the kids with the best grade got prioritized, then the company had to go through a selection process, they rang all the kids, and then the kids all rang the company that they got ranked, and then there was a matching process, and I ended up coming to Hewlett Packard Laboratories, and AT was on an awesome company at the time. Back then there were massive and like very, very innovative laser printers, you know, workstation computer systems, all of that stuff. I was in the HP Lab, which is the research lab where a lot of the real innovative stuff happened, and so it was a dream job. As a student, I get to work on cutting-edge research with all the other PhDs around. I get to write the joint thesis for my bachelor's and my master's with the work there, that was part of the arrangement, and when I graduated, HP just hired me straight into that research lab. So I became one of the researchers, although I didn't have a PhD, and after that, a few years of that, then I went into the industry and write code that people would actually use. I really enjoy my time at HP Lab because you get to do cutting-edge stuff, but we were working on medical informatics at the time, where right now you go to every doctor's, all your records are following you. Back then we actually have a network, distributed system architecture, where every physician workstation that you go to, had your x-ray and everything followed, and then you have knowledge base that actually look at for drug interaction. We actually did that research back in the mid-late 80s, actually, and these are cutting-edge stuff, but then the thing that I find unsatisfying, unsatisfaction at the time for me was we published great paper and then didn't go anywhere. It was not productionized, and I'm just this is so cool. Why can't we bring it to the user? But that wasn't the setup. The setup was research lab, worry about research, and then we would have a tech fair every year, and the general manager of every product division swing by, and then decide what they want to pick up and productize. And so I didn't feel empowering beyond the research phase, so I just had to go find a place where I can write code and people actually use my code. So I went to Silicon Graphics. At the time, we were also trying to invent the future, and we actually did a prototype of that. There was interactive TV, where back then, now we could take for granted a streaming video, video on demand, online game, right? Cooperative game. Back then, we didn't have cell phone, internet yet, and we can cobble 4,000 homes together in an 18-trial that has cable. And then we invent like network protocols and all these things, and we actually found a setup. We used like Tube TV, not even a Flaskin TV, with a set top box on top, which is a Silicon Graphics box, and we can implement online shopping, movie on demand. You're building all of these things without having this infrastructure. Without having any of this stuff right here. Wow. And then Celebrity, like Michael Jackson, came by and saw demos, and we saw a Spielberg, we saw everybody. We thought really, really that was the future, and it was the future. The problem was, we're way ahead of the time, right? Then I learned a big heart lesson. It's not about just the technology. It's about where the world is ready for it, why it is economically feasible. And back then, what was the point where you realized like this is not going to work even though we're doing this awesome stuff? After a year, right? Because it took like $100 million back in 1994 dollars just to provision the head end. Silicon Graphics love it because they sell all these massive servers to pump out video. And then the set top itself is a Silicon Graphics workstation that costs $4,000 or $5,000. People would not buy that, right? Especially like in today's money, that will be like $10,000, $20,000, something like that. Exactly. We wouldn't buy that. The early adopter, enthusiastic maybe, right? But not for the mass market. And so when we, and we did that trial, and we're successful, we definitely all saw the future. And then we did the same trial, a similar trial with a different set of software that we wrote for NTT, Nippon Telephone Telegraph in Japan. We went to Japan, deployed that. Very cool, had a really great time. But then it fizzled out because it would not be commercially viable. And so that was a really first life lesson that I learned is not just the technology, right? You got to be at the right place at the right time and the right price point. And then after that, I went to a startup founded by a former office mate at SCI. So we were doing internet advertising. The internet was about to take off. Then Mosaic Browser Search came out, Netscape was being formed. And in the early days of Netscape, and so we saw very clearly that the advertising model worked for TV. So it has to work for the internet, right? Because all these content, people would use it if it's free, but then who has to pay for it, the advertising. So we, I joined a company initially, we called ourselves Netvertiser, which is like, you know, pay up. And then it changed the name quickly to Net Gravity. And then it's so enterprise software to put ad banners, dynamic ad banners on CNN, on Netscape site and all that. I was one of the very early engineer there. I was a fourth engineer, I believe. Yeah. And so people don't know this, but a buddy of mine, we were the first pay up engineer to put the first dynamically targeted ad on the Yahoo page. And dynamically targeted, meaning that it showed different ads based on ID or like whatever. Yeah, the version before I came in was a script that crawled through and just put a static banner ad and rotated through every hour. But then we got to target it and then we started using cookie. We started at first it was a content of the page and the person and then we actually used that to actually target different ad. And then we have ad sequence and all that stuff. And that was the very first one, of course, we had some success there, that company won public. But another thing that I learned was sometimes you've got to seize the market, right? There's a company that formed right much later than us, but didn't add service bureau. And that took off because it take a lot less investment for people to just stick a banner attack on your HTML page, and then revenues come your way, right? Because the service bureau to kind of stick the ad there dynamically, all that kind of stuff. We had wanted to do that in our company. But then one of our board members said, no, you should focus and get to profit first before you expand. And we went down the profitably path and we then becomes like, you know, a bigger, robust enterprise solution, where the other one is and try to get profitable. And the other one is just expand through the internet like raging wildfire. Then after that, years later, the book got bought by Google. So that was the journey there. There's a lot of lesson there about how to build things. So this was, do I understand that you saw that what happens when there's a big market and you focus on profitability, which should make sense. But a player focuses on growth, even at being non-profit, it might be able to swallow you in the end. That's right. Look at what happened to at Uber, right? We did the right move. I'm starting to see how these things are coming together. So now you're at the start of which almost took off, but not quite. And then what was your next step? That company went public and then got absorbed. And after about seven years there, I had made it to the VP level. I joined as an IC along the way. I knew that from my inspiration at the Silicon Graphics Day that if you want to do something really big, you need to leverage other people. You can't do it with your bare two hands. So then I switched over to the management track and I honed the skill and I got up to directors and senior directors and then ultimately got the VP. And then after about eight years, seven years, eight years there, the dot-com bus happened right at the time. And then I said, well, maybe it's time to prove to myself whether or not I'm just a one hit wonder or I actually have skills that are transferable. Just one thing on the dot-com bus because you kind of swept over it because you've now seen a lot of like ups and downs. But can you take us a little bit back? What actually happened with dot-com bus because the people I talked to, especially were new grads, it sounded very, very scary. What did you feel like and what did people, professionals, engineers around you feel like? The dot-com bus was kind of scary when the correction happened. But before that, there was this exuberance that everything is dot-com, right? Pest.com, Fubao.com, everything is the dot-com. Webban. Yeah, all of that. So I actually have the webban bin and my brush head actually. And so yeah, but then there was a check out eventually, there has to be sound business model that makes sustained profit, right? Growth and profit. Growth alone eventually burns, you know, money and that's not good. You can grow fast, but eventually you have to turn that into profit to be a durable company. And so in that dot-com wave, there were massive companies that emerged, right? There was Yahoo, there's Google, there's Amazon, all of those companies. There's also a bunch of other companies, Webban and others, whatever that would go under because they didn't have like a strong value proposition that lasts the test of time, right? So yeah, it's all about what value you deliver and whether or not it's beneficial and valuable to the customer that they're willing to pay, right? And I think that's one thing that we learned. Which one is like a real fundamental strong business, even though it might not be a profit initially. But which one that's just a me too, right? Just put a dot-com on something and it's hot. There may be a lot of dot-AI things that's going on right now, right? Eventually, some of these things will consolidate, some will go under, some will become really awesome solutions and all that stuff. And so, but the market will sort it out. In the end, the customer will vote on what they want to spend the money on. 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It's really about closing the gap between the speed of AI and the reality of production security. What else is Sonar doing to help reduce outages, improve security, and lower risks associated with AI and agenda coding? Head to sonarsource.com slash pragmatic to find out. With this, let's get back on what Tuan did after the dot-com boom and bust. And there was a lot of layoffs, companies going bankrupt. Did that worry people around you? Did that worry you that your job could be in danger or you might have a harder time switching jobs or was it a very short lived? It lasted a couple years. I remember and during that time, it was definitely hard to get a job, especially for new college grad. That's always the first layer that gets hit, right? When everything retrends, people want more experience, people want a strategic system, folks rather than keep on hiring entry-level. So, hope that you have to continue to invest in, right? So, it starts to become a lot of time. It comes and goes and waves. Yeah, so that was certainly a very scary time. But of course, in the longer range of history, things generally tend to recover, but it caused a rearrangement. And yeah, so during that time, it was certainly tough. However, the way I look at this thing is that talents are always talent, right? So, people are really strong talents and who's really hungry, always try to punch above their weight will always be marketable, right? Even in a downturn. Even in a downturn. So, I think the key thing is how people should, even in peacetime, invest in that skill, never be complacent, constantly try to be better. And then in wartime or in rough time, those things will save you, right? If people just leave a complacent atrophy with the time and then when rough time hit, it's very, very hard to recover from that. And then you went to VMware for this time. Yeah, so let's see. After I went from double click to that, and then I jumped into a four-person company. Again, Licky Roof and everything, classic startup. That, this is it, not succeed. It took about three years or so, got to about 40, 50 people in size, and then kind of ran out of money and then got acquired by another entity that was built with a security appliance product, which try to solve the problem of, you know, intimidation of web services, traffic that are going through. And it was a very interesting security niche, but it's not a mass market thing. And so it's hard for a company to kind of break through like that, right? And, but eventually it went away. But even then, you know, those three years taught me a lot, that you can survive even when you do it from the ground up, then you still have skill that you can pick up, despite the fact that that journey might not end in like a commercial success. But your skills still get better. So you are getting better as a professional, even though that's right. And that's something we have to trust. We invest in ourselves, but of course, we invest in the company or vehicle that we are part of, and ideal case both sides succeed. But if they are in the seat, at least if you work really hard, you gain some skill. And then based on that, then you can then leverage all the things that you learn so far and all the mistakes that you've made, it's got you smarter and better and wiser to look for the next offer. So right after that, I look at a bunch of other things when that company was acquired, and then I went into VMware. Again, when even well, it was a pretty small, not very well known yet. So it was a 40 person organization and so that built software to stitch together. So VMware was still early? It's still early. Yeah, there was two, there was three division, one division that did the workstation desktop app. And then there was the division that does the hypervisor, which is the OS underneath the OS. And then there was my division that was building enterprise software that stitched together all of the hypervisor into like a cloud platform, a management platform. Right. So I was the one for that. There's a 40 people and we kind of built the very first product suite of VMware called Virtual Center that tied to ESX. So that was a really, really fun, right? Very smart people. And then VMware really took off. So virtualization as a whole took off in the early 2000s. VMware was core part of it. It was one of the main things. So it was just a kind of hockey stick-ish experience. It was, not to the extreme of Uber, but it certainly was because it was an industry changing technology. It was a game changer, right? Before that, there was anything like that. But at first people thought, oh, this is a kind of interesting tool on the desktop for you to run a couple of Mac and PC OS on top, on a PC. But the true power was the ESX, right? Yeah. And then that's what you power data center. And then, of course, that's the hypervisor. But I think the key feature that made VMware so useful was the whole vMotion thing. When you take a virtual machine and you can migrate it from hardware to hardware without any procedural downtime of the application run on top. With that capability, unlock the whole cloud thing, right? Because you have a thousand machine, it can look like one. It can look like a machine. And so application inside of a machine will just scale and it will just move itself and it can do whatever you need to do. You can do DR, you can do all kinds of things with it. So that actually makes it very much like a cloud operating system. And then at VMware, we also grew with the company, right? So again, it seems you have this history of you were VP of engineering at the startup, you step down to a small startup, you then join VMware. And eventually you became VP of engineering at VMware, was it all, right? Yeah. I have this weird thing where when I get, when the thing gets large and I start to feel too comfortable, I get nervous. Really? Yeah. And so that's where I double clicked when I got to VP and I managed hundreds of people. I said, is this a fluke or is it real? So I had to go back to a four percent company and try to see if it's real or not. That didn't succeed really well, but the engine was healthy. It was good. And then when a VMware again, it's a smaller company and it go big. And when it get really big again, when you get to a point where you're just running things, rather than breaking ground and doing this thing or you're learning, then you got to do something different, right? So I keep on going back small and when it get big, I might go back small again. Yeah. So I'm seeing the pattern. So you got to get VMware and VMware was doing amazing. What made you look around and how did you find this very small company at the time called Uber or it might have been Uber or Cab? I'm not even sure how it was called. It was Uber at the time already. Uber Cab was way before that. Yeah. Yeah. It was when after eight years at VMware, and sometimes people changed, sometimes company changed, sometimes both sides changed. And so yeah, for me, what changed personally for me was I reached to a point where I didn't feel I could do much more there, right? I'm running 800% engineering team. We're building this software and it's been like the third generation of that software already. We're tweaking, we're adding more features to it. I love my team and all that, but you know, it just more of like keep it steady, keep it growing and add more features. And then the company has also changed along the way. You know, the original founder left, new crew came in and there's a fair amount of changes and personalities and all that. And after a while, it just felt like it's time. So now with your background, like you now have a super impressive background, you probably could have gone anywhere larger or small. What was your search process looked like? And then how did you come across it again? Because Uber was still pretty obscure. Yeah, here's a really interesting thing. People do ask me about what the search process looked like. How did you sit together? I could read like that. My honest answer is I didn't do any of that. And it wasn't luck that you bumble around and you find one thing after another. It's actually something different. If you try to do a really good job at every company you've been working well with all the people that you work with, including your own team, your peer, whatever it is, over time, very slowly, you accumulate a decent reputation in people's mind. And people always come and go throughout the industry. But if you're good with them, to them, whatever, they tend to remember that. And then when you become available, then people come to you. Yeah. But this, how about this, how about this? And then you actually look at all those things and then you can dig in and you can decide. And so that played out multiple times for me in the valley. And it actually, I think the biggest breakthrough was Uber. Again, when I left VMware, I didn't plan to do anything. Right? I'm saying, well, let's sit back and take a look to what's going on. And then Bill Gurley from Benchmark Capital who invests in early investor in Uber. And guess what? His tie was he knew me from that net gravity startup a decade before. And so we kind of knew each other. And then, but of course, when we know someone, you follow their reputation. And it was Bill who come to me after he knew that I'm living VMware. Hey, can you take a look at this one? I'm investing. It could be really interesting. So I went up to his office in Sand Hill and he shared with me the board deck and how the company is growing. And then I understood the business model. Right? To all of you back then, when I kind of recruit some people, it was like, why don't you join a taxi company? Right? Yep. I remember everyone's asking me once I joined. Exactly. And so, but I knew that. And of course, that we have to go to like, pretty rigorous interview process with Travis. And yeah, but ultimately, it's about the connection that leads you to the right thing. But that connection and the opportunity basically tied to your reputation. And then back then, as I looked it up, and you also helped me with this, Uber just raised a Series B, which was $30 million, it was worth, it was worth $300 million, which was sizable, but still not nearly the gigantic company that later became. And one fun fact that I read about is you had this very rigorous interview Travis with process, which was tens of hours or something. Can you talk about how that went? It was impressive that he did that. He committed over 30 hours interviewing me one on one. Wow. Yeah. That's like several days of like, It was two weeks worth of interviewing every single day, a couple hours each day minimum. Yeah, with passion, with intrigue. And we ended up, after a while, I kind of forgot that I was being interviewed. It was like two people kind of sharing ideas, like changing ideas. And sometimes, disagree something and then kind of work it out. And then you showed me, you took a photo of like some topics that you talked about. Can you summarize what those were? Yeah. My very first meeting, I drove up to San Francisco and saw Travis in the office. And we immediately went to the whiteboard and he wrote down all the topic on his head that, you know, he want to talk about with me. It was a really long list. There's a big long list of general topics about hiring and firing and communications and all of that stuff. All design, everything else. And then there's a shorter list of very engineering specific stuff. What about quote quality? What about QA? What about design? All that. And then there is also a list of shorter lists of the five things that he want to see in an engineering team and the culture of the engineering team. And yeah. And so that was the list. And so after we wrote the list, we start talking picking up some item off the list and talk, of course, you know, in two hours, I was supposed to meet him for an hour. It lasts two, which is actually good. We got totally into it. Time ran out. And then as soon as I drove out of the office, I barely get to the exit. I got a call from the recruiter say, go on Travis, I want to see you again and talk some more. And so we did that. And of course, he's very busy. He's traveling around all the regional offices to run the business. And so we set up a Skype session every single day for two hours each day. And we will pick one of the topics. That's why I took a picture, a photo of that whiteboard. And he did the same thing as a list of topics to talk about. And I still have it on my phone today. It's so impressive to me that because we'll show that list in this episode as well, that screenshot, but that the fact that the CEO would go into things like code review. It's the hiring topics, I understand, but that he was so injured in mind that or did you get a sense that he had the vision that technology and engineering would be just key to Uber? Oh, absolutely. I mean, he knew that. And it was very clear from the very beginning that he's viewed the business has two major engine that powers it. One is the operation, you know, business out of right, you got to have real physical thing moving around the world. And then there's technology and technology is a key part of that, right? No one side disappears to the other, but it's required both of those. Yeah. And so that was very key. And I think he also knew what he wants also and what he wants in whoever it is. And so I think this list and this this this series conversation was for him to to vest that. Yeah, later on, I, I, I think either he said something or I figured out that it was actually a simulation of what it's like to work with another person in that capacity. In the end, when we're inside, we're all working with each other all the time. And can we disagree on something? Can we work things out? Do we have generally similar philosophy and principle? And he did himself. Yeah. And so, yeah. And the level of passion and commitment he showed was just really impressive from this side. I can tell you, for example, there's some session when we totally, you know, in the middle of that and two hours gone by, and he had to like stop and catch a flight to go somewhere else. He literally stopped and told me just wait. And he picked up his phone, call his EA and said, can you move my flight and continue the conversation. Who had that level of commitment, right? And passion and stuff like that. And when you see that, it actually draws you in. Yeah. So I guess it was not a question that you joined. But can you recall what was it like from the inside, especially from an enduring point of view, from a systems point of view, from like what was going on? So it was still pretty small. It was about 30,000 writes a day when I pulled the data, the weekend, which is always the busiest time when people move around. Yeah, that weekend, the day, the Saturday or Sunday before I joined, I joined on Monday, was about 30,000 writes a day. And Uber was, I don't know, 20 something cities around the world at that point, 2030. And so it was very modest. The certain things that were going for it already, the enduring team was very young, but pretty scrappy and pretty committed and talented where whatever you need to get done by hook or by group, they'll cobble together, right? And so, and as a result, though, the service was beautiful. Anybody who ride, we only had black car service at the time, but the X-band was beautiful. And for all the people who ride it, that's why word the mouth is reaching around. And yeah, and so that was the really good part. Now, the thing that maybe Travis had foreseen or whatever it is, was the next phase, which as the company grow faster and faster, what happened, right? And by the way, the 40 engineer were very, very young. I think in the 20s all of a minute. And the system was built not to scale, right? It would build for functionality. It's actually together and it's work. Yeah, and it worked beautifully, right? But it wouldn't scale and it would crash and burn all the time, multiple times a week. And that was our lives in the French is the hockey stick actually happened. Yeah, everything breaks. And we have to basically race against time to actually figure out what we're the next most critical thing that would break and how to get ahead of it. And one of the things that Travis always tell me, even from the interview days is you got to see around corner. So I try my very best to see around corner. And one of the first thing I did when the first couple weeks were beyond getting the beginning to know the engineer and build relationship and build trust was to start examining what we currently have and what we need. And dispatch was the first thing. Without dispatch, there is nothing, right? That's what you master riders and drivers. It's our magic service, right? When it has the drivers, the riders and it doesn't match. That's right. And without that, there's no business, right? There's nothing. And so, yeah, and I start that with the first system I look at. And I asked some, I reviewed the architectures, I reviewed the implementation plan and it was very obvious that wasn't going to scale. It was a JavaScript, it's Node.js, and it was a single threaded thing. And the engineer at the time where when the city gets larger and larger, they need more out of that piece of code to power that city, they will move that piece of code into a larger machine with a faster processor. Vertical skilling only gets you access so far. Exactly. So my role also to do thing, but also to teach people along the way. And so, I was just asking leading question to the team and the team only had three people, three or four people at a time. And so, I asked the engineer, okay, what would happen if the city gets larger and you have to support that? Because every city is getting larger, the right volume is getting larger and larger. Then the entry base said, oh, yeah, we just move it to a more powerful processor. And so, what happens if you get to the fastest process you can? Oh, there are multi processors and then you can get a four-way box and then you can put multiple of these processes on them. And then you said, well, you got three or four of these things surfacing the same city, do they talk to each other? Do they share the same state? Not really, right? So it becomes a partition. So pretty soon by asking those leading question, the engineering now discovered a flaw that this thing would not scale, right? And then I had to establish the limit of where is the brick wall. And I basically started, what's the biggest city that we currently have in terms of the right volume? And they say New York City. And it's okay. When is New York City is gonna, we're gonna run our capacity even hand on New York City, even on the biggest box that we can get our hands on, it's about October and it was around May. Okay. And so, it's like, well, we have to rewrite it, don't we? And we have to write it in a really scalable way. And I only have two requirements that I already need. One is a city has to be powered by multiple boxes. Yes. And a box has to power multiple cities. That's it. So you can have N by N. You gave them these two constraints? No new feature necessary. Just make sure that we can do that. And then that allows the business, the company to just pour a whole bunch of hardware behind that. And it will scale. Technically, it will scale infinitely, right? And so, the engineer did that. And the because of the requirements are very simple. We have to do it really, really quickly before we run out of time, run out of runway, not to survive. And so they did that. And we actually deployed that right around August, September, right before it's actually actually here. And then onto the next problem, database, it's going to be the next choke point, right? And then the API model is going to be the next choke point. And we keep on identifying all these things. So there's all these threats coming at us. And we have to establish like, how much runway we have until we like really get in serious trouble. There's no way out. And then get ahead of it. And so, this was been the reason that we had so many rewrites. I joined later, but rewrites were still continuously happening. And I think when you come in, you ask like, why could they not written in properly the first time? Or like, but I guess, do I understand correctly that it was because A, sometimes you just build a system to solve your problem. And B, you don't always know how big this will feel. A good example is the New York problem. And then you take those constraints, and then you build a system. And then if those things change later, you might need to build a different system. Yeah. It also depends on how fast you're growing, that dictate how you make, because the faster you grow, the shorter runway you have to survive, right? Given whatever architecture and system that you currently have. And yeah, the question about how big and possibly grow, nobody knows really, but it's actually not fruitful to pontificate on that. It was all about how much time we have to live, right? We hit the brick wall and it's no way out, right? So yeah, and if that time is really short, then don't overthink it. Just survive that and give yourself enough runway to then live to fight another day is what I like to say. Growing fast like Uber did is a good problem to have for startups until it's not. And the pain points that come with fast growth is a good time to mention our season sponsor, WorkOS. If you're building any SaaS, especially an AI product, surprisingly, you reach the need to build enterprise features like Samo-Ledge cases, directory sync, audit logs, and all the things enterprise customers expect quickly. Building that infrastructure yourself takes months. 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Travis told you you have two months to launch in China. And apparently, launching in China was not as simple as just opening your API and allowing the firewall. What was that project like? I heard it was an absolutely manic and crazy project. Can you take us back? What it was like? Yeah, that was one of the craziest things we've ever done, but it's also one of the most amazing. And now explain why that is so. So I remember very, very clearly right around Christmas time, 2020, 2014, we were all hanging out in the big room in 2015 and Travis made a declaration that, okay, come the new year, I'm going to light it up and we're going to go into China. Okay. And then he turned over to me, it's like, one, I want that. And one of the requirement at the time was that we have to run our services on China soil, right? And data center physically. The physical data doesn't need to be there. Until then, we kind of dabble into the water by powering it from the US. Okay. And we have a limited time to do that, but he's going to light it up. I'm going to do that. And he's like two months. And I said, well, that's really tough. And he said, why is that? Because I can go rack all the machine and copy the software over and take more than two months. And then I have to like explain that it's not that easy, because when you do that, then it's work on day one. And then the two drift and how you're going to maintain that, right? We don't have twice the engineering team to actually manage two different systems that deviate. So the right way to do that is you build re-architects, whatever you need to do, to kind of build one system that can be partitioned, right? So I think there's a huge security concern, right? Because there are anything that runs over there cannot presume to have any level of privacy or anything like that. But over here, we have to protect everything, right? So we have to build that same system that has completed partition of data and controls and everything out so that yeah, nothing bleeds across. But it has to be every time you deploy code, you have to close everywhere, right? So you have to re-arch, rebuild a lot of things. And so I went to the TPM team and asked the team to actually scope it out. And I think the TPM technical program and the traditional program. And I think the best path for us was about six months. And that was the fastest we can even imagine, right? I benchmarked with a few of my friends in industry and they kind of laughed at me. And it's like 18 months minimum, that was it. But that was over. So we didn't think too much about it. It was like, let's do that. And then Travis didn't like the six months. So we kind of sat around four months, right? So we just split the difference. We didn't know anything, but we just want to head down and start getting to it. And so we look at all what needs to change, given like this is the end goal and the requirement. And then everybody started getting really busy, working a lot of hours to make these changes. And four months come and we are still a month or so short. So we slept. And we're in the Travis and he's not too happy about that, but it's fine. And then five months come to round and we're very close, but we're not there. We're about to slip again. And so it was definitely not happy then. But we actually talked it out. And I said the team feel very confident that within a month we can launch. But he had to give us something. That means give us a ability to incrementally launch instead of lighting all the city in China all that we want. Let us do it in phase, right? Every single week we'll launch a number of cities. And then we're going to do it in the process of a few weeks. And then we're done with that. And he said, okay, that's reasonable. But he won the biggest city first. And that's Chengdu. He agrees to the incremental launch, but you need to serve with the biggest city. Start with the biggest first. That's so Travis, no? Yeah, exactly. But it is brilliant though. Now, do you think about it? I thought a lot about that over time. And that was the most brilliant thing because by doing the hardest thing first, once you launch that, everything else is down here from there. The team had this swag. The team know it would go into the next set of city with confidence. Had we done the traditional way, let's start with the safest one, the smallest one for us. The next one we step it up. On the surface, it seemed like, oh, it's a very good risk control measure. But every single week we'll be holding our breath until it's done. It's not done, right? But this time, we did everything we can to do the hardest thing first. And after that, it's just the routine process to roll out the rest. And that, it worked out exactly like that. And so, yeah, we got it done. And a bunch of people were really burnt. And then they take like a month off, go to the beach and did nothing except tear the water. I know some of the, and you did that. But after that, though, we're not fearful of anything. We did that kind of the impossible. And I talk with a friend, the dooper who worked at the IT team at the time. And his job was just to get the servers physically set up. And he said that the timeline was so impossible. I think they had two weeks from start to finish. They had a little bit of time to plan, but they were on the side. They were just, and when I gathered the stories from the software engineers who worked on it, everyone had their own impossible task and project. And they all thought it couldn't not be done. And then somehow it all came together. That's right. None of us thought the whole thing that could be done, but we just got our head done and we just break it apart and just do it one step at a time. And then I think neither does this say, but the China launch was a massive success. Uber started to compete head on head with the leading Chinese provider, DD. And there were, is pretty much head on head, very intense competition all the while competing with the rest of the world as well. That's right. Yeah. Yeah. So that was something incredible. That was something incredible. And I think just the experience having gone through that and doing things that initially you didn't think was possible just increases everyone's confidence and range. And that's what's stressing all about. And I think that as I was saying that Travis like to say that sometimes you have to be willing to redline yourself a little bit, right? And that's how you prove that you can actually do a lot more than you can. That was the fearlessness of the, and the risk-taking culture that he won in the company in the first place. One thing that Uber has been very, very well known about from the outside is microservices. And from the inside, one thing that has been very talked about is a program and platform split. Can you tell us which one came first? And how do we get to as many microservices as we did? The program and platform came first. Yeah. And microservices came later. And the platform and program platform as an organizing structure came first out of necessity. When I came in April of 2013, we had 40 engineers and three product managers. By the end of that year, we had about a hundred engineers and a dozen or so product managers. Even at that really small size, we ground ourselves to a halt with a functional org structure. Imagine, a lot of engineers, there were about up to eight or 10 mobile developers, a number of infrastructure engineers, an venture backend engineer, et cetera, et cetera, and by eight people or so at Dispatch now. But every feature that we want to put out has to be queued up on mobile development bandwidth, Dispatch bandwidth, and it becomes impossible to navigate trade-off because every feature you want to do, you have to go negotiate with so many teams. And so then the team wanted to move fast and started to feel that friction and complain right away. And that's a good thing. We raised the concern. And so I remember Travis and Jeff Holder and I thought that and we got together for a couple of days actually. And I remember we had sticky notes of all the different colors. Each color represents a different function, engineering, product, designer, and we put one person name to each of the sticky notes. And then Travis gave a talk about what are the most important areas of the business that he thinks. And at the time there were like 17 areas that he can think of. The world of Uber can cover 17 areas. It turned out to be a lot more than that. But at the time it was 17. We didn't have enough to fund 17. We had enough to fund seven plus a few more. So we fund seven with partially the next four. And that's it. And the rest can remain empty until we hire more people and we fund it. And so that was the thing. But then as part of that process, we then put sticky note onto each of these areas has to be a cross-functional team because we can no longer afford to run in, no, cross-functional rather than a functional team. Which means that there's like a backend, a mobile and whatever else they need, like a design if they need to, etc. The concept is that team has to have all the skillset necessary to just take whatever they have to do. They just go off and they do that. So that was the principle behind that decision. And then we call some of those programs and some of the platforms. So programs are the team that builds things that end users actually use. And the platform are the thing that builds tools and layer that other program team use. And that was it, sort of horizontal versus vertical kind of thing. And that's that. And then after we defined that, then we start putting the right sticky note onto that box. And then that's how the first version of program platform came about. And then how did microservices started and how did they blossom as much as they did? Yeah, again, none of us wanted to go through that extreme. But lots of time when you are under a lot of pressure and no time to react, other than to survive that scale that keep on coming at you, you have to make a decision that increase speed and velocity. Because speed and velocity allow us to build things quick enough to survive. And so we knew right away that the back end API, which is a monolith, right, is the thing that will prevent speed from happening. So we made a declaration, anything that is new need to be built outside of that as a microservice. Okay. And then there's a team that's dedicated to decompose that monolith, that API monolith, into a bunch of services. We used to call it API, right? Exactly, called API, right? And I think that project name is called Darwin. Yes, Darwin. Oh, I remember. Yeah. And interestingly, had we freeze time, that piece of code could be decomposed in a matter of three to six months. But it took us two years to do that. Because as we peel out a piece of code, the business keep on going forward, right? These hockey sticks are laying on top of each other now as we launch new city and having faster and faster new city and new product UberX. Right. Feature has to be added on, right? And so the philosophy we all operate at a time was no one should be blocking anybody else. No one can block anybody else. And so when a team that needs to build feature, and that thing hasn't been pulled out of monolith, they add to the monolith, right? And then the team that pulls it out, do the best that it can. And then we kind of keep chasing our own tail until eventually, you know, something gets completely pulled out. And as it happened, it bulges up like this, the monolith, right? If you pull out one thing, the remaining stuff grew even faster than the stuff that you put out. So the code base get larger and larger, eventually reach a certain point when it starts to come down. And that's why it took two years. And meanwhile, everything that is new must be on it because we don't keep on adding stuff, right, to the monolith. And so that's how it came up to like, you know, thousands of microservices. But that was out of necessity so that we can just fan out and solve every problem all at once. And then over time, after things stabilize and so the business more mature and growth is not as violent like that anymore. Then the team, we actually have a project called Arc, we're going to look at this stuff and how do we clean it all up. So we put like domain interfaces on top of a whole bunch of microservices that are within the same domain. It's funny because I remember that around like 2016 or so, there was a published Uber blog post that Uber had about 5000 microservices. And I just saw about a few months ago, Uber published another one and they have about 4500. So in that 10 years, the number has gone slowly down, right? It's gone down. But even then, we could put it later on in front of it. Even though right now, Uber has so much more complexity, right? Yeah, that's right. Yeah. The process took a little while. But yeah, the team had to look at everything and how do we simplify that, right? And then the next cent out of that new tool has to be invented by us, Gager, the tracing tool, all that stuff. And so that'd be a really great tool that we open sourced. Let's talk about how and why Uber built so much internal tools and also open sourced a bunch of them. Gager was one of them, but internally we had Schema less, a trip data store, T-Channel, an RPC protocol, RingPop, Gelspecial, Placing, Clay, ServicePrainwork, UMonitor, Observability. And there's like hundreds of others, some of them open, some of them not. How were you thinking about that? Like, does it not seem like a lot of waste for us to build this? Or was it again, necessity? It was mostly necessity. I can't claim that every single one of those things were absolutely necessary, but all the important ones were absolutely necessary. The thing is, when I started, Uber used pretty much all the open source stuff. We used Redis, we used everything, right? Because those, the engineer there just focused on putting together a service that actually moved cars. But then as we scale, we kept on using, pushing the boundaries of the capability of those open source stuff. And to the breaking point. And at a certain point, if we don't invent something, the power our own need, by the way, this is 2013, 14, 15, 16, it's not as mature as it is right now. We did not have the kind of the big tech investment in open source back then. That's right. There was very little and most of the big teams like Google and Facebook, they were keeping their things inside. I remember, like, for example, a very painful example of that we had to face early on was we use Postgres, right? And we get to a certain scale that Postgres would randomly fail and that take our services down randomly. We don't understand. It's inside the kernel. I remember the time where I had to go on LinkedIn begging people who anybody on LinkedIn that has any knowledge of Postgres to be our consultant to help us diagnose this problem. And we spent several weeks and doing that. It was terrifying because I don't mind if we think we can do something of our own problem. It's terrifying when we have a major problem and we depend on somebody else. And we don't know because open source, there's no single person or some single company, I'd be willing to pay anything if someone can give me an answer. But there was no one, right? And so that was one of the motivators to kind of build our own data layers and all of that stuff as well so that we would use this generic database and we end up using MySQL just as table data store. All the logic on top, we have to build for our own use, right? Because then we control it on this thing and we only build the feature that we really need, right? And so that was one of many examples. And eventually we run into other brick wall of scaling. I remember in 2015, right around the holiday, I was taking a holiday trip and I go to the airport. And I took an Uber ride as usual. The receipt didn't come for two days after that, right? Why is that? Things were queuing up. We weren't processing things enough, right? And so, yeah, but that's not a deal breaker for maybe people because they just ride and then receipt come later, that's fine as long as the building and all the stuff, even when you build people late, they don't read their mind that either, right? But as long as the ride happened, the rest of stuff can be processed later, but it's still not great. Okay? When I dug into it, like our data processing capability is at capacity, right? So we have to rewrite a bunch of stuff. And then our ability to monitor things is reaching a breaking point with the open source tool that we use. So the M3 has to be invented, right? And all of that stuff. So we had to do things because we had to scale when we broke all the open source stuff that we use. As Uber, we did unusual things. One of the most unusual projects, which is where you and I met when I joined Uber, was internally called Helix. It was completely writing Uber's app. And as I understand what happened, this Uber's user experience was starting to degrade because it was really cluttered. Travis got a bit fed up with the designer team came up with a solution, which was a very nice and clean UI, which kind of the engineering team looked at it and would have been a full rewrite. And then we just did a full rewrite. Back then, I remember we had a million or two million lines of code, we had two or three hundred mobile engineers working on this. This was a massive business. And there was an extremely tight deadline set. Can you take us back on? Why did we even do this? Because from it didn't feel it felt existential threat from the inside, but it was not like a Google plus versus Facebook existential thing. And how did we decide on that short deadline? Yeah, it seemed like a recurrent theme to keep on coming up with a tight deadline. Everything we do had a tight deadline. It's just that's just how the culture roll. Anything we want to do, we want to do as fast as we can. But going back to why Helix, actually, Travis has a vision. And it's actually not just the designer Travis and Yuki, the lead designer at the time, they pair up and they all the storyboard and everything else. And he has a vision where the current app back then was too limiting. Yeah, it's really good. Push a button, get a ride, all that stuff. But if you want more services to hook in other things, messaging and all these other things as people were riding, the new architecture was much more open to all those things. And so that was the division behind that. And then when we're doing that, the aesthetic is really important, the icon change and all of these things change. And so but that is beautiful, right? That's actually Travis and Yuki. And then of course, when that slashed out, there's so an amount, then the engineering team, the mobile team get involved. And it's not just the mobile engineer, the backend has to be written to everything. We change from the heartbeat where every five seconds we would pull and it was pretty painful to actually push the channel. Yeah, this path with that by that time, it's called real time system now, right? Yes. Yeah, it has to change backend, everything has to change to support the new flows and all that stuff. And so yeah, it took, you know, six, seven hundred engineer all total seven, eight months to actually do it. Then we put it off and it's still live today. It's still on that same architecture. It was so far well thought out. It's almost like future proof in that design. So it's still beautiful today. And you compare that with the previous version. It's actually with definitely the right. Yeah, and it was scalable, user experience. I think no credit in that is like the genius of Travis and Yuki. You ever now send emails to all of engineering on different things. And I remember this really, really emotional email coming from you about naming. I see all this and I understand that young engineer want to have fun. Yeah, we were having fun. Yeah. And name things goofy way. I think the trigger for that was a service named Mustafa. I have no idea what it is. Right. I look at that stuff and by that time we were already very complicated, right? And we have to onboard new engine all the time. We want builders to ram up quickly, et cetera, et cetera. And I can imagine an engineer come in here and if all these weird name have no context to it, better time out tooling wasn't that great either. Did I do blame didn't come into existence yet? Right. And so there's no mapping to be discovered what this really mean. And then those weird naming scheme. So I got to the point where I'm kind of fed up. So I send that email out. Of course, you know, those mass email, sometimes you regret that you send it out because it has some effect, but it didn't really solve. And I think it was very quoted because you specifically wrote this is not a Mickey Mouse shop. Exactly. We're not making a mess out of it. And, and yeah, it was again, it was, it was a growing up phase for everyone in the company. But it was, it was my frustrating time was, look, at this scale and side, we got to take ourselves seriously. We got to do things better faster. And this is not helping. Twan's been talking about improving things as the org scales, such as moving to a more mature naming policy, introducing new and better approaches as the company matures leads us nicely to our presenting sponsor, Statsig. Statsig build a unified platform that enables both experimentation and continuous shipping built on experimentation means every rollout automatically becomes a learning opportunity with proper statistical analysis showing you exactly how features impact your metrics. feature flags, let's you ship continuously with confidence, rollout to 10% of users cash issues early, rollback instantly if needed. And because it's all in one platform with the same product data, teams across your organization can collaborate and make data driven decisions. They have a generous free tier to get started and pro pricing for teams starts at $150 per month. To learn more and get a 30 day enterprise trial, go to statsig.com slash pragmatic. With this, let's get back to Twan. We started to do better names. One other thing that was very, very unique to Uber across the industry and it caused a lot of confusion from the outside is Uber senior level for a while. Uber had a senior engineering level called L five, which is common. And then at some point, you were the leadership team, cut it into two, there was L five a and L five B senior one senior two. Can you tell us about why you did that? And where do you get the idea from? I did that. I'm not a apologize for it. I think it was a good move at a time. And the principle was we want people to grow, right? But we have a very clear definition and an expectation of what it is at the staff and general because we benchmark ourselves to all the great company out there, Google, Facebook and all that. And then I realized that for many engineer crossing from senior engineer, I mean, uh, and you to pass through the senior engineer to get staff, it could be a five year journey. Okay, it's a long time. And so I just want to break it in two so that people get a sense of progress. And then also not everybody can make the staff, right? But some people is good enough to make it to senior to senior to. Yeah. And I think that's a benefit. That's not right. But versus like not doing it and every kind of just get lost in that five year, you know, journey. Um, yeah. And so that was the motivation behind that. So you saw a problem and then this was a solution and it worked. We can say right. It worked for a while, right? And then people were acclimatized to that. And then they start complaining like, oh, why does two level, we need to get the staff faster. And then while I was there, I held on to that because I was a principal and staff is staff compared to the best of the industry. And later on after I left, I think it got really good. They got pushed down. Alphab is now staff. Yeah, it's just to inflation. I didn't want to do the title inflation thing. I appreciate that. Another email that I remember from you is in 2016, you sent an email saying, you've heard the feedback that NGRs are unhappy because their managers not support them. And then what you wrote is like, we are creating a very easy internal transfer process. You can move teams. How was that received? And again, how did you decide that we need to do this? I look at the talent base. And I think it is best for us to create opportunity for people to keep on growing with fresh new challenges within the company because if we don't do that, they would leave the company and seek effort. And then I thought about the next logical step, which is, hey, if people come to us and just resign, they didn't tell us when they interview. And so why the heck do we have like these rigorous process when you have to ask your manager for permission to go to another team? Why do we make it harder for ourselves when our own NGD go from team A to team B, have to ask for all these permission where they don't have to ask for the interview outside. That just makes a sense. Basically, it's easier to interview outside. It was easier to interview outside. So that didn't make any sense to me. And so I was like, well, let's not have that. And that also has maybe a good side effect where a manager now needs to be incentivized to take care of people great, develop them, grow them, put position, the best view, and to the best time for them to grow. And then they not like to leave your own team, right? If they continue to grow. So there's all of that. That back pressure might cause engineer and manager to be a little bit more responsible to. So that was that. And I remember that get quite a bit of pushback because it would be radical at the time, but we just did it anyway. And so that doubted to not be the right thing to move. And I would rather people trust each other. And when an engineer want to go, they should have a really great relationship with the manager where they just talk to the man, hey, look, I want to do this and the manager should be generally supportive. Instead of saying, no, you belong to me, that kind of thing, which is the wrong thing. I have a saying that I share with you guys all the time, right? It's not a jail. We can lock anybody down. Now, everybody have free will. If they want to work, you know, somewhere, they should have the ability to do that. And we should create more opportunity. And then we also, to support that, we publish internal job board, right? Anything on the outside, see, we see on the inside. So, and you should be able to shop within all the opposite of have inside a company and stay with the company and why make it so hard and then they're believing the company. That's just a silly thing. I remember at Uber in some of the meetings, either all hands or team meetings, you gave talks that were memorable. And one of the most memorable, I asked around former Uber folks and Charles specifically, he was on the podcast, he told me that his most vivid memory of you is this talk or this topic about behaving work in the perspective of death. Yeah, I don't remember that exact speech, but I do have that line of thought in my head all the time, right? And sometimes I would share with different audience, I have different context, but it is, it's all about finding one's purpose and not take oneself too seriously, right? If you look at people, the most accounts of people don't take themselves that seriously, right? The more you know, the more you know, you don't know kind of thing. And people who are arrogant tend to like not know enough yet or they have all that, right? So, yeah, so I always take opportunity to remind people to be humble. And the example I use always is use myself, right? I said, look, you know, when you're in an important position, people treat you really well, but don't let that get to your head. It's not you, it's the position you hold. And I remember saying this, we were like the moment I stopped being Cedro Uber, nobody can care or know about me. They were talking about the next CTO, right? And that's always happened, right? The world forget about us, right? So the only thing we can really do is in any job that we do, do the best that we can to help each other, to leave a lasting positive impression in each other. And one day, everything ends, a job and then I'll get to the more bit stuff like life even ended so. And so then I measure myself, like what is my achievement that I would be most proud of? And I said, well, when I'm gone, the thing I'm most proud of is how many people remember how I was good to them or helpful to them. And for some number of years, right? And that is that because I can't take anything with me. And so live in the moment, be as best as you can to everyone and be as constructive as you can. And and leave a good legacy behind you. So that that that was a whole gist of that. It feels to me sometimes there's talk about how you can network better and grow your network. But sounds like this is almost like it's not a hack is just do the work, right? Do the work and then the writing happened, right? But you can't do the work in service of that goal because that's very artificial. I just be genuine, just be yourself, be helpful, be constructive, uplift everybody, help people along the way, coach being doing it altruistically. And let me show you another angle too, which I personally over and over again. It's not only that other people around the industry pull you into good stuff. When you pull in and you don't have people to support you succeed, you would not you would not succeed also. And here in an example at at Uber, right? When I came in, again, the engine, we talk about very, very young in the experience, you do not know how to build system at scale, reliable, all that stuff. And the network that I have to really knew how to do that was from VMware, we're building systems off of we're in operating system, right? Rigorous, principal level engineer, experience, no, like in their sleep, they can do it, right? Right. So when I came in, and when I have to like work with a team on dispatch, I pull in the first engineer from Uber to lean land on that team, right? His name is George. And so he there and then he worked for everybody else or uplift everybody there, right? That and then this was an engineer from VMware. Yeah. Yeah. Yeah. Yeah. And then when I build payment system, has pulled in another a few more one. And then when we get to build schema less, it was a Denmark team, right? I pulled the top four engineer from my VMware team and then I moved them down from one floor to the next in Denmark, so seven office is why we had a Denmark office, which was correct, which was one of the best infrastructure offices at Uber. And they build schema less, they build schema less, they built a lot of other, right? And so now if I weren't a good person doing a good job for them with them, why would they come? Yeah, they wouldn't answer the phone. Yeah, they wouldn't answer the phone, right? But everything I wanted, I call because I really needed help. They all came. Initially, they all asked the same question, why a taxi company? But when they understand that, they came, right? But they came because they still enjoy working with you, right? There are people who work with me for five different companies over 28 years. And that always surprised me. I think this is something that people might overlook a little bit as they're building out offices. I'm talking with founders is one thing is where you can hire, there are things where the good people stay for a long time and there's a lot of value in that. And Denmark kept being very core critical infrastructure. Yeah, core infrastructure software team. And that's one of the things we had to build at Uber because back then when I came in, we didn't build infrastructure for it, right? We just use existing open source stuff, right? And we build that. And another thing that I, you know, discover along the way is great talents are everywhere, but, you know, you have to bring opportunity to them. They don't necessarily relocate from Denmark to San Francisco, right? And so that's why we end up having nine engineering offices around the world. Because we have a lot of work to be done. We didn't go to other places because of car savings and like that. We go there because we have need and we have world class talent and we just cherry picked the world class talent. It doesn't matter what size it is and Denmark team was small compared to team in India, et cetera. But, you know, there was really great talent infrastructure and we'd invest on that. Lithuania, amazing DevOps team. And so we just go to where the talent is and then we bring the great work to the great talent. And then we establish a structure to manage and give people first class ownership of the problem. And then, you know, everybody's kind of equal. At Uber, you talked about several times of your three chores of duty. Which one for these? Yeah, again, it come back down to purpose. So when I do something, I try to be intentional about why am I doing something? What's my purpose of doing that? And so of course, my purpose to come into Uber was, hey, let's build this business. I just be the tech that support the business. And so the first couple of years, 18 months, 24 months, we're fixing a lot of the broken stuff. Things weren't reliable, become more reliable, et cetera, et cetera. Rebuilding basically just getting to work and work well. And then along the way, you know, these things don't end and beginning on a particular day. It just phase in and out, right? So phase two, that was called my second tool of duty was scale, worldwide scale. That was China. That was massive scales everywhere in every dimension. And so, yeah, so at each of those phase, when you're done with that phase, you ask yourself, am I still useful? Do I want to read up my commitment and energies and everything else? And so the first two phase were no question, right? With there to do that. And then as phase two, we're about to wrap up, right? About 2017, we actually kind of stabilized. We're really big now. I was actually asking myself that question, do I need it here anymore? And I was actually about to wrap it up that summer. Because, you know, at that point, we had also another STP that was higher. And I think he's really, really great technically. And I can like feel very, very at peace. Kind of, you know, there's someone who really take it on even better because the person has done even bigger thing at Google, right? Yeah. And then, but that didn't work out. And then we had a really rough year. So then I have to like sign myself to the third tour of duty, which is, and what is the purpose of that? You know, help the company get through the turbulent years. And I had no idea at the time when that phase would end. I just kind of know the condition for that to end, which is whenever the next CEO arrive, right? And then after that, whether that person like me, I like that person, whatever it is, that's to be decided. But that third phase, I have to stick it through because, you know, we owe it to ourselves and we owe it to everyone along the way who have built Uber to that point, right? To get through that turbulent phase. So we did that. And then now when the new CEO come in, and you know, I stayed on until 2020. And so in 2017, I remember it was really turbulent. Travis had to step down for a while. A group of, I think 14 people who were, Travis's Erica Ports, they took over steering the company. You were one of them. So this is the point where you decided that if everything would have gone smooth, you might have actually just left, but you decided to stay on to help the company, help the team to help us get through. Yeah. Because Uber was built by 10,000 to 1000 people, right? Past and present. The fact that people built somewhere in the left or right before the moon, that's so fine. They still work was still in there in some way, right? That led to that Uber that we have there. And it was a really important thing that we all built that many of our lives were. And then just to suck it out, we weren't public, which went good. And then it went okay. And then, of course, COVID happened, which really hit Uber. And a few months later into COVID, you did step down. Why did you leave Uber? And why was the timing when it was, and what motivated you to say, okay, this is a time to go? Yeah, it didn't have anything to do with COVID, really. It, you know, I was, I'm lucky enough to arrive at a point in life where money doesn't matter, right? And so then I asked myself, why am I doing anything? If I wake up every day and spending X number of hours on doing something, why would I do that versus something else? And so it come down to three things. One is, do I really love the mission and what I'm doing? And the second one is do I feel like my being anywhere, right, is making a really big impact? And the third one is am I enjoying the company of people I'm working with, right? And if several of those dimensions are lacking, then at some point is not enjoyable in totality anymore, right? Then it come down to, okay, when you wake up and you spend 50 hours a week doing something where money doesn't matter anymore, I live very modestly, so it doesn't change anything. So then why would I do that versus doing some other thing? And so I think that was, that was the realization at that point, where I'm more like, okay, I'm there doing a big job, but more or less running things rather than, you know, being very much more effective and building the company like in the early days. And so, and at that point, I think it's actually much better for other people who take a crack at that job. Again, it's, you know, like everything ends, right? And so you have to decide yourself. And then give opportunities to other people, right? So like, and they did pick it up. Now, after Uber, I remember you did an interview with a publication, I think you said that you're thinking of retiring or you'll see, but then you were not done. Oh, no, you did other stuff. You had coupon, new bank, fare. Can we talk about what happened? What was your thinking? And you never been left for a moment, honestly. Well, I blame that on COVID. So seriously, that one COVID had everything to do with it. So when I left, we had a plan to travel the entire summer, because our daughter was between eighth grade and ninth grade, when she was about to enter high school. We had African safari plan with all the other travel plan. Everything got shut down. Everything got shut down. All the flight tickets, all the country closed. And so we stuck at home. Right? And I don't remember at the time where I'm the only one who go to supermarket to go and then then like various parts to kind of race through, I pick up what you need and you get out with all the mass and yeah. And so we kind of bored though. And so I am bored. So I sit at home and we all got on Zoom call and lots of people want to kind of chat with me to not surprising. And so I took a bunch of call and one of them was the founder of coupon. And we had really great chat and like our charging person wanted to get a lot of things done. And I really liked that. And I think, well, I'm not doing anything here anyway. So might as well make myself useful. Right? Again, it's about how you spend your time. And so yeah, as I did that and I joined there and I helped some, but I learned also a ton because that's also a very interesting area, right? The Amazon style logistic and the way coupon does it is to talk about five hours, six hours delivery. Wow. Yeah. You order before midnight and the things show up in your doorstep five o'clock in the morning. And when I was there, I joined the delivery truck, putting package in front of people's home like two, three o'clock in the morning. And it's brilliant, right? And all those things that you learn and you learn a whole bunch of these things. And so yeah, it's a really great use of time, right? Given the circumstances. Yeah. And then you became, was it an advisor or a board member at New Bank? A board member. Yeah. And New Bank is for those that don't know and outside of the US or Europe, it is the most successful and highest valued non-US companies, the largest growing bank in Latin America. It's, it's, it's, it's, it's, it's, it's engineering culture. I hear amazing things about you're the first person I'm actually talking about. So what, what did you learn there? And you're still involved, right? Yeah. Yeah. I'm still involved, but as a, as a board member capacity. And for a while, I also took on a more active responsibility to mentor the CTO, right? A couple of them. And so yeah. And so I, I, again, it's all about being useful. We all learn a lot in our journey and working with really smart people, really motivated people younger and in part that knowledge and sharing, you know, what you see and advise, help people move forward better, faster. And, and I find that very fulfilling. And so, so that was that. And the, the culture there is very vibrant. I mean, it reminds me of our early days over when everybody's gung-ho, hard charging, the founders hard charging everybody is. And when I visit there, usually during board meetings, once a year, we kind of go down to Brazil. And we will have all hands with the entire company. And sometimes I also did all hands with the engineering team and do AMA style, the way we all did it. And so it's just very energetic, right? And, and there are, there are many factors to their phenomenal success. One is like very much like Uber, they actually solve the right problem at the right time. There's a whole bunch of unbanked population before New Bank came along and they deliver like leapfrog, like of traditional banking just online on the app and they experienced this beautiful, the NPS score is through the roof. And it ultimately, it adds a lot of value to people, right? Life. And that's why the adoption rate is crazy high, right? And, and, and so yeah, well executed, amazing product vision, phenomenal cultures and energy. And all those factors are very common and like great companies. Then we experienced one of those things at Uber too in the early days. So it's really very energizing being a part of that. And they're doing great. And now you're at CCO at fair. What made you join fair? I took a couple years off when my daughter was finishing high school because I figured that time would not ever come back when she's gone and she's gone now. What was the right choice? Oh, absolutely. I would not take that time back. I'm good. I'm so glad. Yeah. Now 10th, 11th grade and 12th grade. I get to stay home, drop her off, pick her up, cook, hang out together, help with color application, all of that stuff. And so the bond we had was really cool. And as I was thinking about her going to college, I was saying, well, I'm going to have a lot of time on my hand. So what should I do? Here we go again. Exactly. Right. And so should I join another board, which I was about to. And then at the last minute, some partners, Acoya asked me to meet Max, the CEO of fair, and really liked him. Very smart. Again, all the same characteristics, very smart, very hard charging, one to all the right thing. The business is empowered, local businesses. Can we talk a little bit about that? Because from the outside, when you Google fair and I look at it, it doesn't tell you exactly too much. Feels a little abstract from the outside. It is a B2B marketplace, right? Between big brand wholesalers and retailers. So people buy that and then stock their storefront. And so yeah. And so all the traditional two-sided marketplace dynamic apply. And the mission is very similar to Amazon, but even though we will be to see, right, this is B2B, but it's all about what can we do to empower local businesses to flourish, right? So to buy the right thing to sell through, make a profit, grow that business. So basically, this can help small and also large businesses to actually just grow their business. May that be like just getting in inventory. More supply, more successful demand, more demand, more supply, all of that. So yeah, it's like you have really marketplace. This is really fun and very complex. And so I really like that. And I really, when I dig in, I do the interview process and everything else. And again, this company moved really fast. Within a week, everything was finished, including my homework assignment, right? I have to go and present and everything else. And so I really, the company moved really fast. It's energizing. And the culture is super nice and super kind, you know, like no politics. Everybody's just focused on doing the right thing and working with each other, taking care of one another. So it's a trifecta. Doesn't matter if the company is really big or really small, right? But it's got all the ingredients. So I said, wow, maybe that's a good place to jump in and help out. And can you give us a little context on fair in terms of the size of the company, the size of the engineering team, where the hubs are, what the work is like, is it in person, is it hybrid and so on? Yeah, the company is about a thousand person. The engineering team, including the data science team combined is about 300 people. The work we are in the office three days a week. Yeah, three days on a week, the other two are working remotely online. Yeah. And some people throw up more, they live closer to the office. Yeah, the engineering team, there's a portion here in SF, just down the street from here. And a large part is in Canada. They would have a big office in Waterloo, and we have a big office in Toronto. So I make the trip there quite often, every five, six weeks as I'm over there. And what are some interesting engineering challenges that you're excited about right now that you're solving? Oh, right now, clearly the most exciting thing is AI and how is it? I change everything so quickly. Oh, yeah. To tell me, what are you seeing, what's working, what's not at you, like on your teams? In my team as well as in the company, we're using AI to boost everyone, effectiveness and productivity is an output. And so that's one. Within the engineering specifically, we use AI to make certain recommendations better, right? Because the whole job is to help people discover things that would sell really well for their business, etc. And imagine AI as a shopping consultant, right? And all this up. And then coding wise, AI is doing a lot more of the coding now. But we also used different techniques to actually boost engineering productivity. Have you heard of like swarm coding? So swarm coding as in the agents? Yeah, a whole bunch of agents are swarm of agents, right? It's pretty new. So you're using it. So we are really using it and we we're building orchestrators to orchestrate the action of all this agent. And we measure the first the early adopters, and then the bulk of the engineer follow through after we build the more robust tooling. And we see dramatic lift in engineering output. Among the early adopters, the one really efficient at thinking this way, right? Because it's very different from a linear kind of thinking when I write this code right now. It's almost like multi-threaded program with single thread, right? You have to think about all these other things. You have to prompt all the action. And then you have all these code get coming back to you and you have to review it. You have to sit together. Yeah. And it required a different way of thinking and the cognitive load may be a little higher. But the output is dramatic. We have seen our best engineer double their output. I know we're talking about that, but just to make clear, we're talking about not the code output, the actual business output, the impact of their work, right? Yeah. The impact now depends on the evolution of AI, right? So right now, the state of the art right now is it's very easy to make large scale changes, right? Clean up and everything else. So massive productivity increase. Now we're trying to crack the next frontier, which is how we get that level of productivity increase and output building new features on top of a code base that are older, right? It's not like you and I can just go build something brand new, not entangled with anything. It's really fast. The whole thing will generate for you, right? Yeah. But we got millions of lines of code and how do you like deal with that and build a feature on top with all those dependencies and all that stuff, right? Can AI good enough now to help us untangle some of those things along the way, building new things? And so we actually, you know, continue to work on that and figure out how we can actually continue to boost more and more productivity out even building a feature with AI. How do you think AI will change software engineering and what a software engine does and on what skills we value? Yeah. It's already changing. I mean, very rapidly, the changes are fast and anything I've ever seen, including the internet, right? Back then, I remember when we first learned how to do programming, we have to know a lot about the machine architecture. We have to know about virtual memory. And then we have to learn how to syntax and coding. All of that stuff has been abstracted away now, right? It's such a way AI use like, I want X, Y, and Z, blah, and it should be this way and whole thing get constructed, right? So it elevated the level of the playing field where people who don't even know how to program can now create good, decent code and whatever it is, I look on the surface, it'll be really good. So it is game changing, right? It elevates the playing field. Now, then in that level of abstraction, how do you tell the great engineer from the good engineer? Great question. How do you? Well, from what we see so far, the great engineer are still finding a way to leverage this and accelerate the output even more. Then we see the difference between the great engineer and an average engineer is two 3x in terms of their capability. They're more inquisitive. They're at the bleeding edge more. They're more innovative, right? And then there are people who like, okay, well, here's the tool that you give me. I'm going to be two times more productive, right? Because I'm using this tool. It's great. But the great engineer continues to break new boundaries. And so I think that is still a very, you can still, you can look at people and you can see who are the high performer versus who are average. So do I hear correctly that the, what the traits that you're seeing great engineers is, we didn't mention, but it's kind of a given the foundations plus curiosity, curiosity, innovation, fearlessness, willing to innovate, willing to stretch, willing to try new things and break new ground. All of those traits that's interesting. If I think back to like just the upper days or your startup days, that those traits were kind of the traces of the sound out. That's right. Those are things that make someone outstanding versus someone average. So I guess maybe an advice is like, well, I mean, try not like if you were a great engineer before, just don't be complacent and keep using, keep approaching the same way, right? Correct. Yeah. Complacency is death. I mean, like every, the world will move faster and faster. And the moment we stand still, we're falling behind. This sounds like if you, if you work that a fast, fast space startup before, which is this is how it works. Hey, I should be familiar. Welcome to the way it was before. To me, it is a incredibly powerful tool. In the end, it's still a tool. And you can wield the tool properly. You can do extraordinary thing versus you just merely use a tool in a mundane way. You're not going to be a great stuff. So we talked about sand and engineers in this age. I'd like to talk about something that I cannot talk with too many things. Sand out CTOs. You have now been CTO at multiple companies. I lost for VP of engineering. You've been at some of those higher highest engineering leadership and at Uber at fair, you've done an outstanding job as a CTO. What is the most important job of CTO? Yeah, there are a couple of angles to this. One is build a high performance team, right? Talent, culture, all of that. You know, whatever it is that you got to do, put the architecture, put the, you know, develop the talent, prune, you know, bad folks out or whatever it or everything that you need to do to make sure that you have really high talent density. Because when you have team A, team A would just want a high more A level players. And yeah, they're just intolerant of anybody who's not performing, right? So when you get to, but you got to get to that concentration and then it's kind of just self-protecting, if you will, right? And then of course, you have to create an environment where people really trust each other and align and work really well together. Right? Because you put an all-star team together, doesn't mean they work really well. If you don't have like the cultural alignment, right? So that organizational side of things I've always deeply believe in, like if you do that one well, then good outcome will just happen. It doesn't matter what you want to do, right? They will just be able to come out with great results because we have great talent and with great motivation. The other side is you have to look in the future and see around that corner, right? For example, I always think about two years out, what does great need to look like? Okay, do we have the key ingredient, if you will, talents and otherwise to actually get there? Whether it's architects, leadership, whatever, right? And what problem are we trying to solve? What would the business look like? So, you know, the famous Wayne Gretzky quote is, get to where the puck would be. So, yeah, then envision that future and I would share this with everyone at every management level that when you're at any level, your job is to see a little bit further out in your folks, right? Because your folks are busy working on the near term things, right? And then you have to see, because if you don't do that, then no one, it's your job to actually do that. Right? Well, let's put this to the test because right now is the most, so many people including me, see it's an unprecedented time with growth. How do you look around the corner? What do you see around your corner right now? Like what will be coming, maybe not in two years, but even in six months? Well, in six months, you know, we know what we need to do. In fact, it's too short, right? Like these are the things that we... Let's talk about two years. I recently asked OpenAI on what they see in two years and I go, that's too long, let's talk six months. But in context with everything, right? For them, they're trying to reinvent that future and sometimes things are changing too fast there from that context. But from fair the business, yes. We know what business result we want to drive. We know what project we need to execute, right? To me, that's pretty much lock and load. It just requires good execution, right? Good adjustment along the way. To me, 18 to 24 months out is my job to look at while my team is worrying about the six month problem, right? And so for example, there are many areas that we need to clean up, right? There's the data ecosystem that's been old and you know, the same problem we started over too. Something changed upstream, breaking downstream and how you re-looking it up with all this older code base. Then what is the next generation of search and discovery that AI driven, right? Because something needs to look like. Proctivity, right? How can we leverage AI to like double output on future development, right? So all of these things, what does that future need to look like? And do we have the horsepower and to get there, right? In terms of expertise, management and planning, all that stuff. And then if the answer is no, then it's like the next question, how do we actually position ourselves there? Who do we recruit? And so that's the job policy on the sort of the non-management side. And then finally, what advice would you give to a young engineer, someone let's say 25 years older, a new grad who is entering industry right now, lots of change. For folks who are entering the workforce right now, I have to acknowledge it's a very scary time because it's very bumpy. Even at our company right now, we still bring in new grad, but it's come through our intern co-op channel, right? We're not in the world where we just hide massive number of new college grads like the old days anymore, right? But if great people are still finding opportunities, right? We have a healthy cohort of co-op every single four months that come through, right? And the best of the best still get offered from us because if we don't hide those folks today, what senior engineer we will have for years from now, right? You have to feed the talent pipeline and great people, great people, they will learn and grow. And so the opposite will always be there for great talent. So invest in yourself. When a student volunteer doing inter-salt hard problem early on, the earlier and harder you work early on, the better you will have in the future. If you take it too easy right now, then the road in the future might be a little harder. So I think that's the key. And then when you enter the industry, it depends on, I think career phases. I would say the first five, 10 years or so, find out where you learn the most, that push you the most because those are the time that you have the most energy to develop your skill and ramp up really fast. And when you get to senior engineer staff, engineer range, then you know enough to be very dangerous in terms of making a big impact, then seek opportunity where you can make a big impact. Maybe a smaller company will allow you a bigger stage to actually make a huge impact. Take some of that risk and do that. And that phase should be about using what you know and make this big impact as you can and you will learn along the way too. And then when you get to the next phase where hopefully if you're really good, you're already at this, you know, principal engineer, senior staff or on the management side, senior director, VP, whatever it is. And then that point, you learn to give back. You learn to coach and develop people along the way. You'll be leading them and be responsible for very big things. You know, apply that knowledge to do a really great job, but also teach and bring other people along. So different phases, you should change the priority a little bit. Tuan, thank you so much. This was a great conversation. My pleasure. So I hope that everyone will find this useful. What a conversation. So many of these stories have not been told before. And I hope you enjoyed them as much as I did. The microservices story is such a good one. Nobody at Uber planned to have thousands of microservices. It happened because every time they tried to decompose the monolith, the business was growing so fast that other teams were adding to it faster than the decomposition team could pull things out. It took two years to do something that in isolation would have taken three to six months. Uber had unusually violent business growth that resulted in unusually fast cold growth and microservices helped Uber tame its growth. But unless you're growing at the speed of Uber, you probably will not need thousands of microservices. Oh, and fun fact, in 2026, Uber has fewer microservices than they had in 2016. I also found it fascinating how Tuan's entire career was shaped by relationships he built by simply doing great work. Bill Gurley reached out about Uber because he remembered Tuan from NetGravity, a company from a decade earlier that didn't even win this market. The engineers Tuan pulled from VMware into Uber came because they genuinely enjoyed working with him. There was no networking strategy. Just years of being good to people, compounding quietly in the background. Finally, Tuan's point about AI was an interesting one. Complacency is death. The traits that made someone a great engineer before these AI tools, curiosity, fearlessness, willingness to try new things, are exactly the same traits that make someone great with AI tools. The tools changed. What makes people exceptional has not. Do check out the show notes below for more deep dives on Uber and Uber's engineering culture as covered in the Pragmatic Engineering Newsletter and podcast. If you've enjoyed this podcast, please do subscribe on your favorite podcast platform and on YouTube. A special thank you if you also leave a rating on the show. Thanks and see you in the next one.