How Great Tech Leaders Think and Decide | Ex-Meta CTO & Gigascale Founder, Mike Schroepfer

1. 0:00 – 1:33

   Intro

   1. MSMike Schroepfer0:00

      Hi, I'm Mike Schroepfer. Um, I spent 25 years building, starting, and scaling technology companies. Worked at a small startup. Worked at two small startups, actually, in the dot-com boom, then started my own company after the dot-com crash. Sold that off to a bigger company, Sun Microsystems, then joined Mozilla, which made the Firefox web browser, helped ship 1.5, 2.0, 3.0 back when version numbers were small. They weren't in the 70s. And then in 2008, I joined a little social network called Facebook, which was, you know, smaller than MySpace at the time. And then over the next 14, 15 years, led engineering, built data centers, uh, took us into the consumer hardware business with virtual reality headsets, managed multiple billion-dollar acquisitions, built an AI research lab. Sort of built hardware, software, enterprise, and consumer, and scaled a team from, you know, 100 to 35,000-ish. I'm now helping great entrepreneurs through starting a venture capital firm called Gigascale Capital, where we are hunting for founders with big ideas using technological advances to build products that makes people's lives better, and it solves a climate environmental problem, and it turns out to actually be a great business along the way. Easy example of this that is, you know, electric vehicles spew no pollution. They're cheaper to operate. They're cheaper to maintain. They're faster. They're quieter. They have been traditionally more expensive, but as batteries get cheaper and cheaper, they're getting more and more cost competitive. And so we're looking for products that are cost competitive and people love, and then also are better for human health and for the planet. That's a version of what we do. [upbeat music]

2. 1:33 – 2:57

   Scaling Facebook: Building the Backbone Under Pressure

   1. MSMike Schroepfer1:35

      It was August or September of 2008. We had a product that people loved, the website facebook.com. More people were signing up every day. Those are new features being added to the site all the time. At the time, the most urgent problem was scale. Literally, the problem was, you know, every week or every month on keeping the site running. And the software backbone and hardware backbone for building this sort of product didn't really exist. So a lot of the first many years I spent there was scaling and rebuilding the software architecture of the site, and then building the hardware infrastructure to do this. You know, when I first got there, we were releasing space in those data centers and putting servers in them and, and building it up. But because of the financial crisis or the real estate crisis in 2008, people had stopped building large data centers, and so we couldn't get more space, so we had to build our own. And there was a lot of challenges and, you know, we had to learn a lot. None of these things were perfect the first time. We made some mistakes, and we had to fix them. And we had enough humility to know that we had to learn a bunch of new things. And so, you know, the first goal was hiring people who had done data center work, who had done network design, people with the actual background in this space. So the goal was go hire a team to understand this new area and then help us get good. Like many problems in a company, you know, if you're a founder or prospective founder watching this, most of the things a startup does, it does out of just complete necessity. It's like, well, we can't get space. We don't really have a choice here. We have to figure this out.

3. 2:57 – 3:40

   You Can’t Avoid the Hard Problems

   1. MSMike Schroepfer2:57

      One of the lessons that I took away was there's no getting away from the hard problems. You just gotta get to it. So it's like, okay, if we need to solve this problem, let's, like, figure out what are the critical risks, what are the things we understand the least or the highest technical risks, and let's work on those first. I think there's this, like, human instinct to solve tractable problems, so it's kind of like, I've got this task I wanna do, but my room's kinda messy. I'm gonna go clean my home office. I don't wanna go work on the hard thing. And, like, that's a very human sorta trait. So it's, like, important to work against it to say, like, "Actually, no, it doesn't matter my office is messy. Like, if I don't get this pitch done right and get, raise money for this company, then nothing else matters. It doesn't matter if my office is clean or not." So I think that trying to get people focused on the right hard problems and running at them is, is really critical.

4. 3:40 – 5:49

   Why Meta Went All-In on AI

   1. MSMike Schroepfer3:41

      [gentle music] Early years of Facebook, it was sort of just pants on fire all the time. Wasn't totally solved, but it wasn't so much consuming all of our energy that we had a little bit of energy to look forward. 2013, Facebook started the AI research. One of the questions was, you know, should you establish a more broad-reaching research lab? There were things like Microsoft Research or IBM Research or others who were kind of like, could do software theory, could do lots of different areas of technology. The most prescient part of all of this was to say AI is such a big thing and so impactful that you wouldn't wanna spend time on these other things. You'd wanna take all the energy you had and focus it on AI. This is a debate Mark and I had, and I, you know, I think he was the one actually who kind of pushed to say, like, "Let's make sure we just do AI." Part of it was having visibility to what was going on in the ecosystem. There was, you know, this, the ImageNet challenge was this famous academic challenge of object identification that no one was paying that much attention to, except when the first neural net entered the challenge and sort of was so much better than everything else. It is one of these rare moments. You know, there are these moments in tech where something shows up, and it's like, oh, my gosh, that thing is, like, 10% better than anything, a very large amount compared to any other gap. And it used neural nets training on data, so it was a different approach. You then look at that thing and say, like, "Okay, is that at the end of its runway in terms of capability, or is it at the beginning?" And you say, "Well, what's powering it?" You say, "What's powering it is, like, the size of the neural net, the size of the dataset, and the amount of computation you can give it both in training and in inference." And even at the time, it was like, wow, we, we can scale all of those things by thousands of times easily. And so even if we invent nothing new, you could get a lot more out of this. And so this is what I'd like to say is, like, it's a technology with a lot of runway. It's not fully optimized. But, but the point being is as a technology, it was at its infancy in terms of its ability to grow and scale. And those are the things that I think are really exciting, and I think when we've, you know, when Meta's invested in them and when I've invested in them, that's where you have a lot of opportunity versus things that have been optimized for 150 years, and there's just not a lot of room for, for improvement.

5. 5:49 – 8:08

   Take the Leap: Believe Before It Works

   1. MSMike Schroepfer5:50

      Everything always feels obvious in afterthought. And I had definitely seen people who, at the time something was coming out, questioned and doubted it. And then three years later when it was obvious said, "Oh, I knew all along this was gonna be great." And I was like, "Well, no you didn't." Um, and, and I think it's fine. It's like human nature to doubt something until you can touch and feel it. And you had to have take that leap, and not everyone, you know, beginnings of a new technology takes that leap. And they say, "Well, I, I just don't believe it's gonna scale or work." And sometimes they're right. Like, sometimes things don't work and they don't scale. You know, and then every time you get something to a useful point, there's always some other useful point it hasn't done yet. So we, you know, the very first part of AI that started working commercially really well was this sort of ability to analyze images and to say like, "Okay, we can start labeling things in these images and understand them." Um, translation started to work reasonably well, so you could do texts, you know, from one language to another. But this idea of a sort of chatbot that I could talk to that w- had any semblance of intelligence, it was better than anything we had before, but still not very good. At the time, the demos were terrible. You know, you could barely say how many people were in this photo. Is there, is there a cat in this photo? And if you think of this from a, like, what can I do with it standpoint, it wasn't obvious. Like, this w- it wasn't good enough to do anything with. And so you'd have to look at that and say like, "Oh, no, no, that'll get better," which is hard for a lot of people to believe. And then it's not until you can touch and feel, and it's really the kind of the ChatGPT moment where most consumers actually had their first experience with an AI chatbot. And you're like, "Oh, wait, it actually can do some useful things for me now, I believe." You know, you have a similar experience with self-driving cars. Most people, like, conceptually get scared of them, and they're like, "Oh, it'd make me super nervous." I've taken a lot of people on Waymo rides in San Francisco. It's one of my favorite things to do. You get in the back of the car, like five minutes in, you're bored. You're like, "Oh, it's like a better driver [laughs] than, than most distracted, tired humans." Um, and you're like now on your phone, and you're like, "This is boring." And so this is the challenge of new technology, is I have yet to encounter a new technology that until I could show it to you in a way that was obviously useful that you could personally experience, it is so easy to doubt it. Once you get to that point, you've, like, captured all the value. So a lot of the challenge is like how do you identify technologies that have an opportunity to get to that point but aren't there yet? Um, because that's where the sort of place to have impact

6. 8:08 – 10:46

   Three Core Questions for Spotting Breakout Technologies Early

   1. MSMike Schroepfer8:08

      is. So if I'm looking at a new technology trying to decide whether this is something that might be a breakout, you know, transformative technology, I think there's three core things I'm looking for. The first and most important question is sort of understanding the light speed test. As far as we know in experimental physics, you can't go faster than the speed of light. If I was building spacecraft, you know, and I was at 99.9% the speed of light, there's not a lot of room for me to get faster, right? And you say, "I'm gonna make it twice as fast." You're like, that's really, really, really, really hard. You know, if I'm at .00001 the speed of light, I got a lot of room to go before I've hit any theoretical limit. And so for most technologies, my first question is how far away from theoretical maximum is the current version of the thing? Like, how much headroom do you have to scale it in improvement? Is it thousands of times? Is it 1%? Um, that's question number one. Then question number two is are there tailwinds, are there things that are happening that make this technology better that you are not working on? And usually this means that there's some input into the component that improves year over year without your work. In the AI world, for example, the idea of getting more compute power was happening without our effort because NVIDIA, TSMC, ASML, the entire chip ecosystem was developing faster, more powerful chips year over year. So for the same dollar, I could get every 18 months about double the compute power. So I didn't need to do any work. I didn't need to go build a chip team and go do design chips. It's just like every year I got more computation without me doing a single amount of work. So that's question number two is do I have some tailwind making my product better even when I'm asleep? And then number three, which is the hardest, is what problem are you solving, and how important is it to your customer? Your customer could be a consumer, could be a company, but there are plenty of amazing technologies that have made great advances that don't actually solve a problem for people. The biggest example of this is 3D TVs. For a long time, everyone's like, "3D is, it's the next step forward from 2D. It's more immersive." It turns out people don't wanna put, like, special glasses on, you know, and sit in their couch and do it. So 3D TVs were a technological leap that didn't solve a problem for consumers that they cared about, so it's been mostly a failure. What we do with advanced technologies is you try to mock them up and say like, "Okay, I haven't built the thing yet. What's the best sort of proxy of the thing that I can use to show people to get a sense of if I built this thing, would you like it?" That is, that customer exploration is, is really important because ultimately someone has to buy that technology. Some business model have to pay for it. Someone has to buy it, uh, to fund all the R&D, and if there isn't some loop of money there somewhere, it'll eventually die.

7. 10:46 – 12:24

   Why I Left Big Tech to Break the Bottleneck to Progress

   1. MSMike Schroepfer10:53

      To me, the problem was obvious. I mean, it's something that I've been passionate about for a long time. I had the very first Nissan LEAF, which is the first electric vehicle I could buy. Early generation product, so it was terrible. Had very bad range. Really, the question was, is there really anything I can do about it? It just, it feels like a big overwhelming problem, and then it takes either some hubris or some naivete to believe that I can actually have a meaningful impact on it. And I think what I decided at some point was it didn't really matter whether I could. I had to try. And when I think about the problems humanity needs to go tackle, massive amounts of additional clean energy is upstream of everything we wanna do. You know, if you sort of take a longer view of the Industrial Revolution, really what humanity has done is we've harnessed energy, whether it's animals, whether it's fuel or sometimes renewables, do work for us. So instead of manually farming or manually digging, you know, we have machines that do it for us, and that has had a huge uplift in productivity and in human health and happiness. You know, the only way we're gonna get AI progress is by massively increasing energy use. The only way we're gonna get people in comfort and air conditioning with clean water, it's, it's energy. That is the upstream problem to everything you look at. We know how to desalinate water and make clean water. We know how to keep people cool on a hot day. We know how to manufacture lots of different things. We know how to make super intelligent assistants. We don't know how to scale that to 8 billion people without terawatts of additional clean energy, and there are lots of ways to go solve that problem and lots of entrepreneurs off tackling solutions that can, that can have an impact in the world at that scale.

8. 12:24 – 13:25

   Startups Will Solve Sustainability

   1. MSMike Schroepfer12:25

      And it took me a little bit of time to figure out exactly the mechanism to do so, and, and what I figured out was solving sustainability is, is re-engineering ten sort of trillions of dollars in our economy. You know, energy alone is a multi-trillion-dollar business. And if you wanna do that, you can't do it with government money. You can't do it with philanthropy. You need businesses to be investing, and the place to do that is usually through startups, is you've got this cohort of really amazing entrepreneurs out there chasing ideas from fusion to next-generation microreactors to offshore AI data centers, dehydrators for your kitchen. And that my personal experience in building companies over 25 years was just uniquely valuable. As I spent time with entrepreneurs, I realized there was a lot I could do to help them skip over the common mistakes you make when building a team. How do I hire executives? How do I manage sort of product development? All of these sorts of things. And so there's just a great co-align with my skills and sort of the change I wanna see in the world, and it's just awesome to see these products take off in the market. And so there's a lot of exciting

9. 13:25 – 15:34

   Better, Faster, Cheaper: Products People Actually Love

   1. MSMike Schroepfer13:25

      work happening in clean power that is effectively unlimited, meaning we can ten X, five X, you know, multiple orders of magnitude above what we're using today worldwide in power, and power the whole planet. The most exciting and most ambitious of this is fusion, the idea of basically the, the power source of our sun. We know it works in the universe. We've actually made it happen on planet Earth before. We know how to make fusion work. We just haven't yet figured out how to turn it into a reliable power source. If I can make it work, I can build a power plant that requires almost no inputs and produces no emissions. It's completely safe, and we can build kind of unlimited these. Power plant that could power all of Austin, Texas, would require one pickup truck a year of fuel, which is absolutely insane. That would take train loads of, of coal cars or amazing amounts of gas to do the equivalent sort of generation. So from an efficiency standpoint, it's sort of the end game for power generation. So this is energy, and I could talk a lot more about those, but let's talk about, you know, just one other example of something that people watching this might experience at home, which is, um, you know, the idea of throwing away food in the garbage. When you throw food in the garbage, it goes to a landfill, it rots, releases methane. This is a major source of near-term warming and waste. There's a company called Mill. It's a little trash can. It looks like a little pop-up trash can, um, but it's magic. You throw food into it, it dries it up, grinds it up, turns it into what looks like little coffee grounds. You can do this in an average family home for probably a month, and at the end of that month, you have a shoebox size of these grounds. Most importantly, it doesn't smell. You don't have to empty it for a month. And so the, the pitch to consumers is, "Empty your trash less and it stinks less." You know, and, and I kind of laugh. N-nobody really enjoys emptying their trash. And so obviously, you can do it less, and it's smaller and it stinks less, then everyone's excited. And it turns out this is a major diverter of food waste-related emissions. And the thing about this product is if you meet someone who has it, you'll know it because they love it. It's a product that people buy because they love, and it saves a food waste problem, saves a consumer problem. And guess what? This company is making great revenue and, and money on this. This is an example of a better, faster, cheaper. It makes people's lives better, and it solves a climate environmental problem, and it turns out to actually be a great business along the way.

10. 15:34 – 17:17

    Technology Matters — People Matter More

    1. MSMike Schroepfer15:35

       [upbeat music] People have operated big things. People have started companies. Very few people have seen the sort of the scale. You know, I built tens of millions of square footage of data center space. We've shipped tens of millions of consumer hardware products. You know, scaled teams to tens of thousands. Managed lots of multi-billion dollar acquisitions. So, you know, I've had the great fortune of working with an absolutely incredible cohort of people on a, an amazing set of technologies in hardware, in software, in deep research, in, in others. All-- Everything we've talked about here is, is what we bring to bear. How do you find the right problem identification, the technology that has headroom to scale and tailwinds and c-customer demand? And then what we haven't talked about is, is people. You know, a lot of my job ended up being finding out who were the right leaders, technical, organizational, otherwise, to take something forward. And in the startup realm, the team is ultimately what you're betting on. You know, these are the people who are gonna build that company, and we're looking for founders who could take the company as far as possible, meaning the challenge of a company is a company at ten employees at pre-seed or seed is a very different company than a three hundred person company with customers in the series C. And that rate of change is unusual for humans. Like, you don't usually encounter environments that change that much. And so there is a rare set of people who can scale through those changes, and I've had the great fortune of working with many of them. I've had the ability to do it myself. And so a lot of what I'm looking for is that people identification of, like, as we meet thousand founders a year, these are the ten for this year that we think have the best shot at scaling this company into a public company. And that is a lot of the sort of that pattern matching on people's ability to scale is a lot of what we're doing. So coupled with technology and market, you know, it's, it's really people.

11. 17:17 – 19:03

    What Winning Founders Have in Common

    1. MSMike Schroepfer17:19

       I think what we see in founders... You know, there's questions about how do you evaluate it. What we look for in founders is, number one, you need a complete relentlessness, a determination. Building a company is a never-ending series of near-death disasters and a lot of people telling you what you're doing isn't gonna work, and a lot of people saying no. New recruits say no, investors say no, customers say no, and you need to get thirty noes in a row. It doesn't really matter if thirty investors say no if one says yes. I mean, I remember from twenty-five years ago going out on Sand Hill Road trying to convince people to invest in my startup as a first-time founder, and I got a, a ton of noes. You know, I had someone fall asleep in one of our pitch meetings. But then we got the world's best venture capital firm, Sequoia Capital, to say yes, and that was the defining moment, you know, and that, that helped us build a really successful company. And so as a founder, you need to have this determination to just, like, keep going despite setbacks. That's number one. Number two is building a company as a CEO is a different job every single day. You might have to solve technical problems. You might then have to go talk to customers. You might have to go recruit people. You might have to get a lab space. You're gonna be doing a different job every single day. And there's a category of people, I, I call them, like, just consumers of new information, and they have this combination of humility that they don't know something and curiosity, figure out how to learn it. And that combination allows them to do everything. Like, how do I run a board meeting? How do I pitch an investor? Like, these are all things our founders learned how to do successfully. And so you're looking for people who demonstrate this ability to decide they don't know something, and then figure out how to learn it as quickly as possible, um, and/or hire people who know how to do it, you know, for their company. So it's, it's really those two things. It's, like, unrelenting determination and this, like, ability to understand, identify, and learn new

12. 19:03 – 20:59

    There’s No Perfect Founder Checklist

    1. MSMike Schroepfer19:03

       domains on a very rapid clip. Well, the challenge is, you know, everyone tries to distill it down into a, you know, if I could just, like, check off a couple of things in their background, you could find the founder, but that never works. Like, the number of times that if you-- if I make a rule where we only do second-time founders or this and that, I can give you examples that violates that rule. We just have to meet founders and then do our own evaluation of it. That is the most important part of the job, and we evaluate them by meeting them multiple times. We evaluate them by calling references and people that worked with them. But I'll give you a couple of examples of people in clean tech that I think are phenomenal. You've got most well-funded companies working on fusion, Commonwealth Fusion Systems, founded by Bob Mumgaard, who's a plasma physicist. This is his first company. He's never worked at a company before. You know, if I told you to give me a resume for a CEO of a thousand-person company, a plasma physicist is probably not what you would search for. But when you meet him, he is an operator. He has learned very quickly how to build a team, how to rely on others, how to tell a story, how to raise money. He's absolutely phenomenal. What a CEO needs to do is, like, describe in deep clarity the mission of the company and what's important and get a large number of people on board and focused in that direction, and he does an exceptional job. And then you have people like Matt Rogers at Mill. This is his second company. His first company was Nest. And he was sort of great along the way, but he didn't rest on his own laurels. He did what great founders do, which is build a great team around him. What we look for in founders is, you know, the job of building a company is solving hard problems that no one's ever solved before, that you don't actually totally know how to solve. And so we met Mill, they said, "Oh, we're gonna get approval from the US government to take this return food waste and turn it into chicken feed. And, uh, you know, we're gonna get it by X time. We're not exactly sure how to do it, but we're gonna get it done." Like, two or three months later, they're like, "Yep, we got it done. It's approved. We're, we're now doing it." And it's just, like, a series of, like, we're gonna go after this problem, and then we're gonna go solve it. Each individually are amazing, but as a team, figure out how to go take down big problems, and that's the magic of a, of a startup. [outro music]

Episode duration: 20:59