EP 85: Alexandr Wang (CEO, ScaleAI): The 26-Year-Old Powering the AI Industry

29:56And I think that the potential for misuse of the technology is very high, again, for cyber attacks, for bio attacks, bioweaponry, for information warfare, and even stuff. you know the the version of this i think is like almost the most direct or clear is like um you know there's these companies like character ai and replica that where you you can have an ai model that becomes a genuine companion to huge percentages of of the citizens of various countries and if you had a foreign run and foreign operated ai companion company i think that's like the most effective intelligence agency that you could possibly have.

30:40So there's a lot to be worried about in the realm of AI misuse. And it's something that I think is, you know, that's certainly like very concerning. It's something that we as a country, we as a society need to think about. How do we mitigate those risks? There was the executive order from Biden administration. I think we're certainly thinking about those. Hey guys, Rashad here. I'm the producer of The Logan Bartlett Show and wanted to take a quick second to make and ask. We are close to 10 ,000 subscribers and are trying to get there by the end of the year. If you're enjoying this conversation and these episodes, please consider subscribing to the YouTube channel.

31:16Now back to the show. What's something that you believe inevitable about artificial intelligence in the next five years that maybe isn't mainstream or the average person wouldn't fully appreciate? I think there's a bunch of things I'll mention. I think one that you know, most people in AI see and believe, but certainly is not super, is not yet fully mainstream. It's just that these models are going to become very quickly some of the largest investments in most countries. So, you know, if you believe these go from hundreds of millions of dollars to billions of dollars, tens of billions of dollars to hundreds of billions of I mean, there's not that many countries that can afford$100 billion investment, either funded through private industry or funded through the public sector, through the government itself.

32:05And so this very quickly becomes one of the largest economic projects or sort of like scientific projects that the world has seen, which I think it's maybe surprising to people that it isn't that yet. These models, they've cost hundreds of millions of dollars, but a lot of people can afford a few hundred million dollars. Very quickly, it's going to be almost like particle accelerators or like these massive scientific projects in terms of scale of investment. I think the other piece that many people don't think about or I think is just going to slowly blend in is that the percent of time that humans are interacting with other people versus to a model directly, that split is just going to keep accelerating in the direction of the model.

32:55So, you know, there's truly no reason outside of regulation you would believe that the percent of time, that that percentage, the percent of my total time I spend interacting with models is going to decrease at any point for the next few decades. So that's going to increase monotonically. It's already pretty high for me. I interact with ChatGPT quite a bit already. And I think that's a very weird sort of sociological scenario for us to contend with, which is no matter what, these models are going to start eating into all the time you spend talking and interacting with other people. If you believe that the models are only going to get better, if you believe that they're only going to have more interesting data, if you believe the products are going to get better, the monotonicity of the improvement is going to be very weird to think about.

33:45And, you know, maybe these don't happen in the next five years. Maybe these happen over 10 years, 15 years. You know, who knows when they happen? But at some point, you know, people are going to spend more than half of their time talking to models versus humans. There was once kind of a concept or belief that it would be low level sort of manual jobs that would get automated through artificial intelligence. I think increasingly we're finding that what these models are good at are entirely orthogonal to our understanding of what is difficult versus not. How do you think about that orthogonality and what AI is good at versus what it isn't?

34:20And it's off to the side. Yeah, I think this just all boils down to data availability. So going back to it, right, like data is the lifeblood of all these algorithms. Everything they learn, everything they are capable of, they've learned from data. And so it turns out that, you know, by using the internet over the past few decades, and by commenting on Reddit and uploading stuff to the internet, we've happened to have been creating the largest data set of human behavior ever. So anything that we did on a computer, which most of it was fundamentally knowledge work or knowledge related or intellectual because by definition it's abstracted away from the real world, that's what the models have a lot of data on.

35:09So they have remarkably little data of what it's like to pick something up or what it's like to throw a ball or what it's like to manufacture something. All the things that are embodied in the real world, it has very little actual bearing on and very little data. And that's going to be true for a long time. The sort of digital presence of these models and digital intelligence is always going to surpass. Probably perpetuity will be far more advanced than the physical embodied capability. If you think about it from a data availability standpoint, I think it makes perfect sense. And obviously where it gets really weird is the economic impacts of this and what does that mean for the future of labor.

35:54You touched on the three components of model development, I guess, being talent, compute, and data. what do you think the most limiting factor is today and what do you think it will be in, I don't know, five years' time or 10 years' time? I think data and compute are definitely the limiting factors today. Compute has a very clear limit because of manufacturing capability. So the supply chains for both of these, I think, are worth diving into. So, 100 % of high-end GPUs that fuel these models are manufactured in Taiwan today. There are these fabs that TSMC has put tens, if not hundreds of billions of dollars into CapEx to build and continue to refine and improve.

36:52and that's just like a very strong upper bound limit for the compute capability and capacity for these models. So by definition, like if you believe in like continued exponential scaling, it gets pretty hard unless you have like an exponential scaling in the supply chain as well, which is something that, again, economically speaking, is not really feasible today. So compute is both the pinch point today. Obviously, we see how much NVIDIA chips sell for and how much startups want them, but there's also just a clear limiting factor to the exponential growth scenario. Data is as well. So I think a lot of people have observed that, is there more pre-trading data out there?

37:42Have we run out of high-quality tokens? And there's certainly some very lucid arguments by some folks that show that some of the scaling laws will be tough to keep up because we just don't have that much more high-quality data on the internet. And this argument, is video data high-quality data? Is video data not high-quality data? These are the sort of questions. Text is a very unusually compressed form of knowledge and information. Video is much less compressed. So then if you don't have enough pre-training data, where a lot of this is made up for or where there has to be a big scaling to make up for all that is in RLHF and post-training data.

38:23And so I think we're going to start seeing, again, similar kinds of bottlenecks where the amount of human experts who are really what's needed to fuel this sort of like RLHF stages. is human experts become GPUs in their own right, that basically the number and quality of human experts who are fueling model improvement is going to, in and of itself, become another supply chain bottleneck for the industry. As we've looked at GPT-2 to 3 to 4, it seems to be outside and almost like linear development that's going on. But clearly, these are more stair-step functions along the way. Do you think with the constraints we have and what we just talked about, we're going to hit some plateau at some point that's going to require a much bigger unlock of one of these things to really reach that next major step function?

39:17When you talk to people at the leading labs, they spend all their time thinking about the supply chains for these models. So I think that implicitly, you know, if nothing happens, These will be really big bottlenecks. But that being said, I think that this is potentially the greatest human engineering project that we've ever seen. And so I think we're going to figure things out. I think what that means is you're going to start seeing some pretty crazy actions to try to secure and ensure that the supply chains can continue scaling. But again, I think that's kind of the technological imperative that we operate in.

39:57Do you think we're under appreciating as a society the reliance on Taiwan and the political position that Taiwanese find themselves in there and what that means for artificial intelligence for us? One very clear indication of the degree to which we don't appreciate it is just in the multiple gap between NVIDIA and TSMC. TSMC trades a dramatically lower multiple than NVIDIA. NVIDIA is a higher margin company, of course, so some of it's very well-deserved. But TSMC, I think, you know, from my talking with public market investors, they get dinged because of this geopolitical risk. You know, what happens?

40:43Taiwan is just at the sort of like this pressure point for the world. What's your perspective on open source versus closed source models? It seems to be a big debate these days. Do you have any opinions on that? As a company, and my personal point of view is to be quite agnostic to how the technology develops. I think that AI is an incredibly powerful and good technology. I think that, you know, all development on these models is great. And as long as you have safe open source development as well as safe closed source development, I mean, both can be done poorly and unsafely and both can be done safely and well.

41:18And if you have safe development on both, it's great. I think that open source models are probably a requirement to ensure that AI achieves the full economic impact that it can have. There's a lot of scenarios where you need like, you know, you just don't have very much compute. You need a small model running somewhere that probably needs to be an open source model of some form. It doesn't make sense for there to be some small closed source model to fit that need. And so I think it's good for economic growth and economic prosperity that we have open source models. I've heard you talk about the competing curves of AI.

41:59Can you talk about inequality and the competing curves of scale and democratization a little bit more? Because of the scaling laws, as the models become ridiculously expensive to train, tens of billions, hundreds of billions of dollars, potentially even trillions in the future, that very clearly limits the accessibility to the underlying technology, just in the same way that none of us have access to particle accelerators. And that is like the poster child non-democratized technology is a particle accelerator. There's this avenue where that becomes the version of the world. So that's very clearly going to happen.

42:42And that's like one major sort of like tentpole for how the technology develops. And then the other one, which, you know, there's so much will and might within the community to accomplish this is how do you push all these models down the cost curve so quickly that it's like a few years after you have like these incredibly powerful closed source models, you have very good open source models that just get that where the cost curve can be climbed down to really dramatically. quickly. I think we're seeing that in open source models. I think we're seeing like GPT-3.5 level models that have happened very, very quickly and actually are very small.

43:23You know, I think there's some recent things that show that, you know, these 10 billion parameter or even smaller models can perform at the level of GPT-3.5. So this is pretty rapid improvement of the democracy. I think basically you have this, so one curve is the scaling and the other curve is the speed from a frontier model result to democratization of that technology. And these are sort of the push and pull of the entire industry. What is the Turing trap and why is that significant in your mind? Yeah, so the Turing trap comes from this, comes from this great paper that this economist, Eric Bernolfson, professor at Stanford and others sort of wrote.

44:09And the basic premise is, you know, the starting condition of AI, you know, in many ways the invention of AI came from this concept of the Turing test, which is, you know, at what point do you have an AI that can fully imitate a person? And because of that framing, we've thought about AI as a as a replacement for humans, predominantly. So we think about like, you know, when we have AI, it's going to replace humans in the workforce and that will be its impact on the economy, which is, you know, as Professor Bernolfson argues, is a trap. Because what's actually going to happen is, you know, you're going to have AI systems that sort of like slowly walk up the capability curve.

44:57And as they come in, most of the value is going to be generated from sort of basically hybrid human AI systems. And it's going to be like through some very interesting and complex and nuanced interaction between human capability and AI capability that you're going to get these very economically valuable things to occur. And because of that, AI in most outcomes or in most versions of the world actually ends up being a pretty strong creator or sort of net creator of more jobs or net creator of more demand for human labor. And that's, I think, one of the very important messages, which is there's this perception that AI will just take all of our jobs.

45:41No, the answer is like AI is going to create a fundamentally different economy, which has like a fundamentally different mix and kind of job, but that will probably net create greater demand on human labor. There's a lot of, the world's obviously complex and there's a lot of complexities and nuances associated with artificial intelligence, that being one of them. Is there another one that's a general misconception that people have that you would like to clarify or express your opinion, the dissenting opinion of? I think one of the major things that people, when they think intuitively about AI, that they kind of get wrong.

46:16And I think this is, you know, I see this in a lot of places, is sort of this, it's a very easy technology to say, you know, you have an AI, you use GPT-4, you realize, oh, it just hallucinates all the time. And then you sort of like throw your hands up and you're like, oh, this technology is fundamentally limited and it's never going to go anywhere because it hallucinates. And I think the tricky thing about AI is like very hard technology to bet against because every prior instance where you would like use an earlier version of the models, If you use GPT-2 and you said, oh, this thing can count to 10, and you threw your hands up, it's like, there's no future here.

46:55Or GPT-3, you would use it and it's like, it can't solve a simple math problem. You throw your hands up and it's like, this isn't going to go anywhere. I think a lot of people, even in the AI industry, fundamentally don't actually believe in model improvement. And it's a big, it's a shame, honestly, because I think the reality is the model is going to get a lot better. and I think it's hard to imagine how the models will get a lot better, but they will. And we need to be thinking about a world where we're just on this continued track of model improvement. You're a student of geopolitics and how artificial intelligence, I guess, plays in that, so much so that you recently did a TED Talk on the subject.

47:39Can you speak to the battle that you see playing out in artificial intelligence within the geopolitical world that we're in, in particular China and the US? So one of the ways in which AI has been surprising is the degree to which it's become a clear objective and imperative for many, many countries and many geographies around the world. So obviously, much of it was invented in the United States at Google and OpenAI and DeepMind, etc. But very quickly, now you look, you see China is obviously trying to move very quickly. You know, the Chinese tech giants have bought an aggregate of over$5 billion worth of NVIDIA chips.

48:21That's a lot of chips. You see the UAE particularly, but the UAE and Saudi moving very aggressively into the technology, building large data centers. The UAE has open source to successive open source models, one of which is 180 billion parameters. These are very big and serious models that they're building. In Europe, you're seeing some of the best open source models coming from European companies, European startups. And, you know, from my conversations with people from many other countries, there's certainly, there's many others who have like clear aspirations in AI. And so at minimum, it's becoming this technology that a lot of countries are looking at as like, hey, this is really important for our future.

49:03And what's really more concerning is the degree to which certain countries, particularly China, are very clear-eyed about the monumental impact this technology can have. You know, one of the, you know, there's a number of PLA people, the army, the DOD equivalent of China. There's a number of PLA documents that talk explicitly about how AI and other breakthrough technologies could allow the PLA to leapfrog the adversaries, most notably the United States, which is the most powerful military in the world. because we're going to overinvest into our legacy platforms and just upgrading our legacy platforms versus the new breakthrough technologies.

49:53They'll overinvest in the new breakthrough technologies and they can leapfrog us just like China leapfrogged the United States in fintech and payments technology where we pay and all their digital payment infrastructure is, you know, most people believe surpasses the sort of like state of payments infrastructure in the United States. This is the question. The question is, what is the, you know, 40 % of global GDP, US GDP plus China GDP is 40 % of global GDP. So these are like the two behemoths in the economy. And the key question is, is AI the catalyst for China to overtake the United States or at minimum dramatically gain ground versus the United States?

50:42Or is it the technology that allows the United States to ensure that we can maintain global stability by persisting and continuing Pax Americana? If you talk to a lot of political scientists, there's a pretty clear consensus that if Chinese military capabilities catch up to that of the United States, that's a very unstable world. you know, whichever side you're on, that definitely results in greater levels of global instability because, you know, a lot of the global stability or one major portion of the last 80 years of relative peace has been because America has been the clear hard power superpower in the world.

51:31if you have two superpowers you get a high level of you get greater entropy in the system there's more proxy wars there's more overall instability there's more war, there's more death so I think that in this broader battle between democracy and authoritarianism and sort of these different government systems and these different ways that the world can organize AI is one of the major chess pieces in that game. And that's why I think it's critical that, you know, we as Americans or American generals able to maintain that pole position. And maybe speak to the proportionality of what China is spending versus the U.S.

52:14today. For the past few years, at least, China has been spending, the PLA, the Chinese military, has been spending between roughly one and two percent of their budget on AI technologies. And then in that same time period, the USDOD has been spending 0.1 to 0.2 % of our budget on AI technologies. So what the PLA had been forecasting is actually playing out in reality right now, which is we're over-investing into just our legacy platforms, our legacy technologies, under-investing in the breakthroughs. they might reach a breakthrough before us and we might be left, you know, in a situation we don't like.

53:00By the time people hear this, you will have already been to the UK AI Summit. I think you did a great job there, by the way. I think it was really well done. You're heading out tomorrow. Why is attending this important to you? And what are you hoping to accomplish? There's a few threads here that I think are interesting. I think one is ensuring that there is a, there is a track for global cooperation on AI. I think regardless of what you believe, if this technology is as important as I think it is, as many think it is, it's something that requires many, many of the countries in the world to have a clear and open dialogue around.

53:44You don't want anybody going off track and doing things in a way that is opaque to the rest of the world. That's certainly a driver of instability. So I think at minimum, there's a huge amount of just intrinsic value to the world by being an open dialogue between all the countries to discuss the technology. And many of the countries are going to be there, which is great. And kudos to the UK government for creating such a forum. And I think that the other piece that's critical is ensuring that we're thinking about the right risks of the technology. I think we talk a lot about the frontier level risks and some of the existential risks.

54:24And I want to make sure that we're also thinking a lot about the risks of misuse and what are we doing about those and how do we think about those. Um, so, so I think it's important to me to ensure that we have a broader view of particularly geopolitical risks at play, um, and, uh, and, um, and ensure that shapes the sort of global dialogue around the technology. What role do you think the government plays in regulating AI? Yeah, I think it's a, it's a, it's obviously the question of the day, literally with the executive order coming out. But so far, the approach has been to take a very, quite a light touch on regulation of the technology, particularly because we're in such an early stage.

55:07And one of the worst things that you can do for a technology as high potential as AI is to squander the opportunity early on by over-regulating it. So I think that's been smart. I think that the key is the government needs to ensure that the misuses of the technology or the ways in which the technology can be used to create meaningful consumer harm or meaningful harm to the citizen base, that those don't happen, or at least that those are very highly punished and limited, very limited and difficult to do in some way. And so to that end, I think that the, and this is a key part of the executive order, one of the most important things is ensuring that there's the proper testing and evaluation regime for AI systems.

55:55So how do we as a society agree that certain AI systems and use cases and applications are fit for purpose and ready for prime time versus, you know, totally inadequate? And there's versions of this that exist in all sorts of ecosystems. So, you know, the FDA approves drugs. You can't just buy like random molecules off the internet and ingest them and expect that to go well.

56:26there's similar kinds of regulation on planes, obviously, and cars, and these technologies that are potentially very dangerous. And even Apple does a version of this for apps in the App Store. You don't have to be approved by the App Store. So I think this is the key question. And in my conversation with folks in the White House, it's like, this is the industry that needs to exist that doesn't exist. And we at SCALE were trying to play a big part in this topic. We worked with the White House and DEF CON on some of the first public evaluations of these models a few months ago. And our view is that you need to have a pretty clear regime of testers in the private sector with pretty clear regulation and guidelines given by the public sector and a very clear opt-in from the model providers and those implementing AI technology.

57:20I want to back up to the founding of scale and transition from some of those broader topics. So what was the original insight behind the business? What did you recognize at that time that sort of led to its founding? Yeah, I think that the key insight was that, simply put, it's like, if AI were going to grow, the needs on data were going to grow exponentially. So, I mean, it's pretty, and, you know, I had no idea what timeframe that was going to happen, or when that was going to happen, or at what scale, size, and magnitude that was going to happen. But I had a pretty strong conviction that neural networks and AI were going to be more and more ubiquitous.

58:07and if you believe in that, you believe there had to be, you know, infrastructure for data to meet that challenge and sort of like meet that growth. That certainly played out, I think, even in a way that's been surprising to us, which is that the amount of data required for these AI systems and the sort of like hunger for new data has far exceeded, I think, what I originally even would have conceived possible by this time frame. And so you spent how long at Quora before going to school? I grew up in Los Alamos, New Mexico. Parents, physicists at the lab, a lot of physicists at that lab, and then went to work at Quora for about a year.

58:55Those were my foray and taste of what technology was like with the tech. How old were you when you were working at Quora? 17. Okay. So I worked there with 17. And it was pretty eye-opening in the sense that like you really get, you know, it's like the Steve Jobs quote that I think every new employee at Apple hears about, which is like, you know, you sort of realize that everything around you is built by people no smarter or no more capable than yourself. I mean, my colleagues at Quora were brilliant, but it was like crazy to think about. Like this was a site I was spending a lot of time on as a teenager.

59:31and it was built by a team of 100 or so people. And it was this very empowering experience. Then I went to MIT, started training neural networks of my own and the rest is history. And so you went to MIT and you sort of got bored with the learning aspect of academia and wanted to go be a practitioner in the field. Is that fair? Yeah, I think that like the... I think one thing that kind of stuck with me is that the, you know, it was already playing out at this point in 2016 when I started scale, which is pretty clear that the amount of resources that you need to fully. accomplish AI or to see the, you know, see AI through, through the fullness of time, we're going to vastly exceed what was available in academia.

1:00:19And obviously that's true in an almost ridiculous degree now with, you know, hundreds and millions, billions of dollars being used to train the models. But, um, but that was, that was probably the key, the key driver. What inspiration have you taken from Amazon with operations and technology being combined for scale? Yeah, a huge amount. I think Amazon in many ways is one of the most countercultural tech companies in certainly the world. I mean, I think the key insight of Amazon, or they have many key insights, but one of, I think, the key insights of Amazon was that operational excellence is actually a huge driver of tech surplus and tech value.

1:01:09And so Jeff Wilkie, who ran operations there for many years and was the CEO of Worldwide Consumer, so everything outside of AWS, is a very close mentor of mine. And you learn pretty quickly that there was a way of thinking there that is sort of like you just don't see it any other tech company, which is a deep embrace of operational complexity and operations as a discipline. a deep embrace of the sort of like marriage of technology and operations to produce things that are sort of produce linear combinations that are sort of uniquely powerful and uniquely capable and and a extremely pragmatic approach to the business decision making.

1:01:52and those in combination created, I think, one of the greater economic engines of our time. So learned a lot and a lot of what we do at scale is taking that same approach and playbook and sort of philosophy, which is how do you marry operational complexity with fundamental technology breakthroughs to drive an entire industry forward? And Amazon's also been kind of canonical in parallel execution as well. Is that something that you guys think about when executing across a suite of different products you'll offer. Yeah, yeah. And the key, like the beauty of that insight or the key of that insight is that, you know, you figure out how to architect problems such that you have as few dependencies as possible.

1:02:35And so you have as many things that you can sort of like bet on in parallel at once, which is something that investors, I think, are obviously understand quite well. And if you have enough independent bets, then you can sort of double down on the ones that work out and it ends up working quite well. Can you talk a little bit about, I've heard you reference the dichotomy of how businesses are rewarded for predictability but actually benefit from elements of random discovery, maybe using Amazon as an example? Yeah, so if you think about Amazon as a company, it was an online bookstore and then it was the everything store, you know, the online everything store.

1:03:18they created Prime so they created this membership program and then it became the largest data center provider in the world and that last piece sounds so non sequitur if you like tell it that way that it almost seems like it's what a bad author would write into a book you had the everything store and they were so big and bad and then they ran all the computers globally It just sounds so unbelievable. And now if you look at Amazon's market cap, depending on who you talk to, most analysts attribute the vast majority of the value of the company to AWS. So this very unpredictable event that Amazon was going to invent AWS and then build that business is actually the core driver of its market cap and value today.

1:04:12It was a pretty crazy thought because, you know, talk to most growth investors. Most growth investors are trying to very directly understand what will happen to the revenue of this company over the next few years. How predictable is their growth? What's that exactly going to look like? But, you know, the thing that affected their earnings the most was this totally unpredictable event of AWS being invented. And so I think there's sort of this pretty confusing property of companies, which is that, you know, on the one hand, investors think that they're betting on the next few years of execution for the company.

1:04:49But for the best companies, what they're really betting on is in continuous reinvention. I think NVIDIA is actually the best modern example of this, which is that NVIDIA GPU company selling gaming and graphics chips for decades, literally decades. And like 15 years ago, they noticed that people were starting to use NVIDIA GPUs to train algorithms, train AI algorithms because of the parallel computing capability. And they just started investing a huge amount of time, effort, R &D, and their attention towards supporting that use case. and required a huge amount of conviction at that point to like conviction in AI to start that investment that early and, you know, sort of like keep leaning into it so much, even long before it was a needle mover on the financials of the business.

1:05:50But today, NVIDIA is a trillion dollar company almost purely because of AI. And so, you know, if you're an investor in NVIDIA stock 10 years ago, again, it's sort of a very similar thing. It's like, you know, you're evaluating the ability of the company to execute on graphics chips and gaming chips. But the thing that actually matters for whether or not you're going to make a ton of money on the investment is whether or not they invent themselves to be an AI company. And so I think this is the core of markets or the core of companies that a lot of people don't understand is that the you know, the bet, the thing that you're almost always actually betting on is the capability to reinvent.

1:06:35How do you manifest that culturally within scale? You guys obviously, it was scale API once upon a time and scale AI focused on mostly autonomous vehicles. Now it's much broader than that, doing stuff around RLHF. But it sounds like this is something you study and think about. How do you make sure that exists culturally within the business? Great question. That's what I spend a lot of time thinking about. And there's a few things that we do. I think there's certainly a lot more that you can do at all times. I think one is that we create a culture of as much as possible pure meritocracy and one that leans heavily into people who are usually more junior at the company who have ideas that are good being almost like thrown into the responsibility of having to run with those ideas and turn them into something big.

1:07:31And this kind of culture of like, you know, if you have a great idea, first of all, anyone can have a great idea. And if you have a great idea, you have like almost full accountability to realizing it and making it happen. This kind of culture, you know, really is not how most companies operate. Most companies, like everyone can have a good idea. and then like some director or some VP steals your idea and then makes that into their career move. This kind of culture is like, is pretty unique and we really lean hard into it to make it very clear that like, you know, the limit and I've talked to a lot of, I always talk to new people joining the company and people who've been with the company for a bit to make sure this is always true, that the true limit to your impact and future at scale is just like, you know, it's limitless depending on how much you apply yourself, how good your ideas are, you know, how innovative they are, et cetera.

1:08:27That's one. I think another is that we try to be very, we try to always put ourselves, focus on big problems, if it makes sense. So, you know, I think a lot of times, this is, you know, Amazon's version, this is like focusing on the customer. But I think if you have the right sort of fixed point in the system, which for Amazon is the customer, for us is sort of like thinking about the big problems in the industry, then you'll always end up finding, you know, stumbling upon opportunities that continue to be bigger and bigger and bigger and bigger. And by that, I mean, so, you know, we were focused on autonomous vehicles for a very long time, which is a huge problem, you know, a very big, complicated, interesting problem.

1:09:13At a certain point, it became pretty clear that a lot of what we talked about with geopolitics and sort of the importance of AI to the future of the sort of like balance of power between countries, that we had pretty high conviction that that was going to be the case. And we leaned very hard into working with the US government and the US DOD. And a lot of the technology that we built up in servicing the autonomous fuel industry was pretty applicable. But we then took on this much larger problem of how do you ensure American leadership? And how do you ensure that the U.S. stays ahead? And that's such a big problem that, you know, in the course of serving that problem, we stumbled upon much, much larger opportunities than the original opportunities in autonomous vehicles.

1:10:03And the same has been true now with, you know, the big problem is helping to ensure the maximal progress in the AI industry. Like, how do we ensure that these models are the most impactful version of ourselves, that we push for the maximum amount of progress in the AI industry? And that's, you know, the biggest problem of our time. So I think pushing ourselves to be continuously ambitious for what is the North Star of the business, I think, has been critical. I want to ask about interviewing. So you said your favorite interview question is what's the hardest you've ever worked on something? Why do you like that question?

1:10:45Yeah, so I generally think there's like, there really are two kinds of people in the world. There's, and this is like, this is a psychological term, but there's having like an internal versus an external locus of control. So if you have an internal locus of control, it means that you believe the things that happen in your life are actually more a product of what you do and the actions that you take. So you believe a lot more in you're holding the reins on your own life. And if you have an external control, it's the opposite. You believe that the things that happen to you are mostly the outcome of things outside of your control.

1:11:24The world's very deterministic, and you're sort of like a pinball in a big pinball machine. And, um, and I, I really like, you know, if you know how to look for it, this really is like a very clear dichotomy between how people, how people think about their lives. And I find that, you know, I only want to work for people, work with people who have an internal locus of control. And, um, and one way to like look at that is, or one way to like index off of that is seeing how hard do people work at things that matter to them? Right. Cause you know, there's things that matter to everybody, you know, everybody's things that matter to them.

1:12:01But if, if they have an internal locus of control, then they're going to like work their ass off to make sure that the things that matter to them happen the best possible way. If they have an external locus of control, things matter to them, but they sort of like, you know, throw their hands up and, and let, let the world sort of take the wheel. And so by, by seeing how, how hard people work on, you know, the things that matter the most to them, and like, by really like actually quantifying and, and getting a sense for how obsessive were they, how much do they really care, like how small of details do they sweat.

1:12:33You get a pretty clear indication for how much control they believe they have on their life outcomes. What's the single trader characteristic that you're most looking for in hiring? Is it that locus of control or is there something else that stands out? Yeah, I think, you know, there's a few, there were like, we had this early document that we wrote up around like, what do we look for in people that we hire? And there were four traits. One is internal locus of control. Two is problem solvers. So fundamentally, people who are very good at creative problem solving. You give them a problem and they figure out, sometimes you couldn't solve it just by tackling it head on.

1:13:15They'd figure out a way around the roadblock. That's a really important trait. Third, we look for people who are impressive. so um uh we look for people who who you know when you talk with them and you you work with them you were sort of like were genuinely impressed by them and it's kind of a shorthand for people who are just sort of like constantly upping the bar of the organization um which is that like you know if you're impressed by somebody you know you're gonna be very motivated by coming to work every day and work with them and learn from them um so so we we held a pretty high bar there and the last one was people were collaborative.

1:13:50I think that you can have people who are like high looks of control, good problem solvers, very impressive, but just like suck to work with. And so those were the, those were sort of like the North stars for the organization. And it's carried us pretty far. You've spoken about how the prestige around big brands and tech actually kind of perverts and distorts the perspective around hiring in Silicon Valley. And how do you think that's the case? like these big brand names people stay at for a long time? Why is that kind of a contra signal that you'd not look for? You know, I think one of my favorite lines around this is like, you know, if you're recruiting organization looks like a college admissions office, then, you know, you should be pretty scared of something along those lines.

1:14:34And I think it's true, which is that like the reality is it's very hard for somebody at a big tech company to have any sort of real impact. I'm not, you know, this is not too much of an indictment of the big tech companies, but they just hire so many people. They have, you know, a limited scope of problems that really, really matter. And so a lot of the people they hire just ended up working on like a teeny piece of a teeny piece of a teeny piece of a teeny piece of a problem. So if you think about the selection bias, the people who get selected into these very large brand name tech companies are those who they're almost, they're over-optimizing for brand and status relative to impact.

1:15:17By contrast, small startups are like, are literally the exact opposite. Like you're joining a small startup because you're like, wow, I see the five people working on this thing. And like, I know I can come in and have a big impact. I'm just saying they're all doing, they're doing a bad job. But like, I know I can have an impact. But it's not gonna be a cool thing. Like I'm not gonna be able to tell my friends about working this startup. And they're gonna think like, oh, wow, that's really awesome. And so a lot of hiring, you know, a lot of it is like skills based, but a lot of it is also just culturally testing people.

1:15:45And you really want these people who don't care about status, care a lot about impact. And I think, yeah, I think big tech companies negatively select for that. We were talking about zero to one before we got going and how it's kind of been normalized in startup culture. And I think once upon a time, it was very revolutionary. But now I think a lot of the things that they wrote about or Peter wrote about in the book has become kind of status quo in a lot of startups. Is there something that you've read or internalized today or recently about startups that's non-consensus that you think will be at some point in the next couple years around how to operate or work with companies?

1:16:26I think one thing that is certainly non-consensus in the context of the ecosystem, but I think is really, is certainly even in my experience, is that you really, the value of very hardworking people who are not necessarily super experienced in your company. And it's pretty surprising. There are some kinds of companies where it's a small group of very experienced people who build something incredible. That certainly exists. But for the most part, I think most startups are a, you know, the sort of like chaotic buzz or hive of people who are not necessarily super experienced, very, very hardworking, very high aptitude, very capable, and just sort of almost like gradient descent to building these incredible things.

1:17:19And I think that's not super well understood or super adopted by the entire tech ecosystem. A lot of tech ecosystem, I think, is really focused on hiring the experienced people who've been there and done that. I think the other thing, and we're talking a little bit about this, is the importance of having a strong point of view. It's quite interesting. The last generation of tech giants, you know, you think about the Googles and the Metas, even the Apples of the world, the startup advice or the sort of like classic business advice is to have as neutral of a point of view and as neutral a brand as possible so that you have, you know, you can distribute your product broadly.

1:18:06you're not offending anyone, you're having as wide-scale impact, as broad-based appeal as possible. And I think we're very quickly entering a very different era, which is that the right thing to do is to have a pretty strong point of view and to be very loud about that point of view, because that allows you to, A, attract the talent of people who agree with you. so it's like it's incredible for building a positive culture and building a very high talent group it's also very important for your customers because more and more customers whether it's enterprise customers or consumer customers care a lot about working with people who philosophically agree with them and sort of share their points of view and um and it it forces you to keep your company authentic uh and that's kind of it's like kind of a subtle thing but i think Like, you know, I look at a lot of peers in enterprise software, and these enterprise software companies just become, you know, very quickly they stand for nothing.

1:19:10And early on, every company was the product of, like, founders who care a lot, who really, like, sweat every detail. And then invariably, every enterprise company becomes sort of like a, you know, another widget that in the, like, bag of tools or whatever. And I think it's important for companies to maintain a sense of identity and remain authentic to have any chance at the reinvention component I talked about before. Aversion to this scale has never been a particularly cool business. Can you elaborate on that and if that's been a net negative or a net positive for the company over the years? Totally.

1:19:47I think that like, you know, it's funny. We've always operated in very cool spaces, you know, self-driving cars, the current AI. revolution, but we've never been the cool people in those spaces because fundamentally, we're an infrastructure provider. Infrastructure is not that sexy. We actually, for our company, we don't want the people who just want to be cool and flashy and work on this exciting new technologies. We actually really want the people who are willing to roll up their sleeves, get their hands dirty, and work on the unsexy problems in AI that are really, really damn important. So I think it's been very important for building the company in a way that I think is true to the work that we need to do.

1:20:34And I think that the impact has been that the people who join Scale know what they're getting into. They know what role in the ecosystem we play, and they care a lot about that. You have a wonderful office that we're sitting in right now. I've heard you say that you believe you actually should spend money on a nice office space and that that's an important thing to do for your employees. Can you talk to me for a little bit about why that's the case? Yeah, at the risk of sounding somewhat woo-woo. I mean, I do think that the spaces that you're in impact a lot about your thinking. I mean, personally, being in spaces with a lot of natural light is one of the best things I can do for the quality of my thinking.

1:21:20And I think there's a lot of fractal effects here where pretty subtle differences in either the quality of your space or the amount of natural light or the sort of like configuration that you're in with your coworkers can have pretty big impacts to the ultimate end outcome and the quality of thought. So it is one of these things that I think is sort of like almost insidious in how much it matters and sort of unintuitive. What about structuring your day? How do you structure your day for maximum productivity? a day? Yeah, what I often find is the best thing to do is to set some pretty clear goals at the very start of the day to say, what are the most important things for me to get done today?

1:22:03And they can start out pretty small. And then over time, you'll find what your limits are and upsize them. And then I'm in a ton of meetings every day, which is part of the job. But continually, I'll check in how am I progressing against, you know, the clear goals I set. I think that's probably the best, the best thing I would do. I've heard you say that both math and physics growing up, there were clear right answers, but it was violin that was super influential to you because it wasn't just about getting the notes right. Can you elaborate on that point or expand on why and how the violin influenced you?

1:22:43I think one of the things that is like somewhat maddening for people who are very um quantitative um in business is that you're sort of like constantly operating in a little bit of a um of a gray area in the sense of like you'll never really know if your decisions were like lacking we were like fully correct or incorrect and um and most things that matter are like quite hard to measure and you just have to like operate via instinct. And so I think this sort of like, almost like fuzzy thinking or this sort of like more intuition driven kind of thinking is something not super well trained in math and science, and much more well trained in the arts in America.

1:23:31So that's the primary way it's been formative. And I think another thing that's been quite important or quite valuable as part of that is also developing a sense of taste. I mean, I think that so much of the product of a company is an outcome of taste and the degree to which you take that taste seriously. And taste in people, taste in aesthetics, taste in product, taste in how to organize. And so Apple is probably the best example of this, one of the most tasteful companies in the world. And I think it's been important to me to have been in a field where you have to develop taste to be effective and apply that to the company.

1:24:19Your dad's a physicist and mom's an astrophysicist, right? Yeah. How did your childhood most influence the CEO and founder that Alexander Wang is today? I have a great example for this because I just spent the weekend with my parents. And to them, it was really important that the people they worked with and their leaders had this sort of very deep, almost inexplicable passion for the sort of place and the work that they did and the history of the field. and pretty similarly, my parents both watched Oppenheimer many, many times and they told me that we had to keep re-watching it because we had to figure out who were all the physicists.

1:25:11There were physicists in the movie that had a single line or didn't have any lines. They were like, oh, we had to really figure out who played each of the physicists. And so I think there's this level of inexplicable passion for the field of physics that both my parents have and this level of like, sort of like fundamental care and love of the field that I think really rubbed off on me. I mean, my mom had been teaching me about physics ever since I was born, basically. And I think that level of sort of like deep enthusiasm has been quite effective. You wrote a blog post, Hire People That Give a Shit, that I think ties into that and some of the hiring things that we were speaking about earlier.

1:25:52Is there anything else that you would say about how you try to suss out if someone uniquely has the passion for your company versus any other business when you're recruiting and the hiring process? You know, one thing we do, we often ask people like why they're interviewing at scale. And I think you can tell a good answer by how obscure it is. You know, if people are just like, oh, AI is the next big thing and I want to work in an AI company, they're like, oh, OK. Okay. But if they're like, yeah, you know, I was working with one of my friends to train a model, and we spent five hours just looking at the data, and there was one little bug in the data that caused the whole model to not work, and then I realized that this problem was really deeply interesting, and then I applied to scale because that's the right kind of answer.

1:26:42So one of the things that you look for, and Paul Graham, I think, has written very elegantly about this topic is, is sort of a, a reason for people to care about things when there was, when there's like, like an irrational reason to care about things, whether it's because of some curiosity or some sort of like quirk or something like fundamentally irrational, some like reason they care about what we do. And I think that's probably the thing we look for the most is like something that is not, But, you know, there's fundamentally irrational and fundamentally sort of like hard to explain about their passions.

1:27:22Similar to music, right? Practicing music. Maybe people will never know if you cut that last corner. But if you know it, if you really practice it, then it's something that's innate to you. Totally. I've heard you say, maybe you tweeted or something, you've been weird your whole life and that everybody you've ever respected has also been weird. Why do you think being weird is an important trait to being an interesting person and the types of people you resonate with? Yeah, I mean, like, purely statistically, if you're, like, if you're normal, that means you're, like, in the bell curve. And, you know, it's hard to be in the bell curve and accomplish, you know, great things or to have a huge amount of, like, differentiated impact on the world.

1:28:06So I think it's, like, a pure statistical argument. But I think that the thing that I find the most interesting here is being normal is kind of like some approximation for having, generally speaking, pretty mainstream beliefs. And there's nothing wrong with that. But it means that, this is maybe an indictment, but if you're normal, it's pretty easy to simulate a conversation with you. and it means there's like you know in some ways like low information content from from having that conversation whereas if you're weird and you say a lot of very unexpected things and have a lot of unexpected thoughts that's very generative experience so I think interacting with people surrounding yourself with weird people ends up being quite valuable because you just sort of like you get to like bathe in a more entropic and more sort of like fundamentally interesting and diverse pool of ideas and thoughts.

1:29:05That's like the, that's the greatest gift that, you know, you could have. What has you most excited about the future of AI as we look out five, 10 years from now? It's hard to not be excited about, you know, kind of what we talked about, which is potentially the greatest economic invention and the greatest economic engine that humanity will have ever invented. So that's kind of, it's hard to, again, And it's like, that's like fundamentally so incredibly exciting. It's like, it's like as if we're inventing the steam engine times a million, right? So what is this thing that will generate so much economic surplus that lifts so many people into better living conditions that kind of like elevates humanity to such an insane degree?

1:29:49It's such an exciting proposition. And double clicking in that, the deeply exciting components there are, again, the sort of the elevation of the human condition, right? So take healthcare, kind of alluded to it before. Right now, globally speaking, there's roughly a 10x shortage of doctors. So because it takes so much training and it's so expensive to train people and take so much time and resources, there's really, at a global perspective, just like way too few doctors. And even with those doctors, the way healthcare mostly works right now is extremely reactive. Like you go to the doctor, you will, you know, you go to the doctor, you have a problem, you go to the doctor, and sometimes they can fix it easily.

1:30:35Sometimes it's extremely expensive to fix. Most of the times it's very expensive to resolve. And then sometimes it doesn't work out. You know, fundamentally we need a more proactive healthcare system by which you're constantly measuring a lot of things. And, you know, you can deal with these problems very early. And healthcare is just an entire field that like without technology breakthroughs, we're kind of stuck as a species. You know, humanity is like a little bit stuck in the, in like how good you can make healthcare without real fundamental technological advances. So if AI was all of a sudden can give everybody a doctor in their pocket that, you know, enables them to, as soon as they feel something weird, or they think something was going on, or like there's a, you know, there's a weird bump or whatever, they can be proactive about that.

1:31:21It's pretty incredible. That's just one way in which that could have one of the greatest effects to longevity of anything that we do, global lifespan. So those are the things that get me really excited. It's like the full knock on impacts are going to be pretty great. Alex, thanks for doing this. Yeah, thanks for having me.

1:31:49I'll see you next time.