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

Alexandr Wang is a Co-Founder and CEO of Scale AI and one of the youngest self-made billionaires. At 19, he dropped out of college to found Scale AI and has since scaled it into one of the most important companies in the world of artificial intelligence today ($7B). In this episode, Alexandr shares the insights that are driving Scale AI, predictions on the future of AI, and other thoughtful tech and geopolitical takes. (0:00) Intro (1:01) Why data isn't the new oil (2:42) Data is the new code (7:47) Outsourcing problems (10:34) Reinforcement learning and generative AI (16:52) How execs and founders should think about AI (18:59) AI VS computers, the internet, mobile, and other tech trends (22:38) The next two to three years of AI will define decades (26:05) Atomic bomb analogy (28:30) Alexandr's biggest AI worries (31:17) Undercover AI predictions (35:55) Three components of model development (39:57) Under-appreciating our reliance on Taiwan (41:56) Democratization of AI (43:54) The Turing Trap (45:52) Misconceptions About AI (47:29) Geopolitics and Artificial Intelligence (53:00) The UK AI Summit (54:45) What role does the government play in regulating AI? (1:14:05) The risks of hiring from big brands (1:19:36) The business of Scale AI (1:22:25) Influence of playing violin (1:24:19) Alexandr's childhood with physicist parents (1:27:33) Being weird means being interesting (1:29:09) The future of AI Mixed and edited: Justin Hrabovsky Produced: Rashad Assir Executive Producer: Josh Machiz Music: Griff Lawson 🎙 Listen to the show Apple Podcasts: https://podcasts.apple.com/us/podcast/the-logan-bartlett-show/id1606770839 Spotify: https://open.spotify.com/show/5WqBqDb4br3LlyVrdqOYYb?si=3076e6c1b5c94d63&nd=1 Google Podcasts: https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5zaW1wbGVjYXN0LmNvbS9zb0hJZkhWbg 🎥 Subscribe on YouTube: https://www.youtube.com/channel/UCugS0jD5IAdoqzjaNYzns7w?sub_confirmation=1 Follow on Socials 📸 Instagram - https://www.instagram.com/theloganbartlettshow 🐦 Twitter - https://twitter.com/loganbartshow 🎬 Clips on TikTok - https://www.tiktok.com/@theloganbartlettshow About the Show Logan Bartlett is a Software Investor at Redpoint Ventures - a Silicon Valley-based VC with $6B AUM and investments in Snowflake, DraftKings, Twilio, and Netflix. In each episode, Logan goes behind the scenes with world-class entrepreneurs and investors. If you're interested in the real inside baseball of tech, entrepreneurship, and start-up investing, tune in every Friday for new episodes. Executive Producer: Rashad Assir Producer: Leah Clapper Mixing and editing: Justin Hrabovsky Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA 🎥 Subscribe on YouTube: https://www.youtube.com/channel/UCugS0jD5IAdoqzjaNYzns7w?sub_confirmation=1 Follow on Socials 📸 Instagram - https://www.instagram.com/theloganbartlettshow 📱 X - https://twitter.com/loganbartshow 🎬 Clips on TikTok - https://www.tiktok.com/@theloganbartlettshow About the Show Logan Bartlett is a Software Investor at Redpoint Ventures - a Silicon Valley-based VC with $6B AUM and investments in Snowflake, DraftKings, Twilio, and Netflix. In each episode of The Logan Bartlett Show, we sit down with the people behind today’s most important startups and extract the tactics, lessons, and frameworks they’ve learned the hard way. Conversations span hiring to GTM, product, growth, fundraising and everything in between - collectively forming the ultimate playbook to make you a better CEO, investor or board member. Tap follow and enable notifications to stay ahead of the game.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:05Logan Bartlett?

  Welcome to the Logan Bartlett Show. On this episode, what you're going to hear is a conversation I have with Alexander Wang. Now, Alexander is the co-founder and CEO of Scale, a company most recently valued at $7 billion that helps companies use their data as an input into the development of artificial intelligence models. Alexander started this company at 19 after dropping out of school, and it's scaled into one of the most important companies in the world of artificial intelligence today. Really interesting conversation with Alexander about the future of artificial intelligence, including what the risk of catastrophic doom is, as well as his concerns about the potential for artificial intelligence to create further inequality in society. We also talk about his operational lessons, including hiring people that actually give a shit about the problems you're solving, as well as a number of other interesting

• 0:50Logan Bartlett?

  things related to operating a business that has grown so quickly while he's doing so at such a young age. Really fun conversation with one of the more thoughtful executives in the space of artificial intelligence that you'll hear now. Alex, thanks for doing this.

• 1:02Speaker 01

  Of course, thanks for having me.

• 1:04Logan Bartlett?

  So, there was a phrase that was pretty ubiquitous about a decade ago, that data was the new oil. Can you talk about why you reject that view?

• 1:12Speaker 01

  So, I think there's a lot that the phrase gets right. I think that's sort of like, one framing is, so if you went back like two decades, the largest companies in the world were all oil companies. And so, at that point, and less the case now, but oil and petroleum were sort of like the bringers of sort of power and leverage, and mostly economic leverage. So, I think the way in which data is the new oil is that it is, by and large, going to be the main lever for economic power and economic sort of influence over the course of the next few decades. I think the thing that it gets wrong is that...

• 1:56Speaker 01

  Data is not a commodity in the same way. It's not like, not all data is created equal in the same way as oil. So, you know, oil by definition is like, you know, it's like this scarce commodity. But, but data is like, it's far richer than that. You know, data has, has multitudes. You know, you could have data specific to code, or data specific to language, or data specific to law. And each of these pieces of data is quite different. And therefore, you know, when you think about it strategically, it's, it's kind of a different framework you have to apply. You're not just going around hunting for, you know, data wells and just like try to mine them up and resell them.

• 2:36Speaker 01

  You need like a thoughtful strategy by which you're stitching together useful, qualitatively different data sources.

• 2:42Logan Bartlett?

  What does "data is the new code" mean? And how did that serve as a primitive to the founding of scale?

• 2:50Speaker 01

  The basic concept is that the, you know, what is the building block that enables the next generation of, of applications? Right? And, and I think that building block, undeniably for the past, let's say 50 years, has been code. Code has, has enabled many, many revolutions in technology. Most notably, the internet and mobile and, you know, everything that's happened. And, and code was that fundamental building block. And I think as you, as you peer forwards towards the era of AI in a world where, you know, models and algorithms more and more start to be what we interact with, govern the applications we use, like be the sort of like core primitive of our,

• 3:35Speaker 01

  of the, of our technological, technological lives, then data actually becomes the building block. And, and you know, the form of experience for me here was, I was, I was in college at MIT right when Google released TensorFlow. And, and it was like the first, it was the very early moments where like deep learning and large neural networks were starting to become democratized. And I remember using, you know, it was like, I used the exact same algorithm to detect.

• 4:08Speaker 01

  Facial emotions as to detect whether or not my food had gone missing inside my fridge. And nothing had changed, just data. The code was all the same, the algorithms were all the same. You'd run the exact same commands on the terminal. And it was just data was changing the performance of the algorithm. And so the form of experience was basically, if you think about like the next, call it like 50 years of technology that's going to be built. What is going to differentiate one application for another? And what are those building blocks that you're going to compose on top of one another? That's going to make an incredibly differentiated thing or something that delights consumers?

• 4:51Speaker 01

  And that thing was data, which gets at the heart of, I think, the importance of it going forward.

• 4:57Logan Bartlett?

  So that's the insight. Can we walk through to a specific example, what a use case was in the early days that kind of got you going around this?

• 5:06Speaker 01

  Yeah, so the earliest use case was all autonomous vehicles. And so, you know, go back to 2016, 2017 in Silicon Valley. Probably the mega trend was autonomous vehicles and self-driving. And there were many companies being started. A lot of the automakers were starting their own programs. There was the GM Cruise acquisition, which was sort of maybe the starting gun for the entire industry. And all of these autonomous vehicles, one requirement to be self-driving is that you can fully see everything that's on the road. That these cars can drive down the road and can see, oh, there's a person there.

• 5:49Speaker 01

  There's a car there. There's a bicyclist there. There's a cone, construction cone over there. This is what the traffic light says. You know, fully understand the environment around them. And to be able to do that, they required they had to build algorithms that ingested huge amounts of data of, you know, basically tons and tons of examples that where the algorithm could learn from, which are basically in this scenario, this is where all the cars were, in this scenario, this is where all the people were, with this scenario where all the pedestrians were, and then train off of millions and millions of examples like that to build these robust vehicles. You know, it's kind of come full circle now because you have in San Francisco, you have self-driving cars driving around everywhere without drivers in the vehicle, and it's now finally become a reality.

• 6:33Logan Bartlett?

  What did scale play in that in that value chain of getting autonomous cars going? Like, where did you fit in versus where Cruise stopped, or Waymo, or whatever the right example is?

• 6:44Speaker 01

  Yeah, it was specifically in this, in, you know, this data refinement stage where the cars would collect huge amounts of data. They would drive around, you would get tons of footage, video footage, lidar data, radar data, all this, all this sensor data altogether. But in none of that data were there actual examples marked of this is where a person is, this is where pedestrians, this is where bicyclists is, this is where a car is. And so the algorithm had nothing to learn off of. So what we did is we went from raw data to what's called labeled data, or high-quality data from machine learning applications, where all of these examples were marked so that that the model could actually learn, you know, in what situations, what does a person look like, what does a pedestrian look like, what does a car look like, et

• 7:29Speaker 01

  cetera. And, you know, one of the things that we like to say, while I disagree with the framing of the data is the new oil, if data is the new oil, then scale is the refinery. And we sort of underwent this process by which you would convert large amounts of raw data to very high-quality data that could empower your algorithms.

• 7:46Logan Bartlett?

  And why was that a problem that they wanted to outsource to a third party rather than bringing that in-house and building that competency out themselves?

• 7:55Speaker 01

  I think in general, if you look at the the overall AI industry, the sort of like large-scale

• 8:06Speaker 01

  Building blocks or the large scale ingredients for it end up being just such big problems that that that companies deserve to be built to occupy those infrastructure slots. So another way to think about this is like, you know, when I was starting scale, very inspired by Stripe and AWS, these sort of like large scale infrastructure companies that felt very visionary because they basically they realized that there were like the same problems that every company in a sector, every company in the startup industry were going to deal with. And they basically took those and built just almost like consumer level experiences for the developers and and built them to a point where it's like

• 8:48Speaker 01

  so easy to use and the economies of scale were so clear that they just became the defaults within the industry. So if you look at that for AI or for for for machine learning, there were there were kind of three main ingredients. There's compute, so GPUs and other other chips to power the, you know, incredibly data intensive and compute intensive algorithms. And as we've seen, almost the entire industry outsources to Nvidia at this point. There's talent, which there's no way to outsource really, but talent is this place where, you know, these companies obviously are spending huge amounts of money. Engineers at these at these firms are making millions and millions of dollars. They have teams of of hundreds and hundreds of people. So they're

• 9:33Speaker 01

  spending on the order of billions of dollars, you know, on the talent, full stop. And then there's data. And the each of these three ingredients were such, you know, there's such big pieces of the overall AI component tree that if there were companies that could solve them in a very high class way and a very high quality way, you know, they they were going to be used, they're like infrastructure, they're the industry demanded, you know, in infrastructure layer at that at for each of these components. So that's really the way I look at it. I mean, I think that like, there's for each individual company, they have this option, which is, do I build it in house? Or do I use the the sort of

• 10:15Speaker 01

  like industry infrastructure? And most companies take an approach, which is like, there's a few things which where it makes sense for me to build things on my own to differentiate myself. But You have to accept that you're going to do those things, generally speaking, less efficiently than the industry standard because of the economies of scale and network effects that the infrastructure providers have.

• 10:34Logan Bartlett?

  So you got going around that, and obviously, your use cases have been expanded today. We've seen what generative AI looks like, companies like OpenAI and Anthropic and many others. So where do you all play in the reinforcement learning, human feedback paradigm? Can you apply the similar primitives that you got going on there to now this world of generative AI?

• 10:58Speaker 01

  Yeah, so I think one of the craziest things about modern day AI is that most of the capabilities of these models are taught by data. You don't have the AI systems, you still don't have AI systems that are just sort of like learning on their own and just randomly demonstrating these very human skills. They're taught to them by large-scale data sets and human data. So what we do, we build what we call a data engine, which is basically similar framing, kind of the refinery for raw data in the ecosystem. But that data engine powers every leading LLM in the industry today effectively.

• 11:42Speaker 01

  Basically, every large language model is powered using Scale's data engine. And the specific technique or the specific approach is what you just mentioned, reinforcement learning with human feedback.

• 11:53Logan Bartlett?

  Can you explain that for people that maybe don't know that term?

• 11:55Speaker 01

  Yeah, so this was a technique that we actually worked with OpenAI back in 2019, on the very first experiments of. But the basic approach is that you teach a model what good looks like. So you teach a model how to assess whether or not one answer or one response is better than another. And it learns that through a bunch of examples where human experts are sort of teaching it. So a human expert will say, this one's better than that one, and here's why, and the model can learn off of that, and then know what good looks like. And so then, by the time it gets to actually producing results... It has an internal sense of what good looks like and what bad looks like and

• 12:40Speaker 01

  and what's better than another thing. It's called a reward model. And then it does what's called reinforcement learning. It basically uses that internal sense of what good looks like to optimize its own its own responses. And what that means is that this allows the models to actually exceed human performance in a lot of cases because, you know, it's kind of like how every human in the world can be a movie critic, but almost none of us can make a movie. So, you know, each of us can say ways in which a movie could be better or could be improved. And but but, you know, obviously, I can't make a movie. In the same way, if if humans can teach the model what better looks like and how to improve,

• 13:23Speaker 01

  then the model can keep improving even far beyond what human capability is.

• 13:26Logan Bartlett?

  You're in such a unique position to see how customers are leveraging AI. Are there any interesting anecdotes or observations you've had in the last couple months or year, whatever it's been about enterprises, big companies leveraging both you and one of the model providers as well to do do something that you can speak to?

• 13:47Speaker 01

  The really interesting opportunity for enterprises now is that if you look at the models, the best in class models that are that are built today, they're trained off of predominantly public data, so predominantly data from the open internet. But if you think about the total data that's available, the total addressable data, let's say, ninety nine point nine percent of that is actually private proprietary data of some form. One way to kind of like benchmark this is like of the words that you type, that each of us type, what percent of those end up on the open internet? Like a vanishingly small percentage. Most of it is in messages or emails or, you know, memos, these things that, you know, will never end up on the public internet unless you're subpoenaed or something.

• 14:31Speaker 01

  So, so the what that means is that like most enterprises, whether they know it or not, are sitting on troves of data that far exceed, you know, the amount of data that's accessible in these other in these other formats or on the public internet. And so much of the opportunity for enterprises is figuring out ways to Take great base models that are trained off the public internet, but then intermingle them, fine tune them, and specialize them on top of their own data, on top of their own business, their own customers, all of that context to produce things that are sort of like quite uniquely theirs and proprietary and generally differentiated because of you know, all this data that they've amassed in the past. So broadly speaking, that's what we think that the

• 15:16Speaker 01

  this is what we this is the work the direction the world's going to go is enterprise are going to be able to build models on proprietary data that sort of have unique capabilities. And the the exciting thing that's been happening over the past few months is is our work with OpenAI and others and other model providers, you know, we partnered with with Lama to Meta on Lama two as well, and then taking these general purpose models and fine tuning them on top of on top of enterprise corpuses. And so we've built a platform, EGP, which enables this, basically enables enterprises to, you know, take their own enterprise data, fine tune it on top of whether it's GPT-3.5 or other or Lama two

• 16:01Speaker 01

  or other base models over time, and build things that are sort of like uniquely capable for their own for their own use cases, whether it's for customer care and support or for legal applications or for, you know, development and their own development capabilities. And I think that this is, you know, it's incredibly exciting because it's a way for enterprises to get the best of both worlds. You know, all of a sudden I'm leveraging all of the incredible development that's happening in, you know, among this small handful of foundation model providers, while also adding something to it that makes it sort of uniquely mine. So I think that this is the this is the paradigm of the future for enterprise.

• 16:46Speaker 01

  You know, there's obviously a a long way to get there, but I think this is very clearly what the future is going to be.

• 16:52Logan Bartlett?

  Let's say you're an executive or founder at a startup or an enterprise in some decision making role and the core business isn't related to artificial intelligence. What? What should you be doing right now? Or recommendations would you have for someone that isn't at a Fortune 100 that they have people specialized in thinking about it? But the average executive or founder, like, how do you go about discovering what you could potentially use artificial intelligence for, scale for, OpenAI for?

• 17:21Speaker 01

  You should basically first go through and catalog, okay, what are my unique data assets? And, and think through like, hey, if, let's say, you know, one mental model to use is like, let's say there were, there were a person who, you know, was just a superhuman could like read through all of that information more quickly than anybody else. What are the things that that person would be able to do better than anyone else in the world? Right? And that's not like a, that's a pretty rough approximation what this looks like for the model. The models are better at storing information than human brains. And, you know, are not as time limited, as time limited as human brains. So they can read through everything.

• 18:03Speaker 01

  And then what are the unique capabilities that you get from some, from a system that is able to have done that? And so I'd go through that mental exercise and then I would think about, okay, what are the, what are the unique things that I can do from there? So both cost reduction, you know, customer care is a pretty clear example of, of cost reduction or optimization. Or, or what are the offensive things I can do? And then, and then I would just seek to build those out with knowing AI partners, you know, ourselves, OpenAI, Anthropic, you know, these, these companies that are seeing the entire ecosystem play out. And they're basically raced to do that because they think, you know, I certainly believe that, you know, not all businesses immediately,

• 18:46Speaker 01

  but you know, pretty short timeframe is going to be very clear which businesses have embraced AI and which ones are sort of still not running on, on the models. And it's going to become very evident from consumer experience as well as financials.

• 18:59Logan Bartlett?

  How do you, how do you compare the advent or the last five years of artificial intelligence to past trends like the personal computer, internet, smartphone, iPhone, whatever it is? In your mind, the like societal impacts, GDP lift, productivity gains, whatever the right framework is? How do you think about it?

• 19:18Speaker 01

  I mean, my honest take is it's going to be bigger than all of them. But you can sort of like look at it if you, from a few different lenses. I think at minimum, AI is clearly a new consumer paradigm and it's a new.

• 19:32Speaker 01

  It's a new way in which people will expect to interact with technology. And so in that way, you can sort of like say it's at least another mobile. In the sense that, you know, mobile was just this sort of like this mobile and personal computing. There's a massive changes in paradigm and accessibility of a lot of the base technologies. Same thing is happening here with AI. Chatbots are very clearly an extremely popular delivery method for technology. And so as a baseline, you can sort of like baseline it as like new consumer paradigm for technology. But the the upshot is like AI has been a very hyped technology for a long time for good reason. It is the holy grail of unlocking human productivity.

• 20:15Speaker 01

  Because, you know, take the framing of of productivity, which is, you know, let's say roughly speaking GDP per capita, what's the economic output divided by the number of human human heads you have? Well, all of a sudden, if you have technology, so algorithms or AI systems that can start doing pretty meaningful chunks of what would otherwise require humans and people, you have a potentially, you know, ridiculous unlock on on productivity. Another way to look at this is if you take all of US GDP, roughly $27 trillion of US GDP. Software and IT services is about $2 trillion of that. So everything that, you know,

• 21:00Speaker 01

  you or I spend all of our time on is thinking about that $2 trillion of of that $2 trillion bucket of the overall spend, which is not nothing, but it's it's not even 10%, right? 16 trillion of US GDP, so more than half is in services. The biggest buckets of which are are healthcare and the second and the next biggest bucket is financial services. And the potential disruption of this $16 trillion of of services GDP, I think is that's the that's the that's the potential of artificial intelligence. That's what you can totally, you can potentially transform to be, you know, 10x more productive, 10x better for the consumer, 10x better for the, you know, 10x economically more efficient in every way. And that's just sort of like.

• 21:45Speaker 01

  You can't imagine an economic opportunity bigger than that. In many ways, I think it is like it is the biggest economic wave. Until obviously the future, there's like some some new technology that has like the ability to be as impactful. And and the, you know, the key question you would ask is, okay, so to unlock that, you just need to believe that the models will keep getting better pretty quickly. Because like no matter what, if the models keep improving at the rate they're improving now, you know, we're going to end up in that world where the the opportunity to disrupt the economy is just like totally unprecedented. And, you know, I think we don't as a as an AI community see that slowdown happening anytime soon.

• 22:24Speaker 01

  So we're just in in the midst of potentially one of the the greatest economic engines of the world being invented. And that's, you know, I think will be one of the most special technological changes we see.

• 22:38Logan Bartlett?

  You said the next two to three years of AI are going to define the coming two to three decades of the world. What did you mean by that? Was that related to a lot of this productivity gain stuff? Was that geopolitical in that comment?

• 22:49Speaker 01

  I generally take take the stand there's like there's like two ways to look at the world. In terms of, let's say, the balance of power, the balance of countries and whatnot. I think you can look at it from an economic standpoint and you can look at it from a from a hard power standpoint. So probably most of the history of the world before World War Two was was dictated by hard power. And then most of the history of the world for the past, you know, eighty or so years has been dictated by economic power. And you could you would certainly ask the question, which is going to define the, you know, the next eighty years? But at minimum, it's like one of the two. So so if you take that framing, so I think one of the things that's that's quite shocking

• 23:35Speaker 01

  is the next two to three years of AI development, you know, everything we've seen over the past.

• 23:41Speaker 01

  Three years of or four years of AI development is shocking. In 2019 with GPT-2, GPT-2 couldn't count to 10. It would spit out gibberish English, you know, it was totally unintelligible. And then now, four years later, GPT-4 is probably more convincing and eloquent than like most people in the world. And that happened over the course of four years and roughly, you know, on the order of 1000x scale up of models. GPT-2 is roughly 2 billion parameters. GPT-4, depending on the US, is somewhere between trillion to 2 trillion parameters. It's been across a roughly 1000x scale up.

• 24:22Speaker 01

  We've seen just this like transformation from like, you know, worm level intelligence to something quite convincingly human. The next two to three years, you know, many companies are on the record for undergoing another 100x scale up. So, you know, these people will go from spending hundreds of millions of dollars on these models to tens of billions of dollars on these models. And my expectation is that's going to deliver, you know, very, very powerful algorithms that have the ability to impact both of these spheres, both economic power and hard power. So, okay, let's say we're in this like, you know, takeoff scenario of the technology. Economic power, I think the case for economic power is pretty clear.

• 25:01Speaker 01

  If you believe what I just said around it being the most important thing for global productivity or for economic productivity, then whoever gets there first, whoever integrates into their economy the fastest, whoever is able to actually like leverage MB AI first, whichever country or whichever society does that first, is going to have this meaningful leg up from an economic standpoint. And then from a hard power perspective, if you believe the technology is of a similar vein as the atomic bomb, which, you know, we can certainly dive into. But if you believe it's that kind of technology with the ability to, you know, both deter and project hard, deter conflict, project hard power to that degree,

• 25:45Speaker 01

  then it's also going to fundamentally change.

• 25:49Speaker 01

  You know, the balance of military power. So it feels to me like no matter how you slice it, this technology, while today we think about it as like a chatbot, is like at the core of, you know, the balance of power globally for the next, you know, 50 years.

• 26:05Logan Bartlett?

  What part of the atomic bomb analogy do you agree with? What part do you reject? You obviously, it's near and dear. You grew up in Los Alamos, so you have something familiarity with elements of it. Like, what do you believe about that comparison versus not?

• 26:23Speaker 01

  There's a bunch of interesting nuances here. So the atomic bomb was, was obviously primarily a weapon of war. So it was, you know, it is a weapon. And it's something that pretty clearly, you know, we as an entire world could pretty quickly agree like, we didn't want to use that anymore. And so it very quickly became, went from, you know, after a few, a few uses to being this like very clear deterrent for conflict. And this, this huge stabilizer for the globe. The difference with artificial intelligence is that no matter what, we're going to have to use the technology for economic purposes. So there's no scenario in which, you know, the countries of the world are going to get together and say,

• 27:07Speaker 01

  hey, we're not going to use AI anymore. And artificial intelligence is a pretty difficult technology to detect the use of. So part of the issue with AI is, you know, Russia could be using it for cyber attacks today. And it'd be very hard for us to actually like, you know, know that that's what they were doing. It's almost impossible to hide the fact that you used a nuke, right? So because of that, it makes it pretty hard to set the right international standards around the use of the challenge, the fair use of the technology, you know, how, you know, set standards around how terrorists can and should use the technology. And the thing that makes it, that presents challenges as a world,

• 27:49Speaker 01

  which is that like, this is a hard technology to keep in any sort of box. Unlike, unlike nukes. The way in which it's similar, I think, are that it's It's a technology that that has a very steep technological curve and has very clear benefits to scale. So to the degree that the United States can can be the leader or or a few a small set of democratic countries can be the leaders in this technology, then I do think it has the potential to be a huge deterrent towards other countries that are sort of behind on that curve. And that's been that's certainly been the case with atomic weapons and nuclear weapons.

• 28:30Logan Bartlett?

  Do you worry about the catastrophic risk scenario of that fast takeoff and anything that's more nefarious with AI itself rather than being used by foreign entities for things, I don't know, bioweapons or whatever you want to compare it to? Like, do you worry about it in and of itself?

• 28:48Speaker 01

  My my taxonomy on AI risks is sort of like there's three buckets. The first bucket is the AI qua AI risk. So, you know, the AI itself becomes a threat to humanity. That's, personally speaking, not the bucket that I am most worried about or concerned about. And I can, you know, I'll speak more about that. There's the AI misuse category. So, you know, authoritarian countries or terrorist groups misusing the technology. I think it's a very real risk. I think it's like the most real risk that we have. And then there's the last risk, which is sort of like a, you know, a second order effect,

• 29:31Speaker 01

  which is with massive with massive labor displacement, you'll see, you know, all sorts of political instability, domestic instability, populism, social, you know, these kinds of trends in in many developed countries. So the misuse one, I think, is the is is very real. I think it's it's we're seeing overall an increase in terrorism in the globe. And I think that the potential for misuse of the technology is very high, again, for cyber attacks, for biotax, bioweapons, for, you know, information warfare, and even stuff, you know, the the version of this, I think, is like, almost the most direct or clear is

• 30:14Speaker 01

  like, you know, there's these companies like Character AI and Replica that where

• 30:21Speaker 01

  You can have an AI model that becomes a genuine companion to huge percentages of the citizens of various countries. 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. So there's like a lot of, 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, how do we mitigate those risks? There was the executive order from Biden administration. I think we're certainly thinking about those.

• 31:00Logan Bartlett?

  Hey guys, Rashad here. I'm the producer of the Logan Bartlett Show and wanted to take a quick second to make an 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. Now back to the show.

• 31:17Logan Bartlett?

  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?

• 31:25Speaker 01

  I think there's a bunch of, a bunch of things I'll, I'll mention. I think one that, you know, most people in AI see and believe, but, but certainly is not, is not super, is not yet fully mainstream. It's just that these models are going to become very quickly, some of the, 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 dollars. I mean, there's not that many countries that can afford a hundred billion dollar investment, either funded through private industry or funded through the public sector, through the government itself. And so this very quickly

• 32:07Speaker 01

  becomes a, one of the largest economic projects, or sort of like scientific projects that the world has seen, which, which I think it's maybe surprising to people that it isn't that yet. You know, these models, you know, 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 like massive scientific projects in terms of scale of investment. I think the other piece that, that many people don't think about or, You know, I think is just going to slowly blend in is that

• 32:45Speaker 01

  the percent of time that humans are interacting with other people versus to a model directly, that split is just going to keep accelerating the direction of the model. So, um, you know, there's no, there's truly no reason outside of regulation. You would believe that the percent of time, that that percentage, the percent of time I spent, percent of my total time I spent 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, 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, contend with, which is

• 33:26Speaker 01

  no matter what, these models are going to start, you know, eating into all the time you spend talking and interacting with other people. You know, if you believe that the AI, that the models are only going to get better. If you believe that they're only going to have like more interesting data, if you believe the products are going to get better, the, the monotonicity of the improvement is going to be very weird to think about. And, you know, maybe these don't happen 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.

• 33:55Logan Bartlett?

  There was once kind of a concept of belief that it would be low level sort of manual jobs that would get automated through our official 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? And it's off to the, off to the side.

• 34:23Speaker 01

  Yeah, I think this just all boils down to data availability. So, um, 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, you know, uploading stuff to the internet, we've happened to have been creating the largest data set of, um, of human behavior ever. So anything that like we did on a computer, which most of it was, was fundamentally knowledge work or knowledge related or, or intellectual, because, you know, By definition is abstracted away from the real world.

• 35:07Speaker 01

  That's what the models have a lot of data on. So they have remarkably little data of, you know, 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, or, you know, all the things that are embodied in the real world. There is very little actual bearing on and very little data. And that's going to be true for a long time. You know, the sort of like digital presence of these models and digital intelligence is always going to surpass. For probably perpetuity will be far more advanced in the sort of like physical embodied capability. You know, if you think about it from a just like data availability standpoint, I think it makes perfect sense. And obviously where it gets really weird is, you know, the economic impacts of this and the sort of,

• 35:50Speaker 01

  what does that mean for, you know, the future of labor?

• 35:55Logan Bartlett?

  You 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?

• 36:10Speaker 01

  I think data and compute are definitely the limiting factors today. And, you know, compute has a very clear limit because of manufacturing capability. So the supply chains for both of these are, I think, are worth diving into. So, you know, 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.

• 36:52Speaker 01

  And 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 of the supply chain as well, which is something that, again, economically speaking, is not really feasible today.

• 37:16Speaker 01

  So compute is both a pinch point today. Obviously, we see how much Nvidia chips sell for and how much startups want them. But it's also just a clear limiting factor to the exponential growth scenario. Data is as well. So we think, you know, a lot of people observe that like, you know, is there more pre-training data out there? Have we run out of high-quality tokens? And there's certainly like some very lucid arguments by some folks that show that, you know, 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 you know, this argument, is video data high-quality data? Is video data not high-quality data?

• 38:00Speaker 01

  You know, 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. And 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, you know, human experts become GPUs in their own right. That, you know, basically the number and quality of human experts who are fueling

• 38:43Speaker 01

  model improvement is going to, in and of itself, become another supply chain bottleneck for the industry.

• 38:48Logan Bartlett?

  As we've looked at GPT-2 to 3 to 4, it's sort of, it seems to the outside end, almost like linear development, that we're actually, 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?

• 39:17Speaker 01

  When you talk to people at the leading labs, they spend all their time thinking about the supply chains for these models.

• 39:25Speaker 01

  So I think that implicitly, you know, if nothing happens, these will be really big bottlenecks. But that being said, I think that like, you know, this is potentially the greatest human engineering project that, you know, 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, pretty crazy actions to try to secure and ensure that the supply chains can continue scaling. But again, I think that's, that's kind of the technological imperative that we operate in.

• 39:58Logan Bartlett?

  Do you think we're underappreciating 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?

• 40:12Speaker 01

  One very clear indication of the degree to which like, we don't appreciate it is just in the, the multiple gap between Nvidia and TSMC. TSMC trades a dramatically lower multiple than Nvidia. Nvidia is a higher margin company, of course. So, you know, some of it's, you know, 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? Taiwan is just at the sort of like this pressure point for, for the world.

• 40:48Logan Bartlett?

  What's your perspective on open source versus closed source models? It seems to be a big debate these days. Do you, do you have any opinions on that?

• 40:56Speaker 01

  As a company, and, 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, 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. And if you have safe development on both, it's, it's great. I think that open source models are probably a

• 41:27Speaker 01

  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. Doesn't make sense for there to be some small closed source models to fit that need. And so I think, I think it's good for, you know, economic growth and economic prosperity that we have open source models.

• 41:56Logan Bartlett?

  I've heard you talk about the competing curves of AI. Can you talk about inequality and the competing curves of scale and democratization a little bit more?

• 42:05Speaker 01

  Because of the scaling laws, you know, as the models become ridiculously expensive to train, you know, 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 like, none of us have access to particle accelerators. And, you know, that is like, the, the poster child non-democratized technology is a particle accelerator. You know, there's, there's this avenue where the, where, where that becomes the version of the world. So that's, that's very clearly going to happen. And that's like one major sort of like tent pole for, for how the, how the technology develops.

• 42:47Speaker 01

  And then the other one, which, you know, there's so much will and, 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. And I think we're seeing that in open source models. I think we're seeing like, um, GPT three, five level models that have happened very, very quickly. And actually are very small, you know, I think there's, there's some recent things that show that, you know, these, these 10 billion parameter or even smaller models can perform at the level of GPT three, five.

• 43:32Speaker 01

  So, so this, um, this pretty rapid improvement of, of the democrat, I, I think basically you have this, so one curve is the scaling and the other curve is. The speed from frontier model result to democratization of that technology. And these are sort of the push and pull of the entire industry.

• 43:54Logan Bartlett?

  What is the Turing trap and why is that significant in your mind?

• 43:57Speaker 01

  Yeah, so the Turing trap comes from this great paper that this economist, Eric Brinolfson, Professor at Stanford, and others sort of wrote. And 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 replacement for humans predominantly. So we think about like, you know, when we have AI, it's going to replace humans in the workforce.

• 44:41Speaker 01

  And that will be its impact on the economy, which is, you know, as Professor Brinolfson 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. And 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,

• 45:24Speaker 01

  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, the, one of the very important messages, which is, there's this perception that AI will just take all of our jobs. No, 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 than human labor.

• 45:52Logan Bartlett?

  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, dissenting opinion of?

• 46:10Speaker 01

  I think one of the major... Things that people, when they think intuitively about AI, that they kind of get wrong. And I think this is, you know, I see this, I see this in a lot of places. It's 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, the tricky thing about AI is like very hard technology to bet against because every prior instance where you would like use an early version models, if you use GPT-2 and you said, ah, this thing can't count to 10, you like threw your hands up. It's like, there's no future here.

• 46:55Speaker 01

  Or GPT-3, you would use it and it's like, it, uh, it can't solve a simple math problem. You're like, you throw your hands up and it's like, this isn't going to go anywhere. I think that 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 are 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, uh, and we need to be thinking about a world where we're just on this continued track of model improvement.

• 47:29Logan Bartlett?

  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. Can 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?

• 47:48Speaker 01

  So one of the ways in which AI has been surprising is the degree to which it's become a clear, uh, objective and imperative for many, many countries and many geographies around the world. So obviously most, much of it was invented in the United States, um, at Google and OpenAI and DeepMind, et cetera. 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. That'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.

• 48:32Speaker 01

  The UAE has open source two successive open source models, one of which is 180 billion parameters. You know, these are very, you know, 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, 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 the, in AI. And so at minimum, it's becoming this technology that a lot of countries are looking at as like, hey, this is, this is really important for our future. And what's, what's really like more concerning is the degree to which, the degree to which certain countries, particularly China, are very clear eyed about

• 49:18Speaker 01

  the, the monumental impact this technology can have. You know, one of the, you know, there's a number of PLA people, the, 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 its adversaries, most notably the United States, which is the most powerful military in the world. Because, you know, we're going to over invest into our legacy platforms and just upgrading our legacy platforms versus the new breakthrough technologies. They'll over invest in the new breakthrough technologies and they could leapfrog us just like China leapfrogged the United States in fintech and payments technology.

• 50:00Speaker 01

  Where we pay and all their digital payment infrastructure is, you know, most people believe surpasses the, the sort of like state of, of payments infrastructure in the United States. This is the question. The question is.

• 50:16Speaker 01

  What is the, you know, 40% of global GDP? US GDP plus Chinese 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? Or is it the technology that allows the United States to ensure that we can maintain global stability as via by persisting and continuing Pax Americana? If you talk to a lot of political scientists, there's a pretty, a pretty clear consensus

• 51:00Speaker 01

  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. The, if 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, there's more overall instability, there's more war, there's more death.

• 51:44Speaker 01

  So I think that the, you know, in this like broader battle between democracy and authoritarianism and sort of the, these like different government systems and these different ways that the world can organize, AI is one of the major chess pieces in that game. And, and that's why I think it's critical that, you know. We as Americans or American generals able to maintain that pole position.

• 52:10Logan Bartlett?

  And maybe state to the proportionality of what China is spending versus the US today.

• 52:14Speaker 01

  For the past few years at least, China has been spending, the PLA, the Chinese military, has been spending between roughly 1 and 2% of their budget on AI technologies. And then in that same time period, the US DOD 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, you know, we're over investing into just our legacy platforms, our legacy technologies, under investing in the breakthroughs. They might, they might reach a breakthrough before us and we might be left, you know,

• 52:59Speaker 01

  in a situation we don't like.

• 53:01Logan Bartlett?

  By 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 you're really, really well done. You're heading out tomorrow. Why is attending this important to you and what are you hoping to accomplish?

• 53:17Speaker 01

  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, to have a clear and open dialogue around. You know, you 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, of just intrinsic value to the world by being an open dialogue between all the countries to discuss the technology.

• 54:02Speaker 01

  And many of the countries are going to be there, which is great. And, and kudos to the, to the UK government for, for creating such a forum. I think that the, the other piece that's, that's, that's critical is ensuring that, that we're thinking about the right risks of the technology. I think we, we talk a lot about the frontier level risks and the sort of like some of the existential risks. And 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?

• 54:31Speaker 01

  So I think it's important to me to ensure that we have a broader view of particularly geopolitical risks at play 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. 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. And 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.

• 55:16Speaker 01

  So I think that's been, 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 that at least that those are, you know, 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 was a key part of the, of the executive order, one of the most important things is ensuring that there's the proper testing and evaluation regime for AI systems.

• 55:55Speaker 01

  So 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. We don't, you can't just buy like random molecules off the internet and ingest them and expect that to go well. The, the, there's, there's similar kinds of regulation on planes, obviously, and cars and, you know, these, these technologies that. Are potentially very dangerous. And even the even Apple does a version of this for for apps in the App Store. You don't have to be approved by the App Store.

• 56:39Speaker 01

  So I think this is the key. This is the key question. And in my conversation with folks in the White House, it's like this is the this is the industry that needs to exist that that doesn't exist. And we at Scale, we're 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 and our view is that, you know, you need to have a pretty clear regime of of, you know, 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.

• 57:21Logan Bartlett?

  I 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?

• 57:35Speaker 01

  Yeah, I think that the 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. And if you believe in that, you believe there had to be, you know, infrastructure for data to to meet that challenge and sort of like meet that growth.

• 58:19Speaker 01

  That certainly played out, I think, even in a in a way that's that's been surprising to us, which is that the The amount of data required for these AI systems and the sort of like hunger for new data has has far exceeded, I think, what I originally even would have conceived possible by this timeframe.

• 58:37Logan Bartlett?

  And so you spent how long at Quora before going to school?

• 58:41Speaker 01

  I grew up in Los Alamos, New Mexico. Parents, physicists at the lab, a lot of physicists at that at that lab. And and then went to worked at Quora for about a year. Those were my foray and taste of what technology was like with the tech.

• 58:59Logan Bartlett?

  How old were you when you were working at Quora?

• 59:01Speaker 01

  Seventeen.

• 59:02Logan Bartlett?

  Okay.

• 59:02Speaker 01

  So I worked there when I was seventeen. And 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 every every new employee at Apple hears about, which is like, you know, you sort of realize that everything around you is is built by people no smarter or no more capable than yourself. I mean, my colleagues at 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. And it was built by, you know, a team of hundred or so people. And and and it was it was this like very empowering experience. Then I went to MIT, started training neural networks of my own, and the rest is history.

• 59:44Logan Bartlett?

  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?

• 59:54Speaker 01

  Yeah, I think that like the

• 59:58Speaker 01

  I think one thing that kind of stuck with me is that the, you know, it was already playing out at this point in in 2016 when I started scale, which is that it was pretty clear that the amount of resources that you would need to fully accomplish AI or to see the, you know, see AI through through the fullness of time would vastly exceed what was available in academia. And 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 but that was that was probably the key, the key driver.

• 1:00:30Logan Bartlett?

  What inspiration have you taken from Amazon with operations and technology being combined for scale?

• 1:00:36Speaker 01

  Yeah, a huge, a huge amount. I think Amazon.

• 1:00:42Speaker 01

  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 you know, operational excellence is actually a huge driver of of tech surplus and tech value. And so Jeff Wilkie, who ran operations there for many years and was a 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

• 1:01:26Speaker 01

  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 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. And 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 is taking that same approach and playbook and sort of philosophy, which is how do you marry operational complexity with with fundamental technology breakthroughs

• 1:02:10Speaker 01

  to drive an entire industry forward?

• 1:02:12Logan Bartlett?

  And Amazon's also been kind of canonical in parallel execution as well. Is that something that you guys think about when executing across the suite of different products you'll offer?

• 1:02:22Speaker 01

  Yeah, yeah, and the key, like the 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. And 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 then you can sort of double down on the ones that work out and it ends up working quite well.

• 1:02:51Logan Bartlett?

  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?

• 1:03:05Speaker 01

  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.

• 1:03:19Speaker 01

  They created Prime, so they created this membership program. And then it became the largest data center provider in the world.

• 1:03:29Speaker 01

  And that last piece sounds so non sequitur, if you like, tell it that way, that it just, it almost seems like it's, it's what like a bad author would write into a book. That like, you know, you had this, you had the everything store and they were so big and bad. And then they like ran all the computers globally, like it just sounds so unbelievable. And now, if you look at Amazon's market cap, you know, depending on who you talk to, most analysts attribute the vast majority of the value of the company to AWS. So this, 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.

• 1:04:12Speaker 01

  It was a very, like, pretty crazy thought, because you know, talk to most growth investors. Most growth investors are trying to very directly understand what will happen to, you know, 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 it's sort of this like, this pretty confusing property of companies, which is that, you know, on the one hand, investors feel like, think that they're betting on, you know, the next few years of execution for the company. But for the best companies, what they're really betting on is in continuous reinvention.

• 1:04:57Speaker 01

  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

• 1:05:14Speaker 01

  And like 15 years ago, they 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 of time, effort, R and 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. But today, Nvidia is a trillion dollar company almost purely because of AI.

• 1:05:56Speaker 01

  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 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 this is like, you know, this is this is the core of of markets or the core of companies that a lot of people don't understand is that the, you know, the bet, the the thing that you're almost almost always actually betting on is the capability to reinvent.

• 1:06:35Logan Bartlett?

  How do you manifest that culturally within scale? You guys obviously 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 how do you make sure that exists culturally within the business?

• 1:06:56Speaker 01

  Great question. It's what I spend a lot of time thinking about. And there's a few things that we do. I think there's like certainly a lot more that you can do at all times. I think one is that.

• 1:07:07Speaker 01

  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. And 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

• 1:07:50Speaker 01

  and then like some director or some VP steals your idea and then makes that into their career move. This, this kind of culture is like, is pretty unique and, 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 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. That's one. I think another is that we, we try to be very,

• 1:08:34Speaker 01

  we try to always put ourselves, focus on big problems, if that makes sense. So, you know, I think a lot of times, this is, you know, Amazon's version of this is like focusing on the customer. But I think if you have the right sort of fixed point in your, in the system, which for Amazon as a customer, for us to sort of like think about the big problems in the industry, then you'll always end up finding, you know, stumbling upon opportunities that can 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, very, a very big, complicated, interesting problem. At a certain point, it became pretty clear that a lot of what we talked about

• 1:09:16Speaker 01

  with geopolitics and sort of the, 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 this technology that we built up in servicing the autonomous fuel industry was pretty applicable, but you know, we then took on this much larger problem of how do you ensure American leadership? And how do you ensure that the US stays ahead? And that's such a big problem that, you know, in the course of serving that problem,

• 1:09:58Speaker 01

  we stumbled upon much, much larger opportunities than the original opportunities in autonomous vehicles. And 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 themselves? That we push for the maximum amount of progress in the AI industry? And that's 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.

• 1:10:36Logan Bartlett?

  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?

• 1:10:45Speaker 01

  Yeah, 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 like what you do and the actions that you take. So you believe a lot more in like, you know, you're holding the reins on your own life. And if you have an external control, it's the opposite. You believe that like, you know, the things that happen to you are mostly the outcome of things outside of your control, sort of like the world's very deterministic and you're sort of like a pinball and a big pinball machine.

• 1:11:28Speaker 01

  And I really like, you know, if you know how to look for it, this really is like a very clear dichotomy between 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 one way to like, look at that, or one way to like index off of that is seeing How hard do people work at things that matter to them? Right, because you know, there's things that matter to everybody, you know, everybody's things that matter to them. But if, uh, if they have an internal locus of control, then they're gonna like work their ass off to make sure that the things that matter to them happen the best possible way.

• 1:12:09Speaker 01

  If they have an external locus of control, things that matter to them, but they sort of like, you know, throw their hands up and, and let, uh, 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 getting a sense for how obsessive were they? How much do they really care? Like, how small of details do they sweat? You had a very, pretty clear indication for how, how much, um, how much control they believe they have on, on their life outcomes.

• 1:12:42Logan Bartlett?

  What's the single trait or characteristic that you're most looking for in hiring? Is it that locus of control or is there something else that stands out?

• 1:12:50Speaker 01

  Yeah, I think, um, you know, there's a few, there were like, we had this early document, um, that we wrote up around like, what do we look for in people that we hire? And there were sort of like, there were four traits. One is internal locus of control. Two is problem solvers. So, um, fundamentally, people who are like very good at creative problem solving. You give them a problem and they'd figure out like, you know, sometimes it would, you couldn't solve it just by like tackling it on. They'd figure out like a, a way around the, 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.

• 1:13:34Speaker 01

  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 into 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 who are collaborative. I think that you, you can have people who are like, high locus of control, um, 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. Um, it's carried us pretty far.

• 1:14:05Logan Bartlett?

  You've spoken about how the prestige around big brands in 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 contrast signal that you'd not look for?

• 1:14:22Speaker 01

  You know, 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, something along those lines. And 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, there's 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 end 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

• 1:15:06Speaker 01

  large brand name tech companies are those who, they're almost, they're over optimizing for brand and status relative to impact. By 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 not 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 at a 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. And you really want these people who don't care about status, care a lot about impact.

• 1:15:49Speaker 01

  And I think, yeah, I think big tech companies negatively select for that.

• 1:15:54Logan Bartlett?

  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 of years around how to operate or work with companies?

• 1:16:26Speaker 01

  I think one thing that is certainly non-consensus in the context of the ecosystem, but I think is really, is certainly been my experience is that you really, the value of of very hardworking people who are not necessarily super experienced in your company. And it's like, pretty surprising. There are kind of like two, there are some kinds of companies where it's like, you know, a small group of very experienced people who build something incredible. Like, that certainly exists. But, but for the most part, I think most startups are, are a, you know, this sort of like chaotic buzz or hive of people who are not necessarily super experienced,

• 1:17:09Speaker 01

  very, very hardworking, very high aptitude, very capable, and just sort of almost like gradient descent to building these incredible things. And it's, I think that's, that's, that's not super well understood or super adopted by the entire tech ecosystem. You know, a lot of tech ecosystem, I think, is really focused on hiring, you know, the experienced people who, who, 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, 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.

• 1:17:53Speaker 01

  The, 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. You're not offending anyone. You're not, you know, you're having as, as wide-scale impact as, as wide, as broad-based appeal as possible. And, 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

• 1:18:32Speaker 01

  you. So 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 share their points of view. And it forces you to keep your company authentic. And that's kind of, it's like kind of a subtle thing, but I think, you know, I look at a lot of peers in enterprise software and these enterprise software companies just become, very quickly they stand for nothing. And early on, every company was the product of founders

• 1:19:14Speaker 01

  who care a lot, who really sweat every detail. And then invariably, every enterprise company becomes sort of like, 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 that I talked about before.

• 1:19:36Logan Bartlett?

  At Virgus 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?

• 1:19:47Speaker 01

  Totally. I think that like, you know, it's funny, we've always operated in very cool spaces, 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 a, it's been very important for building,

• 1:20:27Speaker 01

  the company in a way that I think is true to the work that we need to do. And I think that the...

• 1:20:37Speaker 01

  The impact has been that we, you know, the people who join Scale know what they're getting into. They know what role in the ecosystem we play and they they care a lot about that.

• 1:20:47Logan Bartlett?

  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 a important thing to do for your employees. Can you talk to me for a little bit about why that's the case?

• 1:21:02Speaker 01

  Yeah, with the risk of sounding somewhat woo woo. I mean, I do think that like the 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 like a one of the best things I can do for the quality of my thinking. And I think there's a lot of fractal effects here where pretty subtle differences in either 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, you know, end outcome and the quality of thought. So 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.

• 1:21:44Logan Bartlett?

  What about structuring your day? How do you structure your day for maximum productivity?

• 1:21:49Speaker 01

  Yeah, what I what I often find is

• 1:21:52Speaker 01

  the best thing to do is to 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? And they can start out pretty small and then over time you'll like find where your limits are and upsize them. And and then, you know, I'm in a ton of meetings every day, which is part of the job, but you know, continually I'll check in, how am I progressing against, you know, the clear goals I set? I think that's probably the best

• 1:22:24Speaker 01

  the best thing I would do.

• 1:22:25Logan Bartlett?

  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?

• 1:22:44Speaker 01

  I think one of the things that is like somewhat maddening for people who are very

• 1:22:50Speaker 01

  Quantitative in business is that you're sort of like constantly operating in a little bit of a of a gray area in the sense of like you'll never really know if your decisions were like lacking were like fully correct or incorrect and and most things that matter are like quite hard to measure and you just have to like operate the 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 in math and science and much more well trained in in in the arts in America. So that that's the primary way

• 1:23:34Speaker 01

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

• 1:24:20Logan Bartlett?

  Your dad's a physicist and mom's an astrophysicist, right?

• 1:24:22Speaker 01

  Yeah.

• 1:24:24Logan Bartlett?

  How did your childhood most influence the CEO and founder that Alexander Wang is today?

• 1:24:31Speaker 01

  I have a great example for this because it was just I just spent the weekend with my parents and to them it was like really important that, you know, the people they worked with and the people and their leaders had this sort of very deep.

• 1:24:45Speaker 01

  Like almost inexplicable passion for the sort of place and the work that they did and the sort of like and the history of the field. And and some almost pretty similarly, like my parents both watched Oppenheimer many, many times. And they told me that, you know, we kept, we had to keep rewatching it because we had to like, we had to figure out who were all the physicists, like which, you know, there were like physicists in the movie that had like a single line or didn't have any lines. And we're like, oh, we had to like really figure out who played each of the physicists. And so I think there's like this, this level of like inexplicable passion for the field of physics that both my parents have.

• 1:25:27Speaker 01

  And this level of like, of sort of like fundamental care and love of the field that I think really rubbed off on me. I mean, my mom would teach, 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.

• 1:25:44Logan Bartlett?

  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. Is there anything else that you would say about how you try to assess out if someone uniquely has the passion for your company versus any other business when you're recruiting and in the hiring process?

• 1:26:03Speaker 01

  You know, one thing we do, we often ask people like why they're, why they're interviewing at scale. And I think you can tell, 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, okay. But if they're like, yeah, you know, I, I like, I was, I was working with one of my friends to train a model and like, we spent five hours just looking at the data and like there was one little bug in the data that caused the whole model to not work. And I realized that, and then I realized that this problem was like really deeply interesting. And then I applied to scale because of that, like that's like the right kind of answer. So, so one of the things that you look for, and Paul Graham, I think, has written very

• 1:26:47Speaker 01

  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, you know,

• 1:27:17Speaker 01

  that's fundamentally irrational and fundamentally sort of like hard to explain about about their passions.

• 1:27:22Logan Bartlett?

  Similar 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 could something that's innate to you.

• 1:27:33Speaker 01

  Totally.

• 1:27:33Logan Bartlett?

  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?

• 1:27:48Speaker 01

  Yeah, I mean, like purely statistically, if you're like, if you're normal, that means you're like in the bell curve. And, uh, and, you know, it's, 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. So I think it's like a pure statistical argument. But I think that the thing that I found, I find the most like interesting here is like, you know, being normal is kind of like a, it's kind of like some approximation for having like, generally speaking, like pretty mainstream beliefs. And, uh, and there's nothing wrong with that, but it means that like,

• 1:28:29Speaker 01

  this is maybe an indictment, but like if you're normal, it's pretty easy to simulate a conversation with you. Um, 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. Um, so I think interacting with people, surrounding yourself with weird people ends up being quite valuable because you're 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. That's like the, that's the greatest gift that, you know, you could have.

• 1:29:09Logan Bartlett?

  What has you most excited about the future of AI as we look out five, 10 years from now?

• 1:29:15Speaker 01

  It's hard to not be excited about, you know, kind of what I 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, 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? It's such an exciting proposition. And double clicking in that the deeply exciting components there are, again, the sort of

• 1:29:59Speaker 01

  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 takes so much time and resources, there's, 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, you have a problem, you go to the doctor and sometimes they can fix it easily. Sometimes it's extremely expensive to fix. Most times it's very expensive to, to resolve and then sometimes it doesn't work out. You know, fundamentally being a more proactive healthcare system by which you're constantly measuring

• 1:30:44Speaker 01

  a lot of things and you know, you can deal with these problems very early. And, and healthcare is just an entire field that like, without technology breakthroughs, we're kind of stuck as a, 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 weird is going on or like there's a, you know, there's a weird bump or whatever, they can, they can be proactive about that, it's pretty incredible.

• 1:31:23Speaker 01

  That's just one way in which like, you know, that could have one of the greatest effects to longevity of anything that we do, you know, global, global lifespan. So those are the things that get me really excited. It's like the, the full knock on impacts are going to be pretty great.

• 1:31:39Logan Bartlett?

  Alex, thanks for doing this.

• 1:31:40Speaker 01

  Yeah, thanks for having me.