The Exchanges

Q Speaking of kind of improving the data set that you have there, I am interested because plugging the gaps in terms of the edge cases, I'm seeing more and more synthetic data creation platforms pop up. How do you think about synthetic data creation platforms, and does that change the landscape and the value of data moving forward?

A Yeah, so synthetic data is a very alluring concept. I think that you'll be able to just generate data to match these edge cases. It hasn't really worked in practice in most situations, particularly when it comes to visual data or even textual data. It really hasn't worked, and a lot of the reasons why it's sort of limited in terms of its overall impact is that at the end of the day, whenever you generate data, there's always some sort of weird artifacts or bias in the synthetic data that you generate. You can see this, like, When people generate deepfakes, there's always some weird artifacts in that data. And the thing is, machine learning models are really good at picking up those little points of bias in a way that they, they basically won't generalize very well. And so all that is to say, I think it's, it's this very alluring idea. And if you talk about a high level, it's very exciting. But if you really dig in on like, where is the state of the technology and how impactful is it really? It's nowhere close to actually impact the curve of machine learning yet.

AI assessment note: “It's nowhere close to actually impact the curve of machine learning yet.”

Q I absolutely love that in terms of the drop out to start scale. Can I ask, A, what did your parents say? And B, in terms of risk tolerance and how you assess risk personally, how do you think about your own risk assessment there?

A Yeah, so I had to tell my parents that I was planning on going back to school. Jury's still out if I'm planning to go back to school, but that's what, that's why I had to tell them to let me do it. And then in terms of risk tolerance, I adopt a very risk-seeking perspective, because the reality is that there's not actually that much risk. When you start a company, the worst case, obviously, is that it fails, but even in that case, it's not that bad, especially if you're young, because if you're in that position, you'll still have plenty of opportunity after the company, and so I think a lot of people overweight risk, particularly when it comes to taking a job at a small startup or starting a company, etc., because they think that the negatives will really weigh on them, but I think You see people who start companies and fail, and then start another one, and it succeeds, and they're just going for it, and it's not really that negative of a, of experience.

AI assessment note: “in terms of risk tolerance, I adopt a very risk-seeking perspective”

Q Before we discuss some kind of company building principles, which I do want to touch on, 10 years time, what does that foundation model there look like? Who's independent? Who's been acquired? What does it look like?

A I think at its core, um, what we've seen about the foundation model race is that it is, uh, incredibly, incredibly expensive. Um, and it is expensive to the level of, you know, These models have gone from crossing hundreds of millions to a billion dollars to maybe multiple billions. I think in 10 years time, maybe they'll cost tens or hundreds of billions. And so there's just not very many entities that have that much discretion and capital to invest into these AI models. So naturally what will happen over time is AI effort, the, the, the foundation model efforts will coalesce around, you know, Uh, nations or the large tech companies over time. And so, you know, the, um, basically you will see the most, you know, all these hyper profitable business models, whether that's a nation state or one of the hyperscalers will, those will be the only entities that could possibly subsidize or, or, or underwrite these massive AI programs. And so I think in the future, it looks like You know, it is a battle of Jack already. It looks like a battle of giants, but you, but at that point, it's even more a battle of giants.

AI assessment note: “foundation model efforts will coalesce around, you know, Uh, nations or the large tech companies”

Q 800 people. You are now getting to the, kind of, bigger company size. It is harder, you know, the, kind of, only hire A plus players, or A players. A players, by definition, are rarer. Can you have 800 A players?

A I think the answer is yes, and I think that, um, you know, what we say a lot internally is, how do we hire the Navy SEALs, not, not the Navy, not that there's anything wrong with the Navy, but how do you have, you know, a really small elite group, um, that is really, uh, where you're really hiring the cream of the crop, and this goes down to process, you know, for us, um, at this point in the company, still, I approve every hire, so I will look at The, I will either indirectly interview or look at the interview feedback and look at the, um, understand every single person who we hire to ensure that we're keeping an exceptionally high bar. And that way if there's.

AI assessment note: “I think the answer is yes”

Q Did you see the reduction of that bar in real time?

A It felt, it was kind of subtle. It was something where, um, you know, You, you would hire all these people in and then, um, and then you notice it like maybe like the next year or the next six, six months later, you would notice it slowly and that the organization, you know, there were, there were challenges that the organization used to be able to, to deal with and solve that slowly just, um, calcified and we weren't able to, we weren't able to get around. And so you'll notice, you know, from the end of 22, you know, where I said we were 700 people to now we're 800 people, the team has mostly kept the same size. And I think what we've really thought about is like, How do we, you know, how do we move towards, but the company, the revenue of the company has grown dramatically.

AI assessment note: “It felt, it was kind of subtle... you notice it like maybe like the next year”

Q It feels like from the outside, scale's hot again. Do you know what I mean? Well, that's, uh, I don't mean that, like, to be super nice or, or not nice. I didn't mean it when saying you're cold, but it's just weird how brands have moments of heat and not heat.

A This is a fascinating thing actually from, um, you know, I, I actually asked, uh, Patrick Coulson this question as well, and Stripe obviously is an incredible company that has, uh, that for a lot of their lifetime I think has been one of the iconic, um, Silicon Valley companies. And I asked him whether or not he thought that the fact that they were such an iconic company, um, was Beneficial in all the hiring they did. And he made an interesting point, which was that, um, and hopefully he's okay with me sharing this, but like he made an interesting point that actually, you know, the, um, the best people they hired He thinks would have been people who would have joined whether or not they were the hottest company in Silicon Valley. Um, you know, it was the, it was the sort of like off the beaten path people who, um, who are actually the, the best hires they could have gotten. And a lot of the people who joined because they were the hottest company in Silicon Valley, you know, for one reason or another, weren't necessarily the, the, the most valuable employees. And so there's this, there's this element where I think the common belief and the common narrative is like, You know, you want to be the hottest company, so you can track the best talent, so you can hyper grow, so you can then go keep growing, and I think that's often so, so difficult, and it's much more about, like, how do…

AI assessment note: “independent of whether the company's hot or not hot, because you will have”

Q What does that look like in the structure of organizations today? Like, do we create new roles for these AI, uh, prompt, not prompters, but, um, kind of, uh, savers?

A Yeah, trainers is one term. AI trainers or, or, uh, or contributors, uh, is another term. I think this process of Of contributing data to AI is actually one of the, uh, highest leverage, uh, jobs that humans can have. And the reason for that is, um, you know, uh, if let's say I'm a mathematician, I can either go into my hole and, you know, do pure math, uh, and, and try to do, uh, try to do pure math research. And there's like one, that's one trajectory for my life. The other trajectory is I use all my skills and talents and intelligence To help make these AI models smarter. And even if I make, let's say I make GPT-IV just like a little bit smarter on math. If I, um, take that little bit of improvement of the model and I sum that up across all the times that GPT-IV is gonna be called and used across every, you know, every math student who's gonna use GPT-IV, every company that's gonna use GPT-IV, um, every developer that's gonna use GPT-IV, that's a huge amount of impact. And so, as a, as a human expert, you have the ability to have society-wide impact by producing data to help improve these models. And I think that's a, you know, what we see is for, you know, scientists, mathematicians, doctors, you know, people who are, you know, human experts in the world, it's an incredibly exciting proposition to be able to, you know, I can transmit my capabilities, intelligence, training,…

AI assessment note: “Yeah, trainers is one term. AI trainers or, or, uh, or contributors, uh, is another term.”

Q When do you feel the press tried to tear you down unfairly?

A So I, I would say that, like, Uh, almost, almost precisely we've had this, this, this, uh, this story where, you know, we had an incredible rise up and an incredible come up maybe when we had our initial, we initially became a unicorn back in 2019, um, and, uh, for the few years after that, you know, it, it felt like smooth sailing, and then starting in about, um, 20, 22, right, when it was the entire tech narrative was Or the entire media narrative, let's say, was tearing down tech companies because, you know, it was, I mean, in some ways it was very fair. You know, all of many, many tech companies received very high valuations. There was, uh, there was an incredible, um, amount of excitement in tech, and then the markets all crashed. And that's, that, and starting in 2022 was when I noticed, um, for us specifically, the tone entirely shifted, where the media engine pointed itself towards, you know, pointing out all the missteps From companies like us or a lot of our peers versus, uh, versus trying to point, you know, trying to take a balanced perspective. And there were even, you know, another example of this is, so starting in about, um, 2020, we, we began working with the US military and the US DoD. Um, and this was, you know, this is obviously long before the current defense tech, um, hype wave and, and long before all that, but it was, it was driven by an intrinsic belief…

AI assessment note: “starting in 2022 was when I noticed, um, for us specifically, the tone entirely shifted”

Q It feels like from the outside, scale's hot again. Do you know what I mean? Well, that's, uh, I don't mean that, like, to be super nice or, or not nice. I didn't mean it when saying you're cold, but it's just weird how brands have moments of heat and not heat.

A This is a fascinating thing actually from, um, you know, I, I actually asked, uh, Patrick Coulson this question as well, and Stripe obviously is an incredible company that has, uh, that for a lot of their lifetime I think has been one of the iconic, um, Silicon Valley companies. And I asked him whether or not he thought that the fact that they were such an iconic company, um, was Beneficial in all the hiring they did. And he made an interesting point, which was that, um, and hopefully he's okay with me sharing this, but like he made an interesting point that actually, you know, the, um, the best people they hired He thinks would have been people who would have joined whether or not they were the hottest company in Silicon Valley. Um, you know, it was the, it was the sort of like off the beaten path people who, um, who are actually the, the best hires they could have gotten. And a lot of the people who joined because they were the hottest company in Silicon Valley, you know, for one reason or another, weren't necessarily the, the, the most valuable employees. And so there's this, there's this element where I think the common belief and the common narrative is like, You know, you want to be the hottest company, so you can track the best talent, so you can hyper grow, so you can then go keep growing, and I think that's often so, so difficult, and it's much more about, like, how do…

AI assessment note: “because you will have, to your point, you'll have moments where you're hot”

Q When do you feel the press tried to tear you down unfairly?

A So I, I would say that, like, Uh, almost, almost precisely we've had this, this, this, uh, this story where, you know, we had an incredible rise up and an incredible come up maybe when we had our initial, we initially became a unicorn back in 2019, um, and, uh, for the few years after that, you know, it, it felt like smooth sailing, and then starting in about, um, 20, 22, right, when it was the entire tech narrative was Or the entire media narrative, let's say, was tearing down tech companies because, you know, it was, I mean, in some ways it was very fair. You know, all of many, many tech companies received very high valuations. There was, uh, there was an incredible, um, amount of excitement in tech, and then the markets all crashed. And that's, that, and starting in 2022 was when I noticed, um, for us specifically, the tone entirely shifted, where the media engine pointed itself towards, you know, pointing out all the missteps From companies like us or a lot of our peers versus, uh, versus trying to point, you know, trying to take a balanced perspective. And there were even, you know, another example of this is, so starting in about, um, 2020, we, we began working with the US military and the US DoD. Um, and this was, you know, this is obviously long before the current defense tech, um, hype wave and, and long before all that, but it was, it was driven by an intrinsic belief…

AI assessment note: “starting in 2022 was when I noticed, um, for us specifically, the tone entirely shifted”

Q When do you feel the press tried to tear you down unfairly?

A So I, I would say that, like, Uh, almost, almost precisely we've had this, this, this, uh, this story where, you know, we had an incredible rise up and an incredible come up maybe when we had our initial, we initially became a unicorn back in 2019, um, and, uh, for the few years after that, you know, it, it felt like smooth sailing, and then starting in about, um, 20, 22, right, when it was the entire tech narrative was Or the entire media narrative, let's say, was tearing down tech companies because, you know, it was, I mean, in some ways it was very fair. You know, all of many, many tech companies received very high valuations. There was, uh, there was an incredible, um, amount of excitement in tech, and then the markets all crashed. And that's, that, and starting in 2022 was when I noticed, um, for us specifically, the tone entirely shifted, where the media engine pointed itself towards, you know, pointing out all the missteps From companies like us or a lot of our peers versus, uh, versus trying to point, you know, trying to take a balanced perspective. And there were even, you know, another example of this is, so starting in about, um, 2020, we, we began working with the US military and the US DoD. Um, and this was, you know, this is obviously long before the current defense tech, um, hype wave and, and long before all that, but it was, it was driven by an intrinsic belief…

AI assessment note: “starting in 2022 was when I noticed, um, for us specifically, the tone entirely shifted”

Q Did you see the reduction of that bar in real time?

A It felt, it was kind of subtle. It was something where, um, you know, You, you would hire all these people in and then, um, and then you notice it like maybe like the next year or the next six, six months later, you would notice it slowly and that the organization, you know, there were, there were challenges that the organization used to be able to, to deal with and solve that slowly just, um, calcified and we weren't able to, we weren't able to get around. And so you'll notice, you know, from the end of 22, you know, where I said we were 700 people to now we're 800 people, the team has mostly kept the same size. And I think what we've really thought about is like, How do we, you know, how do we move towards, but the company, the revenue of the company has grown dramatically.

AI assessment note: “It felt, it was kind of subtle... you notice it like maybe like the next year”

Q Listen, I'm going on holiday next week. I'm going to buy it for that. The best business book ever. That's quite an endorsement, Alex. Tell me, what would you most like to change about Silicon Valley and tech? You're at the epicenter. What would you change?

A I think the main thing is I would like to change. I think particularly for early stage startups, there's a lot of focus around press and getting your name out there and being a sort of like hyped person. And I think the most important thing is to just build a good business actually, and being hyped very rarely ends up helping you build a great business. And so I think the thing that I'd most like to change is sort of this, I think in the press and on tech crunch, et cetera, There's like a focus around, there's a certain kind of focus, which is in many ways, anti counterproductive to building a really successful business, actually. So I think it's sort of like a return to fundamentals would be great.

AI assessment note: “I think it's sort of like a return to fundamentals would be great.”

Q Listen, I'm going on holiday next week. I'm going to buy it for that. The best business book ever. That's quite an endorsement, Alex. Tell me, what would you most like to change about Silicon Valley and tech? You're at the epicenter. What would you change?

A I think the main thing is I would like to change. I think particularly for early stage startups, there's a lot of focus around press and getting your name out there and being a sort of like hyped person. And I think the most important thing is to just build a good business actually, and being hyped very rarely ends up helping you build a great business. And so I think the thing that I'd most like to change is sort of this, I think in the press and on tech crunch, et cetera, There's like a focus around, there's a certain kind of focus, which is in many ways, anti counterproductive to building a really successful business, actually. So I think it's sort of like a return to fundamentals would be great.

AI assessment note: “So I think it's sort of like a return to fundamentals would be great.”

Q kind of asymptote of performance, where actually we'll see this kind of plateauing in performance while we wait for that? And do we think that's like a monthly thing, or do we think that's kind of like self-driving? Do you remember with self-driving, we saw kind of the plateau in performance actually for kind of several years, and actually it was only recently where we see that in fact again.

A It's kind of this interesting thing. So there's, there's three ingredients that go into Um, these AI models are three pillars. So there's compute, of course, there's data, and there's the algorithms. And the history of AI is that, uh, progress comes from sort of, you know, uh, all three of these pillars sort of being built altogether. So, you know, you need, you certainly need a lot of computational capability, but you need the algorithmic advances, algorithmic advances like the transformer originally, or RLHF, or, you know, whatever future algorithmic advances come. Um, and then you need, you need the data pillar to Supported as well. And I think a lot of the plateau that we've recently seen can almost be explained at a very high level from, um, hitting kind of a data wall. So GPT-IV was, was a model basically trained on nearly all of the internet, uh, and using a huge amount of computational capabilities. And a lot of the, a lot of, I think what the industry has been doing over the past few years is scaling the computation dramatically, um, but not necessarily by building up the other two pillars, um, In tandem. So there needs to be, I think, a combination of more algorithmic improvement, but in particular we need to, we need to ensure that there's more data to support it.

AI assessment note: “a lot of the plateau that we've recently seen can almost be explained”

Q kind of asymptote of performance, where actually we'll see this kind of plateauing in performance while we wait for that? And do we think that's like a monthly thing, or do we think that's kind of like self-driving? Do you remember with self-driving, we saw kind of the plateau in performance actually for kind of several years, and actually it was only recently where we see that in fact again.

A It's kind of this interesting thing. So there's, there's three ingredients that go into Um, these AI models are three pillars. So there's compute, of course, there's data, and there's the algorithms. And the history of AI is that, uh, progress comes from sort of, you know, uh, all three of these pillars sort of being built altogether. So, you know, you need, you certainly need a lot of computational capability, but you need the algorithmic advances, algorithmic advances like the transformer originally, or RLHF, or, you know, whatever future algorithmic advances come. Um, and then you need, you need the data pillar to Supported as well. And I think a lot of the plateau that we've recently seen can almost be explained at a very high level from, um, hitting kind of a data wall. So GPT-IV was, was a model basically trained on nearly all of the internet, uh, and using a huge amount of computational capabilities. And a lot of the, a lot of, I think what the industry has been doing over the past few years is scaling the computation dramatically, um, but not necessarily by building up the other two pillars, um, In tandem. So there needs to be, I think, a combination of more algorithmic improvement, but in particular we need to, we need to ensure that there's more data to support it.

AI assessment note: “I think a lot of the plateau that we've recently seen can almost be explained”

Q kind of asymptote of performance, where actually we'll see this kind of plateauing in performance while we wait for that? And do we think that's like a monthly thing, or do we think that's kind of like self-driving? Do you remember with self-driving, we saw kind of the plateau in performance actually for kind of several years, and actually it was only recently where we see that in fact again.

A It's kind of this interesting thing. So there's, there's three ingredients that go into Um, these AI models are three pillars. So there's compute, of course, there's data, and there's the algorithms. And the history of AI is that, uh, progress comes from sort of, you know, uh, all three of these pillars sort of being built altogether. So, you know, you need, you certainly need a lot of computational capability, but you need the algorithmic advances, algorithmic advances like the transformer originally, or RLHF, or, you know, whatever future algorithmic advances come. Um, and then you need, you need the data pillar to Supported as well. And I think a lot of the plateau that we've recently seen can almost be explained at a very high level from, um, hitting kind of a data wall. So GPT-IV was, was a model basically trained on nearly all of the internet, uh, and using a huge amount of computational capabilities. And a lot of the, a lot of, I think what the industry has been doing over the past few years is scaling the computation dramatically, um, but not necessarily by building up the other two pillars, um, In tandem. So there needs to be, I think, a combination of more algorithmic improvement, but in particular we need to, we need to ensure that there's more data to support it.

AI assessment note: “a lot of the plateau that we've recently seen can almost be explained”

Q mean to throw shade, but, like, OpenAI's models are not necessarily better, they've just had better access to data, they've bought more data, whatever, whatever, but data being the central kind of superiority element of why they had better performance in the past. Like, will we see One model get access that others don't. How do we think about like fair and equitable access to data from the model side?

A Well, I actually think to your point, if you think about the competitive playing field of these different model providers against one another, um, uh, they have, you know, data is one of the few, uh, you know, pillar. There's three pillars, right? There's algorithms, compute, and data. Um, and data I think is actually the primary pillar that you could imagine, uh, a real comp, Durable competitive advantage emerging, right? So, so if you think about where their moats in this LLM race or where their moats in this foundation model game, I think data is one of the few areas where you can produce a sustainable mode because the issue is algorithms. That's IP that at some point, you know, the rest of the industry will learn about. Um, compute is, uh, you know, you can have more compute than other people, but other people can just spend more money and buy that compute. And data is one of the few areas where you can actually produce a A long-term sustainable competitive advantage.

AI assessment note: “data is one of the few areas where you can actually produce a A long-term”

Q mean to throw shade, but, like, OpenAI's models are not necessarily better, they've just had better access to data, they've bought more data, whatever, whatever, but data being the central kind of superiority element of why they had better performance in the past. Like, will we see One model get access that others don't. How do we think about like fair and equitable access to data from the model side?

A Well, I actually think to your point, if you think about the competitive playing field of these different model providers against one another, um, uh, they have, you know, data is one of the few, uh, you know, pillar. There's three pillars, right? There's algorithms, compute, and data. Um, and data I think is actually the primary pillar that you could imagine, uh, a real comp, Durable competitive advantage emerging, right? So, so if you think about where their moats in this LLM race or where their moats in this foundation model game, I think data is one of the few areas where you can produce a sustainable mode because the issue is algorithms. That's IP that at some point, you know, the rest of the industry will learn about. Um, compute is, uh, you know, you can have more compute than other people, but other people can just spend more money and buy that compute. And data is one of the few areas where you can actually produce a A long-term sustainable competitive advantage.

AI assessment note: “data is one of the few areas where you can produce a sustainable mode”

Q mean to throw shade, but, like, OpenAI's models are not necessarily better, they've just had better access to data, they've bought more data, whatever, whatever, but data being the central kind of superiority element of why they had better performance in the past. Like, will we see One model get access that others don't. How do we think about like fair and equitable access to data from the model side?

A Well, I actually think to your point, if you think about the competitive playing field of these different model providers against one another, um, uh, they have, you know, data is one of the few, uh, you know, pillar. There's three pillars, right? There's algorithms, compute, and data. Um, and data I think is actually the primary pillar that you could imagine, uh, a real comp, Durable competitive advantage emerging, right? So, so if you think about where their moats in this LLM race or where their moats in this foundation model game, I think data is one of the few areas where you can produce a sustainable mode because the issue is algorithms. That's IP that at some point, you know, the rest of the industry will learn about. Um, compute is, uh, you know, you can have more compute than other people, but other people can just spend more money and buy that compute. And data is one of the few areas where you can actually produce a A long-term sustainable competitive advantage.

AI assessment note: “data is one of the few areas where you can produce a sustainable mode”

Q It feels like from the outside, scale's hot again. Do you know what I mean? Well, that's, uh, I don't mean that, like, to be super nice or, or not nice. I didn't mean it when saying you're cold, but it's just weird how brands have moments of heat and not heat.

A This is a fascinating thing actually from, um, you know, I, I actually asked, uh, Patrick Coulson this question as well, and Stripe obviously is an incredible company that has, uh, that for a lot of their lifetime I think has been one of the iconic, um, Silicon Valley companies. And I asked him whether or not he thought that the fact that they were such an iconic company, um, was Beneficial in all the hiring they did. And he made an interesting point, which was that, um, and hopefully he's okay with me sharing this, but like he made an interesting point that actually, you know, the, um, the best people they hired He thinks would have been people who would have joined whether or not they were the hottest company in Silicon Valley. Um, you know, it was the, it was the sort of like off the beaten path people who, um, who are actually the, the best hires they could have gotten. And a lot of the people who joined because they were the hottest company in Silicon Valley, you know, for one reason or another, weren't necessarily the, the, the most valuable employees. And so there's this, there's this element where I think the common belief and the common narrative is like, You know, you want to be the hottest company, so you can track the best talent, so you can hyper grow, so you can then go keep growing, and I think that's often so, so difficult, and it's much more about, like, how do…

AI assessment note: “independent of whether the company's hot or not hot, because you will have, to your point”

Q What does that mean in terms of the makeup of engineering teams of large enterprises? Do they shrink? Do they focus on different things? Do we just have teams of the world's best prompters? What does that mean in terms of the changing structure of engineering teams? Yeah.

A Well, I think software engineering in general is, um, is going to change dramatically. A lot of what developers spend a lot of time on today, they will not need to spend time on going into the future as the models get better and better at coding. But there's certainly big parts of what they do, which are irreplaceable. And over time, I think that the, the part in particular that's really, um, that's very, very valuable is this Is this sort of like general process of going from what are the, what are my customer problems or what are the sort of like problems I need to solve and like translating those into engineering problems and scoped sort of like, um, you know, tickets almost that can be solved by an AI engineer.

AI assessment note: “translating those into engineering problems and scoped sort of like, um, you know, tickets”

Q What does that mean in terms of the makeup of engineering teams of large enterprises? Do they shrink? Do they focus on different things? Do we just have teams of the world's best prompters? What does that mean in terms of the changing structure of engineering teams? Yeah.

A Well, I think software engineering in general is, um, is going to change dramatically. A lot of what developers spend a lot of time on today, they will not need to spend time on going into the future as the models get better and better at coding. But there's certainly big parts of what they do, which are irreplaceable. And over time, I think that the, the part in particular that's really, um, that's very, very valuable is this Is this sort of like general process of going from what are the, what are my customer problems or what are the sort of like problems I need to solve and like translating those into engineering problems and scoped sort of like, um, you know, tickets almost that can be solved by an AI engineer.

AI assessment note: “translating those into engineering problems and scoped sort of like, um, you know, tickets”

Q I think it was either Elon or Bill Ackman. And it basically showed countries, ah, different kind of creation of EV providers, and it showed like the US, and it was like the US, I mean, without Tesla, it would have been in the dumps, because it would have only had me in General Motors, but China was like up and to the right, for sure. Does that worry you?

A It worries me a lot. Yeah, and I think that the, um, you know, one of the elephant in the room topics, which I think, you know, is an AI community we rarely discuss, is that, um, at its core, this AI technology, uh, Has the potential to be one of the greatest military assets that humanity has ever seen. You know, if you imagine, let's say you had AGI, and you have one country with AGI and another country without AGI, you know, which one will win in a war? Well, probably the one with AGI is gonna, like, figure out all, you know, how to produce all the weapons, or we'll figure out a brilliant military strategy, or we'll be able to hack the other country systems, and it is potentially one of the greatest Military assets that the world's ever seen, potentially even more of a military asset than nukes, right? And so, um, and, uh, if you think about this, you know, we're in a, a geopolitical environment that is, uh, increasingly tense. You know, the amount of conflict in the world is, is, has been monotonically increasing the past few decades. Um, the, uh, you're seeing these, these, uh, multiple wars being fought in the world, um, and some of them without very clear paths to resolution. And you have, there's, Uh, there are totalitarian leaders right now in the world, you know, many of them with, for whom, like, you know, let's say China or Russia, uh, had AGI today, and the United S…

AI assessment note: “It worries me a lot. Yeah, and I think that the... elephant in the room”

Q London. We specialize in, in many things here at long lunch breaks and regulation. Um, and so diminishing on London. Um, but my question to you is I really worry that we're going to see regulatory provisions which stifle innovation because of consumer data protection acts and just unnecessary regulation around data access. Do you think I am justified, and how do you navigate the regulatory access to data question?

A It's a really important point, and I think that, that certainly, um, what we've seen in the EU, uh, is, is a very restrictive approach to data. My personal belief, I don't think that, uh, more permissive, uh, uh, regulations around data are at odds with being a liberal democracy. More sort of liberal, uh, data, data access, uh, provisions Are, are in fact very, very compatible with being a liberal democracy. And I think that, that we as a society need to figure out what the right balance there is and, and how we sort of square the circle. Um, but, uh, but I think, I think this is a very important question because it's almost like, I think in the United States, there's been a huge amount of, of effort and, and, uh, real regulatory effort on terms of how do we ensure that We do not slow down chip production. And how do we ensure, you know, how do we make sure that, you know, um, that we can keep manufacturing huge amounts of chips and the US won't be disadvantaged from that perspective? We need to take a similar lens to data. So how do we, from, from a policy standpoint, both in the US and in the UK, frankly, um, how do we think about ensuring that, uh, as countries, we are not going to, we're not holding, uh, we're not tying one hand behind our backs For future data production for these models.

AI assessment note: “what we've seen in the EU, uh, is, is a very restrictive approach”

Q Did you see the reduction of that bar in real time?

A It felt, it was kind of subtle. It was something where, um, you know, You, you would hire all these people in and then, um, and then you notice it like maybe like the next year or the next six, six months later, you would notice it slowly and that the organization, you know, there were, there were challenges that the organization used to be able to, to deal with and solve that slowly just, um, calcified and we weren't able to, we weren't able to get around. And so you'll notice, you know, from the end of 22, you know, where I said we were 700 people to now we're 800 people, the team has mostly kept the same size. And I think what we've really thought about is like, How do we, you know, how do we move towards, but the company, the revenue of the company has grown dramatically.

AI assessment note: “It felt, it was kind of subtle.”

Q I think it was either Elon or Bill Ackman. And it basically showed countries, ah, different kind of creation of EV providers, and it showed like the US, and it was like the US, I mean, without Tesla, it would have been in the dumps, because it would have only had me in General Motors, but China was like up and to the right, for sure. Does that worry you?

A It worries me a lot. Yeah, and I think that the, um, you know, one of the elephant in the room topics, which I think, you know, is an AI community we rarely discuss, is that, um, at its core, this AI technology, uh, Has the potential to be one of the greatest military assets that humanity has ever seen. You know, if you imagine, let's say you had AGI, and you have one country with AGI and another country without AGI, you know, which one will win in a war? Well, probably the one with AGI is gonna, like, figure out all, you know, how to produce all the weapons, or we'll figure out a brilliant military strategy, or we'll be able to hack the other country systems, and it is potentially one of the greatest Military assets that the world's ever seen, potentially even more of a military asset than nukes, right? And so, um, and, uh, if you think about this, you know, we're in a, a geopolitical environment that is, uh, increasingly tense. You know, the amount of conflict in the world is, is, has been monotonically increasing the past few decades. Um, the, uh, you're seeing these, these, uh, multiple wars being fought in the world, um, and some of them without very clear paths to resolution. And you have, there's, Uh, there are totalitarian leaders right now in the world, you know, many of them with, for whom, like, you know, let's say China or Russia, uh, had AGI today, and the United S…

AI assessment note: “It worries me a lot. Yeah, and I think that the... AI technology”

Q I think it was either Elon or Bill Ackman. And it basically showed countries, ah, different kind of creation of EV providers, and it showed like the US, and it was like the US, I mean, without Tesla, it would have been in the dumps, because it would have only had me in General Motors, but China was like up and to the right, for sure. Does that worry you?

A It worries me a lot. Yeah, and I think that the, um, you know, one of the elephant in the room topics, which I think, you know, is an AI community we rarely discuss, is that, um, at its core, this AI technology, uh, Has the potential to be one of the greatest military assets that humanity has ever seen. You know, if you imagine, let's say you had AGI, and you have one country with AGI and another country without AGI, you know, which one will win in a war? Well, probably the one with AGI is gonna, like, figure out all, you know, how to produce all the weapons, or we'll figure out a brilliant military strategy, or we'll be able to hack the other country systems, and it is potentially one of the greatest Military assets that the world's ever seen, potentially even more of a military asset than nukes, right? And so, um, and, uh, if you think about this, you know, we're in a, a geopolitical environment that is, uh, increasingly tense. You know, the amount of conflict in the world is, is, has been monotonically increasing the past few decades. Um, the, uh, you're seeing these, these, uh, multiple wars being fought in the world, um, and some of them without very clear paths to resolution. And you have, there's, Uh, there are totalitarian leaders right now in the world, you know, many of them with, for whom, like, you know, let's say China or Russia, uh, had AGI today, and the United S…

AI assessment note: “It worries me a lot. Yeah, and I think that the, um, you know”

Q And then I want to finish, Alex, on probably the most exciting of all. What are the next five years for you and for Scalehold? What's that roadmap ahead?

A Yeah, I think what we're really excited about Is going beyond what we've done so far in terms of building this sort of data platform for AI and build out more and more infrastructure for AI in general. And so I think as we look at the next five years, we want to build out all of the pieces of infrastructure, all of the tools, all of the platforms that would allow more and more organizations to be successful, effective, and productive with machine learning so that we can actually kind of realize All this hype that we talked about at the beginning of the show. And so, so we're really excited about, and we want to help existing tech businesses that are doing a lot of machine learning. We want to help enterprises that are just sort of starting to get their feet wet with machine learning. We want to help smaller organizations. Like we want to help every single business that can apply AI into their business process or into their business for positive productivity. We want to help them do that.

AI assessment note: “as we look at the next five years, we want to build out all of the pieces of infrastructure”

Q On the incentives drive outcomes, I loved something you also said. You said, why hiring people who give a shit is harder than it sounds. What do you mean? And how do you think about that when hiring?

A You know, it sounds so simple when you, when you really boil it down, but if you hire people who, you know, we say give a shit internally, but who really, really care, you know, they really, really care about the, their, their work product. They really, really care about the quality of their work. They really, really care about the organization. They care about making sure that the company has an impact. You know, they just, they just really care. And what that means, how that manifests is, you know, they're willing to sweat every single detail. And if they get roadblocked or there's like something in their way, They'll spend the extra, they'll go the extra mile to make sure that they get through those things. You know, that ends up being, that's, that's how startups work fundamentally, is that you have these small teams of people who each care 10 times more than the, than the average employee, 10 or a hundred times more than the average employee inside a big company, and so they end up, you know, you end up just solving so many more problems than, um, than the big company can.

AI assessment note: “if you hire people who, you know, we say give a shit internally, but who really, really care”