20VC: Andrew NG on The Biggest Bottlenecks in AI | How LLMs Can Be Used as a Geopolitical Weapon | Do Margins Matter in a World of AI? | Is Defensibility Dead in a World of AI? | Will AI Deliver Masa Son's Predictions of 5% GDP Growth?

In my career working in AI, I have yet to meet a single AI person that ever felt like they had enough compute. Data centers are the critical infrastructure for building the digital economy. I think that open-way models is a tremendous source of geopolitical inference. I think the work I think the velocity when China's government makes an all-nation commitment is all-industrial commitment is actually a very powerful force that I wouldn't underestimate. This is 20VC with me Harry Stubbings and I am forever grateful of the opportunities that I get to speak to the world's smartest people. And this was a real pinch me moment. I'm learning AI in real-time with you. And so I could not ask for a better guest than the guest today joining me. Andrew and G globally recognize leader in AI. He's the founder of Deep Learning AI, exact chairman of Landing AI, General Partner at AI Fund, he even co-founded Coursera. He's a pioneer in machine learning and has co-authored or authored over 200 papers in machine learning robotics and other fields. And in 2023 he was named the Times 100 AI list as one of the most influential AI people in the world. But before we dive into the show today, are you drowning in AI tools, chat GPT for writing, notion for docs, Gmail for email, Slack for comms, and you're constantly copy-pasting between the more losing context and losing time. This is the AI productivity tax and it's killing your output. At 20VC we're all about speed of execution and superhuman is the AI productivity suite that gives you superpowers everywhere you work. With the intelligence of Grammily, mail and code of built-in, you can get things done faster and collaborate seamlessly. Finally, AI that works where you work, however you work. Superhuman gets you from day one with zero learning curve and it's personalized to sound like you at your best, not like everyone else using generic AI. 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You have now arrived at your destination. Andrew, I've been in admire for a long time so I've been really looking forward to making this happen. So thank you so much for joining me today. Thank you, Harry. Watch the video shows. I really enjoyed your recent way with my friend Martin, Martin Kursa. That was well, that was very memorable. So I actually told to be here. I love Martin. Very, very special man. I want to start with something that you've said before. You said AI is the new electricity and when I think about electricity and where we are today, I want to understand the bottlenecks and everyone seems to suggest that it really is about data, compute and algorithms. Is that the three parameters to which we should think about bottlenecks? And if so, which one do you think is the biggest bottleneck? I would say the two biggest bottlenecks right now, I think electricity is one of them. So in the US, I am honestly worried that many data center operators were stuck in permitting and I know that local community support is important and some people don't want a data center there. But once we build roads and railways as the infrastructure for a certain generation, data centers are the critical infrastructure for building the digital economy and so lack of electricity in America and in a number of Western countries is a problem. And in contrast, I see China building power plants left and right, including nuclear. So that would be interesting dynamic. And semiconductors is another bottleneck. But AI is so complicated. I think we also need more data, we also need more better algorithms all over this worth working on. But in the short term, some constraints with electricity and semiconductors. Can you talk to me about the constraints around semiconductors that you think are most pressing that most people don't realize? First, in my career working on AI, I have yet to meet a single AI person that ever felt like they had enough compute. You know, get us any amount of compute, we will use it all up and say we still don't have enough. So this is a constraint for the last 20 years or so. But what I'm seeing is with the rise of Gen. AI, there are very valuable workloads. For example, AI system coding, you know, it's fantastic. It's making us so much more productive. But if you use cloud code enough, sometimes you get really limited and I find that many companies really have access demand, which is a very rare problem to have. But so many people are one more on inference, one more tokens generated. And we just don't have the semiconductors and data centers and electricity to meet the demand. But you know, there's a lot we could do with AI token generation and it's frustrating when we can't supply enough to people that wanted on the demand side. You know, you get very limited if you use too much. How should I think about that insatiable need for more compute and the improvements that come from it with the recognition that many people say GPT five was the example that scaling laws have been reached to certain extent and a focus on efficiency has been a transition. How should I balance this suit to seemingly differing opinions? It is true that token generation is getting more efficient and cheaper. If you look at OpenAI's Open Weight model, they actually release models are very efficient to run. So I think they did a good job. Was it like 120 billion pounds or something with I think 5.7 billion active. So it's a very efficient model to run. But despite the costs of token generation falling, our demand for it is insatiable. One thing that's happening in AI is if we look at where the buckers of value, one of the big buckers of value is AI's system coding. I think this Hawkins back to an earlier era in a previous generation. I think Google came to dominate, you know, horizontal information discovery like web search, but there's room for lots of verticals when the internet was being built. So we want to have with travel, lost in expedia, fought off a travel, bunch of folks, fought out in retail, a bunch of others, fought out in transportation, social media, and so on. What we're seeing now is chat GPT has such a strong consumer brand. Chat GPT seems to be the dominant player and new gen horizontal information discovery. I think Gemini with his channel advantage through control of Android and Chrome, you know, is a serious player as well. But if that's where horizontal information turns out to be, then there's still plenty of room for lots of verticals to be built out. And one of the clear buckers that really valuable verticals is AI coding assistance where cloud code is, you know, I use that every day, love it, open the i codex, has a lot of momentum as well. But it's clearly making developers so much more productive and efficient that the demand is just through the roof for less, less use more and more of this. One thing I find exciting is I often look at AI coding assistance as a harbinger for what might happen to other job functions as well as the AI marketing tools become more efficient as AI recruiting tools were more efficient. I find it's too difficult more efficient. So I often look at AI coding assistance as a maybe a foreshadowing when we happen as well to other sectors as the tools get better for them too. I had Joel Pinot from cohere and formerly a Facebook on a show recently and she said that AI coding assistance are in the same place that maybe image generation was in 2016, 2017 in terms of maturity. Do you think that's a first state of the environment today or do you not think so? I don't know, I think it spreader along. I think in 2016 image generation wasn't super valuable. I think today AI coding assistance is really actually an AI fund, my head of engineering recie, I would say, hey, let's think about standardizing on tools and basically he said, I need these tools and you have to pry them out of my code dead hands. I think our developers feel really strongly. I myself, I don't ever want to have to code a game over AI coding assistance. I think the tools are really working well but still with a lot of a headroom for how much better they can get. I do just want to go back to the kind of the core border once we said they're about all that true to me, said they're about semiconductors. I think when we look at the build out of data centers today, as you said, regulation has been a big part of preventing that in a lot of ways. Do you think Trump has done more to help or to hurt the progression of AI in the United States from an infrastructure perspective? Over the last few years, the US federal government has done some good things and some less hopeful things. I feel like playing out unnecessary regulations has been a very good move. Even last year, the bipartisan Schumer AI Insight Forum, I think there are a lot of people lobbying the US government to pass stifling regulations. There are a lot of high-top AI safety narratives saying AI could lead to human extinction, which is climate ridiculous statement. So try to get stifling anti-competitive regulations passed off into trying to shut down open source open weight. Fortunately, we'll be back a lot that, but I think the bipartisan shrewd means I far from did a really good job digging to the truth and concluding that the America should be investing in AI rather than you are passing unnecessary regulations to slow it down. I think Trump and then this whole team, David Saxon, Christian and so on, did a good job clearing out unnecessary regulations. On the flip side, one of America's huge competitive advantages has been this ability to attract talent, including high school talent, as well as young talent that may not currently be high school, but could be high school in the future to the extent that America is not investing as much in attracting talent. I think that would be an unforce error. And then I think lastly, investments in science, right? I think helping our institutions of higher education have the resources to train our grad students to invest in science and technology. I think that's really precious and so anything that damages that I think would also be very unfortunate. I gave you a regulatory magic wand, Andrew. What would you change that would have the most significant needle moving impact? America is fortunate to have a lot of very smart people wanting to come here to do really challenging, really tough problems. Many of our low-bell, blores are immigrants, Einstein, Kanalka, Zabos and immigrants. I think continuing to cultivate America as a place to attract great talent to work together in a democratic nation that respects the rule of law, I think that would help us move ahead. I think that securing the semiconductor supply chain would be very valuable as well. A lot of friends in Taiwan, I love Taiwan. And also America's dependency on TSMC is concerning, in case anything happens. And then frankly, there's one very funny thing that happened in society. There was a recent purport showing I think how much America's think AI would be good for them in thuzastic for a versus non-thuzastic. And even though a lot of AI technologies were invented in America, a lot of people don't trust or don't like AI. The joys of what I do, Andrew, is I get to speak to incredible people and then cross-reference what they say. David Con from Sakura said, hey, a really useful barometer for effectiveness is Kan AI replaced the bottom 5% of capabilities of what workforce does. Joel from Cohears said, no, that's crap. The real question is, can it 10X people's ability? Forget the bottom 5% can it 10X? How do you think about a barometer for success of the workforce with AI? In a case of software engineering, it is accelerating the rating of codes. There are so many projects that used to take six engineers half a year to build that today I or one of my engineers can build in the weekend. I hope that we never have to go back to coding without AI systems again because the acceleration to productivity boost is incredible. For example, one weekend I thought I wanted flashcards for my daughter to practice multiplication and she wanted to practice multiplication and she wanted flashcards. So I thought I could either drive to the store and buy a bunch of flashcards for her or if you just use AI to write code for me to generate and print out a bunch of flashcards. And so I did the latter. And so does a very low economic value task, both AI as a coding and could get their done very quickly. Do you think vibe coding is an enduring market? Like do you think everyone will want to code? An accessibility is important or do you think it bluntly just allows builders to build better and more efficiently? I think we need all of the above. I've had mixed feelings about determine vibe coding but nitpicking terminology aside, I think everyone should then to code. What I'm seeing is for a lot of job roles that aren't just software engineering, people that can code can get more done than people that can code. For example, I think my marketer wanted to run a user survey. She wanted something for people to give live feedback and she looked the app store couldn't find anything. So she said, you know what, I'm going to spend two days to code that. And take a take a two days, but my marketer then build a little mobile app where users could swipe left or right to give feedback on some marketing messages. We want to use the test. And because of that, we're able to run user experiments, get feedback. And so it helped her do her job better as a marketer. Whereas in contrast, a marketer that they couldn't code a low app to let people swipe around and give feedback, they were just not helping to do this. We're not have gotten a feedback. We're not having to move forward. They are my best recruiters, not only do they screen resumes by hand, they are writing prompts to get AI to help them screen resumes. Which is amazing, but going to your point on like, oh, people shouldn't be fearful and they are fearful. You see that that would lead to efficiency gains, which mean head count reductions. If you can screen so much more with AI, I'm not into this kind of fear mongering. But like, if you can screen a lot more with AI, I don't need my three other analysts. I think there's a small subset of jobs that frankly are in trouble. But I think for the vast majority of knowledge workers, AI is amazing. There's lots of can't do. So this phantom, AI, Sunday with AI, I think, do everything human can do. I think we're very far away from that. I'll say it all like decades away, maybe even longer. And the trick is if AI could do 30% of recruiters job, maybe 50%, although that feels a little bit high. And then there's another lights 50 to 70% of stuff that we still need the human to do. But there's also clear that if you use a and someone doesn't, that's actually a huge difference in what you can accomplish. So much better off using AI. But because AI can't do everything, there's still plenty of work that we still need humans to do for a lot of job roles. Do you not think we have a white collar talent pipeline problem, though, which is whether you're a consultant or you're a legal associate in the junior ranks. A lot of what you can do is being replaced by AI and they are actually cutting juniors. You're seeing this across the board. And so what we're supposed to fear is we're going to have this talent hole where in 10 years time, there's no juniors to go up into seniors because we've replaced them. I don't think it's as dire as that. I think there is a big problem, but I don't think it's exactly that problem. So let me tell you what I'm seeing in software engineering. The most productive engineers I know, they're not fresh college grads. They are people of 10, 20 years of experience or whatever and really on top of AI and know the AI tools and understand the AI code. So those people experience and on top of AI move faster than anything the world has seen even 102 years ago. One tier down is actually a fresh college grads that are really on top of AI. So I've hired quite a few people, fresh college grads that for whatever reason through the social network community really learned the AI tools and they move really fast. But they're not as good as people experience. One tier down for the fresh college grads is the people with 10 years of coding experience but who had a comfortable job and for whatever reason is still coding like us 2022 before tragedy. I just don't hire people like that anymore, but there are people that you know that the comfortable job they kept coding be all way and they just did not learn AI. I think those people may get into trouble at some point, but there's one other one which is the tier that isn't trouble which is the fresh college grads that don't know AI. One unfortunate thing is university curricular is slow to change. I actually feel pretty bad that even today there are universities graduating CS on the grads that have not made a single call to single API on the internet. Imagine graduating a CS on the grad that has never heard of cloud computing. So what does it call? Oh, I don't need to just run things. That's weird. You just can't be a CS major and not know how to do things on the cloud. And I'm feeling like I'm getting to point where I feel like we've got to not train CS majors without also making sure they know how to use AI to help them with coding but we're also making them know the AI building blocks. That's the cohort of students that entering the job market that's really struggling. But the fresh college grads know AI. We can't find enough of them. So many businesses love to hire those fresh college grads. I just want to touch on the 10X100X engineers that you said are just amazing, amazing. We're seeing pay packets, compensation brands, large than they've ever been. It's three and a half billion dollars in certain cases for a single engineer. Are these justified pay packages given the impact that they are having on companies enterprise value or is this bubble like pay packages that we should be concerned by? It is very hard to say. I know a number of people that got a really huge pay package. I'm actually very happy for them. I think it's great. The funding going into pay AI people really well. I mean, it nicely. Do you think it's a 100 million dollars for an engineer? I worry that you're just not going to be as productive. I give you a 100 million dollars overnight. God, you might buy a nice house and go and hold it and you lose a bit of efficiency. I have lost the confetti friends that, you know, for whatever reason have made a little bit of money. Many of them just keep working really, really hard. Equally before and after, you know, they wound up making a little bit of money. So I find that a lot of the tech culture we do stuff because it's fun because it lets us, you know, hopefully hope other people is a way to change the world. I find that a wealth mixed people become lazy much less than one might guess. I'm intrigued to see how you think about this. You said all the different ways that it could impact many different verticals there. And you said we over-hype, you know, Doomsday scenarios and everything in between. So Andre Capati recently said, "AGI will just blend into 2% GDP growth." I thought it sounded a little bit unexciting. I wanted some seismic shift in productivity increase. Do you think a blend into 2% GDP growth is what you expected? Or do you expect a much more significant 5, 6% like Mass or Sun at Softbank expects? I hope you can. get much closer to 5, 6 or more percent GDP growth. When looking to the future, it turns out one of the most expensive things in today's world is intelligence. This is why it's so expensive, at least in the US, to hire a highly skilled doctor to advise us on the medical condition. I hire a highly skilled tutor to patiently teach our kids because that intelligence, training up that wise doctor, wise teacher, wise advisor is very expensive. So with AI, we finally have a path to make intelligence cheap. And so in the future, if everyone can be assisted by an army of smart, war-informed staff on all of these topics under the sun, that currently only the relatively wealthy in society can afford to hire people for, then individuals will be so much more empowered and able to get so much more done. Lies will be so different that the GDP growth will be massive. Kind of speaking about that democratization of knowledge there and the benefits that come from it, you said a word before which was open about the kind of open weights ecosystem we've seen, we've seen this reversion back to like a closed world in a lot of cases. How do you analyze the state of play today in that open versus closed? It's still very dynamic. So for a lot of American companies, the leading frontier model is often kept closed and then the one tier down model, not quite as good as releases open. I think it's much better than nothing. I'm actually grateful for all the teams that are releasing open source, open weight models. And then the other dynamic is China, especially has been really taking the lead or taking a lead or getting up there in terms of releasing tons of really good open weight models. I would say it's kind of not whatever predicted decade ago that China AI would end up being more open than America AI. Do you think China is wanting an open AI world? It turns out that open is just great for a country's development. So it turns out that when a team releases open source software, circulation of knowledge is much faster to the close by community. And so what I see is when a team in China releases an open way model, then yes, of course American can take advantage of it, but the China economy benefits even more from it because when something is open, it's easier for teams to call each other and say, Hey, buddy, how does this really work? I'm having trouble with this model. It's just that circulation of knowledge is really valuable for innovation. And when the US has more close models and when you know, teams are trying to pay these $100 million salaries to extract talent, then that circulation of knowledge becomes very slow and it slows down the rate of American and European innovation. With the commoditization of the model layer, though, and the opening of it, it actually increases the premium on manufacturing and the ability to manufacture at scale, which China have a much greater ability to do the US. Do you not think that actually leads a lot of their thinking around why they want to remove the strength of US models? I think in addition to increase innovation and circulation of knowledge, which the open way models helps with, I think that open way models is a tremendous source of geopolitical inference. For example, if someday, you know, some kid in some developing nation asks a question about a publicly sensitive topic or asked, Hey, where are the national borders in this case? Or what is the history of this development event? The country of origin of the model they end up using will be delivering some answer, whether the answer is used towards one nation's values or under the nation's values is actually tremendous source of influence and soft power. Like on our open way models are a key part of the AI supply chain, China releasing low cost of free models and today keep our supply chain means is really starting to build up a lead right in build up a commanding use of base. And this is why I think nations with a strong median entertainment industry turns out self career has vastly disproportionate influence because of their leading entertainment industry. So people listen to whatever are you okay, pop whatever and that buys the national of influence. Hollywood was a tremendous source of soft power for America, a painter, certain vision of the American dream, talks about the values of freedom and democracy. This is another frontier of communications and soft power. You have the most fascinating perspective having obviously spent many years at Google and then obviously by do as well. And so having been on both sides of the table in certain respects, we have this kind of strange binary polarization of the AI race China versus the US. You agree with that positioning of China versus the US in an AI race. I think there's a lot of room for corporation and then also some places that will be competitive so first while people sometimes even me talk about the a.I. race, there's no single version of the finish line. It's not one race is AI is a general purpose technology and you could be better aware of the coding better where the answering questions better, where is it helping with you know, marketers and finance and so on. So AI has many different capabilities and there's no one finish line and even with one capability, we're going to keep on improving for a long time. So I feel like because of a PR goes a GI has been high type as it was a finish line. But I don't think it was a finish line. It's just what have continually improving capabilities for decades to come. Having said that nations with stronger AI capabilities are going to be more powerful. The citizens will be more prosperous. The economies will grow faster. So to the extent that different nations incentives are not aligned nations with more powerful AI capabilities will be the do more. If a country is a fantastic electricity grid in another country, power, villages and so on, one country can just use electricity grid to do more manufacturing or industrial world. Just do a lot more that way. You don't think we still underestimate China's ability though. I think we definitely do it in Europe. But I think in the US, respectfully I see a lot of US arrogance around new positioning. And then you go to China and you've been to China and spend a huge amounts of time in China. You realize the speed and the intensity with which they move different level to both Europe and US. Yeah, to be fair, I think US, Europe, China all have problems as well. But having said that, I think the work I think the velocity when China's government makes a whole nation commitment is all industrial commitment. That's a very powerful force with kind of sea level investments in semiconductors in this education system. This is also used AI, share knowledge and then sometimes built this stuff and also so internationally with state apparatus that may or may not be the whole control over rare earth elements. So I think that whole of economy, whole of country efforts is actually a very powerful force that I wouldn't underestimate. Given that, we shouldn't underestimate it. Do you think it's right that we have export controls on chips? If the Nvidia has had a lot of export controls back and forth, do you think that's right or not? I think the export control on chips is largely backfire. The way the US first put restrictions on Huawei and then later on you know, exported Nvidia and AMD and other semiconductors that really incentivize China. So before the export controls, some conductor development in China, it was not, frankly it wasn't moving that fast. You know, it was a nice area, those some investment, but when America did that, then China really accelerated its semiconductor development. And so America incentivized China to do this and it is paying off a China. I think your number of Chinese companies are building offerings that in future chips are less powerful, but maybe a much larger number of chips trying to build offerings competitive with certain the last generation of Nvidia, maybe increasingly the current generation. If I were to analyze just purely, you know, US national self interest, I think that course China to a serious semiconductor industry in a way that may not be hopeful to the US longer. I sit in Europe, obviously living London, you told me you were born in London before this. My question to you is it transparently feels like we are very far behind and people say, you've already lost. How do you feel about Europe's position in a very new world? And what can Europe do to regain some semblance of equality between the US and China? If I had one wish for the European regulators, I spoke with quite a few European regulators, I was hearing things like, we want to believe that it is in regulating AI and that's a competitive advantage. And with all the respect, that's not a competitive advantage. So my one wish for Europe is start regulating so much and just focus on investing and building. The thing is, it's still early in the days of AI, it's still early in the game and Europe has plenty of smart people, let people work hard, don't force them to not work hard, let people that want to work hard work hard and start over regulating and just going invest and build stuff. Where do we most need to be investing? Where we are not investing enough? There's tons of capital going into data centers and intra, we can debate is there a bubble or not, we definitely need a lot of investments, are we getting the point where people are using such esoteric financial instruments to find cash for it that there will be a bubble, we could debate that, right? So we definitely need a lot of investments but when does it become over investment? That's an interesting question. The other place that I think we need to invest in a lot is not just the in fraud data center or foundation model layer, but the application layer. turns out that because of other is having spent billions of dollars to train DCI models, we can now access them for hundreds of dollars or thousands of dollars or whatever for tens of dollars. It's wonderful to build tons of applications, it's just we're not possible before. Now from a VC investment perspective, I've heard some multiple VCs is the cost of trying something out is so low that there are fewer ideas. It's not quite sure where to put massive amounts of capital to work at the application layer. If you look a lot of the application layer investments, sometimes it feels like firms are putting a hundred million dollars so that they can pay open and anthropic, so the open and anthropic can pay in video, which is where all the money is is ending up. Having said that, there's so many valuable bets to be placed at the application layer to just build stuff, but the dilemma is you could do it in a very capital efficient way. So if someone wants to say, I want to put ten billion dollars to work, yes, you can build ten billion dollars worth of data centers. We know how to spend that money, but how do you spend ten billion dollars in building applications? The problem is almost, it only cost me a million dollars to try an idea out. So how do I spend ten billion dollars? It's kind of a problem and also another problem, but I think we should. What does it, because when you look at AI margins, what margins for AI application layer companies, they're terrible. They make no money. They cost a lot of money to build because you have large engineering teams that build them. They cost more or not less. I think it still varies. I'm seeing a lot of green shoots of software applications that were not that expensive to build, and if your LM token usage is not the majority of your expense, if you look at a rapid or a lovable 80% of their pass through is to anthropic. So the dynamic that I'm excited about is, I think that as LM token costs continue to come down, we'll see how the economics change. Right now, LM token is just expensive, but hopefully that will change and the value created is really large. Actually, I remember an earlier era in early days of food delivery, for example, I saw this in both the US and China. There's loss. VC subsidized eating. Right? It was great. But eat food delivery was basically VC subsidized. I think we're seeing that right now with a lot of VC subsidized AI coding, the laws of physics or the laws of finance says that at some point, right, this can't go on forever, but where it settles down, I think there will be some very valuable businesses that are not perpetually VC subsidized, but navigating this crazy VC subsidy world to get to good outcome takes a lot of skill. But having said that, I still want to say, the lot of smaller applications that are not yet doing these, you know, hundreds of millions of dollars, maybe they're doing millions of dollars or tens of millions of dollars of revenue that haven't been quite expensive to build into operate and that I think we'll see continue to grow. The smaller niches, so to speak, they're that continue to grow. How do you think about the question of, you mentioned earlier, brilliantly that articulation kind of horizontal and then the verticals beneath them and Google and now open AI being a horizontal? How do you think about the question of a world of large monolithic models versus much smaller, much more efficient, much more specialized models? And has your mindset changed around which will be more dominant? It will be all of the above. We will have large models and the size models and tiny small models. And the reason I'm confident about that is because the nature of intelligence is diverse. Sometimes we do intellectually really easy tasks like if someone asked me like yesterday, my daughter, she must spell the word butterfly. So I need to tell her how to spell butterfly is a low, you know, is it easy intellectual task? And sometimes I'm sitting down thinking for hours about some complex technical problem. That's really hard. And so intelligence has a range of things we want to do. And so the set of things we want AI to do too has a huge range. If you want AI to do basic grammar check and spell checking, you don't need a trillion per ounce of model. Use a tiny model, maybe run it locally, just do that. But you want to do complex reasoning to write a piece of code. Then yes, having a powerful model is going to do better. I'm actually very confident we'll end up with a huge range of models, small and large, to do the huge range of tasks. Just like we have humans do a range of tasks, it's difficult to save away. Does that mean that you disagree with Andre Kapati when he said that? Useful agents are a decade away. I disagree with that. I think we're seeing useful agent workflows right now. So AI fun, our team has built so many agent workflows for so many tasks where, you know, we just could not even do the task, but for agent workflow. Can you give me an example? I'm fascinated. Sure. So, for example, over a year ago, we thought that after one of the Biden Trump debates, a better or worse, we thought that tariff compliance may become an issue. Unfortunately, we turned out to be right. But so last year, I think it was around August, we started building, started exploring building technology to help with tariff compliance. And by the way, I don't mean we've seen these tariff compliance docs, but frankly, when I look at what it takes to follow these, this paperwork is like, it makes me want to, oh my god, what is this? So, you know, you say import a bicycle. Then you look at the specs of the bicycle. How much does it cause the size of the wheels? There are all these rules and regulations to import a bicycle. It just makes me go, oh my god, I've humans really doing this. So we built agent workflows to read the tariff compliance documents carefully, get the spec for what someone's employed carefully, try to match mixed suggestions. And so this is now one of our portfolio companies called Gire, Gire Dynamics, that, you know, is the increased complexity in tariff compliance has been doing pretty well. We just could not have done this without agent workflows. We have a medical assistant operating an India AI assistant, a Caledist, a helping process legal documents. Many of these workloads we just could not do. So I find that they're useful AI agent workflows already today and a large business is to not just our status. When we look at the high for scalars and I chat to friends and so are large businesses, a bunch of internal workflows that we just could not be doing without these AI agents. When we think about a core of a business, it's margins. And most of these business don't have margins. Do you care about margins when investing today or with absolute respect and it sounds disrespectful? Do you take the kind of utopian view that it will just correct itself with time and with efficiency gains? At some point, the laws of physics or the laws of finance or something, margins do matter. One of the tricky things about AI is we know the technology is going to change. So we don't build assuming the technology will be stagnant. We do build assuming the technology will evolve. So one obvious one token prices have been rapidly falling. Depends who you believe falling. Eight percent year in year or whatever. Kind of frankly, when we build prototypes, we routinely just not worry about token costs. Because the first most important thing is this build a product that uses love. And then what we find is after we build some of this that should happen to me a few times now. We'll build something and not worry about the cost. And then you know, users start to use it. And then our API bill starts climbing and then it is really like kind of you looking at this every few weeks and go, well, this game really expensive. This costing me study of one engineer cost me more than two engineers cost me more than a whole bunch of engineers. All right. But fortunately, when that has happened almost every time so far, we've been like use techniques to bend the cost curve back down even faster than the rate at which token prices are falling on the market. And so I find that absolutely margins are important. But when you have a view for whether technology is going, then it lets you not build for the margins today, but what you can forecast them being in the future. And I think that's an important distinction. But we don't take a blind utopian AGI love, love, love view either. I think that's also overly simplistic. How do you think about defansibility in there? Well, a lot of people suggest that the timed copies reduce significantly. The defansibility itself is questioned in AI. You agree with that and the questioning of defansibility today or not. Motes are changing. I find that most tend to be a function of the industry rather than the function of the technology. So AI is a technology doesn't really offer an answer to the moat for most businesses. So if you're building AI, you know, for drones or legal or whatever, the moat is more of a function of that industry. But one thing that is changing with regard to most is previously software used to be a moat, right? If you know, invest it 10 years, the bill of software is really hard to replicate that. That one moat is much weaker than before. But other moats like are you trying to use AI to accelerate to build a two-sided marketplace which can be very defensible or are you building for a consumer or morphicals who are an enterprise? Are there a brand in reputation or effects, right? They can help you build defensibility there. So I find that the software moat has changed, but are the most tend to be analysis based on the industry? So software moats have changed. And so we now have margins that matter, but we have a little bit more elasticity there. Software moat has changed in terms of like the ability to stay relevant for large enterprises. What are the single biggest barriers that are preventing large enterprises from implementing AI aggressively and prevent themselves being extinct? I think it's very, most large enterprises is actually people and change management. Not data. It's not data. I think it's definitely not data. Not the data is not important, but that's definitely not the bottleneck. The interesting thing about AI hype is There's almost always a gem of truth in the hype. It's just that it's been hyped up 10 times more than the reality. And maybe actually let me give one example that'll come back to data. There's been this buzz about, oh, with AI, we'll have unicorns with one employee. It's like a thing. And it's fine. If you want to build a unicorn starter, put in dollarware with one employee girlfriend. It's a good thing to do. Frankly, if you're a billion dollar valuation, you could afford to pay two employees or even ten. So why do you need to hype it all the way up? To say, let's do this with just one employee. So it's true that team sizes are shrinking. I wouldn't get more done with smaller teams. So that is true. But the hype is then saying, let's build a unicorn with one starter. I find a lot of AI hype is so hard to disentangle because there's a gem of truth in it. It's just been hyped up a lot more. So on data, data is important. But it turns out that data is very verticalized. And you don't need as much of it to get started as you think. So for example, landing AI does a lot of work with financial institutions, healthcare, a lot of financial institutions that plenty of transaction days are, take the PDF file, turn it to earn ready, mark down, text, go process that, find value in that. Like for example, I don't know, we could take a SEC follow-links, large, complex financial tables, very accurately turn those financial tables into Excel spreadsheets, then go get your analyst or AI to analyze that and draw conclusions. So often with bit of strappiness, looking internal data, looking at public data, you can often get some stuff going. And it turns out that a lot of internet data is kind of general purpose data. Most of the world's data is that you're private. And then so a lot of business is actually very valuable transaction data. Sales data, product data, manufacturing data, logistics data, all that data with a strappy team that knows how to use it can actually start to build something good value out of it. Not to say more data wouldn't be even better, but you're not stuck to even take the first few steps or lack of data. And you're out to be to many CEOs of these size businesses, and they say, Harry, are you kidding me? You think we can get security and permissioning for our data and our enterprise? We don't have Slack, we don't have Notion. Everything is custom-built. You're seeing the likes of JP Morgan Goldman's sacks. Absolutely, refuse any chat GPT's building internal systems. Is that the world that we inhabit for enterprise AI adoption? I think we get there. So I find that a lot of enterprises are adopting OMS, chat GBA, and many others. I think today there's still businesses that are still on-prem rather than on the cloud, but we're making progress. Actually, one thing about AI, did this hype that we have AI in two years or whatever? I think that's just for the. That's just. For most visible definition of AI, that's just not going to happen. And just as how long I'll be now into the cloud era, but we still have an awful lot of on-prem jobs, I think that AI adoption, it will be wonderful. There will be tremendous GPT growth, is also going to take much longer than the hype says it will. I actually think that a decade from now, we will still be working to identify valuable applications and enterprises in building them. Having said that, we will make a lot of progress over the next one or two years, but we're not going to be done even 10 years from now. What else does everyone think they know about AI? And it's adoption and implementation that they get wrong? A big one for me. Even earlier this year, we saw some senior business leaders advise people to not learn to code on the grounds that AI will automate it. We'll look back on that as some of the worst career advice ever given, because as coding becomes easier with AI assisting us, a lot more people should learn to code, not fewer. And I'm already seeing, I mentioned the marked example, just now we're building it up for feedback, swiping. But for a lot of job functions, people that know how to tell computer exactly what they wanted to do, so the computer didn't do it for you, they'll just be more powerful. And for the foreseeable future, the language of precisely telling computers what you wanted to do is coding. It doesn't mean you should write code by hand. Writing code by hand is becoming obsolete, right? Really, don't do that. Not, not, not. But they get AI to write code for you. And people can do that, would be more effective and more powerful and have more fun. If we're that early, where a decade's time, we're still going to be looking and identifying areas where it can improve meaningfully. Do we have enough money to fund both the energy and the compute requirements for that 10-year period? Sam Altman has said he needs a trillion dollars, he needs the energy of Japan. If we're 10 years out before we have still not that much improvement, do we have the money to fund it? Oh, I think we'll see plenty of improvement over the next two years. But I think we still won't be done getting even more improvements 10 years from now. One place we're super promising is AI-sistering coding. So we're seeing real productivity gains, real returns. It's really changing the way software is written. It's really been fantastic. It's been there, frankly. So many of my friends is just calling so much more fun with AI to help us out than with all. So we are seeing returns just to be clear. But we still won't be done growing this 10 years from now. But if you look at the term, the secret to success in AI investing is will we see a transition from human labor budgets to software budgets? And if we have that, then Holy Grail, me and you will make a lot of money with our funds and fantastic news because the terms have massively increased or the spends massively increased. If we're like, hey, we're not going to actually lose any people, then actually we don't see that transition from human labor budget to software. Do you think we won't see that transition? To me, the question is, is AI mostly for cost savings or is it for growth? It's difficult to change work, though. So a lot of companies tend to think cost savings. But maybe here's the problem. There's actually one pattern I see. Let's say I have a work task that has five steps. And let's say each step takes 20% of my effort. Like maybe I'm underwriting approvals. Do I prove this low? No, no, no. So let's say for simplicity, the five steps, each 6, 20% of my effort. If you can automate one of those steps, is a 20% cost savings. Which is really nice. It could be great if you're a low margin business, but it doesn't feel like a game changer. What I find is that the more valuable users of AI, it actually requires, it often requires rethinking that work, though. And the pattern I see is instead of taking the 20% cost savings, which you could do. Let's find nothing wrong with that. The two patterns to then getting growth is either do more or do it faster. So in the case of underwriting, making loans. If instead of saving 20% of my human labor, if I can now rework the workflow, to turn around my disease making time. So instead of someone needing to wait two weeks before loan officer looks at it, but we can just give you an initial answer in 10 minutes. That changes the product and let's you drive growth. So that's a faster pattern. And then there's also the more pattern. So another example, there are a lot of businesses that could do high-touch, say, cost for service, only for expensive high-end clients. But if you can now serve a much larger group of people, or let's say financial advice, instead of giving high-touch financial advice to small group of people, if we cannot deliver that quality of service to a lot more people, then that can change the product and let's you drive growth. So instead of cost savings, if AI lets you do something way faster, or lets you take a task and do it a thousand times more. Instead of serving a small number of people, let's serve a lot more people because it's not economic to do so. These are the two patterns I've seen to drive value increases. And I think that would be important for unlocking a lot of this GV growth. You said economic to do so. Do you think it's crucial that we see vertical ownership in terms of the Nvidia own models as well as chip player? And we're seeing Facebook build out data centers more than anyone. We're seeing everyone build out data centers. Is it important that we own every layer of the stack? Or actually, will we see individual participants own horizontal layers of the stack? I think this is evolved over time. I'm going to make an analogy. In the early days of, say, the computing industry, it was a vertical player's their one. Because if you want to connect the keyboard to your computer motherboard, which is a CPU, is it okay if your keyboard has a plus minus five those and your CPU has some of the voters? Is that okay or not? So we didn't know where the API boundaries or if your CPU has your memory laid out a certain way, a compute and your math accelerator, they needed to interoperate with each other. So before we wound up having a clear conception of where the draw lines, whether the API boundaries, the integrated players, IBM back in the day, could solve all the problems and the valuable working products. But as the industry mature, we started to stand as, like in a way, for example, now whether USB standard, before that were other standards. So now you make a computer, some of those things to keep up with, plug them together and it all works. So when the industry is in mature, it turns out where to draw those boundaries, select different participants, do their part and have a still interoperate. That's less clear. But then as the industry matures and as your more standards go, are ever want to publish a compressed OM model on the internet, what's the foul format for that? And you're starting to see more standards, then that makes it easier for individual players to do something and still have it fit into the broader ecosystem. So do you think then like Zuck and Sam are right to be spending as much as they are on data centers? Or should they be patient and wait for the maturation of the industry where they can then be horizontal? I think the open-ended investments have paid off to date. It is fast for the overinvest at some point, but I don't know if that is the point. And then I think also the financial instruments being used by many players to shift risks around have been really interesting. I find that overly complex user financial instruments to shift risks sometimes increases the risks of their being a bubble at some point. So that's something to watch out for. - Do you worry about the circular deals? - It's something to keep an eye on. I'm not alarmed by them. I think things could be more fraught, feel less barfied, things could be more of a bubble and less of a bubble. And these are signs of things feeling a little bit more bubble-ish. When does a sign turn into a big concern for you with these? - You mentioned the sequer article on the $600 billion problem of AI. I am concerned about that. But essentially, my concern for different layers of the stack is different. So what I'm seeing is for the application layer, there is very clear ROI. I think it's fantastic. So someone else trained these models who can build applications for $100,000 or $1 million and start generating ROI. And then I think it is calibrating to the right level of infrastructure investment that is tricky. But having said that, it is also the same time very clear that we do need more let's see, more data centers and more semiconductors. That too is very clear. So we should be investing a lot. And I'm glad we are. But what exactly is the right amount to invest? I think that's the tricky question. It should be a lot though. - You get annoyed by the bubble discussion. - I don't get annoyed by the bubble discussion. I do get annoyed by the hype. And when regulators are calling me up and saying, "Hey, we heard AI could lead to human extinction. "Thankfully much less than that now than a couple years ago." And then instead the consulate should be how could we upscaled the workforce, working with invests, not like how do we slow this thing down? I think the hype has really distorted public perception of AI. Oh, and then one downside of the hype too is without public support of AI, things slow down. So for example, actually one of my friends works a lot with high school students and he told me that he was talking to a girl, a high school student, that he was talking to her about maybe pursue a career in AI. And she said, "You know what? "I heard AI could have something to do human extinction. "I don't want to have anything to do with that." And so this hype turned a high school girl away from working on AI at the time where it'd be so promising for them to leap into AI. And I think this really causes people to make weird decisions both at the individual school student level, as well as at the community level. When the community shuts down building out the data center, they could be good for the community and good for the world. I think that's also unfortunate. I'd love to move to a quick fire round where I say a short statement, but kind of staying on that thread 'cause the first question is, what's your biggest advice to educational institutions to make sure they equip students for a generation of AI? Embrace an update curricula, teach them as much AI as possible. Students are gonna live in a world where they will be using AI and having to help them gotta teach students to do that. I think it'd be different for different fields, but one thing that is clear is get all your students to learn to code. What's one thing you've changed your mind about AI in the last 12 to 18 months? I think my favorite tools keep changing. If you ask me every three months over the last year what my favorite coding tool is, my answer would have kept on changing. Do you think anthropic will beat open AI in the coding wars? Pretty hard to say. Open AI has a very strong consumer brand and that's very defensible. In contrast, developers are more likely to switch coding tools on the dime. So I love Cloud Cloud, I think it's fantastic, but I find myself using open AI codecs much more over the last month. I think overnight codecs is actually gain role momentum and then I'm also keeping an eye on Gemini CLI which I think is also getting better maybe that faster rate than people I've given them credit for. So the coding dev tools and API tools market, the mode there's weaker than having a strong consumer brand. So I think that's something that companies have to sort out. What was your biggest takeaway from Baidu? It's such a different company to anything that we used to in the West. What was your biggest takeaway? I really appreciated the speed and intensity of Baidu and also of the China ecosystem. I think it was really unfortunate that in some parts of the United States advising someone to work hard is due that's publicly incorrect or something. In Europe, I'm just eyes for it. Hopefully the European viewers won't hate me or hate us both for that. I think frankly, I wish people would work for a week and be wildly successful but the practical reality is when people work hard they get more done. Now, I wanna acknowledge that not everyone in every point of their life is in the position of work hard. So yeah, the week after my kids were born, I didn't work that hard. It took time all spent time with the kids for a whole more than a week. And I think we need to respect people in all walks of life including people that for whatever reason I'm not in the position of work hard at that moment. But if someone wants to work hard, go, quote, "Seafjord, make a dent in the universe. Let's empower them and celebrate that." And someone, for whatever situation, kind of work hard, let's also respect that and maybe celebrate that. But I think this is a moment in time where there's so much stuff we could build. People that work hard to learn a lot and build things will accomplish a lot. - Did you do 996? - The term 996 was an explicit term that I use. I find it right these days. I really love what I do. It really doesn't feel like work. But on a lot of my weekends, I'm sitting in a coffee shop, coding away because it's the most fun thing I could do in a Saturday. So I actually don't bother to keep track of my hours. It's probably a lot. - Was the hardest transition element moving from operator to investor? - Oh, one thing about AI fund? Yes, we call ourselves a fund, but frankly, the way we run the fund day to day, we act much more like operators than investors. AI funds adventure studio, and I believe our skill set is actually in building not just in capital as an allocation or whatever. So we work really hard to screen ideas. We talk to customers, I'm sometimes on customer calls myself, and then we bring and found this work alongside us, we're reviewing the product, giving feedback and product, arguing about pricing. So where my day to day life is much more operator. And yes, eventually we have to do the financial diligence I already checked and do follow on, we do all that, but a lot more. - I'm really surrounded. And you're a fund or are you an incubator? - We call ourselves a venture studio or venture builder. Incubator is usually bringing founders that already have an idea. We go earlier than that. We often work of our investors and partners to come with an idea. And only after we have an idea, then we go and try to find the best founder to co-build, to co-founder company with us. So how much ownership do you have them when you make those original investments and seed the company? - It depends. We end up with some common stock for the sweat equity building the company. And then we usually, our first check-in is usually the other $1 million, they're the $4 million cap. So kind of one of 20% ownership or safe. - And so we're basically getting 20 to 25% ownership on entry with a couple of common. - Yeah, plus some common for the sweat equity. The reason we do this is because I find that, while there are VCs that do the competitive deal flow thing, they make a lot of money that way, I think my team's biggest contributions is not fighting over hard deals. It is finding ideas and creating companies that would not exist but for the fact that we and a founder got together to co-founder together. So I think we just create more value in the world by creating new companies rather than only discovering hard companies to try to put money into. - What concerns you most today, Andrew? I love your optimism and your open-mindedness. What concerns you on the flip side? - I am worried about the difficulty of bringing everyone along with us. In previous ways of economic disruption, like when our nations went from mainly agriculture to non-avirculture, someone that was a farmer could keep farming until they retired, but the kids had to learn the different trait, maybe with the city or whatever. The change is so fast this time round that we need people that are alive today to learn new schools, as opposed to we need their kids to learn new schools. And that's actually very challenging and historically I don't think we've ever been good at that. - You do a lot of interviews, Andrew. You speak to many journalists, I'm not a journalist, I've never actually had a job. Do you find the quality of interviewers that ask you questions good? - I think media has an important role to play to curate into some of your knowledge. I think the quality of questions the reporters are asking has been very clearly trending up over time, but there is still the hype element of it that keeps on distorting the information ecosystem. Unfortunately, there are financial incentives and your rigged-view capture and legislative benefits of incentives and certain types of hype. And it does actually wipe out in that scene. I won't name any companies. But I find that their companies with something to lose, whose statements over time have become more moderated. So I find that as your established company, you just say more sensible things, but there are some companies that I think are a greater existential risk. And I find some of those companies that don't want to name to be the worst sources of hype, because you've got less to lose, let's just say a bunch of random stuff. - In many respects, it's lashing out in desperation, I think. When you look at a damn essay, obviously, "Brily and Eater," where you look at Assam even or Dario, all of them I think have moderated that position significantly with the maturing of their companies. - No comment on individuals, but I think that when you're something to lose, you say more sensible things, but when your company faces greater existential risks, sometimes people say weird things, but fundraising, and I think fortunate. - I like to finish on a tone of optimism. What single thing are you most excited for when you look forward to the next decade? So for me, for example, and my mother's got Amaz, I think we'll have incredible medical discoveries. in some diseases that we haven't made much advancements in for years. That excites me. What excites you? Yeah, I'm sorry to hear about you, Mother. Thank you. What excites me? I want to empower everyone to build AI. I think the distance between you and having an idea and building it is now much shorter. And we need not just software engineers to be creating. So in the future, I hope that a lot of people, instead of saying, "Is there an app for that?" And instead of just being a software user, there will be a software creator and we can get there. Then people around the world will be much more empowered, get more done, have more fun. Andrew, this has been such a joy to do. Thank you so much for putting up with my prying and my pressing. You've been amazing and I really appreciate the time. Now, I really enjoy your shows. There's a privilege to be here. Harry. 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