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?

26:05Do you not think we still underestimate China's ability, though? I mean, I think we definitely do it in Europe. But I think in the US, respectfully, I see a lot of US arrogance around your positioning. And then you go to China and you've been to China and spent a huge amount of time in China. You realize the speed and the intensity with which they move. Different level to both Europe and the US. Yeah, to be fair, I think US, Europe, China all have problems as well. But having said that, I think the work ethic, the velocity, when China's government makes an all-nation commitment, it's an all-industrial commitment.

26:38That's actually a very powerful force with kind of state-level investments in semiconductors, in this education system. So, you know, like K-12 kids being trained to use AI, businesses ask to use AI, share knowledge, and then sometimes, you know, build this stuff and also sell 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?

27:16You know, 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 has largely backfired. The way the US first put restrictions on Huawei and then later on, you know, export of NVIDIA and AMD and other semiconductors, that really incentivized China. So before the export controls, semiconductor development in China, it was not, frankly, it wasn't moving that fast. It was a nice area. There was some investment. But when America did that, then China really accelerated its semiconductor development. And so America incentivized China to do this.

27:53And it is paying off for China. I think a number of Chinese companies are building offerings that individual chips are less powerful, but maybe a much larger number of chips trying to build offerings competitive with certainly the last generation of NVIDIA, maybe increasingly the current generation. If I were to analyze just purely US national self-interest, I think that caused China to accelerate a semiconductor industry in a way that may not be helpful to the US long term. I sit in Europe, obviously live in 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.

28:33And people say, oh, 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 be leaders in regulating AI, and that's a competitive advantage. And with all due 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.

29:08And 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 go and 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 infra. We can debate, is there a bubble or not? We definitely need a lot of investments. Are we getting to the point where people are using such esoteric financial instruments to find cash for it, that there'll be a bubble? We could debate that, right? So we definitely need a lot of investments, but when does it become overinvestment?

29:46That's an interesting question. The other place that I think we need to invest in a lot is not just the infra data center foundation model layer, but the application layer. It turns out that because of others having spent billions of dollars to train these AI 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 that just were not possible before. Now, from a VC investment perspective, I've heard from multiple VCs, is bizarrely the cost of trying something out is so low that there are fewer ideas.

30:23It's not quite sure where to put massive amounts of capital to work at the application layer. In fact, if you look at a lot of the application layer investments, sometimes it feels like firms are putting in$100 million so that they can pay open-end anathropic, so the open-end anathropic can pay NVIDIA, which is where all the money 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$10 billion to work, you know, yes, you can build $10 billion worth of data centers.

31:01We know how to spend that money. But how do you spend$10 billion in building applications? The problem is almost, it only cost me a million dollars to try an idea out. So how do I spend$10 billion? It's kind of a problem and also not a problem, But I think we should... Well, 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, 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.

31:38And if your Elm token usage is not the majority of your expense... If you look at a Replit or a Lovable, 80 % of their pass-through is to Anthropic. So the dynamic that I'm excited about is, I think that as EARM token costs continue to come down, we'll see how the economics change. Right now, EARM 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 a lot of VC subsidized eating, right? You know, it was great. We could eat food delivered.

32:18It 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 a good outcome takes a lot of skill. But having said that, I still want to say that a 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 and to operate and that I think we'll see continue to grow.

33:01The smaller niches, so to speak, there that continue to grow. How do you think about the question of, you mentioned earlier brilliantly, that articulation of kind of horizontal and then the verticals beneath them and Google and now OpenAI being the 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'll be all of the above. We will have large models and mid-sized models and tiny small models. And the reason I'm confident about that is because the nature of intelligence is diverse.

33:39Sometimes we do intellectually really easy tasks. Like if someone asked me, like, all right, yesterday, my daughter, she misspelled the word butterfly. So I need to tell her how to spell butterfly. It's an 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 checking and spell checking, you don't need a trillion parameter model. Use a tiny model, maybe running locally, just do that.

34:14But if you wanted 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 just like we have humans do a range of tasks of difficulty, same with AI. Does that mean that you disagree with Andre Kapathi when he said that useful agents are a decade away? I disagree with that. I think we're seeing useful agentic workflows right now. So AI Fun, our team has built so many agentic workflows for so many tasks where, you know, we just could not even do the tasks, but for agentic workflow.

34:54Can 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, for 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 know if you've seen these tariff compliance docs, but frankly, when I look at what it takes to follow this paperwork, it makes me wonder, oh my God, what is this? So you say import a bicycle.

35:31Then you look at the specs of the bicycle, how much does it cost, the size of the wheels. There are all these rules and regulations to import a bicycle. It just makes me go, oh my God, are humans really doing this? So we built agentic workflows to read the tarot compliance documents carefully, get the spec for what someone wants to import carefully, try to match, make suggestions. and so this is now one of our portfolio companies called Gaia, Gaia Dynamics, that because of the increased complexity in tariff compliance has been doing pretty well. We just could not have done this without agentic workflows.

36:05We have a medical assistant operating in India, AI assistant Calatus helping process legal documents. Many of these workflows we just could not do. So I find that there are useful AI agentic workflows already today. And the large businesses too, not just our startups. When we look at the hyperscalers and I chat to friends and some of the large businesses, there's 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 businesses don't have margins. Do you care about margins when investing today or with absolute respect?

36:41And 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 but 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 one obvious one um token prices have been rapidly falling depending on who you believe falling if it's year or 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 just build a product that users love and then what we find is after we build some this actually happened 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're looking at this every few weeks and go, whoa, this is getting really expensive.

37:38This is costing me salary of one engineer. It costs me more than two engineers. It costs me more than a whole bunch of engineers. But fortunately, when that has happened, almost every time so far, we've been able to 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 absolute margins are important. But when you have a view for where the 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 blah, blah, blah view either.

38:14I think that's also overly simplistic. How do you think about defensibility in the AI world? A lot of people suggest that the timed copies reduce significantly, that defensibility itself is questioned in AI. Do you agree with that and the questioning of defensibility today or not? Modes are changing. I find that modes tend to be a function of the industry rather than a function of the technology. So AI as a technology doesn't really offer an answer to the mode for most businesses. So if you're building AI for drones or legal or for whatever, the mode is more of a function of that industry. But one thing that is changing with regard to modes is previously software used to be a mode, right?

38:56If you invested 10 years to build a software, it's really hard to replicate that. that one mode is much weaker than before. But other modes, 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, a more focused enterprise? Are there a brand and reputational effects, right? They can help you build defensibility there. So I find that the software mode has changed, but other modes tend to be analysis based on the industry. So software modes have changed. And so we now have margins that matter, but we have a little bit more elasticity there.

39:32The software mode 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 the biggest barrier in most large enterprises is actually people and change management. Not data. It's not data. I think it's definitely not data. Not that 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.

40:07And maybe actually, let me give one example that will 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 startup, billion dollar bear with one employee, go for it. It's a good thing to do. Frankly, if you have a billion dollar valuation, you could afford to pay two employees or even 10. So why do you need to hype it all the way up to say, let's do this with just one employee, right? So it is true that team sizes are shrinking. We get more done with smaller teams. So that is true. But the hype is then saying, let's build a unicorn with one starter.

40:39I 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, you know, I don't know, Landing AI does a lot of work with financial institutions, healthcare, a lot of financial institutions have plenty of transaction data. Take the PDF file, turn it to earn ready markdown text, go process that, find value in that. Like for example, I don't know, we could take a SEC filings, large complex financial tables, very accurately turn those financial tables into Excel spreadsheets, then go get your analyst or your AI to analyze that and draw conclusions.

41:24So often with a bit of scrappiness, looking at internal data, looking at your 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 actually private. And so a lot of businesses actually very valuable transaction data, sales data, product data, manufacturing data, logistics data, all that data with a scrappy team that knows how to use it can actually start to build something and get 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.

42:01Andrew, I speak 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. and you're seeing the likes of JP Morgan, Goldman Sachs, absolutely refuse any ChatGPT use, building internal systems. Is that the world that we inhabit for enterprise AI adoption? I think we'll get there. So I find that a lot of enterprises are adopting OMS, ChatGP 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.

42:39Actually, one thing about AI, this hype that we have AGI in two years or whatever. I think that's just ridiculous. For most reasonable definitions of AGI, that's just not going to happen. And just as how long are we 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 GPD growth. It's 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 and 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.

43:17What else does everyone think they know about AI and its 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 mockery example just now with building an app for feedback swiping, but for a lot of job functions, people that know how to tell a computer exactly what they wanted to do, so the computer can do it for you, they'll just be more powerful.

43:59And 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. But 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 at a 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.

44:35He 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 where it's super promising is AI-assisted coding. So we're seeing real productivity gains, real returns. It's really changing the way software is written. It's really been fantastic. Frankly, selling of my friends is just coding is so much more fun with AI to help us out than without. So we are seeing returns, just to be clear.

45:10But we still won't be done growing this 10 years from now. But if you look at the TAM, the secret to success in AI investing is where 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 TAMs have massively increased or the spend's 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?

45:49You know, it's difficult to change workflows. 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. Maybe I'm on underwriting approvals. Do I approve this loan or not? So let's say for simplicity, there are five steps, each takes 20 % of my effort. If you can automate one of those steps, it's 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 workflow.

46:29And the pattern I see is instead of taking the 20 % cost savings, which you could do, that's fine, 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 decision-making time. So instead of someone needing to wait, you know, two weeks before a loan officer looks at it, but we can just give you an initial answer in 10 minutes, that changes the product and lets you drive growth. So that's a faster pattern. And then there's also the more pattern.

47:05So another example, there are a lot of businesses that could do high touch, say customer 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 a small group of people, if we can now deliver that quality of service to a lot more people, then that again changes the product and lets 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.

47:40These 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-owned 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 will evolve over time. I'm going to make an analogy.

48:14In the early days of, say, the computing industry, it was the vertical players that won. Because if you want to connect a keyboard to your computer motherboard, which is a CPU, is it okay if your keyboard has a plus minus five volts and your CPU has some other voltage? 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 to draw lines and whether the API boundaries, the integrated players, IBM back in the day, could solve all the problems and build valuable working products.

48:56But as the industry mature, we started to have standards. For example, now we have a USB standard. Before there were other standards. So now you make a computer, someone else makes a keyboard, we plug them together and it all works. So when an industry is immature, it turns out where to draw those boundaries to let different participants do their part and have it still interoperate. That's less clear. But then as the industry matures and there's more standards for if I want to publish a compressed LN model on the internet? What's the file format for that? And you know, it's time to see more standards, then that makes it easier for individual players to do something and still have it fit into the broader ecosystem.

49:35So do you think then like Zuck and Sam are right to be spending as much 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 clearly OpenEye's investments have paid off to date. It is possible to over-invest 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 use of financial instruments to shift risks sometimes increases the risk of there being a bubble at some point.

50:11So 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 frothy or less frothy. 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 Sequoia article on the$600 billion problem of AI. I am concerned about that. But it's interesting, 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.

50:46I think it's fantastic. So someone else trained these models who can build applications for, $100 ,000 or a million dollars 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 at the same time very clear that we do need more electricity, 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. Do you get annoyed by the bubble discussion?

51:19I don't get annoyed by the bubble discussion. I do get annoyed by the hype. 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 of years ago. And then instead, the conversation should be, how can we upscale the workforce? Where can we invest? Not like, how do we slow this thing down? I think the hype has really distorted public perception of AI. Oh, and one downside to 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.

51:53And he told me that he was talking to a girl, a high school student, that he was talking to her about maybe pursuing a career in AI. And she said, you know what, I heard AI could have something to do with 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 a 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 a data center that could be good for the community and good for the world, I think that's also unfortunate.

52:30I'd love to move to a quick fire round where I say a short statement, but kind of staying on that thread, because the first question is, what's your biggest advice to educational institutions to make sure they equip students for a generation of AI? embrace it, update curricula, teach them as much AI as possible. Students are going to live in a world where they will be using AI and having it help them. Got to teach students to do that. I think it'll 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?

53:03I 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 OpenAI in the coding wars? Pretty hard to say. OpenAI has a very strong consumer brand, and that's very defensible. In contrast, developers are more likely to switch coding tools on a dime. So I love CloudCo. I think it's fantastic. But I find myself using OpenAI Codex much more over the last month. I think OpenAI Codex has actually gained real momentum. And then I'm also keeping an eye on Gemini CLI, which I think is also getting better, maybe at a faster rate than people have given them credit for.

53:42So decoding DevTools and the 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're 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's really unfortunate that in some parts of the United States, advising someone to work hard is viewed as publicly incorrect or something. In Europe, I'm chastised for it. Hopefully the European viewers won't hate me or hate us both for that.

54:21I think, frankly, I wish people could work four hours a week and be wildly successful. But the practical reality is when people work hard, they get more done. Now, I want to acknowledge that not everyone in every point in their life is in the position to work hard. So, you know, the week after my kids were born, I didn't work that hard. I took time off, spent time with the kids for more than a week. And I think we need to respect people in all walks of life, including people that for whatever reason are not in a position to work hard at that moment. But if someone wants to work hard, go, quote, Steve Jobs, make a dent to the universe, let's empower them and celebrate that.

54:56If someone, for whatever situation, can't 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 wasn't an explicit term that I use. I find that 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 you could do on a Saturday. So I actually don't bother to keep track of my hours. was probably a lot.

55:32What's 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 Fund's a venture studio, and I believe our skillset is actually in building, not just in capital asset 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 in founders to work alongside us. We're reviewing the product, giving feedback on the 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 and say, I write a check and do follow-on.

56:14We do all that, but a lot more. I'm really sorry, Andrew. Then are you a fund or are you an incubator? We call ourselves a venture studio or a venture builder. Incubators usually bring in founders that already have an idea. We go earlier than that. We often work with our investors and partners to come up 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-found the company with us. How much ownership do you have then when you make those original investments and seed the company? Depends. We end up with some common stock for the equity of building the company.

56:52And then we usually, our first check-in is usually like a million dollars at a$4 million cap. So kind 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 it, while there are VCs that, you know, do the competitive deal flow thing, they make a lot of money that way. I think my team's biggest contributions is not, you know, fighting over hard deals. It is finding ideas and creating companies that would not exist, but for the fact that we and the founder got together to co-founded together.

57:29So I think we just create more value in the world by creating new companies rather than only discovering hot 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-agriculture, someone that was a farmer could keep farming until they retired, but the kids had to learn a different trait, maybe move the city or whatever. The change is so fast this time around that we need people that are alive today to learn new skills, as opposed to we need their kids to learn new skills.

58:13And 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 and disseminate knowledge. I think the quality of questions that 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. them. Unfortunately, there are financial incentives and regulatory capture, legislative benefit types of incentives and certain types of hype.

58:54And that's actually one pattern that I've seen. I won't name any companies, but I find that there are 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 I think are at greater existential risk. And I find some of those companies that I 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 a lashing out in desperation, I think. When you look at a Demis, say, obviously a brilliant leader, or you look at a Sam even, or a Dario, all of them, I think, have moderated their position significantly with the maturing of their companies.

59:35No comment on individuals, but I think that when you have something to lose, you say more sensible things. But when your company faces greater existential risk, you know, sometimes people say weird things, but fundraising and nothing fortunate. I'd 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, my mother's got MS. 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 your mother.

1:00:09Thank you. What excites me, I want to empower everyone to build AI. I think the distance between you 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? They'll say, I built an app for that. And instead of just being a software user, there'll be a software creator. And we can get there. Then people all 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 show so it's a privilege to be here Harry but before we leave you 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 them all, losing context and losing time.

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