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?

Dr. Andrew Ng is a globally recognized leader in AI. He is Founder of DeepLearning. AI, Executive Chairman of LandingAI, General Partner at AI Fund, Chairman and Co-Founder of Coursera. As a pioneer in machine learning Andrew has authored or co-authored over 200 research papers in machine learning, robotics and related fields. In 2023, he was named to the Time100 AI list of the most influential AI persons in the world. Agenda: 03:19 What are the Biggest Bottlenecks in AI Today? 08:51 How LLMs Can Be Used as a Geopolitical Weapon 15:48 Should AI Talent Really Be Paid Billions? 29:07 Why is the Application Layer the Most Exciting Layer? 36:22 Do Margins Matter in a World of AI? 38:02 Is Defensibility Dead in a World of AI? 45:29 Will AI Deliver Masa Son's Predictions of 5% GDP Growth? 49:39 Are We in an AI Bubble? 57:31 Will Human Labour Budgets Shift to AI Spend?

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

• 0:00Speaker 00

  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 weight models is a tremendous source of geopolitical influence. I think the work ethic, the velocity, when China's government makes an all nation commitment, it's all industrial commitment is actually a very powerful force that I wouldn't underestimate.

• 0:29Harry Stebbings?

  This is 20VC with me, Harry Stebbings, 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 Engi, globally recognized leader in AI. He's the founder of Deep Learning AI, exec 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 to the Times 100 AI list as one of the most influential AI people in the world.

• 1:12Harry Stebbings?

  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 them all, 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 Grammarly, Mail and Coda built in, you can get things done faster and collaborate seamlessly. Finally, AI that works where you work, however you work.

• 1:52Harry Stebbings?

  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. Get AI that works where you work. Unlock your superhuman potential. Learn more at superhuman.com/podcast.

• 2:10Harry Stebbings?

  superhuman.com/podcast and once you're moving faster with superhuman make sure you're moving safely with Vanta customer trust can make or break your business and the more your business grows the more complex your security and compliance tools get it can turn into chaos and chaos isn't a security strategy that's where Vanta comes in think of Vanta as your always on AI powered security expert who scales with you Vanta automates compliance continuously monitors your controls and gives you a single source of truth for compliance and risk so whether you're a fast growing startup like Cursor or an enterprise like Snowflake Vanta fits easily into your existing workflows so you can keep growing a company your customers can trust

• 2:54Harry Stebbings?

  my listeners can get $1,000 off Vanta by going to vanta.com/20vc that's V A N T A dot com forward slash twenty vc two zero vc for $1,000 off Vanta

• 3:09Speaker 00

  you have now arrived at your destination

• 3:13Harry Stebbings?

  Andrew I've been an admirer for a long time so I've been really looking forward to making this happen so thank you so much for joining me today

• 3:19Speaker 00

  thank you Harry I've watched a bunch of shows I really enjoyed your recent one with my friend Martin Martin Casado as well that was very memorable so I'm actually thrilled to be here

• 3:28Harry Stebbings?

  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

• 3:52Speaker 00

  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 kind of permitting and you know and I know that local community support is important and some people don't want a data center there but um 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 will be interesting dynamic. And in semiconductors is another bottleneck. But AI is so complicated. I think we also need more data.

• 4:36Speaker 00

  We also need more better algorithms. All of it is worth working on. But in the short term, some constraints with electricity and semiconductors.

• 4:45Harry Stebbings?

  Can you talk to me about the constraints around semiconductors that you think are most pressing? That most people don't realize.

• 4:51Speaker 00

  First, in my career working in 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, is fantastic. It's making us so much more productive. But if you use cloud code enough, sometimes you get rate limited. And I find that many companies really have excess demand, which is a very rare problem to have. But so many people want more L M inference, want more tokens generated. And we just don't have the semiconductors and data centers and electricity to meet the demand.

• 5:36Speaker 00

  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 want it on the demand side. You know, you you get rate limited if you use too much.

• 5:48Harry Stebbings?

  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 G P T five was the example that scaling laws have been reached to a certain extent and a focus on efficiency has been a transition. How should I balance this two seemingly differing opinions?

• 6:10Speaker 00

  It is true that token generation is getting more efficient and cheaper. If you look at OpenAI's Openweight model, they actually released models that are very efficient to run. So I think they did a good job. Was it like a hundred twenty billion parameters or something with, I think, five point seven billion active? So it's a very efficient model to run. But despite the cost of token generation falling, our demand for it is insatiable. One interesting thing that's happened in AI is if we look at where the buckets of value, one of the big buckets of value is AI assisted coding. I think this harkens back to an earlier era. In a previous generation, I think Google came to dominate, you know, horizontal information discovery like web search.

• 6:54Speaker 00

  But there's room for lots of verticals when the internet was being built. So we wound up with Travelocity and Expedia, Forte for travel, a bunch of folks Forte in retail, a bunch of others Forte in transportation, social media and so on. What we're seeing now is ChatGPT has such a strong consumer brand. ChatGPT seems to be the dominant player in the new gen horizontal information discovery. Although I think Gemini with its 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 up. And one of the clear buckets of really valuable verticals is AI coding assistance,

• 7:36Speaker 00

  where Claude Code is, you know, I use that every day, love it. OpenAI 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 become more efficient, AI finance tools become more efficient. So I often look at AI coding systems as a maybe a foreshadowing of what may happen as well to other sectors as the tools get better for them too.

• 8:12Harry Stebbings?

  I had Joel Pino from Cohere and formerly of Facebook on the show recently. And she said that AI coding assistants are in the same place that maybe image generation was in two thousand and sixteen, two thousand and seventeen in terms of maturity. Do you think that's a fair state of the environment today? Or do you not think so?

• 8:31Speaker 00

  I don't know. I I think it's further along. I think in two thousand and sixteen, image generation wasn't super valuable. I I think today, AI coding assistance is really, actually, at AI Fund, my head of engineering recently, I would say, hey, let's think about standardizing on tools. And, you know, basically, he said, I need these tools, and you have to pry them out of my code-dead hands. I think I developed this fear really strongly. I I myself, I don't ever want to have to code again with an AI coding assistant. So I think the tools are really working well, but still with a lot of headroom for how much better you can get.

• 9:06Harry Stebbings?

  I do just want to go back to the kind of the core ballmarks. We said there about electricity, and we said there about semiconductors. I think when we look at like 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?

• 9:26Speaker 00

  Over the last few years, the US federal government has done some good things and some less helpful things. I feel like clearing out unnecessary regulations has been a very good move. Even last year, the bipartisan Schumer AI Insight Forum, I I I think there are a lot of people lobbying the US government to pass stifling regulations, you know. There are a lot of hyped-up AI safety narratives, saying AI could lead to human extinction, which is kind of a ridiculous statement. To try to get stifling, anti-competitive regulations passed, often to try to shut down open source, open weight, fortunately, would beat back a lot of that. But I think the bipartisan Schumer Insight Forum did a really good job digging into the truth and concluding that America should be investing in AI rather than, you know, passing unnecessary regulations to slow it down.

• 10:11Speaker 00

  I think Trump, and then this whole team, David Sacks and 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 skilled, but could be high skilled in the future. To the extent that America is not investing as much in attracting talent, I think that would be an unforced error. And then I think lastly, investments in science, right? I I think hoping 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.

• 10:53Speaker 00

  And so anything that damages that, I think would also be very unfortunate.

• 10:57Harry Stebbings?

  If I gave you a regulatory magic wand, Andrew, what would you change that would have the most significant needle-moving impact?

• 11:05Speaker 00

  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 Nobel laureates are immigrants. Einstein, Konal, for example, was an immigrant. 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.

• 11:48Speaker 00

  And frankly, there's one very funny thing that happened in society. There was recently a Pew report showing, I think, how much America's thinking AI would be good for them, enthusiastic for it versus not enthusiastic. And even though a lot of AI technologies were invented in America, a lot of people don't trust or don't like AI.

• 12:07Harry Stebbings?

  The joys of what I do, Andrew, is I get to speak to incredible people and then kind of cross-reference what they say. David Kahn from Sequoia said, hey, a really useful barometer for effectiveness is can AI replace the bottom 5% of capabilities of what workforce does? Joel from Cohere 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?

• 12:38Speaker 00

  In the case of software engineering, it is accelerating the writing 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 a weekend. I hope that we never have to go back to coding with our AI assistants again because the acceleration, the productivity boost is incredible. I remember, for example, one weekend I thought I wanted flashcards for my daughter to practice multiplication, and she wants to practice multiplication. She wanted flashcards. So I thought I could either drive to the store and buy a bunch of flashcards for her, or I could just, you know, use AI to write code for me to generate and print out a bunch of flashcards. And so I did the latter. And so this is a very low economic value task.

• 13:22Speaker 00

  But with AI system coding, I could get that done very quickly.

• 13:25Harry Stebbings?

  Do you think vibe coding is an enduring market? Like, do you think everyone will want to code? And accessibility is important? Or do you think it bluntly just allows builders to build better and more efficiently?

• 13:37Speaker 00

  I think we need all of the above. I've had mixed feelings about the term vibe coding, but nitpicking terminology aside, I think everyone should learn 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't code. For example, I think my marketer wanted to run a user survey. She wanted, you know, 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 gonna spend two days to code that. It did take two days, but my marketer then built a little mobile app where users could swipe left or right to give feedback on some marketing messages we wanted to use to test. And because of that, we're able to run user experiments, get feedback,

• 14:23Speaker 00

  and so it helped her do her job better as a marketer. Whereas in contrast, a marketer that couldn't code a little app to, you know, let people swipe around and give feedback, they would just not have been able to do this, would not have gotten the feedback, would not have been able to move forward. Today, my best recruiters, not only do they screen resumes by hand, they are writing prompts to get AI to help them screen resumes.

• 14:46Harry Stebbings?

  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 headcount reductions. If you can screen so much more with AI, I'm not into this kind of fear mongering, so. But like, if you can screen a lot more with AI, I don't need my three other analysts.

• 15:04Speaker 00

  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 a lot of stuff it can't do. So this phantom AGI, someday with AI, they can do everything a human can do. I think we're very far away from that. I would say, no, like decades away, maybe even longer. And the trick is, if AI could do thirty percent of a recruiter's job, maybe fifty percent, although that feels a little bit high. There's another like fifty to seventy percent of stuff that we still need a human to do. But it's also clear that if you use AI 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.

• 15:48Harry Stebbings?

  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 A I. And they are actually cutting juniors. You're seeing this across the board. And so what's the fear is we're going to have this talent hole. Where in ten years' time, there's no juniors to go up into seniors because we've replaced them.

• 16:12Speaker 00

  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, you know, ten, twenty years of experience or whatever. And really on top of A I and know the A I tools and understand A I. So those people, experienced and on top of A I. Move faster than anything the world has seen even one or two years ago. One tier down is actually a fresh college grads that are really on top of A I. So I've hired quite a few people, fresh college grads. That for whatever reason, through their social network, community, really learned the A I tools. And they move really fast, but they're not as good as people experience.

• 16:53Speaker 00

  One tier down from the fresh college grads is the people with ten years of coding experience. But who had a comfortable job and for whatever reason is still coding like it's two thousand twenty two before chat G P T. I just don't hire people like that anymore. But there are people that, you know, they did a comfortable job. They kept coding the old way and they just did not learn A I. I think those people may get into trouble at some point. But there's one other one, which is the tier that is in trouble, which is the fresh college grads that don't know A I. One unfortunate thing is university curricula is slow to change. I actually feel pretty bad that even today, there are, you know, universities graduating C S undergrads. That have not made a single call to a single A P I on the internet.

• 17:38Speaker 00

  Imagine graduating a C S undergrad that has never heard of cloud computing. It's like, what is it called? Oh, I don't need to just run things. That's weird. You just can't be a C S major and not know how to do things in the cloud. And I'm feeling, I'm getting to a point where I feel like we've got to not train C S majors. Without also making sure they know how to use A I to help them with coding, but also making them know the A I building blocks. That's the cohort of students that entering the job market that's really struggling. But the fresh college grads know A I. We can't find enough of them. So many businesses love to hire those fresh college grads.

• 18:13Harry Stebbings?

  I just want to touch on the the ten X, a hundred X engineers that you said are just amazing, amazing. We're seeing pay packets, compensation brands, larger than they've ever been. 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 light pay packages that we should be concerned by?

• 18:39Speaker 00

  It is very hard to say. I I know a number of people that have gotten really huge pay packets. I'm actually very happy for them. I think it's great. The funding going into pay AI people really well.

• 18:49Harry Stebbings?

  I mean, nicely, do you think it's a hundred million dollars for an engineer? I worry that you're just not going to be as productive. Like, if I give you a hundred million dollars overnight. God, you might buy a nice house and go on holiday and you lose a bit of efficiency.

• 19:02Speaker 00

  I have a lot of second value friends that, you know, for whatever reason, have made a little bit of money. Many of them just keep working really, really hard. But e- equally before and after, you know, they wound up making a little bit of money. So I I I find that a lot of the tech culture, we do stuff because it's fun, because it lets us. You know, hopefully help other people as a way to change the world. I I I find that wealth makes people become lazy much less than one might guess.

• 19:29Harry Stebbings?

  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 we overhype, you know. Doomsday scenarios and everything in between. Andre Capathie recently said AGI will just blend into two percent GDP growth. I thought it sounded a little bit unexciting. I I wanted some seismic shift in productivity increase. Do you think a blend into two percent GDP growth is what you expect? Or do you expect a much more significant five, six percent like Massa Sun at SoftBank expects?

• 19:58Speaker 00

  I hope we can get much closer to five, six 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. Or hire a highly skilled tutor to, you know, patiently teach our kids, because that intelligence. Training up that wise doctor, wise teacher, wise advisor is very expensive. But 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, well-informed staff

• 20:39Speaker 00

  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. Lives will be so different and the GP growth will be massive.

• 20:55Harry Stebbings?

  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 the 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?

• 21:12Speaker 00

  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 well, taking a lead or getting up there in terms of releasing tons of really good open weight models. I would say it is kind of not whatever predicted a decade ago that China AI would end up being more open than America AI.

• 21:53Harry Stebbings?

  How do you think China is wanting an open AI world?

• 21:57Speaker 00

  It turns out that openness is 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 weight model, then yes, of course, America can take advantage of it, but the China economy benefits even more from it because once 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 closed models and when, you know, teams are trying to pay these

• 22:38Speaker 00

  hundred million dollar salaries to extract talent, then that circulation of knowledge becomes very slow and it slows down the rate of American and European innovation.

• 22:50Harry Stebbings?

  With the commoditization of the model layer, though, and the kind of 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 than 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?

• 23:10Speaker 00

  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 influence. For example, if someday, you know, some kid in some developing nation asks a question about a publicly sensitive topic, or asks, hey, where are the national borders in this case, or what's the history of this event or that event? The country of origin of the model they end up using will be delivering some answer. Whether the answer skews towards one nation's values or another nation's values is actually a tremendous source of influence and soft power. Like it or not, Open Way models are a key part of the AI supply chain.

• 23:53Speaker 00

  China releasing low-cost or free models into that key part of the supply chain means it's really starting to build up a lead, right, and build up a commanding user base. And this is why I think nations with a strong media and entertainment industry, it turns out South Korea has vastly disproportionate influence because of their leading entertainment industry. So people listen to whatever, you know, K-pop or whatever, and that buys the nation a lot 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.

• 24:32Harry Stebbings?

  You have the most fascinating perspective, having obviously spent many years at Google and then obviously Baidu 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. Do you agree with that positioning of China versus the US in an AI race?

• 24:52Speaker 00

  I think there's a lot of room for cooperation and then also some places that will be competitive. So first, while people, sometimes even me, talk about the AI race, there's no single fish in the finish line. It's not one race is AI is a general purpose technology and you could be better at coding, better at answering questions, better at helping with, you know, markets and finance and so on. So AI has many different capabilities and there's no one finish line. And even as one capability, we're going to keep on improving for a long time. So I, I, I feel like because of a PR goals, AGI has been hight up as the finish line. But I don't think it's a finish line. It's just we'll have continually improving capabilities for decades to come.

• 25:35Speaker 00

  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 would be able to do more. Just like, you know, if, if a country has a fantastic electricity grid in another country, you know, because power outages and so on, well, one country can just use the electricity grid to do more manufacturing, more industrial world, just do a lot more that way.

• 26:05Harry Stebbings?

  Do you not think we still underestimate China's ability though? I mean, I think, 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 amounts of time in China. You realize the speed and the intensity with which they move, different levels, both Europe and the US.

• 26:24Speaker 00

  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, this all industrial commitment, that's actually a very powerful force with kind of state level investments in semiconductors, in this education system. So, you know, like K12 kids being trained to use AI. Businesses are asked 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 put a whole control over rare earth elements. So I think that whole of economy, whole of country efforts

• 27:06Speaker 00

  is actually a very powerful force that I wouldn't underestimate.

• 27:11Harry Stebbings?

  Given that, like, we shouldn't underestimate it. Do you think it's right that we have export controls on chips? You know, obviously Nvidia has had a lot of export controls back and forth. Do you think that's right or not?

• 27:20Speaker 00

  I think the export control on chips has largely backfired. The way the US first put restrictions on Huawei and then later on your exported 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, and it is paying off for China. I think your number of Chinese companies are building offerings that individual chips are less powerful, but maybe with much larger number of chips, trying to build offerings

• 28:05Speaker 00

  competitive with certainly 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 caused China to accelerate semiconductor industry in a way that may not be helpful to the US long term.

• 28:23Harry Stebbings?

  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, and 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?

• 28:44Speaker 00

  If I had one wish for the European regulators, I've spoken to 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, 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 stop over-regulating and just go and invest and build stuff.

• 29:22Harry Stebbings?

  Where do we most need to be investing, where we are not investing enough?

• 29:26Speaker 00

  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 love investments, but when does it become overinvestment? That's a, that'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.

• 30:08Speaker 00

  It's wonderful to build tons of applications, it's just were not possible before. Now, from a VC investment perspective, I've heard through multiple VCs, is bizarrely 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. In fact, if you look at a lot of the application layer investments, sometimes it feels like firms are putting in a hundred million dollars so that they can pay Open and Anthropic, so that Open and Anthropic 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.

• 30:51Speaker 00

  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 costs 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 not a problem, but I think we should.

• 31:14Harry Stebbings?

  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.

• 31:29Speaker 00

  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 token usage is not the majority of your expense.

• 31:43Harry Stebbings?

  If you look at a Rappel or a Lovable, eighty percent of their pass through is to Anthropic.

• 31:48Speaker 00

  So the dynamic that I'm excited about is I think that as token costs continue to come down, we'll see how the economics change, right? Right now, 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 lots VC subsidized eating, right? You know, it was great, we could eat food delivered, it 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

• 32:29Speaker 00

  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 and to operate and that I think will, we'll see continue to grow.

• 33:01Harry Stebbings?

  The 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?

• 33:28Speaker 00

  Well, it'll be all of the above. We will have large models and midsize 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 asks me, like, all right, yesterday my daughter, she misspelled the word butterfly, so I need to tell her how to spell butterfly, is a low, you know, is 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.

• 34:11Speaker 00

  Use a tiny model, maybe running locally, just do that. But 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 like humans do a range of tasks of difficulty, same with AI.

• 34:32Harry Stebbings?

  Does that mean that you disagree with Andre Capathie when he said that useful agents are a decade away?

• 34:39Speaker 00

  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 task but for agentic workflow.

• 34:54Harry Stebbings?

  Can you give me an example? I'm fascinated.

• 34:56Speaker 00

  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's like, it makes me wanna, oh, my God, what is this? So, you know, you say import a bicycle. You know, then 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 like import a bicycle. It just makes me go, oh, my God, are humans really doing this?

• 35:41Speaker 00

  So we built agentic workflows to read the tariff 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 Geyer, Geyer Dynamics, that, you know, because of the increased complexity in tariff compliance, has been doing pretty well. We just could not have done this without agentic workflows. We have medical assistance operating in India. AI assistant, uh, calibers, helping process legal documents. Many of these workflows, we just could not do. So I, I, I find that they're useful AI agent workflows already today. And the large businesses too, not just our startups. When we look at the hyperscalers and I chat to friends and saw the large businesses,

• 36:26Speaker 00

  there's a bunch of internal workflows that we just could not be doing without these AI agents.

• 36:31Harry Stebbings?

  When we think about the 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, and it sounds disrespectful. Do you take the kind of utopian view that it will just correct itself with time and with efficiency gains?

• 36:48Speaker 00

  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, own token prices have been rapidly falling. Depending on who you believe, falling eight years, a 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 users love. And then what we find is after we've built some, this actually happened to me a few times now. We'll build something and not worry about the costs. And then, you know, users start to use it. And then our API bill starts climbing.

• 37:33Speaker 00

  And then it is really like, kind of, you look at this every few weeks and go, whoa, this is getting really expensive. This costing me salary of one engineer, costing me more than two engineers, costing me more than a whole bunch of engineers. This is all right, this. 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 in 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. I think that's also overly simplistic.

• 38:16Harry Stebbings?

  How do you think about defensibility in AI? Well, a lot of people suggest that the time to copy is reduced significantly. But defensibility itself is questioned in AI. Do you agree with that and the questioning of defensibility today or not?

• 38:31Speaker 00

  Modes are changing. I find that modes tend to be a function of the industry rather than the function of the technology. So AI as a technology doesn't really offer an answer to the modes for most businesses. So if you're building AI, you know, 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? If you had, you know, invested ten 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, more for consumer and enterprise? Are there a branding reputational effects, right?

• 39:16Speaker 00

  That 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.

• 39:24Harry Stebbings?

  So software modes have changed. And so we now have margins that matter, but we have a little bit more elasticity there. The 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?

• 39:46Speaker 00

  I think the biggest barrier in most large enterprises is actually people and change management.

• 39:52Harry Stebbings?

  Not data?

• 39:53Speaker 00

  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 ten times more than the reality. And 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 with one employee, go for it. 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, right? So it is true that team sizes are shrinking, we get more done with smaller teams, so that is true.

• 40:36Speaker 00

  But the hype is then saying, let's build a unicorn with one startup. I find a lot of AI hype. It's 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 into a 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.

• 41:20Speaker 00

  Then go get your analysts or your AI to analyze that and draw conclusions. So often with a bit of scrappiness, look at internal data, look 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 there's a lot of businesses, actually very valuable transaction data, you know, 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, 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 for lack of data.

• 42:01Harry Stebbings?

  Andrew, 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. You're seeing the likes of JP Morgan, Goldman Sachs absolutely refuse any chat GPTs, building internal systems. Is that the world that we inhabit for enterprise AI adoption?

• 42:26Speaker 00

  I think we'll get there. So I find that a lot of enterprises are adopting OMs, ChatGPT 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, this hype that we have AGI in two years or whatever, I think that's just ridiculous. That's just for most reasonable definition of AGI, that's just not going to happen. And just as how long I'll be now? To 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. It's also going to take much longer than the hype says it will. I I actually think that a decade from now, we will still be working to identify valuable applications and enterprises and building them.

• 43:11Speaker 00

  Having said that, we will make a lot of progress over the next one or two years, but we're not could be done even ten years from now.

• 43:17Harry Stebbings?

  What else does everyone think they know about AI and its adoption and implementation that they get wrong?

• 43:23Speaker 00

  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 market example just now with building an app 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 can 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.

• 44:06Speaker 00

  It doesn't mean you should write code by hand. Writing code by hand is becoming obsolete, right? You know, really don't do that, 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.

• 44:18Harry Stebbings?

  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 ten year period? Sam Altman has said he needs a trillion dollars. He needs the energy of Japan. If we're ten years out before we have still not that much improvement, do we have the money to fund it?

• 44:43Speaker 00

  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 ten 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. It's frankly, so many of my friends, it's coding is so much more fun with AI to help us out than without. So we are seeing returns, just to be clear, but we still won't be done growing this ten years from now.

• 45:14Harry Stebbings?

  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 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?

• 45:43Speaker 00

  To me, the question is, is AI mostly for cost savings or is it for growth? You 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, you know, like five steps, right? And let's say each step takes 20% of my effort, like maybe I'm underwriting approvals, you know, do I approve this loan or not, right? So let's say, let's say for simplicity, the 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 uses of AI, it actually requires, it often requires rethinking that workflow.

• 46:29Speaker 00

  And 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. So another example, there are a lot of businesses that could do high-touch, say, customer service

• 47:11Speaker 00

  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 a lot more People, then that can change the product and let 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 G P growth.

• 47:47Harry Stebbings?

  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 layer? 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?

• 48:10Speaker 00

  I think this will evolve over time. I'm going to make an analogy. In 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 C P U. Is it okay if your keyboard has a, you know, plus minus five volts and your C P U has some other voltage? Is that okay or not? So we didn't know where the A P I boundaries or if your C P U has a, you know, memory laid out a certain way. A compute and your, 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 A P I boundaries,

• 48:50Speaker 00

  the integrated players, I B M back in the day, could solve all the problems and build valuable working products. But as the industry matured, we started to have standards, like for example, now with a U S B standard, before there were other standards. So now you make a computer, someone else makes a keyboard, we've plugged them together and it all works. So when the 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, you know, more standards for ever want to publish a compressed O M model on the internet. What's the file format for that? 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.

• 49:35Harry Stebbings?

  So do you think then like Zach 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?

• 49:45Speaker 00

  I think clearly, open eyes investments have 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, you know, used by many players to shift risks around have been really interesting. I I find that overly used, complex use of financial instruments to shift risk sometimes increases the risk of there being a bubble at some point. So that's something to watch out for.

• 50:13Harry Stebbings?

  Do you worry about the circular deals?

• 50:15Speaker 00

  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.

• 50:28Harry Stebbings?

  When does a sign turn into a big concern for you with these?

• 50:32Speaker 00

  You you mentioned the Sequoia article on the six hundred billion dollar 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. I think it's fantastic because someone else trained these models who can build applications for, you know, hundred thousand dollars 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, let's say, 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.

• 51:17Harry Stebbings?

  Do you get annoyed by the bubble discussion?

• 51:19Speaker 00

  I don't get annoyed by the bubble discussion. I do get annoyed by the hype. I know when regulators are calling me up and saying, hey, we heard AI could lead to human extinction. Thankfully, much less than now than a couple of years ago. And then instead, the conversation should be, how can we upskill the workforce? Where can we invest, you know, not like, how do we slow this thing down? I think the hype has really distorted public perception of AI. Oh, and and and one downside to the hype too is without public support of AI, things slow down. So for example, actually, one of my friends, 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 pursuing a career in AI. And she said, you know what?

• 52:02Speaker 00

  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 a community shuts down building out a data center, they could be good for the community and good for the world. I think that's also unfortunate.

• 52:30Harry Stebbings?

  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?

• 52:43Speaker 00

  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.

• 52:58Harry Stebbings?

  What's one thing you've changed your mind about AI in the last 12 to 18 months?

• 53:03Speaker 00

  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.

• 53:12Harry Stebbings?

  Do you think Anthropic will beat OpenAI in the coding wars?

• 53:15Speaker 00

  Really 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 the dime. So I love Cloudera, 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. So the coding dev tools and API tools market, the mode there is weaker than having a strong consumer brand. So I think that's something that companies have to sort out.

• 53:52Harry Stebbings?

  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?

• 53:59Speaker 00

  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.

• 54:15Harry Stebbings?

  In Europe, I'm chastised for it.

• 54:18Speaker 00

  Hopefully, the European viewers won't hate me or hate us both for that. I 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 of their life is in a 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, save jobs, make a dent in the universe. Let's empower them and celebrate that. If someone, for whatever situation, can't work hard, let's also, right, respect that and maybe celebrate that.

• 55:01Speaker 00

  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.

• 55:11Harry Stebbings?

  Did you do nine nine six?

• 55:13Speaker 00

  The term nine nine six 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. It's probably a lot.

• 55:32Harry Stebbings?

  What's the hardest transition element, moving from operator to investor?

• 55:37Speaker 00

  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 skill set is actually in building, not just in capital asset allocation or whatever. So we work really hard to screen ideas. We talk to customers, you know, 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, is I ready check and to follow on? We do all that, but a lot more.

• 56:15Harry Stebbings?

  I'm really sorry, Angie, then are you a fund or are you an incubator?

• 56:19Speaker 00

  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 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-found a company with us, so.

• 56:40Harry Stebbings?

  How much ownership do you have then when you make those original investments and seed the company?

• 56:46Speaker 00

  Depends. We end up with some common stock for the sweat equity of building the company. And then we usually, our first check-in is usually like a million dollars and a four million dollar cap, so kind of 20% ownership or safe.

• 57:01Harry Stebbings?

  And so we're basically getting 20 to 25% ownership on entry with a couple of common.

• 57:06Speaker 00

  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-found it together. So 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.

• 57:38Harry Stebbings?

  What concerns you most today, Andrew? I love your optimism and your open-mindedness. What concerns you on the flip side?

• 57:46Speaker 00

  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 trade, maybe move to the city or whatever. The change is so fast this time round that we need people that are alive today to learn new skills as opposed to we need their kids to learn new skills. And that's actually very challenging and historically, I don't think we've ever been good at that.

• 58:18Harry Stebbings?

  You do a lot of interviews, Andrew. You speak to many journalists. I'm not a journalist, never actually had a job. Do you find the quality of interviewers that ask you questions good?

• 58:29Speaker 00

  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. Unfortunately, there are financial incentives and you know, regulatory capture, legislative benefit types of incentives and certain types of hype. And it does actually one pattern that I've seen, I won't name many companies. But I find that there are companies with something to lose, whose statements over time have become more moderated. So you know, 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.

• 59:13Speaker 00

  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.

• 59:20Harry Stebbings?

  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. You know, all of them, I think, have moderated their position significantly with the maturing of their companies.

• 59:35Speaker 00

  No, no, no 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 risks, you know, you sometimes people say weird things about fundraising and that's unfortunate.

• 59:47Harry Stebbings?

  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, you know, my mother's got AMAS. 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?

• 1:00:07Speaker 00

  Yeah, I'm sorry to hear about your mother.

• 1:00:09Harry Stebbings?

  Thank you.

• 1:00:09Speaker 00

  What excites me, I want to empower everyone to build AI. I think the distance between your 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 when you get there, then people all around the world will be much more empowered, get more done, have more fun.

• 1:00:41Harry Stebbings?

  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.

• 1:00:49Speaker 00

  No, I really enjoy your shows. It's a privilege to be here, Harry.

• 1:00:55Harry Stebbings?

  But before we leave you today, are you drowning in AI tools? ChatGPT 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? 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 Grammarly, Mail and Coder 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

• 1:01:38Harry Stebbings?

  and it's personalised to sound like you at your best, not like everyone else using generic AI. Get AI that works where you work, unlock your superhuman potential. Learn more at superhuman.com/podcast. That's superhuman.com/podcast. And once you're moving faster with Superhuman, make sure you're moving safely with Vanta. Customer trust can make or break your business and the more your business grows, the more complex your security and compliance tools get. It can turn into chaos and chaos isn't a security strategy. That's where Vanta comes in. Think of Vanta as your always on AI powered security expert who scales with you. Vanta automates compliance, continuously monitors your controls

• 1:02:23Harry Stebbings?

  and gives you a single source of truth for compliance and risk. So whether you're a fast growing startup like Cursor or an enterprise like Snowflake, Vanta fits easily into your existing workflows so you can keep growing a company your customers can trust. My listeners can get $1,000 off Vanta by going to vanta.com/20vc. That's V A N T A dot com forward slash 20vc, 20vc for $1,000 off Vanta.