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?

Summary

Podcast Summary: The Twenty Minute VC (20VC) with Andrew Ng

Episode: 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?
Date: November 17, 2025
Host: Harry Stebbings
Guest: Andrew Ng (Founder, DeepLearning.AI, AI Fund; former Baidu/Google, Coursera co-founder)

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Episode Overview

This episode features a deep, multifaceted conversation between Harry Stebbings and Andrew Ng, a seminal figure in artificial intelligence. They explore the current and future state of AI infrastructure, the paradigm shift in global competition (especially around China and the West), the evolving economics and defensibility of AI businesses, and the human, educational, and regulatory challenges surrounding rapid AI adoption. The discussion is candid, nuanced, and actionable, shot through with Andrew Ng’s signature optimism and realism.

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Key Discussion Points & Insights

1. The Core Bottlenecks in AI Advancement

• Electricity and Infrastructure Shortages
  • "Data centers are the critical infrastructure for building the digital economy. Lack of electricity in America and in a number of Western countries is a problem." — Andrew Ng [03:52]
• Semiconductors and Compute Supply
  • "I have yet to meet a single AI person that ever felt like they had enough compute...so many people want more inference, more tokens generated, and we just don't have the semiconductors and data centers and electricity to meet the demand." — Andrew Ng [04:51]

2. Scaling, Efficiency, and AI Coding Assistants

• Despite debate over scaling laws, demand remains insatiable.
• Coding assistants are far beyond where image generation was in 2016.
  • "I myself, I don't ever want to have to code again without AI coding assistant." — Andrew Ng [08:31]
• Prediction: Coding productivity boosts foreshadow parallel transformations in marketing, recruiting, finance, and beyond. [06:10]

3. Policy, Regulation, and the Global AI Race

• US Regulatory Approach
  • Mixed record; US must continue to attract & cultivate talent, invest in higher education, and secure the semiconductor supply chain. [11:05]
• Comparing US and China
  • China’s national, all-industrial commitment to AI is a formidable force.
  • "The work ethic, the velocity—when China's government makes an all nation commitment, it's an all industrial commitment—that's actually a very powerful force..." — Andrew Ng [26:24]
• On Export Controls:
  • US chip export controls may have "backfired" by accelerating Chinese semiconductor development. [27:20]
• Europe’s Role
  • "My one wish for Europe is: stop regulating so much and just focus on investing and building...Let people that want to work hard, work hard and stop over regulating." — Andrew Ng [28:44]

4. Market Dynamics: Margins, Moats & Application Layer

• Application Margins & Economic Models
  • Current margins in AI app layer are poor, but will improve with falling token costs and technological advances.
  • "When we build prototypes, we're routinely just not aware about token costs...the most important thing is let's build a product that users love." — Andrew Ng [36:48]
• Defensibility (Moats) in the AI Era
  • Software advantage as a moat has weakened. Moats become industry- and execution-specific.
  • "Moats tend to be a function of the industry rather than a function of the technology." — Andrew Ng [38:31]
• Horizontal vs Vertical:
  • Both monolithic and small, specialized models will coexist due to the diversity of intelligence tasks. [33:28]

5. Talent, Education, and Workforce Transformation

• AI Upskilling is Essential
  • "Get all your students to learn to code." — Andrew Ng [52:43]
• Junior Talent Pipeline Concern
  • The real issue: graduates with no AI tooling experience — universities too slow in updating curricula. [16:12]
• Impact on Employment and Productivity
  • AI is amplifying the value and productivity of knowledge workers, not (yet) causing extinction-level job loss.
  • "If you use AI and someone doesn't, that's actually a huge difference in what you can accomplish." — Andrew Ng [15:04]
• On AGI Hype:
  • "This phantom AGI...we're very far away from that. I would say decades away, maybe even longer." — Andrew Ng [15:04]
• Key Skill:
  • Coding (and AI-powered coding) now essential for a wide range of knowledge work.

6. The Geopolitical Leverage of Open Models

• Open-weight models as a source of geopolitical soft power.
• "The country of origin of the model they end up using will be delivering some answer...tremendous source of influence and soft power." — Andrew Ng [23:10]
• China surprisingly ahead in releasing open models, which accelerates local innovation.

7. AI for Economic Growth: Incremental or Transformational?

• Debate over GDP impact: slow "blend" (2%) versus massive uplift (5+%).
  • "I hope we can get much closer to 5, 6 or more percent GDP growth...with AI we finally have a path to make intelligence cheap." — Andrew Ng [19:58]
• Biggest promise comes from reimagining workflows (not just automating slices but enabling more and faster).

8. Large Enterprises: Barriers to AI Adoption

• People and Change Management are the real barriers, not data.
  • "The biggest barrier in most large enterprises is actually people and change management, not data." — Andrew Ng [39:46]
• Enterprise adoption will take time: "A decade from now, we will still be working to identify valuable applications and building them." [42:26]

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Notable Quotes & Memorable Moments

• On the insatiable need for compute:
  "Get us any amount of compute, we will use it all up and say we still don't have enough." — Andrew Ng [04:51]

• On learning to code in an AI world:
  "We'll look back on [advice not to learn coding] as some of the worst career advice ever given." — Andrew Ng [43:23]

• On China's strategy:
  "That whole of economy, the whole of country efforts is actually a very powerful force that I wouldn't underestimate." — Andrew Ng [26:24]

• On hype vs reality:
  "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." — Andrew Ng [39:46]

• On AI’s impact on economic growth and access to intelligence:
  "In the future, if everyone can be assisted by an army of smart, well-informed staff...then individuals will be so much more empowered...and the GDP growth will be massive." — Andrew Ng [19:58]

• On the US & Europe:
  "Stop overregulating and just go and invest and build stuff." — Andrew Ng [28:44]

• On AI margins:
  "At some point, the laws of physics or the laws of finance or something, margins do matter. But...we do build assuming the technology will evolve." — Andrew Ng [36:48]

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Pivotal Timestamps

• [03:52] — Bottlenecks: Electricity, Data Centers, and Semiconductors
• [06:10] — Efficiency Gains in Token Generation; Coding Assistants as Industry Harbinger
• [11:05] — What US Policy Should Focus On
• [16:12] — The Real Problem with the Tech Talent Pipeline
• [19:58] — Will AI Power 5%+ GDP Growth?
• [23:10] — Open-weight Models as Geopolitical Influence
• [26:24] — China’s Unique All-Nation Commitment to AI
• [28:44] — Europe’s Mistaken Focus on Regulation Over Enablement
• [38:31] — The Changing Nature of Moats in the AI Era
• [39:46] — Why Change Management, Not Data, is the CIO’s AI Bottleneck
• [43:23] — The Value of Coding in a World of AI-Enhanced Work
• [52:43] — Advice to Educational Institutions: Embrace AI, Teach Coding to All
• [55:13] — Work Ethic: Learning from China’s Intensity

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Flow & Tone

• The conversation is fast-paced, practical, sometimes playfully combative on viewpoints (e.g., AGI, regulation).
• Andrew Ng comes across as relentlessly optimistic yet sober, repeatedly focusing on value creation, upskilling, and pragmatic policy.
• Harry probes on commercialization, strategy, investment, and societal risk, often using examples from his broad network of tech and venture insiders.

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Final Reflections

What most excites Andrew Ng for the next decade?
"I want to empower everyone to build AI...In the future, I hope a lot of people, instead of saying 'is there an app for that?', they'll say 'I built an app for that.' Instead of just being a software user, they'll be a software creator..." — Andrew Ng [60:06]

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This episode delivers a master class in current AI thinking from both a technical and a business/societal standpoint. Whether you are an operator, investor, policymaker, or curious observer, the actionable takeaways and global perspective make it essential listening—and this summary gives you the map.

Transcript

[A]

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 an all industrial commitment is actually a very powerful force that I wouldn't underestimate because this is 20 VC.

[B]

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 Ng, 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 two 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. But before we dive into the show 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 20 VC we're all about SPE 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. 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 human potential. Learn [e-mail] 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 all 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. My listeners can get $1,000 off Vanta by going to Vanta.com 20VC that's V A N T A.com 20TVC 20VC for $1,000 off Vanta.

[A]

You have now arrived at your destination.

[B]

Andrew, I've been an admirer for a.

[C]

Long time, so I've been really looking forward to making this happen. So thank you so much for joining me today.

[A]

Thank you. Harry. Watched a bunch of shows. I really enjoyed your recent work with my friend Martin, Martin Casado as well. That was very memorable. So I'm actually thrilled to be here.

[C]

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 elect 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?

[A]

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 I know that local community support is important and some people don't want a But once we build roads and railways as the infrastructure for a certain generation, data centers are the critical infrastructure for building the digital economy. And so lack of electricity in America and in a number of Western countries is a problem. And in contrast, I see China building power plants left and right, including nuclear. So that would be an interesting dynamic. And then semiconductors is another bottleneck. But AI is so complicated, I think we also need more data, we also need more better algorithms. All of it is worth working on. But in the short term, some constraints with electricity and semiconductors.

[C]

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

[A]

First, in my career working in AI, I have yet to meet a single AI person that ever felt like they had enough compute. 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 Genai, there are very valuable workloads for example, AI assisted coding is fantastic. It's making us so much more productive. But if you use cloud code enough, sometimes you get very 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 om inference, want more tokens generated, and we just don't have the semiconductors and data centers and electricity to meet the demand. But 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 get very limited if you use too much.

[C]

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 GPT5 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 suit to seemingly differing opinions?

[A]

It is true that token generation is getting more efficient and cheaper. If you look at OpenAI's open wake model, they actually release models that are very efficient to run. So I think they did a good job. Was it like 120 billion parameters or something with I think 5.7 billion active? So it's actually 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 horizontal information discovery like web search. But there's room for lots of verticals when the Internet was being built. So we wound up with Travelocity and Expedia, fought out for travel, bunch of folks fought out in retail, a bunch of others fought out in transportation, social media and so on. What we're seeing now is 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, 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, where cloud code, I use that every day. Love it. OpenAI codecs 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 let's 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 maybe a foreshadowing of what may happen as well to other sectors as the tools get better for them too.

[C]

I had Joel Pinault 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 2016, 2017. In terms of maturity. Do you think that's a fair state of the environment today or do you not think so?

[A]

I don't know. I think it's further along. I think in 2016 image generation wasn't super valuable. I think today AI coding assistance is really actually at AI fund, my head of engineering, Risi, I would say hey, let's think about standardizing on tools. And basically he said I need these tools and you have to pry them out of my code dead hands. I think our developers feel really strongly. I myself I don't ever want to have to code a game without 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.

[C]

I do just want to go back to the kind of the core bottlenecks we said there about electricity and we said there about semiconductors. I think when we look at the build out of data centers today, as you said, regulation has been a big part of preventing that in a lot of ways. Do you think Trump has done more to help or to hurt the progression of AI in the United States from an infrastructure perspective?

[A]

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. Last year, the bipartisan Schumer AI Insight Forum, I think there are a lot of people lobbying the US Government to pass stifling regulations. There are a lot of hyped up AI safety narratives saying AI could lead to human extinction, which I think 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 we'd beat back a lot of that. But I think the bipartisan Schumann Insight Forum did a really good job digging the truth and concluding that America should be investing in AI rather than passing a necessary regulations to slow it down. I think Trump and his whole team, David, Sachs 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 skill talent as well as young talent that may not currently be high skill, but could be high school in the future. To the extent that America is not investing as much in attracting talent, I think that would be an unforced error. And then I think lastly, investments in science. Right? I think helping our institutions of higher education have the resources to train our grad students to invest in science and technology, I think that's really precious. And so anything that damages that I think would also be very unfortunate.

[C]

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

[A]

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, canonical 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. Frankly, there's one very funny thing that happened in society. There was recently a Pew report showing I think how much Americans think AI would be good for them. Enthusiastic for it versus non 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.

[C]

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?

[A]

In the case of software engineering, it is accelerating the writing of code. 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. For example, one weekend I thought I wanted FL cards for my daughter to practice multiplication, and she wanted 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 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. Both AI assisted coding could get that done very quickly.

[C]

Do you think vibe coding is an enduring market? 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?

[A]

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 something for people to give live feedback and she looked at the apps, couldn't find anything. So she said, you know what, I'm going to spend two days to code up. It did take her 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, and so helped her do her job better as a marketer. Whereas in contrast a marketer, they could include a little app to 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 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, which is amazing.

[C]

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.

[A]

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 that can do everything a human can do, I think we're very far away from that. I would say decades away, maybe even longer. And the trick is, if AI could do 30% of a recruiter's job, maybe 50%, although that feels a little bit high, there's another 50 to 70% of stuff that we still need a human to do. But it's also clearer that, 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.

[C]

Do you not think we have a white collar talent pipeline problem though? Which is whether you're a consultant or you're a legal associate in the junior ranks, a lot of what you can do is being replaced by AI and they are actually cutting junior. You're seeing this across the board. And so what's the fear is we're going to have this talent hole where in 10 years time there's no juniors to go up into seniors because we've replaced them.

[A]

I don't think it's as dire as that. I think there is a big problem, but I don't think it's exactly that problem. So let me tell you what I'm seeing in software engineering. The most productive engineers I know, they're not fresh college grads. They are people of 10, 20 years of experience or whatever and really on top of AI and know the AI tools and understand the AI. So those people experienced and on top of AI move faster than, 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 AI. So I've hired quite a few people, fresh college grads that for whatever reason, through their social network community, really learned the AI tools and they move really fast, but they're not as good as people experience. One tier down from the fresh college grads is the people with 10 years of coding experience, but who had a comfortable job and for whatever reason is still coding like it's 2022 before ChatGPT. I just don't hire people like that anymore. But there are people that did a comfortable job, they kept coding the old way and they just did not learn AI. I think those people may get into trouble at some point. But there's one other one which is the tier that is in trouble, which is the fresh college grads that don't know AI. One unfortunate thing is university curricula is slow to change. I actually feel pretty bad that even today there are universities graduating CS undergrads that have not made a single call to a single API on the Internet. Imagine graduating a CS undergrad that has never heard of cloud computing. It's like what is the cloud? Oh, I don't need to just run things. That's weird. You just can't be a CS major and not know how to do things in the cloud. And I'm getting to the point where I feel like we've got to not train CS majors without also making sure they know how to use AI to help them with coding without also making them know the AI building blocks. That's the cohort of students that are entering the job market that's really struggling. But if the fresh college grad has no AI, we can't find enough of them. So many businesses love to hire those fresh college brands.

[C]

I just want to touch on the 10x100x 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 like pay packages that we should be concerned by?

[A]

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

[C]

I mean it nicely. Do you think it's $100 million for an engineer? I worry that you're just not going to be as productive if I give you $100 million overnight. God, you might buy a nice house and go on holiday and you lose a bit of efficiency.

[A]

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

[C]

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 overhype doomsday scenarios and everything in between. Andre capathi recently said AGI will just blend into 2% GDP growth. I thought sounded a little bit unexciting. I wanted some seismic shift in productivity increase. Do you think a blend into 2% GDP growth is what you expect or do you expect a much more significant 5, 6% like Massasana Softbank expects?

[A]

I hope we can get much closer to 5, 6 or more percent GDP growth when looking at 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 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 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 GDP growth will be massive.

[C]

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 open weights ecosystem. We've seen, we've seen this reversion back to a closed world in a lot of cases. How do you analyze the state of play today in that open versus closed?

[A]

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 release as 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 what I would have predicted a decade ago that China AI would end up being more open than America AI.

[C]

I do think China is wanting an OpenAI world.

[A]

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 way 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, this really work. I'm having trouble with this final model. It's just that circulation of knowledge is really valuable for innovation. And when the US has more closed models and when teams are trying to pay these $100 million salaries to extract talent, then that circulation of knowledge becomes very slow and it slows down the rate of American and European innovation.

[C]

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 U.S. do you not think that actually leads a lot of their thinking around why they want to remove the strength of US models?

[A]

I think in addition to increased 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 some kid in some developing nation asks a question about a politically 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 weight models are a key part of the AI supply chain. China releasing low cost or free models and to that key part, supply chain means it's really starting to build up a lead 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 K pop or whatever, and that buys the nation a of lot of influence. Hollywood was a tremendous source of soft power for America. It paints a certain vision of the American dream. Talks about the values of freedom and democracy. This is another frontier of communications and soft power.

[C]

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 versus the us. Do you agree with that positioning of China versus the US in an AI race?

[A]

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 on the finish line. It's not one race. AI is a general Purpose technology and you could be better aware at coding, better ways at answering questions, better at words, at helping with markets and finance and so on. So AI has many different capabilities and there's no one finish line. And even those one capability we're going to keep on improving for a long time. So I feel like because of PR goals, AGI has been hyped up as a 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. Having said that, nations with stronger AI capabilities are going to be more powerful, their citizens will be more prosperous, the economies will grow faster. So to the extent that different nations incentives are not aligned, nations with more powerful AI capabilities will be to do more. Just like if a country has a fantastic electricity grid and another country has power outages and so on, well, one country can just use the electricity grid to do more manufacturing, more industrial work, just do a lot more that way.

[C]

Do you not think we still underestimate China's ability though? I think we definitely do it in Europe, but I think in the US respectfully, I see a lot of US arrogance around your positioning. And then you go to China and you've been to China and spent huge amounts of time in China, you realize the speed and the intensity with which they move different level to both Europe and the us.

[A]

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 all industrial commitment. That's actually a very powerful force with kind of state level investments in semiconductors, in this education system. So like K12 kids being trained to use AI, businesses also use AI, share knowledge and then sometimes build this up and also sell internationally with state apparatus that may or may not really help control over rare earth elements. So I think that whole of economy, the whole of country efforts is actually a very powerful force that I wouldn't underestimate.

[C]

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

[A]

I think the export control on chips has largely backfired the way the US first put restrictions on Huawei and then later on exported Nvidia and AMD and other semiconductors that really incentivized China. So before the export controls, semiconductor development in China, 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 a number of Chinese companies, 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 its semiconductor industry in a way that may not be helpful to the US long term.

[C]

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?

[A]

If I had one wish for the European regulators, spoke with quite a few European regulators. I was hearing things like, 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 stop 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.

[C]

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

[A]

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 become overinvestment? 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, or 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 VC 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 $100 million so that they can pay open and anthropic, so the OpenAntropic can pay Nvidia, which is where all the money is is ending up. Having said that, there's so many valuable bets to be placed at the application layer to just build stuff. But the dilemma is you could do it in a very capital efficient way. So if someone wants to say I want to put $10 billion to work, yes, you can build $10 billion worth of data centers. We 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 IDR out, so how do I spend $10 billion? It's kind of a problem and also not a problem, but I think what does it.

[C]

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.

[A]

I think it still varies. I'm seeing a lot of green shoots of software applications that, that were not that expensive to build. And if your ELM token usage is not the majority of your expense, if.

[C]

You look at a replit or a lovable 80% of their pass through is to anthropic.

[A]

So the dynamic that I'm excited about is I think that as ELM token costs continue to come down, we'll see how the economics change. Right now, ELM 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? 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 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 a good outcome takes a lot of skill. But having said that, I still want to say there are a lot of smaller applications that are not yet doing these 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.

[C]

To grow 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, efficient, much more specialized models. And has your mindset changed around which will be more dominant?

[A]

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. Sometimes we do intellectually really easy tasks. Like if someone asked me, like, all right, yesterday, my daughter, she misspelled the word butterflies. I need to tell her how to spell butterflies. 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 run it locally, just do that, 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 we have humans do a range of tasks at difficulty. Same with AI.

[C]

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

[A]

I disagree with that, 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 we just could not even do the task, but for agentic workflow.

[C]

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

[A]

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, compliance may become an issue. Unfortunately, we turned out to be right. But so last year I think it was around August, we 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 file this paperwork, it makes me wonder, oh my God, what is this? So you say import a bicycle, 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 import a bicycle. It just makes me go, oh my God, are humans really doing this? 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 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. We have Medical Assistant operating in India, AI assistant Callidus helping process legal documents. Many of these workloads 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 free 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.

[C]

When we think about a core of a business, it's margins. And most of these business don't have margins. Do you care about margins when investing today or with absolute respect, and it sounds disrespectful, do you take the kind.

[B]

Of utopian view that it will just.

[C]

Correct itself with time and with efficiency gains?

[A]

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 obvious one token, prices have been rapidly falling, depending on who you believe, falling 80% year on year, whatever. Kind of. Frankly, when we build prototypes, we're routinely just not aware of about token costs. Because the first most important thing is let's build a product that users love. And then what we find is after we build something, this actually happened to me a few times now, we'll build something and not worry about the cost. And then 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. This is costing me salary of one engineer, cost me more than two engineers, cost me me more than a whole bunch of engines. All right. 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.

[C]

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

[A]

Moats are changing. I find that modes tend to be a function of the industry rather than a function of the technology. So AI is a technology doesn't really offer an answer to the moat for most businesses. So if you're building AI for drones or legal or for whatever, the moat is more of a function of that industry. But one thing that is changing with regard to moats is previously software used to be a moat. If you invested 10 years to build a software, it's really hard to replicate that. That one moat 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 difficult defensible, or are you building for a consumer more focused through an enterprise? Are there brand and reputational effects 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.

[C]

So software moats 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 the ability, 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?

[A]

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. And maybe actually let me give one example, then I'll come back to data. There's been this buzz about oh, with AI we'll have unicorns with one employee. It's like a thing and it's fine. If you want to build a unicorn startup billion dollar 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 hire 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 saga. I find a lot of AI hype is so hard to disentangle because there's a gem of truth in it. It's just been hyped up a lot more. So on data. Data is important, but it turns out that data is very verticalized and you don't need as much of it to get started it as you think. So for example, 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 earn ready markdown text, go process that, find value in that. For example, I don't know, we could take SEC filings, large complex financial tables very accurately turn those financial tables into Excel spreadsheet. Then go get your analysts or your AI to analyze that and draw conclusions. So often with a bit of scrappiness, looking at internal data, look at public data, you can often get some stuff going. And it turns out that a lot of Internet data is kind of general purpose data. Most of the world's data is 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 good value out of it. Not to say more data wouldn't be even better. But you're not stuck to even take the first few steps or lack of data.

[C]

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 ChatGPT use, building internal systems. Is that the world that we inhabit for enterprise AI adoption?

[A]

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 are still businesses that are still on prem rather than on the cloud. But we're making progress actually. Actually 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 GPT growth is also going to take much longer than the hype says it will. I actually think that a decade from now we will still be working to identify valuable applications and enterprises 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.

[C]

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

[A]

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 were automated. We'll look back on that as some of the worst career advice ever a 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 mark the 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. 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.

[C]

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 fund both the energy and the compute requirements for that 10 year period? Sam Altman has said he needs a trillion dollars. He needs the energy of Japan. If we're 10 years out before we have still not that much improvement, do we have the Money to fund it.

[A]

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. Frankly, so many of my friends, this 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 10 years from now.

[C]

But if you look at the TAM, the secret to success in AI investing is will we see a transition from human labour 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?

[A]

To me the question is, is AI mostly for cost savings or is it for growth? 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 underwriting approvals, do I approve this loan or not? So let's say for SIMPLICITY There are five steps, each takes 20% effort. If you can automate one of those things, steps is a 20% cost savings, which is really nice. It could be great if you're a low margin business, but it doesn't feel like a game changer. What I find is that the more valuable uses of AI it actually requires, it often requires rethinking that workflow. 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 two weeks before loan officer looks at it, but we can just give you an initial answer in 10 minutes that changes the product and 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 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. 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 GP growth.

[C]

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 players? 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?

[A]

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 CPU, is it okay if your keyboard has plus minus 5 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. But as the industry matured, 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 OM model on the Internet, what's the file format for that, it's starting to see more standards. Then that makes it easier for individual players to do something and still have it fit into the broader ecosystem.

[C]

So do you think then Zuck and Sam are right to be spending as much as they are on data centers, or should they be patient and wait for the maturation of the industry where they can then be horizontal?

[A]

I think clearly OpenAI's investments have paid off to date. It is possible to overinvest at some point, but I don't know if that is the point. And then I think also the financial instruments being used by many players to shift risks around have been really interesting. I find that overly complex 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.

[C]

Do you worry about the circular deals?

[A]

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 bubbleish.

[C]

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

[A]

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. I think it's fantastic. So someone else trained these models who can build applications for $100,000 or $1 million and start generating ROI. And then I think it is calibrating to the right level of infrastructure investment. That is tricky. But having said that, it is also 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.

[C]

Do you get annoyed by the bubble bubble discussion?

[A]

I 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 is that now than a couple of years ago. And then instead the conversation should be, how can we upskill the workforce? Where can we invest? Not how do we slow this thing down? I think the hype has really distorted public perception of AI. And one downside that the hype too is without public support of AI, things slow down. So for example, actually one of my friends works a lot with high school students and he told me that he was talking to a girl, a high school student, that he was talking to her about maybe 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 the high school grow 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, that could be good for the community and good for the world. I think that's also unfortunate.

[C]

I'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?

[A]

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.

[C]

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

[A]

Months? I think my favorite tools keep changing. If you ask me every three months over the last year what my favorite coding tool is, my answer would have kept on changing.

[C]

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

[A]

Really hard to say. OpenAI has a very strong consumer brand and that is very defensible. In contrast, developers are more likely to switch coding tools on a dime. So I love cloud code. I think it's fantastic. But I find myself using OpenAI codecs much more over the last years of couple month. I think OpenAI codecs 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 decoding dev tools and API tools market the moat there is weaker than having a strong consumer brand. So I think that's something that companies have to sort out.

[C]

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?

[A]

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 politically incorrect or something.

[C]

In Europe, I'm chastised for it.

[A]

Hopefully the European viewers won't hate me or hate us both for that. I think, frankly, I wish people could work, 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 the week after my kids were born, I didn't work that hard. I took time off, spent time with the kids for a little 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 Steve 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 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.

[C]

Did you do996?

[A]

The term996 wasn't an explicit term that I use. I find that right these days I really love what I do. 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.

[C]

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

[A]

Oh, one thing about AI Fund. Yes, we call ourselves a fund, but frankly, the way we, we run the fund day to day, we act much more like operators than investors. AI Funds, Adventure Studio and I believe our skill set is actually in building, not just in capital 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 that work alongside us. We're reviewing the product, giving feedback on the product, arguing about pricing. So we're where my day to day life is much more operator. And yes, eventually we have to do the financial diligence. As I write a check and do follow on. We do all that, but a lot more.

[C]

I'M really sorry Andrew. Then are you a fund or are you an incubator?

[A]

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 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.

[C]

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

[A]

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 at a $4 million cap. So kind of 20% ownership or say.

[C]

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

[A]

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 hot deals. It is finding ideas and creating companies that would not exist but for the fact that we and a founder got together to co founder together. So I think we just create more value in the world by creating new companies rather than only discovering hot companies to try to put money into.

[C]

What concerns you most today, Andrew? I love your optimism and your open mindedness. What concerns you? On the flip side, I am worried.

[A]

About the difficulty of bringing everyone along with us in previous waves 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 around 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.

[C]

You do a lot of interviews, Andrew. 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?

[A]

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 regulatory capture, legislative benefit types of incentives and certain types of hype. And 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 you're an 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.

[C]

In many respects, it's a lashing out in desperation. I think when you look at Adamis, 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.

[A]

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, sometimes people say weird things for fundraising, and that's unfortunate.

[C]

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.

[A]

Me?

[C]

What excites you?

[A]

Yeah. I'm sorry to hear about you, mother. Thank you. What excites me? I want to empower everyone to build AI. I think the distance between you 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.

[C]

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.

[A]

I really enjoy your show, so it's a privilege to be here, Harry.

[B]

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