Why The Laws of Startup Physics Have Changed | Ben Horowitz Interview

tldr

The old startup playbook — find a market, build 10x better, sell — is dead. AI has made TAMs unknowable and competitive dynamics unprecedented. Ben argues the CEO's hardest job isn't strategy, it's managing your own psychology through lonely decisions nobody else can understand. Culture is what you tolerate, not what you write on a wall. And the best companies, like the best albums, come from uncompromising authenticity — not chasing what the market says it wants.

Key Takeaways

The New Physics of Startups

The old framework is broken. Traditional VC math — TAM analysis, competition mapping, unit economics — falls apart when AI is creating markets that didn't exist. "What if this market wasn't $50 billion? What if it was $5 trillion?" The scale of possibility has changed the math of every investment decision.
Markets are being created, not captured. The previous startup playbook was: take an existing market, build a 10x better product, sell it. Now with AI, the entire concept of a knowable TAM is gone. You're not sizing an existing pie — you're betting on a pie that doesn't exist yet.
This changes how you evaluate everything. If the market could be 100x what anyone estimates, the "risk" of investing in something with unclear unit economics looks very different. The downside is capped. The upside is unknowable.

Alchemistic Talent & The Kobe Bryant Effect

AI talent is "alchemistic" — you either have the experience or you don't. If you haven't been at Google, Facebook, OpenAI, or Anthropic training giant models with hundreds of millions in compute, you probably can't do it. There's no shortcut. No bootcamp. No self-taught path that gets you there.
Extreme inequality in talent value. A great AI researcher might be worth 1,000x a good one. Not 10x. Not 100x. A thousand times. This breaks every compensation framework and hiring playbook.
The Kobe Bryant Effect. Kobe was worth more than the next 100 basketball players combined to a franchise. The same dynamic now applies to AI talent. One person can change the trajectory of an entire company.

Andy Grove's Management Philosophy

"The output of a manager is the output of the organizations under their supervision." Not your personal output. Not your individual contributions. The team's output. Ben considers High Output Management the greatest management book ever written.
Find the limiting step. Grove's core framework: identify the bottleneck in any process, then optimize everything around that constraint. This applies to manufacturing, software, hiring, sales — everything.
Your job as CEO is to be a multiplier, not a producer. If you're the one doing the work instead of making your team's work better, you're misallocating the scarcest resource in the company — your attention.

The Loneliness of the CEO

"The thing that kills you as a CEO is the feeling that there's nobody you can talk to about a certain class of problems." Your board has an agenda. Your team is looking to you for confidence. Your spouse doesn't have the context. You're structurally alone on the hardest decisions.
The lonely decisions are the defining ones. Firing a friend. Shutting down a product people love. Admitting a strategy was wrong. These are the moments that determine whether a company survives. And nobody can make them for you.
The hardest part isn't strategy — it's managing your own psychology. Not product. Not hiring. Not fundraising. Your own mind. The uncertainty, the weight of decisions that affect real people's lives, the loneliness of knowing you can't fully share the burden.
Ben's advice: find peer CEOs, not mentors. Not someone who's been there and forgot what it felt like. People going through the same thing right now. That's the only relationship that actually helps.

How a16z Was Built

VCs were terrible at helping companies operationally. They wrote checks and gave advice. But advice without operational support is mostly useless. Ben and Marc saw this gap and built a firm around closing it.
The CAA model for venture capital. Build a VC firm that operates like a talent agency — provide actual services: recruiting, marketing, business development, government relations. Companies that got operational help performed dramatically better.
Everyone thought they were crazy. "You're going to spend money on services? That cuts into returns!" But the returns proved them right. The services made portfolio companies more successful, which more than paid for themselves.

Culture Is Action, Not Platitudes

"Your culture is not what you say. It's what you do. And more importantly, it's what you tolerate." If you say you value honesty but let someone lie in a meeting without calling it out, your real culture is that lying is acceptable.
Define culture with specific, shocking rules that force behavior change. Not "we value integrity" — that means nothing. Instead: "We return phone calls from founders within 24 hours, even if the answer is no." Specific. Measurable. Shocking enough to be memorable.
Why shocking rules work. They're memorable, enforceable, and they signal what actually matters. Ben points to samurai bushido, Genghis Khan's codes, the Haitian Revolution — all defined culture through specific behavioral rules, not abstract values.
Ben personally teaches culture to every new a16z employee. No delegation. No exceptions. At any scale. If the CEO won't spend time on culture, nobody else will take it seriously.

Authenticity & The Nas Lesson

Nas made Illmatic by being uncompromisingly authentic. He didn't chase trends. He documented his truth. It became one of the most important albums in hip-hop history — not despite the refusal to compromise, but because of it.
The market constantly pulls you toward the median. Every signal — customer requests, competitor moves, investor advice — pushes you to be more like everyone else. The greatest companies resist that pull.
Great software is like great music: technically excellent AND emotionally resonant. Art and technology aren't opposites. The best products are both. Coding is the new literacy — not because everyone should be a programmer, but because understanding software is now as fundamental as reading.

Timestamps

Time	Topic
0:00	Intro
1:00	The US Tech Advantage
2:49	A Solution for Everything
4:21	The Fragility of Success
7:14	The New Physics of Company Building
10:48	"Alchemistic" Talent
12:57	Inequality and the Kobe Bryant Effect
17:01	Automation History & The Future of Jobs
20:06	American Leadership in the AI Era
22:42	Andy Grove & High Output Management
26:02	The Hardest Part of Being a CEO
29:56	Founding a16z
35:11	Scaling the Firm & Early Mistakes
39:19	Broken Capital Markets
41:23	Why We Don't Do Private Equity
43:29	Culture Is Action, Not Platitudes
49:54	Coding & Art
52:08	Learning from Nas
56:36	Las Vegas: The Future of Tech-Enabled Policing
1:01:03	The Kindest Thing

Relevance to SupportWire & FeatureOS

The "Limiting Step" Framework Applies Right Now

Andy Grove's bottleneck thinking maps directly to both products. FeatureOS is at $15K MRR with a churn problem — the limiting step isn't product quality, it's retention mechanics. SupportWire is pre-launch — the limiting step is getting it into the hands of paying customers. Every week, the question should be: what is the single bottleneck, and is all energy pointed at it?

Culture as Specific Rules, Not Vibes

With a small team, culture gets set by default if you don't define it intentionally. Ben's framework — specific, shocking, enforceable rules — is more useful than generic values at this stage. What are the 2-3 behavioral rules that define how this team operates? Not "we care about craft." Something like: "Every customer email gets a human reply within 4 hours." Specific enough to enforce. Shocking enough to remember.

The Loneliness Problem Is Real at $15K MRR

Ben's point about the CEO having nobody to talk to about certain problems hits hard for a bootstrapped founder. No board. No co-CEO. The decisions about what to kill, where to double down, whether SupportWire is the right bet — those are structurally lonely. His advice: find 2-3 peer founders at a similar stage. Not mentors. Peers in the same fight.

Authenticity Over Market-Chasing

The Nas lesson is a distribution lesson in disguise. The temptation at $15K MRR is to chase every feature request, every competitor move, every trend. But Nas didn't make Illmatic by chasing what was popular. The products that break through — especially in crowded SaaS categories — do it with a clear, opinionated point of view. The distribution weakness won't be solved by being more like competitors. It'll be solved by being unmistakably different.

Manager Output = Team Output

At a small team, it's tempting to be the top individual contributor. Grove's framework is a direct challenge: your output isn't your code, your designs, or your copy. It's the total output of everyone you lead. Every hour spent doing work someone else could do is an hour not spent making the team faster.

Source: https://www.youtube.com/watch?v=ID27juNvAfc

Raw Transcript

Auto-captions from YouTube. Folded by default — expand if you need to grep the source or pull an exact quote.

0:00 When Ben Horowitiz and his partner Mark 0:01 Andre came into the venture capital 0:03 industry, it was very different than it 0:05 is today. You can argue that it is them 0:07 more than almost anyone else that has 0:09 reshaped this industry and matured it so 0:11 much ever since. Andre Horowitz has 0:13 become one of the most important 0:14 institutions, not just investors, but 0:16 institutions in the private investing 0:18 landscape, having achieved a scale that 0:20 no one thought was possible in venture, 0:22 which was always supposed to be this 0:23 small, tiny niche corner of the world. 0:25 This conversation is a bit unique 0:27 relative to some of the other ones that 0:28 Ben has had more recently. I tried to 0:30 understand the shaping forces and 0:32 influences in his life and the ways that 0:34 he thinks America most needs to change. 0:36 He's taken this as his life's mission to 0:38 build a firm that affects outcomes in 0:40 the country, not just in a small niche 0:42 part of the market, but very broadly, 0:44 even having discussions in this 0:45 conversation about his work with, say, 0:47 the Las Vegas Police Department, which 0:48 he's tried to infuse with technology to 0:50 lower crime rates across the system. I 0:52 hope you enjoyed this great and 0:53 wide-ranging conversation with Ben 0:54 Horowitz. 1:03 I think a fun place to begin, Ben, would 1:05 be your take on the state of the 1:07 country. Like what does it feel like to 1:09 you in 2026? I know part of your mission 1:12 is to like directly impact the 1:13 trajectory of the country. We'll talk 1:14 about that a lot. Yeah. 1:15 >> Uh but but begin with what does the 1:18 landscape, the playing field like to you 1:20 today? I think the tech sector is very 1:22 very healthy. America's competitiveness 1:24 is very very good. The entrepreneurship 1:26 culture is outstanding. Uh which you 1:30 know and that's the main thing I look at 1:31 from my lens. If you look at and I go 1:34 kind of all over the world and everybody 1:36 wants Silicon Valley like how can we 1:38 have Silicon Valley in the UK? How can 1:40 we have it in France? the thing that's I 1:42 would say lacking so they have a lot of 1:45 the ingredients right they have um great 1:49 uh talent um they've got great 1:52 universities they have like a definitely 1:55 a worse regulatory environment um in EU 1:58 increasingly bad regulatory environment 2:00 for entrepreneurship but there there's a 2:03 cultural challenge where 2:06 you know succeeding doing something 2:08 larger than yourself making the world a 2:10 better place like those aren't things 2:11 that 2:13 uh young people feel like society 2:16 values. And so the likelihood if you're 2:20 building a company of getting people to 2:23 kind of work for you and dedicate their 2:25 life to a mission like that is just not 2:27 that great. Whereas in the US, it's 2:29 amazing. I think the economy is in much 2:31 better shape than 2:34 >> people realize uh and start to see that. 2:37 Well, we're getting, you know, we've 2:40 done a lot of kind of things to 2:42 stimulate it. You know, we've got lower 2:43 energy prices. We've got much less 2:45 regulation. We've got kind of a more 2:47 user-friendly tax code. Um, and that's 2:51 all starting to kick in now. And then, 2:54 you know, from our perspective, I think 2:55 the bigger thing is AI is, um, 3:00 you know, it's going to impact 3:01 everything. There's almost no problem 3:02 you can think of that you can't go, 3:05 well, we have a real shot at solving 3:06 that with AI. It's from, you know, what 3:09 were the big problems in the US? Auto 3:10 deaths. Well, we got an AI solution for 3:12 that. Cancer, we have an AI solution for 3:14 that. So, you know, the fact that we've 3:17 got a technology where we can address 3:20 everything uh is a real new phenomenon 3:23 >> and and all that's I think going to kick 3:26 in like in a fairly major way over the 3:29 next 12 to 24 months. Why do you think 3:31 12 24 months is is a time frame worth 3:34 mentioning that some of this stuff will 3:35 start to be felt more broadly? 3:37 >> It's all kind of starting to take effect 3:40 now and you know it's got to roll out 3:43 get deployed. Now you know deployments 3:44 of technology in particular in the past 3:46 have taken a long time. Um but you know 3:49 you had to build out the infrastructure 3:51 to do it. So like for cars you needed 3:52 things like roads and traffic lights and 3:54 all that kind of thing. And for the 3:57 internet, you needed, you know, fiber in 3:59 the ground and, you know, people to have 4:01 smartphones and you needed to do a lot 4:03 just to get going. Um, the internet is 4:06 here. So, if you want to use AI, if you 4:08 want to apply it to your business, you 4:09 just do it. Like there is no 4:10 infrastructure that needs to be built to 4:12 adopt the thing. 4:14 >> What could most interrupt this good 4:17 trajectory that America is on where we 4:19 are building solutions using technology? 4:21 Like what are the biggest risks? 4:22 >> I think policy. One of the things my 4:24 father said to me was a bad government, 4:27 no matter how many smart people you 4:28 have, no matter how great a culture you 4:30 have, no matter how great the country 4:32 is, can ruin the whole thing. Venezuela 4:35 was the fourth richest country in the 4:36 world. Crazy, you know, and 4:39 >> then like, you know, communism and and 4:41 that's that. And 4:44 you know if you look at 4:47 how little comes out of so many of these 4:49 countries in Europe that have so many 4:51 smart people and then you know and then 4:52 the ones that went into communism and 4:54 there's so many like genius Romanian 4:57 entrepreneurs John vonman and the number 5:00 of great genius scientists that came out 5:03 of Hungary like this little country and 5:05 then like it was just gone once the 5:07 communists took over is like completely 5:11 like nothing from from inventing 5:13 everything to nothing overnight. And I 5:17 think that that can absolutely happen 5:19 here. We could outlaw AI. Like I think 5:22 there there were like pretty aggressive 5:24 proposals. The last Biden administration 5:27 executive order said that you could not 5:30 sell a GPU without federal government 5:34 approval. Like that was a real executive 5:37 order and it got reversed. But like we 5:38 were that close to being basically out 5:42 of the uh global chip game. So it's it 5:45 is fragile. By the way, like technology 5:48 solutions 5:50 work much better than policy solutions. 5:52 That's the that's the other thing. Like 5:53 policy solutions is very hard to make 5:55 anything work. Uh so if you think about 5:59 um you know co we could tell everybody 6:02 to stay in their house. Well that's got 6:04 some like extremely bad side effects. 6:06 you know, turned out not to work that 6:07 well or like, you know, we could invent 6:11 a drug that cures it or like a vaccine 6:14 that works. It's just hard to have or a 6:16 policy solution like, you know, all the 6:17 policy stuff on climate change, you 6:20 know, and Europe actually, you know, 6:22 reduced emissions and all that, but it 6:24 didn't do anything because like China 6:25 didn't reduce emissions. But if you 6:27 build a technology a really safe nuclear 6:30 efficient or nuclear fusion facility 6:33 then like that that would have a big 6:35 effect and I think in general that's 6:37 true that uh you know and police like 6:41 defund the police did not make anybody 6:43 safer technology does and so you know if 6:47 you really want to change the world if 6:48 you really want to make it a better 6:49 place I think you can build a solution 6:51 for darn near anything. If you want to 6:53 change the world for the better, it's 6:56 never been a better time to be an 6:57 entrepreneur. 6:58 >> I was with a uh local restaurant tour 7:00 yesterday here in New York, one of the 7:02 best, for a couple hours having him 7:04 describe to us how he is planning on 7:06 using AI tooling to improve everything 7:08 about his restaurant business. How do 7:10 you think about the way all of this is 7:12 changing the sort of potentially large 7:15 attractive businesses that you want to 7:16 invest in? Because there's been stick 7:19 with the restaurant example. Toast is a 7:21 great there's many great companies that 7:22 have been built in and around restaurant 7:24 software businesses. It seems like this 7:27 restaurant owner is going to be able to 7:28 have his own spun up operating system 7:30 specific to him not going to need any of 7:32 that stuff. How how is this changing the 7:34 way in which you view investment 7:36 opportunities 7:37 >> on the positive? Uh one everything is up 7:39 for grabs, right? I I think people are 7:41 kind of over uh reacting to that in the 7:43 stock market and so forth and that if 7:45 you look at um existing software 7:47 companies like people think, oh, they're 7:49 all dead. Well, some of these guys are 7:52 extremely hard targets. Like it's not 7:55 that easy to take out Salesforce or SAP. 7:58 You you you would be surprised um even 8:00 with AI like how much uh heavy lifting 8:04 that is. Having said that, it is true 8:06 that you know a lot of these things, 8:09 yeah, you can just make your own, you 8:10 can do it yourself. Uh it's going to be 8:12 a lot easier. That's a just like the 8:15 number of possible interesting companies 8:18 I think went up a lot. I think the other 8:21 thing we're seeing is the products work 8:22 so much better than any technology 8:25 products we've seen in the past that 8:26 revenue growth is so much faster for 8:28 these AI companies and there's many such 8:31 cases of companies coming out you know 8:34 cursor which is ostensibly an IDE like 8:36 what's the biggest ID before cursor like 8:39 I don't know but it wasn't big uh and it 8:41 took probably 12 or 15 years to get to 8:44 that revenue level and you know they 8:46 went over a billion dollars in revenue 8:48 like in no time. So that's super 8:51 interesting. I would say though from an 8:53 investing standpoint, 8:56 the laws of physics of company building 8:59 changed which is going to in affect 9:02 investing in what's currently I would 9:05 say an unknown way. So if you look at 9:09 the one thing you knew if you'd ever 9:12 built a software company is you cannot 9:14 throw money at the problem. 9:15 >> Yeah. Yeah. 9:16 >> Like you know what's a man year? you 9:19 know, 700 IBMmers before lunch. Like 9:21 that uh you know, that phenomenon kind 9:25 of everything was built on because you 9:27 knew if somebody built a great product 9:29 and it took them three years and they 9:30 did it with a small team, Google's not 9:32 going to hire 2,000 engineers and catch 9:34 them. It's just not going to happen. 9:36 That was a law of physics. Now, 9:39 if you have uh the data and you have 9:42 enough GPUs, you can solve damn near 9:45 anything. and kind of we've seen that 9:48 with uh Elon catching the big models in 9:52 no time. I mean, he just took a lot of 9:55 money and a really good data center 9:57 design and some smart engineers. He's in 10:00 the game, you know, like he got in the 10:02 game very fast. 10:03 >> That would have never happened in the 10:05 past. The markets are also seem to be 10:08 much much much bigger than anything 10:09 we've ever seen. So it would cause you 10:12 to think about valuations and kind of 10:15 long-term value and other sorts of 10:17 things in a different way than we have 10:19 in the past. On the one hand, it's like, 10:21 well, when you calculate the long-term 10:23 value, what if this market wasn't, you 10:25 know, $50 billion? What if it was $5 10:28 trillion? And then on the other end, 10:32 well, what if somebody could catch you? 10:34 These are just concepts we've not dealt 10:36 with. So how would the conversations 10:38 feel different to me if I came in you've 10:40 got all these great investors working at 10:41 Andre and Horovitz the the the nature of 10:44 the conversation amongst your teammates 10:46 as they're debating this sort of stuff 10:47 versus four years ago or something where 10:50 where does it feel most materially 10:51 different internally 10:52 >> I would say one of the most different 10:54 things is when you look at AI 10:55 researchers it is really 11:00 a different kind of thing if you haven't 11:03 been at like Google or Facebook or open 11:05 AI or anthropic and like somebody gave 11:08 you hundreds of millions of dollars to 11:09 try and build a giant model and you 11:11 weren't like one of the main people, 11:12 then you probably don't know how to do 11:14 it because you can't learn it in school. 11:17 Um, and you can't learn it in school 11:20 because it's it's a little bit 11:22 alchemistic in nature. You know, you are 11:25 it's it's a little bit of an art. And so 11:28 if you've never done it before, the 11:29 chance of on your very first try of 11:31 building some kind of large model that 11:33 it's going to work well isn't that 11:36 great. Now that's, 11:38 you know, people are coming up to speed 11:39 more. There's more companies. Um, people 11:42 are learning it. But that's kind of why 11:44 you got to this, which from the outside 11:47 world probably looked absolutely bananas 11:50 that, well, why is somebody paying a 11:52 hundred million dollars for an AI 11:54 researcher or a billion dollars for an 11:56 AI researcher? Like, that's the craziest 11:58 thing I've ever heard. 12:00 Well, what if there were only 40 of them 12:02 like in the world? 12:03 >> And you have a$4 trillion dollar 12:04 company. Yeah, then it kind of changes 12:07 the math on it a little bit. And I think 12:09 that's sort of where we were because 12:11 it's kind of the first time we've had a 12:14 need for a technologist that academia 12:17 could produce. That um is kind of 12:22 probably one of the bigger things that 12:24 changed in the conversation is like who 12:25 are all these people? Like we track all 12:27 of them and you know know what they're 12:29 doing but uh it's very different. 12:30 >> Your finance team isn't losing money on 12:32 big mistakes. It's leaking through a 12:34 thousand tiny decisions nobody's 12:36 watching. Ramp puts guard rails on 12:38 spending before it happens. Real-time 12:39 limits, automatic rules, zero 12:41 firefighting. Try it at ramp.com/invest. 12:44 Every investment firm is unique and 12:46 generic AI doesn't understand your 12:48 process. Rogo does. It's an AI platform 12:50 built specifically for Wall Street, 12:51 connected to your data, understanding 12:53 your process, and producing real 12:54 outputs. Check them out at 12:56 rogo.ai/invest. 12:57 The best AI and software companies from 12:59 OpenAI to Cursor to Perplexity use work 13:02 OS to become enterprise ready overnight, 13:04 not in months. Visit works.com to skip 13:06 the unglamorous infrastructure work and 13:08 focus on your product. Everyone talks in 13:10 venture about the power law. The thing 13:12 underneath the power law is a sort of 13:14 inequality. It seems like so many of the 13:17 things that are happening are just 13:19 massive multipliers on the trend of 13:21 inequality in every way. the billion 13:23 dollar researcher, the size of the 13:24 biggest companies, the wealth of the 13:27 people creating those companies. I would 13:29 argue that inequality is a feature, not 13:30 a bug of the American system. But yeah, 13:32 >> I'm curious for you to riff on like the 13:35 nature of growing inequality and the the 13:38 good and the bad associated with that. 13:40 what's happening in AI is sort of, you 13:43 know, I I would just say an extension of 13:44 the Kobe Bryant effect, which is um, you 13:48 know, a basketball player in uh, 13:51 whenever James J. Nay Smith invented the 13:54 game, you know, like there was a limited 13:56 amount of money you could make because 13:58 you basically played the game in front 13:59 of the people who could show up for the 14:02 game and that was it. That's the whole 14:03 market. Um whereas once you add 14:06 television and the global audience and 14:08 these kinds of things, you can get to 14:10 you know a much bigger you can be LeBron 14:12 James, you can become a billionaire and 14:14 that that just was not at all possible 14:17 before. And I think that uh you know we 14:20 kind of first saw that with the internet 14:23 where okay now I can build a product and 14:26 I can get to global distribution very 14:27 fast then all of a sudden I can become 14:29 like extremely rich and then AI is 14:33 another layer on top of that and that 14:36 okay now take that same product and make 14:39 it just more a valuable thing and so 14:41 whoever invents that is whatever the 14:44 internet company was plus+ and so that's 14:47 going to make them even richer. That's 14:49 the kind of bad part of it. I think the 14:51 good part of it is it starting out day 14:53 one like completely democratized like 14:56 the AI 14:58 anybody gets access to like very 15:01 powerful AI uh you know anybody who has 15:03 a phone and now everybody 15:06 most people in the world uh at this 15:08 point have smartphones and now you've 15:10 got like super intelligence uh in your 15:13 phone. So that's a big it's an equalizer 15:17 of the opportunity in a lot of ways that 15:20 I don't think we've ever seen a bigger 15:23 opportunity equalizer than AI. Um in 15:26 that every child can have like a super 15:29 advanced amazing tutor teacher. Um so 15:33 like education 15:36 great education is accessible to all 15:38 now. So I think it's an equalizing 15:40 technology and uh you know there's some 15:44 drive in inequality and this is another 15:46 thing I I learned from my father. He 15:48 said look son life isn't fair and that's 15:51 extremely good advice because it's just 15:53 not going to be fair. Like no matter 15:55 what government or anything tries to do 15:58 it's not going to be fair. And the 16:00 problem is if you create a system that 16:02 tries to correct that it doesn't make 16:03 things more fair. It just transfers all 16:06 the power to the person running the 16:08 system. And that's what happened with 16:09 Stalin. That's what happened with 16:10 Chescu. That's what happened with Pulp 16:12 Pot. That's what happened with Mao. You 16:14 know, not an accident that every single 16:17 system like that went bad because it 16:19 really ends up just being a power 16:21 transfer. When you think about, well, 16:23 what do you want? You'd like everybody 16:26 to have a chance, you know, like don't 16:29 give me no chance. Give me some chance. 16:31 Now, it may not be as big a chance as 16:33 the other guy. It may not be, you know, 16:35 like a perfect chance. Um, but if I have 16:38 the desire, if I've got some capability, 16:40 give me a chance to be something like to 16:42 make my imprint on the world. And 16:46 a system like that is going to end up 16:49 with a lot of inequality. All, by the 16:52 way, all systems end up with a lot of 16:53 inequality. 16:55 But you can try systematically to give 16:59 everybody an opportunity. And I think AI 17:01 does a really good job of that. One of 17:03 the memes that's very popular today is 17:04 that you have a couple years to get some 17:06 capital or you're going to be a part of 17:07 the permanent underclass is like the is 17:09 the phrase that is used on Twitter. And 17:11 I certainly agree that um now everyone 17:13 has the best lawyer, accountant, you 17:15 know, adviser in their pocket and that's 17:17 amazing. Um but what do you think about 17:19 this notion that we're just going 17:21 because of that we need less labor. We 17:23 need it's going to be harder if you 17:25 don't have some capital to begin with to 17:27 accumulate capital and break in. I don't 17:29 I don't necessarily believe that. I'm 17:30 just curious what you think about 17:31 challenges we'll face because of AI 17:33 society. 17:35 >> Yeah, I don't really think that's right. 17:38 I think that I don't think like the the 17:40 door is going to close behind you. I 17:42 think like the opportunities tend to 17:43 multiply 17:44 >> um when you kind of open up a new door 17:47 and open up like a new way of doing 17:49 things. We saw that with crypto. So many 17:51 people who made money on crypto were 17:53 like people who, 17:55 you know, literally didn't have much to 17:58 start with. they just got into the 17:59 technology early um and then they kind 18:01 of parlayed it up. And so if you have 18:03 something that grows really fast, that's 18:06 actually the opportunity for somebody 18:07 with a little bit of capital to make a 18:08 lot of money because it doesn't take 18:10 much. You know, if you bought Bitcoin 18:12 for a nickel, you did really well. Uh 18:16 and all you needed was a nickel. And 18:18 that's uh you know, I think that's the 18:21 nature of these things that go 18:23 hyperbolic. And you know particularly if 18:25 you create something I also think uh 18:28 the the labor market stuff 18:32 I think people are acting as though it's 18:34 very predictable and when it's not at 18:36 all predictable. So 18:40 if you look at kind of the history of 18:42 the of the world um and automation and 18:44 this is what it is. It's a kind of like 18:46 an automation technology. We've been 18:49 automating things since the agricultural 18:51 days. And in in those days, I think 95 18:56 or 96% of all jobs in the US were 18:59 agriculture. Almost all those jobs have 19:01 been eliminated. Um and the jobs we have 19:04 now the people doing agriculture 19:06 wouldn't even consider jobs. And so like 19:08 the idea that we could imagine all the 19:11 jobs that are going to come, you know, 19:13 sitting here, you know, that AI is going 19:15 to enable, I think is low. I think the 19:18 need for like more creativity jobs um is 19:22 going to go way up and the kind of need 19:25 for kind of jobs to process work for the 19:30 creatives will probably uh go down in 19:32 some ways but um I'm not even sure about 19:35 that. You know, we've had AI going right 19:38 imageet was what 2012 and then natural 19:42 language stuff and Burton and all that 19:44 was like 2015 and then you know chatbt 19:46 was 2022 and like where's where's all 19:50 the job destruction? 19:52 You know why hasn't it happened yet? And 19:55 why are you so [ __ ] sure it's going 19:56 to happen next? And why are you so sure 19:58 no jobs are going to be created? I don't 20:00 think it's nearly as predictable as 20:02 people are are saying. How would you 20:04 describe the nature and scope of your 20:06 ambition over the next 10 20 years? 20:10 >> One of the things that I learned um so I 20:13 had a mentor who's a a great great CEO 20:16 by the name of Andy Grove. Um and he was 20:19 uh the CEO of Intel and he kind of 20:22 famously did the the major pivot of them 20:24 out of the memory business into the 20:26 microprocessor business. Maybe the 20:27 greatest tech CEO we've had. Uh and one 20:30 of the things that he he said that you 20:34 know in a way is very obvious but I 20:35 think is um also profound is if you're 20:41 the leader in the industry then the 20:43 growth of the industry is dependent on 20:45 you. Um like you it's up to you to 20:48 expand the market like nobody else is 20:49 going to do it. Uh and so when I think 20:53 about the firm I think of it a lot in 20:54 those terms. The reason America is 20:56 America and and there's many narratives 20:58 on this, but like I think the factual 21:01 one is like we won the industrial 21:04 revolution. We really did. We had Henry 21:07 Ford and we had Thomas Edison. We had 21:08 like great entrepreneurs. They built 21:10 great technology. The technology lead to 21:13 a military lead, led to an economic 21:15 lead, led to cultural dominance. None of 21:17 that was by accident. And had we not had 21:22 all those inventions, had all those 21:23 companies, um, which led to, you know, 21:26 everything from like winning World War 21:27 II, we just won't be, we'd be some other 21:29 thing. We won't be America. So, we're 21:32 there again. Like, this is the 21:35 equivalent change of the industrial 21:37 revolution in terms of how everything 21:39 works, governments, societies, 21:40 businesses. And you know, we're either 21:44 going to be uh the leader of that 21:47 technology, the provider of that 21:49 technology, or we're not. And if we're 21:52 not, we're not going to be um the 21:56 economic superpower, the military 21:58 superpower, the cultural influence, the 22:00 kind of standard of the world that we 22:02 are now. At least I think that would be 22:06 bad. I think you know uh America's been 22:08 kind of good for the world and good for 22:10 giving people a chance like we talked 22:12 about before and so our role in that you 22:14 know you know taking it try try and be 22:17 humble with the role but our role is 22:19 like from a policy standpoint from a 22:21 funding standpoint from a helping people 22:23 build standpoint to make sure that that 22:26 next set of great companies comes out of 22:29 uh America or allied nations a core 22:31 ambition is to do our part in kind of 22:33 helping that 22:34 >> I want to ask about some of the 22:35 ingredients to do that. Well, but just 22:36 as a quick sidebar on Andy Grove, uh his 22:40 book is incredible. Like everyone should 22:41 read High Output Management. Um what was 22:44 it about what what very specifically did 22:46 you learn from him? Like what did you 22:47 see him do that impacted the way that 22:49 you think or behave? 22:51 >> Well, like I'm so overly influenced by 22:53 him, it's hard to even pin it down. But 22:55 so high output management, you know, I I 22:57 actually wrote the new forward um for 22:59 it. Uh which I actually think that's the 23:02 best thing I ever wrote was a forward 23:04 high output management. Um, but the hard 23:06 thing about the reason I wrote that 23:08 forward was um I, you know, it was my 23:11 favorite book and I wrote uh the hard 23:14 thing about hard things was basically 23:16 intended to be um the updated version of 23:19 it. But the the thing in high output 23:22 management that um he did so well that I 23:27 I tried to you know kind of do my own 23:30 version of is 23:33 you know the the the concepts of 23:35 management are easy like 23:41 they you need an eighth grade education 23:43 maybe to kind of understand management. 23:46 It's not like physics. It's it's pretty 23:48 simple. Uh but the psychological part of 23:52 it is extremely difficult particularly 23:54 for a young person to be able to do. Uh 23:58 you know it's it's super 24:00 confrontational. You're having to kind 24:03 of look through the conversation you're 24:05 having to the entire organization. You 24:08 really have to be confusion at times. 24:09 The the good of the of the whole 24:12 supersedes the good of the individual. 24:14 uh and all these things are are really 24:17 complicated to do. His big influence on 24:19 me was me trying to not only absorb that 24:22 but then kind of tell it in a more 24:25 up-to-date kind of modern way. I went to 24:28 visit him. He had this award on the wall 24:30 which was it was literally like um 24:35 manager of the year from for the Santa 24:39 Clara facility of Intel and it was from 24:43 I don't know 1992. I'm like Andy we're 24:46 like the biggest CEO in the world like 24:48 why did they give you the manager of the 24:49 year award for the Santa Clara facility? 24:52 And he goes, "Oh man." He's like, "You 24:54 know, Santa Clara was like the always 24:57 scored like it was the lowest quality 24:59 scores, the lowest [ __ ] score on 25:02 everything at Intel." And so I was just 25:04 like, "I'm going over there and talk to 25:06 them." So I go over there 25:09 and he said, "I brought a roll of toilet 25:12 paper and I put it under my desk, under 25:15 my chair." And you know, I said like, 25:18 "When are you going to get this facility 25:21 up to code?" 25:23 And they just started in with all this 25:27 [ __ ] [ __ ] [ __ ] [ __ ] 25:30 [ __ ] And I [ __ ] reached under my 25:33 chair and put all the toilet paper up. I 25:35 said, "Clean up your [ __ ] 25:39 and tell me when the [ __ ] you're going 25:40 to be up to code." And in two months, 25:42 they were up to code. and they were 25:43 always the highest rated facility 25:45 thereafter you know just on that. Uh so 25:47 they gave them manager of the year for 25:49 that. 25:49 >> When did you first experience the 25:51 lessons that drove his success this 25:54 confrontational psychologically 25:56 difficult aspect of management yourself? 25:58 How would you encourage other people to 26:00 like get a get a taste of it? You can't 26:02 just read about it. Obviously 26:04 >> what happens uh to founders is you 26:07 invent something right now. I've got to 26:09 build a company. you don't know what 26:11 you're doing and you make mistakes and 26:14 then those mistakes really cost the 26:16 company and you lose confidence and that 26:20 leads you to hesitate and that 26:24 hesitation is what kind of causes the 26:27 failure mode. So then either like the 26:30 company's indecisive or they get very 26:32 open. All these guys got so open to 26:34 input from their team and their 26:36 executives and like but you know the 26:39 team doesn't have the full context. Only 26:41 the leaders got the context. So even if 26:43 they're smarter than you, you still 26:46 likely can have better judgment because 26:47 you have all the knowledge. Um, but you 26:51 know, they defer and then if you defer 26:53 to people who work for you, then that 26:56 kind of creates a weird political 26:58 situation because people jump into the 27:00 vacuum of like, you're not making the 27:02 decision, I'll make the decision. And 27:03 then that feels political to everybody 27:05 else. And so that's the pattern people 27:08 run into. And so, you know, you really 27:11 kind of have to build up enough 27:14 confidence in them to have that 27:17 confrontation. The hardest version of 27:19 this, by the way, is the reorg. Uh 27:21 because reorg is basically you're 27:23 redistributing power to make the company 27:25 work better, to like have communication 27:27 be better, to not have as much conflict. 27:29 But what's going to happen is somebody 27:31 who's really good, who you've had for a 27:32 long time, is going to lose power and 27:35 they're going to be [ __ ] pissed. Um 27:38 and so then if you compromise the 27:39 organization so they can maintain their 27:42 power then you've just kind of 27:45 redistributed power from the people 27:47 doing all the work to the executives and 27:49 that's a catastrophe. So it's always 27:52 that kind of thing where people don't 27:55 want to have that conver confrontation. 27:59 They don't want to tell that person look 28:01 the organization's here. you you helped 28:04 us tell here, but like you either have 28:06 to be happy in this new role or it's 28:08 going to be a rap. When you're young and 28:10 inexperienced, you know, it's going to 28:11 hurt to like tell him that, but I don't 28:16 know it's going to help me to do this 28:17 reorg because I don't I'm not 28:19 experienced enough to know that. I've 28:21 never done that before. And so I'm going 28:24 to go with the known avoid hurt to the 28:26 to the theoretical avoid hurt. Um, and 28:29 that's when you wreck your company. And 28:30 and and so that's the pattern. And I, 28:32 you know, I always do my best to like 28:34 lend them my experience on that. 28:36 >> You you were lucky that when you started 28:38 Andre and Horwitz, you and Mark had both 28:39 had tons of operating experience both 28:41 together. 28:42 >> Yeah. I still didn't know what I was 28:43 doing as CEO. 28:44 >> Fair enough. 28:45 >> And he didn't know what he was doing 28:46 either. Like his ideas now, like if you 28:49 ask Mark about management now, like he's 28:51 so different than how he actually did 28:52 it. Um, and it actually makes him mad if 28:55 you talk about it too much because he's 28:56 like, "I got such bad [ __ ] advice. 28:59 They told me to hire all these guys." 29:00 How do you think he's most different? 29:01 Like what would he say is or what do you 29:03 observe to him to be the most different? 29:06 >> I just think he's like way more um 29:10 in control of his own. Like Mark is 29:13 super emotional person. Um and he's just 29:17 way more in control of it than he was 29:19 then. Uh just in terms of just like the 29:21 personality. He used to be like zero or 29:23 100, right? Like so he would be like 29:25 full of emotion like what the [ __ ] are 29:27 we doing? or like I'm just not gonna say 29:29 anything like but nothing in between. 29:32 >> Something I know the least about about 29:34 your firm is like the first I don't know 29:35 what period of time three days, three 29:38 months, three years. 29:40 >> And I'd love to hear about how you 29:42 thought about the business right as it 29:44 was getting started. Of course, we're 29:46 I'm going to come back to what it is now 29:47 and and those ingredients you mentioned 29:49 for having the impact you want to have. 29:51 But uh lots of this is an incredible 29:54 part of the world. Silicon Valley, Wall 29:56 Street, you know, these are institutions 29:57 that make America great. Lots of people 29:59 listening have ambitions to do this sort 30:01 of thing. And I'd love to hear like the 30:04 very very ear early primordial case 30:06 study. Yeah. 30:07 >> Of what it was like and what kinds of 30:08 conversations you were having and what 30:10 your initial ideas were. 30:12 >> So venture capital, first of all, you 30:14 kind of have to understand the the 30:16 context of it was um there hadn't really 30:21 been new top tier venture capital firms. 30:24 So like the the last one before we 30:28 started that you would say is top tier 30:30 was probably Benchmark which ostensibly 30:33 started in 1995 but it didn't really 30:35 because all those guys came from another 30:37 firm called Merryill Pickard 30:39 >> and that firm was like from the 80s and 30:44 there there hadn't really been a new one 30:46 from the 80s and if you looked at why 30:48 every VC was kind of reputationbased and 30:51 so to be top tier you had to have 30:53 invested in Apple and Cisco and Google 30:56 and you know Yahoo and all the great 30:58 companies and you can't from a standing 31:00 start get to that and then if you're not 31:02 top tier in VC you're not going to last 31:05 because yeah in a super hot period 31:08 everybody makes money but the best 31:10 entrepreneurs will only work with the 31:11 top tier firms because that's how you're 31:14 going to recruit great engineers that's 31:16 how you're going to get follow-on money 31:18 like everything comes out of that so 31:20 you'd never take money from a tier two 31:22 if you could get it from tier one and so 31:24 That's why the tier ones always have 31:26 better returns. Um, so we knew we had to 31:28 be tier one, but we had that problem. 31:31 And the idea that we had was, well, 31:35 venture capital is a great product for 31:38 LPs, um, but it's not a great product 31:41 for entrepreneurs. And so, if we could 31:42 build a better product for 31:44 entrepreneurs, then we could win. And 31:47 that was like the the original kind of 31:50 framework. And the idea that we had for 31:52 the product for entrepreneurs was you 31:54 know because we had been entrepreneurs 31:55 was around what you and I had been 31:57 talking about which is well 32:00 if you're like a founder who wants to 32:04 run their own company you're not getting 32:06 much like you need so much you don't 32:09 have the confidence you don't have the 32:10 knowledge you don't have the knowhow you 32:11 don't have the network. Um what if we 32:13 built a firm that like was designed to 32:16 give you enough confidence, power, 32:20 network reach, advice that you could 32:24 actually be a CEO. And so that was the 32:26 whole idea behind the firm originally. 32:29 And then the second idea we had, which 32:31 was the other thing, like VCs didn't 32:34 ever market themselves at all because if 32:37 you're all based on your investing track 32:40 record, it's best that it's just magic. 32:42 Like why say anything? Like keep that a 32:44 secret. And so they weren't talking. And 32:47 so when we went out and talked, like 32:50 everybody covered it. So we instantly 32:52 everybody knew we had this product. 32:54 >> Where did that where did the germ of 32:56 that specific idea come from? like let's 32:58 be fairly loud relative to what others 33:00 do from the very beginning. 33:02 >> Well, it's funny because you know Mark 33:03 and I were talking about it. He said to 33:05 me, he's like why don't VCs market and 33:09 actually it the original thing went all 33:12 the way back to kind of the first uh 33:15 class of VCs which were the investor 33:18 revolution VCs were JP Morgan, 33:21 Rothschild, Goldman Sachs etc. right 33:23 like they were the ones financing these 33:25 things. Um, and it turned out that 33:30 these guys were financing both sides of 33:33 World War II. Um, and so they really 33:37 didn't want any publicity because that 33:38 would have been like an extremely 33:40 [ __ ] bad uh thing. To a large extent 33:43 that just carried over all the way 33:45 through Arthur Rock and 33:47 >> and all these things and then, you know, 33:48 the reputation thing clicked in and it 33:50 was working, so there was no need to do 33:52 it. Um, and we got a lot of criticism 33:54 when we did it. our LPs would say, you 33:57 know, like the other VCs say, you guys 33:58 are egoomaniacs. You name the firm after 34:00 yourself. You're marketing it like this. 34:02 And it was so funny because the reason 34:04 we named the firm after ourselves is 34:06 when we try we raised money in 2009, 34:08 which is right on the, you know, edge of 34:11 the financial crisis. And the big 34:13 objection from LPS was, well, like you 34:16 guys are like really good entrepreneurs. 34:19 You're just going to leave this thing 34:20 and go build another company and then 34:22 we're going to be stuck with the fund. 34:24 And we couldn't get them off of that. 34:26 And so then I had the idea. I was like, 34:28 "Well, why don't we just name it with 34:30 our names and then they know we're 34:31 safe." 34:32 >> Yeah. 34:32 >> And and that worked. 34:34 >> As your business grows, Vanta scales 34:36 with you, automating compliance and 34:37 giving you a single source of truth for 34:39 security and risk. Learn more at 34:41 vanta.com/invest. 34:43 Ridgeline is redefining asset management 34:45 technology as a true partner, not just a 34:47 software vendor. They've helped firms 5x 34:49 and scale, enabling faster growth, 34:51 smarter operations, and a competitive 34:52 edge. visit ridgelineapps.com 34:55 to see what they can unlock for your 34:56 firm. 34:57 >> Um, if you think about the the period of 35:00 takeoff of the firm in 2009 up until you 35:03 reach, let's call it like cruising 35:05 altitude, like when was cruising 35:07 altitude and and and what was the most 35:09 difficult part about getting it from 35:11 takeoff to that point? 35:12 >> Well, I mean, the first thing is we 35:13 really didn't know that much about 35:14 investing. Mark and I had done some 35:16 angel investing, but we not neither of 35:18 us had any venture capital experience. 35:20 And like you know, credit to Sequoia, 35:24 credit to, you know, Greylock and and 35:27 and Kleiner and all the guys who were 35:29 around at that time, you know, they just 35:31 had years and years of of doing it. We 35:34 made more than our fair share of 35:36 investing, 35:37 mistakes, you know, missing things we 35:40 should have done and and then, you know, 35:43 doing things that we shouldn't have 35:44 done. Um, but missing things that we 35:47 should have done was probably the bigger 35:48 one. And 35:50 you know and then the other thing is is 35:52 that kind of how we thought about the 35:54 profile of the investor was wrong. So we 35:57 so overindexed on our idea that we had 36:02 to help the founder uh become a CEO that 36:05 we made it a requirement that you 36:06 couldn't be an investor at Andre and 36:09 Horowitz if you hadn't like founded and 36:12 or run a company. And that, you know, 36:17 was a very good attitude and and set the 36:19 culture of the firm in a lot of ways and 36:21 had good uh things that came from it. 36:23 But I would just say that like most CEOs 36:29 aren't as interested in investing as 36:31 they think they are. Uh and then also 36:34 most CEOs aren't as good at helping 36:37 somebody else learn the job. Uh and so 36:39 those two things ended up being, you 36:42 know, not quite correct. Uh, so we made 36:45 some adjustments, you know, fund one 36:46 just went really well because we, you 36:48 know, we hit the scene hard. It was a 36:50 small fund. We did Skype, we did Slack, 36:52 we did Octa. I mean, like, there was 36:55 Stripe was in there. Like, so like there 36:58 were just too many good things in a $300 36:59 million fund for that thing not to blow 37:01 the doors off. Fund two wasn't as good 37:04 as one. Um, and then by the time we got 37:07 to three, that's when we had uh the 37:12 contention among like, oh, we really 37:15 don't have the right profile for GP 37:18 here. Uh, and there was a while where we 37:20 thought that was going to be a terrible 37:21 fund. It ended up being a great fund 37:22 because we had, you know, Coinbase and 37:24 Data Bricks and Lyft and GitHub, but 37:27 that that one was scary for a while, but 37:29 coming out of that, we kind of knew 37:32 what the firm needed to be. Uh, and so I 37:37 think it was settled down after that, 37:38 you know, it wasn't such a like startup. 37:40 It was like, okay, we got across that 37:43 chasm. But then the bigger thing was we 37:47 always had this idea about software is 37:49 eating the world. um and you know which 37:51 is Mark articulated really well in his 37:53 uh 2011 piece. And so we always felt 37:57 like venture capital firms 38:02 needed to be able to scale uh and that 38:05 the other firms would have trouble 38:06 scaling because of uh the way they 38:09 worked, the way they shared control. Um 38:11 so that could be an opportunity for us. 38:13 Um but we hadn't figured out how to do 38:14 it yet. And then I'd say um starting 38:19 with like the bio and the crypto fund I 38:22 started to get to the kind of 38:23 organizational picture of how we would 38:25 become um be able to address every 38:28 market of technology. Uh and but with 38:33 investing teams that weren't 20 people 38:36 like that doesn't work. So you need an 38:38 investing team of like four or five 38:39 people but you you have to you can't 38:42 address the whole technology market with 38:44 five people. So you have to have 38:45 multiple teams. Having multiple teams in 38:49 a venture capital firm, it was a little 38:51 bit of a novel idea particularly when 38:54 each team has like a platform that helps 38:57 the founder build the company. And so we 39:00 began it really in earnest with the 39:02 crypto fund I think like around 2018 and 39:05 then uh and you know now the whole firm 39:07 is kind of organized that way. If you 39:09 think if we zoom now to today and back 39:11 to what you said which is that the scope 39:13 of your ambition is big as the leader be 39:15 the one to help be the ones that are 39:17 expanding the market. What are the 39:19 components of doing that? Like what is 39:20 the system need that it doesn't 39:22 currently have that you might be able to 39:24 provide? One is um the the capital 39:29 markets are have changed you know 39:33 dramatically with 39:36 not much um 39:39 help. So I went public at 18 months old 39:42 with $2 million in trailing revenue. 39:44 That wasn't a good idea. But companies 39:46 used to go public routinely with $50 39:48 million in revenue. It was fine. Um you 39:51 know now nobody's going public 39:54 >> a billion, right? like you you get to go 39:56 public or something like that and if and 39:58 you're kind of small if you don't have 40:00 that and so you kind of need a lot more 40:03 out of the private markets than VCs are 40:06 built to do and so you know that's one 40:08 of the kind of things we have to think 40:10 about. Another one is the companies in 40:13 the portfolio 40:15 you know they'd leave you at 100 million 40:17 in revenue. They're going public. 40:19 They're out to the races. Well that's 40:20 not true anymore. And so what do you 40:23 need when you get to be 200 million 300 40:26 million in revenue? Well, you need to be 40:28 multi-product, you need to be multi- 40:30 channelannel, you need to be multi- 40:32 geography. Um, so as a venture firm, you 40:36 know, we need to help them and as a 40:38 venture industry, we need to help them 40:39 do that. Like how do I get to Japan? How 40:42 do I uh get to South America? Like 40:46 most venture firms don't provide much 40:49 along those lines. So we kind of have to 40:51 step up to those ideas if we're going to 40:53 have companies in the portfolio at that 40:56 stage. 40:56 >> Do you hope that over time your firm and 40:58 maybe some others like it that have 40:59 become these big institutions and 41:01 venture go on to be sort of like the 41:03 Blackstone you know Apollo type 41:05 companies that are big publicly traded 41:07 you know enduring businesses. 41:09 >> A big huge wave in among venture 41:12 capitalists is uh private equity AI 41:17 rollups. It's a good business idea like 41:18 a really good business idea which is 41:20 okay 41:22 you know just like the spreadsheet kind 41:24 of created the original private equity 41:26 business AI is kind of creating a new 41:29 private equity business where you can 41:30 buy any existing company optimize it 41:33 with AI and it'll be more valuable. 41:37 That's a good idea. It's a good thing to 41:40 invest in. It's not something we're 41:42 going to do for two reasons. Um, one, 41:45 it's like the cultural opposite of who 41:48 we are. So, we're about building new 41:51 things, um, growth, 41:55 believing in the entrepreneur, price 41:57 doesn't even matter. As long as the 42:00 thing succeeds, you're going to do well. 42:02 Private equity is like entry price is 42:05 key. like the I mean know I I had a 42:09 great dinner with Mark Rowan who's a 42:10 super genius uh runs Apollo and he was 42:13 like entry price entry price entry price 42:15 you know we never even think about that 42:17 we think about it but it's not like 42:19 first and foremost at all like thinking 42:22 about containing cost and this and that 42:24 and the other that's just not like what 42:26 a good venture capital frame of mind is 42:28 so like culturally I didn't want to mix 42:31 those two things but more than that like 42:33 I just didn't want to be in a business 42:35 where the way you make money is you 42:37 figure out how to optimize an existing 42:40 thing and you know lay off people and 42:42 that kind of thing. We're about like new 42:44 technology companies building the future 42:46 um taking things forward and I'll leave 42:49 that to the other smart guys in the 42:52 industry. 42:52 >> What if any trade-offs ex feel like they 42:54 might exist at this scale as you 42:56 continue to scale as you consider all 42:58 these different people you're trying to 43:00 serve? Well, the investors internally, 43:01 the LPs, the founders, so many people 43:03 need to nothing's perfect. like what 43:05 what are the trade-offs to the path that 43:08 you've chosen? 43:09 >> I think you know with any scale of 43:11 organization you really have to over pay 43:14 attention to culture um or the culture 43:17 will drift. We probably spend um more 43:21 work on that than than any venture 43:23 capital firm. I'm like you're not 43:24 allowed to join unless you sign the 43:27 culture document. I I spend an hour with 43:29 every single employee teaching them the 43:31 culture. Like it's like that level of 43:33 investment. Um, and then you know we 43:37 really try to enforce it uh hard when we 43:40 can and you know we have pretty good 43:42 consistency but like that's that is hard 43:44 to maintain as you grow. 43:46 >> Can you teach me more about culture? The 43:48 the you've written a book about it. 43:49 You've you've built them. You've studied 43:52 some very interesting cultures that you 43:53 wrote about in the book. if you had to 43:54 teach a seminar or something on like 43:56 what a culture is in the first place and 43:58 then how to design one given what you do 44:02 and who you are and then how to you know 44:04 make sure it it people live by it. 44:06 >> Let me give you kind of like the 44:09 a small but like the probably the most 44:12 important insight which is from Bashidto 44:15 the way of the warrior from the samurai. 44:17 Uh a culture is not a set of ideas. It's 44:21 a set of actions. Um, and so if you 44:24 define your culture as a kind of set of 44:27 ideas, integrity, do the right thing, we 44:31 have each other's backs or any kind of 44:33 like these ideas, they call them 44:34 corporate values, it's actually just a 44:37 bunch of [ __ ] platitudes, it doesn't 44:38 mean anything. The culture has to be 44:40 defined in terms of the exact behavior 44:43 that you want that support that idea. 44:46 What do you have to do to actually be 44:49 that thing that you want to be? And so, 44:52 and it's the little things, you know, 44:56 how responsive are you to your 44:59 colleagues? What's the SLA on returning 45:02 a Slack message or an email? Do you show 45:04 up to meetings on time? Um, 45:07 and and this is like not everybody has 45:09 those ideas, but if you want that idea, 45:11 it's got you've got to manifest it 45:13 through something else. So, like we have 45:15 an idea about like you have to respect 45:17 the entrepreneur. Well, what is that 45:20 behavior? Like one, you can't ever be 45:23 [ __ ] late to a meeting with an 45:24 entrepreneur. I used to find people $10 45:26 a minute in the beginning of the firm to 45:28 reinforce it. And then uh you know, you 45:31 have to get back to an entrepreneur. If 45:34 you say no, like you have to say no. You 45:36 have to explain why you're not 45:37 investing. Um and you know, it has to be 45:41 clear. And we're going to survey that 45:42 entrepreneur after you um say no to make 45:45 sure that you said no and that they had 45:47 a good experience. So like that that's a 45:49 behavior. If you uh try to make yourself 45:53 look good by making an entrepreneur look 45:57 bad, you're fired. So like you get on X 45:59 and say, "Oh, he's selling dollars for 46:01 85 cents." No, no, no, no, no, no, no. 46:04 We're dream builders. We're not dream 46:06 killers. [ __ ] that. We're Somebody wants 46:08 to do something larger than themselves. 46:11 Build a company, you know, make the 46:13 world a better place. We're for that. we 46:14 don't give a [ __ ] what the idea is, you 46:17 know, and or if Sequoia funded them or 46:19 whatever. We'd love that. That that's 46:21 who we are. And so the behavior 46:25 is the culture is the actual thing and 46:29 that gets you the idea as opposed to the 46:32 idea and then figure out how you're 46:33 going to behave. And so that's probably 46:35 the main thing on culture. 46:36 >> Can you say more about the influence 46:37 your dad had on you? You mentioned that 46:39 lesson of nothing's fair or life isn't 46:40 fair. 46:41 >> Yeah. 46:42 >> Tell me about your dad. He was what's 46:44 known as a red diaper baby. Uh he uh my 46:47 grandparents were communists. Like they 46:50 went to secret meetings. They had cards. 46:53 My grandfather was fired during the uh 46:55 McCarthy era from being a a junior high 47:00 school teacher um you know for being a 47:03 communist. And he grew up a communist. 47:05 And he started out um on the left. He 47:08 was uh editor of a there's very famous 47:10 new left magazine called Ramparts 47:12 magazine which he was editor of and he 47:14 uh 47:16 you know was involved in the the Black 47:19 Panthers with Huey Newton and um you 47:21 know the Oakland chapter Eldrich Clever 47:24 and he sort of dropped out of politics 47:25 and he reemerged 47:27 um I guess probably eight years later on 47:30 the right. He really understood kind of 47:32 the ills of communism and socialism 47:34 which which helped me a lot. Like one of 47:36 the things that he said to me that uh 47:38 always stuck with me. He's like, "Son, 47:40 go to the library, 47:44 pick any book on socialism. There's 47:45 hundreds of books. And in that book, I 47:49 guarantee you, you will find page upon 47:52 page, chapter upon chapter of how to 47:55 divide the wealth. You will not find a 47:57 single sentence on how to create how to 47:59 make it." And I was like, "Oh, wow. 48:02 That's not like a very good system, is 48:04 it?" I learned a lot about systems 48:07 thinking from that uh which I you know 48:09 ended up being I'd say very helpful to 48:12 me as uh CEO. He wasn't like uh you know 48:18 this this this new age father he wasn't 48:19 like that you know in the old days your 48:21 father like they wouldn't even talk to 48:22 you till he got to be like 12 and uh you 48:26 know and then you get these little 48:27 snippets of wisdom and like one of the 48:28 ones I actually put in the hard thing 48:30 about hard things but I had uh you know 48:32 I had three kids I was young um and I 48:36 remember there's like 102 degrees the 48:38 air condition was broken the kids were 48:40 going crazy like one of them poured a 48:41 whole bottle of apple juice like a 48:43 gallon of apple juice into the rug 48:45 Apple juice is steaming out of the car. 48:47 But I'm just sitting there looking like 48:48 I was going to die. And my father looks 48:51 at me and he goes, 48:53 "Son, 48:56 you know what's cheap?" I said, "What?" 48:59 He goes, "Flowers. 49:01 Flowers are cheap." I said, "Okay." He 49:03 said, "You know what's expensive?" I 49:05 said, "No, what?" He said, "Divorce." 49:07 And, you know, he had been uh married 49:10 four times, so he knew what he was 49:12 talking about. 49:13 >> Yeah. As you look out today in the 49:14 world, I'm curious what things are 49:16 captivating you most and maybe even like 49:18 most inspiring you. You get you have 49:20 such an interesting perch. You get to 49:22 see 49:23 >> so much at the frontier. 49:26 >> What's going on in coding now is like 49:27 quite phenomenal. You know, like we kind 49:30 of went through this period where like, 49:31 okay, AI can write code, cool. Okay, you 49:34 can vibe code stuff with a lot of 49:35 security holes, fine. Um, but I think 49:39 over the break, over the kind of winter 49:42 break, 49:44 it turned a corner where like really 49:47 really good programmers were going, 49:49 "Whoa, 49:50 >> oh god, 49:50 >> this is this helps me." 49:52 >> Like I just became a hundred times more 49:55 productive and I can't remember any kind 49:57 of 49:58 >> technology where like just all of a 50:01 sudden you wake up and everything the 50:02 whole world just changed like that. And 50:05 that's happening on a 50:08 pretty regular basis I would say. And 50:10 then you know you know we spent a bunch 50:12 of time with uh people in Hollywood who 50:13 are using AI. I think AI will help you 50:16 make movies both better and at much 50:19 lower cost because you can do you know 50:22 you can shoot a scene and then have the 50:25 AI do a variation of that scene. That's 50:27 very very good. Um, and so you don't 50:30 have to do, you know, the really like if 50:33 you're an actress, you have to shoot a 50:35 scene like 15 or 20 times or something. 50:38 Uh, wouldn't it be nice to shoot it 50:39 three times and then you just like take 50:41 the pieces you like and make it what you 50:42 want? Uh, so it's I I think it's a 50:46 little underestimated as a tool for 50:49 creatives. I think um, and I think 50:51 that's true in music, too. You know, I 50:53 was uh kind of a young person when 50:56 hip-hop started and 50:58 the the huge criticism like this is not 51:00 music. They're just taking music and 51:02 they're like remixing it um and they're 51:05 rapping over it and it's a bunch of 51:06 [ __ ] like it's a novelty. But it was 51:08 postmodern art and I think we're going 51:11 to get into uh kind of postmodern art 51:14 with like what people will be able to do 51:16 with AI and music. And that was like one 51:18 of the most exciting 51:21 times in music. Like the invention of 51:23 the new art form is when it gets really 51:26 exciting. 51:27 >> What people in hiphop specific people 51:31 have had the largest impact on you 51:32 personally and and how? Nas is a very 51:35 good friend of mine and um he uh 51:41 he's definitely had a big impact. Just 51:43 the the lens at which 51:46 he sees the world is so 51:51 different um and interesting for me. So 51:55 we're both like very big fans of Rock 51:57 Kim who is kind of like the John Cold 52:00 Train of rap. So, Rak Kim had one of his 52:03 first big song was a song called My 52:04 Melody. And uh Nas and I are listening 52:08 to My Melody and the the first line is 52:12 turn up the bass, pull up a chair, hand 52:14 out a cigar, I'm letting knowledge be 52:16 born. I'm my name. And so he puts it on, 52:19 hands out a cigar. Uh and he pauses it 52:22 and he goes, "Ben, 52:24 why is he handing out a cigar?" And I 52:27 go, "I don't know why." Then he plays an 52:29 line. I'm letting knowledge be born. and 52:30 he's like, "It's a birth bin. He's 52:32 passing out cigars at the birth of 52:33 knowledge." And I was like, "Oh [ __ ] I 52:35 listen that song a thousand times. I 52:36 never heard that." Um, and 52:41 I can't tell you how many times like he 52:44 sees or hears something uh that's there 52:47 that I don't see. So having, you know, 52:50 somebody that I can talk to who has just 52:52 like a completely different perspective 52:54 of all things in life. Um, and it was 52:57 interesting, you know, uh, we did the 52:59 Coinbase deal together, uh, and he had 53:03 called me like two weeks prior to us 53:06 really kind of, um, seeing that, uh, 53:10 because he wanted to learn about 53:11 Bitcoin. So, you know, I explained to 53:13 him how it worked and, you know, he was 53:14 very interested. And then, uh, 53:17 you know, when I was talking to Chris 53:19 Dixon, who was working on the deal, I 53:20 was like, "Tell me about the guys." And 53:21 he's like, "Well, you know, one of them, 53:22 Fred, is like really into hip-hop." I 53:24 was like, "Okay." And so, you know, I 53:26 brought Nas over over to my it's like 53:28 have him come over to my house. There's 53:29 a boxing match on Saturday. You know, I 53:31 had Nas come over and like that's how we 53:34 got that deal. Um, 53:35 >> wow. 53:36 >> But, uh, yeah, he he's just like a I 53:38 would say a big influence on me 53:40 personally. And then he's such a you 53:42 know I um you know I as a whatever as a 53:47 leader and so forth like storytelling is 53:50 and a writer um is important to me and 53:52 he I I think he's one of the great 53:54 storytellers of all times you know like 53:56 just a super genius on that. 53:58 >> Is there a CEO comparable to Nas where 54:01 you know there's this class of guys in 54:03 the 90s where Jay-Z you know I'm not 54:05 just a businessman I'm a businessman. 54:07 Um, and there were these just massive 54:10 franchises that got born. These guys all 54:12 became incredibly successful in the 54:14 business world. And it it felt more um 54:16 like industrialized almost like the 54:18 whole process whereas not like even just 54:20 his album that just came out is it it 54:22 feels just like Ilmatic feels like it 54:25 could have come out then or now. It's 54:27 like this weird timeless quality. He 54:28 still has that somehow. And like 54:30 Premiere, same thing. 54:31 >> Yeah. 54:32 >> Do you know anyone else like that in 54:34 another domain? He seems like such a 54:36 unique 54:38 >> person relative to his peers. 54:41 >> Maybe Jensen Jensen has like this like 54:44 very defined, you know, agree with it or 54:47 not, but it's like this view of 54:50 who he is, what the company is, and so 54:53 forth that's kind of gone 54:56 across 54:58 eras. Um, 55:01 but it's still the same thing, right? 55:03 Like it's not that like it played in 55:06 gaming, it played in Bitcoin, it plays 55:09 in AI, but it it's still Nvidia. Like 55:12 it's not he never thought he had to 55:15 change the name of the company. He's 55:16 gotten better over the years, but in a 55:18 weird sense, it it never felt like he's 55:20 trying to be current, 55:22 which like Nas never kind of feels like 55:24 he's trying to write a hit. 55:27 >> Can you tell the story of the work 55:29 you're doing with the Vegas Police 55:31 Department? And I'm asking about this 55:32 one because it's super interesting, but 55:34 also because it feels like uh an 55:37 interesting different kind of example of 55:39 what the application of this 55:41 constellation of new technologies might 55:42 allow for in terms of improvement 55:44 efficiencies. You know, it's just such 55:47 an interesting case study. 55:48 >> A couple things about the Las Vegas 55:50 Police Force were intriguing to me. The 55:52 biggest one was uh they they're kind of 55:55 they were different than other police 55:58 forces in the country because they're a 55:59 big metropolitan area that's not run by 56:01 the chief of police, but run by the 56:03 sheriff. And the reason that's important 56:05 is the sheriff uh is an elected official 56:09 and does not report to the mayor. So 56:11 they never got caught in the big 56:14 political movement and defund the police 56:15 and they were the one of the only cities 56:17 that didn't reduce the police budget or 56:20 anything like that. So they kind of 56:22 stayed intact and they're also 56:23 interestingly the one or the one that I 56:26 knew that never militarized and they do 56:28 community policing and you can see it in 56:30 the numbers. So the murder clearance 56:33 rate in Las Vegas is the highest murder 56:36 clearance rate meaning they they uh 56:38 solve the murder 94%. 56:41 >> And you know I think San Francisco is 56:43 like 75% and then Chicago's like in the 56:45 30s and the national average is below 56:47 60. And I asked him, I said, you know, 56:49 why why is your murder clearance rate so 56:52 high? And the sheriff, Kevin McMahill, 56:56 said, "Ben, 56:59 you know, when somebody is murdered, 57:01 there's always somebody who knows who 57:02 did it. They just don't talk to the 57:04 police." So, but they talk to us because 57:06 we're part of the community. Like, they 57:08 know us. And so, I was like, "Wow, 57:10 that's a great kind of environment to 57:13 see if this new technology worked." And 57:14 I knew about all the public safety 57:16 technology because we invested in 57:17 through American dynamism. So I I was 57:20 like, look, we're going to become the 57:22 highest tech police force in America, 57:24 hopefully the world, and I'm just going 57:26 to fund it. And so I bought, you know, 57:28 we've got a drone program and we've got 57:30 u, you know, prepared 911 and we've got 57:33 flag safety, you know, AI cameras. If a 57:35 an emergency call, if a 911 call comes 57:38 in or if a gunshot goes off, there will 57:40 be a drone deployed in there within 90 57:42 seconds. And then that drone uh video 57:46 feed will be in every police officer's 57:49 phone in the vicinity like instantly. 57:53 Since we started the program, I think 57:54 crime is down over 50%. Um and then uh 57:59 you know shooting of suspects by police 58:02 is down like close to 75%. But 58:05 everybody's safer. And I think this is 58:06 the thing that that was the most 58:08 surprising to me on the technology 58:11 deployment um is that so when you talk 58:14 to the police they go look the problem 58:16 is the descriptions cause like half the 58:20 violent confrontations. I'm like well 58:22 what do you mean? So somebody jacks a 58:25 car. There's a baby in the back seat. We 58:28 get a description of the car. It's a 58:31 2004 Hyundai that's blue. Well, it's 58:37 really a 2008 Hyundai that's green. But 58:39 we pull a guy over in a 2004 Hyundai 58:42 that's blue and you know that person has 58:45 had bad experiences with the police and 58:48 you know now he's got a gun in the car 58:50 and all of a sudden we've got an 58:52 incident and like an innocent citizen 58:54 gets harmed or police gets shot with AI 58:56 camera. We know that's the car. That's 58:59 it. Uh, and we know there's a baby in 59:01 the car. And so we're not sending one 59:03 guy with a gun to see if that's the guy. 59:05 We're sending a whole squad. Um, and 59:07 we're apprehending them safely. And so 59:11 everything um about like policing is 59:13 inherently dangerous, but intelligence 59:15 makes it dramatically safer. And so I'm 59:18 a huge believer in this technology for 59:21 making everybody safer. You know, 59:23 suspects, criminals, citizens, police, 59:26 everybody. The other kind of uh knock on 59:28 effect is it's kind of put the pride 59:32 back into policing. We used to have a 59:33 big problem in Vegas where uh you know 59:37 because nobody wanted to be a police 59:39 officer, we were lowering the standard, 59:41 but now the standard is really high. So 59:43 between the drone center, which is like 59:45 super state-of-the-art, and then you 59:47 have these um cyber trucks that look so 59:51 like amazingly futuristic and cool 59:53 driving around like everybody wants to 59:55 be a police now. And Las Vegas happens 59:57 to have the highest concentration of 60:00 veterans uh in the country. So plenty of 60:03 like super qualified people to choose 60:05 from. They all want to be police. Uh 60:07 it's so that's all gone really well. 60:09 >> The last question I ask everyone is the 60:10 same. What is the kindest thing that 60:12 anyone's ever done for you? 60:14 >> A mentor of mine, a fellow by the name 60:16 of Ken Coleman, who uh was a big 60:18 executive at Silicon Graphics. And when 60:20 I was um 60:23 uh I guess a sophomore in college, uh I 60:26 got an introduction into him and uh he 60:29 gave me a job as a summer intern. And 60:32 without that job, 60:34 I don't know that I ever get to Silicon 60:36 Valley or or that whole thing. So I 60:39 would say that's probably that that was 60:40 the highest impact. Just he didn't have 60:43 to do that thing that anybody did for 60:45 me. 60:46 >> It may interest you that that is the 60:48 most common form of answer. uh across 60:50 500 of these someone that like took a 60:52 bet when they didn't need to. Ben, 60:53 pleasure to finally do this with you 60:54 after a couple years of uh of watching 60:57 you and and learning from you. So, thank 60:58 you so much for your time. 60:59 >> Thank you, Patrick. It was fun. 61:05 >> You know how small advantages compound 61:07 over time. That's true in investing and 61:08 just as true in how you run your 61:10 company. Your spending system is your 61:12 capital allocation strategy. Ramp makes 61:14 it smarter by default. Better data, 61:15 better decisions, better economics over 61:17 time. 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