Inside the AI Sprint, Understanding Anthropic's Strategy | Tomasz Tunguz, Theory Ventures

12:28Turner Novak:Interesting. And yeah, because I feel like we've all seen those headlines at this point where like, you know, Meta actually had to get rid of the leaderboard, I think was one of the most recent things because people were probably doing something sort of similar where you're like gaming

12:43Tomasz Tunguz:the leaderboard yeah i mean you know you can you can use jet fuel in your car and it'll go faster but like you know will it actually go faster yeah yeah i mean you know racing car fuel octane is the um so this is a fun so hydrocarbons form all of of uh propellants right and there's hexane which is, I think it's C6H6, and then there's septane, and then there's octane, which when you get, you know, it's 87, 89, 91, and that's the amount of octane that exists within gas. And then if you get racing fuel, the octanes are much higher. They're 91, 95, 105. And so the amount of energy per unit volume is significantly higher, and your car will produce a lot more horsepower if you put racing car fuel in it because there's just the explosion is stronger.

13:39Turner Novak:So you actually will go faster. Yeah.

13:42Tomasz Tunguz:I mean, at the top end, sure. I mean, you know, make, provided that everything in your engine holds together, you know, this is not an endorsement of like putting in nitrous into your Prius and seeing if you can break 200 miles an hour. Yeah.

13:59Turner Novak:But I guess it's kind of the same thing with AI. It's like, you may have a super powered model or you're like running all these different parallel tasks, but are you even doing them properly? And like, have you set things up right to make that even worth it?

14:14Tomasz Tunguz:Yeah, exactly. So let's, let's ground this in some numbers, right? So a very small model might be a few hundred million to two, three, four billion parameter models. Those are great for dictation. They're great for grammar, cleanup, transcription, those kinds of things. Then you have the next range of models, which I would put it like the 25 to 35 billion parameter models. And they're almost anything that you can do with a computer aside from coding, you can now achieve with one of those models. And they will be faster on your laptop than they will be talking to Claude or OpenAI. They're just faster at it.

14:49Tomasz Tunguz:And then there's another class of models that's like 120 to 150 billion. Those are not that often used. And then you have the state of the art models, which are trillions of parameters. And they could do architecture and implementation of very sophisticated code or novel math discoveries.

15:08Turner Novak:And then those are the ones. So it's basically, I think I've seen, if you follow what sort of some of Dario and Anthropics positioning, it costs a ton to train these models. And as the revenue starts ramping, you start getting profitable on these models, the older models, but then they're training the new ones, which are even more expensive to make, which makes it so it looks like they're losing money, but the revenue gets even bigger. And it's kind of like these like stacking, super expensive to train work way better. So eventually we get to a point where they just start making a ton of money.

15:44Turner Novak:Is that kind of how, how this is going to go?

15:46Tomasz Tunguz:Or it's, I think it resembles pharmaceuticals more than it resembles software where you might spend three years researching a drug. And then you, I think in pharma, I'm not deep in pharma, but I think you have 17 or 20 years with an exclusive patent on whatever, the next statin to reduce cholesterol. But with AI models, like we said, you have 41 days to be state of the art. And you can see there's a company called OpenRouter, which is an open source router of model calls, you can see the share shifting pretty significantly. So in November of last year, Grok, which is the XAI model had pretty significant share

16:31Turner Novak:above 15 percentage points.

16:32Tomasz Tunguz:Today it has a lot less. And then you can see the share shift as a result of the subsidies from OpenAI or Anthropic on their different products or new models, right? The GPT 5, 6. And so you don't have 17 years to recoup your investment costs. You have to keep running faster. The other dynamic that's really important is as these models and the training data becomes larger and larger, there's a great paper that talks about how the model performance will ultimately converge. And we're seeing this. At the beginning, you could see like GPT, whatever, four was significantly better at agentic tool calling.

17:13Tomasz Tunguz:And then I don't remember exactly what the Claude model was that kind of caught up. And then Gemini was really strong in this particular domain. Maybe it was math. And another one was great at humanity's last exam, which is a knowledge retrieval benchmark. And now they've all, they're adding more and more benchmarks and they're all more complete. And the differences between them are increasingly subtle.

17:35Turner Novak:And so ultimately then the advantage is just, do you have people using it? Like it doesn't even matter how good the model is sort of because they're all the same and it's more so, is it like the behavior or like the, have you captured the workflow in some capacity or? Yeah.

17:50Tomasz Tunguz:I think we're going to get to a place where you reach a minimum viable intelligence, where if you work at any company with a computer, there's whatever, I don't know what it will be, but some minimum, let's say it's a 30 billion parameter model in late 2026. And if you have a computer that you can run it, that's good enough. It's just like, we're not giving everybody inside of a large company a state of the art laptop. Like you have a, whatever, you might have an IBM PC that's pretty good. You might have a MacBook air that's pretty good, but you're not like, you don't have an M3 ultra with 512 gigs of RAM to everybody.

18:25Tomasz Tunguz:You have like a minimum level of performance that's good enough. And then you're kind of upgrade every two years to three years, depending on your company's policy. You can imagine we get to a very similar place with models where you say like, okay, I have the current Gemma model from Google. It's 31 billion parameters and I can do most of my things on my laptop and that's fine for me. And then the frontier models, then they push into the domains of like high performance computing, math research, materials research, chemistry, and really pushing PhD level analysis further and further. And you have some companies, Dow, Corning, pharmaceutical companies who are willing to pay a huge premium for that, but everybody else will use a sort of a mid-range model.

19:13Turner Novak:When you can build anything, Amplitude lets you know how to build the right thing. Use human language to get complex answers about your products, nor manually selecting events or building charts or dashboards, just to ask. Use agents to sense changes in customer behavior, decide what's causing them, and ask you if it's okay to fix it, continuously in the background while you work. Get the answers you need while building directly in the tools you are already in, like Claude, Cursor, Lovable, and more. And for the first time, understand if your agents actually work. Measure quality, debug failures, experiment, and measure their ROI with agent analytics.

19:48Turner Novak:Amplitude. With AI analytics, all you have to do is ask.

20:18Turner Novak:these companies are being valued by the markets.

20:22Tomasz Tunguz:Yes. Okay. So when a company is growing really fast in the public markets, many, many people, not everyone, but many people value it on what's called a forward revenue multiple basis, which is a fancy word to say, estimate the revenue in the next 12 months. And then you take the market cap and divide it by that estimate of the

20:41Turner Novak:revenue growth. It's called a forward revenue, EV to forward revenue multiple.

20:45Tomasz Tunguz:Anyway, Most software company, and you can benchmark the fastest growing software company today at scale, aside from a pure model company is Palantir. And they're growing at like 60, the projected growth rate is 68%, which is mind blowing.

21:03Turner Novak:That's pretty good for their size. Yeah.

21:06Tomasz Tunguz:That's like a midsize venture scale business five years ago. But this is a publicly traded, you know, billion dollar plus revenue business growing at 80%. And they traded a very elevated multiple. people. And then if you look at anthropic, okay, so anthropic year over year grew 30 X and now it's closer to 43 X. They went from a billion in run rate to 43 billion in a year. Right. And it's just like absurd. I mean, just.

21:34Turner Novak:It was like breaks all laws of business, just like ever, like, it's just like impossible for that to happen. You'd think they were like committing like fraud or like scam or just like it's fake. Yeah.

21:45Tomasz Tunguz:They added in the month of April, they added all of snowflakes revenue plus all of Palantir's revenue in a month. Yeah. I mean, just monstrous. So anyway, so, so let's say that, you know, they grew 43 X. Okay. What do you think they'll do next? It's almost absurd. What do you think they'll do next year. But even if they're at 43 and then they get to a hundred and they're valued at 900, well, they're kind of valued around like single digit forward revenue multiples. And then you look at Palantir and it's valued at like, I mean, 30 X, 35 X. And so Anthropix actually trading at a discount, which is kind of wild because the growth rates, 80 % versus

22:33Tomasz Tunguz:4 ,300%. Yeah.

22:35Turner Novak:And is that like, does the market not expect Anthropic to continue to grow that fast? Is it just people saying, okay, this is like not sustainable. It's still growing really fast, whatever. But like, we're going to assume that this slows down or is it like a private market thing? Like it's just because it's harder to get access to it and technically Anthropic can just price it whatever they want, really. Like, is it just not a fair price that's like just out of whack or?

23:02Tomasz Tunguz:One, it's very difficult to project forward revenue. The revenue is non-recurring. I mean, some of it is contracted, but it's unclear. The third part of it is at some point the revenue will be limited. Revenue growth will be limited by just total amount of GPUs. So the Anthropoc and SpaceX AI signed an agreement so that 25 % of the Colossus data center, which is focused on training will now be allocated to Anthropic. But at some point there just aren't enough GPUs. And so what happens to a business that's growing 43X in a year that starts to grow at say 30%, which is still, I mean,$43 billion revenue base.

23:43Tomasz Tunguz:You're talking about adding 12 to 15 billion of revenue a year.

23:48Turner Novak:It's like they just added that in a month and now they're going to add it in a year. Like it's, it's almost like unrealistic to think it's going to slow down that much.

23:58Tomasz Tunguz:Right. But you don't, so what are you underwriting? Like, what do you, what do you think it'll be? I don't know. And so then there, and then there's also the capital intensity. You need to build out these data centers. Do they need to raise debt? What does that look like? How much dilution are you taking as an investor? So there's a lot of unknowns. And then there's this trope, trees only grow so big. Have you ever heard that?

24:22Turner Novak:No, but it makes me think of the law of large numbers or just like, yeah, this company could never get that big. If you look at textbooks, they'd say there's no, it's like the law of physics says you cannot go from one to 43 billion in a year. It's just impossible.

24:39Tomasz Tunguz:Right. And so I remember when we had the first, I was growing up and anyway, I remember when we had the first trillion market cap company and that seemed staggering. And now we have four companies that are around a three, four trillion, maybe five. And I mean, you know, one interesting question to ask, like break the ice at a dinner party of people who really care about finances, when do we have the first$10 trillion company? It's, I mean, I don't know. It's definitely within our lifetimes. Is it 2030? Is it 2035? You have the devaluation of the dollar. And then clearly these companies are growing really fast, but it's a, it's, I mean, it's my perspective, it's inevitable.

25:15Tomasz Tunguz:And so like will Anthropic be the first$10 trillion company? But it's kind of hard to imagine, right? Like who's going to take the other side of that bet? I don't know.

25:24Turner Novak:Yeah. Well, especially when you consider two years ago, they arguably had like no business. Like there like wasn't the thing that exists right now is just not there. Right. And now it's suddenly the fastest growing of all time. So, but I think the, the, the interesting thing, I think you also wrote about this publicly recently. The strategy that they're taking is sort of similar to what Google did, where you're kind of commoditizing the compliments, I think is how you describe it. How do you think then about just the strategy that Anthropics taken then with all the products?

25:56Tomasz Tunguz:Yeah. So Jason, I think it was Jason from a smart bear wrote this blog post in the early 2000s called commoditizing the compliments. And the idea is if you have a really good business, what you want to do is look at all the people who have businesses around you and make all of those products free so that more people end up using your products. That's called commoditizing the compliment. You commoditize everything that's complimentary to you. Okay. So let's make this concrete. So if you're Google and you make money when people click on search ads, you want to make it so that people click on as many ads as possible.

26:33And what's in it?

26:35Tomasz Tunguz:So, and I was at Google from 05 to 08. So I saw a little bit of this from the outside. But okay, so what did they make free? Well, it used to be paid for email. Okay, email was free. And then it used to be that you paid for video hosting because video hosting was really expensive. But then they bought YouTube and made that free. And it used to be that you would pay a license to have an operating system on a mobile phone. Then they bought Android and then they made that free. And then it used to be that you would buy a dedicated GPS device for you to navigate your car from one place to another.

27:08Tomasz Tunguz:And then they ended up buying Keyhole and making Google Maps and Google Earth free. And then they bought all these books and chopped them up and scanned all of them and put them in the index. So it was just driving more and more searches. Google Docs, same thing. So you're just using the internet more. And by virtue of the fact that you're using the internet more, and it was free, so there was less friction, you would go to Google more, and then you would get more ads. So if you're Anthropic, you can run a very similar strategy. You are Anthropic, you are selling inference. You are selling a prediction of an AI system.

27:40Tomasz Tunguz:And then what you want to do is, okay, well, there was all this like workflow software the previous decade. Maybe it's legal software or finance software or account. I'm just picking categories at random here, but you don't really want to charge per seat anymore. That's silly because the amount of money people will pay per seat, maybe it's$500 a seat per month compared to the amount of inference they'll buy,$2 ,000 a month. Just give away the$500 seat and have them buy more inference. You'll make a whole lot more money and then you have less competition. And so I don't know, I don't have any, I'm just observing from the outside, but that's a very game theoretical optimal way of maximizing when you have a really phenomenal business.

28:24Tomasz Tunguz:You just want to make sure everything else is free. So there are many different queries, many queries as possible.

28:29Turner Novak:Yeah. Yeah. And then why does that become so important then paying for the inference that you mentioned? Like, what does that even mean for somebody who doesn't know what inference is? Inference is like when you ask AI a question or the AI does something for you. It's like the process of like them doing the retrieval and doing whatever they do with the GPUs that they then give to you essentially.

28:49Tomasz Tunguz:Yeah, that's right. So, you know, all these systems are basically the word prediction machines. And so when you ask like, what is the capital of Italy, it's then creating a sentence where it's predicting and it anthropic and the other companies charged by the word it's called the token but it's really you know effectively by the word and so the more words that you um you the longer the answer or the greater the amount of information you give the model the more expensive the query so if you have a really large code base and you have lots of or if you have a really large legal case, or if you have lots of PDFs and you want the AI system to analyze it, that's a very expensive query because it turns out that the input tokens or the data that you give the model is somewhere as around 90 % of the overall cost of asking that question most of the time or more, 90 to 95%.

29:48Tomasz Tunguz:And so anyway, inference is what the model is predicting to answer your question. And so, yeah, I mean, you know, if yes, anyway, I'll stop there, but that's the idea. If I can just get the system to ask more questions and it's not like, I don't want to give people the impression. It's not somebody sitting there and typing and asking about a particular case. It's let me create a workflow. So to analyze a startup, let's say it's like, okay, find the backgrounds of the founders, create a bottom side, a bottom upsizing of the market map, help me understand the backgrounds of the team, compare this to the companies and then all of a sudden the tokens that use the amount of information you're feeding to the model number of words that you're analyzing predicting explode so really anthropics business

30:30Turner Novak:model in like their strategy is just get people to do as much as possible in anthropic products just like use it for things yes and this is why open claw is so strategic

30:47Tomasz Tunguz:because what do I do? So OpenClaw is a little assistant that lives on your computer and you can create a task list for an AI. And you can say, find for me the best place to visit in Italy, go and schedule this with this person.

31:06Turner Novak:It's kind of all the things you described earlier that you can do with Claude.

31:10Tomasz Tunguz:Yeah. And so, but instead of doing them synchronously back and forth, you can create a huge long list. And then those tasks can take 30 seconds or they can take three hours. And that's how you token max when we were talking about that's how you jump from a million tokens a day to a hundred million or 500 million tokens per day.

31:31Turner Novak:And Anthropic and OpenAI, it sounds like, want people token maxing technically, like the highest margin version of token maxing, which is probably like a B2B workflow in some capacity. Exactly.

31:44Tomasz Tunguz:So, and you want people and you want people thinking that they no longer want to interact with a computer without AI, which I think many, you know, many people in the valley already there. And because you can just do so much more because I can just enumerate this list of tasks and then Claude or somebody else, some other model would just burn through, burn through that

32:08Turner Novak:backlog. Is there anything that you're not using AI for right now, like on a computer? There are some tasks.

32:14Tomasz Tunguz:I mean, I'm on an Android and so it won't answer SMS messages because that pipeline's broken. But, but no, you really want to stay. I mean, there's this great book called flow, right? Which talked about how do you get into a place where when you're working, you're just directly connected and it's kind of tied. There was a philosopher named Heidegger who talked about the design of tools. And if you think about using a fork, once you learn how to use a The fork becomes an extension of your hand and you don't feel a difference. And I think working with an AI is like that in the sense of I can just tell it.

32:46Tomasz Tunguz:I can use the most native. I don't have to learn to type. It's probably a dying skill. Yeah. You can literally voice talk to it. Yeah. I can just dictate what I want it to do. And then if it has enough information about the way that I work and it has access to my systems and have helped it produce its own, make its own, fashion its own tools, then it can work as if it were me. And why would I, I mean, I'll give you an example. I was on a plane going to Atlanta and they told us in the waiting area, there's no wifi, right? And so half of the people

33:22Turner Novak:are relieved because they can watch a movie, guilt-free.

33:26Tomasz Tunguz:And the other half, the workaholics I'm like, oh gosh, what am I going to do for three and a half hours? And so I sat there and I tried to find a really fast internet connection so I could download a local AI model because now I look at the laptop and I'm like, you're so dumb, right? Or it's the same feeling when you get into a self-driving car and you start operating it and then you get into a regular car because you're someplace. It's like, why won't you drive yourself? Yeah. Right? Yeah.

33:55Turner Novak:Yeah. So I have one more question on this sort of kind of like inference topic. So I actually don't know on like a tackle level how this works. So because the Anthropics business, you could say they basically sit on top of a cloud provider and they're basically like this layer on the cloud provider. How does that actually play out in just in the sense of like how that sort of like that business model works because like do they need to build their own cloud provider eventually because they're just kind of like a gcp wrapper or like a aws wrapper really

34:34Tomasz Tunguz:at the end of the day they can decide right so you can own you can own the buildings and the chips inside where they're called data centers which google does right so let's let's think about this three layer cake. There's the data center. And then there is the chip inside the data center, the GPU, the chip that's analyzing. And then there's the model. So let's look at those three layers. Google has all three. Google manages its own data centers. Google manufactures and designs its own chips called TPUs, tensor processing units. And then Google makes its own model called Gemini and Gemma. And that is a great business.

35:19Tomasz Tunguz:And then you can say, okay, Anthropic does not own the data center. It does not design its own chips. It just makes a really great model. And that looks a lot like Netflix. So Netflix competes with Amazon. Amazon has Prime Video, but Netflix runs a lot of their infrastructure on AWS. Both businesses can succeed. There are pros and cons to each. And so let's look, you know, a great sort of segue is, okay, let's look at, um, SpaceX AI. SpaceX AI has a data center. They don't have chips, so they're missing that middle layer and they have, they have a model. So there it's an Oreo, right? Where they're kind of, well, an Oreo with nothing in the middle.

36:05Turner Novak:Oreo with a vacuum. It's like it's Oreo when you take it apart and lick the icing and then you stick it back together. There you go.

36:14Tomasz Tunguz:That's right. Or you put somebody else's icing in it. Yeah. Yeah. So there'll be different strategies and you need different amounts of capital in order to do that. You'll have very different margin structures. If you can vertically integrate, which means own each layer, I think you will ultimately be significantly better off because you can design the chips and the data centers for your algorithms. Whereas if you're an algorithmic or a model company, you will definitely have a say in how those chips and those systems are designed, but you are not the only customer. Fair.

36:51Turner Novak:And you probably need to have enough scale to justify the investment into all your own stuff because it's not easy and it's not fast and not cheap.

37:00Tomasz Tunguz:No, it takes, I mean, it might take you, I don't, I mean, Google has been developing the TPU since 2012, right? Amazon has been developing their own chips called Tranium and Inferentia, I think for the last five or six years. And it probably takes seven to 10 years to get to a place where you are at state of the art. You have executed enough cycles to really be there. And so at some level of scale, sure. If you're one of the five most valuable companies in the world, Apple has its own chips, all the M1 to M5 silicon that you and I run on our computers. That's proprietary and it's a big advantage.

37:37Turner Novak:So then the play is probably if you're anthropic right now, maybe at some point you need to start doing that, but it's really just get as much adoption as you can, get as much usage, get as much revenue to have cash to work with, to now fund all this stuff. and to your point that you started talking about this, it's just a sprint. Go as fast as you can to get there.

37:58Tomasz Tunguz:Yeah, if you have a significantly better model, you will win share. And the opportunity cost is so huge and the willing to spend is enormous because if your model is meaningfully better, you might add 100 billion to your market cap in a quarter.

38:15Turner Novak:So I think it begs the question, where do you think is a good place to be investing in AI? today? Is it over because Google is virtually integrating and win everything? Maybe Anthropic and OpenAI went on the edges. Is it wide open for startups? Obviously, you're investing in startups, so maybe this is a loaded question, biased question, but what do you think the opportunity is today investing in AI?

38:40Tomasz Tunguz:There are certain markets that are uninvestable because they are on the direct roadmap for the large companies that are incredibly well capitalized. Right. So, I think if you were to start an agentic coding company today, it'd be very difficult because it is probably the most important market and you have so many businesses whose

39:09Turner Novak:roadmaps are pointed in that direction. Is it the most important because it is so tied to that inference thing that we talked about where there's just so much inference is flowing through that?

39:20Tomasz Tunguz:Yeah. Great question. Okay. So why is Agente Coding such a phenomenal product market fit with AI? The first is there is a lot of spend in software. So the market today is really big. The second reason is software engineers are largely very expensive. So there's a lot of labor spend as well. So there's technology spend and there's labor spend. Both are very large. The third is the demand for software, I would argue is infinite. You and I, as we age and all of us will only use more software. We won't use less. And it will become increasingly sophisticated building on the previous software. So you have labor spend, software spend, and a very fast growing market, and then the market with infinite demand.

40:03And then the last thing is, it is a set of tasks that an AI can test whether or not the AI's answer is correct.

40:15Turner Novak:Because it's just so like objective rule-based. And it's just like, you know if you got it correct or not. Yeah. It's like math.

40:23Tomasz Tunguz:Either the equation resolves or it doesn't. And if an AI system can test that itself, well then sure, you can just let it spin overnight until it has satisfied all the different equations, right? Or all the different parameters that you've defined for the piece of software. So that's called a closed loop problem. You can just have the machine spin faster and faster and faster. And so the combination of all those four, it makes it really great, makes these systems perform exceptionally well in software.

40:52Turner Novak:Where is that not the case? well let's say you know we asked it to paint impressionist art

41:01Tomasz Tunguz:i mean you and i can debate like our monaise lily's is a zenith of impressionist art you know you could say no pizarro is is the bee's knees um and so it's subjective it's it's open loop it's uh the blog post how do you when we summarize this great episode that we did together there's no like objectively best blog posts. And so that's not a closed loop problem. And so their AI has a much harder time because you can't just let it spin. You have to say, okay, this is, you have to apply judgment as a person and say, that's enough.

41:36Turner Novak:So, and the reason that people, that all the big, the biggest AI companies are going after agent decoding is because it's ultimately like the biggest TAM and the biggest opportunity. So then you're basically, you're almost like accepting that you're maybe settling, quote unquote, for smaller, less interesting markets. But then there's an opinion to be had of like, well, these are actually still very big markets or there may be very strategic for these other reasons. Right.

42:04Tomasz Tunguz:Yeah. It's like, you know, I mean, after Google in 2006, would you have started a search company? Probably not.

42:11Turner Novak:Maybe did DuckDuckGo? I don't know. I think DuckDuckGo maybe started around then and it's still alive. But yeah, like, I don't know if I would have invested in it.

42:19Tomasz Tunguz:No, it's just really tough because you don't attack your opponent in the area they are strongest.

42:26Turner Novak:So do you think that there may be some jockeying where, I don't know, a company that's not in agentic coding that we all know of and hear of every day just suddenly kind of emerges and has created a position to kind of, you know, ladder themselves in there or something?

42:42Tomasz Tunguz:Well, you know, Cursor, right? There are all the dynamics around Cursor and the brilliant business that they have built. So that's definitely an interesting one to watch. And you have Poolside, which is releasing US open source models. So now sovereign AI, AI that is limited to a particular country has become a critical geopolitical issue. And so you have companies that are building models for India and companies that are building models for Japan and United Arab Emirates. And so maybe there's a market segmentation. You say, I want to be the best agentic coding system for India. Well, okay. There may be a market segmentation there that makes sense.

43:21Tomasz Tunguz:Just the way that you might have a vertical search engine to compete with Google that was focused on travel for a long time. And that was a standalone vertical. So it's not to say that you can't segment and then compete within that segment. I don't think you can just go and say, okay, I want to win the United States agentic coding market as a model provider.

43:42Turner Novak:That'd be tough unless you really have a meaningful scientific advance or mathematical advance. How do you think then about what are the opportunities that are interesting? How do you figure out, is this side market, this other market, this non - incumbent market that they've already captured? How do you figure out what's worth going after?

44:01Tomasz Tunguz:Well, okay. So let's think about the markets where clearly they've demonstrated an interest, the incumbents. So, agentic coding is one. The second one is health. OpenAI has a great team pushing health products. You have Anthropic launching a collection of skills on Monday of this week tied to finance and the automation of finance. That'll be important. There's legal work that's associated. So, the legal market is definitely in scope for them. Anything around like infrastructure and software automation is definitely core. And so those are some of the markets. I'm sure there are more security. Clearly they will push.

44:40Tomasz Tunguz:I don't know if the model companies, I doubt they will dominate that market its entirety. They will be a supplier more than an individual competitor. But there you have six markets where the direct competitive dynamics of the largest AI companies you must consider. and you can either invest and say, I'm going to, I believe a company is sufficiently far ahead that one of the incumbents must buy or partner with them, viable investment strategy. Or you can say, okay, there are 10 markets they really care about and I'm not investing in any of those. I'm going to go pursue markets 11 through 100. And then I'm going to analyze each of those market dynamics.

45:20Tomasz Tunguz:How many competitors are they? How many venture backed competitors are they? How likely is it that the customer population adopts software, right? Like if you're a longshoreman, the odds that you adopt AI, I think are pretty low. But if you are, say, in the business of like back office automation and you are like an insurance company or a third-party logistics company, pretty high. And then the question is, do the model companies care about that market or not?

45:49Turner Novak:So then what's your lens for thinking through kind of this whole like SaaS apocalypse? I feel like we've gone through these waves of people are like, oh, every software company's dead. And then I don't know if now if it's flipped or it's like, they're not all dead. I'm not sure where we're at. It's hard to keep track. But in terms of that side, if you're a mature software company, how do you think about the defensibility?

46:12Tomasz Tunguz:Yeah. Okay. So the public market's value gross, it remains the most important factor as an input to valuation. It's not a 50 to 60 % correlation.

46:22Turner Novak:You're saying the growth rate of a public company, 50 % of its valuation is just depending on how fast it's growing? Yeah.

46:30Tomasz Tunguz:50 % is, yeah, correlates. So it's explained by it. Yeah. And so which are the three fastest growing segments in the public markets? The first is security. The second one is data. And then the third is a core systems infrastructure. All of those have tailwinds from AI. The slowest growing ones are vertical software companies. And then like productivity apps where there are, some of them are seeing negative growth. And then I forget the third, but so there really is a distribution. It's that you can't look at it as the all publicly traded software companies. There's a distribution, the faster growing ones are doing fine.

47:11Tomasz Tunguz:And then the ones that are slowing or contracting will be punished. One really interesting question actually, this be fun with you. Turner is, okay, imagine you are at 2001 and the dot-com crash has just happened. And you're looking at all the venture backed and publicly traded software companies. They were building on-prem software.

47:30Turner Novak:So you would have a CD and you would get a box of software at a store

47:35Tomasz Tunguz:and then you would install it, right? And you're the head of IT for your company. And then after 2002, some number of companies moved to the cloud. Which companies were big during the box software era that transitioned to the cloud, that survived, maybe even thrived?

48:00Turner Novak:So I was born in 1991. So I was about 10 or 11. So I'm trying to give you the perspective I would have as like a public market investor in 21 or 2001 slash 2002. At the time, I'm just trying to think of how it even size it up. I guess looking back in hindsight, maybe Adobe. Yes. Great. Yes. But that is not really what they did in 2001, right? Like it was, they slowly transitioned to the cloud over the past 25 years. But I mean, it probably didn't start in, it probably started in like 2005 or something.

48:36Tomasz Tunguz:Yeah, no, that's right. Okay, so Adobe is a great case. Maybe Salesforce? Salesforce is post-cloud. So they launched directly on the cloud and then their banner was no software, which meant no on-prem software. Okay, maybe Oracle, but I don't know how fair that would be to count. Very fair. Okay. Yeah, so you're on it. So you have the B. So you have Adobe. Adobe, clear market leader with Photoshop and InDesign and all those things. You have Intuit. Yeah, that's a good one. TurboTax and all that stuff. They made the transition dominant in their category. You have SAP, right? 50, 60 year old talk for a company.

49:22Turner Novak:That is like a common though. The AI stuff is all going hard at SAP now. I feel like. Yeah, that's right.

49:27Tomasz Tunguz:Yeah.

49:28Turner Novak:So can they survive again? We'll see.

49:31Tomasz Tunguz:anyway you keep going through this exercise and we were able to name about seven to eight companies that navigated that transition out of how many i have no idea how do you know how many there were i mean you know hundreds i think it's order of hundreds so are there characteristics of some of

49:51Turner Novak:these, like, is it that they had a very specific customer that they served that, and, and were they like, did they have management teams that took the cloud seriously? Maybe like that feels like a big component of it. I think the characteristic is that they were near monopolists. So it almost didn't matter what they did, whether they made the change in three months or 10 years, they just would eventually manage it.

50:21Tomasz Tunguz:You think about Oracle, I mean, transactional databases inside of banks, who's ripping that out, right? It still hasn't happened, right? Intuit, there's nobody else even close. Adobe, name, I mean, before Figma, name a competitor that mattered to Adobe, didn't matter. SAP, can you name another enterprise ERP system. I'm being a bit glib here, but I do think they just had tremendous control or tremendous presence within their markets, which bought them time. They clearly had the resources to be able to figure out how to make the transition. As a result, customers couldn't leave to a better alternative because maybe there were or there weren't.

51:09Tomasz Tunguz:I think it really is a dominant market position that buys you the time and gives you the resources to learn how to transition and maybe affords you the opportunity to buy a market leader and then integrate that DNA plus the product into the next evolution of the business.

51:24Turner Novak:So it'd probably just be paying attention to, you know, in pretty much all these categories, there's probably a bunch of these AI native companies. And it's just seeing these incumbent publicly traded, how does their product seem to be evolving relative to these like new companies that were founded in the past couple of years? And are they able to make these changes fast enough to just continue to keep their dominant position? There's probably a couple, and there's also a lot more that won't do it properly.

51:53Tomasz Tunguz:It's really hard. Yeah. I mean, ServiceNow has about three or four different AI companies, right? They've definitely been aggressive. That would be an example. But there are many companies that really have not yet responded and will need to. Yeah.

52:09Turner Novak:In terms of kind of maybe, I can't remember if we were talking about this before we started recording or not, but just the impact of AI in the economy compared to some of these other economic cycles. Did we hit on this a little bit? Like, I think railroads was like the peak. I think you said it was like 7.6 % of GDP or something like that. Mm-hmm.

52:28Tomasz Tunguz:That's right. Yeah. Yeah. Yeah. So I think we'll be about low to mid 2 % of GDP within this year. And in Q1, 75 % of GDP growth is AI.

52:43Turner Novak:And a lot of this is data center build out.

52:46Tomasz Tunguz:Yeah. So there's the construction, the manufacturing, the assembly, the chips, the networking associated with it. Anyway, and then all the labor that's associated with that. And then the revenue that's generated from it, which fastest growing market. So yeah, I mean, I think 75 % of all US GDP growth, if it continues to grow at this rate, the US overall GDP will continue to grow much faster. And then it will go from 31 to whatever it is, 33 or 35. And then if we can get to 7 or 8 or 10 % of that, you're talking about 3.5 trillion a year of investment going into AI in the intermediate future. This is a big business.

53:26Tomasz Tunguz:It's a big industry.

53:27Turner Novak:Well, and you think about, so like the scale of, I mean, clouds, maybe an interesting example, mobile, like, did they make the economy grow faster? I'm not actually sure. Like they, I feel like they had to have.

53:40Tomasz Tunguz:Oh yeah, of course. I mean, the networking build out, this was, you know, when you were 10 and I was 18, but like the, there were, I mean, before the internet was broadly adopted, everything needed to be connected. Every Every house needed to be connected. Every building needed to be connected. Fiber and copper. And so you had huge GDP, I mean, not nearly close to the scale, but significant GDP when you had Nortel networks and Quest and all the initial internet service providers who were in the telephone companies adding new telephone lines that were ultimately replaced by fiber. That drove a lot of the 99 boom, right?

54:21Tomasz Tunguz:That big network, Juniper networks and Cisco and all those businesses. They were explosive, very similar to this era.

54:30Turner Novak:Yeah. Well, and then, I mean, that begs the question. It didn't end that well, right? In 2001. Like, do you feel like, is there sort of a bear case to be made in terms of just being careful or being cognizant of, you know, where we're at in the technological or economic cycle or the capital, the debt cycle related to all this stuff? Like, is there any kind of thing that you kind of keep top of mind when thinking through that? Yeah.

54:57Tomasz Tunguz:I mean, it is a lot different than 2001 because 2001 revenue models of many of the businesses were not known, right? Like Amazon. Okay, fine. In the fullness of time, but like Peapod, which was yesterday's Instacart.

55:11Turner Novak:We didn't have phones. Like it was literally, you're placing your food delivery order on the computer or whatever.

55:16Tomasz Tunguz:Yes. Right.

55:17Turner Novak:With dialogue. Like it takes three minutes to load. Yeah.

55:22Tomasz Tunguz:So it's a different era. I think you can legitimately say AI converts electricity into work, just the way that gasoline is converted into work if you use a lawnmower. And it can meaningfully improve the productivity. You have like Boris Czerny from Anthropic who talks about he can ship 30 to 50 times as much code with AI as not. Okay, he's turning electricity into real work.

55:47Turner Novak:I think, okay, so what are the things to worry about?

55:49Tomasz Tunguz:The first is, yeah, the credit markets, I mean, you have to, many of these data center build outs are built with 80 % credit. And the, you know, OpenAI, I think SoftBank limited the size of the debt. I think they dropped it by 40 % this morning. So we'll see what happens there. When you borrow money like you borrow money for a house to pay for a mortgage, you are providing the house as collateral to that mortgage. And the lender looks at the house and says, okay, what does the inspection say? How long will the roof last? How much investment? In the very same way, people who are lending to data centers have to look at the GPUs.

56:30Tomasz Tunguz:How long will those GPUs last? Are they productive? Will they fail at some level? And there's a debate about how long those inference GPUs are productive. So that's a big one. So the credit market is definitely one. I think, you know, the argument at some point, like the token maxing wave, I think in the back half of this year will wane and everyone will say like, yeah, you're burning a lot of electricity and you're buying a lot of intelligence, but like what did it do for the company? I think that's definitely coming at some point, but overall it's hard to, it's hard to paint a negative picture.

57:06I mean,

57:07Turner Novak:like part of the negative uh just like general perception is there's gonna be like all this job loss or whatever like hey i think the other one is like water usage in data centers or something like that and contaminating the land noise pollution maybe i don't know what whatever the argument is for the data centers but i i know the other side though with jobs is like it's actually not going to cause job loss if you look like every technological revolution like it always ends up actually creating more jobs yeah what do you think will be or at least what are you seeing Maybe it's still pretty early, but like what kind of new jobs do you think we'll see from a lot of the AI build out?

57:42Turner Novak:Yes.

57:43Tomasz Tunguz:Okay. So let's talk about why there are more jobs. There's not a finite amount of work to be done right there. There's this thing called a lump of work fallacy, which is there's a total amount of work to be done every day across the globe. And there's a certain number of workers and they have to allocate their share. And then once they're done, they go home.

57:59Turner Novak:I've never heard this before, but it makes sense. Yeah.

58:01Tomasz Tunguz:Yeah. But if you're a workaholic or you're married to a workaholic, you know, So there's always more work to be done. My wife's like listening to this, like, yes.

58:13Right.

58:14Tomasz Tunguz:And so, okay, what ends up happening? Well, you know, you used to like write Java code and then somebody used to review that Java code. Well, great. Now you no longer have to write or review that Java code.

58:24Turner Novak:You have to architect that system.

58:25Tomasz Tunguz:And then you have to make sure that system is now resilient. And it turns out in order to compete, you can no longer just offer a point solution. You need to offer six times the breadth of the product. Okay. get to work right and so i think that happens just like across across the board you know the i we looked at the automobile industry united states before interchangeable parts and taylorism you had 80 000 people who are artisans building different components of combustion engine this is

58:57Turner Novak:like before the assembly line too yes right before the model t so just some dudes just sitting in a sitting in a room in a circle banging, banging parts together. Yeah.

59:08Tomasz Tunguz:Making a piston, right. Or camshaft. And yeah, I mean, it worked and they sold cars. And then all of a sudden, well, the price of a model T collapsed, collapsed automobiles. And everybody was driving one. And the number of people working in the U.S. automobile industry within five years went from 50, from 80 ,000 to 500 ,000. And there were a few people working on the line, but there were people marketing the cars. There were people designing the cars. There were people building dealerships. There were people building roads. And so the overall employment exploded, but it wasn't, you know, it's not like, you know, around that time we were looking, there were about, there was about a million manual dishwashers, people who washed dishes for a living.

59:54Tomasz Tunguz:Wow. That's crazy. This is in the U.S. in the U.S. I mean, yeah, every restaurant in the United States needed three or four dishes. 1 % of the population, 2 % of the population. That's crazy. Yeah, or farming, right? Like think about the shift from agrarian farming and people moved to the cities and they found all kinds of new work. And now we have all these incredible industries. So, you know, there are other benefits. You look at the Waymo statistics of how much safer these cars are. 50 ,000 people die in the U.S., unfortunately, on roads. And once we get to a place where we have significant volumes of cars, think about the longevity of the average American will increase as a result of the safety.

1:00:30Turner Novak:Yeah, that's a pretty big one where I hear that a lot is there's millions of people who drive, and this is a significant displacement. We were at dinner probably last week, and I overheard the women beside me talking about this. And the massive concern with them was they don't trust them, but then also like, oh, what about all these drivers are going to lose their jobs from these self-driving cars? I can't support that. I think the argument, though is there's probably still going to be people in these vehicles in a decent amount of cases like long-haul trucking you may still need you know maybe like maybe it's like flat or something there's just more trucks on the road that are enabled by this and you'll still have people in the warehouses that are like unloading them and maybe or maybe it's like you know you sleep there's like a you sleep in the trailer or whatever you should have a nice bed and you're kind of like maintaining the car while it's self-driving across the country or something like that.

1:01:27Tomasz Tunguz:So long haul trucking, average age of a long haul truck I was just looking at is 46 to 47. It's not an industry where lots of young people are gravitating to. And maybe the tastes of new job seekers have shifted and they don't love that lifestyle. And so I think there's two parts to it where ideally we are automating the jobs where there's not a tremendous amount of labor supply.

1:02:00Turner Novak:And that's one of the ways of looking at AI.

1:02:03Tomasz Tunguz:The great place for AI, while it's not perfect, is you have a labor market shortage. You have the hiring manager who needs that job to be done. And therefore, they're willing to accept a 70 % solution. electric pole inspections, long haul trucking, anything to do with sewer inspection. Those kinds of things, AI is phenomenal at. And it can be a very unappealing job. And so maybe there's this generational shift where people's preferences for different kinds of work evolves and the machines take the work that is no longer interesting. I mean, you know, like working, tilling a farm. I'm sure there's some fraction of the population that likes that.

1:02:55Tomasz Tunguz:Great. But you don't have 10 % of the population who wants to go and yoke some ox and oxen and then plow behind them. Right. The preferences change.

1:03:06Turner Novak:Yeah. And I think too about accounting or finance. Right. Back in the day, an accounting department was just a big building. Maybe it was next to the factory with just people literally writing the debits and credits on paper or whatever. like manual invoices. And I mean, some people still do some of this kind of stuff, but now it's literally like, it's a spreadsheet and you type it in and it automatically calculates and like QuickBooks, literally, we were talking into it. Like the software just does it for you. It like calculates the financials. You can literally press a button and like get the final financials.

1:03:37Tomasz Tunguz:We all know what a calculator is. But when I say the word calculator, you imagine, I don't know, like a TI 82 or, you know, an HP calculator. But, but before that was invented, there was a title. Like a human person that was a calculator. Yeah. Yeah. I mean, like, you know, the, the Apollo missions, the, all the math was done by many, much of it by women who, and their jobs were like senior calculator.

1:04:05Turner Novak:They literally had, I think I've seen those pictures where there's a woman who was standing and there's like a stack of papers that she had calculated that was literally taller than her or something. It was just like calculations of the, you know, the route or whatever they had to calculate.

1:04:19Tomasz Tunguz:Yes, the trajectories and the orbits. Yeah, that's right. And so, okay, what happened to all those calculators? Well, you know, they found other, we found other work for them at a higher level.

1:04:29Turner Novak:They didn't have to look up logarithms and big books. Well, maybe instead of like spending literally weeks, just hand calculating the equations, it's just, it's done by the computer. And I'm like, oh, this was wrong. Like the calculation was wrong. Let's see what we need to do to change about this route. And you get into like more strategic work around, you know, the, the, the calculation, this stuff, that's like a rule-based thing really at the end of the day. But yeah, it's just like it to, to the point of like, you know, with, uh, investment firms like finance, right? Like, you know, back in maybe the fifties when they were doing like, you know, selling junk bonds or whatever they were doing, like doing out early stage of LBOs and companies have like this army of people that had to punch out all the calculations versus now it's like a lot of them are doing more sales, more marketing, right?

1:05:14Turner Novak:Like it enables more people to do LBOs, more people to take out credit, more people to raise venture capital because we have this like army, all that the AI is doing all the analysis and this is a good investment. So it's just all these VCs going out and giving money to founders and enabling them to start companies. Maybe where I'm exaggerating this a little bit, but I feel like it just like the productive work shifts towards things that kind of like generate, grow a business or like add more sales, do more things for customers. Yeah.

1:05:44Tomasz Tunguz:I mean, those calculators got into the business of aerodynamics, computational fluid dynamics, quantitative stress modeling on different elements. There's always more work and it's increasingly sophisticated. Yeah.

1:05:57Turner Novak:Are there areas that you think AI is still kind of underrated today? or maybe you're expecting it to get really good in the next couple of years and people are maybe not thinking about it. I know you invested in an advertising company recently.

1:06:12Tomasz Tunguz:Yeah. We're really keen on online ads. I think Google generates something like$120 per user in the United States and ads. And the online ad market in the US is about 450, 460, global, $460 billion. If you think about what ads can do for offsetting the cost of GPUs and also helping consumers find things that they might like, I think it's an absolutely huge market. And so we're very keen in that space. It's been tough, I think, for startups as a whole within the online ads ecosystem, but AI is such a disruptive force that I think there's an opportunity to build a great business and we're lucky to work with a fantastic team there.

1:06:58Turner Novak:Is there anything that's not in the data that you are kind of waiting for or looking at? Like, maybe there is data, but it's not well-known data or it's not matured data. Like it's just kind of early signs of things. Within the online ads ecosystem? Or just in general, like in AI adoption or in usage or.

1:07:17Tomasz Tunguz:I don't think we're seeing the productivity gains yet. So why haven't we seen that? Well, before say October or November of last year, AI systems were great search engines. And then in November of last year, the model started to be really great at executing workflows, multi-step processes. And that's where you really get time compression and work because I can write up a workflow in English. And then I can say, here's a list of a hundred entries in a file. And I want you to run each one of these workflows in parallel. And boom, in 15 minutes, I have the work that I could have done in four days.

1:07:58Tomasz Tunguz:And we're not really seeing that in the productivity statistics yet or in the earnings per share of publicly traded companies, but it will be significant and sustained. I think it will be tremendous. So maybe early next year, mid to late next year, we'll see that.

1:08:17Turner Novak:So what will that show up as? I didn't actually look at this. I just saw Datadog. The day we recorded this is up like 30%. I saw someone make a joke that this is the AI productivity we are expecting. Maybe it is or maybe it isn't related to it, but is it just companies are getting more efficient per employee essentially? Like, is that probably what shows up?

1:08:39Tomasz Tunguz:Yeah, they can do more work per hour, whatever that unit of work is, whether it's like lines of code for a software engineer or customer support cases solved for customer support rep or a company is reviewed by a venture capitalist. It is the throughput of whatever factory you are operating has just gone up because the conveyor belt and the machines can now operate at twice the speed. And that's a really great mental model for it. There's a whole discipline called operations research, which is I have a factory with a factory line. And as I change different components to it, how many more chocolate boxes can I make?

1:09:18And I think with AI, the reality is like, I mean, I think people will, could you see

1:09:25Tomasz Tunguz:like a 30 %? You know, I mean, we just talked about Boris who's at like 50 X, you know, he's clearly, I don't know how many standard deviations out, but can you see like a 3X to a 5X productivity gain for a software engineer on average, maybe 3X? Yeah. And so all of a sudden your software factory is now operating at 3X the throughput.

1:09:46Turner Novak:Is this kind of related to, you put out a study, I think it was about a year ago where you interviewed a bunch of, or you did run a survey with a bunch of, I think it was in go-to-market with sales teams. And basically you found that using AI had zero impact on revenue growth or something like that. Was this, so I guess what was the study and then maybe has this changed in the past year or two?

1:10:11Tomasz Tunguz:We're just about to launch the new go-to-market survey. So we will know this year. Yeah. And I think in retrospect, that's the answer we should have expected because again, everyone had access to a fancy search engine instead of a system that could actually parallelize work. So I think even this year, we will see modest, positive response. And then next year, I would expect to see very significant response.

1:10:37Turner Novak:Interesting. Okay. And then so then how do you think people are actually buying AI today? What are you seeing in terms of maybe companies you've invested in, surveys that you've done? What is seen to be getting purchased? Maybe what's the obvious things, what's the less obvious things? And just what's the general decision-making framework that you see people using? So there are buying committees. There is the line of business owner, VPNs,

1:11:03Tomasz Tunguz:VP products, VP marketing, VP customer support. There is the head of technology, VPNs or CIO, head of security, and then oftentimes general counsel, because there are lots of different data information and security questions around AI. I would say the sales cycles were extremely fast November until March. And now as a result of some of these buying committees becoming more sophisticated, they're slowing a little bit, but they're still much faster than software sales cycles. And one mental model, which is not universally true, but it is useful, is that every leader within an organization will pick a platform that they trust to deliver to them the vast majority of their agents.

1:11:51Tomasz Tunguz:If you're the head of data, you'll pick a company like Monte Carlo and say, great, I trust you to deliver all of these data agents. If you're VP of engineering, you've kind of already done that either with OpenAI or Anthropic or Cursor, one of the three. And same for sales. And many of those categories is still TBD who that brand is, but that's what will end up happening. And as a leader, you'll trust, you'll make a career decision and say, I trust this particular company to deliver for me all the different sales agents I could need.

1:12:24Turner Novak:So then there might be then a sort of like jump ball type opportunity in some of these categories, like with, like in sales, like Salesforce, like do you make a bet on Asian force or whatever all the Salesforce AI stuff is, or Or is there a new, more emerging product or company that's out there that you maybe make that bet on? And I guess it would probably be, it'd probably depend on who the actual decision maker is there and if they use the product. And then if they probably like a bet on like the slope of improvement, like you may say the startup is like added all these new features. They've got so much better, probably making, like you said, like a career bet almost on like this roadmap seems like it's actually going to be super useful for us and will actually drive the needle versus maybe the existing option we're using.

1:13:09Tomasz Tunguz:I don't know.

1:13:09Turner Novak:Is that like a fair way to think about it?

1:13:12Tomasz Tunguz:Right. And today you have general purpose tools. You have low code and code workflow builders that are growing very fast because there has been no specialization. And the most valuable tool now is a stem cell that I can play around with and then have it specialized until I see it germinate and blossom into a workflow that I will then crystallize, which is what happened in software. right in software everyone was building a whole bunch of custom stuff and then you had salesforce that said this is the right way to run a modern sales organization with software and hubspot did the same thing for the smb and then marketo came around and and then the workflows i don't want to say they calcified but they definitely crystallized around best in class and everybody copied that until there was a new platform shift and everything has to be reinvented do you think a lot of those

1:14:00Turner Novak:companies are probably already founded. No, either is wide open. Really? Okay. Any areas that you're most interested in at theory for people listening, if they're like, oh, I'm working on this. Yeah.

1:14:12Tomasz Tunguz:So we're really interested in online ads. So if you do anything in the online advertising ecosystem, please look us up. We're very interested in inference. So we think you can think about inference will be the biggest market. And there are many different kinds of inference there's like a really fast inference or real-time inference there's inference that's for images or video there's inference that is for very long running background tasks and just the way that if you had one percent of the database market you can become a public company like if you have one percent of the inference market you'll be able to be a public company so specialized inferencing is fascinating to us and then another category we're really keen on is email and the automation of email with AI.

1:14:57Turner Novak:Interesting. And is this because agents are going to start reading most of the email?

1:15:02Tomasz Tunguz:Is it? Yeah. I mean, what are the odds are in two years you're logging into Gmail five times a day?

1:15:07Turner Novak:I've been thinking more and more about like how much of my time is just like deleting these like stupid AI emails that I get that just like, it's always the same format where it's like three follow-ups and whatever. And like, they're pretty, I don't know. Yeah.

1:15:20Tomasz Tunguz:Yeah, there's no way. There is no way you're logging into an email account five or six times a day in two years.

1:15:26Turner Novak:So what do you think is going to happen? Like am I just sitting in Claude and like it's pinging me when I get the best ones or something? Or am I only texting or Slack or?

1:15:34Tomasz Tunguz:Yeah, I mean, it will learn what you care about, right? It will learn who you care about and everything else that will either summarize and prioritize or, but it's just no way. I mean, look at the volume of emails you and I both receive and millions of other people do. I don't want to spend my time and neither, you know, archiving this and archiving. And then now it's a text message is about, you know, I don't know how much credit you were offered today, but I can tell you.

1:16:01Turner Novak:I get the calls every day. Every day, I get a call on average about like, yeah, I know a guy who's a borrow some money. Part of me is I've like thought like, should I just do one of these and just get like a hundred thousand dollar like loan or whatever? Like, should I just see what happens if I actually say yes to this?

1:16:21Tomasz Tunguz:It's kind of funny.

1:16:23Turner Novak:So you do these predictions every year. I think the ones you put out for 2026, I feel like we've actually kind of hit on some of them.

1:16:30Tomasz Tunguz:Oh my gosh, it's depressing, isn't it? Half of them are already there.

1:16:34Turner Novak:But one of the ones that you predicted was a lot of liquidity in kind of like the late stage ecosystem. I forget which ones you said, But I think like there's SpaceX, OpenAI, Anthropic, Databricks. I don't know. I don't know if Andrill is considered, if it's like big enough or close enough to IPOing. But there's just like a lot of these companies that are, I don't know, a couple trillion dollars of liquidity. Do you still think that's going to happen? Where I guess we're a couple months in now. And what do you think kind of the impact of that's going to be?

1:17:05Tomasz Tunguz:Yeah, I mean, I think SpaceX, OpenAI, Anthropic definitely go public. Stripe and Databricks. I'm just looking at the blog post now. I don't think that neither one of those happens. If those three go public at 50 billion each, they will raise more money from the public markets than the sum total of all IPOs in the previous decade.

1:17:26Turner Novak:You're saying if each of them, when they go public, if they raise 50 billion on average between the four or five of them, it will be between the three of them. It'll be more money than the last decade of IPOs that they've raised. Yeah.

1:17:38Tomasz Tunguz:I mean, when Facebook went public, it was a$15 billion IPO and it was like unconscionably large and there was one. And now we're talking about three$50 billion IPOs. Sure. Inflation. Okay. Let's say 40 % more US dollars today than they were back then. You know, you're talking about like 16, 18 billion compared to 150. It's still 10X larger.

1:18:03Turner Novak:And so it is bending the public markets in a very real way.

1:18:10Tomasz Tunguz:So they will go public. I think there's a real question of how people become liquid and sell those positions. But the only thing it can be is positive for the ecosystem.

1:18:21Turner Novak:What do you think happens with sort of late stage venture market? Because there's a lot of people, their business model is just like asset management firms. the business model is like getting their cut of these rounds when they happen. Do we basically just have new companies that kind of grow into it and take their place where they're like new trillion dollar private companies that then IPO in another five or 10 years? Okay. So when I started in venture in 2008, there was one billion dollar outcome

1:18:50Tomasz Tunguz:in enterprise software. In the whole year. Yeah. I mean, up until that point, aside from like Microsoft. Oh, so there was one. There had only been one outcome of over a billion dollars. Venture backed. And I remember being in awe of the venture capitalists who had that billion dollar outcome and everybody was like, wow. Now you can start a company and raise over a billion. Yeah. Well, this is exactly the point. And so, and to go public, you needed about 50 to 75 million in revenue and you would raise 35 to 50 million in an IPO. and there was a bank that would underwrite you and take you to market and charge a fee for it.

1:19:34Tomasz Tunguz:And today, many Series A's are larger than those IPOs. Every Series B of significant company is larger. And so the private market has basically taken over that. And, uh, you know, I think that's fine. It's because it's so expensive to go public, but there there's plenty of business there. So my point is the IPOs of 15 to 20 years ago are today's midsize series B's and series C's and there there's plenty. Yeah.

1:20:09Turner Novak:Well, so then does, does the average series B or series C in 10 years, is it like a trillion dollar valuation? like i hope it doesn't continue that direction no that means we're in hyperinflation like pre-war

1:20:21Tomasz Tunguz:germany no no i hope not i think uh it's cyclical right i mean you have the oil industry went through a huge boom and railroad industry went through a huge boom textile industry went through a huge boom automobile industry and so we will have a cycle and when that when that cycle or that downdraft happens no one can predict but uh but it will happen and and then the levels of overinvestment will be exposed. But that's, excuse me, that's healthy. It's really important for us to have recessions and corrections.

1:20:53Turner Novak:So then I guess one question, and when you're, let's say I'm a founder, I'm meeting you for the first time, maybe you do or don't know much about my business and just like the market that I'm in. And so like, what kind of things are you going to be asking me and looking at when I'm, when you're kind of making a decision of what you want to, I don't know, invest in and sort of be exposed to today as a fund? Like, what are the things that are most important to you that you're kind of thinking about?

1:21:16Tomasz Tunguz:Yeah, what does the company look like in seven to 10 years? I think it's probably the hardest question, but the most germane question in this era.

1:21:28Turner Novak:And is it ultimately you're thinking about owning some inference spend? It sounds like you're thinking about advertising that you mentioned. I'm trying to remember the other two. I feel like you mentioned two other things. Email is one. Oh yeah, email is one. So you think a lot about then how do you slide into the sort of like future of how AI continues to eat more software?

1:21:54Tomasz Tunguz:Yeah. And then, you know, I mean, what does the business look like in seven years? You can say, well, we have a technology advantage, some awesome piece of kit that gives us 18 months and we will sustain an 18 month advantage in our market. Very valuable. You can also say, we can sell better than anybody else and build a brand. And brand is probably the only enduring strategic advantage of any company at scale. And so that's also a very viable strategy, but you need a booster rocket, a way of getting a head start relative to the market. So what is the answer there? Yeah.

1:22:31Turner Novak:I feel like that's kind of related to, I guess, going back to theory ventures, I think the name is related to the disconnect in technology. Can you each explain the name?

1:22:42Tomasz Tunguz:Yeah. The website says we craft theories about the future and then help them become a reality. And so we research a lot of different categories and try to understand the history of the category, which we've talked a lot about. And then if we know the history and we can understand the technology innovations that are occurring within it, then maybe what does the future look like? We try to find founders where we are similarly aligned in that vision and then work really hard to help them achieve their dream.

1:23:11Turner Novak:I know you were at Redpoint for about 14 years before you started Theory. What were sort of the seeds of starting to do this? Did you always kind of know you wanted to or was there like a moment where you're like, this is it, I'm doing my own thing?

1:23:24Tomasz Tunguz:Yeah, I had a wonderful time at Redpoint. Many wonderful people there who taught me the business and I'm extremely grateful for it. you know, and then decided to launch our own adventure. And now we're three years into theory and we're 10, I think we'll be 15 people here by the end of the year. So we're off to the races.

1:23:45Turner Novak:And it was, was it just you when you started it or did you end up, you have ended up teaming up with more people that joined you?

1:23:51Tomasz Tunguz:Yeah. Yeah. Yeah. So yeah, the team is 10. It's, it's always been a we and, uh, I'm really grateful. I mean, anybody who starts a company for the people who

1:24:00Turner Novak:join and believe when, you know, we don't have an office and we're all building it together.

1:24:07Tomasz Tunguz:I think it's been, uh, I'm, I'm grateful for their confidence and all their hard work. Yeah.

1:24:13Turner Novak:And I think one thing I was realizing this when I was asking Claude all my things, like trying to prepare this episode, I have not had a lot of people that have spun out from exist, like pretty big funds on the podcast. I've had a lot of people who was like, I started my own thing. I raised from founders I invested in, blah, blah, blah. How did you go about probably getting to meet a ton of LPs over a long period of time, just putting the first fund together? What was the process?

1:24:43Tomasz Tunguz:Yeah. It's enterprise sales. I think raising a venture capital fund is, you're talking about an 18 to a 36-month sales cycle because you are selling a 10 to a 15-year contract. Pretty long contract. Yeah. You're asking for somebody to entrust you with their capital for 10 to 15 years. And so best in class enterprise sales is 15 to 25 % conversion with that sort of sales cycle. And so you need to build a funnel and build relationships and run it, understand, okay, how do we map the account? How do the decision makers feel? How all those kinds of things. Anyway, that's the mental model we applied.

1:25:26Tomasz Tunguz:And I'm grateful to the limited partners and our investors who took a bet on us when it was just a pinched neck and a dream. And yeah, but it really is just working a funnel and building trust.

1:25:39Turner Novak:And I think you did actually a Monte Carlo analysis to figure out what the portfolio is going to look like. I don't actually know exactly what you did, but so why did you do that? What was the process like?

1:25:50Tomasz Tunguz:It's really, I mean, somebody tweeted this recently, which is your fund size is your strategy. What does that mean? Well, in fact, I think it's for us, the opposite is true, which is our strategy determines our fund size. And that's true at every raise, which is this is how many companies we want to invest in. These are the kinds of ownership targets we want. This is how many of them we want in a fund. And this is how much money we want to continue to support them over different rounds. and given what's happening in the market and what 75th percentile series A's go for, you can kind of calculate what that fund size should be.

1:26:25Tomasz Tunguz:And that's the way that we think about fund size at theory. You have to first pick your strategy and then capitalize the business to be able to execute that strategy, which used to be the case for startups and no longer the case. But I think that's really essential. And then you want to put the probabilities on the side of you winning, right? Like what is the 75th percentile exit and 90th percentile exit for a startup? What is that worth? And then what does that mean for our expected value for fund multiples? And putting together all that math is a very, I think it's an absolutely essential function or essential task for early funds, because the greater the confidence you can have in that business model, the more confidence limited partners will have in your ability to execute it.

1:27:17Turner Novak:What is you, I mean, if you're willing to share, like what do you guys assume is like the average kind of outcome look like for an investment that you're making?

1:27:25Tomasz Tunguz:I don't want to be too public about some of those numbers, but many of those numbers are public and you can pull them from pitch book and those kinds of things. But we have our own very special way of underwriting And then, you know, I think a key part of running a fund is you have to make exceptions. And there are exceptional companies that don't fit the mold and you can't have a portfolio full of them, but you can have some.

1:27:52Turner Novak:Yeah, that's fair. And I know you make a lot of things with AI, like personally, like you're just always messing around. What would you say is like the coolest or most interesting thing that you've kind of built or done, whether it's related to theory or just for fun?

1:28:08Tomasz Tunguz:I mean, the thing that's daily useful is a podcast processor listens to 50 podcasts and then pulls out all kinds of interesting statistics and facts. That's really a lot of fun. What do you get from that?

1:28:20Turner Novak:Does it give, take this episode and it would give you the five most interesting bullet

1:28:24Tomasz Tunguz:points that you mentioned or something? What are some interesting statistics? What are some counterintuitive perspectives? What's the overall narrative? And that's, again, parallelization. I don't have the time to listen to 50 podcasts in a day. I'd run out of hours about halfway through. And not get any sleep. Right. So that's not possible, but that's really useful. I think the most effective uses of AI all boil down to parallelization. You have a big, long list of something to do and you don't have enough time. How can you parallelize it? And the crazy part is the GPU, which is the chip that powers all of AI is amazing at parallelization.

1:29:06Yeah.

1:29:06Turner Novak:And you do a lot for your content too. I mean, people probably have come across you. You have a blog that I think you write a couple of times a week. Like I don't think it's quite daily, but actually sometimes you do post multiple times a day. I think if I'm remembering based on the timestamps, like, so you, you post quite a bit. Do you use AI in the process of creating those and coming up with ideas? AI is an incredible editor.

1:29:31Tomasz Tunguz:When I first started writing, I hired an editor. I had this AP English teacher who taught me to love to write, a guy named Mr. Dunn. And so when I started writing, I really wanted to be graded like an AP English student. So I hired a wonderful person who did that. And now AI will do it for you exceptionally well. And so the amount of revisions with AI is, I mean, most blog posts 10 years ago might've had two revisions or three revisions. Blog posts today have 10 or 15 or 25 revisions.

1:30:05Turner Novak:So do you write something that's maybe long-winded or not fully fleshed out? And then you have AI edit it? Do you have a series of prompts, like cloud skills that you've made or something where you're banging, banging through, like you read it, you're like, fix this, fix this?

1:30:21Tomasz Tunguz:Most important thing is to create an outline, figure out the lead, the real story, and then the data points or the supporting arguments, it takes multiple versions. Even after like 20 years of writing is, you know, like cloud code will say, or whatever, Kimmy K two, six will say, you buried the lead.

1:30:38Turner Novak:You buried the most important part, like paragraph 14. You're saying you'll say that to the D to the AI.

1:30:44Tomasz Tunguz:It's like, no, no, it will tell me, I was like, Hey, critique this post. And we'll say you buried the lead. And you feel, I mean, I feel like a freshman in high school. on it. It's just so basic, but it's that, yeah, it's that consistent discipline.

1:30:57Turner Novak:So thanks again for coming on the show. This was awesome. I know you have to run, where can people follow you? Like Twitter, LinkedIn, blog?

1:31:06Tomasz Tunguz:All three, T-T-U-N-G-U-Z. You can find me on LinkedIn and Twitter. And then tomtungus.com is the blog.

1:31:13Turner Novak:Cool. We'll throw links in the show notes for people to check them out.

1:31:16Tomasz Tunguz:Thanks for the conversation, Turner. Really enjoyed it. And I hope you enjoyed it.

1:31:20Turner Novak:Thanks again to this episode's sponsors. Upgrade to Flux with one of the two links in the description and get$1 ,000 off your first$10 ,000 to spend. Put your sales tax on autopilot at numeral.com. And for AI analytics, just ask Amplitude. If you enjoyed this conversation, please like, comment, subscribe, and share this episode with a friend who loves talking AI data. Make sure to check out the back catalog of over 100 episodes with the founders of companies like Robinhood, Sweetgreen, and Mercury, and investors like Gary Tan at YC, and Chathan and Eric at Benchmark. Tune in next week for a podcast and recording live from Allocates Beyond Summit, featuring conversations with a dozen early-stage investors on everything they're seeing on the ground today.

1:32:00Turner Novak:If you don't want to miss it, subscribe to my newsletter, The Split, linked in the description to get each episode plus a transcript emailed directly to your inbox every week. Thanks again for listening. See you next time.

1:32:15Thank you.