C (7:19)

There's a great meme the other day that someone said, imagine if Google had known that users were willing to pay, you know, 100, 200 bucks like they are with ChatGPT, if they had known that people would pay for that with a similarly magically delightful product like Google. Like, God only knows what the market cap of Google would be today. Obviously maybe even more of a dominant market share contributor than it already is. But it is amazing when you think about sort of the reconfiguration of how we will view monetization in this new world. And before we go even further, David, I think these last couple of slides actually set up probably 95% of a lot of the questions around the market. Because if you go Back to Slide 7, Monique, I think that slide is daunting for a lot of folks, in part because obviously you see it bundled with the headlines, but also because it just feels like we've all, many of us had lived through the early 2000s where it just ended in a very less than desirable picture. So maybe if we can summarize what's your case for why it's different and then particularly talk about the timing cycles, because incidentally enough, despite the massive broadband build out and then the glut, we actually of course get the beneficiary of that today.

B (8:37)

But, well, we grew into it. I mean, we ended up growing into it. It was just a time lag in that case and it was not the strongest companies in the world building that out. And the thing to watch will be like, what role does leverage play? And so I read an article this weekend that was like, is there systemic risk in data center build out? And first of all, most of the people with their neck on the line again. But there's a role that as of right now, private capital is playing. There's a role. And the biggest funder of private capital is actually banks or private debt. So banks are the ones that are funding the private debt companies and increasingly they all have insurance companies. So maybe there's insurance companies that are kind of backdoor funding this build out. That's a really good sign for the stability of the build out. So that's like the nature of the supply side, which again, it feels different this time given who's actually doing the build out and who the tenants are. The demand side is the bigger, more interesting thing. So I read a stat yesterday that the time to get to 365 billion searches on ChatGPT was two years. The time for Google to get to 365 billion searches was 11 years. So it's five and a half times longer. So the big story on the demand side for me this time around is AI is built on the back of the Internet and cloud computing. And because of that it sort of allows for immediate global distribution. If you look at the way Google and Facebook started, for example, like they started much more like small build. Both had network effect dynamic which just takes longer. And we didn't have full sort of Internet proliferation across five and a half billion people in the world and smartphones in the hands of everybody able to access the Internet. And so what that means is because of the nature of this technology, you don't have to deliver a new hardware product. And because we have global Internet build out and because we have cloud computing, the whole world can access this. And so if you just take chat GPT again they got to the scale that they're at five and a half times faster than Google, which is staggering. But you know, there's probably, I don't know, a billion, I think the latest they said there's more than a billion monthly active users. There's probably another billion or so people who have tried it. So if you add up all the different platforms and a bunch of people have probably tried Google products and Facebook products, it's probably well over half of the global Internet population has used AI tools already. And we know that there's probably in some shape or form somewhere between 1 and a half and 2 billion active users of these products. So just the speed at which they got to distribution is unlike anything we've seen before. And so that is heartening to me that the supply build out will be utilized maybe in a more predictable way than you know, broadband in the early Internet build out days just because it's built on the back of the previous infrastructure stuff. I also want to comment on the Google thing you said which is like imagine if they could get 200 bucks or something from users. The beauty of as consumers, the beauty of like Google, Facebook, Apple for us is there's not like a clean way to price discriminate for those companies. Like, like if, if, if you know, like they don't know that I'm willing to pay more and they can't charge me more than you know, the other user of an iPhone. But in the case of AI, because of the way the business model is structured and we've already seen some proof points of this, I think there will be greater success. So just today, or I guess yesterday technically OpenAI released their India subscription product and I think it's something like three or four bucks a month that makes total sense. In the US there are high end subscription products that are 200 to 300 bucks a month that are like flying off the shelves like consumers can't buy enough of. And so to me, the real story of the growth in this market is there's going to be an evolution of the business model that allows these companies to address the user base and actually price discriminate, I think in an effective way. So they can do a combination of subscriptions for higher end users and sort of get to the point where they're willing to pay more. And then also probably end up with freemium products where they monetize through some form of advertising. It's hard to speculate now on what that would look like. I think it's probably some form of an affiliate type thing that has like a dirty connotation because that's kind of a backwater industry in today's Internet. But I think it will end up looking like that. And I think, I think the way you see, like one way to see it in the product is if you haven't done this already, go into like one of the Deep Research products and sorry, this is Catherine Boyle calling me about a deal I think, and I don't know how to turn this call onto silent. So one way to try this out in the product is like go into one of the Deep Research products, either, you know, whatever OpenAI or Grok or, or whatever it may be, and have it do like a really sophisticated shopping research project for you. And it comes out with incredible stuff. Like I had it, I had to do this for my son's literally baseball bat because it required a bunch of different specifications and things. And I wanted to look at year over year like, you know, what's the better value and all these things. And it came back with like extraordinary answers. And this is a far superior, you know, experience than research, like typing into Google and then clicking around and you know, having seven sponsored links above, you know, seeing anything organic. So I think there's going to be an opportunity for them to monetize for users. And so the big story, you know, in the case of OpenAI is like there's, you know, whatever, probably 30 to 40 million paying users today. The other platforms are kind of a rounding error relative to that, so maybe add another 10. So there's like 40 million people paying for this stuff today at some level. And you know, there's probably 2 billion using it. So like you know, and again Facebook, Facebook and Google monetize their properties at like, you know, for us users call it between 150 and 200 bucks a year per user. So there's just a massive amount of opportunity to monetize. And on the consumer side there's probably going to continue to be tremendous surplus. I find tremendous surplus in it today. But you know, active, daily, active users of ChatGPT already today spend like 30 minutes at 20, 28, 29 minutes a day on the product. And you know, to put that into context, I think Instagram's like 50 minutes a day and you know, TikTok like sadly is like 70 minutes a day. But like this is like real time spent and real sort of consumer value already. So I just think, I know the question was about like risk on the supply side and the infrastructure build out, but the usage, the actual usage and distribution that we've seen this time around makes me think that it's kind of different. Like we, we have a really good view of demand signals that is far that took many, many years to get. You know, in the case of, you know, the Internet or even in the case of mobile phones just because, you know, you had to manufacture phones and convince people to buy them. So you know, in that way it's built on the back of the previous technology cycles and it, and that's great but it also in my mind de risks the sort of forward growth potential of the, of the AI companies that we're investors in.

C (16:30)

Totally pulling up the website of ChatGPT and doing it, being able to do it accessibly and almost like the party trick of like hey, let me show you the cool thing that I just did like that usability obviously is very different than we, than the prior cycle where we literally had to wait for the infrastructure to be built out and the device and hardware to catch up as well. Our early stage team also did a great post on this topic. If you're curious about the future of commerce, what that looks like with AI and even the recent, if you have any folks on this call who spend time on a public side, you know, one of the things that has been significantly observable is the number of public companies who've reported a decline in referral, traffic and engagement largely because you know, with Google search now they're just doing the summary, the AI summary version of the result below and we could talk about the implications for what that means in terms of the downstream effects as a part of that. But that is certainly something top of mind. We're hearing a lot about from folks in the Fortune 500 just given. You know, obviously this reorients your business on how do they engage with the consumer when you could actually do a really detailed search as David described with, with his son's baseball bat, without ever actually having to go on any website at all.

B (17:43)

I told my son, I'm like, you have no idea how much research I did, man. I, I, I scoured the end of the Internet for you, buddy. And it all dads unite in their.

C (17:56)

Desire to have endless amounts of research on, on, you know, kind of arbitrary things to say the least.

B (18:03)

Here, I'll, I'll send that. There's, there was a good little snippet on X about some of that Google search traffic stuff. It was like IAC and car gurus. Yeah.

C (18:14)

Oh yeah. The earnings call that Martin dropped the other day just so folks can see like again, it's the folks usually what's expected, right. Like folks like Groupon for example, iac, who are seeing the impact in real time. There's one question from Chris we should take while we're just on this slide here and I'm going to throw in another one that's related to it around the shifts in bottlenecks. Right. You know, right now there's obviously this massive bottleneck as it relates to computer, but Chris's question was, is there enough energy to actually power this build out as well? And you know, we should lay into there what we think the next bottleneck actually is beyond energy as well.

B (18:55)

I mean, yeah, as of right now, through our current means of energy production. Yeah, I mean energy is a bottleneck. And so, you know, we've made investments on the nuclear side. I'm quite optimistic that, you know, we now have, you know, like an embrace, I would say, of, you know, nuclear power. I think Three Mile Island's going to get powered back up. The big tech companies are building data centers near, you know, nuclear power plants. You know, we have figured out there there is a lot of natural gas in, you know, places like West Texas that, you know, can be, you can, you can, you can build training large training clusters like very near to them and pretty efficiently power those data centers. But yeah, we're going to find, we're going to need different sources, I think is the short answer. And we're probably most optimistic about nuclear for sure. And then, and then the ability to just build these things, I mean they're like massive scale operations. And so, you know, like one of the most remarkable things we're, we're large investors in XAI and, and one of the most remarkable things about what XAI did is they stood up the biggest data center at the time in you know, like a quarter of the time that anyone else had done the same thing. And they had to do crazy, unnatural things like, you know, get, you know, every backup generator in the, you know, multi state region bought out and, and you know, buy, buy labor off of different projects. But, but they did it. And so just, you know, the actual construction and build, you know, is massive. My view on like chips and infrastructure is production capacity of those will typically scale to meet demand. There's, there's always a dislocation and you know, you've seen it. And so, you know, I think energy ultimately in the Next, call it 5 years will probably be the bottleneck and that's why we're so excited about nuclear and making investments in that area.

C (20:55)

Absolutely, yeah. And just to extrapolate out, you know, once we figure out that piece, which inevitably, you know, with any technology we always do, if then the bottleneck just shifts to another. The big component that I think most folks have not yet realized or zoned in on is the cooling piece. And so you'll see a whole wave of innovation around that part as well. Once you figure out how to generate all this energy, how to actually cool all this stuff down without boiling our oceans. And making the world melt down.

B (21:25)

And making the chips melt down.

C (21:27)

And making the chips melt down. Yes, that's right. There's one question here, David, if I can interject because you are the business model snob, so this is a perfect question for you, which is there's a lot of debate on whether the gross margins of a lot of AI companies should be more scrutinized, I. E. Particularly kind of, there's a lot of turmoil around, for example, the relationship between cursor and anthropic and whether, you know, the growth for a lot of companies might be actually masked by a reliance on some of these models. And also what is the actual unit economics that is considered best in class? Maybe help us distill how you and the team think about that. And particularly, you know, what is this topic around gross margins where you're willing to make some short term exceptions for versus long term, you know, hopefully benefit and output on the other side.

B (22:21)

Yeah, I, I love this topic and I, I, I would just say that like the reason that this industry and this job is so fun right now is because the range of outcomes is so much greater than before. Like the variance is so High. You know, I, I was talking to the team at the off site yesterday and I was like, you remember in 2000, some of them weren't in the industry, but you know, the ones that were in the industry like.

C (22:43)

But I'm more important too.

B (22:45)

I know there's some that weren't born yet. Yeah. So some young folks who are very AI native and very smart. But you know, like late cycle. There's questions that you are trying to answer that are interesting but they're far less interesting. So you're like, oh, how just how big can datadog get? Or just how big can, you know, whatever, pick your SaaS app, like how big can it get? And now we have all these questions around business quality, market power, who the winners are going to be? Even if you are a winner, is there going to be value that accrues to you and where you are in the stack? And so just the range of outcomes is so much higher. And I think if we do a good job in that period of time, what that means is hopefully we can get greater degree of variance in our own outcome as investors. And so I'm very excited about that. So yeah. So what are, what are we thinking about in business model? So one sort of value proposition to the customers is the number one thing that we care about. Like is there customer love of your product and is that love enduring? So you know, if you, if you made me pick two top line stats to look at to assess the business model, it would be gross retention rate. So gross. Because you know, I like looking at, we have, we obviously get to look at net retention rates too. But for gross retention rates, it's sort of like are people getting value out of your product? And what gross retention is, is basically if you have a hundred customers, you know, in absolute terms, you know, dollar weighted, how many of them are sticking around? And so we look for things where like 90% plus customers are sticking around and hopefully they're expanding their usage. And so that would be expressed in, in net retention. But just sort of core value proposition is shown in gross retention and then ease of customer acquisition. And so the way you see that is like organic customer demand, you know, high value of dollars that they're willing to pay relative to how much it costs to acquire them either via marketing or sales. And so if you have things that are sort of like being pulled off the shelves and you have high endurance of the customer relationship, that's typically like the best things that you can get for, you know, business model quality on the top line, you mentioned gross margins. So we care a lot about gross margins and there's a bunch of debate right now around some of the AI native application companies and their gross margins. I think our hypothesis and hope in the market is if there are multiple model providers that are somewhat close to parity, you're going to see input costs go down significantly over time. If you recall, you know, 100x decline in the input cost over the course of two years. The hope would be that that continues and all indications suggest on our side that that will continue and maybe it abates a little bit, but it will continue pretty significantly down over time as long as there's competition at the model layer. And so, you know, coding is one of the areas that people have spent a lot of time scrutinizing in this area and sort of assessing the business, you know, business models and business quality. I'd say relative to like mature SaaS apps, we probably are a little bit more lenient on assessing a company's gross margin today because we strongly believe that their input costs are going to go down over time. And, and because of the model improvements, they'll be able to harness better models and deliver better products to consumers over time. So they won't need to increase price, but they'll deliver a lot more value and stickiness while their input costs go down. That's the hypothesis. I think that's subject to there being multiple players in the market that serve models. Now we're thrilled that GPT5 is out and it's a very credible alternative and it will put pricing pressure on anthropic. Google is also very focused with their Gemini models on coding and we've seen a bunch of really good improvements and promising progress out of them. So as long as there's multiple players in the market, I think you'll continue to see costs go down. And again, in light of that, relative to, you know, sort of mature industry type SaaS stuff or infrastructure stuff, we're a little bit more lenient, you know, know, on assessing a company's gross margins today. We, we don't want to go invest in a bunch of companies with zero gross margins and we don't do that. But you know, if you, if you sort of took like gross margins, retention rates and sort of like ease of customer acquisition, like I, I'd place far more emphasis on making sure that we feel like there's greatness in those latter two and you know, give them a little bit more benefit of the doubt that they can improve on, on the first for sure.

C (27:43)

Yeah, I Flipped back to this because I've seen this personally myself like a dozen plus times. But something you said there really just clicked for me. Which is to say a lot of people try to make comparisons to the.com era and they're like, oh well, remember we measured eyeballs as well, right. Isn't that the akin to in terms of retention or usage? And it's like yes and no. This is reaching such an en masse so quickly because of all the reasons you alluded to earlier, David, in terms of ability to just pull up a website and actually try it. But also people are paying for this. And it's an interesting juxtaposition when you think about the last cycle of things we paid for, so to speak, like a Spotify subscription or a Netflix subscription, where as soon as obviously pressures come from a budget standpoint, those are first to go. But this, you'd probably sacrifice a few things just given how much of an impact it has made either professionally for you personally and accelerated your productivity and hopefully time in terms of acceleration as a part of that.

B (28:40)

Uh, there's this question I'm happy to take Thomas's question.

C (28:44)

Yeah, it's from. This is from Thomas. So it seems like there's downward pressure on the likes of OpenAI on consumer pricing, yet the cash burn of open AI is ramping up to levels not seen before previously, you know, reports of a billion dollars plus per month. How does the cash burn moderate in the future relative to what you think in your opinion?

B (29:02)

Yeah, so, so, yeah, so. So what I was saying is like, I think effectively, like there's greater consumer stickiness than you would think and there have been a tremendous amount of free alternatives thrown at consumers over the last 12 months and it hasn't had any impact on their business. Now that could change over time, but so far what we've seen is, is no effect that creates price pressure. And if you think about what I had described earlier, which is like call it a billion people using it and only 30 million people paying for it, I think there's way more upside to monetize the base than there is risk of price pressure on, you know, today's 30 million people paying for it. And so, so I think there will end up being like if it's a P times Q and this is, you know, we're talking about chat GPT, I would say it probably applies to, you know, most of the consumer facing stuff in the industry. If there's a P times Q which is like price times quantity, which is the really simple way to think about these consumer Internet businesses Q is like at this point they've gotten so big on the, on the monthly active users, like over the course of the next five years, like maybe it gets to 2 billion or something, I don't know. But like the Google and Facebook properties are in the twos billions so there's, it only gets so large. But I think there's a tremendous amount of room to run actually on the upside on the P, on the price. So. So again if you think about, you know, how are they monetizing today? You know, it's 30 million people out of a billion, you know, at a modest subscription. That is not really reflective of like real price discrimination yet. So I suspect the P is probably like a thing that we get surprised on the upside by. You know, I think about like lessons learned from previous Internet era companies. And I remember looking at Internet companies 10 years ago and we would, we would always look at like Facebook and Google and we're like okay, Facebook and Google, they're monetizing their users at X, like that's the max we could get to. And the big story about what's happened over the last 10 years is they've like 8x'd their own monetization of their users. And so I suspect that if they have some thoughtful ways of monetizing free usage while still maintaining trust, there's probably more upside than downside. On pricing to Thomas's question and then on the burn, the, the actual like most of the burn comes from re like research like R and D, you know, and so few future investments. And you know, we could apply this to the whole industry of all the model companies. But we could, you know, we could talk if, if it's specifically open AI, I'm happy to address that one. But on the OpenAI side, I think they're an advantaged position because they have the consumer base and that's stickier. Like my family, like my parents In Kentucky use ChatGPT. Like if there's some better, slightly better model that comes out, like they're not going to switch. And so I think that's pretty durable. And so it's probably better, a better position to be in to fund those research efforts for continued model development. You know, there used to be like a thing which was, you know, enterprise companies are stickier than consumer companies. In this case they're developer like these are like developers buying these things on the B2B side for the most part today, like buying kind of, you know, raw access to the models. And that's not very sticky yet. I think it's possible that it does get sticky over time, but as of right now it's not very sticky. So to the point about coding models, you know, if there's a new coding model that comes along that's better than the latest version from Anthropic, like our coding companies will just switch and it's pretty easy to do because it's an API call and so, so you know, I think interestingly enough it's a little bit different this time where the consumer's a little stickier. I think that gives you an advantage and I think the companies will not irrationally spend on new model development if there's not a financial return. I would say one of the things that we've observed over the last now almost five years of spending time with these companies is a lot of the founders started as like research brain AI people that were like we're going to, there's going to be no economy and like everything's going to end because we're going to have AGI and it's like, and then you know what like has happened, like competitive forces have kicked in and they've become like hard capitalists. And so you know, my, my expectation and from conversations with them, you know, on an ongoing basis is, you know, they're not going to do totally irrational things on the research side if there's not going to be a financial payback for them.

C (33:57)

So can DG also comment on durability of revenue for many of the AI applications built on LLMs that is outside of OpenAI anthropic XAI?

B (34:08)

Yeah, it depends on the nature of the use. So I think some of them are really sticky. So you know, companies, you know, like medical scribe stuff I think is pretty sticky because there's a bunch of Dr. Workflow built around it. You know, customer support I think is pretty sticky. Some of like the high end financial analysis type stuff I think is pretty sticky.

C (34:38)

Can you explain why those specific areas you think are more stickier than others?

B (34:42)

Yeah, I think the more stuff that gets integrated and the more company specific, like kind of rules built around the model stuff you have the stickier it's going to be. And so you know, I think stickiness comes in the form of like in software, in, in applications like the same way it's kind of always come with software. I'm sure one of my early stage partners has written a blog post about it because we talk about it all the time. But you know, it's stuff like integrations, you Know, rules, engines, workflows, you know, and stuff like that and inter, you know, sort of enterprise capabilities.

C (35:19)

So like I think customer for example, the rules to go down, the sequencing of how to troubleshoot are so embedded that you probably wouldn't experiment a ton once you've got a workflow, you know, across multiple different scenarios that.

B (35:34)

Yeah, yeah, or, and even like a style with which something engages. Like these, like a lot of companies that are in customers of these things are like brands and they care about the way that you know like the, the customer support agent interacts and so, so I think that stuff's probably pretty sticky. I think there's a bunch of stuff that's not sticky at all. So some of the emergent behavior that's like, you know. Yeah, I don't want to talk negative about anything but like some of the stuff that's like not as sticky is like experimental usage of tools to build, you know, some of the internal tooling software replacements or like very low end prototyping of websites and things like that. I just think it's like tbd, you know, who the players are and what the use cases are and I think, I think the market for that will probably segment out. Like we're already seeing it some where some of the tools are just being kind of used for prototyping and then some of the cool tools are being used to like actually build and deploy apps and I think you'll see, you know, kind of continued bifurcation of those things. But you know, it's so early. I don't think that like companies are going to vibe code up like their salesforce.com it's just not worth it. Like it's not like core competencies. I hope they would. I wish we could vibe code away.

C (36:57)

Our salesforce.com okay, I'm going to speed run through some of these questions because I recognize we've opened the floodgates on them and I want to make sure we get through as many as possible. So Umberto asked, given the speed of go to market for AI companies, do you think 100 million ARR is the right milestone to measure against or are you starting to move the goalpost on what success is there?

B (37:17)

Yeah, it's so funny. Like you know, it used to be, I can't remember One of the VCs coined the term, you know like triple, triple, double, double or something like that. And it's like that looks like very modest, something like that. Yeah. And then that time compression has like massively gone down. So I gotta look up the stat. We ran the analysis on it recently but it was like the top companies that we've seen have gotten to 10 million, then 100 million four times faster or something like that. For us it's really important that we have market context and real time market context to make judgments about what great looks like. And so I don't have an absolute answer for you but if you were to sit there and look at and like and listen to one of our investment discussions, we are, we are comparing like the growth of you know, XYZ new app to like Cursor and you know, nothing looks like Cursor so it's unfair. But like Cursor and Decagon and a bridge and 11 labs, you know, and not like Shopify and Docusign and you know, those companies. So you know, if you're not like fully in the market and seeing everything and meeting all the companies, you're not going to be able to have that context to assess what grade is at any given time for sure.

C (38:48)

For AI companies. What do you think is a good question from Aditya? What do you think about seat based pricing versus usage space and what are the experiments that some of the startups you've seen do well around configuring pricing in this age of AI?

B (39:03)

Yeah, this is like the big question. I love this question because I feel like on X and other blogs people have written these really long posts about the new prices that are going to happen with AI and to me it's like a little hand wavy. One, it's subject to innovations in the technology side and the products getting better. And then two, we've really only seen a true business model innovation like early, early days in one area and that's customer support because you can kind of like definitively resolve a task. So even with that like it's hard to do. And so, you know, the question maybe to reframe it would be like we had actually we had like licenses, right, like you know, perpetual licenses with maintenance and then we switched to SaaS and that was mostly seat based and that was huge innovation, very disruptive. That was a huge enabler of the startups actually going to beat the incumbent software companies because it was so disruptive at the time. Then we got to usage based and so you know, obviously all the cloud companies run that way, you know, databricks runs that way, snowflake et cetera. And then the hope would be that I've seen and heard about is like with AI, can you just monetize the replacement of tasks Humans do. And so when I talked about the customer support piece, that's the furthest along in the monetizing the tasks that humans do. But that's super, super early in all the other areas. I think it's really early. Like end customers want to buy stuff on seat and consumption based pricing and you know, you kind of got to meet the market where it is. So for now we're not seeing hugely disruptive things. I think as the capabilities get much better and sort of measurability of the task completions gets more objective, perhaps maybe we get there. But I would say it's like super early days and I'm low conviction that we end up, you know, five years from now with all the software companies monetizing in a completely different way.

C (41:24)

Absolutely. I've.

B (41:27)

By the way, sorry, there's one more thing. This also goes to my point that I made earlier about surplus. Like it's going to be really hard to capture that surplus unless you can monetize based on the value of the completed task. But because that's going to be really hard to measure and there's going to be competition in the market, I suspect that a lot of the surplus or savings may end up in the hands of the customer. Again, you could probably still build really interesting good companies that have really high market cap. But that is like, you know, sort of directly related to who captures the value and the surplus. Like, like, like I always say, like the steam engine got invented and like you didn't price the. Like the steam engine didn't get priced based on like the calculation of how many, you know, humans it replaced in doing a specific task. Competitive forces kicked in and you know, it was priced at some like appropriate, you know, competitive level with a return on capital while still like capturing a lot of value but delivering much more value to the end customers or the users of the steam engine. I think the same will happen here for sure.

C (42:36)

Actually it's a good transition for us to actually talk about what this all means for growth given this setup.

B (42:45)

I try and sit in on as many pitches in our early stage practice as I can just because I feel like I learn a lot about what are interesting founders starting to hone in on and what areas are they building. And our early stage team is doing a great job of doing the most exciting early stage deals. So that to me just bodes really well for the next 12 to 24 months of deal activity for us looking out. All right, growth partner. So this is a crazy looking chart. I mean it's not surprising because it was like the first thing we talked about which is, you know, tech markets are big and of course companies are staying private longer. But, but you can see it in the chart here. It used to be that companies would go public, you know, within call it like 5 to 10 year window of, of inception. And despite the fact that the companies are growing much faster and getting better, quicker, they're staying private way longer. So you know, all, all, is that all that is to say, you know, it's like 14 years and I think that's getting, getting even longer if you take the market cap of you know, the, the private markets valued above a billion dollars and you know, we could argue are some of them overvalued, undervalued. But that whole value in aggregate is like three and a half trillion dollars and that's like 11%, three and a half trillion is like 11, 12% of the NASDAQ or 10% of the NASDAQ, something like that. If you go back 10 years ago, that whole private market cap of three and a half trillion was like 500 billion. So over the last 10 years the market cap of these private companies has like 7x. So there's huge growth. Like again that is related to this point which is some of the best companies taking longer, deciding to stay private longer. But you know, it's, it's pretty, it's pretty stark. So the other thing that's going on, I mentioned this earlier with Snowflake is like the public markets are no longer the place of extremely high growth like it, because of this and it's sort of logic, it's logical, it follows. Right, but something like 5% of software and Internet public companies are forecasting 25% plus next 12 months growth. So 95% of the public market universe in software and Internet is growing less than 25%. So if you want access to like the, and this is, it's a little bit too bad because you know, there's obviously implications for retail and stuff like that. But the reality of what's happened is the high growth segment of new technology companies is all living in the, in the private markets now, you know, for the most part. And so I don't see that changing. I do think, you know, Figma and a bunch of good companies, like I think a bunch of good companies will go public. But this is a trend that you know, is pretty long standing and I don't see it reversing anytime soon. So we have a bunch of really good AI companies. Some people have asked, they're like oh, isn't there like crowding that's happened in some of the best companies? And what I would say is like one, we're getting in a lot of these companies much earlier than others are able to and two, even for the ones that are later where you know there are bigger rounds, like it's really important that we have that early relationship because it gives us ball control and access into the rounds and xai first outside money and beyond Elon. So not only you know, and a lot of these are growth fund kind of first money in. So you know, it's important to get in earlier to generate returns but also to position us, you know, with management and to have access to shape litter rounds. If I were just to spend a second on you know, how we have like our shaped our AI strategy there's basically two parts to what we're doing. One is the companies where they're like flying off the page, you know, like undeniable momentum. So you know, companies like Cursor and decagon and 11 and abridge and you know, others. The second bucket is very early deals in the very, very, very best teams in the market. And I say very, very best teams in the market, like with emphasis because I think we're talking about like the top five teams in the world and anything beyond that, we're not doing these companies. They're, they're sort of a different shape, they're growth dollars earlier than normal and there's a higher degree of variance in the business outcomes. But because the teams are so special and so great, we feel like business risk looks very different from capital risk. And so we, we feel like there's, it's kind of, they're kind of asymmetric bets where the asymmetry lies in the fact that we're probably downside protected because the team quality is so high and there's so much demand for talent. Like we would be downside protected even if it doesn't work out. And so high business sort of variance but sort of asymmetric capital or returns profile that looks a little bit different than a typical early stage investment.

C (48:09)

They talk about timelines to exits because on one dimension folks may take the reaction of gosh, private for longer means just extension of hold periods for LSV companies. Maybe the reframe on it is why is that great for, for us but frankly as private investors and why do we think it's actually advantageous in some respects for some companies to actually delay time to ipo? And how are you counseling folks as A part of that?

B (48:36)

Yeah. I mean, this is, it's a trick. It's a tricky issue. I mean, I said it right up front, which is like, we get paid on, on DPI too. Like, you know, that's, that's, that's how we, that's how we compensate ourselves and our teams. And so, you know, we care, we care a lot about it. I would say we're in a fortunate position where our portfolio is, is pretty good. And we've had a number of companies that have, have exited to the public markets or sold themselves. And so, you know, we're not in a position where we haven't been able to deliver some liquidity. For those sort of champion companies that have been private for a long time and, and are getting a lot of coverage. I think each kind of has idiosyncratic reasons why they've wanted to stay private. And I think I'm confident that they will go public. So I think most of them will probably end up going public. It just will take a longer amount of time. One of the big things that we've observed is the private markets have adapted to some of what you get from the public markets. And we've fortunately been very active in some of those situations. So things like tender offers in the private markets. A big thing that is hard for private companies is competing for talent. And part of the reason why it's so hard is because public companies grant RSUs that typically vest on a quarterly basis. And so they hit your account, they're already tax withheld, and it's basically like getting paid a lot of cash. And it's hard for private market companies in some cases to compete with that. So, you know, some of the bigger, better ones have done more regular tenders, and we've been very active in shaping those with the company. So to me, it's a balance. We kind of want what's best for the company. If there's strategic reasons why it benefits them to be private, that's great and we'll help enable that. But we do recognize that obviously, you know, our goal is to, to monetize great investments. We're not there yet where we feel like there's a need to do unnatural things. I just don't see that being the case for sure.

C (50:41)

And I do want to call out as well. I'm going to brag on behalf of you, David, because I do think DPI generally is an issue and challenge across the industry. I don't think it is our issue necessarily. Okay. I'm going to also cover A question here. Thank you for letting me do our commercial, David, on dpi. You know, it's my favorite topic on the topic of when you think about some of the names that we have underwritten, I think there is this broader question of, gosh, there's a lot of names, for example, like the Chat OpenAI's of the world, and also databricks that feel like they're doing more of these tender offers, they're doing more of these kind of SPBs. How do you counter that portfolio composition of names like that versus names like an anduril or flock, where it's probably very, very difficult to get any access. And how do you think about the configuration of the portfolio as a part of that?

B (51:40)

Yeah, I mean, look, I mean, the opening investment that we made, you know, we, we were able to write exactly the check that we wanted to write, you know, in terms of sort of portfolio construction. So to me it doesn't seem. I'm sure some folks have had access because other managers are trying to do SPVs and stuff, but I think that's increasingly hard. It's also sort of our decision on how much to weight these opportunities and build a portfolio. So I happen to think that the return profile of that recent one and databricks is very attractive, but we don't want an entire portfolio of things that we think are like pretty safe. Two X's, you know, where I think the question is, can we make 5x on them? But like, we feel pretty confident we can make 3-4x and 2x is pretty safe.

C (52:34)

Awesome. Speaking of public companies, we had a question from, from Paige here on. Well, actually it's, it's two related questions. What publicly traded software companies do you think won't be disrupted by A8AI and have real moats and any obvious ones that you think it will be. And related to that, I think we had some questions earlier on around how much, if at all, any public companies you do in the fund?

B (52:58)

Yeah, probably very few public companies in the fund. I mean, we'd have to have a really, really strong thesis and relationship with the management team to want to do that again. The public universe is just way slower growing and there's really, really exciting stuff that we can do in the private universe. And so the bar is extraordinarily high, you know, for things in the, in the public markets. And we've done a couple which have worked out, I would say. But you know, the bar is very high and the, the opportunity cost is probably something that may be even higher growth in, in the private markets. Yeah, the what, what companies are going to get disrupted in the public markets? Like I love this question. I don't know. All, all I would offer this framework which is, which is maybe there's three interesting ingredients to consider when you think about the safety or durability of the, of the public software companies. One would be, you know, UI ux. So if, to the extent that we get a complete reimagination of UI UX, you know, that'll be really exciting. Like what is salesforce.com salesforce.com is like a set of really uninteresting checklists and forms that people fill out the most part on the front end. The promise of this stuff, you know, agents is like a way overused term but like the new technology should be able to like do things for you as opposed to like keep your records for you. And so you could imagine a completely reimagined UI UX which is proactive, which is like, hey, instead of you going and inputting things, I'm, I know what you're doing, I'm just going to tell you what to do. And so a total reimagination of the workflow would be one ingredient for a startup to win versus an incumbent. The second would be access to data. So you know, an entirely new form of data that gets sucked in to take the actions on your behalf. Like Salesforce. You know, I mentioned the form thing on the front end. The reason it's really, really sticky is the database on the back end. So to the extent that instead of using that sort of structured database on the back end, you take all of your unstructured data and dump it into a databricks for example, you know, and, and query it or access it through that. That would be another interesting sort of opportunity for a startup to attack. And then the third is a business model innovation. And I mentioned this already, it's super, super early days. But to the extent that startups can come up with a sort of like novel way of shifting the business model in a disruptive form against like seat based pricing, salesforce.com, you know, it would be, it would have a chance to win. So I think for startups to win, I think you need all three and that's just in the head to head stuff. I think startups are already finding interesting windows of opportunity in and around these systems of record which we've made a bunch of investments in. We haven't found the startup that's like got the killer idea for like dethroning salesforce.com yet. I hope we find it. So I know that's an unsatisfactory answer, but at least it's like a. That's the framework I would use to think about those three buckets.

C (56:13)

Awesome. Okay, I'm getting the hook because I know we only have a few minutes left of this webinar, so maybe in the last couple minutes we'll fast forward here to talk about the team. I don't know why it took us over an hour plus to get to this point. So maybe we'll go through that and then David, also how you work with the broader A16Z team. And then I'll bring us all back home with how this all fits in in the broader franchise and firm as well.

B (56:37)

Yes, I love the team. They're very, very smart. They've come together in an awesome way. We have a really strong sort of team subculture. You know, we are very, very, very intertwined with the early stage teams. You know, if you think about like where we get alpha in our business, you know, there's access, which I've talked about already, you know, in terms of like, you know, being already involved in the companies and, and 80% of the time when we make a new investment that's not an early stage investment. One of our early stage folks has some pre existing relationship. So, you know, Access is a big piece of it. Insights is the other piece. Like I think the way you get really, you know, like outsized, upsized, you know, returns in our business is market and product insights. I mean, I think you, you have to do all the analysis around business model and financials because you can make big mistakes if you get that wrong. And we go super deep in each of those. But I think the real outlier opportunities come when you have a market or product insight that maybe the rest of the market hasn't figured out yet. And you know, the fact that we're attached to the early stage team gives us the opportunity to have those and gives us a tremendous amount of leverage to have a relatively small amount of team, small amount of team members given how much, how much ground we cover.

C (57:53)

I will also take the opportunity to brag on your team and my team because I think both of our teams got a 91% on the employee engagement score. So not that we're competing across teams, but you know, this is a good barometer for team subculture, no doubt, and something we always, we do around the firm year round, which is very company like, but hopefully gives you a sense of what we're measuring ourselves across as well in terms of team culture. But I want to spend a minute to illustrate the point that David alluded to around how we work in collaboration with the early stage. And also think about the late stage venture in terms of sitting across all the six different early stage buckets across the firm. And so maybe David, if you want to give just like the high level talk track on if you were to hypothesize what the composition of the portfolio would look like across these six different buckets, that'd be helpful. And then I saw a question around how much we would expect around crypto investments and particularly around token crypto investments. That would be helpful.

B (58:55)

Yeah, absolutely. The best part about where we are is we've got people at the early stage of figuring out where they think they should be spending their time. And typically, you know, our world is like 12 to 24 months downstream of that. So when you see the way we size our funds at the early stage, that's a reflection of what those early stage teams think is their opportunity set and ours sort of follows that. So I think the largest amount of opportunity will continue to be AI infra AI apps next American dynamism. Our early stage team is killing it in that area. Beyond just AI, there's obviously oppressing know sort of market and world need for American dynamism companies. So you had, at the same time you have market need, you had people that figured out how to do this inside of SpaceX and, and Palantir who have then left to start companies. So the talent is there to navigate, you know, a very complex go to market motion. And then you have advances in technology beyond gen AI like autonomy capabilities and vision, advances that enable American dynamism. So we're very excited about that area. We're starting to see a little bit more interesting stuff in AI enabled health things. So we've done a couple. You'll continue to see us active there, but it's probably a little bit less than the AI infra and app side. And then crypto, the way we're doing that is working hand in hand with Chris and the crypto team for their high conviction bets where they're at a stage that they fit the growth fund and I brag on them. I think they're the best crypto investors in the world and we are happy to attach ourselves to that. It certainly depends on the opportunity set. We're seeing really exciting stuff in the enablement of stablecoins and so to the extent that we see a massive takeoff in that market, it could Be more. I always say it would be great if 100% of our investment activity in the growth fund was follow ons because that would mean that our early stage team is absolutely killing it and they're getting a lot of market share. But I think there will always be a place for us to do really attractive new things and we don't build the portfolio based on some target around new versus follow on. It's really best ideas when, where do we have access, right to win, etc.

C (61:23)

Great. And then less than 60 seconds. Do you want to take Annie's question? So on the topic of portfolio construction, how much exposure do you want to top research teams with a wide fan of outcomes versus opportunities with a more narrow range? How many more researchers do you think are actually out there beyond the ones that you've already backed that you will get more exposure to?

B (61:45)

There are extremely high end researchers at some of the big labs that we, we, our early stage team is tracking and I should have mentioned this when we do these, we do them with the AI Infra fund so we would not, you know, we, we rely on them heavily to make sure that we're doing the right assessment of the teams and you know, the research ideas that they're pursuing. You know, in terms of portfolio construction, I, I, I quite like the way that we've done it where you know, we have some, you know, absolute champion companies that we, we think actually have like a lot of room still to run where on the downside we think we would if things really go poorly, like we'd still probably make two times our money. And on the upside, you know, there may not be the opportunity for 10x over five years, but we think there will be the opportunity for 5x over 5 years. The research team with the high variance is a little bit of an output of that great opportunity set. Like right now there's not another Ilia floating around in the AI market. And so we would never try and say, oh, we want 10% of our portfolio to be in these. So we need to find someone, it's more reactive when we do find those people who are really special. I think the only shift that we'll see over time is I think you're seeing a bunch of really, really, really exciting AI apps and American dynamism companies at the earlier stage that are poised to become kind of champion companies over the next five years. And so you'll probably see a lot of activity from us in those kinds of companies.

C (63:17)

Awesome. And with that I'm going to close out here lastly, thank you David. Appreciate it.

B (63:22)

Great to hang as always.

A (63:26)

Thanks for listening to this episode of the A16Z podcast. If you like this episode, be sure to like, comment, subscribe, leave us a rating or review and share it with your friends and family. For more episodes go to YouTube, Apple Podcasts and Spotify. Follow us on x16z and subscribe to our substack@a16z.substack.com thanks again for listening and I'll see you in the next episode. As a reminder, the content here is for informational purposes only, should not be taken as legal, business, tax or investment advice, or be used to evaluate any investment or security, and is not directed at any information investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details, including a link to our investments, please see a16z.com disclosures.