AI, Infrastructure, and the Next Investment Cycle Transcript, AI Summary & Key Points

00:00 Eight of the top 10 valued companies in the world are US tech companies. >> Since JBT came out almost 4 years ago, the market's up 90% which is 17% annualized. The natural instinct is well that's got to come down. We're definitely in a hot period. >> This puts us in a new age of atoms. Global infrastructure investment needs are estimated at $90 trillion through 2040.

00:20 This goes way beyond AI and data centers. It includes power, water, roads, transit, >> live deployments at S&P 500 companies, that's at 69%. Now, if you go to the ultimate barometer, which is a metric tracked over time, that's actually only at 2%. AI is generating major revenue and savings. On the other hand, adoption is still extremely early. >> Today's opportunity is so much around taking these capabilities and harnessing them to build reliable services.

00:49 That's all great, but is it a bubble? Um >> um um. >> Welcome back to the A16Z podcast. I'm David George. I'm here with my colleagues Sarah Wang, Alex Emerman, and Santiago Rodriguez. Today we are walking through 25 key slides from our latest state of markets presentation. the earnings behind the market's rise, the scale of the AI buildout, the evidence of growing adoption, and what this cycle means for hardware, software, and the next generation of private companies.

01:24 We'll explain what the charts show and discuss what we're seeing inside businesses along the way. So, you can follow along whether you're watching or listening. So, Sarah, Alex, Santi, thanks for joining me. >> Of course, >> the day this podcast goes live, we will be releasing our state of markets presentation. And so this is a now yearly tradition from our growth team where we synthesize the biggest trends in tech, AI, infra and markets.

01:47 Uh today we're just picking out a subset of interesting slides and having a discussion about them. So I would direct you to uh look at the whole version which has which is filled with a lot of nuggets. Um the areas that we're going to discuss today are um macro so where we'll talk about capex data centers accelerating demand at a high level and then on the ground takeaways where we will discuss models apps uh and some vertical deep dives.

02:16 Technology is driving an economywide investment boom with rising earnings supporting market gains and AI demand pushing infrastructure spending far beyond earlier forecasts. Hyperscalers are investing all of their near-term operating cash flow to build out this capacity for demand that continues to outstrip supply in almost every case that we see. They are channeling this investment into chips, power, cooling, construction, skilled labor, and much more.

02:42 This expansion coincides with a broader need to modernize physical infrastructure, creating opportunities across industries and potentially lowering shared costs for businesses and households. So with that, let's jump in. First slide, tech is the everything cycle. So um this some of this may be a little bit obvious, but um the numbers are are somewhat striking at this point, right?

03:06 So high-tech equipment, software, and R&D now account for roughly 55% of US capital spending, which is just a staggering number. Um tech is driving the investment cycle obviously across all areas of the economy. uh from software and models to power construction and industrial capacity. Tech is almost 40% of the aggregate value of the whole stock market in the US.

03:29 Um and eight of the top 10 valued companies in the world are US tech companies. So this is a broad story. Uh you're seeing it in capex. This is heavily covered in in the data center buildout. Um it's obviously heavily covered in the amount of capital that's been going into the to fund the model development models model companies together raising I think over $350 billion um at this point.

03:52 Um and just to put it into into very deep historical context because this is probably the analogy that we've seen the most of uh this buildout just surpassed railroads as a percentage of GDP. Um, so you know, exciting times, massive buildout. Uh, we all happen to think that if you fast forward five to seven, maybe 10 years from now, um, you know, we'll be looking at these numbers and they'll probably be 20x higher cumulatively.

04:20 So >> yeah, this this does feel like the next chapter of Mark's software is eating the world. You know, over the last 15 years, software has transformed all these industries. uh but only a small fraction of the population could build it. None of us could. Uh but today, you know, look at us. We all have like, you know, a handful of automations running every night.

04:43 And so if you think about the software demand, that means a lot more compute, chips, power, construction. Um and so it's it's no surprise to see the majority of investment now now covered in tech. So one of the questions that we get all the time from various audiences is okay that's all great but is it a bubble? So the market has reached new highs um and at the same time that it has reached new highs the trading multiples of the market are actually down.

05:11 So stocks are up about 20% while multiples are down about 20%. So what that means is the performance is is driven by fundamental earnings right not not increased multiples. um the S&P 500 earnings multiple is below 20 times. So um you know if you just start with that these are many in most cases very high quality businesses. It's nothing like the dot boom in that way where some of the highest market cap companies in the world um had their massive stock runups based on increases in their trading multiples and would

05:42 would trade in many cases for like a 100 times PE. That's not what's happening here. Um in contrast uh you know some of the memory companies which again are very cyclical uh are trading you know for call it six times seven times forward earnings. So very very different >> and I think there's an important double click here where you know since JVD came out almost four years ago the market's up 90%.

06:03 which is 17% annualized. And I think anytime there's been a 17% annualized growth to four years, the natural instinct is well that's got to come down, right? Like we're we're definitely in a hot period. But when you compare that to, as you said, the market trading for below 20 times earnings growing 15%. This definitely feels a little different than maybe the 2021 period that was recent or the 2000 period when multiples in growth were not really going together.

06:28 >> Yeah, totally agreed. So I mentioned the scale of the capex buildout um you know in the context of of the railroads you know if you just look at the hyperscalers Alphabet, Amazon, Meta, Microsoft and Oracle their capex in 2026 is about $780 billion. That's up from $416 billion in 2025 and uh all expectations point to them spending over a trillion dollars annually from 2027.

06:58 Um so you know the pattern recognition is is each of these computing platforms has supported a much larger population of users and uses. In this case um this buildout can happen so quickly and demand can still outstrip supply because the amount of users is driven by the fact that there's already existing distribution. This trend is built on top of the internet and cloud computing obviously and mobile phones.

07:20 Um and so you know sort of immediately could reach billions of users um you know in contrast to previous technology cycles. Yeah, and I think it's um an important point to double click on just because we've moved well beyond this model of occasional queries, right? If you think about the rise of agents, you have parallel tasks, you have long running tasks.

07:40 And so, I mean, Alex, you mentioned this previously, right? You have tasks going into the evening, during the day when you're doing other things. Um, and so if you think about agents autonomously writing code, searching, carrying out these tasks, um, it just gives these platforms a much larger compute requirement than ever thought before. Yeah, absolutely.

07:59 Totally agree. Um, this one is another one that just points to what is happening happening in the capex side of things. Successive forecasts for the five largest hyperscalers capex have moved sharply higher um in pretty short succession. So spending that once looked like a ceiling, you know, a number of quarters or years out has become near-term um as the demand for the compute keeps keeps expanding.

08:23 Yeah, look, if you look at the chart here, I think at any point in time, I think natural instinct is just to say like the investment will kind of flatline from here. Yet for the last four years, like as an economy, we keep underestimate just the the strength of the trend. And as Sarah said, you know, with model developments, usage of of our installed capacity keeps keeps being taken up.

08:44 So right now, the numbers that David pointed to or the current estimates. >> Yeah. I mean, maybe to use DG's language, I think we'd all call the demand for compute a model buster at this point. Um, I think one anecdote that's telling that that we've all experienced is, you know, Sam Alman, Sarah Frier, they got a lot of flak, you know, a year or so ago for their massive compute uh commitments.

09:08 Uh, they were being reckless and aggressive. And I think at this point, like everyone would say they're incredibly preient with that decision. Um, and even so, two weeks ago, we all saw that they had to pause new subscriptions on their pro plans. Like showing up with a $2,000, you know, uh, you know, service. No, no thanks. Um, pretty amazing, uh, insatiable demand that we're experiencing here.

09:32 >> Yeah. Absolutely. Yeah. Pretty much every, you know, everyone we talk to at every stage of the supply chain uh, is telling us the same thing, some version of the same thing that demand, you know, outstrips supply. uh there are certain elements in the data center supply chain um you know where you can't get access to materials or products until 2028 um and so this is not softened so um you know I I would say at the same time you can look to the hyperscalers and see some evidence of highquality you know business on

10:04 the demand side that you can hang your hat on right so Microsoft Google and Amazon have uh about 1.7 trillion of combined cloud backlog together. Um and those customer commitments are are building rapidly while the platforms invest heavily in the capacity to serve them. So um while right now free cash flow is depressed during this buildout um you know consensus forecasts are showing a recovery from 2028 and substantial growth thereafter.

10:29 Um, you know, you could look to Amazon's actually Amazon's latest earnings call where they did a really good job of explaining this sort of J-Curve dynamic where the useful life of GPUs is actually pretty or TPUs is actually pretty long. Um, and so, you know, you have to build out the shell, the data center, you know, that's a certain amount of time.

10:49 You have to buy the the chips. Um, but you know, those will have a very useful economic life for a long period of time. And it's and it's been longer than I think any of us expected. >> Yeah. Yeah, and I know some of the hyperscalers have frankly gotten dinged for raising uh debt for capex, but um you know, similar to the model lab dynamic, I think the ones who have blinked and been less aggressive have regretted it.

11:09 Um I know on the podcast recently that you did with Gavin, uh Microsoft came up. Um but I think this is uh you know an issue across the board. >> Yeah. The other dynamic that's been spoken about a lot is pricing on the spot markets for existing GPUs which is just another signal that like any GPU that you can bring online is being priced at an attractive rate where like the hyperscalers earning an attractive return.

11:30 Yeah, it's a key point. Um, you know, this is one of the things that we talk about all the time is like each one of these successive waves just creates a tremendous amount of user or consumer surplus. And so what's actually happening right now, at least as far as we can tell, um is, you know, consumers and users get a tremendous amount of value out of this.

11:47 Like that's why they're using it so much. Um you know, the the the vendors who are serving those users are making very good money. Um and then, you know, you go all the way down every level of the stack to the chips where, you know, you have to pay much higher than you did 12, 18, 24 months ago to get access to them. And yet you could still make very high margins and create a tremendous amount of surplus out of the users.

12:12 >> And as we uh you know talk about this capex uh for the hyperscalers uh we should think of it as someone else's order books. So these big platforms with historically you know the largest profits are pouring their cash back into AI infrastructure. Um that's putting pressure on their free cash flow shortterm as we just talked about. Um but it's been a boon uh for chip orders, for power, for construction and that's why this broader technology boom has become an industrial boom and we I mean we've been spending more of

12:41 our time looking at businesses serving this industrial boom and everywhere along the data center supply chain. I think it's interesting because you know traditionally our world was more about monetizing existing IP you know build once and then sell infinitely but with these businesses there's many more complexities that the companies need to manage like you know financing managing vendor relationships predicting capacity and predicting demand and we've seen you know there are some teams that excel at that and some

13:08 teams that have struggled or kind of taken a pause and it's a little bit of a different expertise than we typically spend our time with. This puts us in a new age of atoms. Uh big numbers on this page. Global infrastructure investment needs are estimated at 90 trillion dollars through 2040. Um importantly though, this goes way beyond AI and data centers.

13:28 It includes power, water, roads, transit. Um we're seeing this across our portfolio. You can see this uh at Anderoll with their massive manufacturing facility. Um it, you know, is it's the the scale of it is 8 87 football fields. Uh Whimo, uh they're you know, aggressively expanding their depots. And of course, you know, SpaceX with their hundred billion dollar investment in Louisiana.

13:54 Um betting on the future today from our perspective isn't, you know, what it exactly was in the as it was in the past. As Santi said, it means building factories, expanding infrastructure, uh more skilled jobs, uh and across the country at large. >> And I mean, similar to what we just said on data centers, I think investing in physical infrastructure is very different than like, you know, investing in software.

14:19 And Elon coined the term, you know, the factory is the product many years ago. It's no surprise that a lot of the great founders that we've backed have come out of Tesla or SpaceX because they just learned that, you know, executing on the factory ends up being a massive competitive advantage um as they scale. So, there are many myths that we hear all the time about data centers.

14:40 They're draining all of America's water. Rich people don't want to live near them. Uh and then, you know, one of our favorites, uh my electricity bill is going to go through the roof. It may seem counterintuitive uh but a data center can help lower your electricity rate. A recent US study showed that for every 10% increase in data center capacity uh residential rates went down by 40 bips.

15:02 Um, you should think about it as a power grid is a shared fixed cost space, poles, wires, substations. And so a large stable customer like a data center can help spread those costs across more units of electricity. Simply put, more demand across a shared system is a positive. >> Yeah. Yeah. I mean, yeah, totally agree with what you're saying. Investment in shared infrastructure can also improve the economics for the households and businesses connected to it.

15:32 >> Yeah. And I don't think that point gets enough media attention. Um Dina Powell went on a podcast recently from from Meta and talked about the Louisiana site that they did where they worked with the community to lower um electricity costs. And so there's real examples of that today. It's not just lip service. >> Totally agreed. >> So now we're going to talk about the trends that we're seeing at the model and application layer.

15:54 Um on the one hand, uh as we've sort of previewed, AI is generating major revenue and savings. On the other hand, adoption is still extremely early. Um, and then, you know, I think on top of this, the the trend is very much so that costs are plummeting. And, you know, we previewed agents already, but agents are actually economical now for a much wider range of work.

16:17 And of course, this is changing even as we speak, right? New innovations like Jev by newer labs like Typesafe are taking this to a greater extreme. you're starting to see things like two orders of magnitude uh cost differences really impact the number of use cases. Um it's sort of classic Jevans paradox which is why it's very aptly named. Um and you know I'd say all in all this is an awesome setup for both the model layer and the app layer.

16:43 Um these improvements are creating real opportunities for both growth and profitability among software companies. And that's not to say that everyone will win, but it's no surprise that AI is attracting venture dollars into an very much expanding range of industries. Um, and we're also seeing more companies just reach enormous scale in the private markets.

17:04 So, this is not a new chart, but it is one of my favorite charts, which is the combined scale of revenue for OpenAI and anthropic. Um, and this is one that we we've had sort of in every GP offsite as far as I can remember the last few years. And we have to keep updating it every month because it it just sort of gets out of date that quickly. I think the point here, you can see in the numbers, OpenAI, Anthropic, their combined annualized revenue has climbed to an extraordinary degree.

17:29 Um, we love to show this relative to the greatest software companies in history. Um and you can see that in the right that um if you look at the estimates for revenue added um for the the best software companies ever built um the estimates for net new for the leading labs has s well well surpassed that um so it's really a striking combination of not only scale but also speed to get there >> but we're still pretty early like I think like relative to other platform shifts the interesting thing about the AI shift is we're

18:00 not only early in terms of percentage of population or percentage of users that use AI But still very early on in kind of the share of wallet gains within those users. So it's a little bit different than like you know in 2008 we would model percentage of people with an iPhone. Here we need to think about percentage of people that will use AI products and then to what extent they're using the products.

18:21 >> Yeah, I think that point that we're still so early is such an important one and there's a bunch of metrics in here that I think elucidate that. Um, one is, you know, if you think about live deployments at S&P 500 companies, that's at 69%. So, you know, if you're AI pilled as we all are, u maybe something close to what we would all expect. Um, but then if you move toward quantifiable impact, which is probably a good metric of how far along they are in their deployment, that's 30%.

18:46 Um, now if you go to the ultimate barometer, which is a metric tracked over time, that's actually only at 2%. So we believe that AI is delivering results but there is a huge amount of room to actually deepen its use inside the organization and track those results over time. Um and so moving from these individual deployments that you're seeing to really deeply influential recurring workflows is is the next stage frankly.

19:13 Um, and uh, I I would just say that most the enterprises that we speak with anecdotally, their exposure to AI is still mostly with Microsoft Copilot, which just shows you how far they have to go. >> Yeah. This gap between what the models can do and how they're being used is what makes me so excited about the application layer. Sarah, you did this great, you know, conversation with Ali Godsy at data bricks with Martin and he talked about how, you know, the AI can know a lot about the world but know very little about

19:43 your company. And we're seeing this uh in our application layer companies where they're bridging this gap. Um, and like one example that has stuck out to me over the last few months is if you were to take Revolute, who has a really sophisticated engineering team, they still had to partner with 11 Labs, uh, and take 11's, you know, best-in-class voice model and harness it to connect securely with customer accounts and banking workflows so that when I, as a customer, call, I can have my, you know, problem resolved

20:12 smoothly, efficiently, and, you know, securely. um today's opportunity is so much around taking these capabilities and harnessing them to build uh reliable services. >> So in addition to the the fact that um enterprise adoption is still quite early for AI, uh the other really remarkable trend that we wanted to um put some stats to is just that the power users are totally pulling away.

20:40 Um so you can think of AI spending rising generally across companies but the power users as we talked about the most intensive users are spending much much more. Um so we we looked at uh some Yepit data for this and if you if you look at sort of median AI vendor spending in the top 1% that's roughly eight times the level of the top 10%. >> What's impressive about that is it's almost as much as the 2 to 10% combined is basically the 1%.

21:06 Yeah. So just you got these power users that are are clearly early adopters. >> Yeah, absolutely. And I mean anecdotally, even inside our own portfolio companies, which you could say are um pretty much fully AI native or AI pilled, um you're seeing the top users spend anywhere from call it 7 and a half to 9,000 a month. And the median users again for the most AI native companies probably closer to the cost of like a monthly subscription of you know 200 maybe you're stacking two subscriptions on top.

21:36 So 200 to 400. Um so over 20 times the spend if you think about median versus um sort of top users. >> Yeah. the you know we we talk to our own portfolio companies and you know one of the questions that we discuss is like you know how how how much adoption do they have and how do you measure that and you know if you just look at like the percentage of money that they're spending on AI tools compared to headcount as an example um you know I think like relatively forwardleaning large Fortune 500 type companies are

22:06 probably today at like 1%. M >> um and you know the most forwardleaning >> you know AI build companies in our portfolio can be as high as like 10%. And so all of these are just questions of like how early are we into diffusion and how deep will that diffusion go. >> Yeah. Another example we've seen within our portfolio um you know many enterprises are really excited when they you know buy cursor or cognition for the first time but the reality is that's just like the beginning of the journey.

22:36 the runway uh from just procuring to and trying these tools to like full adoption is is massive. >> And I mean Sarah, you touched on this early, but we're starting to see quantifiable case studies. And you know, we've highlighted a couple here of public companies that actually report on the metrics where they've seen meaningful improvements with AI.

22:54 You know, two two that we call out. One on the cost side. For example, Chime has reported that they've reduced their cost to serve by over 10% a year for the last four years. you know, compounded. We're talking about almost a 50% reduction in cost to serve. You know, happily supported by one of our portfolio companies, Decagon. Uh, but just in general, many initiatives to lower the cost to serve.

23:16 You know, in the revenue side, an interesting one that I found was Shopify here, who, you know, they they launched their AI sidekick. And this AI sidekick helps merchants get up to speed much much faster. So, you know, the percentage of customers that reach five orders within 15 days after onboarding has grown 8%. And that is kind of a metric that Shopify's tracks is once you reach five orders, you know, you're going to stick around and you're going to retain on Shopify.

23:41 So, it's been a meaningful tailwind to their business as well. >> Yeah, absolutely. And I think to your point on um just the cost to serve coming down, right, if you think about the advantages that lower cost to serve gives you, you just have more room to compete. So, you have better pricing. you know, maybe broader service, you can reinvest in growth.

23:58 Um, and so I think the benefits are reaching the customers um the not only the customers but also the the margins and then they rotate that back in. Um, and then I think the other thing that's been really fascinating to see is um, of course they vary so you can't throw everything into one bucket, but the incumbents have done a pretty nice job on u monetizing this as well.

24:20 And um, service now is a good example, right? They've reported more than a billion in um in a in AI ACV um and actually a 9x increase in agentic deployments. Um so you're kind of seeing it across the stack incumbents to newer companies. >> Yeah, one of the interesting things that we've talked about a lot and you know again some of our most forwardleaning companies like Stripe have discussed with us is you know where are they actually putting their incremental AI investment dollars?

24:46 Are they putting it toward things like building new products for customers that could drive higher revenue or are they putting it toward uh optimization or efficiency gains on the cost side? Uh and this is sort of a litmus test for us of like where are the you know founders or CEOs of these companies seeing the most amount of opportunity like the opportunity to drive revenue growth is unbounded to the upside >> whereas the opportunity for cost improvement um you know yes you could take that and reinvest it but that's

25:16 sort of a latent opportunity that will continue to exist and if you think that the best and highest use of your dollars today is to optimize your cost structure what does that say about the revenue new opportunity for you >> and even within cost optimizations there's different flavors right like one is you know lowering your cost to serve which allows you to reinvest and like makes you a better business I think six months ago our industry was really focused on like rebuilding systems of record internally and if your

25:40 engineers are focused on you know rebuilding a system of record to save a couple thousand a couple hundred thousand dollars I expect there to be better uses of like those resources yeah totally agree >> so Sarah referenced with with service now uh seeing massive agenda authentic usage. Um, agents are here. They are performing tasks. Tasks require multiple steps uh which require multiple model calls.

26:06 That helps explain the 14x growth uh in agent token usage on open router. Um those step steps though are becoming more affordable. We're seeing caching uh so the system can reuse background information instead of processing it from scratch each time. Uh I've seen Hebia take advantage of this. They've seen their financial chat workloads uh become 10x cheaper to run.

26:30 Um the same budget that they had before can now support more work. >> Yeah, I think your your broader point is just that lower costs make it practical now for an agent to try check its work, try again, and you can just open up a ton of tasks where maybe reasoning or tool use would have been just prohibitively expensive. Um, and I think this is especially important in cases where reliability is maybe the de facto reason you would or would not use an agent.

26:59 Um, and again, you know, we sort of talked about type safe previously, but imagine what happens when you go an order of magnitude cheaper, two orders of magnitude cheaper. Um, you know, I think the amount of use cases that open up are actually unimaginable. >> Yeah, totally. And, you know, with much uh greater improvement in latency. >> Yeah, absolutely.

27:19 >> Yeah. So, so companies are definitely thinking more and more today about how to optimize latency or performance at large with cost. Um, so take data bricks as an example. They're leveraging routing uh choosing the appropriate model uh for each specific task to per to improve performance and cost. And so their smart router um performed uh better. It solves more problems at 35% lower cost than the strongest individual model.

27:50 Um, another approach we're seeing a lot of right now is fine-tuning. We've seen this with Harvey. We've seen this with Elise AI. In the case of Elise, they fine-tuned a smaller model. Um, so it became much more affordable, 60% cheaper. Um, but also had way lower latency. So live use cases from an audio perspective became tenable. And so uh these engineering gains are making AI more useful but also more affordable.

28:18 >> Yeah. And I think the if you you sort of roll that up um and think about for application business the relevant unit for them is really the cost of getting the customer's job done. And if you can decouple that right you get the customer's job done you charge for that and you can use better routing to make the product more economical. Um, that's pretty magical because now you're not using the most expensive model for each step, but you are getting the job done and that's gonna I think we're going to see more margin

28:43 improvements and and the best app companies. >> It's a nice segue. Yeah, we we've been talking mostly about usage inside of the enterprise, but important to talk about consumer given obviously that's the place where we've had some of the largest outcomes and where we spend a lot of our times. No, we think it's still pretty early. There was a recent survey that showed that only just over 2% of US households actually have a paying subscription for AI.

29:04 You know, maybe subscription is not going to be the best place to monetize the majority of households, but it it has been so far. And the interesting thing about the subscription revenue is it has some of the best retention we've ever seen in consumer. You know, uh, you know, Alex likes to talk about smile curves and it's really, really rare to see a retention profile that not only flat lines, but actually smiles as, you know, the product improves and people come back to it.

29:29 But that's what we've seen with AI, which just demonstrates the value that the subscription is being is is able to deliver to the to the person who pays. >> By the way, just on subscription, what struck me about these numbers, especially if you look at the bottom right chart, is just how small they are. Like think about Amazon Prime. That's over 200 million households.

29:46 Um, Netflix is a great one. >> 70 million households, right? These I had to look it up. I'm like, is that K correct? Um, and so totally agree that it's just getting started. >> Yeah. I mean, our partner Josh Elman does have uh a definition I like that would be counter to that a little bit, which is consumer AI is what I use for my daily life and not necessarily what I expense.

30:10 So in his view he would say like it's maybe not super surprising that 97% of households that are using AI aren't yet paying for it. Um you are right Santi I do like smiling retention curves. Uh we do like to see users coming uh back more and more. You know another common characteristic of large consumer platforms is time spent. Uh so if you were to look at Facebook, Instagram, Tik Tok, Snap, uh they all get 60 or 30 to 60 minutes per day uh from their active users.

30:41 And while the best AI assistants uh and there's many, you know, up and coming right now, uh all aspire to be my go-to application, like I I hope that they are going to be incredibly persistent and always on and super proactive such that they may not be the place where I spend the most time in the future. Yeah, it's going to make our job harder when we can't, you know, look at engagement via outside in data because the agents are working on the background instead of us just looking at like screen time on a social media

31:14 application. But it is important. I mean, I remember like when the newest models come out in December, even in our workloads, you know, we would set off a deep research task or an agent task and then, you know, drive up to the city or something and that's not driving Tesla. >> That's not screen time that, you know, being tracked as as would have been in prior generations of consumer.

31:33 >> Yeah. Totally agreed. >> It's early, but it is a really new and exciting time in consumer. Uh Muse and Instinct are no doubt the new kids on the block, but they've grown really quickly. Uh those along with chat GBT are taking more and more of my queries away from traditional search. Um, but it's a really a dynamic time and so if you are an existing consumer discovery platform, an existing marketplace, you really need to be thinking about your chest moves right now.

32:02 Um, there's a couple questions I'd be asking. First, how much incremental demand can I get from an AI agent? How many more orders can they bring me? And two, how much of my business, how much of my profit pool comes from owning the customer relationship and discovery? Uh last week there was a lot of news where Amazon said you know no thank you to Muse uh but Instacart said yes please.

32:27 And if you think about Amazon X AWS and Instacart uh and look at their advertising revenue it outpaces all of their operating profit. And so advertising revenue owning the customer relationship is incredibly important. But for Amazon, you know, how many incremental new orders or certainly customers am I going to get from connecting to Muse? Not that many.

32:52 Whereas if you look at Instacart online penetration of grocery, still relatively early, a lot more orders to go get. And so the optimistic possibility here is that there's going to be a lot more orders coming from uh from from these applications. Uh historically I've had to, you know, click all these different ways through to process an order. If I if I offload that to an agent, all of a sudden there's no clicks and hopefully more GMV.

33:22 The the trip I once wanted to book but didn't gets booked. The order that we all wanted to place for dinner tonight happens. Um so I'm optimistic there's going to be a lot more GMV and that'll make up for some of this lost advertising revenue. >> Yeah, it's sort of this question though that's open of how does it get compensated for, right? So to your point, you know, Amazon has an over 70 billion advertising business that's extremely high margin flow through that uh you know is totally uh predicated on the fact that

33:52 consumers go to the website and click on the ads and if they don't do that anymore, you know what happens? Um you know today, you know, Meta and Meta and Google famously advertise um you know probably best out of any of the internet platforms. um you know they each make call it 200 bucks plus per per users in the US in developed world um you know in the case of Meta it's sort of a you know it's an entertainment application we'll see you know what happens with that it's probably a little bit safer in the case of Google

34:23 you know um it's funny like the whole talk track around Google two years ago when all of this just happened was oh my gosh what's going to happen to Google's search business and it turns out it's been really resilient part of the reason it's been really resilient is because their very high monetizing ad terms were somewhat safe because they were things like I need to buy insurance >> or you know find me a hotel in this city um you know or things like that that that you can very directly monetize um but that the AI was

34:55 not yet capable of going to take action for on your behalf. If that changes that could be a very different dynamic. >> Yeah. The other thing just to add I think the narrative last week was a little bit like negative sum where it was very negative you know like look at these marketplaces are going to be impacted. I do think like the positive some view of this is number one maybe that $70 billion that's spent on advertising on Amazon just finds a different channel and you know goes elsewhere maybe spent directly on the

35:22 agents through a different form factor or just finds better ways to target people and then two I think you know with the lower friction we might just see more consumption like people might buy more you know and that's just like a positive flywheel that drives economic growth and I think a little bit we're locked into this oh this is bad for profit pools but actually might be just good >> yeah totally >> both could be true by the way.

35:43 >> Yeah. >> Yeah. Yeah. >> That it could be >> there might be profit pools but it could be additive overall to the economy. Like I think we all would be disappointed if we looked back three years from now and there's not a meaningful productivity improvement actually at the overall macroeconomic level which would drive you know consumer spending higher in the case of advertising.

36:00 But it might just be that you know profit pools disappear from certain places and get reallocated. >> The the immediate reaction has been net market cap positive across the ecosystem. the meta gains have far exceeded any of the losses from the marketplaces. >> Yeah, it's a great point. >> Yeah, going going back to our discussion on software and moving maybe more to the the public side.

36:18 Um I think the big trend that jumps out at you from from these charts is that the mix has really shifted toward slower growing more profitable companies. Roughly 75% of the public software sample here is profitable and only 30% is growing at 20% or more >> which is a staggering number that only 30% of the public software companies are growing at 20% plus >> and not only 20% I mean that's 30%.

36:43 If you draw the line at 30% we've discussed like the absolute number of companies is less than five when you know in the private markets basically every company we see and spend time with is growing at well in excess of 30%. >> Yeah. And look, this is not um this is somewhat obvious actually if you look at just you talk to you know IT managers, CIOS etc.

37:04 Um if you look at the growth that they're experiencing and what they spend on AI% >> like the easiest place for those dollars to come from is not spending incremental new dollars on new SAS software projects. >> Yeah, exactly. Um so you know I think we're investors in in some of the you know the SAS software companies including the public markets and the conversations that we're having with them and the founders are very focused on is okay how do I take my existing distribution which in many cases is very very strong

37:31 you know locked in customers um who you know to your point earlier Santi wouldn't go anywhere and apply really interesting new AI products to them where I can drive my revenue growth higher um I think you know in order to sort of dispel the greatest fear fears around the SAS apocalypse. Um, you know, I think we just need to see a period of time where the software companies continue to post things like 98% gross dollar retention, um, which was kind of always the sticking point for why they were so attractive as

38:00 investments. Um, continue to drive efficiency like we've talked about, but most importantly drive, you know, revenue growth acceleration. um which shows that they're sort of safe from a defensive standpoint, but that the offensive investments that they're making are actually going to drive their business to be better. >> I thought your point in the blog post that you wrote a couple months ago on two paths was really interesting as a rule of thumb.

38:22 Maybe share more on the finer point of the percentages. >> Yeah, you know, the more I talked to founders after it, um the more I felt like uh you know, almost everyone that we talked to is like, yeah, I'm trying to drive revenue growth higher. Uh and you know it's uh it's it's it's it's clearly I think the predominant path for the types of companies that we're investors in and that we've backed over the years.

38:44 Um again I think you know maybe relative to a year ago when the fear was like okay everyone's going to vibe code their software systems like that is clearly not what's happening in the market. Um but you know the onus is high on delivering revenue growth acceleration and so we had said you know let's target 10% plus acceleration which is a high number um but with a magical product given the budgets that are available to AI is seemingly doable and so we'll see I think that there are a few of them that we are close to

39:14 where we'll see that over the next 12 to 18 months. Yeah, absolutely. And you know, as with all things, um you can't lump every uh every every company into the software buckets sort of um you know, talk about uh multiples coming down and and sort of growth uh growth decline as well. Um in fact, uh software has been very differentiated and um if you look at this chart here, um you can see that cyber security observability have really stood out um and uh vertical software as well has generally held up um much better than

39:45 the horizontal applications. Um, and I think the framework for how we think about what's been, you know, what's held up or even gone up nicely versus, um, uh, in more secular decline, if you will, um, is that, uh, really you think about how AI changes the customer's need for that product. Um, right. And and I think the cyber security risk um uh associated with AI has been well publicized.

40:08 But as you think about more software and agents creating new security and new monitoring needs um this is uh creating greater demand honestly for the incumbents in in in these markets. And you can see um you know the call out on crowd strike on the on the right right hand. Um and then of course on the flip side applications are facing different degrees of of workflow change right and it's sort of you know if you think about um you know one of our top CEOs Ali Goatsy likes to talk about this chopping block of AI and

40:36 like what's first on the chopping block um and you know you can kind of think about uh on the flip side you know what is actually needed for AI um and you you pair those dynamics together and I think you know it explains a lot of what we're seeing in the public markets. Yeah, I think a lot of this is pretty intuitive with what we're seeing on on the private market side.

40:55 So, as you alluded to Sarah on the on the security front, uh, agents are accessing more and more systems, taking more actions. The like open AI hugging face incident was, you know, an eye opening event for many. Uh, so security is is paramount. And then as we look at the vertical AI companies, these are some of the fastest growing companies we're seeing in the private markets, right?

41:18 Harvey, a bridge, a lease, they are growing faster than any of the precedents ever have in their industry and and that's a reflection of the customers recognizing it's not just the model which we talked about earlier but how you how you or orchestrate that how you build the application around it and vertical specific workflows command uh command these needs >> and I mean we spend a lot of time looking at polar markets less so for investments but we we underwrite exit multiples right so we track that pretty closely and

41:44 I think if we were to recap the year what's happened you know first couple months months there was the SAS apocalypse. We wrote a blog post about like what kind of businesses would do well. And if you fast forward to today, the software index is actually back to, you know, where it started the year, but it's really bifurcated into like some companies that are deemed AI losers and some companies that are deemed winners.

42:06 And, you know, the public market sometimes simplifies things a bit too much. But to your point, the AI winners are not just the ones that can improve their cost structures. They're the ones that are actually accelerating and capturing some of this net new dollars up for grabs that if you're not really capturing, then you're probably not riding the wave.

42:22 >> Yep. >> Yeah. Um, you know, I think, um, on the flip side, we wanted to show showcase, um, some private company operating data that gives us a view of what customers are actually doing. Um, and there's a little bit of a narrative violation in Stripe's SAS customer data. um which actually shows growth accelerating into 2026 across both young and mature businesses.

42:44 >> Stripe calls it the renaissance. Oh, the renaissance. I actually quite like the renaissance. >> I like renaissance. Yeah, >> it's it's very good. It's very good. >> Um to if if you talk, you know, we've been talking about public markets a bit. You know, Sarah talked about private market SAS acceleration. We get a lot of questions around, you know, companies staying private for longer.

43:01 So if you actually look at the top six companies today, you know, Anthropic, OpenAI, Data Brick, Stripe, Wayland, Revolute by last round valuation, they add up to about 2.4 trillion. This is more than the combined market cap of IPOs we've seen in the last 10 years excluding SpaceX, which adds up to 1.7 trillion. So just the amount of activity that happens within our market, again, six companies, 2.4 4 trillion.

43:24 It's almost as big as the Russell 2000, which is, you know, a big index in the public markets where there's hundreds of public managers that spend most of their time in. So, you know, it's increasingly exciting to just spend time in these late stage champions that can keep investing in growth more so than maybe um maximize short-term profit. But David, you can talk a little bit about kind of how we advise companies on the IPO and when to stay private versus public.

43:47 Yeah, look, I mean the IPO, you know, especially for founder-led companies, one of the things that we've talked about and and that I've written about is that the founder is the asset class at this point. And so the bet that we make and part of the reason that it's been a benefit for some of these companies to remain private is they can many times take bigger swings um in the private markets that have longer duration paybacks.

44:09 You know, Zuck and and Elon are kind of obvious exceptions to the rule in the public markets. Um but you know the way the the way you see it manifest in the numbers in companies like data bricks or stripe um is you know massive massive new bets um in new product areas um and you know you can see revenue acceleration that happens as a result uh and so you know you can do that in the public markets um but you know it's going to catch greater scrutiny you know obviously Meta's stock price um you know at the nater you know

44:40 got below 100 bucks a share um you know when people were very very skeptical about their investments in in ARVR. Um, you know, for us, the way we talk about it with our founders is just the IPO is another financing event. And, you know, what do you get out of being a public company relative to what do you get out of being a private company? Um, but clearly you can reach big scale uh in the private markets.

44:59 And there's some other companies that, you know, already talked about running themselves like they were a public company, you know, with like high focus on efficiency and just basically like every metro being tracked. There's also some companies that benefit from maybe like public disclosing of financials and earning customer trust both at the enterprise level and on a consumer level.

45:17 I mean Navan went public and we've seen a reaceleration just on the basis of some of the larger enterprises actually trusting them more because they're a public business. >> Yeah. And then of course there are examples where you know capital needs over time uh could be very very large and so the p the the capital pools in the private markets are very large uh and can and can serve the needs of many of these companies but at some point you know the capital needs may even get too big for the private markets.

45:45 >> That's the public markets. You know in the private markets one of the things that we we spend a lot of time in too is is secondaries. So there's two dynamics at play. You know, one is we see an increasing amount of companies holding tenders for their employees. So if they choose to stay private, you know, allowing employees to get liquidity outside of the public markets.

46:01 And an interesting data point show here is, you know, participation in tenders dropped by Carta has only been 58%. So this is employees choosing not to take liquidity because they have so much conviction in the performance of their business. So, it's the opposite of I think sometimes there's a narrative of like employees cashing out and what we're seeing is actually employees choosing not to cash out because they believe in their company so much.

46:24 >> Imagine SF home prices if that was 100%. >> Exactly. >> It'd be hard. Exactly. Yeah. The the tender off the tender offers that happen, you know, we're obviously like participating in these and and leading these uh pretty frequently. So, we think they're a good thing. Um they do serve two purposes. one, you know, for employees and prospective employees, it is sometimes hard to compete with the liquidity appeal of public markets, RSUs that hit your account on a net tax basis every quarter.

46:50 Um, so, you know, it is a it is a um, you know, a weapon of competition for the private markets that want to compete for employees or retaining their employees um, in the uh, with with with the public market companies. The other side of it is, you know, we're advocates of sort of more frequent resetting of your valuation. Um, and so that keeps your sort of stock price, you know, private stock price fresh for a number of reasons.

47:13 One, it's easier to talk about that with employees and prospective employees. Um, but two, you know, in the event that you want to do M&A like many of our companies have done, um, you know, you have fresh currency that you can use. >> Yeah. No, maybe just the other dynamic. I think people talk about the secondary discount quite a bit because that's a dynamic that we've seen over the last five years where maybe coming out of 2021 and 22, 23, 24, the median discount to the last round price in secondary markets was

47:37 meaningful because there was maybe like a round that was priced too high and it was out of date. But actually what you're seeing today is you know when there are transactions secondary markets the discount to the last round price is basically zero. Yeah. which just speaks to, you know, these these valuations at which companies have raised that are fresh and there's always net new investors willing to pay that same price, which wasn't the case for last couple years.

48:00 >> Yeah, great point. So, um, this is a fun slide that says everything is AI computer. So, AI related companies account for 86% of US VC deal activity in this 2026 snapshot, up from 65% in 2025. Um so you know obviously AI has become the dominant destin destination for venture dollars. >> Yeah. But beneath that AI label the opportunity has broadened a lot right.

48:26 So enterprise apps consumer apps uh but also services semiconductors power defense um a lot of the attention goes to open AI space AI and anthropic but beyond that um our opportunity set has never been deeper and and broader. Last, we thought it would be fun just to talk about some of the areas that we're very excited about right now. Um, you know, obviously we touched on some of the consumer work that will now be done by agents.

48:54 Um, but you know, you could call it longunning agents, you can call it autonomous agents, heavy use consumer. Um, you know, if you if you take the perspective of these uh that you know, you could have consumer agents do all the tasks that you wouldn't want to do to give you things that you otherwise wouldn't have spent time or money on. Um it's very appealing and I think that the uh the distribution of this could happen pretty quickly.

49:15 Robotics is an area where we're spending a lot of time and attention. We happen to think that it could be even larger than LLMs. Um but you know probably 3 to 5 years earlier um and so we think over the next 5 years this is going to be a massive area of investment and excitement. Um autonomy is here. Uh self-driving works. Uh it's a very exciting time.

49:37 uh if you just take you know a step back and talk about the auto industry and transportation industry this is one of the biggest industries in the world that we probably don't talk about as much because we spend so much time talking about AI right now um but you know if you look at miles traveled by Uber andyft or in Ubers and lifts it represents about 1% of miles traveled in the US I think with um you know full networks of autonomous driving cars that are 14 times safer than human drivers we expect that to expand by

50:08 at least an order of magnitude in the coming years. Plus, there's 17 million new cars sold per year in the US. And I think over the next 10 years, those those will all be um be autonomous. Um AI times bio, this is a super exciting area. You know, obviously the folks in the labs are talking about this. Um but you know new drug discovery solving some of the um you know most debilitating illnesses uh or diseases in the world I think is a promise that we all are very hopeful for that we'll see a lot of progress over the

50:39 next 10 years. Personal health. This is another one that I'm very uh excited about for myself uh you know as a as a health maxer. Um, but you know, there's not been a great place uh or avenue to take all of the information about yourself, put it into somewhere and get very hyperpersonalized uh advice. Um, and then, you know, lastly, diffusion uh into the enterprise beyond coding.

51:03 This is one of the, you know, the ones that Sarah you were talking about just we're so early in actual diffusion of the technology into the enterprise um that I think it's going to be, you know, super super exciting. Um and then lastly um you know there's another area that is not inside the AI bucket which is you know what we call American dynamism but um the sort of retooling of the entire um sort of American dynamism stack.

51:28 We've you know we've invested we've been large investors in this area for a while. um you know it's still just a small fraction less than 5% of overall dollars spent um you know in the military uh is sort of you know newer vendors like Andre uh or Seronic or Castellian um and we expect that to grow dramatically uh as needs change. So, so many areas of excitement.

51:51 Obviously, you know, we've been very active in this AI space. Um, and you know, we're optimistic about the effect that it's going to have on the overall economy. The buildout is massive. Um, but we think it's going to be massively productivity enhancing in the US. So, uh, it's a blast to hang out with you guys. Thank you.