The $1 Trillion AI Buildout | State of Markets

a16z’s David George, Sarah Wang, Alex Immerman, and Santiago Rodriguez unpack 25 key charts from the latest State of Markets presentation, from the scale of the AI infrastructure buildout to what adoption looks like inside companies today. They examine why rising markets have so far been supported by earnings rather than multiple expansion, why hyperscaler CapEx is approaching $1 trillion annually, and why demand for compute continues to outrun supply. They also look at the downstream effects of that spending across chips, power, construction, and physical infrastructure. State of Markets Then they move up the stack: OpenAI and Anthropic’s revenue growth, the gap between AI deployment and measurable enterprise impact, the rise of agents, falling inference costs, and what all of this means for SaaS. They close with where the team is spending time next, including consumer agents, robotics, autonomy, AI and biology, personal health, defense, and the continued diffusion of AI across the enterprise. State of Markets

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00David George?

  Eight of the top 10 valued companies in the world are US tech companies.

• 0:05Speaker 01

  Since GPT came out almost four 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.

• 0:13Speaker 02

  This puts us in a new age of atoms. Global infrastructure investment needs are estimated at $90 trillion through 2040. This goes way beyond AI and data centers. It includes power, water, roads, transit.

• 0:29Sarah Wang?

  Live deployments at S and 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.

• 0:46Speaker 02

  Today's opportunity is so much around taking these capabilities and harnessing them to build reliable services.

• 0:54David George?

  That's all great, but is it a bubble?

• 0:58Erik Torenberg?

  AI is driving one of the largest investment cycles in modern history. But is the spending getting ahead of the economics? Today, David George, Sarah Wang, Santiago Rodriguez, and Alex Immerman walk through 25 key charts from our latest State of the Markets presentation. They start with the macro picture, why markets have risen even as multiples have come down, how hyperscaler capex is approaching $1 trillion a year, and why demand for compute continues to exceed supply. Then they look at what's happening inside businesses. AI adoption is growing, but measurable enterprise impact is surprisingly early. At the same time, power users are pulling away, agents are consuming more tokens,

• 1:41Erik Torenberg?

  inference costs are falling, and the fastest moving companies are finding ways to turn those improvements into both growth and better margins. And finally, they look at what comes next, from consumer agents and robotics to autonomy, AI, and biology, and the next wave of enterprise adoption.

• 2:01David George?

  Welcome back to the A16Z Podcast. I'm David George. I'm here with my colleagues Sarah Wang, Alex Immerman, 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. We'll explain what the chart 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.

• 2:39Sarah Wang?

  Of course. I'd be here.

• 2:41David George?

  Okay, so the day this podcast goes live, we will be releasing our State of Markets presentation. So this is a now yearly tradition from our growth team, where we synthesize the biggest trends in tech, AI, infra, and markets. Today, we're just picking out a subset of interesting slides and having a discussion about them. So I would direct you to look at the whole version, which has filled with a lot of nuggets. The areas that we're going to discuss today are 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, and some vertical deep dives. Technology is driving an economy-wide investment boom, with rising earnings supporting market

• 3:26David George?

  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. 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. Okay, first slide, tech is the everything cycle. So some of this may be a little bit obvious, but the numbers are somewhat striking at this point, right?

• 4:10David George?

  So high-tech equipment, software, and R and D now account for roughly 55% of US capital spending, which is just a staggering number. Tech is driving the investment cycle, obviously, across all areas of the economy, 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. And eight of the top 10 valued companies in the world are US tech companies. So this is a broad story. You're seeing it in capex. This is heavily covered in the data center buildout. It's obviously heavily covered in the amount of capital that's been going in to fund the model development. Model companies together raising, I think, over $350 billion at this point.

• 4:54David George?

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

• 5:18Speaker 02

  Yeah, this does feel like the next chapter of Mark's Software is Eating the World. Over the last 15 years, software has transformed all these industries, but only a small fraction of the population could build it. None of us could. But today, look at us, we all have a handful of automations running every night. And so if you think about the software demand, that means a lot more compute, chips, power, construction. And so it's no surprise to see the majority of investment now covered in tech.

• 5:47David George?

  Okay, 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, and at the same time that it has reached new highs, the trading multiples of the market are actually down. So stocks are up about 20%, while multiples are down about 20%. So what that means is the performance is driven by fundamental earnings, right? Not increased multiples. The S and P 500 earnings multiple is below 20x. So if you just start with that, these are, in most cases, very high-quality businesses. It's nothing like the dot-com boom in that way, where some of the highest market cap companies in the world had their massive stock run-ups based on increases in their trading multiples.

• 6:31David George?

  It would trade in many cases for 100x PE. That's not what's happening here. In contrast, some of the memory companies, which again are very cyclical, are trading for, call it six times, seven times forward earnings. So very, very different.

• 6:44Speaker 01

  And I think there's an important double click here, where since GPT came out almost four years ago, the market's up 90%, which is 17% annualized. And I think anytime there's been a 17% annualized growth for four years, the natural instinct is, well, that's got to come down, right? We're definitely in a hot period. But when you compare that to, as you said, the market trading for below 20x earnings, growing 15%, this definitely feels a little different than maybe the 2021 period that was recent, or the 2000 period when multiples and growth were not really going together.

• 7:13David George?

  Yeah, totally agree. So I mentioned the scale of the capex buildout in the context of railroads. 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 all expectations point to them spending over $1 trillion annually from 2027. So the pattern recognition is each of these computing platforms has supported a much larger population of users and uses. In this case, this buildout can happen so quickly and demand can still outstrip supply

• 7:55David George?

  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. And so sort of immediately could reach billions of users in contrast to previous technology cycles.

• 8:09Sarah Wang?

  Yeah, and I think it's 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. 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. And so if you think about agents autonomously writing code, searching, carrying out these tasks, it just gives these platforms a much larger compute requirement than ever thought before.

• 8:37David George?

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

• 9:01Speaker 01

  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 strength of the trend. And as Sarah said, with model developments, usage of our installed capacity keeps being taken up. So right now, the numbers that David pointed to are the current estimates.

• 9:25Speaker 02

  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. I think one anecdote that's telling that we've all experienced is Sam Altman, Sarah Fryer, they got a lot of flack a year or so ago for their massive computer commitments. They were being reckless and aggressive. And I think at this point, like, everyone would say they're incredibly prescient with that decision. 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 service, no thanks. Pretty amazing, insatiable demand that we're experiencing here.

• 10:02David George?

  Yeah, absolutely. Yeah, pretty much everyone we talked to at every stage of the supply chain is telling us the same thing, some version of the same thing, that demand outstrips supply. There are certain elements in the data center supply chain where you can't get access to materials or products until 2028. And so this is not softened. So I would say at the same time, you can look to the hyperscalers and see some evidence of high-quality, you know, business on the demand side that you can hang your hat on, right? So Microsoft, Google, and Amazon have about 1.7 trillion of combined cloud backlog together. And those customer commitments are building rapidly, while the platforms invest heavily in the capacity to serve them.

• 10:46David George?

  So while right now free cash flow is depressed during this buildout, you know, consensus forecasts are showing recovery from 2028 and substantial growth thereafter. 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. And so you have to build out the shell, the data center. You know, that's a certain amount of time. You have to buy the chips. But you know, those will have a very useful economic life for a long period of time. And it's been longer than I think any of us expected.

• 11:24Sarah Wang?

  Yeah, I know some of the hyperscalers have frankly gotten dinged for raising debt for CapEx. But you know, similar to the model lab dynamic, I think the ones who have blinked and been less aggressive have regretted it. I know on the podcast recently that you did with Gavin, Microsoft came up. But I think this is, you know, an issue across the board.

• 11:43Speaker 01

  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. We're like the hyperscalers earning an attractive return.

• 11:57David George?

  Yeah, it's a key point. You know, this is one of the things that we talk about all the time. It's 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, is you know, consumers and users get a tremendous amount of value out of this. Like that's why they're using it so much. You know, the vendors who are serving those users are making very good money. And then you go all the way down every level of the stack to the chips, where 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.

• 12:38Speaker 02

  And as we, you know, talk about this CapEx for the hyperscalers, we should think of it as someone else's order book. So these big platforms with historically, you know, the largest profits are pouring their cash back into AI infrastructure. That's putting pressure on their free cash flow short term, as we just talked about. But it's been a boon for chip orders, for power, for construction. And that's why this broader technology boom has become an industrial boom.

• 13:07Speaker 01

  And we, I mean, we've been spending more of our time looking at businesses serving this industrial boom and everywhere along the data center supply chain. I think it's interesting because 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 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.

• 13:41Speaker 02

  This puts us in a new age of atoms. Big numbers on this page. Global infrastructure investment needs are estimated at $90 trillion through 2040. Importantly, though, this goes way beyond AI and data centers. It includes power, water, roads, transit. We're seeing this across our portfolio. You can see this at Anduril with their massive manufacturing facility. You know, it's the scale of it is 87 football fields. Waymo, they're aggressively expanding their depots. And of course, SpaceX with their $100 billion investment in Louisiana.

• 14:22Speaker 02

  Betting on the future today from our perspective isn't, you know, what it exactly was in the past, as it was in the past, as Santi said. It means building factories, expanding infrastructure, more skilled jobs and across the country at large.

• 14:37Speaker 01

  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. And Elon coined the term, you know, the factory is the product many years ago. And 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 as they scale.

• 15:03Speaker 02

  So there are many myths that we hear all the time about data centers. They're draining all of America's water. Rich people don't want to live near them. And then, you know, one of our favorites, my electricity bill is going to go through the roof. It may seem counterintuitive, but a data center can help lower your electricity rate. A recent US study showed that for every 10% increase in data center capacity, residential rates went down by 40 bips. You should think about it as a power grid is a shared fixed cost base, poles, wires, substations. And so a large stable customer like a data center can help spread those costs across more units of electricity.

• 15:48Speaker 02

  Simply put, more demand across a shared system is a positive.

• 15:51David George?

  Yeah, yeah, I mean, yeah, I totally agree with what you're saying. Investment in shared infrastructure can also improve the economics for the households and businesses connected to it.

• 15:59Sarah Wang?

  And I don't think that point gets enough media attention. Dina Powell went on a podcast recently from Meta and talked about the Louisiana site that they did, where they worked with the community to lower electricity costs. And so there's real examples of that today. It's not just lip service.

• 16:16David George?

  Totally agree.

• 16:17Sarah Wang?

  So now we're going to talk about the trends that we're seeing at the model and application layer. On the one hand, as we've sort of previewed, AI is generating major revenue and savings. On the other hand, adoption is still extremely early. And then, you know, I think on top of this, 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. And of course, this is changing even as we speak, right? New innovations like Jev, buying newer labs like TypeSafe are taking this to a greater extreme. You're starting to see things like two orders of magnitude cost differences really impact the number of use cases.

• 17:01Sarah Wang?

  It's sort of classic Jevon's paradox, which is why it's very aptly named. And you know, I'd say all in all, this is an awesome setup for both the model layer and the app layer. 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 a very much expanding range of industries. And we're also seeing more companies just reach enormous scale in the private markets. So this is not a new chart, but is one of my favorite charts, which is the combined scale of revenue for OpenAI and Anthropic. And this is one that we've had sort of in every GP offsite as far as I can remember the last few years.

• 17:46Sarah Wang?

  And we have to keep updating it every month because it just sort of gets out of date that quickly. I think the point here, you can see it in the numbers, OpenAI, Anthropic, their combined annualized revenue has climbed to an extraordinary degree. We, I love to show this relative to the greatest software companies in history. And you can see that in the right, that if you look at the estimates for revenue added for the best software companies ever built, the estimates for net new for the leading labs has well surpassed that. So it's really a striking combination of not only scale, but also speed to get there.

• 18:22Speaker 01

  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 not only early in terms of percentage of population or percentage of users that use AI, but still very early 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 AI products.

• 18:48Sarah Wang?

  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. One is, you know, if you think about live deployments at S and P 500 companies, that's at 69%. So, you know, if you're AI pilled, as we all are, maybe something closer to what we would all expect. 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%. 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.

• 19:33Sarah Wang?

  And so moving from these individual deployments that you're seeing to really deeply influential recurring workflows is the next stage, frankly. And I would just say that most of 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.

• 19:52Speaker 02

  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 Ghodse at Databricks with Martin. And he talked about how, you know, the AI can know a lot about the world, but know very little about your company. And we're seeing this in our application layer companies where they're bridging this gap. And like one example that has stuck out to me over the last few months is if you were to take Revolut, who has a really sophisticated engineering team, they still had to partner with 11 Labs and take 11's, you know, best in class voice model and harness it to connect securely with customer accounts and banking workflows

• 20:37Speaker 02

  so that when I as a customer call, I can have my, you know, problem resolved smoothly, efficiently and, you know, securely. Today's opportunity is so much around taking these capabilities and harnessing them to build reliable services.

• 20:55Sarah Wang?

  So in addition to the fact that enterprise adoption is still quite early for AI, the other really remarkable trend that we wanted to put some stats to is just that the power users are totally pulling away. 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. So we looked at some Yipit 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%.

• 21:29Speaker 01

  What's impressive about that is it's almost as much as the 2% to 10% combined is basically the 1%.

• 21:35Sarah Wang?

  Yeah.

• 21:35Speaker 01

  But just you got these power users that are clearly early adopters.

• 21:39Sarah Wang?

  Yeah, absolutely. And I mean, anecdotally, even inside our own portfolio companies, which you could say are pretty much fully AI native or AI pilled, you're seeing the top users spend anywhere from, call it 7.5 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, so 200 to 400. So over 20 times the spend if you think about median versus sort of top users.

• 22:10David George?

  Yeah, that, you know, we talked to our own portfolio companies. And, you know, one of the questions that we discuss is like, you know, 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, you know, I think like relatively forward leaning, large Fortune 500 type companies are probably today at like 1%. And, you know, the most forward leaning You know, AI-pilled 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?

• 22:49Speaker 02

  Yeah, another example we've seen within our portfolio. 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. The runway from just procuring and trying these tools to like full adoption is massive.

• 23:11Speaker 01

  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. You know, 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. But just in general, many initiatives to lower the cost to serve. You know, in the revenue side, an interesting one that I found was Shopify here, who, you know, they launched their AI sidekick. And this AI sidekick helps merchants get up to speed much, much faster.

• 23:55Speaker 01

  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 tracks is once you reach five orders, you know, you're going to stick around and you're going to retain on Shopify. So it's been a meaningful tailwind to their business as well.

• 24:13Sarah Wang?

  Yeah, absolutely. And I think to your point on 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 could reinvest in growth. And so I think the benefits are reaching the customers, not only the customers, but also the margins. And then they rotate that back in. And then I think the other thing that's been really fascinating to see is, of course, they vary, so you can't throw everything into one bucket. But the incumbents have done a pretty nice job on monetizing this as well. And ServiceNow is a good example, right? They've reported more than a billion in AI ACV and actually a 9X increase in agentic deployments.

• 24:58Sarah Wang?

  So you're kind of seeing it across the stack incumbents to newer companies.

• 25:02David George?

  Yeah, one of the interesting things that we've talked about a lot, and you know, again, some of our most forward-leaning companies like Stripe have discussed with us is, you know, where are they actually putting their incremental AI investment dollars? Are they putting it toward things like building new products for customers that could drive higher revenue? Or are they putting it toward optimization or efficiency gains on the cost side? And this is sort of a litmus test for us of like, where are the 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, you know, yes, you could take that and reinvest it. But that's sort of a latent opportunity that will continue to exist.

• 25:46David George?

  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 opportunity for you?

• 25:54Speaker 01

  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 engineers are focused on, you know, rebuilding a system of record to save a couple hundred thousand, a couple hundred thousand dollars, I expect there to be better uses of like those resources.

• 26:17David George?

  Yeah, totally agree.

• 26:18Speaker 02

  Sarah referenced with ServiceNow, seeing massive agentic usage. Agents are here, they are performing tasks. Tasks require multiple steps, which require multiple model calls. That helps explain the 14X growth in agent token usage on open router. Those steps, though, are becoming more affordable. We're seeing caching, so the system can reuse background information instead of processing it from scratch each time. I've seen Hebea take advantage of this. They've seen their financial chat workloads become 10X cheaper to run. The same budget that they had before can now support more work.

• 27:03Sarah Wang?

  Yeah, I think 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. 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. 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. You know, I think the amount of use cases that open up are actually unimaginable.

• 27:39David George?

  Yeah, totally. And, you know, with much greater improvement in latency.

• 27:44Sarah Wang?

  Yeah, absolutely.

• 27:45Speaker 02

  Yeah, so companies are definitely thinking more and more today about how to optimize latency or performance at large with cost. So take Databricks as an example. They're leveraging routing, choosing the appropriate model for each specific task to improve performance and cost. And so their smart router performed better. It solves more problems at 35% lower cost than the strongest individual model. 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. So it became much more affordable, 60% cheaper.

• 28:30Speaker 02

  But it also had way lower latency. So live use cases from an audio perspective became tenable. And so these engineering gains are making AI more useful, but also more affordable.

• 28:44Sarah Wang?

  Yeah, and I think that if you sort of roll that up 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. 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 going to, I think we're going to see more margin improvements in the best app companies.

• 29:12Speaker 02

  It's a nice segue.

• 29:13Speaker 01

  It's a nice segue, yeah. 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. You know, maybe subscription is not going to be the best place to monetize the majority of households, but 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, Alex likes to talk about smile curves. And it's really, really rare to see a retention profile that not only flatlines, but actually smiles as, you know, the product improves and people come back to it. But that's what we've seen with AI,

• 29:58Speaker 01

  which just demonstrates the value that the subscription is being, is able to deliver to the person who pays.

• 30:04Sarah Wang?

  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.

• 30:15Speaker 01

  Yeah.

• 30:17Sarah Wang?

  Netflix.

• 30:17Speaker 01

  Netflix is a great one.

• 30:18Sarah Wang?

  70 million households, right? These, I had to look it up, I'm like, is that K correct? And so totally agree that it's just getting started.

• 30:25Speaker 02

  Yeah, I mean, our partner, Josh Elman, does have 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. 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. You are right, Santi, I do like smiling retention curves. We do like to see users coming back more and more. You know, another common characteristic of large consumer platforms is time spent. So if you were to look at Facebook, Instagram, TikTok, Snap, they all get 60 or 30 to 60 minutes per day from their active users.

• 31:09Speaker 02

  And while the best AI assistants, and there's many, you know, up and coming right now, all aspire to be my go-to application, like, 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.

• 31:31Speaker 01

  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 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 agentic task and then, you know, drive up to the city or something. And that's not striving to, that's not screen time that, you know, being tracked as would have been in prior generations of consumer.

• 32:00David George?

  Yeah, totally agree.

• 32:02Speaker 02

  It's early, but it is a really new and exciting time in consumer. Muse and Instinct are no doubt the new kids on the block, but they've grown really quickly. Those, along with ChatGPT, are taking more and more of my queries away from traditional search. But it's a really a dynamic time. And so if you are an existing consumer discovery platform and existing marketplace, you really need to be thinking about your chess moves right now. There's a couple of 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?

• 32:48Speaker 02

  Last week, there was a lot of news where Amazon said, you know, no, no, thank you to Muse, but Instacart said, yes, please. And if you think about Amazon, XAWS, and Instacart, 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. Whereas if you look at Instacart, online penetration of groceries, still relatively early, a lot more orders to go get. And so the optimistic possibility here

• 33:30Speaker 02

  is that there's going to be a lot more orders coming from.

• 33:36Speaker 02

  From these applications. Historically, I've had to click all these different ways through to process an order. If I offload that to an agent, all of a sudden, there's no clicks and hopefully more GMV. The trip I once wanted to book but didn't gets booked. The order that we all wanted to place for dinner tonight happens. 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.

• 34:03David George?

  Yeah, it's sort of this question, though, that's open of how does it get compensated for, right? So to your point, Amazon has an over $70 billion advertising business that's extremely high margin flow-through that is totally predicated on the fact that consumers go to the website and click on the ads. And if they don't do that anymore, what happens? Today, Meta and Google famously advertise probably best out of any of the internet platforms. They each make, call it, $200 bucks plus per users in the US and developed world. In the case of Meta, it's an entertainment application. We'll see what happens with that.

• 34:48David George?

  It's probably a little bit safer. In the case of Google, it's funny, 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 find me a hotel in this city, or things like that, that you can very directly monetize, but that the AI was not yet capable of going to take action for on your behalf. If that changes, that could be a very different dynamic.

• 35:31Speaker 01

  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, like, look at these marketplaces are going to be impacted. I do think the positive sum view of this is, number one, maybe that $70 billion that's spent on advertising on Amazon just finds a different channel and goes elsewhere, maybe spent directly on the agents through a different form factor, or just finds better ways to target people. And then, two, I think with the lower friction, we might just see more consumption. People might buy more, 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.

• 36:08David George?

  Yeah, totally. Both could be true, by the way.

• 36:11Speaker 01

  Yeah.

• 36:12David George?

  That it could be bad. So might be sort of profit pools, but it could be additive overall to the economy. 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 consumer spending higher in the case of advertising, but it might just be that profit pools disappear from certain places and get reallocated.

• 36:32Speaker 02

  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.

• 36:40David George?

  Yeah, that's a great point.

• 36:41Sarah Wang?

  Yeah, going back to our discussion on software and moving maybe more to the public side, I think the big trend that jumps out at you 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.

• 37:04David George?

  Which is a staggering number, that only 30% of the public software companies are growing at 20% plus.

• 37:10Speaker 01

  And not only 20%, I mean, that's 30%. If you draw the line at 30%, we've discussed the absolute number of companies is less than five, when in the private markets, basically every company we see and spend time with is growing well in excess of 30%.

• 37:24David George?

  Yeah, and look, this is not, this is somewhat obvious, actually. If you look at just, you talk to IT managers, CIOs, et cetera, if you look at the growth that they're experiencing, what they spend on AI, like, the easiest place for those dollars to come from is not spending incremental new dollars on new SaaS software projects.

• 37:44Sarah Wang?

  Yeah, exactly.

• 37:45David George?

  So I think we're investors in some of the SaaS 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, locked-in customers, who, 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? I think in order to dispel the greatest fears around the SaaS apocalypse, I think we just need to see a period of time where the software companies continue to post things like 98% gross dollar retention, which was always the sticking point for why they were so attractive

• 38:29David George?

  as investments, continue to drive efficiency, like we've talked about, but most importantly, drive revenue growth acceleration, 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.

• 38:44Sarah Wang?

  I thought your point in the blog post that you wrote a couple of months ago on two paths was really, really interesting as a rule of thumb. Maybe share more on the finer point of the percentages.

• 38:53David George?

  Yeah, the more I talked to founders after it, the more I felt like, almost everyone that we talked to is like, yeah, I'm trying to drive revenue growth higher. 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. Again, I think maybe relative to a year ago, when the fear was like, okay, everyone's going to vibe code their software systems, that is clearly not what's happening in the market. But the onus is high on delivering revenue growth acceleration. And so we had said, let's target 10% plus acceleration, which is a high number, but

• 39:36David George?

  with the 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, where we'll see that over the next 12 to 18 months.

• 39:45Sarah Wang?

  As with all things, you can't lump every company into the software bucket and talk about multiples coming down and growth decline as well. In fact, software has been very differentiated. And if you look at this chart here, you can see that cybersecurity and interoperability have really stood out, and vertical software as well has generally held up much better than the horizontal applications. And I think the framework for how we think about what's been, what's held up or even gone up nicely versus in more secular decline, if you will, is that really you think about

• 40:27Sarah Wang?

  how AI changes the customer's need for that product, right? And I think the cybersecurity risk associated with AI has been well-publicized, but as you think about more software and agents creating new security and new monitoring needs, this is creating greater demand, honestly, for the incumbents in these markets. And you can see the call-out on CrowdStrike on the right hand. And then, of course, on the flip side, applications are facing different degrees of workflow change, right? And it's sort of, if you think about, one of our top CEOs, Ali Gotze, likes to talk about this chopping block of AI and what's first in the chopping block.

• 41:07Sarah Wang?

  And you can kind of think about, on the flip side, what is actually needed for AI? And you pair those dynamics together, and I think it explains a lot of what we're seeing in the public markets.

• 41:17Speaker 02

  Yeah, I think a lot of this is pretty intuitive with what we're seeing on the private market side. So as you alluded to, Sarah, on the security front, agents are accessing more and more systems, taking more actions. The OpenAI hugging face incident was an eye-opening event for many. So security 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? Harvey, Abridge, Elise, they are growing faster than any of the precedents ever have in their industry. And that's a reflection of the customers recognizing it's not just the model, which we talked about earlier, but how you orchestrate that, how you build the application around

• 42:00Speaker 02

  it, and vertical-specific workflows command these needs.

• 42:04Speaker 01

  And I mean, we spend a lot of time looking at public markets, less so for investments, but we underwrite exit multiples, right? So we tracked that pretty closely. And I think if we were to recap the year, what's happened? First couple of months, there was the SaaS apocalypse. We wrote a blog post about what kind of businesses would do well. And if you fast-forward to today, the software index is actually back to where it started the year, but it's really bifurcated into some companies that are deemed AI losers and some companies that are deemed AI winners. 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.

• 42:48David George?

  Yep.

• 42:49Sarah Wang?

  Yeah. I think on the flip side, we wanted to showcase some private company operating data that gives us a view of what customers are actually doing. And there's a little bit of a narrative violation in Stripe's SaaS customer data, which actually shows growth accelerating into 2026 across both young and mature businesses.

• 43:10Speaker 02

  Stripe calls it the renaissance.

• 43:12David George?

  Oh, the renaissance. I actually quite like the renaissance.

• 43:14Sarah Wang?

  I like renaissance, yeah.

• 43:16David George?

  It's very good. It's very good.

• 43:18Speaker 01

  If you talk, we've been talking about public markets a bit. Sarah talked about private market SaaS acceleration. We get a lot of questions around companies staying private for longer. So if you actually look at the top six companies today, Anthropic, OpenAI, Databricks, Stripe, Waymo, Revolut, 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 trillion, it's almost as big as the Russell 2000, which is a big index in the public markets where there's hundreds of public managers that spend most of their time in. So it's increasingly exciting to just spend time

• 44:02Speaker 01

  in these late-stage champions that can keep investing in growth more so than maybe maximize short-term profit. But David, you can talk a little bit about how we advise companies on the IPO and when to stay private versus public.

• 44:15David George?

  Yeah, look, I mean, the IPO, especially for founder-led companies, one of the things that we've talked about 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 in the private markets that have longer-duration paybacks. Zuck and Elon are kind of obvious exceptions to the rule in the public markets. But the way you see it manifest in the numbers in companies like Databricks or Stripe is massive, massive new bets in new product areas. And you can see revenue acceleration that happens as a result. And so you can do that in the public markets,

• 45:01David George?

  but it's going to catch greater scrutiny. Obviously, Meta's stock price at the Nater got below 100 bucks a share when people were very, very skeptical about their investments in ARVR. For us, the way we talk about it with our founders is just the IPO is another financing event. And what do you get out of being a public company relative to what do you get out of being a private company? But clearly, you can reach big scale in the private markets.

• 45:27Speaker 01

  And there's some other companies that already talk about running themselves like they were a public company, with a high focus on efficiency and just basically like every metric 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. I mean, Navan went public, and we've seen a re-acceleration just on the basis of some of the larger enterprises actually trusting them more because they're a public business.

• 45:53David George?

  Yeah, and then, of course, there are examples where capital needs over time could be very, very large. And so the capital pools in the private markets are very large and can serve the needs of many of these companies. But at some point, the capital needs may even get too big for the private markets.

• 46:11Speaker 01

  That's the public markets. On the private markets, one of the things that we spend a lot of time into is secondaries. So there's two dynamics at play. One is we see an increasing amount of companies holding tenders for the employees. So if they choose to stay private, allowing employees to get liquidity outside of the public markets. And an interesting data point show here is participation in tenders struck by Karta 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 employees cashing out. And what we're seeing is actually employees choosing not to cash out because they believe in their company so much.

• 46:50Speaker 02

  Imagine SF Home prices if that was 100%.

• 46:54David George?

  Cycle.

• 46:55Speaker 01

  It'd be hard, exactly.

• 46:56David George?

  Yeah, the tender offers that happen, we're obviously participating in these and leading these pretty frequently. So we think they're a good thing. They do serve two purposes. One, 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. So it is a weapon of competition for the private markets that want to compete for employees or retaining their employees with the public market companies. The other side of it is we're advocates of sort of more frequent resetting of your valuation. And so that keeps your sort of stock price, private stock price, fresh for a number of reasons.

• 47:41David George?

  One, it's easier to talk about that with employees and prospective employees. But two, in the event that you want to do M&A, like many of our companies have done, you have fresh currency that you can use.

• 47:51Speaker 01

  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 in the last five years. For maybe coming out of 2021 and 22, 23, 24, the median discount to the last round price in secondary markets was meaningful because there was maybe like around those price too high, and it was out of date. But actually, what you're seeing today is when there are transactions in secondary markets, the discount to the last round price is basically zero.

• 48:16David George?

  Yeah.

• 48:16Speaker 01

  Which just speaks to these valuations of 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 the last couple of years.

• 48:27David George?

  Yeah, great point. So 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. So obviously, AI has become the dominant destination for venture dollars.

• 48:48Speaker 02

  Yeah, but beneath that AI label, the opportunity has broadened a lot, right? So enterprise apps, consumer apps, but also services, semiconductors, power, defense. A lot of the attention goes to OpenAI, SpaceX, AI, and Anthropic. But beyond that, our opportunity set has never been deeper and broader.

• 49:09David George?

  Last, we thought it would be fun just to talk about some of the areas that we're very excited about right now. Obviously, we touched on some of the consumer work that will now be done by agents. But you could call it long-running agents, you can call it autonomous agents, heavy-use consumer. If you take the perspective of these that 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, it's very appealing. And I think that the distribution of this could happen pretty quickly. 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, but probably three to five years earlier.

• 49:53David George?

  And so we think over the next five years, this is going to be a massive area of investment and excitement. Autonomy is here. Self-driving works. It's a very exciting time. If you just take 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. But if you look at miles traveled by Uber and Lyft, or in Ubers and Lifts, it represents about 1% of miles traveled in the US. I think with full networks of autonomous driving cars that are 14 times safer than human drivers, we expect that to expand by at least an order of magnitude in the coming years.

• 50:38David George?

  Plus, there are 17 million new cars sold per year in the US. And I think over the next 10 years, those will all be autonomous. AI times bio, this is a super exciting area. Obviously, the folks in the labs are talking about this. But new drug discovery, solving some of the most debilitating illnesses 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 next 10 years. Personal health. This is another one that I'm very excited about for myself as a health maxer. But there's not been a great place or avenue to take all of the information about yourself, put it into somewhere,

• 51:22David George?

  and get very hyper-personalized advice. And then, lastly, diffusion into the enterprise beyond coding. This is one of the ones that, Sarah, you were talking about. Just we're so early in actual diffusion of the technology into the enterprise that I think it's going to be super, super exciting. And then, lastly, there's another area that is not inside the AI bucket, which is what we call American dynamism, but the sort of retooling of the entire sort of American dynamism stack. We've invested. We've been large investors in this area for a while. It's still just a small fraction, less than 5% of overall dollars spent.

• 52:06David George?

  And the military is sort of newer vendors like Androl or Sironic or Castellian. And we expect that to grow dramatically as needs change. So so many areas of excitement. Obviously, we've been very active in this AI space. And we're optimistic about the effect that it's going to have on the overall economy. The build-out is massive. But we think it's going to be massively productivity-enhancing in the US. So it's a blast to hang out with you guys. Thank you.

• 52:36Erik Torenberg?

  Thanks for listening to this episode of the A16Z Podcast. If you liked 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 X at A16Z and subscribe to our Substack at a16z.substack.com. Thanks again for listening, and I'll see you in the next episode.

• 53:01Erik Torenberg?

  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 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.