a16z Podcast - The $1 Trillion AI Buildout | State of Markets

Episode Date: September 30, 2026

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 MarketsThen 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 MarketsResources:Follow David George on X: https://x.com/DavidGeorge83Follow Sarah Wang on X: https://x.com/sarahdingwangFollow Alex Immerman on X: https://x.com/aleximm Follow Santiago Rodriguez on X: https://x.com/santiago__rdz Read David’s piece ‘There are only two paths left for software’: https://a16z.com/there-are-only-two-paths-left-for-software/ Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that 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. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Eight of the top 10 valued companies in the world are U.S. tech companies. Since JOTDBT came out almost four years ago, the market's up 90%. We're just 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 Adams. 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. 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. That's all great, but is it a bubble? 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 Zimmerman walk through 25 key charts from our latest state of the market's presentation. They start with the macro picture, why markets have risen, even as multiples have come down, how hyperscalor capax 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. 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. Welcome back to the A16Z podcast. I'm David George. I'm here with my colleagues Sarah Wang, Alex Emmerman, and Santiago Rodriguez.

Today, we are walking through 25 key slides from our latest state of markets presentation. The earnings behind the markets 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 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. I'll be here. Okay, so the day this podcast goes live, we will be released. 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 capbacks, 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 gains and AI demand pushing infrastructure spending far beyond earlier forecasts. Hyper-scalers 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? So high-tech equipment, software, and R&D now account for roughly 55% of U.S. 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 U.S.

And eight of the top 10 valued companies in the world are U.S. 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. 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 here we're wrong. 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. 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&P 500 earnings multiple is below 20 times.

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 runups based on increases in their trading multiples. It would trade in many cases for 100 times 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. And I think there's an important double click here where since Jad JPD 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 gross to the 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 20, 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. Yeah, totally agreed. So I mentioned the scale of the CAPEX build out on the context of the railroads. If you just look at the hyper scalers, Alphabet, Amazon, Meta, Microsoft, and Oracle, their CAPX in 2026, it's about $780 billion. That's up from $416 billion in 2025. And all expectations point to them spending over a trillion dollars 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 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, in mobile phones. And so sort of immediately could reach billions of users in contrast to previous technology cycles. 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. 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. 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 hypers, CAPEX, have moved sharply higher in pretty short sessions. 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.

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 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. 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. Yeah, absolutely. Yeah, pretty much everyone we talk to at every stage of the supply chain is telling us the same thing, some version of the same thing that demand outstripped 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 business on the demand side that you can hang your hat on. 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. 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 know, 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 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. Yeah, I know some of the hyperscalers have frankly gotten dinged for raising debt for CAPX, but 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. 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 that an attractive rate where like the hyperscalers and earning an attractive return. Yeah, it's a key point. 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, 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 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. And as we, you know, talk about this CAPEX for the hyperscalers, 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. 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. 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, 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 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 Adams. 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 Anderol with their massive manufacturing facility. 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. Betting on the future today, from our perspective, isn't what it exactly was as it was in the past, as Santi said. It means building factories, expanding infrastructure, more skilled jobs,

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. 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. 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 U.S. study showed that for every 10% increase in data center capacity, residential rates went down by 40 bibs.

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. Simply put, more demand across the shared system is a positive. 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. Yeah, 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. Totally agree. 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, buy 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. 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 an 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 it is one of my. favorite charts, which is the combined scale of revenue for open AI and anthropic. And this is one that we we've had sort of in every GP off site as far as I can remember the last few years. 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. Open AI Anthropic, their combined annualized revenue has climbed to an extraordinary degree. We love to show this relative to the greatest software companies in history. And 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 NetNew 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. But we're still pretty early. Like I think 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 little. earlier on 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. 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&P 500 companies, that's at 69%. So, you know, if you're AI-pilled as we all are maybe something closer 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. 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. Yeah, this gap between what the models can do and how they're being used is what makes me so excited about the application there. Sarah, you did this great, you know, conversation with Ali Gozzi at Databricks with Martine,

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 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 resolve smoothly, efficiently, and, you know, securely. Today's opportunity is so much around taking these capabilities and harnessing them to build reliable services. So in addition to the fact that enterprise adoption is still quite early for AI, the other Another 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 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%. Yeah. But you got these power users that are clearly early adopters. 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, seven 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, so 200 to 400. So over 20 times the spend, if you think about median versus sort of top users. Yeah, you know, we talk 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? 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. 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 supportive 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. 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. So it's been a meaningful tail into their business as well. 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 can 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 service now is a good example, right? They've reported more than a billion in AI ACV and actually a 9x increase in agitic deployments. 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 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. 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? 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 dollars, I expect there to be better uses of those resources. Yeah, totally agree. 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 OpenRouter. 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 Hebia 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. 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 TypeSafe 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 opened up are actually unimaginable.

Yeah, totally. And, you know, with much greater improvement in latency. Yeah, absolutely. 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. But it also had way lower latency. So live use cases from an audio perspective became tenable. And so these engineering gains are making making AI more useful, but also more affordable. 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. It's a nice segue. 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 all over our times. No, we think it's still pretty early. There was a recent survey that showed that only just over 2% of U.S. 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, which just demonstrates the value that the subscription is able to deliver it 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. Netflix. Netflix is a great one. 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. 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 expect. 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. And while the best AI assistance, and there's many 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. Yeah, it's going to make our job harder when we can't, you know, look at engagement via outside end data because the agent are working on the background instead of 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 agenetic task and then, you know, drive up to the city or something. And that's not striving to I, Tesla. That's not screen time that, you know, being tracked as would have been in prior generations of consumer. Yeah, totally agree.

It's early, but it is a really new and exciting time in consumer Muse and instincts are no doubt the new kids on the block, but they've grown really quickly. Those, along with ChatGBT, GBT, are taking more and more of my queries away from traditional search. But it's 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. 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? Last week, there was a lot of news where Amazon said, you know, no thank you to muse, but Instacart said, yes, please. And if you think about Amazon, X-AWS, 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 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, 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 get booked, the order that we all wanted to place for dinner tonight happens. So I'm optimistic that 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, you know, 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, you know, what happens? You know, today, you know, meta and Google famously advertise, you know, probably best out of any of the internet platform. You know, they each make, call it 200 bucks plus per users in the U.S. and developed world. 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, you know, 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 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. 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 sum view of this is number one, maybe that's $70 billion that's spent on advertising on Amazon just finds a different channel. And, you know, 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, 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. Yeah. Yeah, that it could be bad for 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. But it might just be that, you know, profit pools disappear from certain places

and get real-cared. 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, that's a great point. 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,

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%. 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 well in excess of 30%. Yeah. And look, this is not, this is somewhat obvious. Actually, if you look at just, you talk to, you know, IT managers. CIOs, et cetera, if you look at the growth that they're experience and 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. Yeah, exactly. So, you know, I think we're investors in some of the, you know, 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, you know, locked in customers 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. I think, you know, in order to sort of dispel the greatest fears around the SaaSpocalypse, 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, which was kind of always the sticking point for why they were so attractive as investments. continue to drive efficiency like we've talked about, but most importantly drive, you know, 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. I thought your point in the block was that you wrote a couple months ago on two pads was really, really interesting as a rule of thumb. Maybe share more on the finer point of the percentages. Yeah, you know, the more I talked to founders after it, the more I felt like, you know, almost everyone that we talked, to talk to is like, yeah, I'm trying to drive revenue growth higher. And, you know, it's, it's, it's, it's, it's, it's, it's, it's, it's, it's, it's, it's, it's, it's, it's, it's, it's, it's, it's, and that we're investors in and, and that we've backed over the years. 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, um, um, but, you know, the onus is high on

new growth acceleration. And so we had said, you know, let's target 10% plus acceleration, which is a high number. 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 where we'll see that over the next 12, 18 months. You know, as with all things, you can't lump every, every company into the software bucket and sort of, you know, talk about multiples coming down and sort of growth decline as well. In fact, software has been very differentiated. And if you look at this chart here, you can see that cybersecurity observability 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 how AI changes the customers 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, you know, the call out on Crowdstrike on the right hand. And then, of course, in the flip side, applications are facing different degrees of workflow change, right? And it's sort of, you know, if you think about, you know, one of our top CEOs, Ali Goetze, likes to talk about this chopping block of AI and what's first on the chopping block. 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. 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 Open AI 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, a bridge, a lease. 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 it and vertical-specific workflows command these needs. 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? You know, first couple months, there was this escap apocalypse. We wrote a block 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. 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. Yep. Yeah. You know, 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. Stripe calls it the REN assess. Oh, the Rennas Sass. I actually quite like the Rennas Sats. I like Rennas Sass. Yeah. So it's very good. It's very good. If you talk, you know, we've been talking about public markets a bit. You know, Sarah talked about private market SaaS acceleration. We get a lot of questions around, you know, companies staying private for longer. So if you actually look at the top six companies today, you know, Anthropic, OpenEI, Data, Brick, Stripe, Wayman, 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, 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 Blade States champions that can keep investing in growth more so than maybe 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. Yeah, look, I mean, the IPO, you know, 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. You know, Zuck and Elon are kind of obvious exceptions to the rule in the public markets. But, you know, the way you see it manifest in the numbers in companies like Databricks or Stripe is, you know, massive new bets in new product areas. and you know you can see revenue acceleration that happens as a result.

And so, you know, you can do that in the public markets, but, you know, it's going to catch greater scrutiny. You know, obviously Meadows stock price, you know, at the Nader, you know, got below 100 bucks a share, you know, when people were very, very skeptical about their investments in ARVR. 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? But clearly you can reach big scale in the private markets. And there's some of our 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 for maybe like public disclosing of financials and earning customer trust, both at the enterprise level and on a consumer level. I mean, Navand went public and we've seen a reacceleration 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 could be very, very large. And so the capital pools in the private markets are very large and can conserve 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. That's the public markets. You know, in the private markets, one of the things that we spend a lot of time in two is secondaries. So there's two dynamics at play. You know, one is we see an increasing amount of companies holding tender.

for the employees. So if they choose to stay private, you know, allowing employees to get liquidity outside of the public markets. And an interesting data point show here is, you know, participation in tenders struck 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 things. 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.

Imagine SF home prices, if that was 100%. Exactly. Exactly. Yeah, the tender offers that happen, you know, we're obviously like participating in these and leading these pretty frequently. So we think they're a good thing. 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. So, you know, it is a, it is a, you know, 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, you know, we're advocates of sort of more frequent resetting of your valuation. And so that keeps your sort of stock price, you know, private stock price fresh for a number of reasons. One, it's easier to talk about that with employees and prospective employees.

But two, you know, in the event that you want to do M&A, like many of our companies have done, 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 the 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 where you're seeing today is, you know, when there are transaction secondary markets, the discount to the last round price is basically zero. Yeah.

Which just speaks to, you know, 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. 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, you know, obviously AI has become the dominant destination for venture dollars. Yeah, but beneath that AI label, the opportunity, has brought in 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. Last, we thought it would be fun just to talk about some of the areas that we're very excited about right now. You know, obviously we touched on some of the consumer work that will now be done by agents. But, you know, you can call it long-running agents, you can call it autonomous agents, have you used consumer.

You know, if you take the perspective of these 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, it's very appealing. 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, you know, probably three to five. years earlier. 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, 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. But, you know, if you look at miles traveled by Uber and Lyft or in Uber's and Lyfts, it represents about 1% of miles traveled in the U.S. I think with, you know, 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, plus there's 17 million new cars sold per year in the U.S. And I think over the next 10 years, those will all be, be autonomous. Yes. AI times bio, this is a super exciting area. You know, obviously the folks in the labs are talking about this. But, you know, new drug discovery, solving some of the, you know, 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, you know, as a health maxer. But, you know, there's not been a great place. or avenue to take all of the information about yourself,

put it into somewhere 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, you know, 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. You know, it's still just a small fraction, less than 5% of overall dollars spent. You know, in the military is sort of, you know, newer vendors like Andrel or Serronic or Castellian, and we expect that to grow dramatically as needs change. So so many areas of excitement Obviously, you know, we've been very active in this AI space And, you know, we're optimistic about the effect That's going to have on the overall economy.

The buildout is massive But we think it's going to be massively productivity enhancing in the U.S. So it's a blast to hang out with you guys. Thank you. Thanks for listening to this episode of the A16D podcast. If you like this episode, be sure to like, comment, subscribe, leave us a rating or review And share it with your friends and family.

or more episodes go to YouTube, Apple Podcasts, and Spotify. Follow us on X, A16Z, and subscribe to our substack at A16Z. com. Thanks again for listening, and I'll see you in the next episode. As a reminder, the content here is for informational purposes only. Should not be taken as legal business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any 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 forward slash disclosures.