a16zAI, Infrastructure, and the Next Investment Cycle
EVERY SPOKEN WORD
50 min read · 10,047 words- 0:00 – 0:59
Intro
- DGDavid George
Eight of the top 10 valued companies in the world are US tech companies Since ChatGPT came out almost four years ago, the market's up 90%, which is 17% annualized. The natural instinct is, well, that's gotta come down. We're definitely in a hot period. This puts us in a new age of atoms. Global infrastructure investment needs are estimated at $90 trillion through 2040. This goes way beyond AI and data centers. It includes power, water, roads, transit
- SWSarah Wang
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.
- DGDavid George
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?
- SWSarah Wang
Um.
- DGDavid George
Um. Um. Welcome
- 0:59 – 6:28
Is it a bubble?
- DGDavid George
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 build-out, 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. 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. Uh, today we're just picking out a subset of interesting slides and having a discussion about them, so I would direct you to, uh, look at the whole version, which has, which is filled with a lot of nuggets. Um, the areas that we're gonna discuss today are, um, macro, so where we'll talk about CapEx, data centers, accelerating demand at a high level, and then on-the-ground takeaways, where we will discuss models, apps, uh, and some vertical deep dives. Technology is driving an economy-wide investment boom, with rising earnings supporting market gains and AI demand pushing infrastructure spending far beyond earlier forecasts. Hyperscalers are investing all of their near-term operating cash flow to build out this capacity for demand that continues to outstrip supply in almost every case that we see. They are channeling this investment into chips, power, cooling, construction, skilled labor, and much more. This expansion coincides with a broader need to moderz- to modernize physical infrastructure, creating opportunities across industries and potentially lowering shared costs for businesses and households. So with that, let's jump in. First slide, Tech Is The Everything Cycle. So, um, this, some of this may be a little bit obvious, but, um, the numbers are, are somewhat striking at this point, right? So high-tech equipment, software, and R&D now account for roughly 55% of US capital spending, which is just a staggering number. Um, tech is driving the investment cycle, obviously across all areas of the economy, uh, from software and models to power, construction, and industrial capacity. Tech is almost 40% of the aggregate value of the whole stock market in the US, um, and eight of the top 10 valued companies in the world are US tech companies. So this is a broad story. Uh, you're seeing it in CapEx. This is heavily covered in the, in the data center build-out. Um, it's obviously heavily covered in the amount of capital that's been going into the, to fund the model development, models, model companies together raising, I think, over $350 billion, um, at this point. Um, and just to put it into, into very deep historical context, 'cause this is probably the analogy that we've seen the most of, uh, this build-out just surpassed railroads as a percentage of GDP. Um, so, you know, exciting times, massive build-out. Uh, we all happen to think that if you fast-forward five to seven, maybe 10 years from now, um, you know, we'll be looking at these numbers and they'll probably be 20X higher cumulatively. So here we are. Yeah. This, this does feel like the next chapter of Mark's "Software Is Eating The World." You know, over the last 15 years, software has transformed all these industries, uh, but only a small fraction of the population could build it. None of us could. Uh, but today, you know, look at us. We all have like, you know, a handful of automations running every night. And so if you think about the software demand, that means a lot more compute, chips, power, construction. Um, and so it's, it's no surprise to see the majority of investment now, now covered in tech. So one of the questions that we get all the time from various audiences is, "Okay, that's all great, but is it a bubble?" So the market has reached new highs, um, and at the same time that it has reached new highs, the trading multiples of the market are actually down. So stocks are up about 20% while multiples are down about 20%. So what that means is the performance is, is driven by fundamental earnings, right? Not, not increased multiples. Um, the S&P 500 earnings multiple is below 20 times, so, um, you know, if you just start with that, these are h- m- many, 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, um, had their massive stock run-ups based on increases in their trading multiples and would, and would trade in many cases for, like, 100 times PE. That's not what's happening here. Um, in contrast, uh, you know, some of the memory companies, which again are very cyclical, uh, are trading, you know, for, call it six times, seven times forward earnings, so very, very different. And I think there's an important double-click here where, you know, since ChatGPT came out
- SRSantiago Rodriguez
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? Like we're, we're definitely in a hot period. But when you compare that to, as you said, the market trading for below 20 times earnings growing 15%, this definitely feels a little different than maybe the 2021 period that was recent, or the 2000 period when multiples and growth were not really going together.
- 6:28 – 13:15
The $780B hyperscaler CapEx race
- DGDavid George
Yeah, totally agreed. So I mentioned the scale of the CapEx build-out, um, you know, in the context of, of the railroads. You know, if you just look at the hyperscalers Alphabet, Amazon, Meta, Microsoft, and Oracle, their CapEx in 2026 is about $780B. That's up from $416B in 2025. And, uh, all expectations point to them spending over a trillion dollars annually from 2027. Um, so, you know, the pattern recognition is, is each of these computing platforms has supported a much larger population of users and uses. In this case, um, this build-out can happen so quickly and demand can still outstrip supply because the amount of users is driven by the fact that there's already existing distribution. This trend is built on top of the internet and cloud computing, obviously, and mobile phones. Um, and so, you know, sort of immediately could reach billions of users, um, you know, in contrast to previous technology cycles.
- SWSarah Wang
Yeah, and I think it's, um, an important point to double-click on just because we've moved well beyond this model of occasional queries, right? If you think about the rise of agents, you have parallel tasks, you have long-running tasks. And so I mean, Alex, you mentioned this previously, right? You have tasks going into the evening, during the day when you're doing other things. Um, and so if you think about agents autonomously writing code, searching, carrying out these tasks, um, it just gives these platforms a much larger compute requirement than ever thought before.
- DGDavid George
Yeah, absolutely. Totally agree. Um, this one is another one that just points to what is happening, happening in the CapEx side of things. Successive forecasts for the five largest hyperscalers' CapEx have moved sharply higher, um, in pretty short succession. So spending that once looked like a ceiling, you know, a number of quarters or years out has become near term, um, as the demand for the compute keeps, keeps expanding.
- SRSantiago Rodriguez
Yeah, look, i-if you look at the chart here, I think at any point in time, I think natural instinct is just to say, like the investment will kind of flatline from here. Yet, for the last four years, like as an economy, we keep underestimate just the, the strength of the trend. And as Sarah said, you know, with model developments, usage of, of our installed capacity keeps, keeps being taken up. So right now, the numbers that David pointed to are the current estimates.
- AIAlex Immerman
Yeah, I mean, and maybe to use DG's language, I think we'd all call the demand for compute a model buster at this point. Um, I think one anecdote that's telling that w- that we've all experienced is, you know, Sam Altman, Sarah Friar, they got a lot of flak, you know, a year or so ago for their massive compute, uh, commitments. Uh, they were being reckless and aggressive. And I think at this point, like everyone would say, they're incredibly prescient with that decision. Um, and even so, two weeks ago, we all saw that they had to pause new subscriptions on their pro plans, like showing up with a $2,000, you know, uh, you know, service. No, no thanks. Um, pretty amazing, uh, insatiable demand that we're experiencing here.
- DGDavid George
Yeah, absolutely. Yeah, pretty much every, you know, everyone we talk to at every stage of the supply chain, uh, is telling us the same thing, some version of the same thing, that demand, you know, outstrips supply. Uh, there are certain elements in the data center supply chain, um, you know, where you can't get access to materials or products until 2028. Um, and so this has not softened. So, um, you know, I, I would say at the same time, you can look to the hyperscalers and see some evidence of high quality, you know, business on the demand side that you can hang your hat on, right? So Microsoft, Google, and Amazon have, uh, about $1.7 trillion of combined cloud backlog together. Um, and those customer commitments are, are building rapidly while the platforms invest heavily in the capacity to serve them. So, um, while right now free cash flow is depressed during this build-out, um, you know, consensus forecasts are showing a recovery from 2028 and substantial growth thereafter. Um, you know, you could look to Amazon's, actually Amazon's latest earnings call, where they did a really good job of explaining this sort of J-curve dynamic, where the useful life of GPUs is actually pretty, or, or TPUs, is actually pretty long. Um, and so, you know, you have to build out the shell of the data center. You know, that's a certain amount of time. You have to buy the, the chips. Um, but, you know, those will have a very useful economic life for a long period of time, and it's, and it's been longer than I think any of us expected.
- SWSarah Wang
Yeah, and I know some of the hyperscalers have frankly gotten dinged for raising, uh, debt for CapEx. But, um, you know, similar to the model lab dynamic, I think the ones who have blinked and been less aggressive have regretted it. Um, I know on the podcast recently that you did with Gavin, uh, Microsoft came up, um, but I think this is, uh, you know, an issue across the board.
- SRSantiago Rodriguez
Yeah, the other dynamic that's been spoken about a lot is pricing on the spot markets for existing GPUs, which is just another signal that, like any GPU that you can bring online is being priced at an attractive rate where like the hyperscalers are earning an attractive return.
- DGDavid George
Yeah, it's a key point. Um, you know, this is one of the things that we talk about all the time is like each one of these successive waves just creates a tremendous amount of user or consumer surplus. And so what's actually happening right now, at least as far as we can tell, um, is, you know, consumers and users get a tremendous amount of value out of this. Like, that's why they're using it so much. Um, you know, the, the The vendors who are serving those users are making very good money. Um, and then, you know, you go all the way down every level of the stack to the chips where, you know, you have to pay much higher than you did 12, 18, 24 months ago to get access to them, and yet you could still make very high margins and create a tremendous amount of surplus out of the users.
- AIAlex Immerman
And as, as we, uh, you know, talk about this CapEx, uh, for the hyperscalers, uh, 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. Um, that's putting pressure on their free cash flow short term, as we just talked about. Um, but it's been a boon, uh, for chip orders, for power, for construction, and that's why this broader technology boom has become an industrial boom.
- SRSantiago Rodriguez
And we... I mean, we've been spending more of our time looking at businesses serving this industrial bur- 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.
- 13:15 – 18:21
The new age of atoms
- AIAlex Immerman
This puts us in a new age of atoms. Uh, big numbers on this page. Global infrastructure investment needs are estimated at $90 trillion through 2040. Um, importantly though, this goes way beyond AI and data centers. It includes power, water, roads, transit. Um, we're seeing this across our portfolio. You can see this, uh, at Anduril with their massive manufacturing facility. Um, it, you know, is, it's the, the scale of it is ei- 87 football fields. Uh, Waymo, uh, they're me- you know, aggressively expanding their depots, and of course, you know, SpaceX with their $100 billion investment in Louisiana. Um, betting on the future today from our perspective isn't, you know, what it exactly was in the pa- as it was in the past, as Santi said. It means building factories, expanding infrastructure, uh, more skilled jobs, uh, and across the country at large.
- SRSantiago Rodriguez
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, um, as they scale.
- AIAlex Immerman
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, uh, and then, you know, one of our favorites, uh, my electricity bill is gonna go through the roof. It may seem counterintuitive, uh, but a data center can help lower your electricity rate. A recent US study showed that for every 10% increase in data center capacity, uh, residential rates went down by 40 bps. Um, 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 a shared system is a positive.
- DGDavid George
Yeah. Yeah, I mean s- yeah, I totally agree with what you're saying. I mean, investment in shared infrastructure can also improve the economics for the households and businesses connected to it.
- SWSarah Wang
Yeah, and I don't think that point gets enough media attention. Um, Dina Powell went on a podcast recently from, from Meta and talked about the Louisiana site that they did where they worked with the community to lower, um, electricity costs. And so there's real examples of that today. It's not just lip service.
- DGDavid George
Totally agreed.
- SWSarah Wang
So now we're gonna talk about the trends that we're seeing at the model and application layer. Um, on the one hand, uh, as we've sort of previewed, AI is generating major revenue and savings. On the other hand, adoption is still extremely early. Um, and then, you know, I think on top of this, the, the trend is very much so that costs are plummeting and, you know, we previewed agents already, but agents are actually economical now for a much wider range of work. And of course, this is changing even as we speak, right? New innovations like Jev, by newer labs like TypeSafe, are taking this to an, a greater extreme. You're starting to see things like two orders of magnitude, uh, cost differences really impact the, a number of use cases. Um, it's sort of classic Jevons paradox, which is why it's very aptly named. Um, and you know, I'd say all in all, this is an awesome setup for both the model layer and the app layer. Um, these improvements are creating real opportunities for both growth and profitability among software companies, and that's not to say that everyone will win, but it's no surprise that AI is attracting venture dollars into an very much expanding range of industries. Um, and we're also seeing com- 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 OpenAI and Anthropic. Um, and this is one that we, we've had sort of in every GP offsite as far as I can remember the last few years, and we have to keep updating it every month because it, it just sort of gets out of date that quickly. I think the point here, you can see it in the numbers, OpenAI, Anthropic, their combined annualized revenue has climbed to an extraordinary degree. Um, we, uh, love to show this relative to the greatest software companies in history. Um, and you can see that in the right, that, um, if you look at the estimates for revenue added, um, for the, the best software companies ever built, um, the estimates for net new for the leading labs has surpa- well surpassed that. Um, so it's really a striking combination of not only scale, but also speed to get there.
- SRSantiago Rodriguez
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're, we need to think about percentage of people that will use AI products and then to what extent they're using the AI products.
- 18:21 – 20:28
Only 2% of enterprise AI is truly tracked
- SWSarah 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. Um, one is, you know, if you think about live deployments at S&P 500 companies, that's at 69%. So, you know, if you're AI-pilled as we all are, um, maybe something closer to what we would all expect. Um, but then if you move toward quantifiable impact, which is probably a good metric of how far along they are in their deployment, that's 30%. Um, now if you go to the ultimate barometer, which is a metric tracked over time, that's actually only at 2%. So we believe that AI is delivering results, but there is a huge amount of room to actually deepen its use inside the organization and track those results over time. Um, and so moving from these individual deployments that you're seeing to really deeply influential recurring workflows is, is the next stage, frankly. Um, and, uh, I, 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.
- AIAlex Immerman
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 Ghodsi 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, uh, in our application layer companies where they're bridging this gap. Um, and like one example that has stuck out to me over the last few months is if you were to take Revolut, who has a really sophisticated engineering team, they still had to partner with Eleven Labs, uh, and take Eleven's, you know, best-in-class voice model and harness it to connect securely with customer accounts and banking workflows so that when I, as a customer call, I can have my, you know, problem resolved smoothly, efficiently, and, you know, securely. Um, today's opportunity is so much around taking these capabilities and harnessing them to build, uh, reliable services.
- 20:28 – 31:34
Power users spend 20x the median
- SWSarah Wang
So in addition to the, the fact that, um, enterprise adoption is still quite early for AI, uh, the other really remarkable trend that we wanted to, um, put some stats to is just that the power users are totally pulling away. Um, so you can think of AI spending rising generally across companies, but the power users, as we talked about, the most intensive users are spending much, much more. Um, so we, we looked at, uh, some Yipit data for this, and if you l- 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%.
- SRSantiago Rodriguez
What's impressive about that is it's almost as much as the 2 to 10% combined is basically-
- SWSarah Wang
Yeah
- SRSantiago Rodriguez
... the 1%.
- SWSarah Wang
Yeah.
- SRSantiago Rodriguez
So just you got these power users that are, are clearly early adopters.
- SWSarah Wang
Yeah, absolutely. And I mean, anecdotally, even inside our own portfolio companies, which you could say are, um, pretty much fully AI native or AI-pilled, um, you're seeing the top users spend anywhere from, call it, 7.5 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. Um, so over 20 times the spend if you think about median versus, um, sort of top users.
- DGDavid George
Yeah. The... You know, we, we talk to our own portfolio companies and, you know, one of the questions that we discuss is, like, you know, how, how, how much adoption do they have and how do you measure that? And, you know, if you just look at like the percentage of money that they're spending on AI tools compared to headcount as an example, um, you know, I think like relatively forward-leaning large Fortune 500 type companies are probably today at like 1%.
- SWSarah Wang
Hmm. Yeah.
- DGDavid George
Um, 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?
- AIAlex Immerman
Yeah. Another example we've seen within our portfolio, um, you know, many enterprises are really excited when they, you know, buy Cursor or Cognition for the first time, but the reality is that's just like the beginning of the journey. The runway, uh, from just procuring to... and trying these tools to like full adoption is, is massive.
- SRSantiago Rodriguez
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 re- actually report on the metrics where they've seen meaningful improvements with AI. You know, two, two, two that we call out. One on the cost side, for example, Chime has reported that they've reduced their cost to serve by over 10% a year for the last four years. You know, compounded, we're talking about almost a 50% reduction in cost to serve, you know, happily supported by one of our portfolio companies, Decagon.
- SWSarah Wang
Mm-hmm.
- SRSantiago Rodriguez
Uh, but just in general, many initiatives to lower the cost to serve. You know, on the revenue side, an interesting one that I found with Shopify here who, you know, they, they, they launched their AI sidekick, and this AI sidekick helps merchants get up to speed much, much faster. So, you know, the percentage of customers that reach five orders within 15 days after onboarding has grown 8%, and that is kind of a metric that Shopify tracks is once you reach five orders, you know you're gonna stick around and you're gonna retain on Shopify. So it's been a meaningful tailwind to their business as well.
- SWSarah Wang
Yeah, absolutely. And I think to your point on, um, just the cost to serve coming down, right? If you think about the advantages that lower cost to serve gives you, you just have more room to compete, so you have better pricing, you know, maybe broader service, you can reinvest in growth. Um, and so I think the benefits are reaching the customers, um, the, not only the customers, but also the, the margins, and then they rotate that back in. Um, and then I think the other thing that's been really fascinating to see is, um, of course they vary, so you can't throw everything into one bucket, but the incumbents have done a pretty nice job on, uh, monetizing this as well, and, um, ServiceNow is a good example, right? They've reported more than a billion in, um, in A- in AI ACV, um, and actually a 9X increase in agentic deployments. Um, so you're kinda seeing it across the stack, incumbents to newer companies.
- DGDavid 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, uh, optimization or efficiency gains on the cost side? Uh, and this is sort of a litmus test for us of, like, where are the, you know, founders or CEOs of these companies seeing the most amount of opportunity? Like, the opportunity to drive revenue growth is unbounded-
- SWSarah Wang
Mm-hmm
- DGDavid George
... to the upside.
- SWSarah Wang
Totally.
- DGDavid George
Whereas the opportunity for cost improvement, um, 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-
- SWSarah Wang
Interesting
- DGDavid George
... what does that say about the revenue opportunity for you?
- SRSantiago Rodriguez
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 thousand... a couple hundred thousand dollars, I expect there to be better uses of, like, those resources.
- DGDavid George
Yeah, totally agree.
- SWSarah Wang
Mm-hmm.
- AIAlex Immerman
So S- Sarah referenced with, with ServiceNow, uh, seeing massive agentic usage. Um, agents are here. They are performing tasks. Tasks require multiple steps, uh, which require multiple model calls. That helps explain the 14X growth, uh, in agent token usage on OpenRouter. Um, those set- steps, though, are becoming more affordable. We're seeing caching, uh, so the system can reuse background information instead of processing it from scratch each time. Uh, I've seen Hebiya take advantage of this. They've seen their financial chat workloads, uh, become 10X cheaper to run. Um, the same budget that they had before can now support more work.
- SWSarah Wang
Yeah, I think your, your broader point is just that lower costs make it practical now for an agent to try, check its work, try again, and you can just open up a, a ton of tasks where maybe reasoning or tool use would've been just prohibitively expensive. Um, and I think this is especially important in cases where reliability is maybe the de facto reason you would or would not use an agent. Um, and again, you know, we sort of talked about type safe previously, but imagine what happens when you go an order of magnitude cheaper, two orders of magnitude cheaper, um, you know, I think the amount of use cases that open up are actually unimaginable.
- DGDavid George
Yeah, totally. And, you know, with much, uh, greater improvement in latency.
- SWSarah Wang
Yeah, absolutely.
- AIAlex Immerman
Yeah, so, so companies are definitely thinking more and more today about how to optimize latency or performance at large with cost. Um, so da- take Databricks as an example. They're leveraging routing, uh, choosing the appropriate model, uh, for each specific task to perfor- to improve performance and cost. And so their smart router, um, performed, uh, better. It solves more problems at 35% lower cost than the strongest individual model. Um, another approach we're seeing a lot of right now is fine-tuning. We've seen this with Harvey. We've seen this with Elise AI. In the case of Elise, they fine-tuned a smaller model, um, so it became much more affordable, 60% cheaper, um, but also had way lower latency, so live use cases from an audio perspective became tenable. And so, uh, these engineering gains are making AI more useful but also more affordable.
- 31:34 – 36:14
Amazon blocks Muse, Instacart opens up
- AIAlex Immerman
Totally agreed. It's early, but it is a really new and exciting time in consumer. Uh, Muse and Instinct are no doubt the new kids on the block, but they've grown really quickly. Uh, those along with ChatGPT are taking more and more of my queries away from traditional search. Um, but it's a really a dynamic time. And so if you are an existing consumer discovery platform, an existing marketplace, you really need to be thinking about your chess moves right now. Um, there's a couple questions I'd be asking. First, how much incremental demand can I get from an AI agent? How many more orders can they bring me? And two, how much of my business, how much of my profit pool comes from owning the customer relationship and discovery? Uh, last week there was a lot of news where Amazon said, you know, "No, no thank you" to Muse, uh, but Instacart said, "Yes, please." And if you think about Amazon, X AWS, and Instacart, uh, and look at their advertising revenue, it outpaces all of their operating profit. And so advertising revenue, owning the customer relationship is incredibly important. But for Amazon, you know, how many incremental new orders, or certainly customers am I gonna 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 gonna be a lot more orders coming from, uh, f- from, from these applications. Uh, historically, I've had to, you know, click all these different ways through to process an order. If I, if I offload that to an agent, all of a sudden there's no clicks and hopefully more GMV. The, the trip I once wanted to book but didn't gets booked. The order that we all wanted to place for dinner tonight happens. Um, so I'm optimistic that there's gonna be a lot more GMV, and that'll make up for some of this lost advertising revenue.
- DGDavid George
Yeah. It's sort of this question though that's open of how does it get compensated for, right? So to your point, you know, Amazon has an over $70 billion advertising business that's extremely high margin flow through that, uh, you know, is totally, uh, predicated on the fact that consumers go to the website and click on the ads. And if they don't do that anymore, you know, what happens? Um, you know, it-- Today, you know, Meta and, Meta and Google famously advertise, um, you know, probably best out of any of the internet platforms.
- AIAlex Immerman
Absolutely.
- DGDavid George
Um, you know, they each make, call it 200 bucks plus per, per users in the US and developed world. Um, you know, in the case of Meta, it's sort of a, you know, it's an entertainment application. We'll see, you know, what happens with that. It's probably a little bit safer. In the case of Google, you know, um, it's funny, like the whole talk track around Google two years ago when all of this just happened was, "Oh my gosh, what's gonna happen to Google's search business?" And it turns out it's been really resilient.
- SWSarah Wang
Mm.
- DGDavid George
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"
- SWSarah Wang
Mm-hmm
- DGDavid George
... or, you know, "Find me a hotel in this city," um, you know, or things like that, that, that you can very directly monetize, um, but that the AI was not yet capable of going to take action for on your behalf. If that changes, that could be a very different dynamic.
- AIAlex Immerman
Yeah.
- DGDavid George
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-
- AIAlex Immerman
Yeah
- DGDavid George
... look at these marketplaces that are gonna be impacted. I do think like 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, 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, the... And, 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 it might be just good.
- SWSarah Wang
Yeah.
- AIAlex Immerman
Yeah. Totally.
- SWSarah Wang
Definitely.
- AIAlex Immerman
Both could be true, by the way.
- DGDavid George
Yeah. Yeah. That it could be bad for certain 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-
- AIAlex Immerman
Sure
- DGDavid George
... productivity improvement actually at the overall macroeconomic level, which w- 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 reallocated.
- AIAlex Immerman
The, the immediate reaction has been net market cap positive across the ecosystem. The meta gains have far exceeded any of the losses from the marketplaces.
- DGDavid George
Yeah, that's a great point.
- 36:14 – 42:23
The SaaS bifurcation
- SWSarah Wang
Yeah. Going, going back to our discussion on software and, and moving maybe more to the, the public side, um, I think the big trend that jumps out at you from, from these charts is that the mix has really shifted toward slower growing, more profitable companies. Roughly 75% of the public software sample here is profitable, and only 30% is growing at 20% or more.
- DGDavid George
Which is a staggering number that only 30% of the public software companies are growing at 20% plus.
- AIAlex Immerman
And not only at 20%, I mean, that's 30%. If you draw the line at 30%, we've discussed, like, the absolute number of companies-
- DGDavid George
Less than five, yeah
- AIAlex Immerman
... is less than five, when, you know, in the private markets, basically every company we see and spend time with is growing at well in excess of 30%.
- DGDavid George
Yeah, and look, this is not, um... This is somewhat obvious, actually. If you look at just-- You talk to, you know, IT managers, CIOs, et cetera, um, if you look at the growth that they're experiencing, what they spend on AI-
- AIAlex Immerman
100%
- DGDavid George
... like, the easiest place for those dollars to come from is not spending incremental new dollars on new SaaS software projects.
- SWSarah Wang
Yeah, exactly.
- DGDavid George
Um, so, you know, I think we're investors in, 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, um, who, you know, to your point earlier, Santi, wouldn't go anywhere, and apply really interesting new AI products to them where I can drive my revenue growth higher? Um, I think, you know, in order to sort of dispel the greatest fears around the SaaSpocalypse, um, you know, I think we just need to see a period of time where the software companies continue to post things like 98% gross dollar retention, um, which was kind of always the sticking point for why they were so attractive as investments, um, continue to drive efficiency like we've talked about, but most importantly, drive, you know, revenue growth acceleration, um, which shows that they're sort of safe from a defensive standpoint, but that the offensive investments that they're making are actually going to drive their business to be better.
- SWSarah Wang
I thought your point in the blog post that you wrote a couple months ago on two paths was re- really interesting as a rule of thumb. Maybe share more on the finer point of the percentages.
- DGDavid George
Yeah. You know, the more I talk to founders after it, um, the more I felt like, uh, you know, almost everyone that we talk to is like, "Yeah, I'm trying to drive revenue growth higher."
- SWSarah Wang
[laughs]
- AIAlex Immerman
[laughs]
- DGDavid George
Um, and-
- AIAlex Immerman
[laughs]
- DGDavid George
... you know, it's, uh-
- SWSarah Wang
Fair enough
- DGDavid George
... it's, it's, it's, it's clearly, I think, the predominant path for the types of companies that we're investors in and, 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 gonna vibe code their software systems, like, that is clearly not what's happening in the market. Um, but you know, the onus is high on delivering revenue growth acceleration. And so we had said, you know, let's target 10% plus acceleration, which is a high number. Um, but with a magical product, given the budgets that are available to AI, is seemingly doable. And so we'll see. I think that there are a few of them that we are close to where we'll see that over the next 12 to 18 months.
- SWSarah Wang
Yeah, absolutely. And y- you know, as with all things, um, you can't lump every, uh, every, every company into the software bucket and sort of, um, you know, talk about, uh, multiples coming down and, and sort of growth, uh, growth decline as well. Um, in fact, uh, software has been very differentiated. And, um, if you look at this chart here, um, you can see that cybersecurity and observability have really stood out. Um, and, uh, vertical software as well has generally held up, um, much better than the horizontal applications. Um, and I think the framework for how we think about what's been, you know, what's held up or even gone up nicely versus, um, uh, in, in more secular decline, if you will, um, is that, uh, really you think about how AI changes the customer's need for that product, um, right? And, and I think the cybersecurity risk, um, uh, associated with AI has been well-publicized. But as you think about more software and agents creating new security and new monitoring needs, um, this is, uh, creating greater demand, uh, honestly, for the incumbents in, in, in these markets. And you can see, um, you know, the call-out on CrowdStrike on the, on the right, right hand. Um, and then of course, on the flip side, applications are facing different degrees of, of workflow change, right? And it's sort of, you know, if you think about, um, you know, o- one of our top CEOs, Ali, uh, Ghodsi likes to talk about this chopping block of AI and, like, what's first on the chopping block. Um, and you know, you can kind of think about, uh, on the flip side, you know, what is actually needed for AI. Um, and you, you pair those dynamics together, and I think, you know, it explains a lot of what we're seeing in the public markets.
- AIAlex Immerman
Yeah. I think a, a lot of this is pretty intuitive with what we're seeing on, on the private market side. So, uh, as you alluded to, Sarah, on the sec- on the security front, uh, agents are accessing more and more systems, taking more actions. The, like, OpenAI hugging face incident was, you know, an eye-opening event for many. Uh, so security is, is paramount. And then as we look at the vertical AI sub 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, and that's a reflection of the customers recognizing it's not just the model, which we talked about earlier, but how you, how you or- orchestrate that, how you build the application around it. And vertical specific workflows command, uh, command these needs.
- DGDavid George
And I, I mean, we spend a lot of time looking at public markets, less so for investments, but we, we underwrite exit multiples, right? So we, we track that pretty closely, and I think if we were to recap the year, what's happened, you know, first couple months there was the SaaSpocalypse. We wrote a blog post about, like, what kind of businesses would do well. And if you fast-forward to today, the software index is actually back to, you know
- SRSantiago Rodriguez
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 AI 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.
- DGDavid George
Yep.
- 42:23 – 48:43
Stripe's Renaissance data
- SWSarah Wang
Yeah. Um, you know, I think, um, on the flip side, we wanted to sh- showcase, um, some private company operating data that gives us a view of what customers are actually doing. Um, and there's a little bit of a narrative violation in Stripe's SaaS customer data, um, which actually shows growth accelerating into 2026 across both young and mature businesses.
- DGDavid George
Stripe calls it the RenaSaaS.
- SRSantiago Rodriguez
Oh, the Renaissance.
- DGDavid George
I actually quite like the RenaSaaS.
- SWSarah Wang
I like RenaSaaS, yeah.
- DGDavid George
It's, uh, it's very good. It's very good.
- SRSantiago Rodriguez
Um, to me-- if, 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, OpenAI, Databricks, Stripe, Waymo, and 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 late-stage champions that can keep investing in growth more so than maybe, um, maximize short-term profit. But David, you, you can talk a little bit about kinda how we advise companies on the IPO and when to stay private versus public.
- DGDavid George
Yeah. Look, I mean, the IPO, you know, especially for founder-led companies, one of the things that we've talked about and, and that I've written about is that the founder is the asset class at this point. And so the bet that we make, and part of the reason that it's been a benefit for some of these companies to remain private, is they can many times take bigger swings, um, in the private markets that have longer duration paybacks. You know, Zuck and, and Elon are kind of obvious exceptions to the rule in the public markets. Um, but you know, the w- the way you see it manifest in the numbers in companies like Databricks or Stripe, um, is, you know, massive, massive new bets, um, in new product areas. Um, and you know, you can see revenue acceleration that happens as a result. Uh, and so, you know, you can do that in the public markets. Um, but you know, it's gonna catch greater scrutiny. You know, obviously Meta's stock price, um, you know, at the nadir, you know, got below 100 bucks a share, um, you know, when people were very, very skeptical about their investments in, in AR/VR. Um, you know, for us, the way we talk about it with our founders is just the IPO is another financing event. And, you know, what do you get out of being a public company relative to what do you get out of being a private company? Um, but clearly you can reach big scale, uh, in the private markets.
- SRSantiago Rodriguez
And there are similar companies that, you know, already talk about running themselves like they were a public company, you know, with like 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.
- DGDavid George
Yeah. And then of course, there are examples where, you know, capital needs over time, uh, could be very, very large. And so the pro- the, the capital pools in the private markets are very large, uh, and can, and can serve the needs of many of these companies. But at some point, you know, the capital needs may even get too big for the private markets.
- SRSantiago Rodriguez
That's the public markets. You know, in the private markets, one of the things that we, we spend a lot of time in too is, is secondaries. So there's two dynamics at play. You know, one is we see an increasing amount of companies holding tenders for 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 tracked by Carta has only been 58%. So this is employees choosing not to take liquidity because they have so much conviction in the performance of their business. So it's the opposite of... I think sometimes there's a narrative of like employees cashing out. And what we're seeing is actually employees choosing not to cash out because they believe in their company so much.
- DGDavid George
Imagine SF home prices if that was 100%.
- SWSarah Wang
[laughing]
- SRSantiago Rodriguez
Exactly.
- DGDavid George
It'd be hard. Exactly. Yeah, the ten- the tender off- the tender offers that happen, you know, we're, we're obviously like participating in these and, and leading these, uh, pretty frequently. So we think they're a good thing. Um, they do serve two purposes. One, you know, for employees and prospective employees, it is sometimes hard to compete with the liquidity appeal of public markets, RSUs that hit your account on a net tax basis every quarter. Um, so you know, it is a, it is a, um, you know, a weapon of competition for the private markets that wanna compete for employees or retaining their employees, um, in the, uh, with, with, with the public market companies. The other side of it is, you know, we're advocates of sort of more frequent resetting of your valuation. Um, and so that keeps your sort of stock price, you know, private stock price fresh for a number of reasons. One, it's easier to talk about that with employees and prospective employees. Um, but two, you know, in the event that you wanna do M&A like many of our companies have done, um, you know, you have fresh currency that you can use.
- SRSantiago Rodriguez
Yeah. No, maybe just the other dynamic, I think people talk about the secondary discount quite a bit 'cause that's a dynamic that we've seen in the last five years. Where maybe coming out of 2021 and '22, '23, '24, the median discount to the last round price in secondary markets was meaningful because there m- was maybe like a round that was priced too high and it was out of date. But actually what you're seeing to date is, you know, when there are transactions in secondary markets, the discount to the last round price is basically zero.
- DGDavid George
Yeah.
- SRSantiago Rodriguez
Which just speaks to, you know, these, these valuations at which companies have raised that are fresh, and there's always net new investors willing to pay that same price, which wasn't the case for the last couple years.
- DGDavid George
Yeah. Great point. So, um, this is a fun slide that says everything is AI computer. So AI-related companies account for 86% of US VC deal activity in this 2026 snapshot, up from 65% in 2025. Um, so you know, obviously AI has become the dominant desti-destination for venture dollars.
- AIAlex Immerman
Yeah, but beneath that AI label, the opportunity has broadened a lot, right? So enterprise apps, consumer apps, uh, but also services, semiconductors, power, defense. Um, a lot of the attention goes to OpenAI, SpaceX AI, and Anthropic, but beyond that, um, our opportunity set has never been deeper and, and broader.
- 48:43 – 52:06
Robotics, autonomy, bio: what's next
- DGDavid George
Last, we thought it would be fun just to talk about some of the areas that we're very excited about right now. Um, you know, obviously we, we touched on some of the consumer work that will now be done by agents. Um, but you know, you can call it long-running agents, you can call it autonomous agents, heavy-use consumer. Um, you know, if you, if you take the perspective of these, uh, that, you know, you could have consumer agents do all the tasks that you wouldn't wanna do to give you things that you otherwise wouldn't have spent time or money on, um, it's very appealing, and I think that the, uh, the distribution of this could happen pretty quickly. Robotics is an area where we're spending a lot of time and attention. We happen to think that it could be even larger than LLMs, um, but you know, probably three to five years earlier. Um, and so we think over the next five years, this is gonna be a massive area of investment and excitement. Um, autonomy is here. Uh, self-driving works. Uh, it's a very exciting time. Uh, if you just take, you know, a step back and talk about the auto industry and transportation industry, this is one of the biggest industries in the world that we probably don't talk about as much because we spend so much time talking about AI right now. Um, but you know, if you look at miles traveled-
- AIAlex Immerman
Mm.
- DGDavid George
-by Uber and Lyft or in Ubers and Lyfts, it represents about 1% of miles traveled in the US. I think with, um, you know, full networks of autonomous driving cars that are 14 times safer than human drivers, we expect that to expand by at least an order of magnitude in the coming years. Plus, there's 17 million new cars sold per year in the US, and I think over the next 10 years, those will, those will all be, um, be autonomous. Um, AI times bio, this is a super exciting area. You know, obviously the folks in the labs are talking about this. Um, but you know, new drug discovery, solving some of the, um, you know, most debilitating illnesses, uh, or diseases in the world, I think is a promise that we all are very hopeful for, that we'll see a lot of progress over the next 10 years. Personal health, this is another one that I'm very, uh, excited about for myself, uh, y- you know, as a, as a health maxer. Um, but you know, there's not been a great place, uh, or avenue to take all of the information about yourself, put it into somewhere, and get very hyper-personalized, uh, advice. Um, and then, you know, lastly, diffusion, uh, into the enterprise beyond coding. This is one of the, you know, the ones that, Sarah, you were talking about. Just we're so early in actual diffusion of the technology into the enterprise, um, that I think it's gonna be, you know, super, super exciting. Um, and then lastly, um, you know, there's another area that is not inside the AI bucket, which is, you know, what we call American dynamism, but, um, the sort of retooling of the entire, um, sort of American dynamism stack. We've, you know, we've invested, we've been large investors in this area for a while. Um, you know, it's still just a small fraction, less than 5% of overall dollars spent, um, you know, in the military, uh, is sort of, you know, newer vendors like Anduril, uh, or Ceronic or Castellan. Um, and we expect that to grow dramatically, uh, as needs change. So, so many areas of excitement. Obviously, you know, we've been very active in this AI space, um, and you know, we're optimistic about the effect that it's gonna have on the overall economy. The build-out is massive, um, but we think it's gonna be massively productivity-enhancing in the US. So, uh, it's a blast to hang out with you guys. Thank you.
Episode duration: 52:20
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