The $1 Trillion AI Buildout | State of Markets

22:04so over 20 times the spend if you think about median versus sort of top users.

22:10David George: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 one percent 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

22:48Alex Immerman:that diffusion go yeah another example we've seen within our portfolio um you know many enterprises are really excited when they, you know, buy cursor or cognition for the first time. But the reality is that's just like the beginning of the journey. 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.

23:26One on the cost side, for example, Chime has reported that they've reduced their cost to serve by over 10 % a year for the last four years. You know, compounded, we're talking about almost a 50 % reduction in cost to serve. You know, happily supported by one of our portfolio companies, Decagon. But just in general, many initiatives to lower the cost to serve. You know, in the revenue side, an interesting one that I found was Shopify here, who, you know, they launched their AI sidekick. and this AI sidekick helps merchants get up to speed much, much faster. So, you know, the percentage of customers that reach five orders within 15 days after onboarding has grown 8%.

24:03And that is kind of a metric that Shopify tracks is once you reach five orders, you know, you're going to stick around and you're going to retain on Shopify. So it's been a meaningful tailwind to their business as well. Yeah, absolutely. And I think to your point on just the cost of 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, 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.

24:35And then I think the other thing that's been really fascinating to see is, of course, they vary. So you can't throw everything into one bucket. But the incumbents have done a pretty nice job on monetizing this as well. And ServiceNow is a good example, right? They've reported more than a billion in AI ACV and actually a 9x increase in agentic deployments. So you're kind of seeing it across the stack incumbents to newer companies.

25:02David George:Yeah, one of the interesting things that we've talked about a lot and, you know, again, some of our most forward-leaning companies like Stripe have discussed with us is, you know, where are they actually putting their incremental AI? investment dollars? Are they putting it toward things like building new products for customers that could drive higher revenue? Or are they putting it toward optimization or efficiency gains on the cost side? And this is sort of a litmus test for us of like, where are the, you know, founders or CEOs of these companies seeing the most amount of opportunity? Like the opportunity to drive revenue growth is unbounded to the upside.

25:37David George: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.

26:07And 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 like those resources.

26:17Alex Immerman: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 Hebbia take advantage of this. They've seen their financial chat workloads become 10x cheaper to run. The same budget that they had before can now support more work.

27:02Yeah, I think your broader point is just that lower costs make it practical now for an agent to try, check its work, try again. And you can just open up a ton of tasks where maybe reasoning or tool use would have been just prohibitively expensive. And I think this is especially important in cases where reliability is maybe the de facto reason you would or would not use an agent. And again, you know, we sort of talked about type safe previously, but imagine what happens when you go an order of magnitude cheaper, two orders of magnitude cheaper. You know, I think the amount of use cases that open up are actually unimaginable.

27:40David George:Yeah, totally. And, you know, with much greater improvement in latency. Yeah, absolutely.

27:45Alex Immerman:Yeah. So 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 also had way lower latency.

28:32Alex Immerman:So live use cases from an audio perspective became tenable. And so these engineering gains are 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.

29:07And that's going to, I think we're going to see more margin improvements in the best app companies.

29:12Alex Immerman: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 a lot of our time. 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.

29:46You 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 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?

30:19These, I had to look it up. I'm like, is that K correct? And so totally agree that it's just getting started.

30:25Alex Immerman:Yeah, I mean, our partner, Josh Ellman, does have a definition I like that would be counter to that a little bit, which is consumer AI is what I use for my daily life and not necessarily what I expense. So in his view, he would say, like, it's maybe not super surprising that 97 % of households that are using AI aren't yet paying for it. You are right, Santi. I do like smiling retention curves. We do like to see users coming back more and more. You know, another common characteristic of large consumer platforms is time spent. So if you were to look at Facebook, Instagram, TikTok, Snap, they all get 60 or 30 to 60 minutes per day from their active users.

31:09Alex Immerman:And while the best AI assistants, and there's many, you know, up and coming right now, all aspire to be my go-to application. like I hope that they are going to be incredibly persistent and always on and super proactive such that they may not be the place where I spend the most time in the future. Yeah, it's going to make our job harder when we can't, you know, look at engagement via outside-in data because the agents are working on the background instead of us just looking at like screen time on a social media application. But it is important. I mean, I remember like when the newest models come out in December, even in our workloads, We would set off a deep research task or an agentic task and then drive up to the city or something.

31:52And that's not striving to IESLA. That's not screen time being tracked as it would have been in prior generations of consumer. Yeah, totally agree.

32:02Alex Immerman:It's early, but it is a really new and exciting time in consumer. Muse and Instinct are no doubt the new kids on the block, but they've grown really quickly. those along with ChatGPT are taking more and more of my queries away from traditional search. But it's a really 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. 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?

32:48Alex Immerman:Last week, there was a lot of news where Amazon said, you know, no, no, thank you to Muse. But Instacart said, yes, please. And if you think about Amazon 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 these applications.

33:38Alex Immerman:Historically, I've had to click all these different ways through to process an order. If I offload that to an agent, all of a sudden, there's no clicks and hopefully more GMV. The trip I once wanted to book but didn't gets booked. The order that we all wanted to place for dinner tonight happens. So I'm optimistic there's going be a lot more GMV and that'll make up for some of this lost advertising revenue.

34:03David George:Yeah, it's sort of this question though that's open of how does it get compensated for, right? So to your point, 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 platforms. You know, they each make, call it 200 bucks plus per users in the US and developed world. You know, in the case of Meta, it's sort of a, you know, it's an entertainment application.

34:47David George: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 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.

35:27David George: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 that are going to 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.

35:58Like 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.

36:08David George:Yeah, totally. Both could be true, by the way. Yeah. 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 reallocated.

36:32Alex Immerman: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 30 % is growing at 20 % or more.

37:04David George:Which is a staggering number that only 30 % of the public software companies are growing at 20 % plus. 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 experiencing, what they spend on AI, like the easiest place for those dollars to come from is not spending incremental new dollars on new SaaS software projects.

37:45David George: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.

38:30David George: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 blog that you wrote a couple months ago on two paths was really interesting as a rule of thumb. Maybe share more on the finer point of the percentages? Yeah. You know, the more I talk to founders after it, the more I felt like, you know, almost everyone that we talk to is like, yeah, I'm trying to drive revenue growth higher.

39:03David George:And, you know, it's clearly, I think, the predominant path for the types of companies that we're investors in and that we've backed over the years. Again, I think, you know, maybe relative to a year ago when the fear was like, OK, everyone's going to Vibecode, their software systems, like that is clearly not what's happening in the market. 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. But with a magical product, given the budgets that are available to AI is seemingly doable. And so we'll see.

39:41David George: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. You know, as with all things, you can't lump 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, you know, 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 customer's need for that product.

40:30Right. 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, on 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 like what's first in the chopping block.

41:06And, you know, you can kind of think about on the flip side, you know, what is actually needed for AI and you pair those dynamics together. And I think, you know, it explains a lot of what we're seeing in the public markets.

41:18Alex Immerman: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 like OpenAI hugging face incident was, you know, an eye-opening event for many. So security is paramount. And then as we look at the vertical AI companies, these are some of the fastest growing companies we're seeing in the private markets, right? Harvey, Abridge, Elise, they are growing faster than any of the precedents ever have in their industry. And that's a reflection of the customers recognizing it's not just the model, which we talked about earlier, but how you orchestrate that, how you build the application around it.

42:00Alex Immerman:And vertical specific workflows command these needs. And I mean, we spend a lot of time looking at public markets, less so for investments, but we underwrite exit multiples, right? So we 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 SaaS apocalypse. We wrote a blog post about like what kind of businesses would do well. And if you fast forward to today, the software index is actually back to, you know, where it started the year. But it's really bifurcated into like some companies that are deemed AI losers and some companies that are deemed winners.

42:32And, 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.

43:10Alex Immerman:Stripe calls it the renaissance. Oh, the renaissance. I actually quite like the renaissance. I like renaissance, yeah. 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 markets, 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, Anthropik, OpenAI, Databricks, Stripe, Waymo, Revolut by last round valuation, they add up to about$2.4 trillion. This is more than the combined market cap of IPOs we've seen in the last 10 years, excluding SpaceX, which adds up to$1.7 trillion.

43:46So just the amount of activity that happens within our market, again, six companies,$2.4 trillion, it's almost as big as the Russell 2000, which is a big index in the public markets where there's hundreds of public managers that spend most of their time in. So it's increasingly exciting to just spend time in these late-stage champions that can keep investing in growth more so than maybe maximize short-term profit. But David, you can talk a little bit about kind of how we advise companies on the IPO and when to stay private versus public.

44:15David George: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, massive new bets in new product areas.

44:54David George: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 Meta's stock price, at the Nader got below$100 a share when people were very, very skeptical about their investments in AR, VR. For us, the way we talk about it with our founders is just the IPO is another financing event. And what do you get out of being a public company relative to what do you get out of being a private company? But clearly you can reach big scale in the private markets. And there's some other companies that already talk 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.

45:37There'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 reacceleration just on the basis of some of the larger enterprises actually trusting them more because they're a public business.

45:53David George:Yeah, and then of course there are examples where, 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 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. That's the public markets. You know, in the private markets, one of the things that we spend a lot of time in, too, 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.

46:29And 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. What we're seeing is actually employees choosing not to cash out because they believe in their company so much.

46:50Alex Immerman:Imagine SF home prices if that was 100%. Exactly.

46:55David George:It'd be hard. 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, 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, we're advocates of sort of more frequent resetting of your valuation.

47:35David George:And so that keeps your sort of stock price, private stock price, fresh for a number of reasons. One, it's easier to talk about that with employees and prospective employees. But two, in the event that you want to do M &A, like many of our companies have done, you have fresh currency that you can use. Yeah. No, maybe just the other dynamic, I think people talk about the secondary discount quite a bit, Because that's a dynamic that we've seen in the last five years 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 was maybe like around those price too high and it was out of date.

48:09But actually what you're seeing today is, you know, when there are transactions in 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.

48:27David George:Yeah. Great point. So this is a fun slide that says everything is AI computer. So AI-related companies account for 86 % of US VC deal activity in this 2026 snapshot, up from 65 % in 2025. So, you know, obviously AI has become the dominant destination for venture dollars.

48:48Alex Immerman:Yeah, but beneath that AI label, the opportunity has broadened a lot, right? So enterprise apps, consumer apps, but also services, semiconductors, power, defense. A lot of the attention goes to OpenAI, SpaceX, AI, and Anthropic. But beyond that, our opportunity set has never been deeper and broader.

49:09David George:Last, we thought it would be fun just to talk about some of the areas that we're very excited about right now. 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 use consumer you know if you 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 and I think that the 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.

49:52David George:And so we think over the next five years, this is going to be a massive area of investment and excitement. Autonomy is here. Self-driving works. It's a very exciting time. If you just take, 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 Ubers 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.

50:38David George: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 autonomous. 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. as a health maxer. But there's not been a great place or avenue to take all of the information about yourself, put it into somewhere and get very hyper-personalized advice.

51:26David George: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 you know, super, super exciting. And then lastly, you know, there's another area that is not inside the AI bucket, which is, you know, what we call American dynamism, but the sort of retooling of the entire sort of American dynamism stack. We've, you know, 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 Saronic or Castellian.

52:12David George: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 it's going to have on the overall economy. The build out is massive. But we think it's going to be massively productivity enhancing in the U.S. So it's a blast to hang out with you guys. Thank you. Thanks for listening to this episode of the A60Z podcast. If you liked this episode, be sure to like, comment, subscribe, leave us a rating or review, and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts, and Spotify.

52:50David George:Follow us on X at A16Z and subscribe to our Substack at a16z.substack.com. Thanks again for listening, and I'll see you in the next episode. 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.

53:37Thank you.