Startup Valuations, Biotech’s Crunch & AI’s Massive TAM | E2147

I can't overstate how transformational it's going to be to give high quality gold standard medical knowledge to every person on the planet. I mean we are really going to put the world's greatest doctor at the fingertips of every single person on earth. And this is I think in some ways the biggest thing that has ever happened to medicine.

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Welcome back to Twist. This is Alex and today we have something special for you. We have just closed the books on Q2 and just opened a new chapter in the third quarter. So it's the perfect time to sit down with some of the smartest investors out there in the world to get the down low on what they're seeing, what's going on and how they are placing bets or investments if you want as we dig into the new quarter. Joining me today are Astasia Meyers, a general partner at Felicis. She invests precede through Series A with themes including AI infra, developer tools, enterprise software, cybersecurity and my favorite open source. Aazia. Hi.

Hi there.

And then we have Matt Turk, a partner at Firstmark. He invests seed through Series A thematically looking at enterprise software in the US and EU and then as he puts it, AI up and down the stack. Matt, welcome.

Thanks for having me.

And then finally we have DA Wallach, a general partner at Time Bioventures. He invests Series A through Series C thinking about things like biotech, diagnostics, care delivery and healthcare. Da, welcome to the show.

Thanks.

All right, so I'm really glad that we're here today because we really just finished Q2. I'm looking forward to earnings reports and we're already off to a pretty hot start in terms of M and A. But I want to take a look at just deals and deal making in Q2 for the data that I can see. It does appear that overall deal velocity slowed down but perhaps in the earlier stages, total capital invested went up. So I'm curious, Astasia, and starting with you, how much pricing pressure did your firm face in the second quarter and are you still able today to find attractive entry points for new investments?

Absolutely, yeah. I think what we're seeing in these numbers is that it's a really interesting story around the founder quality going up and having larger ambition across these teams. Early stage rounds are fewer but larger. And I believe that's because we're witnessing the seismic shift in terms of the caliber the market opportunity and the ambition of these early stage founders. The AI explosion is democratizing access for tools so that they can build faster than ever before. We're seeing them increase and achieve higher ARR faster in the life cycle of the business. And with that we're seeing a flight to the quality companies. We definitely believe this is a huge moment in time and just the beginning for AI investment. And so we're excited about these early stage teams that can not only go after $10 billion markets like before, but $100 billion markets.

Matt, same question over to you. Similar dynamics from your side?

Yeah, absolutely. And look, to keep it real, I'm not at all able to find attractive entry prices. I've been at firstmark for over a decade now and the last two or three investments I've made were by far the highest evaluation investments I've made in my career. It's fairly routine at this stage that anything interesting in AI is at 9 figures pre money. At the series A level, everything is hyper competitive and it's something like I've never seen before. On the flip side, it's also something like I've never seen before in terms of how exciting a moment it is. And just to build on what Estrad just said, all of this is true. We're living in a paradigm shift, but that comes at a price.

Yeah, apparently a very high price. Da. I am not going to lie a little bit less familiar with the realm of biotech investing. So I'm curious, are you seeing similar dynamics in your part of the market?

Well, I don't want to be a wet blanket here, but biotech is sort of in the Great Depression right now and we're about three years into it. And the reasons why we got there are complex and we can get into some of those later. But the bottom line is that the biotech capital market has basically been shut down and that has been led by the public markets, which have been effectively totally closed to public offerings for some time now. So with the exit doors Effectively closed. The private market has suffered tremendously. It's been incredibly difficult for companies to find capital. That is even true of companies in biotech with an AI theme. They may be able to attract generalists who are maybe not super historically embedded in the sector, but when it comes to the specialist biotech investors, even the AI companies are not garnering a lot of excitement right now. So it's a very difficult time. That makes it an interesting time to navigate as an investor and a difficult one. And we can get into some of the nuances of that as the conversation evolves.

Just really briefly, when I was learning about venture capital and IPOs, one of the rules of thumb was that biotech companies tended to go public earlier and that it was a capital formation function. So da does that just mean that because the IPO market has been so rigid or closed, if you will, that a main, a fundraising mechanism and liquidity mechanism at the same time is being closed is putting you into a depression, as you put it.

That's exactly right. And so if you think about, you know, Matt and Anastasia can speak to stasia, sorry, can speak to this better. But in the past decade or two decades in the tech VC market, so much capital has come into the private market and it's enabled companies, as everyone knows, to stay private much longer. And by contrast, the same thing has not happened in biotech. And so companies still need to go public merely to access the capital that is required to develop drugs clinically. So going public is generally the beginning of the clinical journey for biotech companies. It's when they start to raise the capital sufficient to take them into human trials. And they'll often stay public for several years as they move through those trials, either towards an eventual commercial launch as an independent company or M and A to one of the big pharmas.

I would love to double click on this if this is okay. This is very interesting to me coming from the enterprise software AI side of the house, which is there's such constant interest in what we're doing from a private and public markets perspective. I'm wondering what are the dynamics in terms of different investors in biotech that is demonstrating or causing this effect where other capital sources are not coming in earlier.

Yeah, it's great, great question. I would say that the public market is driven by a combination of biotech specialist hedge funds that just do biotech for a living and and then the usual suspects when it comes to the big multi strategy hedge funds or the large mutual funds. So think everyone from Citadel to Fidelity to T Rowe Price and historically, biotech companies require both of these constituencies to survive and to IPO and then do follow on financings. So what's really happened in the past several years is that the generalist investors were rotated out of biotech essentially all at the same time, leaving only the specialist funds. And even in the aggregate they're not large enough to carry all of these companies by themselves. So essentially everyone has been on hold waiting for the generalists to come back into the sector. And why they have not is multifaceted. Some of it has to do just with the relative opportunity that they've had in large cap tech. And you know, if you could earn 18, 20, 22% a year with ostensibly low risk, why would you bother gambling on these super risky binary biotech stocks? So I think it's been the competition over that essentially public risk capital that has led to this shortage of available capital for these companies.

Well, that's an enormous bummer. So I guess, da, does that mean that there's an interesting opportunity for funds to pivot into biotech, given that we were just talking about how prices elsewhere are super elevated. But I presume that they're, I'm not going to say down in the dumps in your sectors, but I mean, certainly probably more affordable.

One of the themes we talk about over and over again on this week in startups is making sure you do your chores. I'm no expert on these things. I have some experience. Stephen Estes from CLA is an expert. Let's talk about being cash efficient. Tell us about efficiency and what you see in the, in the top tier startups in your practice.

We're seeing kind of an interesting trend out there where companies aren't needing to raise quite as much as they had in the past. You really have to be careful as a founder to only take on as much money as you really need. You got to do the forecast and you got to do the modeling and you got it dialed in and get it right. Otherwise you're going to end up either not raising enough capital to get to where you're going and you're going to have to go get venture debt or go back, have an extender to the round, or you're going to give up too much of the company because you just didn't recognize how much money you actually needed.

Yeah, very important to get this stuff right, folks. And that's really a bummer when startups don't do things in a button up. I always have a great partner, a good partner to have on this Adventure. While things change, my friend Stephen over at CLA. Visit cla connect.comtech and don't forget to mention that your boy J. Cal sent you that CLA Connect start today.

Well, look, this gets at the fundamental, one of the fundamental paradoxes of all venture capital or investing even more generally, which is that high prices tend to foretell low expected returns and low prices, by contrast, tend to foretell high expected returns. And the challenge is that when something happens that is substantive, like AI, where we've got a series of legitimate breakthroughs that have occurred, it can be hard to tell whether the high prices are warranted or not, whether the high prices do in fact mean low expected returns, or whether they mean that people are even at those prices underestimating the potential. And you should jump on the train because you don't want to be left behind. So I think in biotech it's a bit, it's a bit of that kind of situation. We have low prices, we all know that. That's very clear. They're historically low in the public markets and they're progressively becoming historically low in the private markets as people sort of stop the extend and pretend type of behavior. And so I think over a sufficiently long time horizon, there's no doubt that there is enormous upside. But predicting exactly when that transfer of the sort of risk appetite of the market's going to occur is unclear.

So thinking about the high entry price, lower expected returns point, Matt, one thing I'm trying to sort out is are the companies that you mentioned are raising at nine figure valuations at the series A level overpriced or simply high performing. I'm going to show a chart here. This is one of many of this particular genre. This one happens to be from stripe. It just shows the median months to 5 million RR for kind of current top AI companies being 24 months and then for previously leading SaaS companies taking 37 months. So to me, we're seeing a lot of these companies outperform historical norms, which would mean that they should have a higher value attached to them earlier on. Matt, does the, the prices we're seeing reflect that fairly or are they still, in your view, a bit over their skis?

Time will tell. Time will tell.

That's true.

Venture is a long game. But when you're talking to yourself at night, what do you say?

Whenever I manage to catch some sleep over all of this. Look, in the venture capital world, we're in the business of taking risk and we can all see that there is a huge paradigm shift that's happening. I think you have to play the game on the field at any point in time with discipline. But you can't not invest in AI right now. So yes, there's that happening on the one hand. And by the way, it's a select group that was a top 100 that you showed on the chart. So it's not every AI company. You could absolutely make an argument on the other side that a lot of this isn't proven. There's like the well publicized discussion around experimental budgets. There is a big open question around retention. So Cursor seems to have great retention. Others do not. For many others, the jury is out. Nobody knows. And then there's the question of gross margin, in particular negative gross margins, where there's a lot of reports that some of those companies operate at a, you know, at a deficit in terms of a gross margin. So at some point they will need to grow into all of that. There'll be a reckoning of some sort. Particularly as you get closer to public markets, the pressure intensifies. So there is a big question mark. But equally, as I said at the beginning a second ago, we're in the business of investing in the products and the companies of tomorrow. So you have to play the field on the game. The game on the field.

I'd love to add on to this, if this is okay, how we think about investing at the earliest stages and the value of the company as a function of the total addressable market and how big that business could become in the span of time. And when we talk about platform shifts, previously we talked about on premise to cloud or mobile to consumer Internet, but in both cases it was software replacing legacy software. The AI shift is totally different. Here at Felicia's we really think about they can go after labor budgets now. This is a $35 trillion market, absolutely insane. Like if you look at the NASDAQ top 10% companies or the Fortune 500 budgets, only 10 to 15% is actually software. So the markets that these AI companies are playing in are larger than ever before. They're absolutely huge. So I think that's one of the reasons that there's a willingness to at early stage, to not only pay premiums for exceptional teams and differentiated technology, but because the markets are really large. Another slice that we took was actually looking at how much budget could these companies go after. So if you look at AI software products and infrastructure increasing the S&P 500 net margins by 5%, that's essentially 600 billion a year in profits. And you can imagine Software can go after 20 to 30% of that. So that's 100 billion of new spend. And if you apply a 10x multiple on that, that's 1 trillion of new market value. So that's all up for grabs for AI startups that hasn't been there before. And so we're just super excited that these markets are bigger than ever. These teams are more ambitious and they're growing faster than ever before.

Well, on one hand that's fantastic news. Everyone loves big tam, big opportunities. On the other hand, today Microsoft laid off another, I forget six or nine thousand people. And so it does seem to be taking a bite Asia. So we'll see how that bet plays out. But I will always worry a little bit about short term dislocations in the labor markets. But I don't think we can have a revolution in the technology world without impacting labor markets. It seems to come with that. And so perhaps this is just the way things go. All right, so going from broad to the specific, I want to ask you each about your port codes and how Q2 went and kind of an on the ground basis. Let's start with you. Da. I'm most familiar with Iterative Health, but you also have investments into Nanomosaic and Deepsight other companies. How is Q2 for your firms? Tailwinds, headwinds, surprises.

Yeah, I'd say operationally many or most of our companies are doing great and they are beneficiaries of when we started this fund, which was basically 21 because they had some time before the market kind of crashed in biotech when they could spend risk capital developing their products. And that is the nature of most of the companies we back, which is that the intention is that they're going to spend and therefore lose a lot of money for five or ten years developing products. And then ideally those products are going to be very profitable once they're FDA approved and out on the market. So we fortunately have, for example in Iterative Health, a company that got its product approved and on the market and therefore has been out there actually selling and generating revenue. And so they're on a great trajectory. I'd say the companies that are having the hardest times are those that really have no choice but to continue operating in that risk capital financed model where you know, if you've got a drug that is in pre clinical development, there's no pivot you can make that will start generating profits tomorrow if you can't raise cash. The entire premise of the sector is that you lose money for years while you develop product, but that the upside at the end of the tunnel can be adequate to compensate you for that. So I'd say the companies that are highly dependent on capital are having a hard time. Everyone is finding it difficult to raise money. The companies that are less dependent on that, I think are, you know, flexing their advantages right now because they're getting to actually do business in a marketplace where a lot of others aren't.

I appreciate that. Matt, same question over to you, but don't answer about Synthesia from your portfolio because I know they crossed 100 million ARR in April, so that's a gimme elsewhere. Blockers, tailwinds, surprises. What are you seeing on the ground for founders out there who are listening?

Well, all my companies are crushing it, thank you very much.

Oh, well, all right, next question. Astasia, over to you.

No, look, I think everybody that's in generative AI is experiencing incredible tailwinds right now. Buyers in companies are just out trying products, buying products, importantly, deploying products at some scale this year, which is different from last year. So if you're so lucky to be in that market is just, it's just amazing and incredibly exciting. So you mentioned Synthesia, you know, I mean a certain company called ada, which, which is part of that customer service automation, where the generative product that they launched has been doing incredibly well as well, growing like 3,4x year on year at some scale and we see it everywhere. And then the companies that are sort of derivative of that AI wave, that are supporting the AI wave, are also seeing incredible growth. A company like Clickhouse, in which we're investors, which has a bunch of AI use cases, is also filling the pool. So wherever you are in the stack, from the very bottom infrastructure to all the way in applications, if you're in that wave, that's fantastic. If you add side of the wave, it's a little different.

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But just to be clear, it feels like you're describing relatively strong product market fit. If they're feeling the pull, that implies that they've nailed something. So going back to your earlier point about concerns about innovation budgets and churn and so forth, are you seeing those potential risks crop up amongst the companies that you're mentioning or is their revenue proving to be thus far pretty durable? Perhaps in and around traditional SaaS metrics?

Yeah, I think for the ones I mentioned it so happens like they all have a pretty impressive NDR or like net revenue retention numbers that show that the enterprise keep buying more and more. So you know Synthesia at this Stage is in 70% of Fortune 100 companies which is sort of incredible when you think about how long they've actually been in market. So those are traditionally the hardest customers to penetrate and they've been able to just land but expand across use cases and look it's a fantastic company and they have been doing an incredible job. I think also in fairness it speaks to the moment that those enterprise buyers are willing to and excited to bring in innovation quickly and then expand quickly and I think that's an incredibly favorable market if you will position. Now at the other end of the spectrum for sure there's a bunch of companies that are still early in product market fit trying different things and they may or may not have staying power but that not any different from prior wave.

So asasia, that all sounds pretty bullish. I want to ask about your port because I have to admit you have two of my favorites. I think browser use is super cool and I was a big alum arena user before it became an actual company. But same question to you, what's surprising you in Q2? What are you seeing as a tailwind? How are the founders doing and what lessons?

Yeah we see similar things across our portfolio both at the AI app layer and infrastructure. Something that I do want to call out is FELICIS has been really early investors in some of these infrastructure businesses. So browser use. We led the inception around N8N and Supabase who are benefiting from these Gen AI apps. We partnered with them one very, very minimal amount of revenue and they've evolved to take the reins of these categories that they operate in. To see the benefit of working with the Genai apps like Supabase is a huge beneficiary of lovable bolt v0. We partnered with them because they had an incredible product and team and we saw the big opportunity to be the world class transactional database and because they had built such a good product, you know, when these other platforms came along, they were the first partner and first choice for these other founders. So that's been really cool. If I was a founder at the earliest stages, thinking about, you know, areas that are top of mind, I would really prioritize marketing and community development because the barriers to entry for starting a new business are lower. Now we don't need to rack and sack servers. Thanks to cloud service providers, we have gen AI coding products like Cursor. More people are empowered to be builders, which is beautiful, but you also need to get your tech out there and people need to learn about it. If no one knows about it, no one's going to care. And so for pre seed inception teams, make sure you equally prioritize marketing and community development to break through the noise.

Is that why Matt tweets so much?

That's my AI.

Oh, it's your AI.

That explains my Sylvesia avatar that tweets.

Are you actually here today or is this just an ad for your portfolio companies?

You know, the uncanny valley has been crossed live today.

Nice.

Such a large Turing test, if you will. Let's stick with AI for a minute and then we'll move back to other topics. I know there's quite a lot else to discuss, but I've been thinking a lot about the AI foundation model companies and how their work has been improving. Da. We often talk about this in terms of how well they can code or follow tests and so forth. But there's also companies out there like I think bio optimists working on foundation AI models for the biology space for folks out there who are more familiar with the enterprise software side of things. What's the state of play in terms of AI advancements in the health care and biotech world and how much does that unlock new opportunities for your thesis?

I think there's tremendous opportunity both on the health care side and on the biotech side. And I'll distinguish between the two. And I think our, our thesis here is a little bit contrarian relative to the rest of the market. So there has been a lot of excitement over the past three, four or five years specifically around AI and biotech. And the theme has essentially been that AI is going to come in and make drug discovery cheap, fast, easy, simple. Where in the past it's been incredibly challenging, incredibly time consuming, exponentially expensive. And the, the, the reason people have believed that is because we've had some genuine breakthroughs, I'd say most notably AlphaFold, which was DeepMind's product that was able to computationally predict protein structures simply from knowing the sequences of those proteins. And that was a true, true breakthrough. It built upon several decades of experimental discovery. There was something called the Protein Data bank that that was trained on. And so it was really an amazing tribute to 40, 50 years of molecular biology and then some amazing new capabilities of AI models and DEMIs Hassabis and John Jumper won the Nobel Prize for that. So you can't downplay how important it is. But really what we're seeing with that and other associated innovations in biotech is a new approach to molecular biology or cell biology that can happen inside the computer as opposed to in a petri dish.

So da on that point, are we actually going to be able to have models that can simulate a cell, like a cell with mitochondria and all the other things we learned about in.

I think we're moving towards it. And you've got in particular a lot of philanthropic efforts like the Chan Zuckerberg Initiative and the ARC Institute that are targeting in specifically on that opportunity, virtual cell models. So I would bet on their success. But having virtual cells, as cool and revolutionary as it is, does not fix what sucks the most about drug development, which is that the only way we find out if drugs are safe and effective today is to put them into human beings. And we have to put them into enough human beings that we can get statistical significance around our assessment of their safety and efficacy. So to me it stands to reason that until we can truly simulate whole human biology in a way that is reliable enough that we would trust it to stand in for the real people, it's not going to disrupt this fundamental bottleneck, which is really what makes drug development a challenging endeavor. So I'm more pessimistic maybe than some about the near term prospects for this technology to transform biotech or drug development.

Given the liquidity issues, given the depression era economics for companies in your space, and given the long time horizons to figure out how to kind of solve bottlenecks using AI. Da why are you investing where you are? It sounds incredibly difficult and like you're running uphill on your hands with your feet tied. Our friends also on the show are having a much easier time of it.

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Of being long winded, I didn't get to the optimistic part of my answer.

Oh, I'm so sorry.

Please outside that is, that is the health care side. And when I, when I use the term health care, what I'm referring to is everything we've all experienced when we've been patients or when family members have been patients, doctors, hospitals, insurance companies, pharmacies, sort of the last mile of medicine where it actually interacts with patients. And we are fundamentally constrained, and have been for all of human history, by the fact that we have very few doctors relative to the number of people that require care. And so while it may still be a decade or two away that we can virtually model humans for the purposes of clinical trials, I think we're already pretty close to virtually modeling doctors. And this vision of the AI doctor in everyone's pocket for free is one that I would bet on heavily. And I think that what we're going to see in the upcoming years, it's already started with the general purpose LLMs. Already patients are taking this into their own hands. And when they're you know, getting confused or overwhelmed by their experience navigating the traditional healthcare system. They're going home at night and they're asking ChatGPT a series of questions about their prostate cancer or about their COPD or about whatever else they're dealing with. And they're finding it enormously helpful. So, you know, I don't want to downplay all of the challenges that people rightly point out. We need these things to be reliable. We have to be very, very careful about errors because these are life and death applications. But I can't overstate how transformational it's going to be to give high quality gold standard medical knowledge to every person on the planet. I mean, we are really going to put the world's greatest doctor at the fingertips of every single person on earth. And this is, I think in some ways the biggest thing that has ever happened to medicine. So we have great reason for optimism in healthcare innovation. We have plenty of early evidence that biotech itself is going to be revolutionized over the long run. I just think that's a longer thesis to play out.

Well, I hope full doctor employment continues for the sake of my household income. Astasia, over to you on kind of a similar question, but admittedly over in your domain, I've been watching foundation model companies slowly creep into the application layer. Things like Claude code. We're seeing I think also anthropic work on customer support agents, that sort of thing. I'm curious how much concern you have or your founders have that you talk to about these companies eventually taking over more and more of the market with what they build and reducing the amount of space that there's left for startups to build, innovate and go to market.

Yeah, you know, competition is always risk. We could see that. OpenAI anthropic identified different verticals where their models were finding the most success and efficacy, really landing with co generation as a key use case. And it was kind of predicted that they would move this direction over time. We're not surprised by this. I've been an infrastructure investor for 10 years. There's always a large incumbent in the space that's trying to win the mass market. In the early days it was Dell, EMC and Cisco. Then we went to the cloud with AWS and GCP and Azure. What sets founders apart that build in these competitive spaces is the touch that they have with the end users and the customer relationship management, the core differentiation of the product and extreme velocity and execution to go win the market. You know, there was a very well known Amazon product called Redshift that was a cloud data warehouse. And yet we got Snowflake, the largest enterprise software IPO of all time. Similarly, we have databricks confluent a number of these vendors that go head to head against publicly trade. Yeah, head to head against cloud service providers. It's going to be the same thing in AI apps. The agile, smart, aggressive, early stage teams have a shot of continuing to win these markets. So while there is a risk of more competition, it does validate the space. It also could provide acquisition opportunities for these teams as we're starting to see. So from our perspective this is not new. Competition always exists and there's a precedent in the infrastructure world of really large companies going after being built, going after the incumbents.

Matt, does all that track with what you and your firm think?

Yeah, I think that's well said. You know, last year Everybody was saying LLMs are going to become commodities, but I think the nuance is that never meant that LLM companies were going to be commodities. And the way they make money and become super large is by monetizing the LLMs, in part by providing those as API, but in large part by building application on top of them. And you know, OpenAI is in some ways has always been, I mean at least for the last few years, an application company was the accidental consumer company. And clearly what they're building is, you know, the next Google. They're a consumer company, they are an enterprise company. There were reports that they were doing the Palantir playbook in the enterprise. They are a developer company, they're trying to do all the things at the same time which makes them such a fascinating company. So you know, it's pretty much what Sam Altman previewed last year. If you're right in the middle of, you know, horizontal applications of AI, whether consumer or enterprise, and you're trying to do productivity, you know, stuff, you're, you're going to be competing very directly with them. And by the way, I think that's partly the rationale behind Grammarly buying Superhuman. If you're in that productivity world, you need to build product and distribution real fast because they're coming after you. Having said that, the rest, whether that's legal or finance or vertical industry specific kind of applications, I think there's plenty of room to build great companies there because as it turns out, building what was known last year as a thin wrapper, actually it takes a lot of work, a lot of, you know, industry specific workflows, a lot of industry specific integrations and OpenAI and Anthropic and are going to do those. So there's, there's plenty of room to build fantastic companies.

Conventional wisdom in the era moves very quickly. We have gone from LLMs or commodities. Therefore OpenAI is doomed to, okay, now it's worth $300 billion.

Alex, I got a quick question about that for all of you, which is, you know, and here we all get to kind of speculate because this is not in our investment sweet spots, I think any of us. But OpenAI at 300 billion, is that too expensive? Is it a deal? You know, like what would each of your pain threshold be for the valuation of OpenAI at which you'd say this is absolutely overpriced.

Well, I'll answer last and give our friends for a shot here. Astasia. Matt, who wants to jump off the plank first? Okay, I'll call on you, Astasia.

Oh, you know, I think there's huge upside in OpenAI today. We just kind of talk through their multi product approach. You know, you have the infrastructure layer that they're building with the models. They're moving into more developer tools and platform as well as up the stack for AI applications. I don't think it's unreasonable to imagine this becoming one of the largest companies in the world. So would I buy at 300?

Probably. Would you buy at 500 with your own money? Not speaking for Felicis, just literally Anastasia Myers, your checkbook, Probably. Okay, Matt.

It'S a complex question. If you look at the current revenue growth rate and the sheer number in terms of revenue versus most recent valuation, the multiple is actually not that crazy at all. But all of this happening at massive scale, that's a question about what that means, how much more room there is to grow given the many, many billions we're talking about. So I'm personally bullish on the company and would probably be a buyer now personally and in probably the near future. Having said that, the firms that put billions and billions of dollars into that company, I just admire that from afar. That would be so incredibly nerve wracking for me because there's many ways this can go wrong and it's still a nonprofit company. There was a CEO question that came up. There is, you know, management questions. There is, you know, a bunch of developers and researchers that leave that getting poached by meta this, you know, like every day they say a reason to just give an ulcer to somebody that has put billions of dollars in the, in the company. So much power to my colleagues that do that and play that game. You know, as bullish as I am on the company, I just find it incredibly scary.

Well, I mean whenever you make a big gamble, it's pretty scary. We've all made a bluff that in a poker game that we've hoped would get by and didn't and we've all been on the other side of that. I think the growth to 10 billion ARR DA to answer your question, putting OpenAI at effectively 30x ARR makes it very, very cheap for its technology, I would say level of prominence, how quickly it's shipping and just given its growth rate, it feels cheap. Pain threshold. Okay, so today at 10,600,000,000 would be 20x arrangements. Given what I've heard about a lot of startups that some of our fine friends may have put money into recently, that's still incredibly cheap. So I think I'd pay up to 30x for it today. So about a trillion which sounds ridiculous but you know, I watched Google go public and I know what generationally defining companies can become in time. And if it's already this large and growing this quickly and this important, sure I take a flyer on it for I don't know, a third of my net worth. But you can't ask the question without answering it. So take a sum.

I, it's, it's really hard. I mean I, constitutionally, I'm a value investor and I, you know, I think, I don't say that to be sanctimonious about it. I just think it's really difficult to price something like this those. I mean it wasn't long ago that we had no trillion dollar companies and the first ones that we got were companies that had been building for decades. So you know, sure, there's a possibility we're in a completely new regime where companies can become worth a trillion dollars in just a few years. I don't know, I guess it hasn't happened yet and they may be the first ones to do it. And then there's obviously the question of what kind of multiple do you need to take the relevant risks. So you know, for it to be a 10x from here is a lot harder than for it to be a 2x. And then you have to compare that opportunity to all the other opportunities where you think you can get 2x and ask whether you're taking more or less risk.

Well, on that point I am, I'm pulling up a chart for us. This is from a CO2 deck that I think just underscores why we're seeing companies Become effectively trillion dollar firms faster. This shows how long it took Anthropic to go from 0 to 1 billion in revenue. Took about, I guess this is 21 months. And then it took three months to go from 1 to 2, and then two months to go from 2 to 3. And the latest reporting is that it's now, per the information, at $4 billion in annual run rate. So when you're seeing growth like that, it's hard to sit on the sidelines. To Matt's earlier point even.

I think the other thing is I have trouble, you know, it'd be interesting people's comments on that. Just as a user, it's unclear to me what the cogs are on these LLMs and I get the impression sometimes that I'm being metered even when I'm paying for the premium subscriptions. So I'll feed it a spreadsheet with, you know, 50 stock tickers and I say fill in this information for all 50 and it gives me back the first five, you know, and then it basically tries to trick me into not asking it for the rest of them. I say give me an Excel spreadsheet where you put all of these values in and then it gives me a link and it doesn't work. And so it's hard for me to tell whether the models are actually incapable of doing this work, which I find hard to believe, or if the companies are basically metering usage. The experience I had with Gemini kind of gave me that impression relative even to the other ones, where there were things that it couldn't do that the others were doing. But it felt like the inability was more a matter of willingness to go spend compute on something that wasn't particularly complex than it was on the fundamental capabilities of the model.

Well, there's two things that come into that. One is just how much compute capacity we have as a species or a company or as a platform. And then there's also just how much we're using or how much it costs. And Matt had a great tweet talking about the impacts of this on startups from the other day, saying that sure, the cost of inference keeps dropping, but as of right now, seeing a lot of AI startups at 50ish gross margins, not 80% like SaaS companies. And Matt, what I was trying to get to a little bit earlier was we talk about how quickly conventional wisdom becomes wrong in the AI world. So sticking to DA's broader point in your tweet here, does this persist as a problem or does this go away as we Add more compute. And as we see compute limitations decline.

Look, that's certainly the, the bet that we all making collectively as an industry that the cost of inference is going to continue dropping. I mean, certainly the last year seems to validate that the hypothesis. And I think we all on board with that. But that's the bet. That's the bet. And there's a lot that needs to happen for this, both on the LLM companies, inference providers, but also on the company side, like the level of optimization that you need to be able to do if you're a company that builds AI into your product, into the product that you deliver to your users. But my point was just when you talk to people, and that tends to be little bit the case in our industry, people think that a lot of things are a foregone conclusion and just operate as if the reality of today was that intelligence is basically free. So that was just a tweet to remind people that at least as of now, that's not the case. And I'm seeing this across companies that leverage AI in their product for every single interaction that the product has with users, like heavy users of AI. But that's, that's a reality of today. And as I mentioned earlier, there's a bunch of companies that have negative gross margins. Right. Where, you know, to a large extent, we are in the pre IPO Uber phase of the AI cycle reminds me of that too. Yes.

But Matt, for the young folks listening who weren't here for that, why don't you explain what that means?

Yes. So it turns out that Uber's showing up at your doorstep in one minute and being half of the price of a yellow cab in New York was not a miracle of modern economics, but the result of the whole industry being subsidized by VCs. And I think we are at that stage as well, where VCs like us are betting, as we should, on where the puck is going, which is all of the cost part of the AI equation being much lower. But the reality as of today is that it's not. And the money comes from VCs. So when you look at the anthropic numbers, I have no idea what the percentage is, but for a fact, I know that a chunk of this is coming from Anastasia and me, and a chunk of the Nvidia revenue that everybody has been raving about for the last few years come from US VCs and also the general public through Microsoft. So it's all very circular and I think you can be super bullish on AI while recognizing that there is an element of fragility that's built into all of this, which is that we are in the supply build phase of the market where founders are building, big companies are building, VCs are investing, and all of this on the basis of a future bet that it's all going to work out, but demand needs to materialize on the other side and we need as an industry to stick the lending within a few months or maybe a year. If there is a true disconnect between the supply phase and the demand phase, then we might be in trouble for a minute. Yeah.

According to producer Claude, the average gross margin for public SaaS companies today is 73 to 77%. Asasia, before we set AI aside, same kind of question over to you. What are you seeing amongst your companies? Are gross margins an actual problem or something that's going to be solved by whatever the next trillion dollar data center that gets announced will bring to market?

Similarly, I spend most of my time at the infrastructure layer and I think an interesting deployment mechanism we're seeing in our portfolio is the bring your own cloud model where they're actually running their infrastructure in the customer environment in their vpc. And so we actually see really strong gross margins.

VPC being virtual private cloud.

Yes, exactly. And so we actually see very strong gross margins with that profile and it actually gives the sellers of that technology a lot more leverage when they're competing in bake offs against fully hosted or cloud service providers. And so we can see gross margins there that are 85% plus. And often those teams are using AI as part of their product experience and they're working with open source models and distilling them so they can be run effectively.

You know, you said earlier that we've moved past the era of Rack and Stack servers because we got cloud and now you're talking about people having better economics on their VPCs or I presume, their own in house. Iron so are we ironically going back to a world in which you don't want to pay someone else's cloud margin if you're going to have good AI margins on the inference you provide to your customers?

So we see it in both approaches where there are still very large scale companies and financial services, health care, etc. That have their own data center environments and our products can sell into those data centers, but they can also be deployed in aws or Azure, etc. As well. So I'm actually very excited about that deployment model because I think it gives power back to the founders against the incumbents. And it's wonderful to see those gross margins again.

All right, well, we mentioned health care, so we got to bring DA back in. Da, Are you seeing your companies able to sell into customers that have their own vpc, or are they mostly living and existing in an Azure vanilla world?

Yeah, frankly we don't have a lot of portfolio companies that are in the software business. And so I can't speak to intelligently. But I will say, you know, the, the world of enterprise hospital, it is a unique and interesting one. And in particular the opportunity there that keeps coming up is around cyber. You know, these, particularly with AI attacks. Now, these are just some of the most vulnerable and mission critical information systems that exist in the world and they're all kind of running around trying to figure out how to manage the risk. Today they don't know exactly what that risk looks like, but they know it's big.

We've been spending some time looking at Genai applications and healthcare and we're particularly excited about the role of Voice for improving client and patient care, decreasing administrative overheads, helping with insurance claims. So I think that a really, really cool area for healthcare is the application of conversational AI.

Yeah, that's definitely true. And one of the things that we're very conscious of is that physicians today, partly because of the shortage that I described earlier, report just very pervasive levels of burnout. And Alex, it sounds like you've got family in this business, so you understand directly. And so, you know, part of that is, or a big part of that is driven by what any of us have experienced, which is when you go to the doctor's office, they basically sit there staring at their computer, doing data entry during the whole visit. And so to your point, Voice is potentially an escape hatch from that. And you know what would be much better is obviously if the doctor could look at you, look you in the eyes, do a physical exam, focus on the patient, not on their, you know, 2000 era software, and doing data entry during a visit that's already painfully short.

Well, here's to bringing more technology to healthcare. So that way everyone's doctor has more than 30 seconds to talk to them. I think we will all appreciate that. But let's put AI down for a minute and talk about founders. One of the more interesting trends that I've seen in the last couple of years is one the rise of solo founders as a more common founding unit and also a decline in what I might call party round of founders. So here's a bit of data from our Dear friends, over at Carta, that shows, just as I said, more and more founders are single shop, and we're seeing roughly steady 2 and 3 founder numbers. But once you get to 4 and 5, it's pretty infrequent. Now I'm curious if the VC market is becoming a bit more willing to back solo founders in the AI era. Matt, we've heard some people discuss how there's going to eventually be $1 billion company founded by one person with no other staff. A bit of a meme at this point, but certainly shows where people are thinking. How has your firm changed its expectations around founding team size?

Yeah, one founder plus one AI, Right? That's the new founding team. Many, many eyes. Yeah, look. That's interesting data I hadn't seen that seems to corroborate what we see. I wouldn't say we ever had a particularly strong sense in favor or against solo founders. We have some. We have many founding teams. We have, you know, founding teams that are brothers. We have founding teams that are married couples. So we never had a sort of predisposition in favor against certain, certain formats. I would just say that, you know, from personal experience and watching many, many companies over the years and working with many companies over, you know, solo founders is cool at the beginning. It can get very lonely very quickly. So, you know, you could argue that on the other end, founder divorce is one of the worst things in the world. But by and large, you know, I've found that teams of like two to three founders, like people, tend to be happier in good times and more challenging times.

Astasia, same question over to you. Any change in how Felicis approaches this or is 2 to 3 still the. The place you want to write the most checks?

Yeah. We similarly have been agnostic to the number of founders. Typically we do see two founders as the most common, but it is good to have a friend in the trenches with you. Many of our solo founders eventually do kind of anoint that number two on the team that becomes a safe space to work with. But I've, I, for me, when I was looking at that chart, I was most surprised that there were actually so many teams with five founders. I think I really seen that before. I don't think we really see it now.

It's down to just 4% of the market, all the way from 11 back in 2015.

But some that's also a bad idea from, from experience, like for anybody listening to this because it solves a problem in the short term. Fast forward seven, eight years, you're the very successful CEO founder number two or three or four of a company and you own 3% of the company.

Well yeah, but then we have companies like DataBricks that had seven founders and it seemed to be doing pretty well. But I'll take that. That's the absolute outlier. I want to go towards team size in general next because asprazine founding teams maybe get a little bit smaller on average. We are also seeing companies trying to do quite a lot more with less. And Astasia, you mentioned earlier about how much of the labor market we can attack with technology. That appears to be showing up in how teams at startups hire or don't. Back in the day the old riff was you raised and then you hired, took your burn up and then tried to grow like hell. Talking to a lot more founders lately it seems that there's less of an appetite for rapidly growing headcount. I'm curious one how much that showing up in your portfolio estasia and then two if that changes burn dynamics or how long these companies have in between funding rounds if they are spending less on people.

Totally. I want to take you back to 2020. Okay.

Oh God.

Growth at all costs. Five years ago like hit the top line numbers and I think there's a lot of learnings for investors and founders coming out of that era and so kind of seeing the high highs and the low lows of what it takes to build companies when it's growth at all costs. I think this next generation of founder is a little bit more pragmatic and sensible but they're also empowered by these AI native products that they can adopt so they can get more leverage out of the founding engineers and they can potentially be more empowered to do the BDR work and sales and marketing and customer success without needing a huge team. So I think it's a learning from the past era of building but also these gen AI products that are just giving people more and more leverage.

And then on the burn front does a decreased need to staff up early give these companies more flexibility, more firepower to spend on marketing? What is unlocked by having less spend? In that human resources line item we.

Are seeing that the burn numbers are lower. Of course that gives them more Runway and we're seeing them often repurpose it towards the go to market side of the house. So thinking about marketing content and collateral events, et cetera as a way to continue to get strong ROI from their investments and building a community and getting customers.

Matt, same question over to you about hiring. I don't know Trends, if you will, and then how that impacts overall startup economics in the earlier stages.

Yeah, it's actually kind of funny. I was tweeting that the other day. Everybody's talking about the one person, $1 billion company, but equally every company that we come across and you know, by definition it's a self selected group. Like Everybody is raising 20, 30, 40 million and when we ask them, you know, what for, especially for the companies that are not building their own models, they're not going to burn on gpu. So like what do you use the, what do you need the money for? And a lot of it is just hiring people. So you know, it's like a little bit of an irony or I guess, you know, there. And we're seeing some of that. We're seeing, especially in the early stages, we're seeing people be very efficient using a lot of coding tools and all the things. But very quickly as you start scaling just a little bit, then we sort of back to having a bunch of people. And that's, that's just the reality of the, of the, of the beast. You can build product faster, but for all the functions you need to have.

People da over in your side of the fence. Is there a similar dynamic in which people are hiring a little bit less earlier on or is the unlocks that we're describing on the Estasia and Matt side of things not as pertinent in healthcare and biotech?

In biotech in particular, I'd say the major shift in really the past two decades has been towards an ability to outsource more and more and to thereby, you know, build what can be very valuable companies with relatively few full time staff. And so that is a trend that we're seeing. Obviously people embrace more and more with capital constraints. But I'd say the other force that's exemplifying that is the Chinese biotech scene. And this has been a major story this year. What we're seeing are these Chinese startups, in some cases subsidized by the government. The whole sector's being heavily pushed by the CCP right now. But I don't want to take any credit away from these entrepreneurs who are really doing remarkable work. And I think what we're seeing from China is that companies with a virtual model basically leveraging what are called CROs in our industry, these outsourced service providers that can do specialized work in drug development and drug discovery, they're able to do things in six months or a year that people in the US are used to taking two to four years. And so that's a real wake up call to VC and to, I think, the entrepreneurs, because if they're seeing these Chinese founders do it, then they've got to figure out a way to match that. And from the standpoint of the biggest acquirers, which are largely global pharmaceutical companies, there's a sort of indifference to geography. I mean, it's not, I don't want to speak for Apple or Nvidia or anyone else in the Valley, but you know, they may have some home court bias, but, you know, a Swiss pharmaceutical company is just as happy to buy a new drug out of China as it is to buy it out of New Jersey. So there's a real global competition at foot and US startups are needing to figure out how they play into it.

Do you think we should have more state level investment here in the United States, given what you said about the investments that the CCP is making? Not to get political, but I'm just.

Curious about the evidence.

It's a great question because it's inevitably political. I mean, people are, are disputing right now the cuts that are in a lot of cases inflaming academics and folks in my industry because what they are feeling is that the US Government is basically pulling back from this industry at exactly the wrong moment. I think what's critical if I try to step outside of my own vested interest though is to really ask what is in the long term strategic interests of the country. And think about France. Matt, you're French.

Yes. Where is this going?

Yeah, we knew he's French because he pronounced it nuance earlier.

Here's why I bring up your homeland. I had this insight when I was in France last year, which is that, you know, as you're familiar with, people have a great lifestyle, but they complain a lot about the lack of entrepreneurship, how hard it is to get rich. Da da da da da. And it occurred to me that, you know, there is a trade off there with the way that say we do things or that China does things. But it's not as if the French people do not get the technology. They get the same iPhones and the same LLMs. They basically get to benefit from the innovation that is produced in these bloodthirsty markets like the United States. And so every society has to ask what is the pro and what's the con of prioritizing innovation on their soil? And in the case of biotech, I think it's clearly the case that, you know, from a national security standpoint, we don't want to be Dependent, say, on adversaries for critical drug ingredients. But that's very different from the question of whether we need to be the ones producing the next big breakthrough. I mean, we want the next big breakthrough for the benefit of our patients. But if that breakthrough happens in Switzerland or France, it's not clear that we're not going to get it. So I think it's a complex calculus and there's no substitute for the democratic process sort of weighing it rigorously.

All right, Matt, answering for not just France, but all of the eu, your response?

Look, I think that, you know, old perceptions die hard. And, you know, it may be different in biotech, but in tech. Thinking that the EU in general, and France in particular has not fully embraced innovation is missing the big story of the last few years. France currently is obsessed with startups, and that starts at the very top of the government. And you could argue whether the government should be in that conversation in the first place or not. But every kid that comes out of a top elite school in France today wants to do a startup. And there's a deep tradition around software, deep tradition around AI, and it's gone from zero to hero in the space of like, four or five years. And sometimes I want to talk about this, like today, many times I don't want to talk about it because that gives me a comparative advantage to go to France and win those deals. But there is a lot that's happening right now, and in particular in AI, like Mistral being the most famous company, but there are many, many others. Having said that, DA directionally as a society, I don't disagree. There's a lot of work to be done, but there is that Nucleus that has completely transformed in the last few years.

I would just add that when we think about breakout AI companies today, the name Lovable comes to mind. They just crossed 75 million ARR, and they're based out of Sweden and they're about to raise, according to the FT, a $2 billion valuation. So there's one company that could easily become the next $10 billion public company from the EU. So something to keep in mind. Now, Estasia, when DA mentioned academia and the connection between industry and startups, I think you were beginning to nod your head. I know you recently interviewed Letta, one of your portfolio companies, about making the jump from academia over to the world of entrepreneurship. I'm. I'm trying to find some silver lining in some of the cuts that we've been seeing. And I'm curious, are we going to see more academics that may see Their time at universities in the ivory tower come to an earlier end. Are they going to break into industry or are we just cutting off our own legs at the knees here?

Yeah, so we're strong believers in academia and have deep partnerships with many universities. This question of how do we 10x the number of founders that are coming out of academia we took to heart and that's the reason we launched our Fellows program. So each summer we bring really bright AI students from places like Stanford, mit, Berkeley, among others, where they have this incredible technical aptitude, but maybe haven't gotten the exposure to what it's like to become an entrepreneur and founder. And so during this week we have talks from founders and hear real stories from people who are just a few steps ahead of them so they can get that exposure. And really the response has been incredible. We just had our Felisa Fellows program and we had four times the number of applicants this year compared to last. And it really speaks to there is a hunger from students, Undergrads, Masters and PhDs to learn more about our world tech and venture. We personally believe there's huge untapped potential here where we bring together amazing researchers with builders and kind of help create that bridge between the two communities. The other thing that has been really interesting is you highlighted I interviewed one of my portfolio companies, leda, that was alongside Jan Stoika from Databricks and the Sky Computing Lab that he runs out of Berkeley. And that program is really designed for students who have an entrepreneurial mindset. They have a really strong belief in the value of open source as a vehicle to share your research, but also get the experience of working with users who become contributors and customers. Our recommendation for students as well is build but then release it and learn from that experience how to work with users, how to bring your technology to market and kind of test the waters. So we're super encouraging of this. If you are a student listening, reach out to us. We'd love to talk to you about your work and the path forward to become a founder and commercialize it.

Just because people will ask what's the best way to get a hold of you Estasia if people wanted to reach out to you on that exact point.

Yeah, it's astasiasis.com so a s t a s I aelisis.com simple enough.

All right, I want to squeeze in a little bit of notes on exits here because we've talked in passing about the superhuman Grammarly deal we just saw yesterday that Figma filed to go public. I absolutely adore an S1 filing and early data kind of makes it seem that Q2 was a bit more fecund than we might have expected in terms of total value of M and A and even a couple of IPOs. So just briefly, as Stasia, how good was Q2 for you guys in a returns context? Are your LPs calming down a little bit and looking ahead? How excited are you by the back half of this year for exits?

So we don't publicly speak about our returns and our dpi. Of course we are very encouraged that publicly one of our portfolio companies, Weights and Biases, was acquired by coreweave. So that was very exciting for not only Lucas, other founders and the entire team, but we are just pumped about that opportunity for this next phase of the business. In the second half of the year, we think that there could be a really interesting opening for both traditional exit channels of IPO as well as the opening of M and A markets. We haven't talked about it, but I thought it was very interesting that Dylan at Figma actually commented that he would become acquisitive as well, at scale, mind you. So it's encouraging to see these businesses have a path forward.

Yeah, checkbooks seem to be opening. Matt, same question to you. How was Q2 for you in an exit terms and are your LPs happy and what's your expectation for the rest of the year?

Yeah, plenty of encouraging signs across the board, you know, Q2, but in general, the last two or three quarters we actually had a bunch of exits, some of which just happened, you know, for like smaller amounts. But that's exactly what you want to, because it's healthy for a portfolio that the companies that are not scaling should be cleaned up by the large companies the way it's always worked. But even that kind of stopped for a while. It seems to have resumed. And then we had larger ones. We're talking about Superhuman, but we had a comparable size acquisition, an insurance company or an AI for insurance company called EvolutionIQ that was purchased for actually higher than was reported in the press, 850 million. So you know, great, we'll take it. And so there's been a bunch. And whether that was Q2 or Q1 or whatever, the secondary market has been active as well. And then in terms of IPOs, you know, like a lot of VCs, like we already, like we've been waiting for things for a while. So, you know, from, you know, discord to data in the enterprise AI world to RO in the health world, like we have a, a whole slate of companies that are in general in that pre IPO zone. And I'm very encouraged by what's been happening.

All right, now, Da. Because you started off by saying that the exit market for your industry is troubled, I'm not going to ask you the same question. But instead, I want to close our chat with this. Two of your portfolio companies, Conceivable Life Sciences and Billion to One, are both working in the broader fertility space. It's a world that I've run through over the last couple years, so I'm pretty familiar with it. I just want to end with some positive thinking here. What's the chance that the companies you're either investing in or seeing can expand fertility treatments to the point to which we all can stop worrying about global birth rates over the next five to 25 years? Because I feel like we're all sick of talking about it.

Yeah, I think the global birth rate issue is it's both about people's ability to reproduce easily in today's modern lifestyle and also their interest and willingness to do so. And that latter consideration is a more challenging one to think about for me, because who knows, maybe you got to think about housing cost and education cost and all these very lofty topics. When it comes to the actual technology, though, again, we're in a renaissance right now of the basic biology. And two of those companies that you mentioned are big success stories in our portfolio so far. Billion to One is relevant during pregnancy, and it's a company whose test very easily enables expecting families to find out whether the fetus has any congenital diseases. And it's a replacement, basically, for amniocentesis, which was the big needle in the belly that no one liked. So that company's grown tremendously over the past few years and is now effectively becoming a standard part of every pregnancy. Conceivable is doing something that's much crazier and more, I'd say, futuristic, which is it's completely automating in vitro fertilization with robotics. And it's pretty shocking when you learn just how variable the results of IVF can be as a result of human handling. So embryology, which is really the art of reproductive medicine, involves handling sperm, eggs, doing the fertilization, freezing the embryos, in some cases biopsying embryos to test them for genetic conditions. And all of this today is done by human hands. And if those human hands are a little shaky on Tuesday, you might end up with a worse result than on Monday. So we think that there are huge gains to be had by totally automating this, and when you automate it, you not only improve the consistency of the process and thereby the technical outcomes, but you also can start to reduce the cost. And to me, one of the great no brainers in healthcare investing today is this opportunity to democratize access to ivf. And it's very simple to see how big that prospect is because you can look at other countries where the cost of IVF are socialized and that gives you a sense of basically what the demand is if cost were not a concern for patients. And it's multiples of the numbers that use IVF in the United States and the other markets where it's not covered. So we see this great opportunity, both with this conceivable company, but then with other technologies to make IVF something that is accessible to every family that wants it. And I really think it should be sort of like a human right. To the extent that we're covering medicine as effectively a human right in advanced economies, I don't see why fertility medicine should be any different.

I think the word you missed there was most, most advanced economies. There are a handful of exceptions, but I do think we've had an overall very bullish conversation. Astasia pointed out that the TAM for AI companies is much larger than just technology budgets today. Matt thinks we're going to see more M and A pop up in the back half of the year, which is going to be great for everyone's portfolio construction. And DA's companies are solving the global birth rate crisis. So between all of that, ladies and gentlemen, it's going to be one hell of a back half of 2025. Thank you all so much for coming and taking part. I would love to do this again in 6 months to check in on how we're doing, but in the meantime, thank you all. This is Alex, this is Twist. We'll see you next time. Bye.