David Friedberg: 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 and this is i think in some ways the biggest thing that has ever happened to medicine this week in startups is brought to

SPEAKER_02: you by cla innovation takes balance cla's cpas consultants and wealth advisors can help you get from startup to where you want to end up get started now at claconnect.com tech century your team should be focused on shipping features not chasing down bugs new users get three months free of the team plan which covers 150 000 errors go to century.io twist and use the code twist and public you take investing seriously public does too build a multi-asset portfolio and earn an industry leading 4.1 apy on your cash with no fees or minimums learn more at public.com twist hello and welcome

SPEAKER_03: 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 felices she invests pre-seed through series a with themes including ai infra developer tools enterprise software cyber security and my favorite open source astasia hi hi there and then we have matt turk a partner at first mark 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 wallick 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 a but i want to take a look at just deals and deal making in q2 for the day 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

SPEAKER_08: and are you still able today to find attractive entry points for new investments absolutely yeah i

SPEAKER_12: 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 error 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 dollar markets like before but 100 billion dollar markets matt same question over to you similar dynamics

SPEAKER_18: from your side yeah absolutely and uh look uh to keep it real uh i'm not at all able to find uh attractive entry prices uh i've been at first mark for over a decade now and the last two or three investments i've

SPEAKER_19: made were by far uh the highest valuation investments i've made in my career it's fairly routine at this stage that anything interesting in ai uh is at you know nine figures uh pre-money at the series a level uh everything is hyper competitive and um uh you know it's it's it's something like uh 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

SPEAKER_18: it is and i just uh just to build on what it says i just said like all of this is true we're living in a paradigm shift uh but that comes out of price yeah apparently a very high price da i am not gonna

SPEAKER_22: lie a little bit less familiar with the realm of biotech investing so i'm curious are you seeing similar

SPEAKER_24: dynamics uh in your part of the market well i don't want to uh be a wet blanket here but biotech is

David Friedberg: 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 uh with the exit doors effectively closed the private market has suffered tremendously it's been incredibly difficult for companies to find capital um that is even true of companies in biotech with an ai theme uh they may be able to uh attract generalists who are maybe not uh 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

SPEAKER_28: and a difficult one and we can get into uh some of the nuances of that as the conversation evolves

SPEAKER_30: just really briefly when i was learning about in venture capital and ipos one of the rules of

SPEAKER_03: 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

David Friedberg: putting you into a depression as you put it that's exactly right uh and so if you think about you know matt and anesthesia can speak to astasia sorry can speak to this uh better but uh in the past decade or two decades in 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 um commercial launch as an independent company or uh m a to one of the big

SPEAKER_35: pharmas i would love to double click on this okay this is very interesting to me um coming from the

SPEAKER_12: 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 uh investors in biotech that is demonstrating or causing this effect where other capital sources are

David Friedberg: 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 then the usual suspects uh when it comes to the big uh multi-strategy hedge funds or the large mutual funds so think everyone from citadel to fidelity to t row price and historically biotech companies require both of these constituencies to survive and to ipo and then do follow-on financing so what's really happened in the past several years is that the generalist investors 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 uh 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 uh available capital for these companies well that's an enormous bummer so i

SPEAKER_03: guess da does that mean that there's a 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

SPEAKER_30: i'm not gonna say down in the dumps in your sectors but i mean certainly probably more affordable

SPEAKER_42: 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 steven 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

SPEAKER_46: 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've got to do the forecasting you've 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

SPEAKER_47: this stuff right folks and that's really a bummer when startups don't do things in a button-up way always have a great partner a good partner to have on this adventure while things change my friend

SPEAKER_50: steven over at cla visit cla connect.com slash tech and don't forget to mention that your boy jacal sent you that's cla connect.com slash tech start today well look this gets at the fundamental one of the

David Friedberg: fundamental paradoxes of all venture capital or investing um even more generally which is that high prices tend to foretell low expected returns or and and and low prices by contrast tend to foretell high expected returns and the challenge is that when something happens that is substantive like ai you know where we've got a series of legitimate breakthroughs that have occurred uh it can be hard to tell whether the high prices are warranted or not you know whether the high prices do in fact mean low expected returns or whether they mean uh you know that people are even at those prices underestimating the potential and you should jump on the train uh because you don't want to be left behind so i think in biotech it's a bit uh it's a bit of that kind of situation we we have low prices we all know that that's very clear um 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 uh type of behavior and so i think over a sufficiently long time horizon there's no doubt that there is enormous upside but um predicting exactly when that transfer uh of the sort of risk appetite the market's going to

SPEAKER_03: occur is 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 uh 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 five million arr for kind of current top ai companies being 24 months and then for previously leading uh sas 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 touch to them earlier on uh matt does the the prices we're seeing reflect that fairly or are they still

SPEAKER_57: in your view um a bit over their skis time will tell and time will tell that's true venture is

SPEAKER_58: a long game but uh when you're talking to yourself at night what do you say

SPEAKER_18: uh whenever i manage to catch some sleep over all of this um look um you know it's it's when in the

SPEAKER_19: venture capital world we're like in the the business of taking risk um and uh we can all see that there is a huge paradigm shift that's happening uh i think you have to play the game on the field at any point in time you know with discipline but like you can't not invest in ai right now um so yes there's

SPEAKER_18: that happening on the one hand and by the way it's a select group right that was a top 100 that you showed on the chart so it's not every ai company um 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 uh experimental budgets uh there is a big open question around retention so cursor seems to have great retention others uh do not for many others the jury is out nobody knows uh and then there's the question of uh gross margin and in particular negative gross margins where there's a lot of reports that some of those companies uh operate at a you know at a deficit in in terms of a gross margin so at some point they will need to grow into all of that there will be a reckoning of some sort particularly as you get closer to public markets the pressure intensifies uh so there is a big question mark uh but equally as i said at the beginning uh a second ago we we're we're in the business of of investing in uh the products and the companies of tomorrow so we have you have to play the

SPEAKER_15: the field on the game the game on the field i'd love to add on to this if this is okay you know 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

SPEAKER_12: 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 felices we really think about they can go after labor budgets now this is 35 trillion dollar market absolutely insane like if you look at the nasdaq top 10 companies or the fortune 500 budgets only 10 to 15 percent 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 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 uh budget could these companies go after so if you look at ai software products and infrastructure increasing uh the s p 500's net gross net margins by five percent that's essentially like 600 billion a year in profits and you can imagine software can go after 20

SPEAKER_15: to 30 percent of that so that's a hundred billion of like new spend and if you apply 10x multiple on that that's one 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

SPEAKER_64: everyone loves big tam big opportunities on the other hand you know today microsoft laid off another

SPEAKER_03: i forget six or nine thousand people and so it does seem to be taking a bite astatia so we'll see how that that plays out but i 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 uh let's start with uda um i'm most familiar with iterative health but you also have investments into nano mosaic and deep side other companies how is q2 for your firms tailwinds headwind

David Friedberg: surprises yeah i'd say um operationally many or most of our companies are doing great and um they are beneficiaries of when we started this fund which was basically 21 uh because they had some time before the market kind of crashed in biotech uh when they could spend risk capital developing their products and that is the nature 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 um adequate to 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 uh i think are you know flexing their advantages right now because they're getting to uh actually do business in a marketplace where a lot of others aren't i appreciate that matt same

SPEAKER_03: 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 uh elsewhere blockers tailwinds surprises what are you seeing

SPEAKER_19: 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 uh you know i think everybody

SPEAKER_18: um that's in generative ai um is experiencing incredible tailwinds right now um you know by buyers and 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 uh so lucky to be in that market is just uh it's just amazing and incredibly exciting um so you mentioned synthasia um you know uh i mean there's a company called ada which which is part of that customer service automation uh where the uh generative product that they launched um has been doing incredibly well as well going like three four x uh year on year um at some scale and uh you know we see it uh everywhere and then the companies that are sort of uh derivative of that ai wave that are supporting the ai wave are also seeing incredible growth uh you know a company that click house in which we're investors which has a bunch of ai use cases is also feeling the pool so wherever you are in the stack from the very bottom uh infrastructure to all the way in applications if you're in that wave that's fantastic if you're outside of the wave

SPEAKER_74: it's a little different faders be honest how much time is your team wasting on debugging if you're like most startups it's way too much time that's where century comes in it's a real-time error monitoring and tracing platform so you know exactly when something breaks where it happened and why no more 1am slack threads or digging through endless logs nope let's entry identify debug and resolve the problem while you're keeping your team informed with live slack updates jira syncs and meet seer century's new ai debugging agent it's like a new engineer that knows your entire code base seer is going to find the root causes of the issues you're having in your code base 94 of the time and it can generate merge ready pull requests with optional tests to prevent regressions here's the bottom line you're going to ship faster your team isn't drowning in bug alerts and instead of grinding through logs your developers are back building your beautiful functional delightful product new users get three months free of the business plan that covers 150 000 errors go to century.io twist and use the code twist

SPEAKER_77: that's s-e-n-t-r-y dot i-o twist and use the code twist but just to be clear it feels like you're

SPEAKER_30: 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 uh innovation budgets

SPEAKER_03: and churn and so forth are you seeing those potential risks crop up amongst the companies that

SPEAKER_30: you're mentioning or is their revenue proving to be thus far pretty durable perhaps in and around

SPEAKER_82: traditional sas metrics yeah i think i think for the the ones i mentioned it's what happens like

SPEAKER_18: they all have a pretty impressive um ndr or like net revenue retention uh numbers that show that the enterprise uh keep buying more and more so uh you know synthesis at this stage is in 70 percent 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 uh customers to penetrate and they've been able to just land but expand across use cases and look it's it's a fantastic company and they uh have been doing an incredible job i think also in fairness it speaks to the moment uh that those enterprise buyers are willing to uh and excited to bring in innovation uh quickly and then expand uh quickly and i think that's an incredibly favorable market if you will position now the other end of the spectrum for sure there's a bunch of companies that are still early in product market fit trying uh different things and uh you know they may or may not uh have a staying power but uh that not any different from prior uh prior wave

SPEAKER_03: so associate that all sounds pretty bullish i want to ask about your report because um i have to admit you have two of my favorites i think browser use is super cool and uh i was a big lm arena user before it became an actual company um but same question to you what's surprising you in q2 what do you see in this tailwind how are the founders doing and what lessons yeah we see similar things across our portfolio both

SPEAKER_12: at the ai app layer and infrastructure something that i do want to call out is felices has been really early investors in some of these infrastructure businesses so browser use we led the inception around n8n and super base who are benefiting from these gen ai apps um we you know we partnered with them one very 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 geni apps like super base is a huge beneficiary of lovable bolt v zero you know 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 uh for these other founders um 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 uh marketing and community development because the barriers to entry for starting a new business are lower now we don't need to rack and stack 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 attack out there and people need to learn about it if no one knows about it no one's going to care and so for

SPEAKER_15: pre-seed inception teams make sure you equally prioritize marketing and community development to break

SPEAKER_90: through the noise is that why matt tweets so much that's my ai oh it's your ai that explains what

SPEAKER_68: is your avatar that tweets are you actually here today or is this just an ad for your portfolio

SPEAKER_93: companies you know the uncanny valley has been crossed live today nice it's such a bit a large turing test

SPEAKER_03: if you will um 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 um 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 biooptimists working on foundation ai models for the biology space um for folks out there who are more familiar with the enterprise software side of things what's the state of play in terms of uh ai advancements in

SPEAKER_30: the healthcare and biotech world and how much does that unlock new opportunities for your uh thesis

David Friedberg: i think there's tremendous opportunity both on the healthcare side and on the biotech side and i'll distinguish between the two uh 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 reason people have believed that is because we've had some genuine breakthroughs um i'd say most notably alpha fold which was deep minds uh product that was able to computationally predict protein structures simply from knowing the sequences of those proteins and that was a true breakthrough it it built upon several decades of experimental discovery uh 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 hasabis 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

SPEAKER_03: on that point are we actually going to be able to have models that can simulate a cell like a a a cell that with mitochondria and all the other things we learned sure i think we're moving towards it and you've got

David Friedberg: in particular a lot of philanthropic efforts like the chan zuckerberg initiative and the ark 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

SPEAKER_30: the depression era economics for companies in your space and given the long time horizons to figure out

SPEAKER_03: 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 uh our friends also on the

SPEAKER_74: show are having a much easier time of it we've got ai helping us out with everything now driving our cars writing our code isn't it about time you put these innovations to work on your portfolio where it really counts you need public.com the investing platform for people who take their money seriously you can buy and trade everything on public.com building out a multi-asset portfolio including stocks bonds options cryptos etfs and more and these yields are no joke but what really sets public apart are there ai power tools and features check this out you can ask public a question about any stock and get a quick straightforward answer you want to understand why one of your stocks jumped or maybe it crashed well public has a clickable ai generated summary right there on the performance chart and in their income hub you can get a monthly breakdown of the earnings from every one of your companies plus a forecast for the year ahead and there's the queue tool which allows you to plan and execute multiple trades at once with real-time price updates so go to public.com twist today to fund your new account in five minutes or less that's public.com twist paid for by public investing

SPEAKER_32: full disclosures in podcast description at risk of being long-winded i didn't get to the optimistic

David Friedberg: part of my answer oh i'm so sorry please 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 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 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 health care system they're going home at night and they're asking chat gpt 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 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 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 a longer thesis to play out well i hope full

SPEAKER_64: doctor employment continues for the sake of my uh household income uh astasia over to you on kind of a

SPEAKER_03: similar question but admittedly over in your domain i've been watching foundation ai model companies slowly creep into the application layer things like claude code uh 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

SPEAKER_12: and go to market uh you know competition is always a risk uh we could see that open ai anthropic identified

SPEAKER_119: different verticals where their models were finding the most success and efficacy really landing with code

SPEAKER_12: generation as a key use case and it was kind of predicted that they would move this direction over time um we're we're not surprised by this you know i've been an infrastructure investor for 10 years there's always a large incumbent in the space that's trying to win the 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 um what sets founders apart the build in these competitive spaces is uh you know 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 in the market you know there was uh a very well-known uh 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 um 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 uh this is not new competition always exists and there's a precedent in the infrastructure world of really large companies going after uh being built going after the incumbents matt does all that track with

SPEAKER_18: what you and your firm think yeah i think that's well said um you know last year everybody was saying uh uh llms are going to become commodities but i think the nuance is uh that never meant that llm companies were going to be commodities and the way they make money and become super large is by monetizing uh the llms in part by providing those as api but in large part by building application on top of them and uh you know open ai is in some ways has always been i mean at least for the last few years an application company it was the accidental consumer company uh and uh clearly what they're building is uh you know the next google right they're a consumer company they are an enterprise company there were reports that they were uh 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 uh so uh you know it's pretty much what uh sam altman previewed last year if you're right in the middle uh of uh you know horizontal applications of ai whether consumer or enterprise and you're trying to do productivity um you know stuff you're you're going to be competing very directly with them uh and by the way i think that's partly the rationale behind uh grammarly uh buying superhuman you know if you're in that productivity world you you need to build product and distribution real fast because they're coming after you having said that uh the rest uh you know whether that's uh legal or finance or you know vertical industry-specific kind of applications i think there's plenty of room uh to build great companies there because as it turns out uh building what was known last year as a thin wrapper actually takes a lot of work a lot of uh you know industry-specific workflows a lot of industry-specific

SPEAKER_62: integrations and open ai and the tropic are not going to do those so there's there's plenty of room to

SPEAKER_03: build fantastic companies conventional wisdom in the ai era moves very quickly we have gone from llms or commodities therefore open ai is doomed to okay now it's worth 300 billion dollars yeah etc

David Friedberg: i got a quick question about that for for all of you which is you know and and here we all get to kind of speculate because i this is not in our our investment sweet spots i think any of us but opening i at 300 billion is is that too expensive is it is it a deal you know like what what would what would each of your pain threshold be uh for the valuation of open ai which you'd say this is

SPEAKER_03: absolutely overpriced well i'll answer last and give our our friends for a shot here astasia matt who

SPEAKER_11: wants to jump off the uh plank first okay i'll call on you astasia oh you know i think there's huge

SPEAKER_12: upside in open ai today we just kind of talked through their multi uh product approach you know you have the infrastructure layer that they're 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

SPEAKER_131: to imagine this becoming one of the largest companies in the world so you know i would i buy at 300

SPEAKER_03: probably would you buy at 500 with your own money like not speaking for felices just literally astasia

SPEAKER_18: myers your checkbook probably okay matt um it's a it's a it's a complex uh question i would um you know if you look at the current revenue growth rate and and the sheer number in terms of revenue versus the you know most recent valuation the multiple is actually not that crazy at all um but uh you know uh all this happening at at massive scale that's a question about uh you know what what what that means how much more room there is to grow given the many many billions we're talking about uh so i'm i'm personally bullish uh on the company it would probably be a buyer uh now personally and in the probably the near future uh having said that uh you know the the the the firms that put billions and billions of dollars into that company uh i i um just admire that uh from from afar i mean that would that would be so incredibly nerve-wracking for me because you just you know there's many ways this can go wrong uh and uh you know it's still a non-profit company uh the you know there was a ceo question that came up there is uh uh you know management questions uh there is uh you know a bunch of developers uh and and researchers that leave that getting poached by meta this you know like every day they say a reason to just uh give an ulcer uh to somebody that has put billions of dollars in the in the company so much power to my colleagues that that do that and play that game um you know uh as 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

SPEAKER_64: we've all made a bluff that uh in a poker game that we've hoped would get by and didn't and we've all

SPEAKER_03: been on the other side of that i think the growth to 10 billion in arda to answer your question putting open ai 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 give it its growth rate it feels cheap pain threshold okay so today at 10 billion 600 billion would be 20x arr 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 uh sure i take a flyer on it for i don't know a third of my net worth but yeah you can't ask the

David Friedberg: question without answering it so take us home i it's it's really hard i mean i constitutionally i'm a value investor and i you know i i think uh i i don't say that to be sanctimonious about it i just think it's really difficult to to price something like this um 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 have been building for decades so um you know sure there's a possibility we're in a completely new regime where uh 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 um and then there's obviously the question of you know what kind of multiple do you need to take the relevant risk so um 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

SPEAKER_29: well on that point i am uh i'm pulling up a chart for us this is from a uh a co2 deck that i think

SPEAKER_03: just underscores why we're seeing companies become uh effectively trillion dollar firms faster this shows how long it took anthropic to go from zero to one uh billion in revenue took about i guess this is 21 months and then it took three months to go from one to two and then two months to go from two to three and the latest reporting is that it's now per the information at four billion dollars in annual room rate so when you're seeing growth like that it's hard to sit on the sidelines to matt's

David Friedberg: earlier point even the other thing is i have trouble you know i'd be interested in 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 um 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 um 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 um but it felt like the inability was more a matter of willingness to go spend compute uh on on

SPEAKER_36: something that wasn't particularly complex than it was on the fundamental capabilities of the model

SPEAKER_03: all 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 uh how much it costs and matt had a great tweet uh 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 50-ish gross margins not 80 like sas companies and matt what i was trying to get to a a little bit earlier was we talked 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

SPEAKER_18: the bet that we're all making collectively as an industry that the cost of inference is going to continue dropping i mean certainly the last year uh seems to validate that the hypothesis uh and um you know i think we all on board with that but like that's that's the bet that's the bet um and there's a lot that needs to happen for for this uh both on the um you know the lm 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 uh to to your users um but my point was just uh you know when you talk to people and that tends to be a little bit the case in our industry people think that a lot of things are a foregone conclusion and just operate as if uh the reality of today was that intelligence is basically free uh so that was just a tweet to remind people that at least as of now that's not the case and i'm i'm seeing this across uh companies that leverage ai in their product for every single interaction that the product has with with users like heavy users of of ai but that's that's the reality of today and as i mentioned earlier there's a bunch of companies that have negative gross margins right where um you know

SPEAKER_149: to a large extent we are in the um pre-ipo uber phase of the ai cycle reminds me of that too yes

SPEAKER_22: for the young folks listening who weren't here for that why don't you explain what that means

SPEAKER_18: yes so um it turns out that um uber is uh showing up at your doorstep in one minute and being half of the price of a yellow cab in new york uh was not a miracle of modern economics uh but the result of the whole industry being subsidized by vcs uh and um uh you know i think we are at that stage as well where uh vcs like us are betting as we should on uh where the puck is going uh which is uh all of the cost part of the ai equation being much lower uh but the reality as of today is that it's not uh and and the money comes from vcs so when you uh look at the anthropic numbers like i've no idea what the percentage is uh but for a fact i know that a chunk of this is coming from astacia and me uh you know and so essentially and a chunk of the nvidia revenue that everybody has been raving about uh for the last uh you know of years come from us vcs and uh also the general public through microsoft so it's all very circular and look 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 uh on the basis of a future bet that uh it's all going to work out uh but demand needs to materialize on the other side and we need as an industry to stick the lending within you know a few months or maybe a year if there is a true disconnect between the supply phase uh and the demand phase then uh you know we might be in trouble for for a minute yeah according to uh producer claude the average gross

SPEAKER_03: margin for public sas companies today is 73 to 77 percent astasia 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 um whatever the next trillion dollar data center that gets

SPEAKER_159: announced uh will bring to market similarly and uh you know i spent most of my time at the infrastructure layer and i think an interesting uh deployment mechanism we're seeing in our portfolio

SPEAKER_12: is the bring your own cloud model where they're actually running their infrastructure in the customer

SPEAKER_15: environment in their vpc and so we actually see really strong gross vpc being virtual private cloud yes exactly and so we actually see very strong uh 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 they're 85 percent plus and often those teams are using ai as part of their product experience and they're working with open

SPEAKER_12: source models and distilling them so they can be run effectively you know you said earlier that we

SPEAKER_03: 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

SPEAKER_12: there are still very large-scale companies and financial services healthcare 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 um i'm actually very excited about that deployment model because i think it gives power back to the founders um against the incumbents and it's wonderful to see

SPEAKER_03: those gross margins again all right well we mentioned healthcare so we got to bring da back in um da are you seeing uh your companies able to sell into customers that have their own bpc or are they mostly living and existing in a in an azure aws vanilla world yeah frankly we don't have a lot of

David Friedberg: portfolio companies that are in the software business um and so i can't speak to it intelligently but i i will say you know the the world of enterprise uh hospital uh it is a unique and interesting one and in particular the opportunity there that keeps coming up is around cyber um you know these uh particularly with ai attacks now um 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

SPEAKER_119: it's big we've been spending some time looking at gen ai applications and and healthcare and we're

SPEAKER_12: particularly excited about the role of voice for improving uh client and patient care um decreasing administrative overheads helping with insurance claims um so i think that the a really really cool

SPEAKER_15: area for healthcare is the application of conversational ai yeah that that's that's definitely

David Friedberg: true and you know 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 um to your point voice is potentially an escape hatch from that um and you know what would be much better is obviously if the doctor could look at you look you in the eyes uh do a physical exam focus on the patient not on their uh you know 2000 era software and doing data entry during a visit

SPEAKER_36: that's already um painfully short well here's to bringing more technology to healthcare so that way

SPEAKER_03: everyone's doctor has more than 30 seconds to talk to them i think we will all appreciate that uh 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 two and three founder numbers but once you get to four and five 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 a billion dollar company founded by one person with no other staff um a bit of a meme at this point but certainly shows where people are thinking um how has your firm changed its expectations around founding

SPEAKER_174: team size yeah one founder plus one ai right that's the the new founding team many many ais um yeah uh

SPEAKER_18: look that's interesting data i hadn't seen that um seems to corroborate what we see uh i i wouldn't say we ever had a particularly strong sense uh in favor or against solo founders we have some uh we have many founding teams uh we have uh you know funding teams that are brothers we have funding teams that are married couples uh so we we never had a uh sort of predisposition in favor or against certain certain formats um i would just say that um you know from personal experience and watching many many companies over the years and working with many companies over the years um you know solo founders is cool at the beginning it can get uh very lonely very quickly uh so um you know uh you could argue that on the other end uh founder divorce is one of the worst things in the world uh but by and large uh you know i've found that teams of like two to three uh founders like people tend to be happier in in good times and

SPEAKER_03: more challenging times astasia same question over to you any change in how felices approaches this or is two to three still the uh the place you want to write the most checks yeah we similarly have been

SPEAKER_119: agnostic to the number of founders typically we do see two founders as the most common um but it is good

SPEAKER_12: to have a friend in the trenches with you um many of our solo founders eventually do kind of anoint that number two on the team um that becomes a safe space uh 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 uh with five founders i think i've not really seen that before i don't think we really see it well now it's down to just

SPEAKER_181: four percent of the market all the way from 11 back in 2015 uh but sometimes that's also a bad idea

SPEAKER_18: from from experience uh like for anybody listening to this because um it it solves a problem in the short term uh fast forward uh you know seven eight years you're the very successful uh ceo founder number two or three or four of a company and you know you own three percent of the company well yeah

SPEAKER_185: but then we have companies like databricks that had seven founders and it seemed to be doing pretty

SPEAKER_181: well but i'll take that that's the absolute outlier uh i want to go towards team size in general next because as we've seen founding teams maybe get a little bit smaller on average we're 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 showed up in your portfolio astasia and then two if that changes burn dynamics or how long these companies have in between funding rounds

SPEAKER_35: if they are spending less on people totally i want to take you back to 2020 okay oh 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

SPEAKER_12: 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 big 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

SPEAKER_181: does a a decreased need to staff up early give these companies more flexibility more firepower to spend on marketing what is unlocked by having less uh spend in that human resources line item we are

SPEAKER_12: seeing that the burn numbers are lower of course that gives them more runway and we're seeing them often repurpose it towards uh the go to market side of the house so uh thinking about uh marketing content and collateral events etc as a way to continue to get strong roi from their investments and

SPEAKER_181: building of community and getting customers matt some question over to you about uh hiring i don't know trends if you will and then how that impacts overall startup economics in the earlier stages

SPEAKER_17: yeah it's actually kind of funny i was uh tweeting that the the other day um you know everybody's

SPEAKER_18: talking about the one person uh one billion dollar uh company but equally uh every company that uh we come across and you know by definition it's a self-selected group like everybody's 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 like they're not going to burn on on gpus so like what do you use the what do you need the money for and a lot of it is just hiring people so so you know it's like a little bit of an irony or i guess uh uh you know there and um uh we we see some of that we're seeing especially in the early stages we're seeing uh 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 right you can build product faster but

SPEAKER_03: for all the functions you need to have people uh 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

SPEAKER_70: describing uh on the astasia and matt side of things not as pertinent in healthcare and biotech

David Friedberg: in biotech in particular i'd say the major shift in really the past two decades has been uh towards an ability to outsource more and more and to uh 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 uh obviously people embrace more and more with capital constraints but i'd say the other force that's um 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 the ccp right now but um i 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 cro's in our industry these outsource service providers that can do specialized work in drug development and drug discovery uh they're able to do things in uh you know six months or a year that people in the u.s 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 uh you know if they're seeing these chinese founders do it um 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 um you know 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 uh 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 uh there's a real global competition at foot and um u.s startups are

SPEAKER_36: needing to figure out how they uh play into it do you think we should have more um state-level

SPEAKER_181: investment here in the united states given what you said about uh the investments that the ccp is making

SPEAKER_208: not to get political but i'm just curious no it's it's a great question because it's inevitably

David Friedberg: political i mean people are are disputing right now the cuts that are uh in a lot of cases in flaming academics and folks in my industry because what they are feeling is that the u.s 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 you know like what is in the long-term strategic interests of the country and you know think about france matt you're french yes where is

SPEAKER_214: this going yeah well we know these things because you pronounce it here's why i bring up your homeland

David Friedberg: um i 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 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 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 uh weighing it uh rigorously all right uh matt

SPEAKER_218: answering for not just france but all of the eu your response um look i i um think that um

SPEAKER_18: um you know old uh perceptions die hard uh and um you know it may be different in biotech but uh in in tech um thinking that the eu in general in france in particular uh has not fully embraced innovation is missing the big story of the last few years uh france currently is obsessed with startups and that's um you know 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 uh but uh every kid uh that comes out of a top elite school in france today wants to do a startup uh and there is a deep tradition around software deep tradition around ai and it's gone from zero to hero in the space of like four five years and um sometimes uh you know i want to talk about this like today many times i don't want to talk about it because uh that gives me a comparative advantage to go to france and win those deals uh but there is a a lot that's happening right now and uh in particular in ai you know like mistral being the most famous company but there are many uh many others having said that da like 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

SPEAKER_181: last few years i would just add that when we think about you know 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 that uh according to the ft a two billion dollar valuation so there's one company that could easily become the next 10 billion dollar public company from the eu so something to keep in mind now astasia 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 um 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 uh 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 uh legs at the knees here yeah um so we're

SPEAKER_11: strong believers in academia and have deep partnerships with many universities this question

SPEAKER_12: of how do we 10x the number of founders that are coming out of that academia we took to heart and that's the reason we want launched our fellows program so each summer we bring really bright ai students from places like stanford mit berkeley among others where they had 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 felices 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 letta that was alongside jan stoica from databricks and the sky computing lab that he runs out of berkeley and you know 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 and so 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

SPEAKER_79: ask what's the best way to get a hold of you astasia if people wanted to reach out to you on that exact

SPEAKER_227: point yeah it's astasia at felias felices.com so a-s-t-a-s-i-a at felices.com simple enough

SPEAKER_181: all right uh i want to squeeze in a little bit of notes on exits here because we've talked uh in passing about the superhuman uh grammarly deal we just saw yesterday that figma filed to go public i absolutely adore and s1 filing and early data kind of makes it seem that q2 was a bit more fecund than we might

SPEAKER_03: have expected in terms of total value of mna and even a couple of ipos so just briefly astasia uh how good was q2 for you guys in the returns context are your lps calming down a little bit

SPEAKER_70: and looking ahead uh how excited are you about the back half of this year for uh exits so we don't

SPEAKER_12: publicly speak about our returns uh and our dpi of course we are very encouraged that publicly one of our portfolio companies weights and biases was acquired by core weave so that was very exciting for not only lucas other founders and the entire team but um we are just pumped about that opportunity to for this next phase of the business in the second half of the year um we think that there could be a really interesting opening for both traditional exit channels of ipo as well as the opening of mna markets um 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 um at scale mind you so it's it's encouraging um to see these businesses have a path forward yeah checkbooks seem to be opening matt same question to you

SPEAKER_03: uh how was q2 for you in an exit terms and are your lps happy and what's your expectation for the rest of

SPEAKER_18: the year yeah i'm plenty of uh encouraging signs across the board uh you know q2 but uh in general the last uh two or three quarters uh we actually had a a bunch of uh exits some of which uh just uh happened um uh you know for like smaller amounts but that's exactly what you want to see uh because it's healthy for a portfolio that the companies that are um you know not scaling should be cleaned up by the large companies is the way it's always worked but like even that kind of like uh stopped for a while it seems to have resumed um okay and then uh you know we had larger ones uh you know we're talking about like uh superhuman but like we had a comparable size acquisition uh uh an insurance company or in the ai french company called evolution iq that was uh purchased uh for actually higher than was reported in the press for like 850 million so you know great we'll we'll we'll take it uh and um so there's been there's been a bunch uh and whether that was q2 or q1 or whatever the secondary market has been um active as well and then uh in terms of uh ipos you know like a lot of vcs like we we already like we've been waiting for this for for a while so you know from uh you know discord to uh did i coup in the enterprise ai world to row in the health world like we have a whole uh slate of companies that are in generally in that pre-ipo zone and uh i'm very encouraged by what's been happening all right now da because you

SPEAKER_181: started off by saying that the exit market for your industries 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 uh conceivable life sciences and billion to one are both working in the broader fertility space it's a world that i've 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 uh what's the chance that the companies you're either investing in or seeing can uh expand fertility treatments to the point at 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

David Friedberg: of talking about it yeah i i think the global birth rate issue uh is it's both about people's ability to reproduce easily in in today's modern lifestyle and and also their um interest and and willingness to do so and uh that that latter consideration is a more challenging one to think about for me because who knows you know maybe that that 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 you know success stories in our portfolio so far billion to one is relevant during pregnancy and it's a it's a company whose test uh very easily enables expecting families to uh 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 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 health care investing today is this opportunity to democratize access to ivf and it's very simple to see how big that uh 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 um 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 uh i don't see why fertility medicine should be any

SPEAKER_181: different i i think the word you missed there was most most advanced economies uh there are a handful of exceptions uh 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 uh matt thinks we're going to see more mna 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

SPEAKER_70: coming and taking part i would love to do this again in six 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