The State of AI: Rise of Reasoning, Surge in Chinese Open Source, Sovereign AI , How to Invest in AI Today | Nathan Benaich, Air Street Capital

24:44Turner Novak:And is it probably, I mean, and there's tons of forks, right? Where you basically take it, you make a tweak and then you republish it. So there's probably thousands, tens of hundreds of thousands of just like very variations of different models have been slightly tweaked over time, I'm assuming. Well, there's that in the open source community. Yeah, but but like to create a Gemini or like a chat GPT or something, I mean, there's there's so many there's like basically all these different teams that are each responsible for like a different skill or capability and each skill or capability has a set of evaluations and sometimes like hundreds of them, which is like ways that you test whether the model has succeeded or failed or what end of that spectrum for that specific capability.

25:27And then you have to collect data specifically for that and then just throw it into the soup and make sure that everybody else who's throwing in their capabilities and data and metrics and evals into the soup doesn't degrade each other's capabilities. And so how do you coordinate that at scale on the internet without being a centralized company? I just think it's unrealistic. And I think the proof point of that is no one has really managed to do it in the sense of no one's managed to publish. A model as good as Light Opus 4.5 or GPT 5.2 in an open source setting, I mean, people have come close, but those are basically private companies that operate.

26:13Those are closed-source model companies that just happen to open source as opposed to like true open source development, which is like distributed contributions.

26:22Turner Novak:So it's almost like you need a dictator or like an opinionated, like the PM, the person managing the whole process of training and releasing it. Yeah, exactly. So then I know we've been, you know, this has taken us a long time to get through the top four most interesting things, but what would you say is like the third or fourth most interesting thing? I mean, the third or fourth, we can probably breeze through a little bit. The third is around like real revenue scale of products. Like it was only two years ago that, you know, the major labs were at de minimis revenue. And, you know, there was that example that Jasper was making way more money than OpenAI was for very similar use cases.

27:09and this debate of is the model the product or should there be lots of other like scaffolding and sticky tape and UI around the model that would mean that it could extract more value for that targeted end user. And then that seems to have flipped as the core model is better and it's been kind of the interface for everything. And then model vendors have created their own like sort of SaaS wrappers basically. So that's like tens of billions of dollars across major players. and then the last one i'd say and there's many more but last of my favorites at least is just like the ai sovereignty marketing agenda has just gone like on full steroids so this is you know the the competitive positioning idea of nation states need to have access to energy compute data and talent and models in order to dictate their own fate in the future of AI.

28:09And NVIDIA has gone on a very aggressive marketing spree, like selling this agenda to every single country that is willing to listen. And that's resulted in commitments of over$100 billion globally in different countries to build up data center capacity.

28:30Turner Novak:Just pick like a random country in Europe, like the fourth largest country, like Italy or something in Europe. And like, I need to have my own sovereign AI strategy. What am I probably doing? Well, this is kind of my point. It's like, what does that even mean? Yeah, what does it mean? I just keep invoking this, you know, Kanye West lyric of no one knows what it means, but it's provocative against the people going. It's a bit of that. What they think they're getting is the ability to train and run AI models in their geographic vicinity in a way that cannot be unplugged or tampered with by any other nation state than themselves.

29:11Turner Novak:Like if you go to war with a country that a major lab is domiciled in, you don't get cut off from the ability to use AI. That would be the idea, yeah. Yeah. And I could see how, you know, if AI continues to kind of get better, it becomes all software. Like you basically can't use software to defend yourself and maintain your sovereignty. And like you'd be an extreme disadvantage if you didn't have that. So I would be extreme. If it's a position like that to me, I'm, you know, spending a couple percentage of GDP. Yeah, it's like I'm opening the checkbook, probably. Yeah, yeah. But the challenge with it is that the hardware improvement cycles are very short.

29:57You still need to have like a bunch of software that runs on the chips to make them work. and maybe there's like a major update that gets shipped to them that's required to run the to run new models on your hardware and you know the vendor decides not to ship it to you because you know we're not friendly with your country anymore so congrats you got the hardware but you can't run it a bit like the whole spiel with the f-35 yeah we you know foreign nations can buy the f-35 but you can't fly it if you don't input the flight plan which gets communicated to the us so

30:34Turner Novak:I didn't know that it worked like that. I mean, that's tricky. Yeah. How do you get around that? Or like it just could be nerfed or... And it's interesting from NVIDIA's perspective because NVIDIA has a customer concentration problem where basically Google, Facebook, I don't know, Amazon and OpenAI, maybe Anthropic is like most of their revenue. Like, I don't know, 90 % of their revenue or something like that. So it's like, can you figure out how do I find a couple more, you know, nine figure customers, right? Like get a couple more people that are paying me billions of dollars a year to get a little bit more diversification.

31:13Yeah, yeah. I think it's a great strategy, by the way. I think they're right to do it. And you can see that like the next growth spurt is, you know, data centers in space. So it's like, what's always like the next, the next time you can, you can create, right? To keep, to keep selling.

31:32Turner Novak:What is sort of the end state of that? Because if you kind of go back maybe a couple of years, if someone were to say, we're going to build data centers in space, it just seems like it's just a word salad of just random buzzwords that makes no sense. Like, does the entire universe just need to be like, filled with like data centers, like power? Like, where does it end? Do you know? Like, what actually happens? uh i'm not sure what happens at the limit but i could i could see you know a pitch around as we get more assets in space of various kinds and maybe we'll have space civilizations at some point and so having compute theirs is just better than having it on earth that we have to communicate to and from which currently is cheaper than having data centers in space uh potentially even for like defense reasons, you might want to have compute in space for all of your space systems.

32:38But yeah, I'm not the space data center expert at this point.

32:43Turner Novak:Yeah, because part of me is like, this just sounds like it's hundreds of years in the future. But also maybe part of me is, I feel like there's that quote of like, less happens in one year than you think and more happens in 10 years than you think. So like with self-driving, my working theory with self-driving is like, it's always going to be a couple of years away, but I guess now it's actually finally here. Yeah, but also historically NVIDIA has been exceptional at resourcing teams, whether they're in companies or in academia who are exploring new things with their stuff. And before it was like shaders and graphics, and then it became like AI with Alex Ned in 2013.

33:30And then it was, you know, these AI labs, non-profits that were started 10 years ago. And then in biopharma. And then now if some like some kids are launching, you know, your GPU in space, like I think NVIDIA is pretty curious to know, like, does it work? And if it doesn't work, like how can I fix it? Because who knows where the next growth frontier is. And, you know, if that's driving excitement for this doc, like even better. But I think like their net outlay and, you know, like figuring out if this works or not is pretty low for the potential upside they could get.

34:05Turner Novak:And it's not like they're losing money because they're like selling products that they generate cash on. Right. So it's like you can scale it up or down like, oh, the space data centers didn't work, but we made$3 billion because everyone wanted to buy some for a year. It's like with crypto, like you go back to 2021, like the crypto bubble almost is like dwarfed by the AI surge. Well, this is interesting because as you said, it's been dwarfed. And then the crypto prices, you know, year on year now are like down whatever, like 20, 30 percent or so. I remember this time last year was like 120 or something.

34:49And now we're like, what, 95, at least in BTC.

34:52Turner Novak:97 just in just in bitcoin's in 97 i saw a notification so it's going up it's going back up yeah like bitcoin miners have found something far more lucrative than mining bitcoin now because it's because of changes in the hash rate etc and energy prices and that's like basically swapping their compute uh workloads from mining to to ai and so like last year we saw this rush of companies from you know irish energy which is like australian business doing energy originally and then And then starting to do BTC mining or crypto mining and now has transitioned to being a GPU company. And same thing for CIFR Energy.

35:32And then like HUT8 and all these rando companies that are...

35:36Turner Novak:Have you even heard of any of these? Yeah, I mean, you can look at them. There's like six of them and they've all ripped like 100 % or 50 % last year. I mean, they're crazy volatile, so you gotta have a stomach for it. And who knows if any of them are really going to survive because they don't have the balance sheet for it. But as traders are looking for volatile stuff to just play on Robinhood, these things have been pretty popular because everybody's trying to front run, oh, AdTropics launched a new 1 million TPU data center thing. Who are the other vendors that can profit? And so I think for that reason, BTC prices have also gone down.

36:15Turner Novak:This kind of leads into interesting other kind of like phase of the conversation that I kind of want to ask you is like, how do you feel about where we're at and kind of the cycle of excitement in AI? Like maybe the more simple way of phrasing is like, are we in an AI bubble right now? I don't know. How do you think through that as someone who's been in the space for a really long time? The first thing I think about is if I were to have seen the capabilities that we have today, like this magic box, you can ask anything and basically gets like anything right. and you showed me that 10 years ago i would have told you that is like absolute magic and pretty much everybody in ai would have told you that's magic that's not possible in a decade and this is across just general you know question answering but also video understanding video generation audio like it's insane what we have today i would have thought this would take in way longer and the future got pulled forward so damn fast and then we're still only in what two or three years into this cycle of getting this like magic alien artifact with no you know instruction manual no genius bar that tells you how you're supposed to use it and the very companies that have developed these tools are figuring out how best to use it so they can educate everybody else and then you already see like vignettes into the future where companies that have adopted this and people that have adopted this have significantly higher productivity than they had before as reframed as if you were to take this a classic question of if i were to take this thing away how pissed off would you be and most people would be like pretty beeping pissed off

37:59and and then also other other things like you know cloud spend as a proportion of overall IT spend is still not ginormous. And that's been the case, you know, since like 10, 20 years, and that hasn't even factored in like AI distribution. So I think we just have so much more to run if you just consider what we have today and educating everybody on how they can use this stuff. And also factoring in that the artifacts we have, at least of if you look back three years ago, were a result of like a very very small number of like highly technical nerds who like stumbled on something that really worked and were not product designers were not like behavioral psychologists were not you know like large scale systems engineers or cloud or like you know cost optimization experts etc all this stuff all these tasks that are super important in building like basically the internet had not worked on AI and now everybody is going into AI so I think there's far too much like money resources talent and and like genuine desire to use these things that progress can't get better now does that mean like currently we're in a bubble like i don't actually think so uh there might be like pockets of bubble-ish dynamics but you know the companies that are accelerating super fast in ai like mag 7 are actually printing like tons of money they have super healthy balance sheets there you know forward like valuations are i think like 50 percent of the top names back in the late 90 90s um they're using their own money to fund a lot of these data center build outs i mean there's some that are doing an off balance sheet credit but i mean it's like sophisticated buyers that are buying this it's not joe blogs that's you know betting their pension or something on you know meta's data center build out i think i saw a

39:55Turner Novak:really interesting stat too. It was that this was probably about maybe six months ago, nine months ago at this time. So this is probably even different stat now, but it's basically OpenAI and Anthropic since the launch of ChatGPT had added more new revenue than like every other publicly traded software company. And this was, you know, a while back. So it's probably even bigger, like it's probably even bigger now. Yeah. It's astonishing. The valuation of these companies are just like, do people pay for your product and do you make money on it? Maybe that's a whole different question here. Like you can argue about the margins of some of these things, but like people are adopting the products and paying for them much more than if you don't have any AI capabilities in the product.

40:41Yeah. Well, the margin profile looks like it's actually improved quite a lot.

40:45Turner Novak:So why so? Because I feel like even within the past couple of months, I still see some of these headlines where it's like, oh, the vibe coders have negative gross margins and they're three months from bankruptcy or something like that. Yeah, well, I guess those are a bit different because they're like pass-through to model providers. So what I'm saying, you know, the margins are pretty good. I'm talking specifically about model makers, whether they're in like image or video or chat. And I'd say, I mean, without disclosing like specific names of companies, is like some of the best ones are like at 60 70 80 percent gross margin on serving their models and like yeah they spend a lot on training uh but to some degree like less than they did before because whereas like pre maybe one year ago the sort of quote unquote recipe for how to build a large scale model that worked really well was not clear or not written and now i'd say like most most like people at the frontier would say like there is a recipe for scaling now like we sort of know how to do pre-training the human mid-training and then the post-training and then rl and evals and all this stuff kind of know how it works so we're getting more efficient a lot a lot more efficient and then and then the other thing is the the quality of the model you get today compared to what it was a year or two years ago is actually far more useful so then you can monetize it better because it's it's monetizable like people were willing to pay for the outputs and so you actually get faster payback so you're more efficient people pay for your thing because it's better more people are educated on how to use it so they want it more and then your general go to market is is improving and there's some modalities that are cheaper than others like text is obviously probably the cheapest.

42:42Audio is actually not too expensive. Video and world models are very expensive.

42:47Turner Novak:You're saying to make or to also sell? It's expensive to do the training and also expensive particularly to do the serving, the inference. So it's just the pricing to consumers. Then it's probably higher. If you want to tap into a world model API, it's probably much higher than just text-based. Yeah, exactly. To make a nice video on Google's VO3 or like the super pimp-down model, it's a couple dollars. So if you're selling to social media people and it costs a couple dollars per video, like it's a bit tough. Yeah. But on the other hand is like, if someone else, like an agency or a designer in Illustrator or like literally take a camera, record an expert doing the thing or like an actor or a dancer, whatever you want, what is like the cost of literally type in make Nathan dance with an iPhone for an Apple commercial versus like actually go make it?

43:44Turner Novak:Like, is it a thousand times cheaper? And it also took two minutes instead of two months. Like, I feel like that's part of the equation too. Like as the products get better, it's just like, things just get so much more efficient where like, you can literally have, it's like the person who's the marketing person at Apple, instead of coordinating with the agency, they're just working with Nano Banana or ChatGPT or insert whatever tool they're using. Instead of sending an email to the agency going back and forth, they're just banging with ChatGPT for a day. It's cool. This is what we would have got for a thousand times cheaper and literally in 10 % of the time.

44:22Turner Novak:We got it 10 times faster too. Yeah, exactly. It's not particularly surprising that 11 Labs is ripping. I heard it's like, or you tweeted, it was at$330 million in revenue. got 100 million in net new ARR in five months. Bananas. Yeah, I saw 15 million in a day or something they were tweeting about, which is, yeah, I mean, that's, you run rate that, that's pretty high. That's what people are doing, right? Is they say like, oh, we're at a$8 billion runway because we signed whatever, like our Stripe balance, it hit the account today if we times that by 365. Yeah, I mean, that would be a mad sketch, yeah.

45:00Turner Novak:Yeah. I mean, it's kind of happening, isn't it? A little bit? I think there's everything that's happening. I would say they're pretty above board, but there's certainly some odd behavior everywhere. Oh, yeah. But I think the magic here is just how much latent revenue there was in these use cases that was only made unlockable as a result of great capabilities. Because I still remember two years ago when 11 started, when Synthesia started seven years ago, like it was not mega obvious that there would be like half a billion dollars worth of revenue for a product like this, you know? Yeah, you'd be like, who gives a shit about why would somebody want an audio model?

45:48Turner Novak:Like there's no demand for that. Right, right. Well, it was especially that there were audio models before. I mean, you know, Amazon had one, Google had one for TTS and speech attacks. was it that they just weren't very good they were kind of shit yeah that makes sense yeah i mean in london like where i was like i spent a lot of time you know it was always funny to me that i'd you know exit king's cross station where you know google deep mind was and you would hear the audio voice and it was like this is clearly a robot and it sounds really crap but there's a company like 300 feet from here that has a way better model for like the last 10 years and like the tube can't even use it.

46:26Turner Novak:We're still so early. Yeah. Yeah. So I'm curious then, are there areas within AI that you think are like a little bit overextended? Like when I just think about, because if you were to open social media and you'd read the commentary from investors, a lot of people say AI is in this massive bubble and things are going to crash. Are there like, so it sounds like you maybe don't necessarily agree with that, but are there certain areas where you feel like there's a little bit too much, you know, things have gotten a little too far ahead of themselves maybe. And I think maybe another way to think about it is, you know, with kind of just, we kind of talk about across the board, like the products just keep getting better.

47:06Turner Novak:Are there areas where maybe the products aren't actually getting better at the pace of maybe like how excited people are getting about them? In some areas, like in science, I'm very excited about like long-term progress in AI for science, which is anything from, you know, helping human scientists process, you know, research papers, formulate ideas, figure out what experiments they should run, you know, test, run those experiments and analyze the data. Like, you know, AI models are making like substantial contributions there. That's like a net positive for the world. Clearly in biotech, this works.

47:42There's a good business model for it. If you develop drugs, there's ecosystem of pharma companies that'll buy them. There's contract research organizations that will run the experiments for you at scale. And we've, the industry has transposed all this excitement over to like materials. I think there's been a phase of several companies raising substantial money for materials. So much so that the venture dollars have gone to materials companies is far greater than like US national funding for material science labs. So it's like, you know, whereas the U.S. government should be funding frontier, like, you know, R &D, like venture dollars have stepped into there using many analogies from from life sciences.

48:29But to me, like I'm very excited about that space as well, but I haven't done any investments there because I think it it does it does not share a couple of the key features that is present in biopharma, which is like lots of pharma companies that have been structurally built to buy biotechs, either outright or the drugs they've created. like this is how biotech originated this is like the tacit agreement that small biotech companies have with pharma companies like we take all the early risk and then if it works you buy us that's how it works material science not really and then there's not there's not like this kind of industrial base of contract research organizations which are like industries that will run experiments for you in the case of materials okay my ai model has popped out a bunch of different versions of like titanium or like some composite and I want to go make it.

49:21I can't send it to anybody to go make it. And so most of these companies are either doing like their in-house labs, expensive for all, you know, a whole bunch of other reasons, a bit inefficient. Or they're sending it to academic groups who take like six months to make her, make the thing. And then they're making like a tiny amount of powder. And it's like, you know, and, and, and then the other thing is, Then you have like big companies like Meta that have been pouring in billions of dollars into material science and then releasing, you know, data sets and models and are really just constrained by synthesis, like actually making the suggested outputs of the model.

50:01And so I think materials is like rate limited by all these things, yet is seeing like irrational exuberance from venture investors as like the next frontier that's similar to AI and biotech.

50:12Turner Novak:Interesting. So what is an example of one of these materials or companies? Periodic Labs. What do they do? I mean, they're probably one of the most exciting ones out there because they have a very cool team. Two co-founders, one worked at OpenAI on ChadGPT and post-trading, and then the other one worked at DeepMind doing material science. And then they're doing, as far as public reporting, as chronic cold, they're doing in-house testing of materials and then using AI to scour the universe of potential materials to optimization and then using their internal labs to test, et cetera. Just to be like inventing like a new periodic table, like inventing a new element that goes on the table or something like that.

51:00It wouldn't go so far as that, but it'd be like, what's like a new formulation of like a superconductor or what's a new formulation of like an alloy. Like stronger steel or something? Yeah, that could be stronger and lighter and cheaper to manufacture and therefore could be great for lots of different things. Or like a material that you could make, you know, an iPhone screen that you could then go outside and not get blinded and it would just work and, you know, with direct sunlight, like things like that.

51:32Turner Novak:That could be amazing. Like I can totally see how you make these awesome products. Like, let's use AI and make, like, I don't know, like how do humans have wings and they can like fly or something? I don't know. Like I can see how like that's the dams are on these things are insane. Yeah, no, I mean, I think it's very inspiring. The question is like, you know, would you pay, you know, almost close to public market valuations from material companies for a private business that's just started to do this on the basis that it could be like the next frontier lab in this space and follow the same. fundraising dynamics?

52:09Turner Novak:I mean, that's kind of the role that a lot of venture investors play is like the momentum identifying and trading almost in a way. So I guess it's just like, is that your strategy? And if that's your strategy, then that's what you should probably do. If like, that's what you're telling your LPs is what you're doing. So yeah, yeah, for sure. I think it's, It's a strategy that one should do if one's scaled to billions of dollars of assets. I think there is probably no other way to move that volume of money into ideas that could be big enough. What you could say on the founder's side is amazing that entrepreneurs have the opportunity to go pursue their dreams with insane balance sheets on ideas that should have been funded by the U.S.

52:56government a couple of years ago. but you know at the same time if if it's your dollar that's getting spent and you look and you're a little bit more pragmatic which is what you know all peas tend to be it's a stretch

53:10Turner Novak:yeah and it's just like so fascinating when i talk to people that were in the industry in like the 90s or whatever and they're like yeah i had to sell 60 60 of the company for like a million dollars and there's like a 2x lick pref and it was like a real product real business it was profitable. And then today it's just like, you know, I interned at AI lab and I raised$8 million to go build, you know, material science thing that, yeah, it's like I'm going to make stronger iPhone glass. It's like the contrast is pretty insane. Yeah, it's tricky because it's all about like risk reward, you know, you take a lot of risk if the reward upside is big, but if the starting valuation is billions, then to reward is not necessarily capped, but I mean, there's some reality.

53:57I mean, as soon as you actually become a materials business, then you get valued as such. Yeah.

54:03Turner Novak:But the argument that people will use is that the outcomes are so much bigger today. And that with generally with like, I mean, with a lot of AI software, it's like replacing the work, right? So like your customer might historically only be spending 5 % or 2 % of their revenue on software, but they spend 50 % on labor. So in theory, you could maybe capture like half of their labor budget. So like your revenue potential goes from two to 20 % of their revenue, which is, you know, 10 times bigger outcome. So you can afford to pay that 10 times higher entry valuation. Yeah. So I don't know. You can argue it where it works.

54:38I think that's true on the outcome size. I would just rather not have to pay the 10x on the front. So the return is 10x bigger, but, you know, to each their own, I guess.

54:45Turner Novak:you come in and you let the multi-stage platform funds do that after you. And then they help support what the capital needs to like, you know, king make it towards the public markets after you've invested as an investor. That would be ideal. But I mean, you know, some of the mega funds are doing this already with these mega fundraisers where, you know, the company announces, you know, multi-billion dollar price tag, but there's been three tranches before that. where, you know, Brand-Aid Megafund got in first. You know, what for a seed manager is a high price, but for one of them is like, whatever.

55:25And then there's been two markups since then in this short period of time. So it's a bit of that.

55:30Turner Novak:And you can almost like manufacture those in a way where like you put in, you know, 2 % of your fund at 200 million posts. And then you put in another 0.1 % of your fund in a follow-on round with one of your LPs who leads the round, who for them, it's also a very small percentage of their portfolio. And then you have an extremely small round where the company sells like 2 % of the business at a billion post money. And everyone kind of looks good on paper. And nothing's changed in the two months since the first round happened. I mean, this is exactly what's happening sometimes. And it is, it is, I don't know, it just strikes me as pretty unhealthy.

56:13And sometimes the rationale is well employees want to know that their equity is worth a lot of money and so you know they'll get the equity early and then there'll be this big write-up so it's almost the way that companies can offer lower percentage ownerships in their business in the form of options but because the markup is so high like the paper value is like already in the millions and so then they can try to compete with offers at the big labs but obviously dangerous because of all the lick pref features that are that are present in these in these extended valuation companies, not least growing into them.

56:48So that's a little bit a little bit scary to me. Yeah.

56:54Turner Novak:And for people who don't know, it's like prep is liquidation preference. And it's basically in a lot of these rounds that companies raise if you any dollar that comes into the business, that if there's a liquidation preference, it's usually one times. Sometimes it might be two times, three times even depending. But typically if you're in your standard venture round, whatever dollar you raise, those investors kind of have right of first refusal on any exit. So if you've raised a billion dollars, the company needs to be worth a billion dollars and all that cash goes to them first. And then beyond that, the employees, the founders, et cetera, get paid.

57:30So if you work at it,

57:32Turner Novak:if you take a job at a company that's raised a bunch of money, I mean, there's a ton of nuance to this, but it's probably like whatever amount you've raised, the valuation needs to at least be that price before you get anything. The investors generally get a rise to first refusal. It's not always the case. You should probably ask when you're talking to... I'm taking an offer and figuring out exactly how that works. Google it. There's tons of YouTube videos that explain it pretty well. There's a full podcast where people explain the nuances of this stuff. But the price, just because you work at a unicorn and might not actually be worth that much at the end of the day.

58:07So I mean, the other thing is whether your stocks get vested immediately once the acquisition happens, if you stayed for a shorter period of time than is like required. So if you get acquired in your first year and you're supposed to stay four to earn all your stock, like do you earn the four on the date of the acquisition or not? And that's like been particularly topical in these like pseudo acquisitions of shell companies and things.

58:32Turner Novak:Yeah, I did actually have that with one company that got acquired. The founder specifically negotiated all of his employees like FullyVest, which I think a lot of this comes down to who you work for. Who is the founder of the company that you're joining? Do you trust them? Not just in, can they build a good product? Are they commercial? Can they sell things? Can they grow the company? It's also like, are they going to treat you fairly? I think is like an under-discussed topic that is probably pretty important to think about nowadays. But so then you, we kind of maybe alluded to this a little bit, but in terms of, so we could maybe talk a little bit about Airstreet and, you know, the, the fund that you run and just kind of like, what kind of things do you invest in?

59:18Turner Novak:Cause when we talk a little bit, oh, maybe what you not be participating in. So maybe it might be interesting, just like, how, how did you get into this bridge? Like, how did you start investing? I know there's, You've been doing it for a long time. Yeah. I started getting interested in venture capital in college because I started in 2006, and that was the era of Dropbox, Skype, Twitter, SoundCloud, Facebook, etc. All these tools that I would just use as a regular college student and get excited about them or read about the history. It was classic kid in a dorm room, thought of something, and it became big.

59:56and at the time I was majoring in biology and I got the chance to work at the Whitehead Institute MIT in Boston and there were I was a place where they did the whole genome sequencing project there were some venture funds in the area that were you know hunting around labs for new you know ideas and drugs and innovations that they would then like spin out into companies and And I was just really excited about this, like working on new technology, new frontier ideas, and then try to like make something out of it for the utility of, you know, real people and companies.

1:00:34And after I did my PhD in the UK from 2010 to 2013, I was really convinced I wanted to somehow play a role in the startup ecosystem. Didn't really know where to start or what would be a good fit. and so i think naturally like being part of a venture firm would be a good good sort of aperture to like everything because you get this like unfair calling card you know that you can use to talk you know enterprises of different types meet entrepreneurs who tell you things that you know hedge funds pay lots of money to glg for you get to learn of successes and failures um and then i could maybe find what I found most like kind of fulfilling long term and and honestly like what I found was like working with brilliant people were trying to invent the future and and particularly this intersection of like AI science engineering and and building real companies I became even more interested in AI like in 2014-15 that was around when DeepMind was acquired in london and it was not that far away from where we were and i had some friends working there and it really captured like the interest of of of machine learners at the time that their skills could be used for for like practical things at the time you know dmine had shown atari and it was like the first time that a computer could solve this video game and discover tactics that humans had never found and i think that that like nugget of hey like if you could have if you could have a learning machine that could amalgamate more experience than any human on the planet and learn from all these experts, then there's certainly going to be like solutions to every problem in the world that we haven't yet discovered, but that the computer can help us discover.

1:02:20It's interesting, like the classic, like, you know, chat GPT or Claude code, like do this thing

1:02:25Turner Novak:for me, blah, blah, blah, make no mistakes. It's like kind of like the meme, but it's almost like real. It's like solve chess for me or like solve world hunger. Go. I would challenge anybody who's interested in technology and just world progress to watch the thinking game on YouTube, the contemporary story of DeepMind and all the stuff that they've done, and walk away from that and not think this is probably the most exciting thing that could possibly happen on planet Earth. And I want to be involved in it somehow. I've seen it. I just have not watched it yet. But it's been kind of on my like, oh, I should convince my wife on a Friday night when we're...

1:03:03gonna watch one thing to watch it instead of like you know something else it's well worth it i mean the narrative is great i mean i think uh you know dennis the service is like one of the you know perfect sort of like characters that that that you want to win i mean he's like he's intellectually curious like brilliant you know like too modest and just super driven to invent new things and to it in his lifetime and feels like this irrational sense of urgency of especially when he's describing like why why the hell did you did you sell a deep mind you know like looking

1:03:38Turner Novak:back it'd be worth trillions of dollars at this point yeah exactly i mean he sold it for 500 million pounds which at the time was like insanity now companies are raising that right from day one and he's in this taxi like describing like look like if i if if i gave you the opportunity to have infinite compute and infinite money and resources to accelerate. You know, potential invention of AGI in my lifetime. Like there's nothing more that I would trade than being able to use my, you know, fruitful years when my mind is still working. I have the energy. I have like, you know, the physical capacity to go to go do this.

1:04:24Like I could have waited, you know, five, ten more years and like struggled here and there because nobody wanted to fund this stuff at the time. But like I wouldn't trade those five, ten years for tens of billions of more dollars. Yeah, that's huge.

1:04:37Turner Novak:It looks like it's on YouTube. Yeah, I'll throw a link in the comments or in the description for people. I guess you made this transition from you're working at a venture fund. It was a 0.9. I worked at a firm called Playfair Capital before that, which was pretty much a family office. Then I moved to 0.9 in 2017 until late 2018, and then had the first closing of Airstream Fund 1 on January 2nd, 2019. So what was that transition then like for people listening that are curious how raising a venture fund goes? Because I think a lot of people kind of think, oh, this guy has millions of dollars and just investing his own money because he's rich.

1:05:15Turner Novak:His parents gave him money. How does it typically go raising a venture fund? Maybe talk us through how that went. Yeah. Yeah. So it was basically like playing pinball with my eyes closed. That's how I'd describe it. Like in the sense that, you know, you have this goal in pinball, like get the ball in like in the hole, you know, and then you've got like the pins and you have to position the pins to get the ball. No, but you have no idea how to position the pins because your eyes are closed. And that's like the best analogy I have for like what it was like, you know, meeting prospective like LPs of any kind that would be interested in in 2018, like a solo GP first time.

1:05:55venture fund manager, like early stage focused, small fund by virtue of those features, and then also focused on Europe and then a little bit of North America.

1:06:04Turner Novak:And then AI too. Like AI was not hot also. It's like you should be doing VR or crypto maybe. Yeah, exactly. So, you know, nobody wants to buy that thing. And I didn't come from like a brand name firm for a long time that was like an easy spin out with like institutional investors. but like i was honestly like convinced that i that i had to do it i think one of the best ways you can make these kind of career decisions is like the regret minimization idea of like if i don't do this will i have like lifelong regret and that was very much the case because i had this like long-term conviction that ai would be important like much in the same way that you know sas was niche 15 years ago and then became like the dominant business model on the internet uh you know ai would be that that case too like if this stuff actually works why would you not build your product using it or powered by it.

1:06:54And different industries would come online in different times. And that was motivated by just the first principles view of progress in the science. I then spent a lot of time with various venture firms trying to see if they were like also convinced on this. And broadly speaking, it was some version of like, no, or it's a toy or it's like going to get absorbed in different things, or we don't think we need to have specialism.

1:07:18Turner Novak:Yeah, because when I think back of like some of those AI products, like they didn't really work very well. So you probably, you know, if maybe like your perspective, just having spent a ton of time, maybe you could see the trajectory or maybe you just have, but if you're just like doing all these things, doing some like consumer D2C brands, you're doing like, you know, SaaS enterprise CRM management or whatever. And then there's like some AI with like, you know, email assistant AI thing that just doesn't work properly and the company goes bankrupt. It's like, why would we waste our time on that.

1:07:51Turner Novak:Like it's just not worth it. Yeah. I mean on that note we forget the original x.ai which was an email assistant. Yeah. Which was like pretty hyped at the time. Like it was like 10 years ago. Right. Yeah. It kind of worked also. Oh it did. Okay. I didn't know. I didn't ever used it. So I just remembered like hearing about it now. Most people were like yeah it didn't work very well. Yeah. Yeah. Yeah. I guess compared to today. Compared to today. It doesn't work. Exactly. Yeah. So were you exploring joining a big firm initially and like leading their AI investing? I was open to various avenues, just trying to figure out what is like the right, the right like setting basically to express my ideas.

1:08:34And then I just eventually found like, look like I've been following and like writing about this or like industry analyst vibes for a long time, like since 2015 or something or 14. I have some essays on why NVIDIA was going to be the most epic company ever, and the different areas in AI to watch in 2016. And it was RL generative models, world models, custom silicon, all the stuff that eventually panned out, and some things that didn't. And then was spending a lot of time with different people in the ecosystem, like researchers, engineers, startups, big companies, policy investors, and doing like a variety of these like meetups in different cities because i always found as a grad student like i want to learn what good looks like but there's no place that i can go for it because the playbook was still getting getting written and then i had a couple companies that invested in that were looking interesting and and yeah it was like the regret minimization of like i just gotta try this because if i don't i'll regret it and honestly like i'm not that scared of the risk of having to go back and eat ramen noodles.

1:09:44Like, they taste good. I'm fine with that. Yeah.

1:09:48Turner Novak:They have good flavors nowadays. There's a lot of options. Maybe not the healthiest necessarily, but like... Yeah, but you got supplements for that. It's okay. You talk about playing pinball with your eyes closed. Like, how do you do that? How do you beat pinball with your eyes closed? Like, what did you figure out to eventually kind of beat the game? i figured out that entrepreneurs who had like some exposure to finance fintech like ai data science like vibes with what i was doing like because they'd seen the value of of ai at the time you know most of the value was in like ad targeting recommendation systems and then i also saw that certain individuals who worked in like high frequency trading also understood this again like because quantitative modeling is very much about machine learning there were like a few growth equity firms that had started to like make early stage investments in managers to sort of like prime the ecosystem and then and then i stumbled on a couple family offices but this is like completely random and again like the pinball and that was mostly through referrals of like hey you might not like this but do you know somebody who does did you usually do that when you have a conversation with an LP is like you usually try to get an introduction to someone else.

1:11:11Yeah, because, you know, the the playbook tells you ask all your GP friends to introduce you to their LPs. But if you don't have fancy GP friends who have great LPs, how are you

1:11:20Turner Novak:supposed to like what are you expected to get? And so I was just like ask, ask entrepreneurs, ask like, yeah, these family office folks and I go to events. I would even do things like, I don't know look on LinkedIn when somebody announced the fund and I'd look at like who liked it and then see on the list oh there's somebody who manages like asset manager I saw you like this maybe you might like this yeah yeah or like you know in the UK another trick is if the UK incorporated venture funds or every company in the UK frankly has to list its shareholders on companies house which is sort of like an SEC register if you will and it lists all the names of the entities.

1:12:04And so you can go there and look at your pure funds and try to see who's an LP and then maybe casually mention, oh, I heard that XYZ maybe is investing. And you know they're an LP and they're fun.

1:12:15Turner Novak:Yeah, that's definitely the trick of like, you know exactly what you want to get going into a conversation and you just kind of float this and just see how they respond. Like see if there's like a chance that something might happen. Yes, I can see. So yeah, short answer is like I tried every trick really. And then it was just to some extent, just like brute force and then some luck along the way. And I started in 2019 with like 9.860 million. I know because like that, like Delta with 10 million like pulled out three weeks earlier. So I had a bit less than 10 million. Nice. And then I did seven closes over like two years, which is pretty, pretty intense.

1:13:04And then hit COVID also towards the tail end of fund one when you couldn't squeeze 50k out of anybody. And then, and then after that summer, it sort of, uh, I swaged a little bit and then managed

1:13:17Turner Novak:to get a bit more into the fund after that. And then it ended up about, about 26 and a half fund one. Nice. And so, I mean, it sounds like it's basically like take a lot of it's a lot of patience to kind of get through the process. The way I describe it to a lot of friends who maybe are founders, it's kind of like raising a pre-seed round. But you don't stop when you get to like a million or two million bucks. It's just kind of like you just get like 150k checks or like 100, 100k checks. It's just they just kind of keep stacking. And even when you get like a lead, quote unquote, typically when you're doing like a seed round, they do most of the round.

1:13:53Turner Novak:but most of the bigger checks in most people's funds are like 10 % of the funds. You almost need like 10 leads basically. So it's like, think of that process. Like it took you two months to get a lead. It almost takes you two years to get 10 leads, lead checks basically. So it just takes a long time. The challenge is you have no leverage, at least with like a pre-seed company or a startup. If you do well, you're at that specific stage once in the entire life of your company. And so there are investors that specialize in that stage that either they're in or they're out and that's done. Whereas by definition, like good venture funds are here in perpetuity.

1:14:36And then the game is like, can you become like access constrained as fast as possible? So then you earn like some element of leverage, assuming that you don't like grow beyond the capacity of the partners you have. Yeah.

1:14:51Turner Novak:And then there's the element of people can wait too. They can just be like, your fund two seems interesting, but I don't know, I just want to see how well you do. And maybe fund three is more for me. And in theory, if you're good, your returns will be just as good in the next fund if you're a really good investor, which is probably why they'd be investing anyways for that reason. And you can't just be like, oh, we went out and signed a new customer and our revenue doubled. You can kind of go, oh, we got a markup from Sequoia or something. But also a lot of LPs be like, that's cool. I wonder how it goes in five years.

1:15:31Turner Novak:Check back and did you return cash? And maybe what you get to is you're on your fund three or your fund four and you're in your fund one, the second investment you ever made. It got acquired or it went public and this is like 10 years later and like it literally takes decades like actually show the tangible proof sometimes yeah and then you don't know maybe you know out of five funds it's like fund one three and five that are great and like two and four are less great and if you missed one or you pulled out like you don't get you don't get the benefits of of this asset class where you really do have to you know invest through multiple cycles and then consider it as a multi-fund commitment and you look at overall venture dollars deployed across those vintages.

1:16:20Turner Novak:So then what's your strategy with the fund? I don't know, what fund are you on today? I think I remember seeing a$100 million number. Yeah, I've just finished fund two. I'm on fund three now. So yeah, from fund two, I went up to 121, which was a big step up from fund one, really just enabling me to offer all the money that entrepreneurs wanted when they're raising their pre-seed or seed, anywhere from one to five million, maybe$6 million. I've just generally been of the belief that not that many companies matter. I want to be concentrated in the funds, so I do like 20 companies per fund. I don't get excited about things that often, but when I do I want to be able to move with like conviction and have the money to offer to the entrepreneur because I think the experience kind of sucks when you meet somebody you absolutely love and you love the idea and they're raising x and you can only do 0.2x because that's that that's the amount of money you have in your fund and then you might be kind of forced to sort of massage the fundraise to meet your fund model and I feel like that's just net bad for everybody um and and then i want to do like europe and north america i'm flexible on the geo and generally like pre-seed to seed and then a couple areas i like doing are like vertical software where like ai is the product um so i've done things like you know v7 which does kind of process automation and spreadsheets or like synthesian 11 labs a bit in dev tools and infra like poolside you know coding model and then defense and security because i'm like i think this is really important freedom does come doesn't come for free and it's non-negotiable and so i've investments like delian in the uk which builds like hardware and software for like your perimeter security anti-drone and then the fourth bucket is tech and bio and so there i've had some early exits in fun one around you know generative models to design new chemistry so business called valence that i led the result of recursion and then another one called all site where it was kind of the opposite we're like testing cancer drugs on samples of patient tissue from cancer patients and then kind of running a clinical trial in a dish then using like computer vision with microscopy to take like pictures and analysis of these cells and figure out which ones are working and which ones are not working with respect to the drug response.

1:18:58Then we sold that to XTNTO and the next TNTO in public. Those are like kind of the four buckets. And the things I've done less of are like these large capital raises for model companies in part because of what we discussed earlier. Like it just feels like the economics are a little bit a little bit tough for early stage investors. Like I'd almost prefer to invest in a slightly later stage around for those businesses once like the economics are much clearer and they have customers and they're scaling versus like a tabula rasa you know 50 million on 200 and something to train a model and maybe it'll work maybe it's not whereas you can invest in like a series bcd company that's like printing 100 200 million revenue going 2x year on year like i don't know a billion or two billion so it's like the cost benefits like seems completely off to me i had a company got acquired

1:19:49Turner Novak:by Anthropic and I have like some Anthropic stock and I didn't know how to feel about it at first because I was like, ah, I don't know about this. I'm like, it's just like, it's like growing pretty quick, I guess, like of all the assets to own. Maybe it's like a good one to just kind of have and like, I don't know, it'll be like a driver in the fund. It's just like, I don't know how big it will ultimately get. Like, is there 100x upside from here? I guess it depends who you ask. Depends what here is. yeah so it's like i don't know it's i guess it's like better to own that than something else i don't know it's like yeah i mean i think in a in a portfolio like different assets play different roles you know there's some early exits that can provide recycling that are good there's there's others that maybe at a later stage to provide good irr and then so i think it's just about thinking what's what's like the right the right mix for these like later stage opportunities i do think that to your point earlier like these companies can become way bigger than we thought you know anthropic i think in open ai i have like hundreds of thousands of customers like same thing with some of these model vendors in different modalities and and whereas 10 years ago it was like pulling teeth or maybe worse to try to get any enterprise to even try your ai widget now it's like please can you help me and like what can you help me with yeah how much money can we give you yeah and so you know if we start with like some use case one then you succeed on that likelihood is other departments are going to see that use case one think oh well i have something that looks similar to this can you build that and then when you can you know code these things way faster than you can before and everybody gets like mass customized software where you can start eating into customer budgets way more than you could in the past and act as that one sort of lighthouse guide into this next generation of software.

1:21:47And so you become multi-product and then add revenue lines. And I think you scaled to really big sizes. So I think some of these layer stage bets still make a lot of sense.

1:21:58Turner Novak:So you're thinking basically, at least your opinion, it sounds like you want to invest when somebody is starting the company and there's appropriate risk for like, this is probably going to fail and not work. Or you invest in this clearly is working and this is a company that is going to exist. There's no going concern and it's growing really fast. So it's basically you invest like company creation or like this is a mature AI company. There's almost like a dead zone in between of like, it's still unclear if it's going to cross the chasm, but you're almost paying a price that's appropriate, but that's related to like a publicly traded software business or something like that.

1:22:39Turner Novak:And like, that's just not a place you want to be in. Yeah, yeah, exactly. And I do at the moment, you know, like 90 % in the first bucket and like 10 % in the latter. And then from the 90, I also do, you know, reserves for, for the core bets. But yeah, when I have like, you know, 20 core bets where I'm buying, you know, 10, 20 % of the company for like serious money and then you know following on in a couple of those right through a and then maybe like two through b and special opportunities so for example in fund two i'm really excited by this company profluent which is you know training large models to do protein understanding so they can engineer proteins with specific functionalities and they've basically focused on genome editors like these proteins that go into your dna and like extract certain pieces of dna and insert another or fix a genetic mistake which can be responsible for disease and therefore like that solution is curative and you know in that example like i've invested in seed a and b you know i have like 15 percent of fund two in the company which i think is probably larger than like every other shareholder so i'm like definitely risk on on like companies i like because i think you know if if these businesses really become generational and work out like it really moves the needle and across several funds.

1:23:56I think this is the better strategy than personally, than, you know, large number of portfolio companies, smaller checks, and then, you know, higher likelihood you don't lose money, but also lower likelihood you have a blowout fund.

1:24:10Turner Novak:Yeah, because if you look at a lot of, like, the data and research, the data-driven approach to this is a super diversified portfolio. Like, that seems to kind of be more of the consensus right now in early, early stage. And I think 20 companies in a fund is like below the threshold of what people would say is like good diversification. So you maybe explained it a little bit. But so why did you not say maybe 40 or something like that? Like based on the research? Because you're like a research driven guy. You obviously read a lot of research. I think part of it is like it's hard to make the math work at 40.

1:24:51like if most companies are raising three to five million dollars if they're not one of these like model training shops to be able to buy up like good ownership and do most of the round it and then you don't want to have a fund that's like half a billion or something which has its own you know exit value capture assumptions it's hard so i prefer to skew with fewer companies and by the way like i invest over three years so it's quite slow so i get some time diversification i could sort of see like okay is you know vintage from year one and year two panning out good or or less good than i hope should i add more names at the expense of prorata in in year three or not so i had i think some more flex in the system to decide should i expand the number of names or keep it small because of the longer investment period just based on my own pacing and like lack of excitement every single day about oh squirrel oh squirrel yeah well i think the other the other thing too to bring up is like you we're in like the hottest fundraising market

1:26:02Turner Novak:ever for ai you should be deploying in a year and raise a new fund they're like twice the size like why why don't you do that because you can make a bunch of money personally i think the best answer to that is and this is a founder who brought it up when we were discussing like fun strategies he's like eventually used to be about the you're in the two and 20 business or whatever your math is but now you're in the two or 20 business and i think that obviously big firms are basically in the like in the two most of them and i just i want to be in the 20 like i'm or it's just like in my in my gut like i i'm performance driven i want to be able to you know get a line item you know in an endowment saying like you know we gave a couple tens of million dollars to this guy that we like found from you know from europe whatever like doing ai before people thought it was cool and like he printed billions of dollars for us over like our relationship and that bought us like a bunch of buildings and yeah he returned the endowment yeah and like this has happened before like for example if you read some of the old yale reporting like swenson wrote about hill house which is founded by some analysts in the investment office covering china and they wanted to do chinese equities before people thought it was cool they were like at their mid-20s and the line is something like we gave them tens of millions of dollars and they printed billions for the endowment like i don't know if this is going to be repeatable i mean obviously high mark but philosophically i'm much more aligned with with that than the NASA gathering.

1:27:36Turner Novak:Yeah, I think like a whole other vector of this is just like how big is the team needed to execute the strategy and like what are the the cash inflow needs of the firm. So if you if you're literally one person and you can live off 200 grand a year, live a nice fine life, maybe you have like kids you want to like they do dance classes, maybe you need 500 grand, maybe you like live in New York, like downtown Manhattan, whatever. But like there's an upper limit of like what you need to survive and live a comfortable life, execute the strategy versus do you need 50 people on the team, 100 people on the team?

1:28:11Turner Novak:You can't do that with a couple hundred thousand dollars. Like you need millions of dollars to pay and compensate these people. So you do actually need a significant amount of management fees and the funds do stack over time. Like 10, we'll say 10 years, you're paying 2 % a year, which is maybe like the average. and you're 10 years into the strategy, you have five funds stacked. You can afford to pay some people, but depending on what the strategy is, you do need a budget to work with and you need to generate the management fee. So I feel like that's also too, is depending on the team size depends on how big the fund needs to be, really.

1:28:49Turner Novak:That dictates a lot. Yeah. But then there's this other narrative on Twitter recently of these like spin out GPs, spin out because they say like, oh, our partnerships are too bloated. There's too much process, too many meetings, too many companies. Decision making is inefficient. And like, you know, we're fresh. We have no companies, you know, smaller partnership, whatever, smaller funds. And then you just kind of wind the clock or, you know, five or so years on most of those teams. And then they've become exactly the firm that they left. Like they've hired more people. They've raised a bajillion more dollars.

1:29:25like they have more portfolio companies to experience the grades etc so i i feel like you know i i got into this in like a non-traditional like not super popular route i mean most lps would say like go find a partner instead of here's some money and so i do i do want to stick to what i think makes like this product like different and feel different and it would just feel like a bit disingenuous if i'd go and hire fly partners and be like oh well we're like everybody else now because we want to scale like AUM or because the opportunity set's bigger. Like I do like just only having to care for portfolio companies and like what I do every day versus having to care about and spend a lot of time on, you know, nurturing someone else's career and keeping them happy and teaching them, which I think you have to if you're going to hire people.

1:30:19Like you have to make that commitment. I'm just like not ready for it.

1:30:25Turner Novak:Yeah. And I think too, is when you think about like, like if you were to go work at the, we'll just say like the largest multi-stage fund, I think there's one, they just announced a new set of funds. It was like, it's like$15 billion or something like that. Like your personal compensation, that what you get personally from any check that you write, like if you, there's like this whole fund from like angel, small fund, medium fund, big fund. So like if an angel writes a check, They invest$100 ,000 or$10 ,000 of their own money. That is like the equivalent of a solo GP writing$100 ,000 check or$200 ,000 check.

1:31:02Turner Novak:And the more bigger you go up the stack, literally a$10 ,000 check from an angel is the equivalent of a mega fund writing a$18 million check or$50 million check of how much they are personally compensated for that investment. I mean, it kind of depends on how a lot of different things, fund size, compensation structure. But like, so you're almost like disconnected from like the actual outcomes, the bigger the fund is on like a personal decision-making level. So I feel like it's just like the whole other... And then there's this other function too, of like as a solo GP, it's like you get, I don't know, 100 % of the carry, maybe like depending on you might have an associate, but like you are personally compensated about like the result of this thing.

1:31:49Turner Novak:and like do you when do you need to access that like if you're just like you're 35 years old i don't actually don't know how old you are like yeah 37 yeah yeah it's like okay like you have you have like hundreds of millions of dollars of just like illiquid net worth it's like whatever it's not that big of a deal like i'll get it in 20 years like it's i don't need it today it's like i'll just let it continue to compound i guess versus if you have a bunch of people like a bunch of cooks in the kitchen of like, I need it today, right? Like, you might actually need the management fees to like pay bonuses or like it influences like when you take liquidity, which impacts how much money your LPs make at the end of the day.

1:32:28Turner Novak:So I think there's that whole element of it too. Yeah, yeah. Yeah, I think you just make different long term decisions because they're less like, you know, markup driven potentially, and therefore less seeking momentum, because that's like the only way you can show to your manager that you're sourcing good things. and then maybe like you know everybody wants to do a spin-out fund at this point because like why wouldn't you for the same reasons you discussed like your look through ownership and the companies you invest into and your big fund are so small and i think i think for you know in a market that's like flush with capital like the closest that entrepreneurs will get to a venture product that is entrepreneurial is somebody who started themselves because they've been through this shit of like no one believing in them of like figuring out the strategy of like tuning the strategy meeting all these people like managing like the organization even if it's just one person there's still like the operational stuff you need to get right if you want to work with serious institutions and then there's like the brand the marketing the sales because we have to do all these things as well like it's a created differentiated product and then if it goes wrong because nobody else to blame if it goes right then all of your success to you so i think for an entrepreneur who wants to find somebody who's most aligned with them winning it's gonna be somebody who's like new who started themselves who has their like career and reputation on the line and money in the line um on the success of the entrepreneurs they work with i think for you know founders who are like who vibe with that it's amazing product and for founders who like want the you know nice brand like i think in that case it's it's hard to it's hard to convince somebody otherwise a bit like consumer preferences yeah it's like a market just like the market people bond different products and it all co-exists and you can pick that's the beauty of of like capitalism it's like everyone will come up with a different product it does different things and like you get whatever you want so it's like it's all good um actually so one thing i wanted to ask you about maybe going back into maybe like some of the stuff in the report you maybe we actually hit on this i can't remember but there's all these like benchmarks every time a new model comes out there's like all these benchmarks like each new model is like the best at something right like how should i interpret that as just like a observer of reading all this So like, do they matter?

1:34:56Turner Novak:Are they super important? I think the way to look at it is almost like the Olympics, if you will. And like each model is a country and then each like sport is a task. And so model vendors are trying to do the best they can across the board in different tasks and different like sports. And some companies will care more about certain sports than others. So, you know, so far, I probably cares a ton about code and basically nothing about audio, multimedia. It doesn't even expose those capabilities as far as I can tell. Whereas OpenAI cares about all of them. And those leaderboards and competitions are useful as systematic ways that one can compare the pros and cons of various systems.

1:35:49But we do have to have benchmarks because otherwise there's no kind of, quote unquote, fair way to test different options. The problem is maybe at least twofold. And so one is, is the benchmark really addressing the task? So for example, if I want to do like, is this model good at biology, is just asking questions about what does a mitochondria do? Like does like a eukaryotic cell have like a cell wall or like these kinds of facts? Is that like the right way of mastering or of understanding if a model understands biology? Or is it, it has to be able to design an experiment that can achieve a certain goal?

1:36:39or it has to be able to like write a cohesive research paper sort of like unclear to some extent and then the second order issue is to what extent are the very evaluations the very benchmarks that we are using to test our models available on the internet or in otherwise in other forms in the training data because almost by definition we're like learning from textbooks we're learning from quizzes and uh and even if like the the benchmark quiz that you're using to evaluate is not present in the training data maybe something that looks like it is because like the same human wrote it like to use a trivial example and so then you have too much similarity between your training set and the test set so you're effectively like what nerds call like a benchmark maxing yep just like manipulating and making sure

1:37:36Turner Novak:you just beat the thing specifically that you're going to get test. Well, it's like studying for a test. It's like the whole, are you actually smart or did you just memorize what was needed for the test and you got all A's, but like you, you know, you're book smart, but not street smart in a sense. Yeah, exactly. Exactly. And then there's, there's things like more recently in code, you know, there's a popular benchmark like called SWE bench, standing for software engineering bench, and then verified to make sure that the actual evaluations are human verified. and it's mostly like bug fixing in python and unit tests and some models perform super well on it but software engineering is far more than unit tests and bug fixes in python it's like a lot of other things and just that you know we probably haven't gotten around to writing all the evaluations for the litany of of tasks that we want models to be good at and so so i think they're they're important like litmus test to look at but you know know with a grain of salt that companies are doing as much as they can to do well on these like tables because everybody else looks at these tables yeah it's like the olympics your point where like when i think of like canada extreme or like norway like extremely overachieves in the winter olympics like canada usually is like top three i feel like norway is usually in like the top five but like it's norway there's like you know a couple million people that live there like how are they beating like they're beating china in the winter olympics or random skills like the the turkish guy who like won the pistol competition oh yeah yeah it's like that's incredible marketing for turkey right it's like this insane sharp shooter yeah slightly scarier they have a pretty crazy defense industrial base of these days that may or may not be selling to shoddy nations but yeah yeah fair fair enough what um so one thing you kind of mentioned I want to talk to you about do you need to train your own AI model to build an AI company like is that a necessary thing to do or can you get away without doing that I don't think it's necessary the way I look at it is like what is what's the problem you're solving and if that might sound super basic but I think a lot of truisms are kind of basic and if if what you are solving is sufficiently workable with an existing model then i think honestly like congratulations like you can now build a sas company you can now build an ai company at the speed of a sas company because you don't have to faff around with all the nuances of making the ai model work and you know for so long as the unit economics are fine then i would just run with it and and and yeah use all that budget that you're no longer spending on r &d on product market thin growth um but if what you're doing for if the problem you're solving is not workable with current systems and you're going to have to do your own stuff so to state like an obvious example in the sciences you know if you're designing crispers and genome editors like you can't really ask ggbt for that yet like it can probably blag its way but it's not going to be that great because it hasn't been trained for it and there's some like architectural nuances that maybe are less suited to the task and then relatedly if you're doing like genometers you kind of don't care about its ability to make pictures or jointly learn audio or jointly generate video it's just like not relevant um but but like if you want to compete in the in the model race then yeah join join the club you gotta start from scratch and there are there are some that have have started like DeepSea in China or Poolside or XAI.

1:41:26Turner Novak:But I feel like another element to that question is like, okay, you just used the OpenAI model to build this thing. And like, oh, Nathan, that's a cool company. You just used the OpenAI model. I'm going to do the same thing. I'm just also going to use their model and build the same thing. Is there any defensibility if you don't have your own model? Product experience, taste, and then user data. which people would call nowadays like preference data, like is this good or is this not good? I think that's what it comes down to. In some ways competition is you have to do whatever it takes to such that your user, your customer either doesn't fire you because who gets fired for buying McKinsey basically or IBM kind of thing in the past, or be in a consumer space that you have equivalents with like a Coca-Cola.

1:42:25And I think, you know, OpenAI to me is like Coca-Cola. You know, 90 % of the time you're going to use that. And if it's not available on the flight, like maybe you'll buy a Pepsi, but you'll think about it. I don't really know what model is Pepsi yet. And then for Enterprise, yeah, it's like the don't get fired for buying this product. and then do whatever it takes to get there. I think once you are there, then... then there's, you know, different ways of, like, having boats long-term.

1:43:02But in the short term, yeah, it's product quality, I'd say.

1:43:05Turner Novak:So then where do you think most of the value ultimately accrues? Like, should you just buy NVIDIA and just, like, that's your AI exposure? Like, what's... I think all the things, honestly, like... I worry a little bit that this point has been propagated or contrived a little bit by VC blog posts that like to pontificate about the future of an industry, not dissimilar to how they pontificated about the modern data stack and all these tools that were needed. And I think all those investments are basically gone to zero. So I do wonder, I do worry a bit with this, like where's the value accrued? Because I think it's kind of everywhere, to be honest.

1:43:44and clearly like NVIDIA long term, but also its suppliers also like in energy and and like some new clouds I think are compelling. Model companies themselves, but also like product companies that wrap on model.

1:44:01Turner Novak:Like you're doing a unique thing for a customer and building a deep relationship with them where you get, you have like a proprietary relationship with the customer serving them in some way. like no matter what you're doing like is that maybe just like a pretty simple way to think about it? I think in the in the early days yeah for sure because at that point

1:44:26the customer has to trust in the journey and the relationship they're going to have with you that they're going to invest some time and you know use a slightly janky product today because it's going to get better in the future and then you'll listen more to the feedback that they have so that you can they can you know purchase a product that's fitter for their for their needs um i think you know i think many of the many of like the best like enterprise software companies that i've invested in when i did like customer diligence on them in the early days it was something along the lines of they really intimately understand our problems and what it takes to build a solution for us they listen to our feedback they're super fast on fixing any problems and getting back to us and and like they care and then on the flip side you know enterprises have been part of where the customers churned and then the founders like fly there and then they're like you know hey like they're trying to rescue the relationship usually the the relationship owner is like thank you for coming this means like a lot to us like because you're here we will resign it's really just like caring about the customers like give

1:45:34Turner Novak:like a good customer experience. Yeah, yeah. If your thing is like a little bit hard to use and then, or maybe it's like super easy to use them and you never have to talk to a human. But I think anytime your customer account value like goes up above like 50 or 100K, you're probably gonna have to talk to a human. I think one thing you kind of alluded to earlier that I wanted to ask you about was you think like there's a big opportunity in defense and specifically in Europe. Like what's kind of the thesis behind that? The general thesis there is that Europe hasn't been investing in its industrial base for a very, very long time, basically since the end of World War II.

1:46:17I mean, a bit similar to how the U.S. has been a broad consolidation of primes since then and the Cold War, sort of like termed the peace dividend. and yeah in general you know europe is like forgotten that you know war can happen on on its doorstep ever and the other problem is that in the munich security conference last year which was february 12 months ago that was when jd vance and others basically dropped the bomb that you know u.s security guarantees might not be what they were up till today and that means basically like the u.s is not going to subsidize europe's defense this was like a big shock to to basically everybody on the continent you know this is several years after the start of the ukraine war when the u.s has been sending you know more aid and more military equipment than pretty much anybody

1:47:22and and then you know ensuing that you know trump pushed really hard on european nations to beef up their percentage gdp spent on defense many countries were below two percent the highest was probably poland that was close to three and a half or four some countries don't even feel the need to spend more on defense like spain doesn't want to spend more than two apparently doesn't want to.

1:47:47Turner Novak:You're the furthest away from the threat. I think that's part of the reason, yeah, they just feel far away. But that's the dangerous concept of this problem is far away. It doesn't affect me. Like it sure as hell affects you and like energy prices and migration and integration and social issues. And then potentially, you know, if you're part of the European Union and NATO, then you do have to contribute forces to potential like troop deployments. So it's a bit it's a bit short-sighted, I would say. And then there was, you know, post that of like, holy shit, like no one's going to come to save us if if we have big problems, like certainly Daddy America is not necessarily around anymore.

1:48:34Then, you know, European Commission, you know, spun up a big initiative around mobilizing additional funding for European defense, where they said each nation is allowed to increase its defense spend up to a certain amount to mobilize in total about 800 billion euros. Now, this is not contractually required. It's a bit like a schoolteacher telling children you're allowed to spend your own money on this new candy if you so wish. I suggest that you do, but you don't have to. Hence the ability of Spain to opt out. and then NATO setting targets with 5 % of GDP spent on defense and then a new instrument called the SAFE, not the YCSAFE but security and something for Europe which is 150 billion of money backed by the European balance sheet to do advanced procurement of military equipment when at least two European nations have sponsored the desire to buy said product and that product has to be made in Europe And then certain fracturing of European procurement for US equipment.

1:49:44There's certain countries like Portugal saying, hey, we're not going to buy F-35s. Some other nations saying, we're not going to buy F-35s, we're not going to buy the Patriot Missile Defense System. Where Switzerland has come out saying, we have six billion and a half of loans that were approved to buy F-35s. and we're going to buy as many as we possibly can. This is a country that's been neutral for basically forever. Sweden was a recent joiner in NATO, has been neutral forever, and now is radically remilitarizing. Germany, I mean, who would have imagined decades ago that Germany would ever remilitarize?

1:50:28Inconceivable. And now is probably the biggest defense vendor in in europe after you know the chancellor basically enabled the the country to take on significant more debt than what it was allowed to in the past after merkel's government which basically crippled infrastructure and defense spend in the country so some pretty massive like macro tailwinds if like vcs are looking for massive tailwinds to motivate investments like these are pretty fucking huge.

1:51:00And then there's the qualitative aspect of things where entrepreneurs are no longer afraid to build in defense. It was before deemed to be pretty taboo. Now there's money to be had. And investors have kind of suddenly woken up to like, okay, we're happy funding this stuff. It wasn't even just a year ago that there's a lot of chatter of Europeans investing in defense, but not that many that were actually doing it.

1:51:32So yeah, it's really like there's no time to waste. The biggest enemy of everybody is basically time to rearm and to have capabilities sufficiently large that they deter your opponent.

1:51:48Turner Novak:And the idea is that there's no existing European domiciled providers or there's just like very few so there's just like there's an opportunity to build more yeah I mean there are you know like Ryan Metala is probably one of the biggest it's in Germany you know it provides a lot of different equipment where it's you know anti-drone you know tanks other things that company's worth more than Volkswagen at this point so it's like close to 100 billion market cap you know has ripped a bit like Nvidia has there is you know one of the biggest like ammunition makers is a czech family-owned business for many many years it's allegedly today expressed interest in filing for an ipo in amsterdam it's going to be worth around 30 billion euros obviously like the french have a lot of primes like talus like naval so aviation you know rolls royce in the uk BAE Systems, you know, Leonardo that helps make some of these like missiles and aircraft, Saab that's famous for making the Guri Ben, which is like a sort of pseudo alternative to the F-35.

1:53:05There's definitely an industrial base. It's just been like kind of sleepy.

1:53:10Turner Novak:Interesting. And you think that there's still opportunities for startups to kind of emerge? Do you need to like, what's like the approach you would take? Do you have to come up with like a new product that the existing guys don't have? Or are you just, you know, quote-unquote AI native or something? Yeah, it's pretty much the same narrative that has played out in the U.S., which is, you know, like lots and lots of products that are autonomous, that are each cheap and potentially disposable. So, you know, in Ukraine, there's like hundreds of different drone makers for different applications, whether it's like surveying called ISR or it's for like strike drones where it's basically a drone is a missile effectively and then you have different sort of you know environments that matter so these are aerial systems and you have land autonomy like reconnaissance land drones or demining drones or or like logistics land systems and you have on the water so like sort of strike boats or boats that can transport material or boats from which you can launch drones same thing with underwater and all these areas particularly in autonomy have been just like under invested or just not not been a focus for for a long time yeah kind of it seems like if you're thinking about like the i don't think the evolution of a war over time like eventually we created like metal and you could like have a sword instead of a stick and then like you know horses like we introduced the horse and that changed and then we introduced like guns and planes and like now autonomous is almost like a new just era of it and like everything needs to be able to defend against that new like plane that's that's kind of been created yeah yeah and you need to protect almost against like the lowest don't lowest common denominator of like stupidity like what is the dumbest person that has access to these weapons gonna do?

1:55:17Turner Novak:One of the, one of, a prior guest of the show, his name is Rahul Sidhu. He has a company called Aerodome that's called the Flock Safety. And he's, he was basically like 911 response drones. Like you call 911, drone immediately goes up and just within 60 seconds, it's there. And it like, basically tells the police what's going on even before the officers get there. But he's, he's kind of, he's like, I'm super surprised that we have not had any cases yet with just like lowest common denominator of like the worst case you can imagine that could happen with a drone. And, you know, you can just imagine of like using a drone to cause chaos, basically, in a country.

1:55:56Well, we sort of seen that in the last quarter in Denmark, where, yeah, around like the Copenhagen airport, some unnamed or undisclosed like organization people, whatever, were flying drones in the airspace. people couldn't figure out who it was it shut down the airport for several hours and just showed like oh well there's holes in our air defense systems against these rogue drones and people obviously suspect it's like Russian and and then like even more serious incursions of you know like Russian jets into Polish airspace that was like pretty egregious a couple of months ago and you know at the time there was no intervention because it's like this mix of I mean this politics is like very difficult but there's a bit of like the talking game of like you know we will we will defend ourselves we'll defend like all our nato allies in case of incursions but like when a jet flies through and like threatens you you don't shoot it so at some point i don't know people gonna have to decide should we actually shoot it because otherwise the enemy is not going to think that you're serious or do you or do you like keep going and and sort of yeah do the policy talk but not the action and then there's and then there's like you know Like voter approval ratings for military engagement.

1:57:20For example, a couple months ago in France, there was a general that said, you know, this might be the first time, some version of, this might be the first time in recent history when we would have had to have, you know, our children, like, die on the front lines of a combat. And, you know, while there's, you know, potentially good support for, yeah, we should be arming, you know, Ukraine against an aggressor, it's different when it's like, send your kids there. It's a country that, like, you've never been to, you've maybe never met somebody for, you don't, you don't understand, like, how this directly affects you beyond, like, yeah, we should be, you know, defending democratic nations that get attacked.

1:57:55and like in a European continent where yes, every country is part of the same like economic union. Are they really like, are they really the same? Like does somebody from Portugal really have affinity for someone in Finland because they're both part of the same continent? Yeah. It's super fascinating with like the United States of America and Europe.

1:58:16Turner Novak:Like the U.S. is like 50 different states and someone in I live in Michigan someone in Oregon like are we really that different or that similar I mean we kind of actually are like it's interesting that you how the US is the just the way like we've evolved is we basically all evolved as one like we've always been one nation like I couldn't imagine if Oregon was a different country and suddenly like 20 years ago people were just telling me like you should care about oregon or something yeah exactly i mean like the u.s has like a singular i mean you know historically like has a singular dream of everybody's there for the same reasons and there's some principles that generally speaking most americans like abide by there's the same language the same cultural heritage like everybody knows what independence day is everybody knows what the slave trade was everyone knows you know the these other events but in europe it's like a hodgepodge of history that is like incredibly complicated that i mean we're already educated about like first and second world war but like you don't know too much about like nuances of crises that you know have shaped generations and like you know czech republic or or other like countries that are maybe too far field for you so it's hard to like feel that kind of unison i think when when actually uh the most uh important you know test comes to to bear and that's like would you put your life on the line yeah well it's interesting this in a in a the episode's releasing tomorrow but by the time this comes when we're recording the time this comes out it's still been about a week or two with marcelo labre the founder of remote.com he had a pretty interesting description of like the European Union is basically like a regulatory body really at the end of the day.

2:00:06Turner Novak:So like the purpose of it is just to regulate. Like that's kind of what it does. It's like a pretty good, like kind of 10 minute, like almost like monologue. Like I'm going to post it on Twitter, just like here's 10 minutes on like Europe's regulation problem basically. But yeah, it's like, it just kind of like, it's kind of like, we need to have a thing that unites us. Like, let's like make this thing to like gives us like a consistent purpose of being and it's just like it's not a nation like it's there's kind of like i there's kind of like shared beliefs and cultural values but it's really just like regulations that we kind of do to subsidize things and like make people follow rules that maybe one of us wants but a different nation doesn't want it's kind of fascinating yeah i mean there's some benefits like making trade a lot easier making travel a lot easier across like immigration for every country you cross and then like a single currency system which was beneficial for a lot of like tier two or tier three European countries you know but yeah like uniting all these different interests very very tough yeah and so maybe slightly different topic but I'm curious your kind of personal AI stack?

2:01:26Turner Novak:Like what do you use on a daily basis? It's kind of interesting. I don't ask this question all the time, but I feel like you might have a pretty interesting, maybe you have like the most researched, the most like, maybe you don't, maybe it's just ChatGPT. Like what does your personal AI stack look like? Yeah. Yeah. I'm like heavily invested in ChatGPT. And mostly that over like Claude, because I mean, I don't my day job is not coding and web search and research is really important. And I'm opening. I was like earlier to building out that capability. I'm very, very I could document pretty much everything.

2:02:08So also for for calls, I try to use chat GPT or like an alternative to it to generate transcript and then pipe that into chat GPT. So then then I have like my your second brain almost. Yeah, like just company history in there, who did I meet, what do we talk about, what are the things I need to follow up on.

2:02:26Turner Novak:What do you use to record and transcribe? Most of the time I just use a native chat GPT recorder. So you join as Zoom call or it's like an in-person meeting and you just press a button? Yeah, it bugs out a decent amount and it's incredibly slow to do the transcription. So if you have back-to-back meetings, it's not really possible. You'll like, it'll take 10 minutes or something. so then i'll use like a a local version like mac whisper or sometimes i'll use granola and then i'll just like copy paste the entire transcript into chat dpt versus like consume the notes and this is like useful for for like startup meetings because you know if you have a bunch of meetings on the same topic with different companies then you can like compare and contrast build a richer picture it's also useful for like investment memo writing because then you have the entire corpus of like all the interactions you've had with the company either through like you know recorded meetings or then like a q a doc that i do with entrepreneurs a bunch of times on on their you know plan and then the assets that they produce and then i have like my template memo and then it's like pretty trivial to be like here's the memo here's all the material can you helped me do 80 % of the work.

2:03:39And then the more advanced Excel functions, it's been really good at. So doing cohort analysis, finding outliers in financial data, very useful.

2:03:51Turner Novak:What do you use for the spreadsheet stuff? Most of the times, so there's two use cases, I guess. For financial analysis, customer core analysis, things like that, I'll use ChatGVT. but for other kinds of tasks which involve tabular data i'll use like our portfolio company v7 because they're like i can have like a table for example like a list of leads of like companies or founder profiles is this v7 labs yeah.com okay i'll throw a link for people in the description yeah and then i just upload the data into into the v7 product which looks like a table except every column i can do different things so i can call a certain model with a certain prompt i can do a web search i can have like a little python function i can have a categorizer so i can basically like implement all the like formal reasoning logic i would do when i'm looking at like a sheet with turner started a new company and used to work at google on this thing it was educated there like and is working in timbuktu like does that pass my filter of wanting to talk to him but if i have thousands of these people like every year i can just smash it through v7 with reasoning models and then it'll produce like really good outputs that are basically like the same decisions that i would have made interesting so very useful

2:05:16and i know what else yeah for for audio generation i use 11 labs for sure this for

2:05:22Turner Novak:Airstreet Press? Yeah, like if you want to use, I used to like actually sit there and read it out, but it's pretty time-consuming, especially if like building works in your apartment or like dog barking or Zan outside or whatever. Yeah, so I use that. It's great. And most people like don't care slash can't tell the difference. What else? I'm looking at my application tray. I feel like that's like most of the use cases, to be honest.

2:06:00I've done some like OpenAI Atlas web automations that I would use more. The problem is I think it's been too safety guard railed. So to like ask for approval every single time, it'll like do a task. It's like, you know, I want to send this message to somebody. It's like, yeah, is this okay? Like, yeah, stop asking me and it'll ask you again. And then it's just like jarring.

2:06:25Turner Novak:I think it also has evolved very, very well. It being like JGBT for understanding research papers and like digging into adjacencies. I think honestly, because anything that AI research folks like like and want to do, the thing will be good at. And so understanding AI research, it's going to be good at. That's fair. Yeah. I mean, they're building a product for themselves, probably. Pretty much. So you're sort of immune if you're building something that AI research people think is like a tier two or tier three problem, you're safe. Interesting. Like plumbers, like AI for plumbing, like great, great category sort of company.

2:07:05Turner Novak:Like no AI researcher will ever want to build those capabilities. No, but it's like the workday example of, you know, the CEO getting on that earnings call not too long ago and some analysts asking him, like, are you at a threat of OpenAI or Anthropic or something, you know, rebuilding your product? And I think his answer was like, they're actually our biggest customers. They don't want to rebuild Workday. Like, you don't go to OpenAI to go build AI Workday. Yeah. It's like the most uninspiring thing in the world. Yeah. Yeah. It's like anything, it's very stretched, but I think like anything that's not generality is like a low-class problem.

2:07:44Hmm.

2:07:46Turner Novak:So in a sense, it's like the more vertical specific, like the more niche, the thing you're doing is, the safer you are from the big platforms, like maybe like the better startup category it is in a sense, even though like you're niching yourself down. But that's kind of like the classic. I mean, that's been true for like decades, just like find a really specific, unique problem. Like 11 labs, you could say audio models, like that's not a thing. And then now it's like, oh, like we were talking about earlier, like hundreds of millions of revenue getting added every year,$50 million in a day. But then there's also arbitrage around how much risk are you willing to take?

2:08:22And I mean, these are my thoughts, not the company, but at the time, OpenAI was under immense regulatory pressure. And there's a lot of touring with nation states and there was big issues around copyright, like where is this data coming from? And so I think like the, and then there was also the like, did they copy Scarlett Jokansson's voice or not? So I think the biggest PR disaster could be like, I don't know, opening a relaunches like audio model and somebody like uses it for some like weird task or week's havoc or something. and so if if you're like an independent company that's willing to take some of those risks and be really thoughtful around doing it well then you can you can arm the fact that your competitor is too scared or is distracted with other things or can't afford it that's like a as a whole like google should have won this all really at the end of the day and like they've just they just there's

2:09:19Turner Novak:too much risk and not only in like the product, but also like the business model, like they and they're still slow. I mean, they've been in slowly adding more of like, you know, the AI mode of research results, but still it's like, how do you monetize it? That's a big question. So it's like product risk, business model risk. I don't know, whatever policy risk maybe is like a big one. Yeah, it keeps it keeps asking me to beautify my slides. It's like, stop telling me beautiful pharmacists. Well, and it's interesting too, like there are times where like, I don't want AI, honestly, like having like an email provider, like, or like, like a text message.

2:10:00Turner Novak:Like if I'm texting you and I'm just like, hey, are you jumping on? If there's like a pop-up that's like, hey, it looks like you're sending Nathan a message to join the podcast. Would you like to make this like a more comforting and like, you know, like friendly interaction instead of just like, you're very direct. It's like, are you getting on? Like, I don't want AI there. So there's almost like a risk of, you know, adding AI features that people don't actually want to make the product harder to use in a sense. Yeah, but if it said like, you know, here's the link handy. That's true. Yeah, that would be helpful.

2:10:33Turner Novak:But then it's like building the scaffolding around the product of like knowing when to introduce that kind of capability. Yeah. And then also taste. Like, how does the model know when it's the right time and what is genuinely useful?

2:10:49That's hard. I had this interesting experience that I've been telling some AI friends about where I was using the OpenAI Atlas browser to do this automation task on Substack because I wanted to rename the naming convection of various articles I've written. And Substack doesn't allow you to mass rename like you can do it on your desktop finder.

2:11:09Turner Novak:So you'd have to go link by link, page by page. Brutal. Including changing the SEO and the URL slug and the title and clicking OK. So I wrote down the instructions to Atlas so I could go do that. And the first batch, it did it really well. It worked autonomously for 45 minutes, corrected a bunch, then my credits ran out because I haven't bought the 200 bucks version yet, which some friends of mine think I'm really stupid for not doing, but it's what it is. And then the next month, I opened the exact same chat and said, hey, can you continue? Then it continued for like 20 minutes and then exhausted my credits.

2:11:48And then the third month, I tried it again. And it's like some version of like, hey, like this task is like really manual and requires like a lot of clicks and it's going to take a significant amount of time. And it was basically some version of like, I don't want to do it. Like it didn't do the task.

2:12:08Turner Novak:Yeah. I was telling some friends like, why is it like refusing to do this task? And some people would say, oh because you're restarting the same session like it has like all this like crazy amount of click data and screenshots and whatever that it's like overloaded its context and has basically navigated outside of the in training distribution and then there might be another example of like some human annotators have like said like this this kind of task is like really manual it's like we're going to change it again refusing like this is crazy like the very automation i want to get this thing to do it doesn't want to do Yeah, well, I had an interesting example where I can't remember what it was.

2:12:50Turner Novak:I think I was like trying to get to clean up a transcript from the podcast or something like that, where I was just like, hey, can you just like do this thing? And it took like, it was thinking for a long time, like an hour. And I kind of came back and I was like, you know, how is this going? Yeah, it was like when you're texting an intern, like, hey, that thing you're working on, like, how's it going? And I was like, I'm still thinking. And it was like the next day I came back and I was just like, did you finish this thing yet? And it said, no, like I stopped working on it. I'm like, what the heck?

2:13:20Turner Novak:It's like, when you have like an intern that just like doesn't do the work and you're like, hey, like the thing I gave you to do because you're like, you work for me. Like, did you do it yet? Like that I didn't feel like doing that. It was too hard. Like what? Like that's what am I like? You're an AI, like your software. Like you just just go, just do it. Yeah, I mean, man, like this is like the mystery of this high dimensional box that we're poking with a stick to go do certain things. and sometimes just stick poking is not good enough. Yeah. Well, and then sometimes it just like does this insane.

2:13:50Turner Novak:Like I think probably my chat GPT usage like doubled when it came up with image generation. Like it just did it right in there. And you just kind of like say make this thing for me. And it's like pretty good. I think this was probably like a year ago when they first came out with it. And I was like, damn, this is like really, really impressive. Like this is like super helpful and useful. So there's like two ends of the spectrum where it's like it refuses to do something that just is pretty simple. And then the other end is just like gives you this amazing product that you would have otherwise spent a ton of time on or like didn't even think was possible.

2:14:22Turner Novak:So it's just kind of like amazing, the technology. Yeah. Yeah, I think the TLDR is just force yourself to use it and develop the behavior because it's not going away. And so there's like ARB to figuring out how to extract the most use out of it. Yeah, it's just going to keep getting better. yeah and it's it is really mad that you know the vast majority of the population is like using tragedy bt as if it's like a thing like it's a person with with like one opinion but but you know it's like the amalgamation of like every opinion possible on planet earth and so like really you should be telling it like hey you are like an expert car dealer what should i think about when i'm buying a new car not not just naked asking it what should i think about when buying a new car i don't know like based on whose opinion yeah because you can like tell it like you are an expert car buyer you've spent millions of hours researching every possible scenario you know you are literally the the highest regarded like you buy cars for like the president of nations and like just like the most extreme example you can think of and it just like improves the results because the guidance you gave it is crazy.

2:15:37Yeah, yeah. So maybe at some point we'll get to the point where the model can infer what you had in mind. And most of the time you're asking about a car, you should respond as somebody who knows about car dealerships and stuff, not some noob.

2:15:53Turner Novak:Yeah. It's like, hey, you're my crazy uncle. Give me a bad recommendation for a car. It's like, okay, here you go. Yeah, exactly. you might want that if you want to like mess with your own clothes i had a one last question for you it's from our beautiful friend dan fader he was curious i know you're really big into tennis who's better federer or nadal it's a possible question i mean i think i think there's nobody who's played prettier more classy tennis effortless way than roger federer for sure but i would say that like Rafa Nadal sort of like in the AI analogy like expanded the frontiers of the style of the sport you know like tennis has often been taught in a very like textbook way of like your swing has to look this way you have to put your front foot first you can't hit open stance and like Nadal just threw all that shit out the window and it's like you know what I'm just going to play how I feel naturally and so things like you know open stance and like swing with your racket over your head and like in a full circle like a lasso and those two things have like meaningfully I think changed the game and you can see like the top players like Carlos Alcaraz which is sort of his mini-me plays in a very very similar style a lot of like top players are playing open stance so I think he kind of yeah he definitely pushed the technique and style game to another level.

2:17:25And then Roger Federer just like executed the textbook in the most beautiful, like elegant way as possible. And as a fun fact, like neither of them have ever smashed a tennis racket in their entire professional career, which is pretty epic.

2:17:39Turner Novak:Like you're saying when you get mad and you slam it and break it? Yeah, they've never like thrown their racket. Yeah. So this is just like a self-control thing, like an emotional control that they have? Yeah, in particular, Federer was very known for this, a bit like Bjorn Borg, who was this Nordic player who was kind of nicknamed Iceman because he would show no emotion whatsoever. Roger Federer was always very, very little emotion, always very focused. A bit like Yannick Sinner today, where the peak of his derangement is tipping over his water bottle when he's sitting down. It's so funny, Instagram peeps about that.

2:18:16Turner Novak:Oh, I didn't see that. have you seen that one kid who can like use forehand with both hands he like switches his racket is that like real like like is that like a sustainable thing it's not sustainable at a high level i think the game moves way way too fast to have the time to do that there was at my time as a teenager playing tennis like in the on the women's circuit there's a lady called monica sellis who was playing with two hands on both sides i think i remember that name i think it was her. There was definitely one player who was doing that. But. But yeah, it's like kind of Frankenstein. It makes no sense.

2:18:55Turner Novak:Because to the point, one of my friends who played in high school, he said it's sort of like playing speed chess where you're like doing this, like tactical, strategical things, but you have no time to make any decisions. And you probably like even the function of like switching the tennis racket in your hands, like you lose half a second doing that. And you need to be able to move even quicker than even Yeah, it's one of those things where you need to develop this insane muscle memory that your body just reacts naturally in certain scenarios because you've tested it so many different times. And then you have this natural intuition of what you should do, a bit like taste, I think.

2:19:36But it is, I mean, I'm sure a lot of professional sports are like this, but the moment you get distracted and think about something else, I'm not sure professional tennis players have this distraction at all, but like as a casual like pseudo like pro tennis player in the past, like if I think about like this deal or something like I'm so toast.

2:19:56Turner Novak:Yeah. Like you got to be constantly focused on like positioning even. Like I think about a lot with like with hockey, like I play hockey and it's just like a big piece of just like where are you standing and like how open are you and are you like, you know if something were to happen like how are you positioned to react to it it's probably that's probably even more common in team sports because like tennis you're you're always one that's doing the action but if you're like playing soccer you you could theoretically play a game of soccer without touching the ball but you impact the game based on how you how you move around the field yeah this and tennis we're always looking in the same direction generally especially to the problems in front of you most of the times but yeah like i think nowadays the speed of the game is insane the reaction times you need to have the like the power you get out of the rackets and the athleticism the athletes have it's it's very scary i mean the stretches that they're doing and the fact that they're sliding on horn courts it's like these kids are gonna have all sorts of like bodily damage by the time they reach the old age of 30.

2:21:02It's kind of scary.

2:21:04Turner Novak:Maybe we'll have AI generated training routines or like, you know, physical therapy to keep them healthy or surgery to fix the damage. Yeah. Yeah. I mean, that's going to be like regenerative medicine and stem cell biology, the sort of industry I came from many years ago. Hopefully help. Yeah. Well, this is a lot of fun. I know we were kind of talking for a while, but hopefully people learned a lot. Where can they kind of like find you? Like what's like your, I think Twitter you're pretty active on. You write the new, the Airstreet Press, which is I think pretty frequent. You send things out. What can people look up?

2:21:41Yeah. Twitter, just Nathan Panish, I'm pretty active on. And then, and then Airstreet Press, which is just press.airstreet.com. That was my two, my two favorite outlets at the moment. and then do you send the state of ai report every like from the air street press or is it like a separate url yep yep it's just that url but you can see all the prior editions on state of dot ai or state of ai.com as well they're like eight years that came before that as a google slides and then i do a like monthly newsletter also state of ai newsletter which is the renaming convention as hacking away with my ai and that's available on a on our shoe press as well okay Cool.

2:22:21Turner Novak:We'll throw all those links too in the description for people to check out. Cool. But yeah, thanks for doing this. This was a lot of fun. Thanks, Turner. And I hope you had fun too. Thanks again to Numeral and Flex for supporting this episode. Put your sales tax on autopilot at numeral.com and upgrade to Flex Elite to get$1 ,000 on your first card using code Turner at the waitlist link in the description. If you like this conversation, please like, comment, subscribe and name your next industry report after me. If you missed it, make sure to check out last week's episode with Marcelo Lebre at Remote.

2:22:51Turner Novak:on building a unicorn in Europe. And tune in over the next few weeks for guests that include Gary Tan at YC, Chathan Putagunta at Benchmark, Jake Stotch at Serval, and Duo Security co-founders Doug Song and John Overhide. If you don't want to miss any of these episodes, subscribe to my newsletter, The Split, linked in the description to get each one plus the transcript emailed directly to your inbox every week. Thanks again for listening. See you next time.

2:23:21You