Speaker 1: Hello, and welcome to another episode of the Odd Lots Podcast.
Speaker 2: I'm Joe Wisenthal and I'm Tracy Alloway.
Speaker 1: Tracy, I mean, I think it goes without saying that
Speaker 1: the appetite and interest in anything related to AI and
Speaker 1: making money from AI continues unabated.
Speaker 2: Yes, I think that's accurate. Well, it's interesting because you
Speaker 2: kind of see it from two different sides at the moment.
Speaker 2: So there are a lot of companies that are talking
Speaker 2: about investing internally in AI technology, and then there are
Speaker 2: a lot of investors talking about investing in AI in
Speaker 2: one way or another.
Speaker 1: I mean needless to say. You know, you like throw
Speaker 1: a dart at nasdextoc some they're talking about, you know,
Speaker 1: the way AI incorporated. It's also funny because like you
Speaker 1: like read the uh the earnings transcript of like a
Speaker 1: grocery storagechep. Yes, and the seats we'll be talking about,
Speaker 1: you know, how AI is going to help them.
Speaker 2: Wasn't this Kroger?
Speaker 1: I think so. And to be fair, like I think
Speaker 1: they actually kind of legitimately have been investing. So it's
Speaker 1: totally but like they mentioned AI one company I don't
Speaker 1: like mention it like a dozen times.
Speaker 2: I'm pretty sure it was Kroger. Yeah, But I think
Speaker 2: I've said this on the podcast before. It does feel
Speaker 2: like there are some people out there who at this
Speaker 2: point are basically using AI as a synonym for any
Speaker 2: type of software, just like we use software, we're using AI.
Speaker 2: But it begs the question of how to smartly invest
Speaker 2: in a technology that clearly a lot of people are
Speaker 2: enthused about. But it's also kind of hard to disaggregate
Speaker 2: a lot of the marketing from the reality.
Speaker 1: Well, and the thing that gets me in that I'm
Speaker 1: still trying to wrap my head around too, is like,
Speaker 1: especially for the big tech incumbents, And I'm thinking of
Speaker 1: like Alphabet, Google or whatever, Like they have an amazing
Speaker 1: business model right now, right, like people search for something
Speaker 1: and then you're telling the machine exactly what you are
Speaker 1: looking for, and then the machine knows, okay, well, here
Speaker 1: are some ads that we can put And I know,
Speaker 1: like obviously Google is you know, a head very like
Speaker 1: front of the curve in terms of AI tech, and
Speaker 1: they have their own large language models and all of
Speaker 1: that stuff. But does anyone know that like this is
Speaker 1: going to turn into like a money making thing for them?
Speaker 2: Right?
Speaker 1: Will it be anywhere close to this amazing money printing
Speaker 1: machine that they built when they built the Google search box.
Speaker 2: Well, I think that's a really good question. And also,
Speaker 2: so far we have seen the incumbents come out as
Speaker 2: the big winners of a lot of this new technology,
Speaker 2: and I think a that's been unexpected. If you'd ask someone,
Speaker 2: you know, five years ago, who was going to be
Speaker 2: the big winner in AI, I don't think anyone would
Speaker 2: have said microsom right, right, And so that also raises
Speaker 2: a question of Okay, how did the giants monetize this?
Speaker 2: To your point? And then secondly, are there going to
Speaker 2: be new players who somehow come in and find a
Speaker 2: better way to do it right?
Speaker 1: Like how disruptive it will be?
Speaker 3: Yeah?
Speaker 1: Well I am excited because I believe we do have
Speaker 1: the perfect guest. We're going to be speaking with Josh Wolf,
Speaker 1: founding partner and managing director at Lux Capital, who has
Speaker 1: been investing in AI since long before it was cool,
Speaker 1: long before everyone started asking like chat gpt to like,
Speaker 1: you know, write a song about the FED and the
Speaker 1: style of Johnny Cash or whatever like long before No
Speaker 1: I feel seen, I feel I'm describing myself. I'm describing
Speaker 1: both of us long before we were all doing that,
Speaker 1: and so he's going to talk to us about how
Speaker 1: he thinks about making money in AI and where the
Speaker 1: value is going to crue in identifying investments. So, Josh,
Speaker 1: thank you so much for coming on Odd Laps.
Speaker 3: Joe Tracy, great to be with you.
Speaker 1: Can I ask you a question for real? Like we
Speaker 1: obviously have this boom, etc. But the tech's been around.
Speaker 1: Did people basically just get excited because like someone finally
Speaker 1: put a good ux on top of this technology for
Speaker 1: a while and suddenly like, oh my god, Like was
Speaker 1: that sort of what catalyzed this current stage of enthusiasm.
Speaker 3: I think the lay answer is what catalyzed this was
Speaker 3: a sense of conjuring magic. People felt like they were
Speaker 3: effectively casting spells, like you said, whether it was a
Speaker 3: Johnny Cash song conjured to you know, to talk about
Speaker 3: the FED, but it's the feeling that somebody had a superpower.
Speaker 3: So I think that's what catalyzed that. Where it was
Speaker 3: a feeling of positive surprise where people were like, oh
Speaker 3: my god, like I just created magic and that you know,
Speaker 3: classic cliche that any sufficiently advanced tech is indistinguishable for magic.
Speaker 3: This felt like magic now the roots of it go
Speaker 3: back over a decade, and that's what the public doesn't see.
Speaker 3: And part of that is the bones, and part of
Speaker 3: it is the brains the tech infrastructure. So you start
Speaker 3: with the GPUs. Now, we've known computing for decades and
Speaker 3: Intel was the dominant force. Intel made CPU central processing units,
Speaker 3: and there was this thing off on the side that
Speaker 3: was just doing graphic processing and it was for video games.
Speaker 3: I've got this mental model, this framework where a lot
Speaker 3: of people say that the most dangerous words in investing are,
Speaker 3: this time is different. I actually think that there's a
Speaker 3: secret that people can follow, which is that the most
Speaker 3: valuable words are whenever you hear a parent say it
Speaker 3: will rot your brain, that basically presages it predicts the
Speaker 3: next ten billion dollar industry. So think about this. I
Speaker 3: mean literally nineteen sixties those hip shaken, Johnny Cash, Elvis,
Speaker 3: you know, rock and Roll, It'll rot your brain. Boom
Speaker 3: ten billion dollar industry. You know, seventies, you know personal
Speaker 3: computers and chat rooms, and and you know nineties the
Speaker 3: Internet and these online chat rooms and then gaming. My god,
Speaker 3: you know, these kids are turning into couch potatoes. Get
Speaker 3: them off the video games, the video game players of
Speaker 3: yesterday are today's you know, robotic surgeons and drone pilots.
Speaker 3: But what's really important is the tech that was underlying
Speaker 3: that these massively multiplayer games and people demand and ever
Speaker 3: higher video resolution and PlayStation competing with Xbox, completing with Nintendo.
Speaker 3: It created these chips that took in video from a
Speaker 3: fifteen billion dollar market cap at the time when Intel
Speaker 3: was one hundred and fifty billion a few years ago,
Speaker 3: and today it's a trillion dollar business. We had invested
Speaker 3: in this company going back about eight years. It was
Speaker 3: four people off the Stanford campus, your classic garage, and
Speaker 3: they were literally in a garage at the slack the
Speaker 3: Stanford Linear Accelerator, sort of secret group, and they were
Speaker 3: trying to develop self driving cars. And we had put
Speaker 3: about twenty five million into this small team called Zookes,
Speaker 3: which was a zoo for robotics. It was a silly name.
Speaker 3: And we go in there and I see all these
Speaker 3: people playing video games and I start to get a
Speaker 3: little bit upset. I say, we just put a lot
Speaker 3: of money into this company. What are all these engineers doing.
Speaker 3: And the founder turned to me and said, no, no,
Speaker 3: you don't understand the cars that you see outside on
Speaker 3: these tracks that are running, they're ingesting information at the
Speaker 3: rate of one second per second, what we call reality,
Speaker 3: and they're taking light ar and radar and visual cues
Speaker 3: and thermal sensors and vibrational sensors and all that and
Speaker 3: they're processing it. But inside these rooms air conditioned, these
Speaker 3: people are not playing video games. This is not grand
Speaker 3: theft or this is not call of duty. The machines
Speaker 3: are actually running simulations and they are training the cars
Speaker 3: thousands of simulations a second, and the machines don't know
Speaker 3: the difference between reality and simulation. And I was like,
Speaker 3: oh my god, okay, this is pretty amazing. What are
Speaker 3: these things running on And they said, those two guys
Speaker 3: over there are from this company, in Vidia, and we
Speaker 3: have chips that aren't on the market yet and they
Speaker 3: are able to do processing like never before. And that
Speaker 3: sent us on this path as investors in AI. And
Speaker 3: from there we found this amazing team that was developing
Speaker 3: like the next gen GPUs and was a guy Navine Row.
Speaker 3: He had a company called Nirvana Systems. Intel buys them
Speaker 3: within a year of US investing for about three hundred
Speaker 3: and fifty million, becomes the core kernel of Intel's AI system.
Speaker 3: We going back that guy again, and just last week
Speaker 3: Data Bricks bought his new company called Mosaic for a
Speaker 3: billion three congratulations, thank you, wild Ride. But you just
Speaker 3: try to find these people that have this irreverent view
Speaker 3: and they sort of see the future and they invented,
Speaker 3: and you get behind them.
Speaker 2: So maybe if I could just step back for a second,
Speaker 2: and this will sort of maybe tell us a little
Speaker 2: bit more about what you're doing in the space. But
Speaker 2: how do you evaluate AI opportunities between the hardware? So
Speaker 2: the chips where they're so far seems to have been
Speaker 2: a lot of excitement and activity versus some of the
Speaker 2: software and the sort of underlying models.
Speaker 3: Today in video really has the lead. It's very hard
Speaker 3: for people to compete. Obviously, there's all kinds of considerations
Speaker 3: of geopolitical dependency and TSMC and ASML and who's helping
Speaker 3: to make these chips. But there's an entire very sophisticated
Speaker 3: stack of semicab equipment manufacturing, IP design so that people
Speaker 3: can make these chips and then the chips themselves. And
Speaker 3: these things are very expensive. I mean, these h one
Speaker 3: hundred chips from Nvidia one hundred thousand dollars. They are
Speaker 3: in scarce supply. One of the other really interesting things
Speaker 3: right now in this chip domain that people should watch for.
Speaker 3: And then I'll tell you where I think in Vidia
Speaker 3: is actually quite vulnerable and they're not just pure monopoly here.
Speaker 3: Anytime that there's hype in a sector, just like you
Speaker 3: were talking about, you know, Kroger's adding AI to their name.
Speaker 3: You saw this in the dot coms, you saw it
Speaker 3: in the Internet, you saw it in mobile everything exactly.
Speaker 3: And you know, look, they lower the cost of capital,
Speaker 3: they take advantage of people's irrationality, they capitalize. And what
Speaker 3: happens in every field, the hype gets high, the cost
Speaker 3: of capital gets low, Hundreds, if not thousands of new
Speaker 3: companies get funded, ninety nine percent of them fail, and
Speaker 3: from that detritus it becomes the comminatorial fodder for the
Speaker 3: next wave. So interestingly, with crypto, crypto people were like
Speaker 3: clamoring for these GPUs. They couldn't get enough of them
Speaker 3: so that they could do bitcoin mining. As that market went,
Speaker 3: you know, hyperbolic, and then crashed. Now you have all
Speaker 3: these xsgpus and you're starting to read headlines about the
Speaker 3: bitcoin miners that are selling their GPUs now to the
Speaker 3: AI researchers, and they're able to do it now, you know,
Speaker 3: cents on the dollar of what they paid before. So
Speaker 3: on the hardware side, that's very interesting. But our bed
Speaker 3: is that there's something different that's happening than betting on
Speaker 3: the next chip. Okay, there's More's law which everybody knows about.
Speaker 3: There's Rocks Law, which basically is that the cost of
Speaker 3: these semiconductor fabs increases exponentially even as our chips get cheaper.
Speaker 1: And sorry, which law?
Speaker 3: Which law Rocks named after Arthur Rock, who is one
Speaker 3: of the you know, first vcs, one of the first
Speaker 3: funders of Intel in East Coast og VC. So they
Speaker 3: called it Rocks law because basically, the cost to build
Speaker 3: these fabs, to make the chips keeps getting more expensive
Speaker 3: every year and a half or two years. It used
Speaker 3: to be you know, one hundred million, then it's a billion,
Speaker 3: and it's ten billion and so on. Okay, why is
Speaker 3: that relevant to make these foundation models that we're all
Speaker 3: using behind the scenes, GPT three and GPT four and
Speaker 3: what comes next. It used to be a few million
Speaker 3: dollars maybe ten million to train GPT three GPT four
Speaker 3: is estimated in the low hundreds of millions of dollars.
Speaker 3: And whatever comes next, people believe is going to cost
Speaker 3: about a billion dollars. Why because they have to buy
Speaker 3: all these in video chips. So in video is telling everybody,
Speaker 3: you've got to get these a one hundred chips. We
Speaker 3: make these h one hundred chips. We make. The reality
Speaker 3: is actually that there's this interesting vulnerability. This is where
Speaker 3: we make our speculative bets. Now we might be wrong,
Speaker 3: but this is what we're betting. We're betting that that's
Speaker 3: not going to be the case, that it's not going
Speaker 3: to be just the domain of open AI, that it's
Speaker 3: not going to be anthropic, that it's not going to
Speaker 3: be just the big giants. And we'll talk about how
Speaker 3: those guys are all intertwined, like you said, with the
Speaker 3: Microsofts and the Googles and the Metas, etc. Because that's
Speaker 3: an interesting dynamic. In Vidia has a language, a computer
Speaker 3: language that people program on and it's called Kuda Cuda,
Speaker 3: and this has been the dominant form, but it's really vulnerable,
Speaker 3: and it's vulnerable interestingly because of Facebook Meta. They came
Speaker 3: up with this language called Pi Torch and a lot
Speaker 3: of the developers are moving to pitorch. It's open source,
Speaker 3: it allows people to do a lot of the AI processing,
Speaker 3: but hardware agnostic, meaning you don't have to use an
Speaker 3: in video chip in video. If you use in video chip,
Speaker 3: you've got a program on Kuda. These guys are saying,
Speaker 3: we can use an AMD chip, we can use an
Speaker 3: ASK an application specific integrated circuit. They are saying, we're
Speaker 3: not going to be beholden to this. So there's two
Speaker 3: competing software languages that are merging sort of quietly. One
Speaker 3: is called PyTorch and one is called Triton. And Triton
Speaker 3: is from open Ai. People probably trust that one a
Speaker 3: little bit less, and Pietorch is totally open source but
Speaker 3: originated from Meta, which is really interesting.
Speaker 1: Man, I'm already learning a lot because I was not
Speaker 1: familiar with PyTorch. You know. So obviously, as you mentioned,
Speaker 1: and we talked about GPT three, GPT four, chat GPT,
Speaker 1: this magical search box that got that got everyone's attention.
Speaker 1: This sort of like what came out of open Ai.
Speaker 1: But there are others that are building chat pots. How
Speaker 1: many winners can there be? A like sort of like
Speaker 1: how do you think about winners? Either at the foundational
Speaker 1: model level, like if we started odd lots GPT, is
Speaker 1: that a worthwhile area or are you thinking like some
Speaker 1: of these problems are kind of solved and it makes
Speaker 1: more sense to focus on building something on top of
Speaker 1: one of these like core winners like GPT four and
Speaker 1: build some sort of a specific application for an industry
Speaker 1: that uses a foundational model that already exists.
Speaker 3: You're thinking exactly right, and you know you should be
Speaker 3: a VC because we're betting on the ladder that you're
Speaker 3: gonna start to get these generalized models which are wowing everybody,
Speaker 3: although you know, you start to look at some of
Speaker 3: the usage pattern classic thing, right, people get really excited
Speaker 3: about the thing, then it starts to die off. Maybe
Speaker 3: it's the summer, but maybe it's reached a little bit
Speaker 3: of a plateau of incremental interest. Okay, so let's break
Speaker 3: this down in terms of the models. You've got open AI,
Speaker 3: which will continue to invest a huge amount of money,
Speaker 3: continue to develop models, continue to wow people. Their next
Speaker 3: thing will be quote unquote multi modal instead of just
Speaker 3: text or voice transcription. You'll start to have all kinds
Speaker 3: of interesting things where like you said, you know, make
Speaker 3: me the Johnny Cash song, give me a full music video,
Speaker 3: print out, all kinds of crazy images. You know, it'll
Speaker 3: it'll do four different things, and that'll be really limited
Speaker 3: by people's creativity. But it's going to be general. Now.
Speaker 3: One of the problems with the general stuff GPT four
Speaker 3: was Plint was trained on the public Internet, and proud
Speaker 3: of the problem in the public Internet is that you
Speaker 3: got a lot of information, but you also have a
Speaker 3: lot of misinformation. It was trained on Reddit and Twitter
Speaker 3: and all kinds of repositories of public info, and so
Speaker 3: it's going to hallucinate. It's going to give you bs answers.
Speaker 3: So you have people saying, Okay, that's a problem, there's
Speaker 3: white space, let me solve it. And it's probably going
Speaker 3: to be financial and healthcare is our guests, where you
Speaker 3: get very specialized models that need to have high accuracy,
Speaker 3: and they're going to be smaller models. So instead of
Speaker 3: these giant models, they're going to be smaller, more bespoke,
Speaker 3: more industry verticalized. Even Bloomberg Bloomberg GPT people I think
Speaker 3: are really fascinated by what that's going to portend. Because
Speaker 3: you have a proprietary data set, you've got a locked
Speaker 3: and user base and sort of a social network and
Speaker 3: you have reliable, high quality data. So I think that's
Speaker 3: going to be the next wave. It's going to happen
Speaker 3: in financial data. My bet is on Bloomberg. It's going
Speaker 3: to be in healthcare with some of the major healthcare systems,
Speaker 3: and I think that that's sort of the next wave. Now,
Speaker 3: when you look at the big players today with the
Speaker 3: big foundation models, open ai, if you're really honest about it,
Speaker 3: they're captive to Microsoft. Microsoft did an incredibly clever deal.
Speaker 3: They knew that there's no way that the DOJA or
Speaker 3: the FTC would allow them to actually acquire open ai,
Speaker 3: so they structured a deal in a way that they
Speaker 3: effectively control it, but without doing an acquisition. You look
Speaker 3: at Google, they're closely tied up with Anthropic, and a
Speaker 3: lot of these deals are interesting because what happens is
Speaker 3: the company gets a giant equity investment. In this case,
Speaker 3: I think Anthropic got about three hundred million from Google,
Speaker 3: but that money sored around trips. Google gets equity, Anthropic
Speaker 3: gets cash. That cash then goes back to Google and
Speaker 3: is spent on compute, so they get to book it
Speaker 3: as revenue in Google Cloud. Now Meta is really interesting
Speaker 3: because just like you said before, Tracy, nobody would have
Speaker 3: thought Microsoft was the leader or would be a leader
Speaker 3: in AI. Meta, you know, has been under congressional scrutiny
Speaker 3: and has been the sort of evil villain of consumer
Speaker 3: and social media and disrupting and destroying our democracy and
Speaker 3: all this stuff. And then they made this bet on
Speaker 3: the metaverse, which nobody cares about, but this idea of
Speaker 3: this fetiverse that they're starting to talk about with threads.
Speaker 3: It's really interesting because they are embracing this idea of
Speaker 3: open source. Now. They're not doing it benevolently because they
Speaker 3: think it's a good thing. It's in their self interest.
Speaker 3: They want to be the sort of network that is
Speaker 3: connected to everything else. They don't want to be siloed,
Speaker 3: and they get to use it as a little bit
Speaker 3: of achine to stave off the regulatory scrutiny and the
Speaker 3: public criticism. But I think you've got to watch Meta
Speaker 3: really closely. In the coming weeks, new releases of open
Speaker 3: source models that are going to really compete with open AI,
Speaker 3: lots of partnerships with interesting companies knowing that they themselves
Speaker 3: couldn't possibly do an acquisition.
Speaker 2: You mentioned data just then, And this is something I've
Speaker 2: been thinking about. But when it comes to AI technology,
Speaker 2: what's the most important factor? Is it access to reliable
Speaker 2: data as you mentioned, maybe reliable and exclusive sources of
Speaker 2: big data, or is it the sort of like underlying
Speaker 2: modeling technology. And I guess another way of framing it
Speaker 2: is like, are the big winners going to just be
Speaker 2: companies like you know, banks, insurers that have huge data
Speaker 2: sets that they can do things with.
Speaker 3: I think so. I think that today it has been
Speaker 3: a little bit of ignorance arbitrage, meaning the people that
Speaker 3: really were in the know were the model makers, the
Speaker 3: people that could design the algorithms to do the predictive
Speaker 3: analysis and make the models. All those models are either
Speaker 3: held proprietary in the case of like open AI, or
Speaker 3: in the case of one of our companies, which has
Speaker 3: one of the most powerful repositories and one of the
Speaker 3: most ridiculous names, Hugging Face. One of my partner's amazing
Speaker 3: guy Brandon Reeves. He says, you know, there's these French
Speaker 3: PhD computer scientists and mathematicians. They're hanging out in Brooklyn
Speaker 3: and they've got this company called Hugging Face. And here's
Speaker 3: the irony they started out as a chatbot, you know,
Speaker 3: almost like Joaquin Phoenix and her, and then they became
Speaker 3: this open source repository for all all the models, and
Speaker 3: now hundreds of thousands of models, including corporate models that
Speaker 3: are hosted there and constantly improving, and it's all open source.
Speaker 3: And the irony is that OpenAI started as this open
Speaker 3: model company has become the world's greatest chatbot. So it's
Speaker 3: sort of inverse. So hugging Face is making these models,
Speaker 3: and they were the beneficiary very early on of everybody
Speaker 3: trying to deploy the models. They could run them on
Speaker 3: Hugging Face, they could use the cloud compute that they provide.
Speaker 3: And now, Tracy, to your point, you're starting to see
Speaker 3: people saying, Okay, the thing that we want to do
Speaker 3: is build on top of all of these models. What
Speaker 3: was expensive and scarce and rare before was the compute
Speaker 3: and the algorithms, and those are becoming increasingly abundant. So
Speaker 3: what is scarce today reliable data and proprietary data and
Speaker 3: the data sets, like you said, it could be big banks,
Speaker 3: could be consumer data, could be Amazon retail spending information
Speaker 3: could be Spotify with user's behavior, it could be healthcare
Speaker 3: systems appropriately anonymized and protected and compliant with HIPPA, but
Speaker 3: being able to collect all this information and have it
Speaker 3: do high quality inference and training. So you have to
Speaker 3: train the models on the data, and then you have
Speaker 3: to be able to do the predictability, which is the
Speaker 3: inference from somebody putting in a prompt. I will say
Speaker 3: the area that we're probably the most excited about, which
Speaker 3: is not something that the everydayly person is going to
Speaker 3: spend time doing. They're not going to be making those
Speaker 3: Johnny Cash songs or conjuring images on mid Journey and
Speaker 3: Dolly is biology. And the key breakthrough here is there's
Speaker 3: something in all these models called a context window, and
Speaker 3: all it means is basically how much information you can
Speaker 3: put it. And if you ever tried to take like
Speaker 3: a long transcript let's say, of odd lots and throw
Speaker 3: it into a context window, it might say, oh, it's
Speaker 3: too long, right. The context window for open AI has
Speaker 3: been about eight thousand, eight thousand tokens Anthropic now has
Speaker 3: one hundred thousand, and that's growing. What that means is
Speaker 3: the amount of data that you can put into a
Speaker 3: single prompt is growing exponentially. If you think about the
Speaker 3: human genome, if you think about genetic data, where you
Speaker 3: have millions of tokens that you need to be able
Speaker 3: to put into this effectively. That is the next domain
Speaker 3: where you're able to put in huge amounts of information
Speaker 3: and do all kinds of predictive things, from designing new
Speaker 3: proteins to discovering drugs. And that's an area where not
Speaker 3: only are the markets enormous, the information and the expertise
Speaker 3: is very narrow and specialized. And I think it's going
Speaker 3: to completely upturn farm and biotech in a giant way.
Speaker 1: That's really interesting. You mentioned you're talking about some of
Speaker 1: these investments that the hyperscalers, the tech giants have made,
Speaker 1: and I hadn't really appreciated that dynamic before. It's sort
Speaker 1: of like easier to link it easier for Microsoft to
Speaker 1: link up with an open AI than to make a
Speaker 1: big acquisition that's going to get on the you know,
Speaker 1: the headlines for regulators. It's easier for you know, alphabet
Speaker 1: and anthropic et cetera as the VC. And also the
Speaker 1: point about how a lot of that cash just comes
Speaker 1: back in terms of all these companies' compute bill is
Speaker 1: extremely interesting as a VC. Can you talk a little
Speaker 1: bit about this dynamic there seems to be a lot
Speaker 1: because of this of corporate VC in AI specifically, and
Speaker 1: how that sort of changes the game as a non
Speaker 1: corporate VC or as an independent VC firm when you're
Speaker 1: thinking about evaluating companies, the presence of these big the
Speaker 1: VC arms of the large corporations and how that sort
Speaker 1: of changes the game.
Speaker 3: Great, great question, And I'll give you sort of three
Speaker 3: quick angles here. The first is how we think about
Speaker 3: ultimately making money and exiting as a VC, and how
Speaker 3: these people play in the ecosystem. The second is a
Speaker 3: related question about how do we ultimately exit our companies,
Speaker 3: meaning how do we sell them to a large incumbent
Speaker 3: when you have all this congressional scrutiny and it's very
Speaker 3: improbable today that Microsoft or Meta could do a big acquisition.
Speaker 3: It's just you know, it's a regulatory and problem. And
Speaker 3: then the third is a geopolitical angle here that I
Speaker 3: think is stually going to change that. So on the
Speaker 3: first one, I always say that it sounds a little
Speaker 3: bit cheesy, but we do a lot of hard signs investing,
Speaker 3: a lot of deep tech investing, and I like to
Speaker 3: say like the first law of thermodynamics, energy is not
Speaker 3: created or destroyed. Risk and value are not created or destroyed.
Speaker 3: They just change form. And every risk that I can
Speaker 3: identify in an early stage company AI or biotech or aerospace,
Speaker 3: whatever it is, if I can kill that risk, if
Speaker 3: I can actually say, Okay, there's financing risk or tech risk,
Speaker 3: or management risk or product risk or customer risk, whatever
Speaker 3: it is, kill that risk. A later investor coming after
Speaker 3: us should pay a higher price and demand a lower
Speaker 3: quantum of return because they're taking less risk, and I
Speaker 3: should get rewarded for taking the early risk. So I
Speaker 3: sort of think about it as destroying risk to create value.
Speaker 3: Why do I say that, Because if we take an
Speaker 3: early stage risk in a company to prove that the
Speaker 3: tech works, I want those corporate vcs coming in. I
Speaker 3: want them coming in. I want them paying a higher
Speaker 3: price than we did, providing a lower cost of equity
Speaker 3: than we did, and helping to both validate and create
Speaker 3: some competition. So I'll give you an example. Runway mL
Speaker 3: a bunch of interesting scientists. One of them was an
Speaker 3: intern back in the day at Hugging Face became a
Speaker 3: co founder of this company, Runway. Runway basically said, we
Speaker 3: can take the cutting edge models that we're developing. They
Speaker 3: actually were the developer of stable diffusion, and we're gonna
Speaker 3: make videos. We're gonna start with two second videos. You
Speaker 3: talk to the CEO there, Chris, he will say, within
Speaker 3: the next two years, you have a full feature movie
Speaker 3: that is entirely generated by people sitting at a computer
Speaker 3: and just prompting angles, lighting, actors, expressions, the I mean,
Speaker 3: it's like a little bit hard to fathom. It's like
Speaker 3: looking at YouTube when it was two hundred and forty
Speaker 3: pixels versus like eight k today. But it's gonna happen,
Speaker 3: and it's interesting.
Speaker 1: Totally full featured Hollywood film. Everything perfect except the hands.
Speaker 3: Exactly, although I think they'll get the hands right and
Speaker 3: there'll even be some you know, unique special effects. But
Speaker 3: the sound, the lighting, the angles, everything. I think we're
Speaker 3: two years from something that actually you think, oh my god,
Speaker 3: that was made by AI and it'll probably be a
Speaker 3: shorter film, but it's it's coming. Okay. Why do I
Speaker 3: say that. You just had one hundred and forty million
Speaker 3: dollar financing announced a week or two ago, Google in
Speaker 3: Video and Salesforce, and those are three great companies one
Speaker 3: is on the data side, one is on the sort
Speaker 3: of strategic side, one is on the hardware side that
Speaker 3: wants them to use their compute. All of those guys
Speaker 3: are now linked with this company. And so Google's competitors
Speaker 3: are looking at this, and salesforce doesn't want to be
Speaker 3: left behind, and and Vidia is looking, and AMD is looking.
Speaker 3: And so the more corporate strategic folks that you get in,
Speaker 3: the more competitive juices start to flow, and it increases
Speaker 3: the chance for the founders and for us that not
Speaker 3: only do you get good strategic partners, but you set
Speaker 3: up competitive dynamic for future exits. And so that's typically
Speaker 3: you know, great companies get bought, and they get bought
Speaker 3: because there's petitive fervor from a corp dev person at
Speaker 3: one of the big companies that says, we can't let
Speaker 3: our competitors get this. Okay. So that was the second thing,
Speaker 3: which is against this regulatory backdrop. You know, who's going
Speaker 3: to allow these big companies to actually buy these small companies.
Speaker 3: And I'm hopeful, Okay, this is wishful thinking. This is
Speaker 3: more prescriptive than observant. I think that the regime today
Speaker 3: is very focused post twenty sixteen in the election and
Speaker 3: the chaos and social media and all the abuses, particularly
Speaker 3: that you saw at Facebook with users and fake information
Speaker 3: and misinformation. I think that we are turning our turrets
Speaker 3: of attention from Congress on the wrong targets. I think
Speaker 3: that focusing on the domestic industry and trying to slow
Speaker 3: it down and prevent acquisition and prevent failures and prevent
Speaker 3: these companies from buying and competing is exactly what some
Speaker 3: of our peer adversaries overseas would love. China and particularly
Speaker 3: the CCP, would love nothing more than for AI in
Speaker 3: the US to slow down and for all of these
Speaker 3: iterations and experiments to have problems and further to be
Speaker 3: a disincentive for vcs to want to fund these things
Speaker 3: because they'll never get out. And I actually think that
Speaker 3: you'll see some sea change coming in the next few
Speaker 3: quarters year two where people say, Okay, wait a second.
Speaker 3: You know, it isn't that we've met the enemy and
Speaker 3: he is us. We actually have to have domestic competitiveness,
Speaker 3: and one of the great assets that the country has
Speaker 3: is competitive great technology companies, and we need to let
Speaker 3: them thrive so that we can compete, particularly with China's CCP.
Speaker 2: Just going back to what you said about a fully
Speaker 2: AI generated movie. When I hear something like that, it
Speaker 2: sounds incredibly exciting. It also sounds very sci fi and
Speaker 2: difficult to wrap my head around in various ways. But
Speaker 2: it kind of leads into a very basic question, which is,
Speaker 2: what is it like to invest in a an AI
Speaker 2: right now? So how are you actually doing your due diligence?
Speaker 2: You know, if someone comes to you with an opportunity
Speaker 2: for investing in a new technology, is it like all
Speaker 2: of us sat here in the office playing around with
Speaker 2: chat GPT? Is that basically the thrust of due diligence
Speaker 2: on this technology or something else? And then, secondly, how
Speaker 2: competitive is it right now from a venture capital perspective
Speaker 2: to get in on some of these investments, because I imagine,
Speaker 2: given the level of excitement, there is a lot of
Speaker 2: money crowding into the space.
Speaker 3: So the latter all answer first, which is it's very competitive.
Speaker 3: I mean, anybody that can write a check is a competitor. Now,
Speaker 3: if you are a founder, you know, just like if
Speaker 3: you are a star high school athlete or a star
Speaker 3: high school scholar, you want to go to the places
Speaker 3: that reflect the quality of your craft, and so you
Speaker 3: might want to go to Yale or Princeton or Stanford,
Speaker 3: or you might want to go to Vanderbilt or Duke
Speaker 3: and you know in play or Michigan and playball. And
Speaker 3: so I think it's the same thing where great founders
Speaker 3: want to work with great firms. And lux and Sequoia
Speaker 3: and Andresen and a handful of others, you know, have
Speaker 3: brands that confer to a founder that we are highly selective,
Speaker 3: that we have a great network, that we can be
Speaker 3: value add But anybody can fund any of these companies.
Speaker 3: There's always somebody that's got a roommate who's got a
Speaker 3: mother or father that gave them some money and they
Speaker 3: became early investors in this company and they made a ton.
Speaker 3: And so our view is that we are competing on
Speaker 3: one hand with everybody. Now. The second thing is that
Speaker 3: I always say there's this five year psychological bias, which
Speaker 3: is that you want to be invested today where you
Speaker 3: know we were five years ago, and so I'm trying
Speaker 3: to figure out what's the next thing three four, five
Speaker 3: years ahead that people don't yet appreciate. So you know,
Speaker 3: I mentioned biology. Now you know all the listeners can
Speaker 3: go out and try to find the next models in biology.
Speaker 3: But it's a hard or more complex thing, and I'm
Speaker 3: confident that there there's a fewer number of investors that
Speaker 3: actually understand or have the networks of the connection. So
Speaker 3: we have some slight competitive advantage there. When we're evaluating
Speaker 3: these things, you're looking at the credibility of the founders.
Speaker 3: Many of them happened to be academic published papers, so
Speaker 3: you can see, for example, the people behind Runway who
Speaker 3: published the papers that led to stable diffusion, or the
Speaker 3: team that came out of Google that published the paper
Speaker 3: on transformers. Not the robots of course, like optimist prime,
Speaker 3: but the underlying algorithms that led to chat GPT. Every
Speaker 3: one of the people on those papers have basically gone
Speaker 3: on to start companies and raise money. People that were
Speaker 3: at open Ai have the pedigree, they learned what works,
Speaker 3: they went and started anthropic and so there's this sort
Speaker 3: of like just like if you go back in finance
Speaker 3: to like the Drexel days where they spawned you know,
Speaker 3: Apollo and Carlisle and Jeffries and all these it's the
Speaker 3: same sort of thing. There's a diaspora that's coming out
Speaker 3: of a small group of people and you can reference
Speaker 3: the credibility and then yes, you sit with them and
Speaker 3: you look at what their demos are. And we like
Speaker 3: to say that we believe before others understand. And so
Speaker 3: when we had that Runway team in or we had
Speaker 3: the Hugging Face team in, you know, it was very
Speaker 3: raw and very crude, and you have to sort of
Speaker 3: squint and see the future that they're seeing. And then
Speaker 3: we don't fully fund companies. You give a little bit
Speaker 3: of money and you say how much money will accomplish
Speaker 3: what in what period of time? And who will care?
Speaker 3: Are we going to get paid for the risk that
Speaker 3: we're taking and funding you? But I would say right now,
Speaker 3: if you're funding anything that is application focused, anything that
Speaker 3: is to your earlier point in the wrappers around the
Speaker 3: user interface, most of those things are just features. They're
Speaker 3: not companies. Most of those are going to be competed
Speaker 3: away by one hundred other examples. A great example of
Speaker 3: that is and I can't even remember the name of
Speaker 3: it now, but there was something that went out and
Speaker 3: it was an app and you could pay twenty bucks
Speaker 3: and it would give you one hundred versions of yourself,
Speaker 3: you know, as a comic book hero and a cowboy
Speaker 3: and a black and white and long.
Speaker 1: I think we signed up for the one month version
Speaker 1: of that. Yeah, I don't know. I probably forgot to.
Speaker 3: Get and spiked and then it's done. And you're going
Speaker 3: to have tons of those things where it spikes and
Speaker 3: it's done and it ends up being a feature integrated
Speaker 3: into Going back to the earliest point you made, many
Speaker 3: of the big tech companies, the Adobes and the Microsoft's,
Speaker 3: it's going to be in all their suites. I have
Speaker 3: one contrarian take here, which is a bit odd as
Speaker 3: an investors part of a partnership who's funding the deep
Speaker 3: tech roots of the semiconductors and the infrastructure and the
Speaker 3: networks and the models and the algorithms. And despite doing
Speaker 3: all of that, I actually have a view that what
Speaker 3: we are doing right now humans talking, even though it's
Speaker 3: through digital communications in an analog way, that that is
Speaker 3: actually going to become the scarce thing. You are going
Speaker 3: to be flooded, I mean utterly inundated by emails, texts,
Speaker 3: tweets that are not written by humans. And that's not
Speaker 3: like two years away, that's like two weeks. The increasing
Speaker 3: percentage of the communications that you receive even by the way,
Speaker 3: from people that you know and love and trust are
Speaker 3: not going to be written by them. They're not going
Speaker 3: to be spoken by them. You know, voice is going
Speaker 3: to be the next domain where it's going to be
Speaker 3: very hard. You're going to be getting a voicemail and
Speaker 3: the voicemail was not actually spoken by your spouse or
Speaker 3: your cousin or kid. I can already and I've already
Speaker 3: trained a model on my child and I was able
Speaker 3: to trick my wife on It was you know, funny
Speaker 3: and scary. The point of this is if that happens,
Speaker 3: and as that happens, you will start to grow increasingly
Speaker 3: distrustful of many of the communications you get. It'll be
Speaker 3: you know, this form of chat fishing is what I
Speaker 3: call it. And you'll start to just pine for in
Speaker 3: person communications and there will be private clubs that form
Speaker 3: where people come and no devices and you just know
Speaker 3: that you're talking to a human because increasingly that will
Speaker 3: be scarce. So the great irony here is the flood
Speaker 3: of money and talent and productivity in AI and deep
Speaker 3: technology is probably going to bring us closer to our
Speaker 3: innate humanity.
Speaker 1: It sounds like the answer is tracy, and I need
Speaker 1: to do a lot more online pub quizzes and other
Speaker 1: live event I'm that sounds good to me. Can I
Speaker 1: ask a question though about if it's like if all
Speaker 1: you have to do is sort of be a graduate
Speaker 1: from one of the right universities and have your name
Speaker 1: on some paper that's on like the archive website, what
Speaker 1: does that mean for recruitment from the companies that you
Speaker 1: are investing in? And when they want to go out
Speaker 1: and hire someone, how is the challenges? Like, no, that
Speaker 1: person who they probably want to hire can also raise
Speaker 1: one hundred million dollars And how difficult is it to
Speaker 1: recruit if there's just like so much money going to
Speaker 1: you know, a potentially smart founder.
Speaker 3: You know, this has been the plague if you're an
Speaker 3: investor for arguably the past decade in tech broadly, and
Speaker 3: it's a weird phenomenon. And what I mean by that
Speaker 3: plague is everybody thought that they could raise money. Amy
Speaker 3: could They all had a friend or a colleague or
Speaker 3: a former you know, associated or roommate who raised money
Speaker 3: and they literally had this reaction he or she just
Speaker 3: raised that money that valuation? What an idiot? I can
Speaker 3: go and do that? And so that set it comparable
Speaker 3: for them to say I'm going to go do it,
Speaker 3: and you get this sort of collective craze and so
Speaker 3: talent gets disfuse and disperse. It's increasing the cost of
Speaker 3: hiring employees. Valuations are going up, money is being misallocated,
Speaker 3: like the whole thing. Okay, all of that crashed about
Speaker 3: a year and a half ago, once rates started rising
Speaker 3: and the back boom ended and all that except for
Speaker 3: this one domain of AI. And so if you actually
Speaker 3: look at the hiring data, and you know, you have
Speaker 3: to parse this to see whether it's you know, signals
Speaker 3: that they're posting jobs or they're actually hiring. But all
Speaker 3: of the layoffs that you saw, you know, the tens
Speaker 3: of thousands at Meta and Google and Microsoft, et cetera,
Speaker 3: you're starting to see this spike back up in some
Speaker 3: of the data. And what are they doing they're hiring,
Speaker 3: whether it's low level jobs for data entry and processing
Speaker 3: and cleaning, or whether it's cutting edge algorithmic design for AI.
Speaker 3: There was an existential panic at Google and it's been
Speaker 3: reported when open ai came out, you know, the all
Speaker 3: hands deck meeting that people had of my god, we
Speaker 3: got to throw a ton of talent and money at
Speaker 3: this so that we don't get left behind. So right
Speaker 3: now it's a bit of an utter craze. You got
Speaker 3: to be really careful. Most of the incumbents are the winners.
Speaker 3: And there's going to be some small companies that end
Speaker 3: up with these really interesting novel approaches that are not
Speaker 3: raising a ton of money, almost out of necessity. They're
Speaker 3: doing these cheap, very focused models and arguably, and here's
Speaker 3: another interesting thing, training on distributed computing instead of having
Speaker 3: these big centralized clusters of compute. We for example, back
Speaker 3: to a company called together Compute, and his basic hypothesis was,
Speaker 3: can I train very sophisticated models on all of the
Speaker 3: excess compute from the crypto craze or from idle computers?
Speaker 3: And can I do it for a fraction of the
Speaker 3: cost of what open ai did. And the answer was yes.
Speaker 3: And so you're going to see lots of these small
Speaker 3: companies that come. And I always say, like, whenever the
Speaker 3: DOJ comes in and starts looking at monopoly concerns from
Speaker 3: the big companies, it's never the DOJ that disrupts the
Speaker 3: big company that people are concerned with. It's always some
Speaker 3: small competitor. It happened with Microsoft in the late nineties.
Speaker 3: Google came along. It wasn't the DOJ, it was Google.
Speaker 3: You know, it happened with Facebook and Google. It's happened
Speaker 3: with open Ai and now Facebook, and it'll happen again,
Speaker 3: and it'll be four guys or girls in a room
Speaker 3: in Croatia or Singapore or Mexico City or Silicon Valley
Speaker 3: that come up with the crazy new thing that disrupts
Speaker 3: the big incumbent.
Speaker 2: What impact do you see of AI on how the
Speaker 2: sort of tech industries slash VC is organized or operating
Speaker 2: at the moment, Like, do you see people start to
Speaker 2: respond to the idea that, well, maybe a lot of
Speaker 2: coding is going to be done by AI in the future.
Speaker 2: Are people sort of like reorganizing themselves or reorienting themselves
Speaker 2: ahead of some of this technology.
Speaker 3: Definitely. When you look at the copilot, which is both
Speaker 3: from people like GitHub and open ai and others, it's basically,
Speaker 3: how can we either help you code or how can
Speaker 3: we completely if you look at code Interpreter completely write
Speaker 3: the code for you. You just describe in general lay language
Speaker 3: what you want to do, and it will in Python
Speaker 3: create the code. So pretty much every company will have
Speaker 3: a form of computer programmers in the software that they use,
Speaker 3: whether it's open source or proprietary, that is constantly developing
Speaker 3: and iterating their own applications, and it'll touch everything from
Speaker 3: customer service to radiology and X ray image analysis to
Speaker 3: Bloomberg queries, and people will be able to basically have superpowers.
Speaker 3: What it means is you'll have the elite codevods that
Speaker 3: are sort of always on the edge trying to figure
Speaker 3: out the next thing, and then you'll have the average
Speaker 3: coders that are basically really leveled up and almost indistinguishable
Speaker 3: from the prior elite coders. So I do think that
Speaker 3: what once was really scarce and really valuable, which was
Speaker 3: top notch what people call ten X coders, is now
Speaker 3: going to become increasingly commodity and people will start looking.
Speaker 3: For example, Now the real value is not can you
Speaker 3: code for the current moment. It's like, are you an
Speaker 3: amazing prompt engineer? Like I can't draw, I can't code,
Speaker 3: I can't write, you know, twelve line stanzas, but I
Speaker 3: can prompt pretty well. And so it shifts the capability
Speaker 3: to the creativity of somebody that really wants to describe
Speaker 3: and control the machine again almost more like casting a
Speaker 3: spell than the person that actually has the discrete technical capability.
Speaker 1: Can I ask you know you mentioned that roughly, I
Speaker 1: don't know, a year and a half or so ago,
Speaker 1: or maybe two years ago, this sort of the model
Speaker 1: that had been working like this incredible trade, this incredible
Speaker 1: line go up decade or whatever for tech and VC
Speaker 1: like did sort of crumble to some extent, and we
Speaker 1: saw the big plunge of the NASDAK And I'm sure
Speaker 1: there were like tons of funds raised in twenty twenty
Speaker 1: one and twenty twenty that are like deep underwater and
Speaker 1: all that stuff. And everyone knows that when you're at
Speaker 1: the table now looking at companies, do you still is
Speaker 1: there still pain and paranoia and fear from that? Have
Speaker 1: there been like or has the pain of that been
Speaker 1: internalized in the way or is it you know what?
Speaker 1: We're just back in at games, back on phone mocycle,
Speaker 1: back on let's go like how much are that? How
Speaker 1: much are their scars from the sort of crash of
Speaker 1: twenty twenty one.
Speaker 3: I think people that put a lot of money to
Speaker 3: work in twenty twenty, twenty twenty one feel a lot
Speaker 3: of pain they invested at record high multiples. They made
Speaker 3: the presumption, which was a fair presumption to make that
Speaker 3: if you fund something there's going to be later stage
Speaker 3: capital or a robust public market to follow you on.
Speaker 3: All of that is gone, you know. So so I
Speaker 3: think we went from what I called what everybody called
Speaker 3: fomo if you're missing out to what I call sobs,
Speaker 3: which was the shame of being suckered. And that wasn't
Speaker 3: just that wasn't.
Speaker 1: Just can I just say, you know what, Can I
Speaker 1: just say there's no shame? And you know people buy that.
Speaker 1: It's hard to know at the top is but I
Speaker 1: think about like the person who paid half a million
Speaker 1: dollars or millions of dollars like for the board ape
Speaker 1: nft is that's the ultimate sob. You can never live
Speaker 1: that down. Okay, keep going. Sorry, I just thought about
Speaker 1: that recently and that was in my head. Okay, keep going.
Speaker 3: But you know, I got a friend, Zach bisin Ed
Speaker 3: who wrote the book back in the day on.
Speaker 1: We've had Yeah, that's amazing exactly.
Speaker 3: So, but this is this is one of the things
Speaker 3: that I love to say, which is not some crazy
Speaker 3: personal insight. It's just an observable truth, which is that
Speaker 3: technologies change, and businesses change, and rules change and policies change.
Speaker 3: Human nature is a constant. Greed and fear is a constant.
Speaker 3: That is what made Buffett and Monger brilliant. You know,
Speaker 3: it's what Howard Mark's chronicles all the time. It's what
Speaker 3: you guys cover the excesses of human emotion. And so
Speaker 3: a lot of this is actually capturing. I love finding,
Speaker 3: like where are people not paying attention? Where is attention scarce?
Speaker 3: Because where attention is scarce, valuations are going to be low.
Speaker 3: And we always say, oh, you know, just like everybody
Speaker 3: says they're contrarian investors, we want people to agree with us,
Speaker 3: just later. And that's the key. Okay, going back to
Speaker 3: your question, you know we went from fomo fear of
Speaker 3: missing out to sobs the shame of being suckered. And
Speaker 3: there was an important reason for that, which was the
Speaker 3: disappearance of two major players, at least symbolically, and that
Speaker 3: was SoftBank and Tiger. And why was that important? Because
Speaker 3: you know, you had a venture firm maybe a decade
Speaker 3: ago that said the price you pay for a company
Speaker 3: doesn't really matter because there's only ten companies that matter
Speaker 3: amongst all the ones that are funded. And if you
Speaker 3: would have funded LinkedIn or Facebook at five billion or
Speaker 3: ten billion or twenty, it wouldn't have mattered, right, And
Speaker 3: so that sort of set a precedent that I think
Speaker 3: was a bit insidious and danger to say the price
Speaker 3: doesn't matter, you just have to be in the right companies.
Speaker 3: Of course, that's only obvious in hindsight. So you had
Speaker 3: lots of people that lost valuation discipline. It skewed control
Speaker 3: and leverage to the founders over from the investors, you know,
Speaker 3: and you saw weak governance, and you saw fraud and
Speaker 3: excess and all that kind of stuff, and it's starting
Speaker 3: to wash out, you know, if not fully, but the
Speaker 3: disappearance of those two players. Symbolically they were the top ticking,
Speaker 3: marginal price setting investors. SoftBank was paying insane prices, and
Speaker 3: of course, you know, they were all kind of shenanigans
Speaker 3: of them marking up their own book and pricing up
Speaker 3: again and all that kind of stuff, and Tiger sort
Speaker 3: of took a passive indexation and approach, which was something
Speaker 3: that was widespread in the public markets, but they did
Speaker 3: in the private markets and say they said, we're just
Speaker 3: going to be in all the companies, and the winners
Speaker 3: will make up for the losers, and it'll work when
Speaker 3: those kinds of players disappear. Now, all of a sudden,
Speaker 3: you have a more rational scrutinizing market of people who
Speaker 3: are afraid of paying ex prices, feel like they need
Speaker 3: to get a better deal. You're seeing down rounds in companies.
Speaker 3: You have a morale spin and decline where employees now
Speaker 3: have underwater stock and need to be refreshed. And here's
Speaker 3: where things get really interesting. We went from this domain
Speaker 3: where I called it the megas and the minnos. The
Speaker 3: megas were the giant funds that were, you know, ten
Speaker 3: billion plus and they were writing these giant checks. And
Speaker 3: the minnos were the thousands of small, sub hundred million
Speaker 3: dollar funds that were just doing all the seed investing.
Speaker 3: Both of those guys have been squeezed out, and so
Speaker 3: now you have a smaller base of capital. You can
Speaker 3: see it in the data. LPs have pulled back the
Speaker 3: you know, the champagne has stopped flowing down the pyramid
Speaker 3: of glasses. Gps are struggling to raise capital. You know,
Speaker 3: we closed a billion two fund in ten weeks, which
Speaker 3: for US was amazing, and it was signal of great
Speaker 3: support of our LPs and great founders. A lot of
Speaker 3: funds out there right now are downsizing. It's taking them
Speaker 3: a lot longer to raise and all of that is
Speaker 3: a rational reaction to a retraction. So I don't think
Speaker 3: you see the same fomo. I think you see a
Speaker 3: lot more fear. People don't want to pay higher prices.
Speaker 3: The only area where there's an exception is inside of AI.
Speaker 2: You know, I just have one more question, And you
Speaker 2: sort of touched on this earlier where we were, well,
Speaker 2: you were talking about parallels between now and the sort
Speaker 2: of dot com era and the idea that well, you know,
Speaker 2: maybe eventually some big winners will emerge from this new technology,
Speaker 2: whether it's AI or the Internet as it was in
Speaker 2: you know, the late nineteen nineties, early two thousands. What's
Speaker 2: the case for investing in AI right now? Rather than
Speaker 2: waiting a little bit to see where the dust settles,
Speaker 2: maybe wait to see who those big winners are, or
Speaker 2: maybe at the very least get a little bit more
Speaker 2: clarity on how this whole thing is going to be
Speaker 2: structured or organized.
Speaker 3: Well, the argument for waiting is, by the time you know,
Speaker 3: it's already fully factored into a price. The contract to
Speaker 3: that is you pay a high price for cheery consensus,
Speaker 3: as Buffett historically said. And so if everybody agrees that
Speaker 3: in Nvidia is the winner, you know, that to me
Speaker 3: gives me pause for concern. You know, Jensen is running high,
Speaker 3: he's got the iconic leather black jacket, he's becoming the
Speaker 3: sort of you know, next profit of tech. Those are
Speaker 3: all signals that are like, okay, just like the classic
Speaker 3: you know, sports illustrated curve, simple reversion to the mean,
Speaker 3: Like what happens? Where's the vulnerability there? To me is
Speaker 3: the question. And I gave you guys and listeners a clue,
Speaker 3: which is that Kuda, their language system, is vulnerable to
Speaker 3: these other ones of py Torch from open source, originated
Speaker 3: from Meta and Triton from open Ai. And that means
Speaker 3: that AMD could actually come from behind and start to
Speaker 3: take share. It's something that people are skeptical about. So
Speaker 3: I would say that if you're thinking about investing now,
Speaker 3: it's too late. It really is, you know, five year
Speaker 3: psychological bias. You want to be invested five years ago,
Speaker 3: where everybody wants to be today and vice versa. So
Speaker 3: i'd be thinking about what are the improbable things that
Speaker 3: are likely to happen in the next wave. I'll give
Speaker 3: you one company that I think is interesting that lux
Speaker 3: is not invested in. It's a public company and we
Speaker 3: do private but cloud Flare. You know, if you go
Speaker 3: back to the Internet early days, one of the winners
Speaker 3: in the infrastructure was Akamai, the people that were sort
Speaker 3: of caching and they were helping to shape the structure
Speaker 3: of the Internet. Cloud Flare is very interesting because they
Speaker 3: have a lot of compute infrastructure at the edge of
Speaker 3: the network. And you hear about this in sort of
Speaker 3: a hype way sometimes the edge edge inference, edge compute.
Speaker 3: It's a real thing. Very simply, you're talking on a
Speaker 3: mobile device or you're on your computer right now, you
Speaker 3: have to go up to the cloud, and the cloud,
Speaker 3: you know, which is basically a bunch of servers somewhere
Speaker 3: with high bandwidth interconnectivity processes. Then you have another domain
Speaker 3: which is on device, so you you know, do something.
Speaker 3: The models get smaller, the chips get better. On your
Speaker 3: Apple device or your Android or your iPad, you're able
Speaker 3: to run the AI model there. Cloud Flare is caching
Speaker 3: a lot of these models and hosting them very close
Speaker 3: to the users, and they're doing it in thousands or
Speaker 3: tens of thousands of places all over the world. So
Speaker 3: I think that they, you know, probably a twenty billion
Speaker 3: dollars market cap company, billion revenue, fifty sixty percent growth.
Speaker 3: I think that they might be poised and aren't one
Speaker 3: of the names that are on the tips of people's
Speaker 3: tongues that are benefiting. But we see them in all
Speaker 3: the infrastructure behind a lot of our companies.
Speaker 1: Interesting and a little investment tip, but for people listening.
Speaker 3: Yeah, it's just it's you know, do your work investigated.
Speaker 3: But it's something that is just not on the front page.
Speaker 3: And I think that they're poised in the same way
Speaker 3: that if I go back ten years when I'm in
Speaker 3: that room in our startup and I got the benefit
Speaker 3: of this legal inside information of seeing these guys from
Speaker 3: Nvidia making these chips that were the soul of the
Speaker 3: new machine, you know, in the proverbial Tracy kittersense, I
Speaker 3: just see that this infrastructure from folks like out Flair
Speaker 3: is probably going to win.
Speaker 1: So I just have one more question as well, and
Speaker 1: it actually also is sort of on the public market side,
Speaker 1: but you know, going back to a company like alphabet
Speaker 1: and obvious and I sort of talked about this in
Speaker 1: my introduction, and you know, obviously they've made a lot
Speaker 1: of AI investments and you know they've been doing research
Speaker 1: for a long time. Nonetheless, though, like the core business
Speaker 1: for now and for probably least the medium term is
Speaker 1: going to be what we call, you know, Google dot
Speaker 1: com or something like that, and enter a search and
Speaker 1: get served a really compelling ad because it's very good
Speaker 1: at that. Like, in your view, how confident should people
Speaker 1: be that some of these big companies can find you know,
Speaker 1: can actually produce revenue and income. I mean, like inference
Speaker 1: is a lot costlier, I presume than a typical search query.
Speaker 1: We don't know what the advertising is going to look
Speaker 1: around it, et cetera. We don't really know. Like, do
Speaker 1: you think it's obvious that these big companies are going
Speaker 1: to find ways to actually sell something profitably from this tech?
Speaker 3: I do, if there's good, strong leadership. And that sounds
Speaker 3: like a you know, a weasel answer, but you know, historically,
Speaker 3: if you look at Satia and Microsoft and you look
Speaker 3: at Google, I just feel like Google was run by
Speaker 3: the inmates for a very long time, and this competitive
Speaker 3: near existential threat from open Ai has given a sense
Speaker 3: of urgency for them to refocus and say, Okay, we
Speaker 3: got to stop with all of the social stuff that
Speaker 3: is happening internally and we got to really focus on
Speaker 3: what we are roots were. Google hasn't really had a
Speaker 3: killer product, I mean, a true new product in over
Speaker 3: ten years. But what's interesting is YouTube is a big
Speaker 3: winning I mean, that was a great acquisition. It's a
Speaker 3: thriving product. It's generating a lot of money. Hopefully they
Speaker 3: don't go crazy and spend, you know, like everybody else
Speaker 3: in the streaming wars. But just like Facebook, right, Facebook
Speaker 3: dot com is dead right, what makes money for Facebook
Speaker 3: is everything else the Instagram and WhatsApp and if Threads
Speaker 3: takes off, you know who knows, you know, and they're
Speaker 3: able to capture some modicum of the enterprise value that
Speaker 3: has been destroyed by Elon with Twitter. So it's all
Speaker 3: of these ancillary product categories inside of the mothership that
Speaker 3: I think that people are cranking and figuring out how
Speaker 3: do we make this work. Google's prominence and search I
Speaker 3: think is going to persist. I think it'll extend into
Speaker 3: other domains. I think it's less likely to be threatened
Speaker 3: by a lot of the AI stuff. They'll integrate it
Speaker 3: barred when it first launched, socked. Now it's not bad,
Speaker 3: you know, Incremental search results are pretty good, but the
Speaker 3: corpus of information that they have from my photos, to
Speaker 3: my emails to my calendar, I'm pretty locked in. And
Speaker 3: I'm relatively trusting of Google, also relatively, if not high,
Speaker 3: trusting of Apple, and I've historically been very low trusting
Speaker 3: of Meta. I always say that whenever Meta launched is
Speaker 3: a product, the one feature that it lacks is trust.
Speaker 3: And I think they're realizing, even if it's a little
Speaker 3: bit of a showcase facade for both the regulator regulators
Speaker 3: and the critics, that they really have to double down
Speaker 3: on trust. And one of the ways to do that
Speaker 3: is a lot of open source stuff. So really watch
Speaker 3: for Meta to embrace open source in a giant way.
Speaker 1: Josh Wolf Lux Capital. That was a great conversation, great
Speaker 1: overview of sort of the market right now. Thank you
Speaker 1: so much for coming on out Law. It's got to
Speaker 1: have you back again.
Speaker 3: Joe Tracy, great to be with you.
Speaker 1: Tracy, can I just say, you know, I don't know,
Speaker 1: listeners might not. I'm an amateur, you know, songwriter, and
Speaker 1: my only goal is to get something published before like
Speaker 1: the computers are just like so good at it. That's
Speaker 1: like my you know, I've like maybe I have like
Speaker 1: a window of like a year or two. I just
Speaker 1: want like one public you know. I just want to
Speaker 1: have like one something someone singing one of my songs,
Speaker 1: and then the computers can do their things.
Speaker 2: I mean, I do think this is kind of most
Speaker 2: disturbing aspect of this whole AI discussion, which is that
Speaker 2: so far it seems to mostly apply to the fun
Speaker 2: stuff songwriting, poetry, making movies, and we're still sort of
Speaker 2: doing all the dredge work ourselves. But that was a
Speaker 2: really interesting conversation, I do think. So, I don't know,
Speaker 2: I take Josh's point about getting in early on some
Speaker 2: of this, but I'm looking at a chart of what
Speaker 2: am I looking at Google, you know, since the IPO,
Speaker 2: and if you got in in two thousand and seven,
Speaker 2: two thousand and eight, like I think you'd still be okay.
Speaker 2: You would have missed like maybe the life changing money,
Speaker 2: but you'd still be up significantly on your investment. So
Speaker 2: I do wonder. Obviously there's a lot of excitement around
Speaker 2: the prospects of AI and what it means for various companies.
Speaker 2: But I also feel like if you waited for the
Speaker 2: dust to settle a little bit, you wouldn't necessarily be
Speaker 2: automatically losing it.
Speaker 1: I like how your question and your point here is
Speaker 1: not really is basically like questioning the entire premise of
Speaker 1: venture venture capital, Like that is actually the entire subtext
Speaker 1: of question.
Speaker 2: After twenty twenty two, I think that's a valid question.
Speaker 1: I would just wait for it all to go public
Speaker 1: and yeah, yeah, it's fine by the index. The other
Speaker 1: thing which I hadn't appreciated, which I thought was really interesting.
Speaker 1: You know, obviously I know that you know, Microsoft, Open AI,
Speaker 1: Google or alphabet anthropic, but the sort of like the
Speaker 1: way in which some of this may be a function
Speaker 1: of the regulatory department. I had not really like appreciated
Speaker 1: that and why, Like, Okay, it's it's gonna be hard
Speaker 1: to like make big acquisitions, so you just invest in
Speaker 1: companies who spend most of their money with you, Like yeah, yeah,
Speaker 1: it's like sort of I hadn't appreciated that element.
Speaker 2: No, I think that was a really interesting angle and
Speaker 2: actually explains a lot of the choices and decisions that
Speaker 2: are being made at the moment, because sometimes you look
Speaker 2: at them and you're like, this is this is interesting.
Speaker 2: But I'm not sure I completely see what's happening here.
Speaker 2: But if you look at it from a regulatory slash
Speaker 2: reputational angle, it makes a lot of sense.
Speaker 1: Uh, you know what, I'm really excited about what Bloomberg
Speaker 1: Bloomberg GPT did. Josh said it's going to be one
Speaker 1: of the winners.
Speaker 2: I feel like we should be doing a disclaimer here.
Speaker 1: We work for Bloomberg. It's fairly obvious we worked for Bloomberg,
Speaker 1: but we did not tell Josh to say that Bloomberg GPT.
Speaker 1: But there's point about who has actually had well quality
Speaker 1: data is interesting?
Speaker 2: Yeah, And I would say so far, a lot of
Speaker 2: the excitement is around the chip makers, some of the
Speaker 2: incumbents like Microsoft. I haven't seen people get really excited
Speaker 2: about like insurance companies as an AI play yet, but
Speaker 2: I think there's something there. The other thing I wanted
Speaker 2: to say, and you asked this question about the user interphase,
Speaker 2: and I actually think it's really important in the story here.
Speaker 2: And this is where I would draw a parallel with
Speaker 2: blockchain and crypto, which is the interesting thing about crypto
Speaker 2: was that you could participate in this as a sort
Speaker 2: of normal person. You know, you could open a wallet
Speaker 2: of some sort and buy whatever your preferred cryptocurrency is,
Speaker 2: so you could participate in it. And I think having
Speaker 2: something like open ai and various other models that you
Speaker 2: can play around with like clearly has drawn in that
Speaker 2: additional Oh yeah, like that is a big part of it.
Speaker 1: Absolutely. I do think like it's just like we've all
Speaker 1: had the jaw dropping moment which is like you didn't
Speaker 1: really get with crypto. It's like, yeah, you could do it,
Speaker 1: but then it's like, Okay, now I have this coin
Speaker 1: in my wallet, right, that is true, And then but
Speaker 1: then you just like you know, literally it takes you
Speaker 1: ten seconds to like be blown away.
Speaker 3: Is just so powerful. Yeah, shall we leave it there?
Speaker 1: Let's leave it there?
Speaker 3: All right.
Speaker 2: This has been another episode of the All Thoughts Podcast.
Speaker 2: I'm Tracy Alloway. You can follow me on Twitter at
Speaker 2: Tracy Alloway.
Speaker 1: And I'm Jill Why Isn't Though? You can follow me
Speaker 1: on Twitter at the Stalwart. Follow our guest Josh Wolf
Speaker 1: on Twitter. He's at wolf Josh. Follow our producers Carmen
Speaker 1: Rodriguez at Carmen Arman and dash Ol Bennett at dashbot.
Speaker 1: And check out all of the Bloomberg podcasts under the
Speaker 1: handle ad Podcasts. And for more odd Lots content, go
Speaker 1: to Bloomberg dot com slash odd Lots, where we have
Speaker 1: transcripts and a blog and a weekly newsletter. And check
Speaker 1: out the discord where we chat about all these things
Speaker 1: twenty four to seven. We even have an AI room
Speaker 1: in there. Some of the questions from the conversation I
Speaker 1: sourced from there, so go check it out Discord dot
Speaker 1: gg slash odd Lots.
Speaker 2: Also, if you enjoy odd Lots, if you appreciate conversations
Speaker 2: like the one we just had with Josh, please leave
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Speaker 2: for listening in