Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.
Speaker 2: Hello and welcome to another episode of the Odd Lots Podcast.
Speaker 3: I'm Joe Wisenthal and I'm Tracy Alloway.
Speaker 2: Tracy, let's talk about AI some more.
Speaker 1: Okay, Well, we could just have AI write the script
Speaker 1: for us. We could give ourselves some time.
Speaker 2: No, I don't think the technology is there yet, you
Speaker 2: know what, so I can't say who. Maybe I was
Speaker 2: trying to a professor recently and she said something really
Speaker 2: interesting to me. I'm not supposed I'm not sure it's fine,
Speaker 2: I think, And she said, like, you know, there's all
Speaker 2: this anxiety about you know, kids cheating on their essays
Speaker 2: or having Chad g GBT write their essays for them,
Speaker 2: and like, you know, supposedly professors are like tearing their
Speaker 2: hair out trying to figure out what to do about this,
Speaker 2: and the AI detector don't really work all that well particularly,
Speaker 2: but apparently like it seems like the solution is to
Speaker 2: just grade them as regular essays no matter what. And
Speaker 2: it sounds like at this point all the chat GPT
Speaker 2: essays are basically solid c essays, and so if you
Speaker 2: just sort of like take them at phase value, even
Speaker 2: if you think they might be AI generated at least
Speaker 2: at this point, it doesn't seem like a way at
Speaker 2: least for college students to write good essays.
Speaker 1: Yet, so our baseline, our average is chat GPT.
Speaker 3: Now, yeah, that's basically can you beat the bot?
Speaker 1: Did you see the thing someone was tweeting about one
Speaker 1: of the tells for AI generated words is if you
Speaker 1: use the word delve. Yes, I saw that, which, as
Speaker 1: someone who I'm sure has used delve numerous times on
Speaker 1: this podcast and in my writing, I thought was a
Speaker 1: little unfair. It is a little it's a cliche, but
Speaker 1: that doesn't mean it's from AI.
Speaker 2: Although being said, there's more to AI than just chat
Speaker 2: GPT obviously in chatbots, and this sort of has come
Speaker 2: up on a couple episodes recently, but only like very tangentially.
Speaker 2: People talk about the use of like AI and industrial applications,
Speaker 2: and I've seen a lot of stuff. There have been
Speaker 2: a couple Bloomberg articles about some startups that sort of say, like, okay, well,
Speaker 2: like what if we trained robots the same way we
Speaker 2: trade large language models, where you feed them just like
Speaker 2: tons and tons and tons of real world data, and
Speaker 2: so that yeah, like you sure you still have to
Speaker 2: solve the mechanical engineering part. But then what if that
Speaker 2: allows them, like all this training data to do more
Speaker 2: advanced industrial things like I don't know, like make a
Speaker 2: pizza or you know, be a more powerful humanless assembly
Speaker 2: line or.
Speaker 3: Something like that.
Speaker 2: Where it's like we see all these oppressive robots and
Speaker 2: videos like Boston Dynamics, but I never know like if
Speaker 2: any of this is like quite there yet in terms
Speaker 2: of having its value.
Speaker 1: Yeah. So the robotics aspect of AI is something that's
Speaker 1: incredibly interesting to me. It kind of makes me think
Speaker 1: about the world that we want to see. So it
Speaker 1: would be great if if we had physical robots who
Speaker 1: are able to do stuff like clean up a house
Speaker 1: or take care of an elderly family member or something
Speaker 1: like that. It's not so great if all of our
Speaker 1: technological prowess basically goes into writing satirical lyrics. Yes, via
Speaker 1: chat GPT, like that's fun. I can do that myself,
Speaker 1: But what I really need is someone to vacuum or
Speaker 1: dust the house.
Speaker 3: Do the laundry.
Speaker 2: Yeah, exactly, really nice. All right, Well, I just want
Speaker 2: to jump right into it because we really do have
Speaker 2: the perfect guest. We're going to be speaking to someone
Speaker 2: who has been investing in AI for a long time.
Speaker 2: A lot of vcs like started investing in AI last
Speaker 2: year obviously, but this is someone who has been investing
Speaker 2: in AI for quite some time before it became the
Speaker 2: hot new thing. We spoke to him last July and
Speaker 2: had a great conversation about what he was seeing in
Speaker 2: the space. So I'm really pleased to welcome back on
Speaker 2: the show. We're going to be speaking with Josh Wolfe,
Speaker 2: co founder and managing partner of Lux Capital. Josh, thank
Speaker 2: you so much for coming back on ODLTS.
Speaker 4: Great to be on. I feel like I should say
Speaker 4: hello and like a robot voice.
Speaker 3: So what's interesting to you these days? What are you
Speaker 3: seeing out there that gets you excited?
Speaker 4: Well, you know, you guys started this off with AI
Speaker 4: and with an AI. Look, we've had what I would
Speaker 4: call maybe a little bit pejorative, a little bit looting crude.
Speaker 4: We've had chips everybody knows that we talked last time
Speaker 4: about in AMD. We've got chatbots. You already have some
Speaker 4: of these guys that are starting to fail. They've raised
Speaker 4: billions of dollars and in some cases just you know,
Speaker 4: relatively undifferentiated, big debate between open source that is approaching
Speaker 4: the ASSYMP tote of achievement that the big private models
Speaker 4: have and then being a little bit lout you've got chicks.
Speaker 4: What do I mean by that? Most of the applications
Speaker 4: in AI are the mundane and passe on the one side,
Speaker 4: which would be like customer service and basic call center
Speaker 4: supplementation or substitution. And then at the other end you
Speaker 4: have people that are spending tens of thousands of dollars
Speaker 4: a month in some case on AI girlfriends and people
Speaker 4: that are doing what they often do with technology which
Speaker 4: is used for prurient interests. So that to me the
Speaker 4: two barbel extremes in AI, where people are actually making
Speaker 4: money and profits serving demand for basic human instincts I
Speaker 4: guess and needs. That's interesting. Overall, you're seeing a big
Speaker 4: shift from the compute piece to the energy piece, meaning
Speaker 4: people now recognize this bottleneck in AI is not going
Speaker 4: to be so much about the chips. We also talked
Speaker 4: about this months ago when we said, look, you don't
Speaker 4: necessarily need these in video chips for inference, the part
Speaker 4: that most people do when they're querying all these models.
Speaker 4: You do need them for training, but the power levels
Speaker 4: on these things are just enormous. There was a Dell
Speaker 4: earnings call that they think sort of accidentally leaked that
Speaker 4: this in VideA B one hundred Blackwell chip is going
Speaker 4: to be a thousand wide draw of power, which is
Speaker 4: like forty fifty percent more than these eight one hundred chips.
Speaker 4: Why does that matter because now you got to figure
Speaker 4: out how do you supply the energy for that. And
Speaker 4: a tidbit which I thought was interesting was Amazon acquired
Speaker 4: a nuclear powered data center in Pennsylvania. They've spent six
Speaker 4: hundred and fifty million dollars, they got about a gig
Speaker 4: lot of power. And I think that that is going
Speaker 4: to be a trend. I think it's actually going to
Speaker 4: usher in a wave of what I like to call
Speaker 4: out elemental energy, but demand for nuclear power to power
Speaker 4: these big AI data centers.
Speaker 1: Yeah, it's kind of funny when you think about it.
Speaker 1: I don't think anyone expected uranium to end up being
Speaker 1: an AI play, but here we are. I want to
Speaker 1: go back to something you said, and you actually brought
Speaker 1: it up the last time we spoke to you last year,
Speaker 1: and it was the idea of I guess the novelty
Speaker 1: of some of these more public facing AI projects, And
Speaker 1: I think you pointed out last year it's really fun
Speaker 1: to use some of these things, like generate a bunch
Speaker 1: of cartoon versions of yourself or whatever, but it might
Speaker 1: not be a sustainable business model, and it might end
Speaker 1: up being a functionality that is eventually incorporated into another
Speaker 1: platform or a different project. Have you seen any example,
Speaker 1: specific examples of more public facing novelty AI start to
Speaker 1: like go away? I think you mentioned a few failures recently.
Speaker 4: Yes, You've had a whole bunch of companies that basically
Speaker 4: took chat, GPTA, GPT three or four and put a
Speaker 4: wrapper around it, basically meaning give the average user who
Speaker 4: doesn't know how to use these or even do prompts,
Speaker 4: some means to interact with it. And those things raised
Speaker 4: a bunch of money. They made these things accessible, and
Speaker 4: they've sort of gone away. The foundation models themselves that
Speaker 4: are undergirding all of this themselves are also starting to
Speaker 4: be relatively commoditized away. And some of these things have
Speaker 4: prominent people and have raised a lot of money, but
Speaker 4: they are what I would consider failing. Take inflection. You've
Speaker 4: got Mustapha Solimon, super smart guy, co founder of deep
Speaker 4: mind raised I think a billion and a half for
Speaker 4: that company. You know, I'm gonna be careful here because
Speaker 4: Microsoft has been probably the savviest actor in this entire game,
Speaker 4: figuring out that they can acquire things by doing things
Speaker 4: in a clever way, skirting FTC and DJ oversight. They
Speaker 4: effectively control open ai. As I think we also talked
Speaker 4: about heard, particularly going into the end of last year
Speaker 4: when there was all the drama around open Ai. Sadia said, look,
Speaker 4: if open ai went out of business, we own it,
Speaker 4: control it. We've got all the data up left, right center,
Speaker 4: you know, all around them. Same thing with this company Inflection.
Speaker 4: They did I think a six hundred and fifty six
Speaker 4: hundred and seventy five million dollar license for the technology
Speaker 4: that basically was a payment above and beyond what the
Speaker 4: venture investors had made. Venture investors made a little bit
Speaker 4: of money, not a lot. Key management went over to Microsoft.
Speaker 4: But Microsoft has been very clever. So back to your question,
Speaker 4: I think the big are going to get bigger and
Speaker 4: are going to be most of the beneficiaries here, Microsoft, Adobe, Amazon,
Speaker 4: Amazon themselves. Coming up on the one year anniversary of Bedrock,
Speaker 4: they're going to announce that they've got the best performing
Speaker 4: model with Anthropic, which they've made billions of dollars of
Speaker 4: investments in now sort of competitor to Opening Eye CHATGPT.
Speaker 4: They're also going to renounce something with one of our
Speaker 4: companies that hasn't yet been publicly disclosed in biology. That's
Speaker 4: going to be one of the two biggest waves next
Speaker 4: biology and what you started the conversation with robotics. If
Speaker 4: you just take robotics as an example, I think in
Speaker 4: one of our companies, Hugging Face, which is one of
Speaker 4: the main repositories for all these open source models, there's
Speaker 4: something like sixty thousand text generations models. You know, it's
Speaker 4: like fifty nine thousand, seven hundred something now, but just
Speaker 4: an enormous number of text generation models. This is in
Speaker 4: like two or three. It's like everybody's doing this. Everybody's
Speaker 4: trying to do it all, basically trying to predict the
Speaker 4: next word based on the prior one. What this transformer technology,
Speaker 4: which was invented at Google, ended up parlaying into guess
Speaker 4: how many robotic models there are? Fifty nine thousand in
Speaker 4: text generation? Guess how many robotic models there are?
Speaker 1: A fraction.
Speaker 4: I mean, I'm obviously leading with my phone are but
Speaker 4: nineteen robotic models. Okay, so you got that to me.
Speaker 4: As a venture investor, we're just always looking at where's
Speaker 4: their abundance, where's their scarcity? Their scarcity of robotic models? Now, why, Well,
Speaker 4: it's relatively easy to train on the open Internet. You've
Speaker 4: got Wikipedia, you've got YouTube videos. You know, whether you're
Speaker 4: not you're supposed to be doing that or not. Like
Speaker 4: the woman who was asked it, soa hey, how did
Speaker 4: you train these things? Was it on YouTube? And she
Speaker 4: gave it and you probably saw that, So there's gonna
Speaker 4: be all kinds of copyright stuff on that. Robots are hard.
Speaker 4: Why Most of the robotic stuff that has been out
Speaker 4: in the world, like you talked about in your intro,
Speaker 4: is constrained in manufacturing facilities, in work cells on an
Speaker 4: assembly line, very specific, parametrically constrained, so very few degrees
Speaker 4: of freedom of what they're actually doing. The robots themselves
Speaker 4: might have multi access scripts and controllers, but they're not
Speaker 4: moving around very freely. You have exceptions with like Amazon
Speaker 4: which acquired Kiva, moving the warehouse inventory stuff around, but
Speaker 4: again relatively xyz access not unstructured environments. You and I
Speaker 4: and our listeners. We all thrive every day in unstructured
Speaker 4: environments and that is where you need enormous training data.
Speaker 4: You can't search the internet for that, So how do
Speaker 4: you do it. There's a few things that have emerged,
Speaker 4: and you mentioned some articles. We funded a company that
Speaker 4: recently came out of STELL called Physical Intelligence stead of
Speaker 4: artificial intelligence, Physical Intelligence, and it is the krem Dela
Speaker 4: Creme team from Stanford and Berkeley. You've got some open
Speaker 4: ai folks, You've got Google Deep Mind folks. They took
Speaker 4: investment from open ai US and a bunch of other
Speaker 4: vcs and they are just twenty four to seven training
Speaker 4: robots doing all kinds of crazy things like folding, laundry, pouring.
Speaker 4: Determined but to let these robots encounter unstructured environments and
Speaker 4: then be able to thrive in them. The next thing
Speaker 4: that you're going to see are visual models where you're
Speaker 4: effectively giving, like an ikea sketch or you're drawing something,
Speaker 4: and you're being able to instruct the robot to have
Speaker 4: a sense of intuitive physics of how the world works
Speaker 4: and how things might connect to each other and then
Speaker 4: learn from that. And then we're also training these robots
Speaker 4: with simple verbal cues. So there's a video you can
Speaker 4: see online from some of the researchers where they are
Speaker 4: picking nuts and m and ms and separating them, you know,
Speaker 4: just as a task of being able to sort and
Speaker 4: filter with precision and dexterity, and if they picked a
Speaker 4: wrong one, you can actually instead of physically grabbing the things,
Speaker 4: say stop grab the Eminem's not the nuts. And now
Speaker 4: it knows that. So I think that we're about to
Speaker 4: unleash in robotics what will become a chat GPT like
Speaker 4: moment where people are so used to seeing robots and
Speaker 4: they see the arms, and they've seen West World and
Speaker 4: this kind of stuff, and suddenly something happens that just
Speaker 4: blows your mind. And I think that's coming soon.
Speaker 2: That's pretty exciting because, like I said, you know, for
Speaker 2: like at least a decade, I've been watching those Boston
Speaker 2: whatever online, those youtubes, and at this point I'm sort
Speaker 2: of convinced that it's like basically a content generator because
Speaker 2: it never seems like they're like crazy robot dogs or
Speaker 2: anything like ever become commercial. But maybe this is the
Speaker 2: missing link. But you've brought up like three different avenues
Speaker 2: we could go on and I want to sort of
Speaker 2: eventually hit on any of them. Here's a specific question,
Speaker 2: and then we can maybe get back to robotics. This
Speaker 2: element where there is such a shortage of advanced, cutting
Speaker 2: edge talent, people who really know how to do this.
Speaker 2: And you mentioned that guy that got hired by Microsoft
Speaker 2: from his other companies as an investor in AI or
Speaker 2: robotics startups. Is this a dynamic that's different than in
Speaker 2: other software or other tech investing. Basically, this sort of
Speaker 2: like highly skilled tech qman risk.
Speaker 4: Basically yes, in that you always are looking for what's
Speaker 4: scarce and you want scarce talent. If anybody could do this,
Speaker 4: it's just not that valuable companies would get funded. Vcs
Speaker 4: would fund forty of them at the same time, or
Speaker 4: maybe four hundred of them. Contrast to things where it's
Speaker 4: very web based or the old groupons of yesterday. This
Speaker 4: is highly technical, often PhD scientists. The vast majority of
Speaker 4: the founding teams that we've backed at companies like Covariant
Speaker 4: or format or this new company Physical Intelligence, they're all
Speaker 4: PhDs that are coming out of Stanford, Carnegie, Mel and MIT.
Speaker 4: Some of the best robotics programs in the world, and
Speaker 4: there's lineage of these great professors. Many of them have passed,
Speaker 4: but for example, there's this one guy, Hans Morvec used
Speaker 4: to be a carnegiemail and I got to meet him
Speaker 4: when he was still alive, but he was one of
Speaker 4: the very early pioneers in robotics. And he's got this
Speaker 4: paradox that insiders in the robotics world called the Morvek paradox,
Speaker 4: which is this weird counterintuitive phenomenon which is basically like
Speaker 4: all the stuff that we think is really hard is
Speaker 4: actually pretty easy for AI, and all the stuff that
Speaker 4: we find totally intuitive and easy, like riding a bike,
Speaker 4: that's really hard for robots. So there's this great paradox
Speaker 4: that some of the most brilliant researchers are working on,
Speaker 4: which is how do we do the kind of stuff
Speaker 4: that a four year old can do very intuitively with
Speaker 4: these very complex, expensive machines. And there's all kinds of
Speaker 4: considerations we could talk about about where are these arms
Speaker 4: coming from? The acquisitions that China has been making from
Speaker 4: what historically was a lot of German companies. I mean
Speaker 4: when I say arms meeting the robotic arms they can
Speaker 4: move thing. And then there's this great philosophical debate that
Speaker 4: it hasn't yet come to four. But I believe will
Speaker 4: and investors are sort of lining up on this. I'm
Speaker 4: on the opposite side of some people are funding humanoid robots.
Speaker 4: And the reason that I say I'm on the opposite
Speaker 4: side of it is I don't really believe in them. Yes,
Speaker 4: you would want somebody to help take care of your
Speaker 4: grandma and maybe provide some companionship. But this idea of
Speaker 4: the movies of these ex machina kind of robots that
Speaker 4: embody a human form. We know that engineering is better
Speaker 4: than evolution. If we were inventing a car tomorrow, it
Speaker 4: would be a terrible idea to take Fred Flintstone and
Speaker 4: use his feet, you know, to power these stone wheels.
Speaker 4: We know that an actuator and the axle and an
Speaker 4: engine or just better, and evolution didn't create that. Why
Speaker 4: would we create these humanoid hands where if I'm twisting
Speaker 4: the cap off of a bottle, you know I can
Speaker 4: only I have to turn my hand like seven times
Speaker 4: to do that. Whereas if I was just designed the
Speaker 4: perfect robot, I'd have a little suction cup that would
Speaker 4: go on top. It'd have like a drill bit mechanism.
Speaker 4: It would quickly twist it off and then it would
Speaker 4: Swiss army knife, you know, swap out for the next
Speaker 4: technical gripper capability. So I think that people are misguided
Speaker 4: and they're basically going to end up doing things for
Speaker 4: like prosthetics or you know, something that's sort of Westworld like.
Speaker 4: But I think the practical robots that we're going to
Speaker 4: all be using in our homes are going to look
Speaker 4: nothing like these humanoid robots.
Speaker 1: This is funny. This is very reminiscent of a weird
Speaker 1: conversation I used to have with my dad. He had
Speaker 1: like some sort of bugbear around the shape of aliens hands,
Speaker 1: and he was like, why are they always shown or
Speaker 1: depicted in these illustrations as having like human like hands
Speaker 1: or sometimes even three fingers, Like why wouldn't they have
Speaker 1: just evolved to the next level of very very efficient physiology. Anyway,
Speaker 1: One thing I wanted to ask, and I'm trying to
Speaker 1: think how to frame this question or what the right
Speaker 1: word is, but how open source is robotics in the
Speaker 1: sense of, like how much of the technology is shareable
Speaker 1: or replicable? Because I feel like one of the reasons,
Speaker 1: and you touched on it earlier, but one of the
Speaker 1: reasons we have seen this boom in AI is because
Speaker 1: you can go to places like hugging Face and download
Speaker 1: a bunch of code, open source code, and build off
Speaker 1: of it and it sort of multiplies around itself. But
Speaker 1: is there any aspect of that all in robotics or
Speaker 1: is it just much more proprietary.
Speaker 4: The hardware piece has historically been very proprietary, although there's
Speaker 4: lots of knockoff things. There is a Chinese company which
Speaker 4: is increasingly dominating the field. A lot of people don't
Speaker 4: know the name called Unitry Uni Tree Unitry that is
Speaker 4: sort of copying the Boston Dynamics robots that Joe was
Speaker 4: talking about and that you see in Black Mirror episodes
Speaker 4: and those kinds of things. On the software side, it
Speaker 4: really because it has the same kernel, the same origins
Speaker 4: as many of the AI software things that led to
Speaker 4: large language models and came from transformers academic roots and
Speaker 4: academics like to share and publish, and of course you
Speaker 4: can patent certain things, but by and large, the early
Speaker 4: systems something called ROSS, as you might guess, robot operating system.
Speaker 4: People that were doing something called r duino, which is
Speaker 4: sort of for hobby programmers with hardware and software at
Speaker 4: the intersection Poppy. There's a handful of these things. But again,
Speaker 4: now you're in this mode where you need to find
Speaker 4: training data and you need to do the work and
Speaker 4: spend the time, and that costs money. So you will
Speaker 4: have a mix of open and closed models. And if
Speaker 4: you take a company like Physical Intelligence, their mo their
Speaker 4: motive is is we want to build the operating system
Speaker 4: that any robot can basically use to navigate the world.
Speaker 4: They want to build the brains for the robots as
Speaker 4: opposed to the robots themselves. And there's an interesting philosophical
Speaker 4: and scientific tangent which Barbara Tavski, who is a friend
Speaker 4: and she was the partner of now the late Danny Connoman,
Speaker 4: who was also a friend, her work and she was
Speaker 4: far less famous, but I think actually more important work
Speaker 4: is all about motor function, and her hypothesis is that
Speaker 4: the entire reason for the existence of the brain, like
Speaker 4: the sole purpose of it is to actually produce movement,
Speaker 4: movement towards food or a mate, or to run from
Speaker 4: a prey or predator, which in turn is doing the
Speaker 4: same kind of thing, and I think that some of
Speaker 4: the most interesting philosophical questions about consciousness and memory, spatial perception,
Speaker 4: embodied cognition, gesturing. I mean, I'm wildly gesticulating while I'm
Speaker 4: talking now with my hands. It's just an innate thing
Speaker 4: being able to do mental simulations, like when we think
Speaker 4: about human brains and machine brains merging. I actually think
Speaker 4: it's really going to be very revelatory as these robotic
Speaker 4: systems advance and us understanding that's a lot of the
Speaker 4: purpose of thinking and intelligence is actually just about moving.
Speaker 4: And so that's a pretty cool side effect that I
Speaker 4: think is going to come from all the commercial and
Speaker 4: speculative ventures stuff that we do and funding these things.
Speaker 2: You keep saying things that like jog me onto something
Speaker 2: else that I was meaning to ask you, but you
Speaker 2: mentioned this impulse.
Speaker 3: That academics like to share their work, and.
Speaker 2: That reminded me of something that someone was telling me.
Speaker 2: So you know, maybe I'm sure you are on this
Speaker 2: site a lot. But for listeners, there's this site archived
Speaker 2: dot org where people like publish research and sort of
Speaker 2: like an open source, sort of ungated manner about all
Speaker 2: kinds of scientific and computer things and just today on
Speaker 2: the artificial intelligence page, there's like fifteen new papers and
Speaker 2: they have headlines like autonomous evaluation and Refinement of digital
Speaker 2: agents or a modular benchmark framework to measure progress and
Speaker 2: improve LLM agents. And this guy was who I was
Speaker 2: talking to, made the contention that's like all this stuff
Speaker 2: is being published and investors, a lot of vcs don't
Speaker 2: really have these sort of like technical chops to judge
Speaker 2: a lot of this research.
Speaker 3: It's like the hear.
Speaker 2: Take my money meme, I'm curious, like what you see
Speaker 2: in this space where it's like there must be a
Speaker 2: lot of investors like yourself who are like wowed by
Speaker 2: PhDs like doing all kinds of stuff. It's like, we
Speaker 2: got a one hundred x breakthrough in the energy efficiency
Speaker 2: of this in video chip by training the model differently,
Speaker 2: like how do you evaluate the science and how risky
Speaker 2: is that for investors with a sort of like plethora
Speaker 2: of ostensible breakthrough is happening left and right.
Speaker 4: Well, you're right, there's endless I mean it is why
Speaker 4: there's this endless frontier and sort of the batter for
Speaker 4: bush sense, and it's always very exciting and you need
Speaker 4: to have a very high filter for these things. Not
Speaker 4: everything is commercent. Sometimes there's just a breakthrough, but maybe
Speaker 4: that breakthrough can be licensed to a company. And so
Speaker 4: the people who are actually commercializing this and thinking about
Speaker 4: capital allocation and recruiting teams and then deciding these are
Speaker 4: our top three priorities, and even though there's forty other
Speaker 4: really exciting things that we could and maybe should be doing,
Speaker 4: we're just not going to do that. Now. That's really
Speaker 4: what company building is about. And so oftentimes we might
Speaker 4: have a brilliant scientist, but maybe that person just isn't
Speaker 4: a good salesperson. They can't tell a narrative and convince
Speaker 4: people to move across the country and join them, they
Speaker 4: can't raise capital, and therefore they're not going to be
Speaker 4: a great entrepreneur and they're probably better off as a scientist.
Speaker 4: But when we're making evaluations, it's how much money is
Speaker 4: going to accomplish what in what period of time? And
Speaker 4: who's going to care. It's like if you were playing poker.
Speaker 4: You're looking at your hand, You're figuring out how much
Speaker 4: money do you have to anti up for the next round,
Speaker 4: And then what's the exogenous what's the outside view, what's
Speaker 4: the market going to say, and will they care. That's why,
Speaker 4: you know, we talked a little bit about this, but
Speaker 4: very skeptical about other fields that people are funding right now,
Speaker 4: in fusion or in quantum computing. I'm so skeptical about
Speaker 4: them in part from hard and cynicism of twenty years
Speaker 4: of people pitching us things that are always about unbreakable
Speaker 4: cryptography and femtosecond and kneeling of this quantity, and I'm like, so,
Speaker 4: what most of the things that people promised, you know,
Speaker 4: about unbreakable cryptography or molecular modeling for people are doing.
Speaker 4: They're just not using quantum computers. They're using GPUs, they're
Speaker 4: using in video chips, they're using new algorithms. So I've
Speaker 4: been very skeptical about that, also been very skeptical about
Speaker 4: fusion for the same sort of reason that to your point,
Speaker 4: there's like this ignorance arbitrage that people take advantage of.
Speaker 4: They take advantage of that Investors don't fully understand something.
Speaker 4: It's hot, it's on the front page of a newspaper
Speaker 4: or magazine or you know whatever. It's a buzz and
Speaker 4: they want to play in it, and so they invest
Speaker 4: in it. And that's how you get frauds. So we're
Speaker 4: always looking and basically trying to say, is this academic
Speaker 4: practitioner commercially minded. They're probably not going to leave their jobs,
Speaker 4: so we're only getting them twenty percent of their time.
Speaker 4: Is the intellectual property reel. And then oftentimes to you're
Speaker 4: pointing about the papers, there is a high reference ability
Speaker 4: in papers. A paper that is cited enormously or immensely
Speaker 4: has a lot more credibility because you have the vainglorious
Speaker 4: error detection and correction of other scientists who are trying
Speaker 4: to shoot that person down who has all the fame
Speaker 4: for doing it, trying to seize that mantle. And so
Speaker 4: scientists are not a benevelent bunch. They're just as competitive
Speaker 4: as an investigative journalist trying to break the story or investor,
Speaker 4: or an A and R REP for a music band
Speaker 4: trying to get there before everybody else. And we're no different.
Speaker 4: They're no different, But that's how all this stuff progresses.
Speaker 1: Talk to us about the robotic arm. Yeah, I'm going
Speaker 1: to take the bait.
Speaker 3: Yeah.
Speaker 4: The first point I would make is I don't believe
Speaker 4: in these humanoid robot arms with like fingers and you know,
Speaker 4: high dexterity. I think it's cool Parler trick, but I
Speaker 4: just I think we should have robotic arms that look
Speaker 4: more like Swiss army knives that are swapping and moving stuff.
Speaker 4: And you can see a bunch of these things online.
Speaker 4: You know, different tools for different tasks and be able
Speaker 4: to do them instantaneously when you out to the industry structure.
Speaker 4: You've got Fanik, which is a major Japanese player. They
Speaker 4: started with industrial robots. They do factory automation, but they've
Speaker 4: been a key player, probably a twenty five thirty billion
Speaker 4: dollar enterprise value company. You've got ABB, which is Swiss
Speaker 4: Swedish multinational industrial robot arms. Those are most of the
Speaker 4: things that you would see in even a Tesla gigafactory
Speaker 4: or something where Elon's talking about all their automated things
Speaker 4: and they've got some of these ABB arms or the
Speaker 4: other one is Kuka. Kuka which is a German company
Speaker 4: and they were one of the great leaders. They got
Speaker 4: bought by a Chinese company, I want to say twenty
Speaker 4: sixteen or twenty seventeen, and China's made a bunch of
Speaker 4: I think very smart investments acquiring technologies that were a
Speaker 4: little bit before their time, And I think it presents
Speaker 4: some geopolitical things that probably in five or ten years
Speaker 4: will be looking at and saying, my gosh, how is
Speaker 4: the dominant robot arms supplier or robot body supplier be
Speaker 4: akin to like being dependent on TSMC, a Chinese company.
Speaker 4: So I think there's going to be nash robot companies
Speaker 4: that form in the same way you're starting to see
Speaker 4: national AI companies form.
Speaker 2: How does a company like Physical Intelligence, how are they
Speaker 2: solving the data problem? Because like you said, there isn't
Speaker 2: just the equivalent of a robot Internet where they can
Speaker 2: watch millions of hours of a robot or I'm trying
Speaker 2: to do something, or humans doing something or whatever. What
Speaker 2: is the approach by which the sort of the token
Speaker 2: problem is being solved?
Speaker 4: I would call it the easy way to do it,
Speaker 4: which is actually quite trivial and hard, is doing what
Speaker 4: people originally did with robotic surgery. So we had a
Speaker 4: company called Ors Surgical Robotics. We saw that JJ for
Speaker 4: six billion dollars. It started with surgeons operating these things
Speaker 4: in like a telerobot, so they had little pinchers on
Speaker 4: their fingers and you know, from five feet away or
Speaker 4: in a totally clean operating room. They were operating, but
Speaker 4: it was their hands being teletransmitted to the device. And
Speaker 4: so that is the first way which has come up
Speaker 4: with one hundred different tasks, maybe the highest frequency things
Speaker 4: like washing dishes, folding clothes, again unstructured environments, being able
Speaker 4: to do them in multiple different houses, multiple different heights,
Speaker 4: multiple different you know, wet clothes, dry clothes. Being able
Speaker 4: to pour coffee, being able to have the dexterity to
Speaker 4: open up a ca cup. I actually don't drink those.
Speaker 4: I think they're disgusting, but you know, put it into
Speaker 4: a coffee machine.
Speaker 3: They don't drink any of that.
Speaker 4: Slow and there it's an engineer that is operating them
Speaker 4: and the movement of compensating for gravity, how much force,
Speaker 4: how much tension, how much pressure. That's all information. It's
Speaker 4: information that historically has not been captured and some of
Speaker 4: that is then extensible. And this is a really cool thing.
Speaker 4: You can see some of these robots. You might have
Speaker 4: five different robots, but they have this amazing thing called
Speaker 4: transfer learning. You teach one robot a thing, and suddenly
Speaker 4: the other robot, which is disconnected, you know, to or
Speaker 4: it's connected through the Internet through it, but it can
Speaker 4: actually learn what that robot just learned and perform the task.
Speaker 4: So that's actually pretty eerie and pretty cool. It's the
Speaker 4: same sort of thing like if I had one roll
Speaker 4: that saw where I tossed a ball in a room,
Speaker 4: but three other robots didn't know, they would instantly know
Speaker 4: because they have the eyes of the first robot. So
Speaker 4: there's all kinds of training like that. Then there's schematics
Speaker 4: and drawings. So I was alluding to this before of
Speaker 4: like Ikea drawings, but once you can take diagrams and
Speaker 4: schematics and actually use visual language models, it's something that
Speaker 4: Opening Eyes a partner here with physical intelligence, and they
Speaker 4: have pioneered some of that. That's going to be wild too,
Speaker 4: where you can literally just show an Ikea drawing and
Speaker 4: the robot goes with a constraint set of limited pieces
Speaker 4: that they have to put together, a set of screws
Speaker 4: and wrenches and whatnot, and they can completely assemble whatever
Speaker 4: it is, a nursery thing or furniture or desk, which
Speaker 4: I think is going to be pretty wild for people
Speaker 4: to see too.
Speaker 1: That would actually be a major quality of life cure,
Speaker 1: not having to put together ikea furniture. That'd be amazing.
Speaker 1: Could you talk a little bit more about how robots
Speaker 1: are learning and I guess, like what the different types
Speaker 1: of learning are or different patterns of learning, and what
Speaker 1: you've seen that's been most promising so far.
Speaker 4: You have two main categories or maybe three. You've got
Speaker 4: supervised learning, so there you've got input data. The robot
Speaker 4: is learning, it's being corrected, it's being told in some
Speaker 4: cases like I described before, whether it's voice or a
Speaker 4: gesture or nudge. Then you have unsupervised learning, where the
Speaker 4: robots are basically training on unstructured data. They get to
Speaker 4: discover patterns, they encounter the world, they encounter boundaries, gravity,
Speaker 4: those kinds of things, and it might be slower in
Speaker 4: that case, but they're reducing the dimensions for error. There's
Speaker 4: something that I coined this term. I call it MBTFU,
Speaker 4: which is meantime between f ups. But you want that
Speaker 4: to be as long as possible. If you go back
Speaker 4: to like the early rumbas, you know, a roomba would
Speaker 4: not know if it was cleaning up a spill of
Speaker 4: chocolate milk, or if your dog made a mess and
Speaker 4: it smears. It like to tell all over your floor, right.
Speaker 4: You want to increase as long as possible the meantime
Speaker 4: between me, you know, basically error reduction. Men, you've got
Speaker 4: reinforcement learning, imitation learning, where you might be controlling the
Speaker 4: robot or having it mimic you. There's this idea of
Speaker 4: transfer learning, where a single robot learned something, but it
Speaker 4: can transfer it to different robots or from a different domain.
Speaker 4: So people are trying lots of different approaches, and the
Speaker 4: more different mechanisms you have, people are then going to
Speaker 4: figure out, okay, which is the least data intensive, or
Speaker 4: which has the lowest latency and is the quickest, or
Speaker 4: which is the best system for training a robot that
Speaker 4: you put it in a totally unstructured environment and without any training,
Speaker 4: sort of what they call it zero shot learning. It's
Speaker 4: able to figure out from prior knowledge. I know that
Speaker 4: I can go through that chair. I know that it swivels,
Speaker 4: I have to turn it this way. I know how
Speaker 4: much force I need to pick up a general coke
Speaker 4: can and those kinds of things. And I think, again,
Speaker 4: it's going to be enlightening of how much we as
Speaker 4: we navigate any given minute in our life. Take for granted,
Speaker 4: all this intuitive tacit knowledge that we have about the
Speaker 4: physical world, and there really is this intuitive physics of
Speaker 4: how we move around. Robots are going to learn.
Speaker 2: That is really simple question. In the next five years,
Speaker 2: is it plausible that I'll have a robot in my
Speaker 2: house where I could take all the clothes out of
Speaker 2: the dryer, drop it into something and have it turned
Speaker 2: into folded clothes. And or if not that, what could
Speaker 2: be that chat gpt of robots that's right around the corner.
Speaker 4: Well, one thing which I posited to the team because
Speaker 4: I was thinking about exactly that. You know, what would
Speaker 4: be a really cool thing. I lose stuff in our
Speaker 4: apartment all the time. We have a bunch of different rooms.
Speaker 4: I would love to basically say, has anybody seen which
Speaker 4: is often what I do to my wife and three kids?
Speaker 4: Has anybody seen my wallet? Has anybody seen my glasses? Okay,
Speaker 4: just announcing that you can see that a robot with
Speaker 4: a series of things in your home that would have
Speaker 4: visual identifiers and visual learning machine learning to be able
Speaker 4: to spot object in a video frame could say yes, Josh,
Speaker 4: I know exactly where they are and go and retrieve them.
Speaker 4: So fetch and retrieving objects in the home to me
Speaker 4: would be a pretty cool thing. Where'd I put that
Speaker 4: remote or whatever? And the robot basically knows because they
Speaker 4: can go through the DVR of the home and they
Speaker 4: basically know where it is and they can fetch it
Speaker 4: and retrieve it with the right physics. Folding laundry. I
Speaker 4: don't know how to handicap that, and we can do
Speaker 4: that now pretty.
Speaker 2: Crudely through That's the one I want because I have
Speaker 2: a swall in New York City apartment. I don't lose
Speaker 2: things like too much. Basically, like, I need that fold
Speaker 2: I need that folding robot.
Speaker 4: What are you gonna pay for that? Though? I don't know.
Speaker 4: You know, would you pay five grand ten grand for
Speaker 4: a robot that folded your clothes? Probably not. So it's
Speaker 4: it's gonna have a high. That's why most of these
Speaker 4: things have found their way into industrial uses first, and
Speaker 4: over time they'll get cheaper and cheaper, they'll get better
Speaker 4: and better. But you know, look, I'm one of the
Speaker 4: few people that have an Amazon astro It's a robot
Speaker 4: that you know, rolls around the house and you can
Speaker 4: tell to go into a room. You can put something
Speaker 4: in the back of it and take it to It
Speaker 4: can do facial identification for my family, so I can
Speaker 4: say where's quinner body, and they'll find my younger kids
Speaker 4: and I can send a message noise the hell out
Speaker 4: of my wife. But I think it's sort of cool,
Speaker 4: and yeah, every robot that comes out, I'll be an
Speaker 4: early adoptor, and yeah, we like funny things.
Speaker 1: So one of the interesting things about the current tech
Speaker 1: cycle and all this enthusiasm for AI is that so
Speaker 1: far it's been the big incumbents who seem to be
Speaker 1: winning here. And part of that is because the capital
Speaker 1: investment needed is so large, the amount of data needed
Speaker 1: is so large when it comes to robotics, would you
Speaker 1: expect to see a similar thing? And then added on
Speaker 1: to that, could you have a situation where, you know,
Speaker 1: if you are a large manufacturer, or perhaps you are
Speaker 1: a company that has a lot of proprietary data, like
Speaker 1: an insurance company or a financial company or something like that,
Speaker 1: could you develop your own robotics Like would that be
Speaker 1: your edge here? And could you potentially just do it yourself?
Speaker 4: On the first case, if I look at the current landscape,
Speaker 4: the one company in sort of the big magnificent seven
Speaker 4: would be Amazon, just because they have already been investing
Speaker 4: in this and for a long time. Jeff Bezos is
Speaker 4: very passionate about robots. So I think there's a DNA
Speaker 4: there where doing things that enable them to do the
Speaker 4: three things that Jeff loves to do historically, which is
Speaker 4: increased choice and availability, increase convenience for customers and lower prices,
Speaker 4: and factory automation, warehouses delivery. Even when they bought our
Speaker 4: company for a little over a billion dollars called Zookes
Speaker 4: that was doing autonomous driving, they have a long term
Speaker 4: intention to be able to do twenty four to seven
Speaker 4: right hand turn lanes, navigating around the city with a
Speaker 4: human that's basically delivering last miles kind of stuff. And
Speaker 4: so so I could see Amazon doing that. Microsoft, I
Speaker 4: don't really see them getting into robotics in a significant way.
Speaker 4: They have some R and D efforts Google and we
Speaker 4: talked a little bit about this phenomenon, but they are
Speaker 4: the third or fourth group where we have taken a
Speaker 4: team out of a big tech We did it with
Speaker 4: Google with a company called Osmo to create basically Shazam
Speaker 4: for smell. We did it with a BIOAI company called
Speaker 4: Evolutionary Scale out of meta that's going to be announced
Speaker 4: more publicly soon. And then we did it with this
Speaker 4: team out of Google that became a physical intelligence between
Speaker 4: Google deep Mind and open AI and Stanford and Berkeley.
Speaker 4: So I think that this is going to be more
Speaker 4: the startups. The beneficiaries of the money will still be
Speaker 4: Nvidia and some of the chip players, some of the
Speaker 4: hardware providers, because we need the hardware to be able
Speaker 4: to train the robots. But I really think that this
Speaker 4: is an open field. And again just going back to
Speaker 4: that stat of how many large language model there are
Speaker 4: for chatbots and how few there are for robots. I
Speaker 4: think it's a big opportunity. Now five years from now
Speaker 4: we might have a bubble in robots, but today I
Speaker 4: think it's it's it's a really exciting field, just.
Speaker 2: An existing AI and competitive advantage. So when I first
Speaker 2: in the year two thousand, I think encountered Google, I
Speaker 2: used it. I was like, this is way better than
Speaker 2: Yahoo or anything else. I never like stopped after that.
Speaker 2: Again with chat bots, just going back now, I even
Speaker 2: talking about robotics here. It's like I and I like
Speaker 2: got a chet GPT progount or whatever early on, and
Speaker 2: I thought it was pretty cool and I like use
Speaker 2: it for some stuff. And then like the new version
Speaker 2: of Claude came out from Anthropic and I was like, oh,
Speaker 2: this is actually kind of cool and I kind of
Speaker 2: like it more. I don't really know why I like
Speaker 2: it more, but for some reason, I like it more
Speaker 2: and I had no problem switching over. Could it be
Speaker 2: possible that some of these like core like models do
Speaker 2: not prove to be as sticky or have as deep
Speaker 2: moat as people expect.
Speaker 4: Totally Inflection, which launched there is called PI just wasn't
Speaker 4: that good. They've now gone to Microsoft Anthropic. Yeah, at
Speaker 4: first was a little bit behind. They are the most
Speaker 4: formative model. They're the best performing one. They're the one
Speaker 4: that I also like, you use the most. Why it's fastest,
Speaker 4: it has a little bit more.
Speaker 3: It seems to talk a little bitter.
Speaker 4: Yeah, but here's an interesting thing to your point about
Speaker 4: you know, sort of motes and Google early on most
Speaker 4: searches for Google, and remember Google's two hundred and eighty
Speaker 4: billion dollars add revenue generated, you know, enormous Google. Most
Speaker 4: of those are like five to ten word searches, right,
Speaker 4: so you put something in your like, I don't know,
Speaker 4: restaurant in West Village or you know whatever. Okay, Perplexity,
Speaker 4: which I don't know if you've used, and we met
Speaker 4: the founder early on and we ended up not investing
Speaker 4: and it was probably a miss for us, but the
Speaker 4: founder they are focused. You know what I'm going to
Speaker 4: do the point two percent of searches or the one
Speaker 4: percent of searches that are longer formed where people really
Speaker 4: want to ask a full question. And I see a
Speaker 4: lot of people that are using it and it's not
Speaker 4: coming up with some Wolke answer or some pithy, you know,
Speaker 4: rock like response. It's actually a well researched, footnoted, sources,
Speaker 4: cited response. So to me, the two things that I
Speaker 4: use most on the chap outside right now are clawed
Speaker 4: from Anthropic and Perplexity. But in six months there might
Speaker 4: be something totally different. And the sheer number of models
Speaker 4: that are available on open source. Who knows what Apple
Speaker 4: ends up doing here and that being integrated. You know,
Speaker 4: Series sucked, but so did Alexa. Amazon's making moves Apple
Speaker 4: will too. So yeah, but you know, use you mount
Speaker 4: for a second. All of this stuff and Venture Venture
Speaker 4: is going through this downturn and the one area where
Speaker 4: there are very high valuations and a lot of money
Speaker 4: flowing and a lot of talent is been in AI,
Speaker 4: and therefore the future returns on these things are going
Speaker 4: to be lower. And that's why we at Lucks decided,
Speaker 4: you know, we're going to focus on AI in the
Speaker 4: physical world. Having done all the stuff over the past
Speaker 4: five years, I would say there's this five year psychological bias.
Speaker 4: Everybody wants to be invested today where you should have
Speaker 4: been five years ago, So hugging face and Mosaic and
Speaker 4: which we sold the data bricks. We were in that
Speaker 4: five years ago. Today we're really interested in biology and
Speaker 4: robotics and AIS use in those. And then I have
Speaker 4: a really weird theme which is so sexy because it's
Speaker 4: unsexy to me and to others. You understand accounting, vast
Speaker 4: majority of venture investors and make startup people don't. But
Speaker 4: do you take capex. Capex is made up of two pieces,
Speaker 4: growth and maintenance. And everybody's been funding growth, growth, growth, growth, growth,
Speaker 4: investor growth. So I got interested in maintenance. Why because
Speaker 4: you have trillions of dollars of assets infrastructure, hospital systems,
Speaker 4: energy systems, buildings that need to be maintained and every
Speaker 4: generation and every new startup and every new investor always
Speaker 4: wants to do the new new thing. It's why we
Speaker 4: get new music, and we get new food, and we
Speaker 4: get new fashion. But there's all these neglected assets, and
Speaker 4: I think you can apply new technology to maintaining these systems.
Speaker 4: And so I've become obsessed with this unsexy theme of maintenance,
Speaker 4: which I think is going to become a hot area
Speaker 4: over the next few years.
Speaker 1: Well, you mean maintenance of physical infrastructure. So the idea
Speaker 1: that you could have, I don't know, a little row
Speaker 1: bought that goes around your factory or a bunch of
Speaker 1: highways and sort of surveys it for cracks or things
Speaker 1: that it thinks needs to be fixed.
Speaker 4: Totally, could be infrastructure for transportation, it could be inside
Speaker 4: of hospitals where there are road routine things. And it's
Speaker 4: also oddly, and I know you guys have covered this,
Speaker 4: but the idea that AI is really coming for the
Speaker 4: white collar workers. You know, you joke that you could
Speaker 4: talk about AI and generate a script, you know, based
Speaker 4: on AI.
Speaker 1: But oh no, it wasn't a joke, very serious.
Speaker 4: They always thought that they were relatively insulated and it
Speaker 4: was the blue collar workers. But let me tell you
Speaker 4: the guy that put me in business, Bill Conway, the
Speaker 4: founder of the Carlile Group. He's spending all his philanthropic money,
Speaker 4: or a significant portion of it, funding nursing schools. Why
Speaker 4: because he identified a very high magnitude impact. Because we
Speaker 4: have such a shortage of nurses in this country. That's
Speaker 4: an opportunity for maintenance where robots and technology can play
Speaker 4: a role. How do you augment and help nurses plummers.
Speaker 4: We have a massive shortage of plumbers this country, and
Speaker 4: so I actually think that the blue collar workers, empowered
Speaker 4: by technology and maintaining all of these systems around us,
Speaker 4: are actually going to be a winning combination.
Speaker 2: I want to talk about another aspect of I guess
Speaker 2: AI investing, which is that in this sort of the
Speaker 2: SaaS wave, the twenty tens a decade like compute was
Speaker 2: very cheap, right, and so basically like that part, you're
Speaker 2: like plug into aws and it's sort of yeah, I know,
Speaker 2: it probably costs some money, but is not a big
Speaker 2: lineup or a line item.
Speaker 3: Ultimately for a lot of these companies, How does that change.
Speaker 2: In twenty twenty four when you're dealing with an AI
Speaker 2: company and electricity bills exist or hardware accumulation, depending on
Speaker 2: where they are in the stack. How is an investor
Speaker 2: do you think about like the changing I guess people
Speaker 2: will talk about like you know, shifting, you know, having
Speaker 2: to spend more on capex versus op X relative to
Speaker 2: the prior generation of tech startups. How does that play
Speaker 2: out in the investments you choose?
Speaker 4: It's a great question in the AI world, and then
Speaker 4: I'll give you the biology world. On the AI side,
Speaker 4: you know, take opening Eye. These are all rumored numbers,
Speaker 4: not you know, nothing's fully confirmed, but two billion, maybe
Speaker 4: three billion of revenue. I think about ten million people
Speaker 4: paying twenty bucks a month or thereabout, and probably one
Speaker 4: hundred million users. You know, I don't know how many
Speaker 4: of those are unique, but they're not making money on that.
Speaker 4: They're losing several billion dollars today because you have these
Speaker 4: upfront costs, big cap X, a lot of training, you know,
Speaker 4: and then you try to maybe do some big enterprise deals.
Speaker 4: A company like hugging Face is profitable because they're not
Speaker 4: doing they're just hosting it and you know, letting people
Speaker 4: run in frints and then charging and making margin on
Speaker 4: that kind of stuff. So that to me is interesting
Speaker 4: of the people that spend a ton of money and
Speaker 4: they've got to earn it back. And can you get
Speaker 4: pricing power by going from twenty bucks a month to
Speaker 4: thirty bucks a month? And maybe you get that because
Speaker 4: now you have open AI premium where you have access
Speaker 4: to say Sora for video generation or something like that.
Speaker 4: So that's going to be a big question on are
Speaker 4: these profitable investments? Not? Are they cool? Not? Are they
Speaker 4: world changing? Absolutely, but are they profitable investments? And look,
Speaker 4: the market may not care if they're profitable. Market funds
Speaker 4: all kinds of profitable thing that they believe in the
Speaker 4: narrative in the story. But thinking about fundamental businesses and
Speaker 4: the economic changes between Capeck and op X, I think
Speaker 4: in AI it's very hard if you are building out
Speaker 4: your data centers, trying to do your own training, your
Speaker 4: own inference, hosting these models, it's very hard. Biology we
Speaker 4: will see an AWS moment where instead of you having
Speaker 4: to be a biotech firm that opens your own wet
Speaker 4: lab or moves into Alexandria real estate which is specialized
Speaker 4: in hosting biotech companies in all these different regions, approximate
Speaker 4: to academic research centers. You will be able to just
Speaker 4: take your experiment and upload it to the cloud, where
Speaker 4: there are cloud based robotic labs. We funded some of these.
Speaker 4: There's one company called Stratios. There's a ton that are
Speaker 4: going to come on wave. And this is exciting because
Speaker 4: you can be a scientist on the beach in the Bahamas,
Speaker 4: pull up your iPad, run an experiment. The robots are
Speaker 4: performing ninety percent of the activity of pouring something from
Speaker 4: a beaker into another, running a centrifuge, and then the
Speaker 4: data that comes off of that, and this is the
Speaker 4: really cool part. Then the robot and the machines will
Speaker 4: actually say to you, hey, do you want to run
Speaker 4: this experiment, but change these four parameters or these variables,
Speaker 4: and you just click a button yes, as though it's
Speaker 4: reverse prompting you, and then you run another experiment. So
Speaker 4: the implication here is that the boost in productivity for science,
Speaker 4: for generation of truth, of new information, of new knowledge,
Speaker 4: that to me is the most exciting thing. And the
Speaker 4: companies that capture that, forget about the societal dividend, I think,
Speaker 4: are going to make a lot of money.
Speaker 1: Yeah, this actually reminds me of the conversation that we
Speaker 1: had regarding snack food innovation and this idea that you
Speaker 1: can use a sort of factorio like simulation just to
Speaker 1: run new processes through your factory and see how they
Speaker 1: would actually work out and what the supply chain might
Speaker 1: look like. But not to give in to my five
Speaker 1: year bias too much and overly focus on chat GPT.
Speaker 1: But where are we in terms of context window expansion,
Speaker 1: because this is something we spoke about with you last year,
Speaker 1: and I think for a lot of people it's probably
Speaker 1: one of the overriding annoyances with something like chat GPT,
Speaker 1: the fact that you can't actually copy and paste that
Speaker 1: much text into it, and that you are limited in
Speaker 1: terms of the output that it actually gives you. Have
Speaker 1: there been major advancements since we last spoke to you.
Speaker 4: Well, the CLAW three is one of the largest, and
Speaker 4: then you've got all kinds of interesting collaborations. You've got
Speaker 4: Nvidia and Microsoft died one with a huge number of tokens.
Speaker 4: You've got a one to one labs that has this
Speaker 4: thing called Jurassic Again, a lot of people are making
Speaker 4: headway here, but we are I think a year away
Speaker 4: from you being able to upload hundreds of PDFs thousands
Speaker 4: of books if they're not already immediately referenceable and be
Speaker 4: able to detect pattern change amongst documents, summarize and unearth,
Speaker 4: you know, the entirety of key concepts, and then I
Speaker 4: think the most valuable thing will be it prompting you
Speaker 4: to say, here's a question you didn't ask about all
Speaker 4: these documents that you just uploaded. So yeah, I think
Speaker 4: we're just keep increasing the context window. But that said,
Speaker 4: most of the history of innovation is just like, keep
Speaker 4: increasing this factor, and then somebody else comes along and
Speaker 4: invent something. It's like that factor doesn't matter anymore. You know.
Speaker 4: My favorite iconic example of this is like sailboats. You
Speaker 4: find these sailing ships back in the day, they just
Speaker 4: kept adding more and more sales, Like these things started
Speaker 4: to look ridiculous, you know, and then somebody invents the
Speaker 4: electric motor, and you have a motor both, so I
Speaker 4: think we'll have the same sort of thing. And then
Speaker 4: people figure out, hey, there's a better architecture here than
Speaker 4: just constantly increasing the context window. And some of that
Speaker 4: might be with memory retrieval and being able to reference
Speaker 4: other models and just go into the archive of what
Speaker 4: they have. So yeah, that's going to keep expanding.
Speaker 2: Josh woolf Lux Capital, thank you so much for coming
Speaker 2: back on odd lots.
Speaker 3: Always great to get an update on what you're interested in.
Speaker 4: It was great to be with you guys.
Speaker 2: Yeah good, you're prepared already to ingratiate yourself and to
Speaker 2: blend in with the human art robot.
Speaker 3: Thank you so much. That was fantastic.
Speaker 2: First of all, Tracy, I really like talking to Josh
Speaker 2: and always like getting an update. I really do want
Speaker 2: the folding the clothes folding robot though, Like I actually
Speaker 2: think that's a really big deal and would make almost
Speaker 2: everyone's life better if they didn't have to worry about
Speaker 2: folding clothes.
Speaker 1: I agree, it would be far more useful to have
Speaker 1: something doing physical tasks like folding laundry versus telling you
Speaker 1: where you're lost, Yeah, is in your desk.
Speaker 3: I want I need that folding robot.
Speaker 1: I mean, I will say, I know everyone likes to
Speaker 1: make fun of Alexa as well, but our house we've
Speaker 1: kitted out all the lights on. They're all those smart
Speaker 1: bulbs because we don't have any overhead wiring, so like
Speaker 1: everything has to be lamps. So if you didn't have
Speaker 1: a robot, that was able to turn on all of
Speaker 1: your appliances at once in a room. It would be
Speaker 1: incredibly annoying because you would be going from lamp to
Speaker 1: lamp to lamp. So it does make a difference in
Speaker 1: my daily life at least, I mean, there's so much
Speaker 1: to pull out from that. So one thing that I
Speaker 1: thought was interesting from an industrial policy perspective was Josh's
Speaker 1: discussion of some of the robotic capabilities being developed in
Speaker 1: places like China and the idea that we might have
Speaker 1: another Chips Semiconductor like situation on our hands where we
Speaker 1: wake up in ten years and realize that a primary
Speaker 1: component of robotics is being built much more efficiently and
Speaker 1: cheaply elsewhere outside of the US or the West. And
Speaker 1: then the other thing I thought was interesting was the
Speaker 1: idea of leap frogging, right, So, I think a lot
Speaker 1: of people, myself included, when we think of technological advances,
Speaker 1: it's like, can this do this thing slightly faster? Can
Speaker 1: it do it on a slightly larger scale, to the
Speaker 1: point about the context window and expansion there. But you
Speaker 1: can leap frog in technology, as Josh was saying, and
Speaker 1: you can go from the sailboat to the motor boat,
Speaker 1: or you could bypass a human evolution, for instance, and
Speaker 1: instead of having humanoid robots, you could have a Edward
Speaker 1: scissorhands like thing with a Swiss army knife on the
Speaker 1: end of his arm.
Speaker 4: Yeah.
Speaker 2: That made a ton of sense to me, which is like,
Speaker 2: if you're like starting from scratch, like it's not obvious
Speaker 2: that the human form that was developed over millions of
Speaker 2: years through evolution is necessarily the thing you want to
Speaker 2: create or recreate to do various tasks that you need.
Speaker 2: There was a lot in there that I liked. The
Speaker 2: thing that he would talk about at the end. It
Speaker 2: sort of sounded like cloud kitchens, but for biology les. Yeah,
Speaker 2: so if you just have all the robots do it
Speaker 2: and then they can prompt you for other ideas, that's interesting.
Speaker 3: It does seem exciting. The idea of ways to.
Speaker 2: Accumulate training data for these sort of like you know
Speaker 2: that you could maybe solve the mechanical engineering, but without
Speaker 2: you know, there's no equivalent of like all of the
Speaker 2: text on Reddit or Wikipedia or whatever that change or
Speaker 2: you know, Google books or YouTube. So like having to
Speaker 2: recreate that as a bottleneck for building robots was really interesting.
Speaker 2: I love the term he used I think was ignorance arbitrage, Yeah,
Speaker 2: which is a really great term. So it's like, yeah,
Speaker 2: like in a lot of like pure science spaces, you're
Speaker 2: going to get investors who are willing to throw money
Speaker 2: at someone who just like has a really good idea
Speaker 2: on paper because that person is smart.
Speaker 1: Well, I think this is also the really unusual thing
Speaker 1: about this particular cycle, which is the dominance of the
Speaker 1: incumbents and the fact that on the one hand, you
Speaker 1: do have a bunch of open source software and to
Speaker 1: some extent you can take something off of a repository
Speaker 1: and you can pitch it to investors and say this
Speaker 1: is the next big thing, and they might not have
Speaker 1: the technological expertise to actually evaluate that. But when it
Speaker 1: comes to making you know, actual advancements in something like robotics,
Speaker 1: it does feel like you have to have an edge
Speaker 1: in one respect or another. You either have to have
Speaker 1: the capital to deploy or you have to have access
Speaker 1: to that data.
Speaker 4: So I don't know.
Speaker 1: I guess we'll see how it shakes out.
Speaker 2: I guess we'll have Josh back next year, yeah, next, next,
Speaker 2: next Springer summer to see what the next big thing is.
Speaker 1: Then hopefully he can bring a robot with him of some.
Speaker 3: Sort or folding robot.
Speaker 1: Yeah, all right, shall we leave it there.
Speaker 3: Let's leave it there.
Speaker 1: This has been another episode of the All Thoughts podcast.
Speaker 1: I'm Tracy Alloway. You can follow me at Tracy Alloway and.
Speaker 2: I'm joll Wisenthal. You can follow me at the Stalwart.
Speaker 2: Follow our guest Josh Wolf. He's at Wolf Josh. Follow
Speaker 2: our producers Carmen Rodriguez at Carman Erman dash, Ol Bennett
Speaker 2: at Dashbot and kel Brooks at Kelbrooks. Thank you to
Speaker 2: our producer Moses on Them. For more odd Lags content,
Speaker 2: go to Bloomberg dot com slash odd Lots, where we
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