Josh Wolfe: The ChatGPT of Robotics is Coming

interesting to me. I'm not supposed I'm not sure it's fine, I think, And she said, like, you know, there's all this anxiety about you know, kids cheating on their essays or having Chad g GBT write their essays for them, and like, you know, supposedly professors are like tearing their hair out trying to figure out what to do about this, and the AI detector don't really work all that well particularly, but apparently like it seems like the solution is to

just grade them as regular essays no matter what. And it sounds like at this point all the chat GPT essays are basically solid c essays, and so if you just sort of like take them at phase value, even if you think they might be AI generated at least at this point, it doesn't seem like a way at least for college students to write good essays.

and I've seen a lot of stuff. There have been a couple Bloomberg articles about some startups that sort of say, like, okay, well, like what if we trained robots the same way we trade large language models, where you feed them just like tons and tons and tons of real world data, and so that yeah, like you sure you still have to

solve the mechanical engineering part. But then what if that allows them, like all this training data to do more advanced industrial things like I don't know, like make a pizza or you know, be a more powerful humanless assembly line or.

chat GPT, like that's fun. I can do that myself, But what I really need is someone to vacuum or dust the house.

the space. So I'm really pleased to welcome back on the show. We're going to be speaking with Josh Wolfe, co founder and managing partner of Lux Capital. Josh, thank you so much for coming back on ODLTS.

of these guys that are starting to fail. They've raised billions of dollars and in some cases just you know, relatively undifferentiated, big debate between open source that is approaching the ASSYMP tote of achievement that the big private models have and then being a little bit lout you've got chicks. What do I mean by that? Most of the applications in AI are the mundane and passe on the one side, which would be like customer service and basic call center

supplementation or substitution. And then at the other end you have people that are spending tens of thousands of dollars a month in some case on AI girlfriends and people that are doing what they often do with technology which is used for prurient interests. So that to me the two barbel extremes in AI, where people are actually making money and profits serving demand for basic human instincts I

guess and needs. That's interesting. Overall, you're seeing a big shift from the compute piece to the energy piece, meaning people now recognize this bottleneck in AI is not going to be so much about the chips. We also talked about this months ago when we said, look, you don't necessarily need these in video chips for inference, the part that most people do when they're querying all these models. You do need them for training, but the power levels

on these things are just enormous. There was a Dell earnings call that they think sort of accidentally leaked that this in VideA B one hundred Blackwell chip is going to be a thousand wide draw of power, which is like forty fifty percent more than these eight one hundred chips. Why does that matter because now you got to figure out how do you supply the energy for that. And a tidbit which I thought was interesting was Amazon acquired

a nuclear powered data center in Pennsylvania. They've spent six hundred and fifty million dollars, they got about a gig lot of power. And I think that that is going to be a trend. I think it's actually going to usher in a wave of what I like to call out elemental energy, but demand for nuclear power to power these big AI data centers.

an AI play, but here we are. I want to go back to something you said, and you actually brought it up the last time we spoke to you last year, and it was the idea of I guess the novelty of some of these more public facing AI projects, And I think you pointed out last year it's really fun to use some of these things, like generate a bunch of cartoon versions of yourself or whatever, but it might not be a sustainable business model, and it might end

up being a functionality that is eventually incorporated into another platform or a different project. Have you seen any example, specific examples of more public facing novelty AI start to like go away? I think you mentioned a few failures recently.

be relatively commoditized away. And some of these things have prominent people and have raised a lot of money, but they are what I would consider failing. Take inflection. You've got Mustapha Solimon, super smart guy, co founder of deep mind raised I think a billion and a half for that company. You know, I'm gonna be careful here because Microsoft has been probably the savviest actor in this entire game, figuring out that they can acquire things by doing things

in a clever way, skirting FTC and DJ oversight. They effectively control open ai. As I think we also talked about heard, particularly going into the end of last year when there was all the drama around open Ai. Sadia said, look, if open ai went out of business, we own it, control it. We've got all the data up left, right center, you know, all around them. Same thing with this company Inflection.

They did I think a six hundred and fifty six hundred and seventy five million dollar license for the technology that basically was a payment above and beyond what the venture investors had made. Venture investors made a little bit of money, not a lot. Key management went over to Microsoft. But Microsoft has been very clever. So back to your question, I think the big are going to get bigger and are going to be most of the beneficiaries here, Microsoft, Adobe, Amazon,

Amazon themselves. Coming up on the one year anniversary of Bedrock, they're going to announce that they've got the best performing model with Anthropic, which they've made billions of dollars of investments in now sort of competitor to Opening Eye CHATGPT. They're also going to renounce something with one of our companies that hasn't yet been publicly disclosed in biology. That's going to be one of the two biggest waves next

biology and what you started the conversation with robotics. If you just take robotics as an example, I think in one of our companies, Hugging Face, which is one of the main repositories for all these open source models, there's something like sixty thousand text generations models. You know, it's like fifty nine thousand, seven hundred something now, but just an enormous number of text generation models. This is in

like two or three. It's like everybody's doing this. Everybody's trying to do it all, basically trying to predict the next word based on the prior one. What this transformer technology, which was invented at Google, ended up parlaying into guess how many robotic models there are? Fifty nine thousand in text generation? Guess how many robotic models there are?

you train these things? Was it on YouTube? And she gave it and you probably saw that, So there's gonna be all kinds of copyright stuff on that. Robots are hard. Why Most of the robotic stuff that has been out in the world, like you talked about in your intro, is constrained in manufacturing facilities, in work cells on an assembly line, very specific, parametrically constrained, so very few degrees

of freedom of what they're actually doing. The robots themselves might have multi access scripts and controllers, but they're not moving around very freely. You have exceptions with like Amazon which acquired Kiva, moving the warehouse inventory stuff around, but again relatively xyz access not unstructured environments. You and I and our listeners. We all thrive every day in unstructured environments and that is where you need enormous training data.

You can't search the internet for that, So how do you do it. There's a few things that have emerged, and you mentioned some articles. We funded a company that recently came out of STELL called Physical Intelligence stead of artificial intelligence, Physical Intelligence, and it is the krem Dela Creme team from Stanford and Berkeley. You've got some open

ai folks, You've got Google Deep Mind folks. They took investment from open ai US and a bunch of other vcs and they are just twenty four to seven training robots doing all kinds of crazy things like folding, laundry, pouring. Determined but to let these robots encounter unstructured environments and

then be able to thrive in them. The next thing that you're going to see are visual models where you're effectively giving, like an ikea sketch or you're drawing something, and you're being able to instruct the robot to have a sense of intuitive physics of how the world works and how things might connect to each other and then learn from that. And then we're also training these robots

with simple verbal cues. So there's a video you can see online from some of the researchers where they are picking nuts and m and ms and separating them, you know, just as a task of being able to sort and filter with precision and dexterity, and if they picked a wrong one, you can actually instead of physically grabbing the things, say stop grab the Eminem's not the nuts. And now

it knows that. So I think that we're about to unleash in robotics what will become a chat GPT like moment where people are so used to seeing robots and they see the arms, and they've seen West World and this kind of stuff, and suddenly something happens that just blows your mind. And I think that's coming soon.

eventually hit on any of them. Here's a specific question, and then we can maybe get back to robotics. This element where there is such a shortage of advanced, cutting edge talent, people who really know how to do this. And you mentioned that guy that got hired by Microsoft from his other companies as an investor in AI or robotics startups. Is this a dynamic that's different than in other software or other tech investing. Basically, this sort of like highly skilled tech qman risk.

is highly technical, often PhD scientists. The vast majority of the founding teams that we've backed at companies like Covariant or format or this new company Physical Intelligence, they're all PhDs that are coming out of Stanford, Carnegie, Mel and MIT. Some of the best robotics programs in the world, and

there's lineage of these great professors. Many of them have passed, but for example, there's this one guy, Hans Morvec used to be a carnegiemail and I got to meet him when he was still alive, but he was one of

the very early pioneers in robotics. And he's got this paradox that insiders in the robotics world called the Morvek paradox, which is this weird counterintuitive phenomenon which is basically like all the stuff that we think is really hard is actually pretty easy for AI, and all the stuff that we find totally intuitive and easy, like riding a bike,

that's really hard for robots. So there's this great paradox that some of the most brilliant researchers are working on, which is how do we do the kind of stuff that a four year old can do very intuitively with these very complex, expensive machines. And there's all kinds of considerations we could talk about about where are these arms coming from? The acquisitions that China has been making from

what historically was a lot of German companies. I mean when I say arms meeting the robotic arms they can move thing. And then there's this great philosophical debate that it hasn't yet come to four. But I believe will and investors are sort of lining up on this. I'm on the opposite side of some people are funding humanoid robots. And the reason that I say I'm on the opposite side of it is I don't really believe in them. Yes, you would want somebody to help take care of your

grandma and maybe provide some companionship. But this idea of the movies of these ex machina kind of robots that embody a human form. We know that engineering is better than evolution. If we were inventing a car tomorrow, it would be a terrible idea to take Fred Flintstone and use his feet, you know, to power these stone wheels. We know that an actuator and the axle and an

engine or just better, and evolution didn't create that. Why would we create these humanoid hands where if I'm twisting the cap off of a bottle, you know I can only I have to turn my hand like seven times to do that. Whereas if I was just designed the perfect robot, I'd have a little suction cup that would go on top. It'd have like a drill bit mechanism. It would quickly twist it off and then it would Swiss army knife, you know, swap out for the next

technical gripper capability. So I think that people are misguided and they're basically going to end up doing things for like prosthetics or you know, something that's sort of Westworld like. But I think the practical robots that we're going to all be using in our homes are going to look nothing like these humanoid robots.

just evolved to the next level of very very efficient physiology. Anyway, One thing I wanted to ask, and I'm trying to think how to frame this question or what the right word is, but how open source is robotics in the sense of, like how much of the technology is shareable

or replicable? Because I feel like one of the reasons, and you touched on it earlier, but one of the reasons we have seen this boom in AI is because you can go to places like hugging Face and download a bunch of code, open source code, and build off of it and it sort of multiplies around itself. But is there any aspect of that all in robotics or is it just much more proprietary.

and those kinds of things. On the software side, it really because it has the same kernel, the same origins as many of the AI software things that led to large language models and came from transformers academic roots and academics like to share and publish, and of course you can patent certain things, but by and large, the early systems something called ROSS, as you might guess, robot operating system.

People that were doing something called r duino, which is sort of for hobby programmers with hardware and software at the intersection Poppy. There's a handful of these things. But again, now you're in this mode where you need to find training data and you need to do the work and spend the time, and that costs money. So you will

have a mix of open and closed models. And if you take a company like Physical Intelligence, their mo their motive is is we want to build the operating system that any robot can basically use to navigate the world. They want to build the brains for the robots as

opposed to the robots themselves. And there's an interesting philosophical and scientific tangent which Barbara Tavski, who is a friend and she was the partner of now the late Danny Connoman, who was also a friend, her work and she was far less famous, but I think actually more important work is all about motor function, and her hypothesis is that the entire reason for the existence of the brain, like the sole purpose of it is to actually produce movement,

movement towards food or a mate, or to run from a prey or predator, which in turn is doing the same kind of thing, and I think that some of the most interesting philosophical questions about consciousness and memory, spatial perception, embodied cognition, gesturing. I mean, I'm wildly gesticulating while I'm talking now with my hands. It's just an innate thing being able to do mental simulations, like when we think

about human brains and machine brains merging. I actually think it's really going to be very revelatory as these robotic systems advance and us understanding that's a lot of the purpose of thinking and intelligence is actually just about moving. And so that's a pretty cool side effect that I think is going to come from all the commercial and speculative ventures stuff that we do and funding these things.

site a lot. But for listeners, there's this site archived dot org where people like publish research and sort of like an open source, sort of ungated manner about all kinds of scientific and computer things and just today on the artificial intelligence page, there's like fifteen new papers and they have headlines like autonomous evaluation and Refinement of digital agents or a modular benchmark framework to measure progress and

improve LLM agents. And this guy was who I was talking to, made the contention that's like all this stuff is being published and investors, a lot of vcs don't really have these sort of like technical chops to judge a lot of this research.

that breakthrough can be licensed to a company. And so the people who are actually commercializing this and thinking about capital allocation and recruiting teams and then deciding these are our top three priorities, and even though there's forty other really exciting things that we could and maybe should be doing, we're just not going to do that. Now. That's really what company building is about. And so oftentimes we might have a brilliant scientist, but maybe that person just isn't

a good salesperson. They can't tell a narrative and convince people to move across the country and join them, they can't raise capital, and therefore they're not going to be a great entrepreneur and they're probably better off as a scientist. But when we're making evaluations, it's how much money is going to accomplish what in what period of time? And who's going to care. It's like if you were playing poker.

You're looking at your hand, You're figuring out how much money do you have to anti up for the next round, And then what's the exogenous what's the outside view, what's the market going to say, and will they care. That's why, you know, we talked a little bit about this, but very skeptical about other fields that people are funding right now,

in fusion or in quantum computing. I'm so skeptical about them in part from hard and cynicism of twenty years of people pitching us things that are always about unbreakable cryptography and femtosecond and kneeling of this quantity, and I'm like, so, what most of the things that people promised, you know, about unbreakable cryptography or molecular modeling for people are doing. They're just not using quantum computers. They're using GPUs, they're

using in video chips, they're using new algorithms. So I've been very skeptical about that, also been very skeptical about fusion for the same sort of reason that to your point, there's like this ignorance arbitrage that people take advantage of. They take advantage of that Investors don't fully understand something. It's hot, it's on the front page of a newspaper or magazine or you know whatever. It's a buzz and they want to play in it, and so they invest

in it. And that's how you get frauds. So we're always looking and basically trying to say, is this academic practitioner commercially minded. They're probably not going to leave their jobs, so we're only getting them twenty percent of their time. Is the intellectual property reel. And then oftentimes to you're pointing about the papers, there is a high reference ability

in papers. A paper that is cited enormously or immensely has a lot more credibility because you have the vainglorious error detection and correction of other scientists who are trying to shoot that person down who has all the fame for doing it, trying to seize that mantle. And so scientists are not a benevelent bunch. They're just as competitive as an investigative journalist trying to break the story or investor, or an A and R REP for a music band

trying to get there before everybody else. And we're no different. They're no different, But that's how all this stuff progresses.

You've got Fanik, which is a major Japanese player. They started with industrial robots. They do factory automation, but they've been a key player, probably a twenty five thirty billion dollar enterprise value company. You've got ABB, which is Swiss Swedish multinational industrial robot arms. Those are most of the things that you would see in even a Tesla gigafactory or something where Elon's talking about all their automated things and they've got some of these ABB arms or the

other one is Kuka. Kuka which is a German company

and they were one of the great leaders. They got bought by a Chinese company, I want to say twenty sixteen or twenty seventeen, and China's made a bunch of I think very smart investments acquiring technologies that were a little bit before their time, And I think it presents some geopolitical things that probably in five or ten years will be looking at and saying, my gosh, how is the dominant robot arms supplier or robot body supplier be akin to like being dependent on TSMC, a Chinese company.

So I think there's going to be nash robot companies that form in the same way you're starting to see national AI companies form.

it was their hands being teletransmitted to the device. And so that is the first way which has come up with one hundred different tasks, maybe the highest frequency things like washing dishes, folding clothes, again unstructured environments, being able to do them in multiple different houses, multiple different heights, multiple different you know, wet clothes, dry clothes. Being able to pour coffee, being able to have the dexterity to open up a ca cup. I actually don't drink those.

I think they're disgusting, but you know, put it into a coffee machine.

transfer learning. You teach one robot a thing, and suddenly the other robot, which is disconnected, you know, to or it's connected through the Internet through it, but it can actually learn what that robot just learned and perform the task. So that's actually pretty eerie and pretty cool. It's the same sort of thing like if I had one roll that saw where I tossed a ball in a room, but three other robots didn't know, they would instantly know because they have the eyes of the first robot. So

there's all kinds of training like that. Then there's schematics and drawings. So I was alluding to this before of like Ikea drawings, but once you can take diagrams and schematics and actually use visual language models, it's something that Opening Eyes a partner here with physical intelligence, and they

have pioneered some of that. That's going to be wild too, where you can literally just show an Ikea drawing and the robot goes with a constraint set of limited pieces that they have to put together, a set of screws and wrenches and whatnot, and they can completely assemble whatever it is, a nursery thing or furniture or desk, which I think is going to be pretty wild for people to see too.

that case, but they're reducing the dimensions for error. There's something that I coined this term. I call it MBTFU, which is meantime between f ups. But you want that to be as long as possible. If you go back to like the early rumbas, you know, a roomba would not know if it was cleaning up a spill of chocolate milk, or if your dog made a mess and it smears. It like to tell all over your floor, right. You want to increase as long as possible the meantime

between me, you know, basically error reduction. Men, you've got reinforcement learning, imitation learning, where you might be controlling the robot or having it mimic you. There's this idea of transfer learning, where a single robot learned something, but it can transfer it to different robots or from a different domain.

So people are trying lots of different approaches, and the more different mechanisms you have, people are then going to figure out, okay, which is the least data intensive, or which has the lowest latency and is the quickest, or which is the best system for training a robot that you put it in a totally unstructured environment and without any training, sort of what they call it zero shot learning. It's able to figure out from prior knowledge. I know that

I can go through that chair. I know that it swivels, I have to turn it this way. I know how much force I need to pick up a general coke can and those kinds of things. And I think, again, it's going to be enlightening of how much we as we navigate any given minute in our life. Take for granted, all this intuitive tacit knowledge that we have about the physical world, and there really is this intuitive physics of how we move around. Robots are going to learn.

Has anybody seen my wallet? Has anybody seen my glasses? Okay, just announcing that you can see that a robot with a series of things in your home that would have visual identifiers and visual learning machine learning to be able to spot object in a video frame could say yes, Josh, I know exactly where they are and go and retrieve them. So fetch and retrieving objects in the home to me would be a pretty cool thing. Where'd I put that

remote or whatever? And the robot basically knows because they can go through the DVR of the home and they basically know where it is and they can fetch it and retrieve it with the right physics. Folding laundry. I don't know how to handicap that, and we can do that now pretty.

tell to go into a room. You can put something in the back of it and take it to It can do facial identification for my family, so I can say where's quinner body, and they'll find my younger kids and I can send a message noise the hell out of my wife. But I think it's sort of cool, and yeah, every robot that comes out, I'll be an early adoptor, and yeah, we like funny things.

expect to see a similar thing? And then added on to that, could you have a situation where, you know, if you are a large manufacturer, or perhaps you are a company that has a lot of proprietary data, like an insurance company or a financial company or something like that, could you develop your own robotics Like would that be your edge here? And could you potentially just do it yourself?

and factory automation, warehouses delivery. Even when they bought our company for a little over a billion dollars called Zookes that was doing autonomous driving, they have a long term intention to be able to do twenty four to seven right hand turn lanes, navigating around the city with a human that's basically delivering last miles kind of stuff. And so so I could see Amazon doing that. Microsoft, I don't really see them getting into robotics in a significant way.

They have some R and D efforts Google and we talked a little bit about this phenomenon, but they are the third or fourth group where we have taken a team out of a big tech We did it with Google with a company called Osmo to create basically Shazam for smell. We did it with a BIOAI company called Evolutionary Scale out of meta that's going to be announced

more publicly soon. And then we did it with this team out of Google that became a physical intelligence between Google deep Mind and open AI and Stanford and Berkeley. So I think that this is going to be more the startups. The beneficiaries of the money will still be Nvidia and some of the chip players, some of the hardware providers, because we need the hardware to be able to train the robots. But I really think that this

is an open field. And again just going back to that stat of how many large language model there are for chatbots and how few there are for robots. I think it's a big opportunity. Now five years from now we might have a bubble in robots, but today I think it's it's it's a really exciting field, just.

it for some stuff. And then like the new version of Claude came out from Anthropic and I was like, oh, this is actually kind of cool and I kind of like it more. I don't really know why I like it more, but for some reason, I like it more and I had no problem switching over. Could it be possible that some of these like core like models do not prove to be as sticky or have as deep moat as people expect.

restaurant in West Village or you know whatever. Okay, Perplexity, which I don't know if you've used, and we met the founder early on and we ended up not investing and it was probably a miss for us, but the founder they are focused. You know what I'm going to do the point two percent of searches or the one percent of searches that are longer formed where people really

want to ask a full question. And I see a lot of people that are using it and it's not coming up with some Wolke answer or some pithy, you know, rock like response. It's actually a well researched, footnoted, sources, cited response. So to me, the two things that I use most on the chap outside right now are clawed from Anthropic and Perplexity. But in six months there might be something totally different. And the sheer number of models that are available on open source. Who knows what Apple

ends up doing here and that being integrated. You know, Series sucked, but so did Alexa. Amazon's making moves Apple will too. So yeah, but you know, use you mount for a second. All of this stuff and Venture Venture is going through this downturn and the one area where there are very high valuations and a lot of money flowing and a lot of talent is been in AI, and therefore the future returns on these things are going

to be lower. And that's why we at Lucks decided, you know, we're going to focus on AI in the physical world. Having done all the stuff over the past five years, I would say there's this five year psychological bias. Everybody wants to be invested today where you should have been five years ago, So hugging face and Mosaic and which we sold the data bricks. We were in that five years ago. Today we're really interested in biology and

robotics and AIS use in those. And then I have a really weird theme which is so sexy because it's unsexy to me and to others. You understand accounting, vast majority of venture investors and make startup people don't. But do you take capex. Capex is made up of two pieces, growth and maintenance. And everybody's been funding growth, growth, growth, growth, growth,

investor growth. So I got interested in maintenance. Why because you have trillions of dollars of assets infrastructure, hospital systems, energy systems, buildings that need to be maintained and every generation and every new startup and every new investor always wants to do the new new thing. It's why we get new music, and we get new food, and we get new fashion. But there's all these neglected assets, and I think you can apply new technology to maintaining these systems.

And so I've become obsessed with this unsexy theme of maintenance, which I think is going to become a hot area over the next few years.

a role. How do you augment and help nurses plummers. We have a massive shortage of plumbers this country, and so I actually think that the blue collar workers, empowered by technology and maintaining all of these systems around us, are actually going to be a winning combination.

They're losing several billion dollars today because you have these upfront costs, big cap X, a lot of training, you know, and then you try to maybe do some big enterprise deals. A company like hugging Face is profitable because they're not doing they're just hosting it and you know, letting people run in frints and then charging and making margin on that kind of stuff. So that to me is interesting of the people that spend a ton of money and

they've got to earn it back. And can you get pricing power by going from twenty bucks a month to thirty bucks a month? And maybe you get that because now you have open AI premium where you have access to say Sora for video generation or something like that. So that's going to be a big question on are these profitable investments? Not? Are they cool? Not? Are they world changing? Absolutely, but are they profitable investments? And look,

the market may not care if they're profitable. Market funds all kinds of profitable thing that they believe in the narrative in the story. But thinking about fundamental businesses and the economic changes between Capeck and op X, I think in AI it's very hard if you are building out your data centers, trying to do your own training, your

own inference, hosting these models, it's very hard. Biology we will see an AWS moment where instead of you having to be a biotech firm that opens your own wet lab or moves into Alexandria real estate which is specialized in hosting biotech companies in all these different regions, approximate to academic research centers. You will be able to just take your experiment and upload it to the cloud, where there are cloud based robotic labs. We funded some of these.

There's one company called Stratios. There's a ton that are going to come on wave. And this is exciting because you can be a scientist on the beach in the Bahamas, pull up your iPad, run an experiment. The robots are performing ninety percent of the activity of pouring something from a beaker into another, running a centrifuge, and then the data that comes off of that, and this is the

really cool part. Then the robot and the machines will actually say to you, hey, do you want to run this experiment, but change these four parameters or these variables, and you just click a button yes, as though it's reverse prompting you, and then you run another experiment. So the implication here is that the boost in productivity for science, for generation of truth, of new information, of new knowledge,

that to me is the most exciting thing. And the companies that capture that, forget about the societal dividend, I think, are going to make a lot of money.

But where are we in terms of context window expansion, because this is something we spoke about with you last year, and I think for a lot of people it's probably one of the overriding annoyances with something like chat GPT, the fact that you can't actually copy and paste that much text into it, and that you are limited in terms of the output that it actually gives you. Have there been major advancements since we last spoke to you.

You've got a one to one labs that has this thing called Jurassic Again, a lot of people are making headway here, but we are I think a year away from you being able to upload hundreds of PDFs thousands of books if they're not already immediately referenceable and be able to detect pattern change amongst documents, summarize and unearth, you know, the entirety of key concepts, and then I think the most valuable thing will be it prompting you to say, here's a question you didn't ask about all

these documents that you just uploaded. So yeah, I think we're just keep increasing the context window. But that said, most of the history of innovation is just like, keep increasing this factor, and then somebody else comes along and invent something. It's like that factor doesn't matter anymore. You know.

My favorite iconic example of this is like sailboats. You find these sailing ships back in the day, they just kept adding more and more sales, Like these things started to look ridiculous, you know, and then somebody invents the electric motor, and you have a motor both, so I think we'll have the same sort of thing. And then people figure out, hey, there's a better architecture here than

just constantly increasing the context window. And some of that might be with memory retrieval and being able to reference other models and just go into the archive of what they have. So yeah, that's going to keep expanding.

to pull out from that. So one thing that I thought was interesting from an industrial policy perspective was Josh's discussion of some of the robotic capabilities being developed in places like China and the idea that we might have another Chips Semiconductor like situation on our hands where we wake up in ten years and realize that a primary component of robotics is being built much more efficiently and

cheaply elsewhere outside of the US or the West. And then the other thing I thought was interesting was the idea of leap frogging, right, So, I think a lot of people, myself included, when we think of technological advances, it's like, can this do this thing slightly faster? Can it do it on a slightly larger scale, to the

point about the context window and expansion there. But you can leap frog in technology, as Josh was saying, and you can go from the sailboat to the motor boat, or you could bypass a human evolution, for instance, and instead of having humanoid robots, you could have a Edward scissorhands like thing with a Swiss army knife on the end of his arm.

sort of sounded like cloud kitchens, but for biology les. Yeah, so if you just have all the robots do it and then they can prompt you for other ideas, that's interesting.

which is a really great term. So it's like, yeah, like in a lot of like pure science spaces, you're going to get investors who are willing to throw money at someone who just like has a really good idea on paper because that person is smart.

the technological expertise to actually evaluate that. But when it comes to making you know, actual advancements in something like robotics, it does feel like you have to have an edge in one respect or another. You either have to have the capital to deploy or you have to have access to that data.

have transcripts of blog and a newsletter. And if you want to chat about all of these topics, including AI and robotics, there's a room in that in the lot Discord chatroom Discord dot gg odd lots.