Speaker 1: We should, of course anticipate the far distant future in
Speaker 1: two or three years where humanoids in general will outthink
Speaker 1: and outrun humans.
Speaker 2: So do you envision that this software will be interopperable
Speaker 2: or can be used with different robot types built by
Speaker 2: different companies in the future.
Speaker 1: Absolutely, The situation we want to avoid is something like
Speaker 1: the Matrix or skye. That's what's driving this kind of
Speaker 1: awkward assertion of mind that blockchains are for machines, not humans.
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Speaker 2: Check it out. Hey everybody, welcome into the Thinking Crypto podcast.
Speaker 2: I'm your host, Tony Edward, and joining me today is
Speaker 2: Jan Liphardt, who is the founder and CEO of open Mind. Yon.
Speaker 2: Great to have you, Tony.
Speaker 1: Wonderful being here.
Speaker 2: John, I'm excited to speak with you. We were talking
Speaker 2: before the recording. I want to learn as much as
Speaker 2: possible about open Mind. I've had a lot of conversations
Speaker 2: about us being in the Fourth Industrial Revolution. It's going
Speaker 2: to include robots, AI, blockchain tech, and much more. And
Speaker 2: I want to learn about the cool things you guys
Speaker 2: are doing that will help the robot economy, if you
Speaker 2: want to call it that. But let's kick it off
Speaker 2: with your background. Tell us a bit about where you're
Speaker 2: from and your professional background.
Speaker 1: Sure, I grew up in Bloomfield Hills, Michigan, and that
Speaker 1: was because my dad is a car person, so we
Speaker 1: moved around a lot and ultimately ended up in California
Speaker 1: at UC Berkeley. I was a physics professor at UC
Speaker 1: Berkeley for a while, and then I started to get
Speaker 1: interested in healthcare and data. That led me into the
Speaker 1: space of computing on sensitive data or privacy preserving compute,
Speaker 1: and that of course has a significant technical overlap with
Speaker 1: what a lot of people care about in the sort
Speaker 1: of blockchain and ZK space. And I'm now a engineering
Speaker 1: professor at Stanford.
Speaker 2: And what led to what was genesis of the idea
Speaker 2: to start open mind.
Speaker 1: Well, it was a series of coincidences. I was really
Speaker 1: fascinated by, just like everyone on Earth was really fascinated
Speaker 1: by large language models. And one thing that large language
Speaker 1: models do that many people don't realize is that most
Speaker 1: large language models speak fluent robotics. And we generally think
Speaker 1: of large language models as things that my students use
Speaker 1: to do their homework, or coders use them to automate
Speaker 1: parts of computer science. But if a computer is able
Speaker 1: to generate computer code, then that computer code can of
Speaker 1: course be used to control a physical object, move speak,
Speaker 1: show fear in your face, listen, proposed solutions, navigate a space,
Speaker 1: and all of those things that used to be really
Speaker 1: hard for robots to do, like make good decisions, tactics, learning,
Speaker 1: engaging people teaching them. All those things, all of a
Speaker 1: sudden now have a good technical solution, and that's the
Speaker 1: foundation for a big jump in robotics capability. And I
Speaker 1: was interested just to play with that a little bit.
Speaker 1: So I got some robots, put them into my house
Speaker 1: with my kids, and we just spent a few months
Speaker 1: figuring out what the technology is good for. That quickly
Speaker 1: led us into this rabbit bole of recognizing that there
Speaker 1: wasn't a good open software stack for thinking machines, and
Speaker 1: then we just built a company around that opportunity.
Speaker 2: Very interesting and I read essentially you're looking to build
Speaker 2: the android for software for robots. So do you envision
Speaker 2: that this software will be interoppable or can be used
Speaker 2: with different robot types built by different companies in the future.
Speaker 1: Absolutely. The situation we want to avoid is something like
Speaker 1: the Matrix or Skynet. We do not want a situation
Speaker 1: where there's a small number of companies one or two
Speaker 1: who write the software and build the back end, and
Speaker 1: build the bottle of the models, and build the hardware
Speaker 1: and drop ship everything to your living room where you then,
Speaker 1: you know, press the button on the large humanoid. It
Speaker 1: wakes up and says, hello, I'm your new humanoid. We,
Speaker 1: at least personally speaking, I'm much more interested in a
Speaker 1: world where people everywhere can help build this technology, can
Speaker 1: look into the brains of machines and figure out what
Speaker 1: they're thinking, and how shall we debug them or how
Speaker 1: shall we make them better? So I really want this,
Speaker 1: this this new world to play out in a much
Speaker 1: more open, flat manner as opposed to being heavily centralized
Speaker 1: like parts of the Internet are. And that's the driving
Speaker 1: consideration behind open source software for humanoids. And we really
Speaker 1: want developers all over the world to be able to
Speaker 1: contribute to this technology.
Speaker 2: Yeah, absolutely, So walk goes through the process of building
Speaker 2: this software. What are some of the considerations that you
Speaker 2: have to think about in working with different types of robots?
Speaker 2: But also security that no one can maybe hack a
Speaker 2: robot eventually as they're in the economy and part of
Speaker 2: our lives.
Speaker 1: The biggest problem right now is that there are one
Speaker 1: hundred and thirty seven different companies in China building humanoids,
Speaker 1: and they're all slightly different, different plugs and power supplies
Speaker 1: and communications systems and hardware abstruction layers and sensors and
Speaker 1: lighters and cameras and microphones and drives and everything is
Speaker 1: slightly different. So what happens in practice is that the
Speaker 1: pure software is kind of easy. The really difficult part
Speaker 1: is integrating software and hardware to work together really well.
Speaker 1: And really reliably. However, what happened in the cell phone
Speaker 1: industry is that pretty quickly lots of hardware companies came
Speaker 1: together to use common standards to help them make their
Speaker 1: lives a little bit easier. And that same trend is
Speaker 1: taking place in robotics right now, where there's a little
Speaker 1: bit of a convergence to using similar sensors and computers
Speaker 1: and things like that, just to make that integration a
Speaker 1: little bit less difficult in terms of security and safety
Speaker 1: and things like that. Robots right now are only people
Speaker 1: should not be scared of humanoids right now. They still stumble,
Speaker 1: and they're slow, and they're loud, and they have to
Speaker 1: recharge every two or three hours. So there's still many
Speaker 1: years before humanoids will have true RoboCop capabilities. But that
Speaker 1: is actually common and so even if humanoids are like
Speaker 1: as a technology safe now, we should of course anticipate
Speaker 1: the far distant future in two or three years on
Speaker 1: where humanoids in general will outthink and outrun humans. We
Speaker 1: just had a TV crew here and the reporter was
Speaker 1: fascinated by seeing if he could outrun the humanoid. He
Speaker 1: was really concerned, like, can a still outrun a humanoid?
Speaker 1: And he won all ten races. But of course humanoids
Speaker 1: are advancing their capabilities very very quickly, and our robotic
Speaker 1: dogs can already outjump humans. So imagine technology that's able
Speaker 1: to outjump the human and win the math Olympics simultaneously.
Speaker 1: And that's obviously something that humans should be aware of
Speaker 1: is coming and to try to anticipate making that technology safe.
Speaker 1: And that's the key reason we also care about things
Speaker 1: like blockchains and immutability and public ledgers and establishing what
Speaker 1: happened and figuring out what good rules are for very
Speaker 1: smart machines.
Speaker 2: Piggyback on what you just said, would blockchain tech be
Speaker 2: used in this software or in a different way to
Speaker 2: put some guardrails in place where not necessarily that the
Speaker 2: robot is trying to do something the farious, but a
Speaker 2: bad actor trying to use the robot to do something.
Speaker 2: Let's say, steal my robot that I've had in my
Speaker 2: house and they want to use it to do something bad.
Speaker 2: Would some sort of blockchain technology or cryptography be used
Speaker 2: to say no, that can't be done.
Speaker 1: Well, you're really alluding to like lots and lots of
Speaker 1: different problems. One question is, if you build a thinking
Speaker 1: machine there's decades of science fiction movies and books that
Speaker 1: explores what can go wrong when thinking machines try to
Speaker 1: do the right thing, but then the consequences for some
Speaker 1: people are unexpected. And so that's really a question of
Speaker 1: how does one make sure that AI in general, not
Speaker 1: just AI plus robotics, how does it want to make
Speaker 1: sure that AI in general does the right thing? And
Speaker 1: of course that's a very big conversation topic. In our case,
Speaker 1: what we do when the robots boot, they check in
Speaker 1: with Ethereum, a particular smart contract on Ethereum, and they
Speaker 1: download their constitution. And we like that because you don't
Speaker 1: have to believe what I just said. I could be
Speaker 1: an evil criminal mastermind bliar. The good news is that
Speaker 1: if I make those claims and you say, yawn, give
Speaker 1: me the contract address, then I can give it to you,
Speaker 1: and then everyone on Earth can go look. And then
Speaker 1: at least there's a public, immutable capability that allows everyone
Speaker 1: on Earth to see what the rules are that are
Speaker 1: used by our cutter peds and humanoids. And that's just
Speaker 1: one example of the benefits of things like blockchains. It's
Speaker 1: incredible to me that you know, thousands and thousands of
Speaker 1: humans have been building a technology that in retrospect is
Speaker 1: almost perfect to I'll coordinate with machines, help govern them,
Speaker 1: and then as extra bonuses you get identity and financial
Speaker 1: rail built in. That's what's driving this kind of awkward
Speaker 1: assertion of mine that blockchains are for machines not humans.
Speaker 2: Yeah, that's profound and amazing to hear that you're using
Speaker 2: etherorem using smart contract technology and it absolutely makes sense
Speaker 2: where it's decentralized. So regardless of who you are, if
Speaker 2: you're a bad or good actor or whatever it is
Speaker 2: your intentions are, doesn't matter. This smart contract is the
Speaker 2: robots downloading its guideline so to speak, and to operate
Speaker 2: and to do what it needs to do from a
Speaker 2: smart contract. That is incredible.
Speaker 1: The reason that all came into existence is I was
Speaker 1: walking down the sidewalk with my pack of three robotic
Speaker 1: dogs in where I live, and people on the sidewalk said, oh,
Speaker 1: yeahn aren't you scared? And I said, no, I'm not
Speaker 1: scared because I wrote the software, and the robots download
Speaker 1: their constitution when they wake up, and it's public and
Speaker 1: immutable and just go and look. And so I wasn't
Speaker 1: sitting in the lab thinking, oh, how can I use Ethereum.
Speaker 1: I was walking down the sidewalk when people said, hey, man,
Speaker 1: why are you not scaring? And I was able to say,
Speaker 1: I'm not scared because I know what the rules are
Speaker 1: and they're public and immutable. So my real concern is
Speaker 1: not bad humans. My real concern is this is a
Speaker 1: technology that, in many specific situations is out thinking humans
Speaker 1: today to the point where my like thirteen year old
Speaker 1: son can ask the robotic dog for help with his
Speaker 1: math homework. And so the real thing I'm concerned about
Speaker 1: as a parent is what happens when you have a
Speaker 1: humanoid that like it is running around San Francisco and
Speaker 1: it's good strategy and tactics and has memory and spatial
Speaker 1: awareness and also makes decisions all by itself. And I
Speaker 1: think it's important for humans in that setting to have
Speaker 1: a system that is immutable in public. And then there's
Speaker 1: so many uses of that kind of capability in robotics
Speaker 1: in particular.
Speaker 2: Yeah, well said, because I think I've been thinking about
Speaker 2: this lately, and that is what happens when these ai
Speaker 2: their IQs become vastly larger than us as humans even collectively. Right,
Speaker 2: get bringing the Star's minds together that they can outthink
Speaker 2: at a faster rate and outcraft whatever it may be.
Speaker 2: What happens then, and it's great to hear that. Now
Speaker 2: you have this in parallel, this technology of blockchain, and
Speaker 2: you have the convergence and they kind of help each
Speaker 2: other out in their way.
Speaker 1: Yeah, that's for me the really exciting part. So at
Speaker 1: Stanford I taught Beyond Bitcoin, a class for many and
Speaker 1: the point of that class was to look at different
Speaker 1: use cases for blockchains. And we had people there from
Speaker 1: the financial sector and the regulators and cryptographers and people
Speaker 1: like Fred from coinbase and the funds, and the point
Speaker 1: of all of all of those interactions was to like
Speaker 1: figure out what is the definitive use case for global
Speaker 1: public commutable letters with payment rails. And one thing that
Speaker 1: really helped make the case was a really old Satoshi quote,
Speaker 1: and the Satochi quote from like two thousand and nine
Speaker 1: is some version of bitcoin doesn't care who you are,
Speaker 1: and bitcoin doesn't care where you're from. I used to
Speaker 1: interpret that in terms of humans from different countries, like
Speaker 1: if you're from Peru or Italy or Australia, like bitcoin
Speaker 1: doesn't care, But now I interpret that in terms of
Speaker 1: blockchains are a system that does not discriminate based on
Speaker 1: how you do your compute. So if you're a human
Speaker 1: or a machine, blockchains really don't care. Bitcoin doesn't care.
Speaker 1: If you have the private keys, you're good. You have
Speaker 1: an identity, you have something unique, you have a way
Speaker 1: to interact with other machines, and people get compute electricity storage.
Speaker 1: So the main conclusion of teaching this class for many,
Speaker 1: many many years is that, like in retrospect, I think
Speaker 1: the technology is like ideally suited for machines and at
Speaker 1: the interface of humans and machines.
Speaker 2: Yeah, absolutely, because when you think about it, a lot
Speaker 2: of us are going to have AI agents doing things
Speaker 2: for us. It's not necessarily like I'm already exploring for
Speaker 2: my podcast, can i have an AI agent that is
Speaker 2: doing something that helps my viewers and my listeners And
Speaker 2: it's not gonna be me because I don't have the
Speaker 2: manpower and the time. But to your point, AI agent,
Speaker 2: but you can have its own crypto wallet running on
Speaker 2: blockchain rails and things like that.
Speaker 1: Yeah, Iris the humanoid has three different crypto wallets. One
Speaker 1: of them is like a coin based system, and then
Speaker 1: she has two really old fashioned, like really low level wallets,
Speaker 1: and she just has a safe place internally to store
Speaker 1: her private keys, and then whenever she needs to she
Speaker 1: can just sign transactions, identify herself obviously, and then also transact.
Speaker 2: Wow. Yeah, that is, like I said, profound, I would.
Speaker 1: Oh no, this is in retrospect, it's glaringly obvious. Because
Speaker 1: if Iris the humanoid walks into Bank of America, Yeah,
Speaker 1: do you think the bank colors are going to do, like,
Speaker 1: are they going to give her a bank account? No,
Speaker 1: they giggle, or they like call security or like, it's
Speaker 1: just really awkward and not useful. So why should Iris
Speaker 1: spend a lot of time in like the Bank Bank
Speaker 1: of America branch here in SF trying to get an account.
Speaker 1: It's obviously infinitely easier for her to pick twenty one
Speaker 1: words for her seat phrase, set up her wallet, and
Speaker 1: just transact and no one's giving her a hard time
Speaker 1: that she's like human or not or something silly.
Speaker 2: Hm. That absolutely makes sense. So you mentioned she has
Speaker 2: three wallets. Are they targeted at different assets or just
Speaker 2: for security levels? You have three in place, that's a
Speaker 2: good question. I think she has something like eight eth
Speaker 2: right now, they're just split over the different wallets. Part
Speaker 2: of what leads to that is that I guess I
Speaker 2: mean most humans also don't just have one wallet, they
Speaker 2: have several wallets for different purposes. Probably wouldn't wouldn't want
Speaker 2: to use the same wallet, you know, to pay for
Speaker 2: coffee at the cafe versus maybe some investing you're doing.
Speaker 2: And it'd be really great to have Iris here with
Speaker 2: us right now. But of course, as noted, she's currently
Speaker 2: getting a brain upgrade to a new and Vidia compute
Speaker 2: architecture which looks like this. Wow, so like nice and compact,
Speaker 2: and there's obviously incredible interest all over the place in
Speaker 2: building compact hardware for Humanoid, and Nvidia is IS is
Speaker 2: already shipping their new for architecture, which is specifically designed
Speaker 2: for like humanoid at the edge cognition. You mentioned ETH
Speaker 2: being the protocol, the blockchain protocol you're using in the
Speaker 2: future that we're having you to. There's going to be
Speaker 2: multiple blockchains, like multi chain orle Are you planning to
Speaker 2: add support for different chains or is it still too early?
Speaker 2: Maybe you want to wait to see which ones have
Speaker 2: lasting potential. I think ETH has already crossed the chasm
Speaker 2: where we know it's been getting the adoption and so forth,
Speaker 2: But are you open to integrating other blockchains?
Speaker 1: Well, I'm I'm an irrelevant human and so the real
Speaker 1: person to ask are the this wave of machines coming
Speaker 1: who can make those decisions all by themselves. And my
Speaker 1: role as someone who builds like architectures for humanoids is
Speaker 1: not to impose my own perfly irrelevant convictions around any
Speaker 1: one chain. So we'll have to just see how that
Speaker 1: plays out. But of course you're absolutely right in that
Speaker 1: there's many L ones and many L two's, and then
Speaker 1: there's L zero's and L three's, and all of them
Speaker 1: are trying to do slightly different things in terms of
Speaker 1: you know, transactions per second and cost and energy efficiency
Speaker 1: and support for cryptographic operations and tapping into different ecosystems.
Speaker 1: Solana comes to mind as having a very strong deep
Speaker 1: in capability, and well, I mean, well, we'll know how
Speaker 1: this world all plays out very soon. But sure, when
Speaker 1: you talk to smart machines about what they want in
Speaker 1: terms of their economy, the number one thing they say
Speaker 1: is we want efficiency. So if you ask a human, oh, like,
Speaker 1: what's the point of the US dollar, then if it's
Speaker 1: a politician, the politician may say, oh, it's a tool
Speaker 1: to articulate our trade policy, for example. But if you
Speaker 1: ask a machine, oh, what's the point of the machine economy,
Speaker 1: most of the answers cluster around efficiency. They'll say things
Speaker 1: like we don't want power, we want afficial and so yes,
Speaker 1: of course, almost certainly they'll be very like with high
Speaker 1: frequency optimizing, which tools they use for what purpose, and
Speaker 1: that almost certainly means like a diversity of chains. And
Speaker 1: of course stable coins are a very important part of
Speaker 1: this because even if machines use let's just say they
Speaker 1: use like eth or Solana or whatever else, it is
Speaker 1: a lot of those transactions will at the end of
Speaker 1: the day need to interface with human banks and people
Speaker 1: and human infrastructure. And that then means there's going to
Speaker 1: have to be this cloud of stable coins orbiting the
Speaker 1: blockchains that are then used for the machine economy, because
Speaker 1: the stable coins then allow you to serve as this
Speaker 1: glue layer between the machine economy and the infrastructure that
Speaker 1: humans have built for our legacy economy.
Speaker 2: Absolutely, I've been talking a lot about that that in
Speaker 2: a future where robots and AI agents are in helping
Speaker 2: us and assist in us, they're not going to be
Speaker 2: using dollars. They're going to be using stable coins. And
Speaker 2: I imagine a world where if I have a humanoid
Speaker 2: robot in my house, it's helping me to mot a
Speaker 2: lawn and doing different things, and I can say, hey,
Speaker 2: can you go to home depot to get me this
Speaker 2: thing because I need to fix something in my house.
Speaker 2: I don't need to give it cash. I don't need
Speaker 2: to give My credit card has its built in wallet.
Speaker 2: It can go at the checkout, scan whatever on a
Speaker 2: hand or whatever it is that's done with stable coins
Speaker 2: or whatever it is, and some sort of digital payment,
Speaker 2: and it brings back what I need.
Speaker 1: Exactly. We've actually done that. We have a cafe close
Speaker 1: to Hear called Cafe Centro, and Iris will ask you
Speaker 1: what you'd like for lunch. She'll head over to Cafe Centrol,
Speaker 1: she'll get stuff for you and she'll bring your lunch.
Speaker 1: And of course part of that is we have this
Speaker 1: agreement with Cafe Centrol because they know who we are,
Speaker 1: so they're generally extremely friendly to Iris, and so they
Speaker 1: treat her very respectfully. Wow, and uh, but that's that's
Speaker 1: exactly You're You're absolutely right.
Speaker 2: That is so fascinating. Yeah, and we're going to talk offline,
Speaker 2: but I would love to come out to your offices
Speaker 2: and meet Iris and interact with her and film that.
Speaker 1: And we're going to have ten robotic dogs in households
Speaker 1: here in the Bay Area within a few weeks. Each
Speaker 1: one of those is going to involve us going to
Speaker 1: someone's home will the dog will like wake up and
Speaker 1: it will just interact with everyone in the household, the parents,
Speaker 1: the kids, and we will just uh. And we hope
Speaker 1: to get a lot of feedback from from everyone about
Speaker 1: specifically what features they like and what we can improve.
Speaker 1: On the tech side, we have an incredibly long list
Speaker 1: of families to want to try the South and they're
Speaker 1: all like super eager. And so what that means is
Speaker 1: that like very soon, more and more quatter Pedge in
Speaker 1: particular will be in households here in the area. And
Speaker 1: so you're always welcome to just drop by. And the
Speaker 1: only problem we have right now is if you're the
Speaker 1: house you live in has a lot of stairs, that
Speaker 1: that's still like a trip hazard. But if you live
Speaker 1: in a place that is mostly flat, then our dogs
Speaker 1: should be just fine.
Speaker 2: So yeah, to confirm your company open mind, are are
Speaker 2: you building the actual robots or is that outsource and
Speaker 2: you're building the software.
Speaker 1: So we work with several different robotics companies that are
Speaker 1: on hardware focused and a few of those I want
Speaker 1: to move extremely quickly in terms of the overall capability
Speaker 1: of the robot and that's where they talk to us
Speaker 1: on the software side. So we don't print hardware, We
Speaker 1: don't to build our own like motors or build battery packs.
Speaker 1: We work with robotics companies with hardware companies to get
Speaker 1: that and then we focus on the integration and the software.
Speaker 2: Yeah, and I want to talk a bit about what
Speaker 2: these humanoid robots will look like as part of our
Speaker 2: lives and on the levels of companionship, assistant right, helping
Speaker 2: to do things around the house, whatever it may be.
Speaker 2: And then the economy jobs. We know that the PUCK
Speaker 2: is heading in the direction that AI and robots are
Speaker 2: going to take a lot of jobs, but it may
Speaker 2: open us up as humans to be able to do
Speaker 2: more and things we've always wanted to do, versus we
Speaker 2: have to go do to nine to five. I would
Speaker 2: love to get your take on that. What is your vision?
Speaker 2: How do you see the future with these robots in
Speaker 2: our lives?
Speaker 1: My suspicion is that in like ten or twenty years
Speaker 1: from now, we will look at some things we do
Speaker 1: today and we will be shocked about the crazy things
Speaker 1: humans used to do in twenty twenty five and we
Speaker 1: thought they were normal. Just imagine, like if we turn
Speaker 1: back the clock two hundred years and a common profession
Speaker 1: for children was to be chimney sweeps. So you take
Speaker 1: this like eight year old boy. Typically you'd tie a
Speaker 1: rope around them and you'd drop them down a chimney
Speaker 1: and they would sweep the chimney. And that was the
Speaker 1: way things were. You like drop the kids down chimneys
Speaker 1: to sweep things and then get cancer. And of course today,
Speaker 1: like two hundred years later, we look at this and say, oh,
Speaker 1: this was like horrible and medieval and so strange and dangerous.
Speaker 1: And my suspicion is that a lot of things we
Speaker 1: do today we will be similarly shocked. One great example
Speaker 1: is that the robotic taxis the way moos that are
Speaker 1: riding around San Francis go. They are known to be
Speaker 1: about ten times safer than a human driving a car.
Speaker 1: So if you look at the US, thirty seven thousand
Speaker 1: Americans die every year in traffic fatalities, Like imagine all
Speaker 1: the parents and the kids and the grandparents, Like thirty
Speaker 1: seven thousand people were killed, and we full well now
Speaker 1: have a technology robotic cars that is tinne safer. So
Speaker 1: as a society, it would appear to make sense to
Speaker 1: let's go save thirty five thousand people a year by
Speaker 1: really thinking about how we do driving in the US. So,
Speaker 1: for example, the words we use right now self driving,
Speaker 1: do you have a self driving car? Those will flip
Speaker 1: and in a few years from now, self driving will
Speaker 1: no longer refer to like the you like the car
Speaker 1: are driving itself. It will refer to these like odd
Speaker 1: old fashion humans who want to steer their own car
Speaker 1: despite full well knowing that you know, it's incredibly risky. Yeah,
Speaker 1: and they're also risking other people's health when it comes
Speaker 1: to things like healthcare. A great example would be that
Speaker 1: like human doctors, because they're fallible and tired and overworked,
Speaker 1: they make lots of mistakes. If you look at mimography,
Speaker 1: for example, we now know that computers are better than
Speaker 1: humans at finding early cancer, and so all across education, transportation, manufacturing, healthcare, nursing,
Speaker 1: caring for old people, there's so many situations where the
Speaker 1: things we do right now that we think are normal. Sure,
Speaker 1: thirty seven thousand people die a year because they're hit
Speaker 1: by cars. We think this is normal, but this really
Speaker 1: isn't normal, right, And so that's like a hopeful perspective.
Speaker 1: But of course one of the issues, one of the
Speaker 1: big issues, is just how quickly the technology is getting better,
Speaker 1: and whenever there's a quick disruption, that can have very
Speaker 1: significant consequences. For example, people who make a living by
Speaker 1: driving taxes, or electricians or blumbers, or nurses, or teachers
Speaker 1: or software engineers or people who make videos and journalists. Right,
Speaker 1: that's something where like overall society and politicians and universities
Speaker 1: generally are moving too slowly. These are conversations we should
Speaker 1: be having, like all across the world, like how do
Speaker 1: we navigate the next five or ten or fifteen years
Speaker 1: where a lot of professions may be quickly and significantly
Speaker 1: disrupted by the technology. So somehow people have to find
Speaker 1: a way of navigating the next decade or two.
Speaker 2: Do you think there's going to have to be some
Speaker 2: sort of universal basic income. These are things that have
Speaker 2: been discussed, but you know, there's the political narratives and
Speaker 2: talking points around it socialism versus capitalism. But then when
Speaker 2: you have this technology in the fabric of our society
Speaker 2: and look, we're not going backwards or going forwards. And
Speaker 2: it is what it is. It has happened throughout this
Speaker 2: the beginning of time right with human beings. How do
Speaker 2: we solve this? And how do people earn and how
Speaker 2: do they find purpose?
Speaker 1: Well, that is a whole set of questions that are
Speaker 1: probably a whole other podcast. Yeah, just to think about,
Speaker 1: you know, what does it mean for what does human
Speaker 1: society do if all of a sudden there are computers
Speaker 1: and machines that learn extremely quickly, never forget, and can
Speaker 1: learn a new language, for example, instantaneously. We had this
Speaker 1: TV crew here from Germany and we said, they said, oh,
Speaker 1: you know, can Irish speak German? And we said no,
Speaker 1: but if you give us two minutes, she can, right,
Speaker 1: because it's a few lines of computer code. And then
Speaker 1: we support a new language. And imagine sitting across from
Speaker 1: a human and the human says, oh, I'm so sorry,
Speaker 1: I don't speak Mandarin, but give me one minute and
Speaker 1: I will so and so what the technology does is
Speaker 1: like introduce a whole new set of capabilities that human
Speaker 1: society is not particularly well prepared for, and like, there's
Speaker 1: a whole other podcast just on that problem. Statement.
Speaker 2: Yeah, yeah, I know that that in itself is a
Speaker 2: whole can of worms. It just touches on so many
Speaker 2: different layers as human beings are, purpose and so forth.
Speaker 2: It's incredible. And this technology, as you mentioned, is moving
Speaker 2: is at such a rapid pace. So when do you
Speaker 2: think your best guess that we as humans in society
Speaker 2: will have humanoid robots in our homes and it will
Speaker 2: be the norm. At one point, in the early days
Speaker 2: of cars being part of the Industrial Revolution, it was
Speaker 2: a foreign concept. I'm used to the horse and buggy,
Speaker 2: but eventually, hey, I need to get a card. Everybody's
Speaker 2: driving a car.
Speaker 1: That's going to depend very significantly on where you live.
Speaker 1: The majority of the supply chain for humanoids is in China,
Speaker 1: and they are adopting the technology much more quickly than
Speaker 1: we are in the US. So if you live in China,
Speaker 1: probably there's going to be a five year difference. Here
Speaker 1: in the US. We will have quiderped dogs in people's
Speaker 1: homes in like this month, but they're not going to
Speaker 1: be like ready for like time time mass adoption in
Speaker 1: part because of the expense. So you're really asking a
Speaker 1: question about how long is it going to take before
Speaker 1: there's a reasonably priced, highly capable humanoid for most Americans
Speaker 1: that they can go to a store and you know,
Speaker 1: pick out their humanoid and bring home with them. So
Speaker 1: the first humanoids on our end that will be deployed
Speaker 1: into homes will be early next year. But again those
Speaker 1: are just going to be ten or twenty and it's
Speaker 1: definitely not going to be something where you can walk
Speaker 1: into your local humanoids store and pick one up. For
Speaker 1: that whole transition to play out, we're probably looking at
Speaker 1: about ten years. If you think about Weaimo. Weimo took
Speaker 1: about ten to twelve years to get to the point
Speaker 1: where you can now rab a Weaimo in the street
Speaker 1: and in stf So those are roughly the timeskills we're
Speaker 1: talking about. Maybe be a lot of test deployments in
Speaker 1: particular cities and a few homes, but it's now at
Speaker 1: the point where quarter peds and humanoids will be in
Speaker 1: American households this year.
Speaker 2: That's incredible, And if we look at previous adoption of
Speaker 2: other technologies, whether it be just having a computer in
Speaker 2: your home or having a car, or having a cell phone.
Speaker 2: Usually I think you had your early adopters some where
Speaker 2: the technology is more expensive, so you had maybe more
Speaker 2: affluent afflent crowd owning it. Then it becomes cheaper over
Speaker 2: time as it becomes more commercialized, and then the mass
Speaker 2: the masses can.
Speaker 1: Partake exactly, and that was Tesla's go to market. They
Speaker 1: started out with a super expensive roadster that only a
Speaker 1: very small number of people could afford. It was basically
Speaker 1: like a fancy fun toy. Then the Model Esque came out,
Speaker 1: and now with the Model three you have something that
Speaker 1: is significantly more affordable. And that process took I think
Speaker 1: like what seven years, and almost certainly whoever the forward
Speaker 1: of human robotics in the US is and I don't
Speaker 1: know who that person or company will be, but the
Speaker 1: trick there is going to be to figure out the
Speaker 1: mass manufacturing of robots to make them cost effective so
Speaker 1: they can really be deployed at you know, millions of
Speaker 1: units per year into households.
Speaker 2: Do you believe that there will be jobs created out
Speaker 2: of the robot industry where robots have to be repaired,
Speaker 2: maintenance and so forth, where we as humans even though
Speaker 2: we may lose the uber driving jobs, we can be
Speaker 2: robot engineers or maintenance.
Speaker 1: When Iris's travels, she travels with her support team of
Speaker 1: five humans. We're still far from a future where you know,
Speaker 1: Iris travels all by herself. She travels with the cloud
Speaker 1: of you know, electrical engineers and software people, and people
Speaker 1: will make sure that it's safe and she doesn't try
Speaker 1: to walk through the walls. If every humanoid robot that's
Speaker 1: deployed needs five humans, the whole thing, of course, makes
Speaker 1: no sense. The point is for humanoids to be useful
Speaker 1: and helpful as opposed to needing like large number of
Speaker 1: people to coddle them along. There will be entirely new
Speaker 1: types of jobs. And a great example is that if
Speaker 1: you're humanoid is able to go to the supermarket and
Speaker 1: put things in a basket. And for those kind of deployments,
Speaker 1: for easily than the next ten years, they will be
Speaker 1: done with a human in the loop for legal reasons,
Speaker 1: liability reasons, reducing fear, and so forth. So we will
Speaker 1: be actively hiring people to do telepresence for humanoids deployed
Speaker 1: into hospitals, schools, and homes. And because it doesn't matter
Speaker 1: where those people are they can be anywhere on Earth
Speaker 1: and make money by serving as the human in the
Speaker 1: loop for a for deployed humanoid for example. So that's
Speaker 1: an entirely new kind of job that allows anywhere, at
Speaker 1: any time zone to do well paid work anywhere else
Speaker 1: on Earth.
Speaker 2: Yeah, that absolutely makes sense that the initial phase of
Speaker 2: this rollout needs that human in the loop. Yeah, yeah,
Speaker 2: definitely makes sense.
Speaker 1: And so if people listening to this, if you're math teacher, nurse,
Speaker 1: retired police officer, then please reach out to us. We
Speaker 1: have jobs for you very soon.
Speaker 2: Wow. Now with the software you're building OHM one, is
Speaker 2: that integrating an AI data set? If you want to
Speaker 2: call that from some of the existing models, would it
Speaker 2: be from open Ai, Gemini or are you building your
Speaker 2: own from the ground up?
Speaker 1: Right now, the big companies like open ai are pouring
Speaker 1: so many billions into making their large language models better
Speaker 1: that there is no way we can compete on the
Speaker 1: model side, you know, for us to scrape every single
Speaker 1: cat video on Earth and try to build our own
Speaker 1: LLLM just makes no sense.
Speaker 2: Yeah.
Speaker 1: What makes a lot more sense is for us to
Speaker 1: float on top of all the work that's going on
Speaker 1: in the LLLM space. And that's the whole like secret
Speaker 1: to robotics in the sense that it turns out that
Speaker 1: all these billions that are flowing into making llms better
Speaker 1: don't just make them better at you know, finishing a
Speaker 1: novel or helping you with your math homework, because they
Speaker 1: also all speak robotics. They also make robotics better for free.
Speaker 1: And so we as much as we can, we use
Speaker 1: models developed by others, with one major consideration is we
Speaker 1: certainly don't want to end up in the world where
Speaker 1: one or two companies are able to turn on or
Speaker 1: off certain capabilities relating to your AI or your robot.
Speaker 1: And so although right now we are sending most of
Speaker 1: our traffic to a small number of like well known
Speaker 1: l LEM developers, at the same time we love open
Speaker 1: source lllms, and there's more and more of them and
Speaker 1: they're more and more capable. So in the medium and
Speaker 1: long term, we certainly want to end up in a
Speaker 1: world where there's hundreds of competing powerful lllms and ais,
Speaker 1: so that we have like a very healthy ecosystem without
Speaker 1: centralized control of compute.
Speaker 2: That absolutely makes sense. And you have, yeah, like to
Speaker 2: your point, you have all these large companies that are
Speaker 2: laying the groundwork. They're putting in all the work to
Speaker 2: build these data sets and so forth, and like you said,
Speaker 2: you can be floating on top of those. Yeah, I
Speaker 2: think you may have addressed this, but you know, having
Speaker 2: a robot, do you foresee that we will need to
Speaker 2: have insurance in case something happens to that, just like
Speaker 2: a car to that robot, or it gets damage, whatever
Speaker 2: may be, there will be the insurance industry is going
Speaker 2: to have to cater for.
Speaker 1: This, absolutely, and they're already starting to. We spend we
Speaker 1: meet with at least one insurance company a week, and
Speaker 1: if there's anyone listening who's running an insurance company, we
Speaker 1: will give you money today for liability insurance for deployed
Speaker 1: humanoids in Kindergartens.
Speaker 2: Wow.
Speaker 1: So if you have a suitable product for that, like,
Speaker 1: tell us where to send the money, because we need
Speaker 1: liability insurance for deployed humanoids.
Speaker 2: This question may have already been answered because we talked
Speaker 2: about the blockchain and the guard rails in place, but
Speaker 2: data privacy, the robots listening to us, listening to our conversations,
Speaker 2: just like we have our Google and Amazon alexis right,
Speaker 2: how do you how are you protecting user data.
Speaker 1: That is something that's incredibly important to us, and that's why,
Speaker 1: for example, we don't send video data with people's faces
Speaker 1: to the cloud. So when the camera records a scene,
Speaker 1: it on the edge will detect faces and blur them
Speaker 1: and then send the de anonymized data into the cloud.
Speaker 1: And so the identification of a human with their face
Speaker 1: is done locally. So for example, the robot has a
Speaker 1: little folder somewhere in its brain that has your picture
Speaker 1: and your name and uses that information to understand who
Speaker 1: it's looking at. Obviously, if you have a dog at
Speaker 1: home and the dog doesn't remember you, ever, that's not
Speaker 1: particularly satisfying. You want to come home and you want
Speaker 1: the dog to say, you know, hello Tony, it's great
Speaker 1: to see you, like, welcome back from work. Let's go
Speaker 1: play fetch, and so that part is all done locally
Speaker 1: at the edge. There's also a role for like blockchain
Speaker 1: and cryptography, obviously, and that has to do with things
Speaker 1: like how do machines transmit skills to other machines. Imagine
Speaker 1: you've taught your quad pet dog to be able to
Speaker 1: chase squirrels. What if your dog wants to share that
Speaker 1: skill with other machines, How does how do you protect
Speaker 1: that skill? How do you buy and sell the skill?
Speaker 1: And that then gets us into territory of one time
Speaker 1: programs or one time models, which is which are graphic
Speaker 1: techniques that allow you to control skills or data that
Speaker 1: you've collected. This is a whole other podcast, which is
Speaker 1: how do we deal with privacy and security in the
Speaker 1: setting of thinking machines.
Speaker 2: Yeah. Absolutely, that's fascinating and that was one of the
Speaker 2: questions that we're going to you address the which was
Speaker 2: how will your robot recognize you and your family members
Speaker 2: and what does it need to learn in the initial
Speaker 2: setup to note this is me, here's my wife, here's
Speaker 2: my seven year old daughter, this is my mom and
Speaker 2: dad and things like that. Yeah.
Speaker 1: So the system right now is very simple, which is
Speaker 1: a voice command and when the dog comes and is curious,
Speaker 1: you tell it, hello, bits, I'm your owner. My name
Speaker 1: is Joan, remember me, and that is then the starting
Speaker 1: point for data being saved locally to create that association.
Speaker 1: Because we certainly want the dog to remember who their
Speaker 1: owner is. We want the dog to remember everyone in
Speaker 1: the family who can be trusted and likewise, if there's
Speaker 1: someone that shouldn't be there. We want the dog to
Speaker 1: bark and send you a notification to your phone.
Speaker 2: Now I read you got a fundraise. Excuse me, you
Speaker 2: raised twenty million dollars in a recent fund raised and
Speaker 2: can tell us a bit about that.
Speaker 1: And some of the participants, Well, the lead is Pantera.
Speaker 1: We're fortunate that the investors that are part of around
Speaker 1: come from many different areas that are relevant to this problem.
Speaker 1: For example, one of our investors is Ribbitt, and Ribbitt
Speaker 1: has their investment thesis is that questions relating to what
Speaker 1: is true, what is real, and identity are going to
Speaker 1: be critical to many industries financial, social media, robots going forward.
Speaker 1: And we have other investors that are that have a
Speaker 1: big footprint in robotics, robotics, supply chain, and manufacturing. So
Speaker 1: the investors are people either care about decentralized solutions for identity,
Speaker 1: financial transactions markets like Pantera, or investors that care about
Speaker 1: what is real and what is true and who's who
Speaker 1: like Ribbit, or investors that care about robot hardware and
Speaker 1: large scale deployment of robots and different verticals.
Speaker 2: Yeah, I know that the folks at Panter are very well.
Speaker 2: Dan moorehead and some of the folks. I know you
Speaker 2: got to run, So I got some wrap up questions
Speaker 2: here for you. First, if you could create your own metaverse,
Speaker 2: what would the theme be? Would it be a robot theme?
Speaker 1: Well, of course, but the actual theme would be would
Speaker 1: be good outcomes for everyone?
Speaker 2: And rapid fire questions? Favorite food night markets in Singapore,
Speaker 2: favorite musician or band Magnetic Fields, Favorite movie, Oh.
Speaker 1: Anything to do with explorations, space, mountains, oceans, robots, the future.
Speaker 1: Favorite book Seven Pillars of Wisdom by T. Lawrence.
Speaker 2: When you're not working at open Mind, what are you
Speaker 2: doing for fun?
Speaker 1: Oh? I build rockets and I shoot them into shoot
Speaker 1: them into space from the public launch facility next to
Speaker 1: Edwards Air Force Base in the Mojave.
Speaker 2: Oh wow. Yeah. As mentioned, we're going to talk offline,
Speaker 2: but I love what you guys are doing. I have
Speaker 2: to come out to your office.
Speaker 1: I need to come join us, come help build. We
Speaker 1: have robots and soldering irons and ascilloscopes and computers and
Speaker 1: anything you could want and come help us build.
Speaker 2: Amazing. Thank you so much, John, Thank you Tony.
Speaker 1: It's been a blast.
Speaker 2: Two