Speaker 1: Pushkin.
Speaker 2: When we started this show in twenty twenty two, the
Speaker 2: standard line about driverless cars was driverless cars have been
Speaker 2: five years away for the past fifteen years, because it
Speaker 2: seemed like they were just always around the corner, always
Speaker 2: just a few years away, but they never quite arrived.
Speaker 2: Nobody says that anymore. Today, in several cities around the country,
Speaker 2: getting a ride from a driverless car is just a
Speaker 2: normal thing people do, and it'll become normal in more
Speaker 2: and more and more cities over the next few years.
Speaker 2: Driverless cars are here now, so now we can ask
Speaker 2: what's next. I'm Jacob Goldstein and this is What's Your Problem,
Speaker 2: the show where I talk to people who are trying
Speaker 2: to make technological progress. My guest today is Boris Soffman.
Speaker 2: He's the co founder and CEO of Bedrock Robotics, a
Speaker 2: company that's figuring out how to retrofit heavy equipment to
Speaker 2: make it work autonomously. Boris's problem is this, how do
Speaker 2: you teach machines not just to drive, but to do
Speaker 2: things like grade roads and move heavy things around construction sites.
Speaker 2: Boris's company is starting with excavators, and they plan to
Speaker 2: have their first commercial excavators autonomously digging holes on construction
Speaker 2: projects next year. Later in the interview, Boris goes big.
Speaker 2: He argues that if Bedrock succeeds, the company could help
Speaker 2: push forward a broad wave of new building in America.
Speaker 2: But first we talked about his time at Weimo and
Speaker 2: how the wild evolution of the autonomous vehicles that he
Speaker 2: worked on there led him to start Bedrock.
Speaker 1: The time at Weimo was this incredible period where I
Speaker 1: was there for about five years from mid twenty nineteen
Speaker 1: till through spring twenty four, and that was this really
Speaker 1: big period where it was going through this like fifteen
Speaker 1: years of R and D, and then it finally transitioned
Speaker 1: into this hockey stick of like growth that is helping today.
Speaker 1: And so today waymos at over one hundred million miles
Speaker 1: fully driver lists. It's at five times safer than a human.
Speaker 1: It's millions of miles every single week, and so it's
Speaker 1: kind of scaling itsponentially and it's like a genuinely fantastic product.
Speaker 1: But I was there when we were like stressing over
Speaker 1: the first hundred miles, and it felt like the most
Speaker 1: incredible achievement to just go like ten fifty hundred miles
Speaker 1: to completely drop less, say now that happens at like
Speaker 1: hundreds of thousands of miles every single day. And so
Speaker 1: one of the things that really broke through it made
Speaker 1: that possible is this shift to machine learning and data
Speaker 1: driven approaches as a core of the autonomy stack.
Speaker 2: And just to be clear, like that's as opposed to
Speaker 2: a more like euristics or rule based kind of model,
Speaker 2: like the old school twentieth century eyes, like, well, just
Speaker 2: tell the car all the rules of how to drive
Speaker 2: and then it'll drive. Like that's what you're comparing machine
Speaker 2: learning to.
Speaker 1: Yeah, like drive, I'm going to like embed the cost
Speaker 1: functions and all this. So yeah, so like large scale
Speaker 1: search and heuristics and rules and you can embed them
Speaker 1: now inside those heuristics, and so you can solve almost
Speaker 1: any given problem with that sort of approach.
Speaker 2: So you can solve one problem, but you can't solve
Speaker 2: like every single possible problem that would ever arise when
Speaker 2: you're driving, which is actually what you have to solve
Speaker 2: to do full autonomy.
Speaker 1: Right right with activating whackamall, where like you fix one
Speaker 1: problem and it becomes harder and harder to kind of
Speaker 1: scale the other ones. And so and so there was
Speaker 1: this really conscious shift at WEIMA, which was kind of
Speaker 1: bold at the time because it feels obvious in hindsight,
Speaker 1: It wasn't obvious at all back then. The shift to
Speaker 1: like really embracing this as a data driven solution where
Speaker 1: you're learning from human driving and human behavior.
Speaker 2: And when you say you're learning, you mean the machine
Speaker 2: learning model. The AI basically is learning.
Speaker 1: The machine learning model, that's right. And so you're basically
Speaker 1: taking giant scales of data one hundreds of thousands of
Speaker 1: millions of miles, and you're learning the model of how
Speaker 1: do you drive and how do you interpret this? Like
Speaker 1: infinite compocity and kitchen sink of contacts, like all of
Speaker 1: this sensored data, the road structures, the things you see
Speaker 1: around you, the way people are moving. How do you
Speaker 1: go from a kind of engineered and you know, kind
Speaker 1: of trained solution into one that is like dominantly a
Speaker 1: warned solution.
Speaker 2: I mean, as you're doing that, you're sort of riding
Speaker 2: the historic machine learning AI wave, right, Like that's right.
Speaker 2: Presumably you're able to do that at that moment because
Speaker 2: of this explosion in AI, which is basically machine learning, right,
Speaker 2: you know that.
Speaker 1: And you couldn't have done that five ten years ago.
Speaker 2: That's what you're kind of coming out of at way MO,
Speaker 2: that's driving this success at way MOO. How does that
Speaker 2: set you up for what you're doing a bedrock?
Speaker 1: The most shocking thing was just how well does generalized
Speaker 1: from San Francisco to Los Angeles, Phoenix in Austin and
Speaker 1: then becomes almost like a qualification problem eventually, where you're
Speaker 1: using data to fill some gaps, but like you need
Speaker 1: less and less of it, less and less new things
Speaker 1: surprise you, like you're just your competency kind of expands.
Speaker 1: And then we unified the technology stack between cars and
Speaker 1: trucks where even jumping from a cargo a truck was
Speaker 1: needed maybe ten to fifty percent more data, but it
Speaker 1: was fundamentally like you're using data to explain why how
Speaker 1: you operate a very different potages.
Speaker 2: This is like a big truck, This is like an
Speaker 2: eighteen wheel truck. When Google was working on that, yeah,
Speaker 2: are way most like.
Speaker 1: Fifty three foot trailer like eighty thousand pounds. And so
Speaker 1: that was the big moment where we started thinking about
Speaker 1: where else can you apply this? What are the places
Speaker 1: where you have all this diversity of challenges and capabilities
Speaker 1: that benefit from this type of versatility and also have
Speaker 1: the ability to jump between platforms in this really natural way.
Speaker 1: And so we looked at a lot of spaces and
Speaker 1: really settled on automation of specialized typing machinery, and so
Speaker 1: the sipes of machines that you see in construction, like
Speaker 1: excavators and wheeloaders and bulldozers, but also frankly the sort
Speaker 1: of machines you see in all sorts of industries like
Speaker 1: agriculture and mining and lumper and garbage movement. And so
Speaker 1: you have these very diverse types of machines that are
Speaker 1: interacting with the world around them. They're fairly still moving,
Speaker 1: they're in semi controlled environments, and at the end of
Speaker 1: the day, there's astronomical scale at which they great at
Speaker 1: and a lot of the learnings that we experienced a
Speaker 1: weymle actually transfer over incredibly well. But the physics of
Speaker 1: the problem is a lot less aperspherial. It's actually like,
Speaker 1: really tackle this and get the market.
Speaker 2: Basically, big vehicles operating in semi constrained environments doing things
Speaker 2: to the world, interacting with the world in some physical
Speaker 2: way in addition to just driving across it.
Speaker 1: That's right, and these are slow moving vehicles where you're
Speaker 1: already on a closed site with people who are assumed
Speaker 1: to be knowledgeable about the world around you, and you're
Speaker 1: moving at like five off an hour, for example, You're
Speaker 1: able to slow down and stop. You're able to minimize
Speaker 1: your exposure. You always have a minimum safety condition of stopping.
Speaker 1: Your complexity is less abound interactions with others on the
Speaker 1: road and more about the interactions with the world around you.
Speaker 1: And so you can actually tackle safety not through this
Speaker 1: sort of statistical methods that drove a lot of the
Speaker 1: mileage that we collect, but through a much more direct
Speaker 1: measure of your competencies in order to just make sure
Speaker 1: that you're like you're actually capable of hurting somebody.
Speaker 2: Yeah, more like like an industrial robot al most right.
Speaker 1: That's right. Yeah, it's like a traditional Susis engineering, Yeah.
Speaker 2: Where it's like, look, even if it can't do the
Speaker 2: thing every time, that's fine. Just make sure it's not
Speaker 2: gonna like go crazy and kill somebody.
Speaker 1: That's right. You can make it to that. Like your
Speaker 1: worst case is your productivity suffers, but safety wise, you're
Speaker 1: you're you're solid. And so that's like incredibly enabling because
Speaker 1: your long tail is now no longer safety. It's the
Speaker 1: versatility what you can do.
Speaker 2: Why did you start with excavators?
Speaker 1: So excavators are the most highly utilized machine, Like they're
Speaker 1: usually the highest volume in fleet. So between twenty and
Speaker 1: twenty five percent of fleets are excavators.
Speaker 2: Fleets are just like big construction heavy equipment.
Speaker 1: Yeah, like a general contractor that alone, like a thousand
Speaker 1: machines two hundred, two hundred and fifty will probably be
Speaker 1: excavators on average.
Speaker 2: And it's basically what a kid would call it digger, right,
Speaker 2: It's like at a digger bucket an arm, and it
Speaker 2: like digs stuff up.
Speaker 1: It's like the equivalent of an arm. Like you can
Speaker 1: like dig stuff, you can demolish stuff, you can swap
Speaker 1: your tools, you can like lift pipes and put them
Speaker 1: in a holes. They can do a ton of stuff
Speaker 1: with it. It's kind of crazy. It really is a
Speaker 1: personal machine. It also makes it very complicated to work.
Speaker 1: So there's like seven degrees of freedom sometimes eight, and
Speaker 1: so it's one of the hardest machines to learn, and
Speaker 1: it takes four to five years to really become an expert.
Speaker 1: And there's a huge difference between an expert and novice,
Speaker 1: and so you kind of have this situation where it's
Speaker 1: a huge volume of work and it's really hard to learn,
Speaker 1: and so you have a really deep pull in the
Speaker 1: market for it, meaning.
Speaker 2: A lot of demand, like a lot of people want
Speaker 2: somebody who can drive one of these or a machine
Speaker 2: that could do it.
Speaker 1: Well, let me tell you about the demand. I've never
Speaker 1: seen such a divergence of supply to demand like in
Speaker 1: my career in any case, where on the demand side,
Speaker 1: you have this astronomical construction industry that's already two trillion
Speaker 1: dollars a year in the US, that's obviously very heavily
Speaker 1: building and having machinery work. And then you have this
Speaker 1: like shortage of operators that already existed, but it's going
Speaker 1: in the wrong direction where forty percent of construction workers
Speaker 1: is retiring in the next ten years. Our partners are
Speaker 1: consistently having trouble filling labor. We've met some that have
Speaker 1: one hundred percent turnover.
Speaker 2: Everybody leaves every year.
Speaker 1: It means like a third of people might be lifers,
Speaker 1: but then like two thirds transition more than once per year,
Speaker 1: and you're constantly backfilling the skill sets very a ton
Speaker 1: and one of them said that for every one person
Speaker 1: entering the workforce of the quality that they look for,
Speaker 1: their seven leaving. And so we end up having is
Speaker 1: this shortage of ability to meet this demand, and so
Speaker 1: prices go up, jobs don't get done. And what's interesting
Speaker 1: is it's not that's like isolated industry that's like just
Speaker 1: on its own kind of like having these sort of challenges.
Speaker 1: It's a horizontal it supports every industry. You can't build
Speaker 1: data centers free eye without it, you can't build houses,
Speaker 1: you can't build energy facilities.
Speaker 2: Of like a rate limiting skill or rate limiting machine.
Speaker 1: It's a whole country. That's exactly right. And then you
Speaker 1: have this need that starts with labor. But then there's safety.
Speaker 1: It's huge amounts of safety challenges. You have, you know,
Speaker 1: huge predictability challenges. So if you actually could soften out
Speaker 1: some of these constraints, we would build more, more work
Speaker 1: would get done, and it would like stimulate the whole economy.
Speaker 1: And so that's what's actually pretty exciting about this opportunity.
Speaker 1: It's not as yours some game at all good.
Speaker 2: So, like you decide to focus on excavators, you you know,
Speaker 2: you raise money for your company, Like, do you go
Speaker 2: out and buy a whatever, a million dollar excavator. You
Speaker 2: go to what is it bucket and shovel dot com
Speaker 2: and buy yourself an excavator.
Speaker 1: They're like three hundred thousand to five hundred thousand, so
Speaker 1: they're like still expensive. Yeah, they're pretty expensive.
Speaker 2: So I mean like, did you buy one like we did?
Speaker 1: Yeah?
Speaker 2: Did you drive it?
Speaker 1: Of course, like you have to. It's like it's like
Speaker 1: a ride of passage. Yeah, they're really fine. I even
Speaker 1: took a lessoners. There's a place in Vegas ALESSI drive
Speaker 1: excavators and take away and from like a trade operator.
Speaker 1: It was pretty fun.
Speaker 2: So, oh, that's genius. You get to break things. I
Speaker 2: was actually as I was walking here today to the
Speaker 2: train that there's a playground and there was an excavator
Speaker 2: just like breaking up the asphalt and then like prying
Speaker 2: it up. I was like, that looks awesome.
Speaker 1: This is the funniest thing. Anybody that gets an excavator,
Speaker 1: like your inner six year old comes out. Suddenly you
Speaker 1: have like the most mature, sophisticated person is trying to
Speaker 1: be all professional. You get an excavator and just like
Speaker 1: get like a giant glob of mud and then like
Speaker 1: bring as hig as a can and then like PLoP
Speaker 1: it down and see what happens. It's amazing, and it's like,
Speaker 1: without fail, everybody kind of reverts back to this sort
Speaker 1: of a world. Yeah, we drove and I think we
Speaker 1: bought like a half dozen excavators at this point, and
Speaker 1: then we also then use a lot of excavators from
Speaker 1: our partners who are general contractors and subcontractors.
Speaker 2: And it's just what a year or so ago that
Speaker 2: you started, like not that long in.
Speaker 1: This less than a year and a half, yeah, like
Speaker 1: last last minute, seah, it's a pretty been a good run.
Speaker 2: How much is sort of commodified of autonomy, right? How
Speaker 2: much is just like, well, we're gonna buy this, this,
Speaker 2: and this in terms of sort of hardware and we
Speaker 2: know the software, and then how much is like, oh,
Speaker 2: here's the things we have to figure out that nobody
Speaker 2: knows how to do.
Speaker 1: So it's one of the other enablers that's like way
Speaker 1: better than what we would have had to go through
Speaker 1: ten years ago. In the space, we can use a
Speaker 1: lot of the existing components on lighter on cameras, on
Speaker 1: Imus GPS, there's a lot of tailwind from automotive great
Speaker 1: cameras and compute.
Speaker 2: That's like accelerating and like presumably they're buying those things
Speaker 2: at scale you can like you've got to go get
Speaker 2: them cheap. Essentially, you're like, give me one of those,
Speaker 2: one of those, one of those.
Speaker 1: That's right, because you're gonna go to millions and millions
Speaker 1: of units as like every car against an autopilot equivalent
Speaker 1: over the next two three years. Oh, that's starting to
Speaker 1: kind of pump in a cost. So that helps. And
Speaker 1: then even the platform, we can retrofit existing machinery.
Speaker 2: When you say the platform, what do you mean in
Speaker 2: this context.
Speaker 1: Platform is like the car, the truck, the excavator, and
Speaker 1: the way in this case the excavator. Yeah, the machine,
Speaker 1: so like the excavator itself. Construction machines, particularly this latest generation,
Speaker 1: they're really well designed to where they're already effectively drive
Speaker 1: by wire, which means that every signal going through the
Speaker 1: machine is electric, and so we're able to spice into
Speaker 1: it and both read and write to these signals.
Speaker 2: And control them electric meaning like computerized basically.
Speaker 1: Meaning like you have a joystick that operates the excavatory
Speaker 1: that's not physically connected to the hydraulics. It's an electric
Speaker 1: signal that's connected to the hydraulics, and so the same
Speaker 1: for every signal and the machine, like the sense the
Speaker 1: pressure gages, and so we're able to both get the
Speaker 1: data from the machine as well as control the machine
Speaker 1: through a non invasive integration where we can upit a
Speaker 1: machine with our sensors and compute suite in like less
Speaker 1: than three hours and make it autonomy capable and it's
Speaker 1: completely reversible. We could never do that with a car
Speaker 1: or a truck because the platforms were just not designed
Speaker 1: this way.
Speaker 2: Yeah, so there's a lot that's there already. The excavators
Speaker 2: themselves are sort of easy to integrate with lots of
Speaker 2: off the shelf technology. What's not there, Like, when you're
Speaker 2: coming in, what do you have to sort of build
Speaker 2: that nobody has built before?
Speaker 1: Nobody has actually created autonomy that can solve the really
Speaker 1: nuanced problems that you need to solve in order to
Speaker 1: operate like an excavator in construction tasks.
Speaker 2: So let's talk like specifically, you buy your excavators, you
Speaker 2: got your hardware, you know, whatever, what basic AI model
Speaker 2: you're going to use, But like, what's a specific thing.
Speaker 2: You have to figure out well a.
Speaker 1: Lot of things. So first of all, it's not trivial
Speaker 1: to tap into these machines and to build a platform
Speaker 1: or kind of an integration around it where you can
Speaker 1: read them, you can control them, and so you have
Speaker 1: to basically create like a wrapper around the machine and
Speaker 1: also design in a way that scales later to new machines. Right,
Speaker 1: So that part's hard. It's a big autonomy problem. So
Speaker 1: in that respect is no different than what we tackle
Speaker 1: with a WEIMO, where you have to train massive scale models.
Speaker 1: You have to collect a huge amount of data.
Speaker 2: And in this case it's like about the digging, like
Speaker 2: it's about the digging dumb question? Is the digging the
Speaker 2: hard part? Like what happens when the thing hits the ground.
Speaker 2: There's different kinds of stuff in the ground.
Speaker 1: Like tell me in the category of what's new, suddenly
Speaker 1: a car doesn't change the environment around it. You have
Speaker 1: a really complicated tough earth and soil, and you got
Speaker 1: to figure out the physics of how you dig through it,
Speaker 1: how do you deal with clay versus top soil, how
Speaker 1: do you deal with rocks inside? Then so, now if
Speaker 1: you want to use simulation to solve parts of these problems.
Speaker 1: You have a very complicated simulation problem because you have
Speaker 1: to solve not just the sensor side, but also the
Speaker 1: manipulation side. You have to structure this problem in a
Speaker 1: way where you're learning from the data you collect in
Speaker 1: order to actually capture the nuances of how do you
Speaker 1: actually interact with the environment. You have to then control
Speaker 1: and execute it the right way. You have to think
Speaker 1: about how do you actually define the goal?
Speaker 2: So from the user point of view with an excavator,
Speaker 2: like how does it look like? I just I want
Speaker 2: to dig a hole of this size at this spot?
Speaker 2: Or like what is it?
Speaker 1: Yeah, and this is a journey like fast forward you
Speaker 1: know next year where card customer does this right, So
Speaker 1: what they would do is they would give it a
Speaker 1: model of what they want the earth to be dug
Speaker 1: towards and so that would be like a three D
Speaker 1: representation of the depth. So with length depth, here's the edges,
Speaker 1: here's the constraints. The pickups for the trucks are going
Speaker 1: to be over here and then go to work. And
Speaker 1: basically these projects, like these machines will work for many
Speaker 1: months at a time, continually just digging earth and working
Speaker 1: it towards the right foundation, and the system will respect
Speaker 1: that boundary and basically dig down to that level. It'll
Speaker 1: have precision to the edges that you've defined, it'll do
Speaker 1: it in a sequence that makes sense so that you
Speaker 1: don't trap yourself in a hole, for example. And then
Speaker 1: you specify where you're going to have dump truck pickups.
Speaker 1: Then there's projects where this happens for nine months straight
Speaker 1: or twelve months straight with like many machines, and so
Speaker 1: what you basically need to specify is what you want
Speaker 1: to dig to, and that what you want to dig
Speaker 1: to ends up basically being the foundation that you're working
Speaker 1: towards for whatever you're going to construct there. And we
Speaker 1: want that to be versatile. So sometimes you're just taking
Speaker 1: off a layer of top soil. Sometimes you're digging eight
Speaker 1: feet deep. Sometimes you're taking an existing stockpile and moving
Speaker 1: in somewhere else. So there's a lot of permutations of this.
Speaker 1: Sometimes you're taking rubble from a demolition job and loading
Speaker 1: it onto trucks, right, and so what's nice is that
Speaker 1: you start to see patterns over and over again, and
Speaker 1: this sort of work.
Speaker 3: We'll be back in just a minute.
Speaker 2: So the data problem is interesting here, right, Like you
Speaker 2: were talking about the physics, just the physics of an
Speaker 2: excavator is quite different, right, Like it's pushing down on
Speaker 2: the ground, which is pushing back on the excavator, and
Speaker 2: like as you say, the ground is changing because of
Speaker 2: its work, and there's not Well where do you get
Speaker 2: the data?
Speaker 1: Data is actually an interesting mix. We get it both
Speaker 1: on our test sites and also with our design partners.
Speaker 1: So we were working with general contractors and subcontractors. Today
Speaker 1: we have five that we're partnered with across southern states
Speaker 1: like Arizona, Texas, and so we're getting data on their size.
Speaker 1: We're getting data on our sites.
Speaker 2: It's not that much data, right, Like I mean, I
Speaker 2: guess my reference point is always like image net or
Speaker 2: you know, the Internet for large language models. It does
Speaker 2: seem like a sort of recurring problem in robotics type
Speaker 2: AI applications is sparsity of data.
Speaker 1: How do you get it?
Speaker 2: Yeah?
Speaker 1: Yeah, And so this is where there's a few kind
Speaker 1: of intersuties. So first of all, like our partners together
Speaker 1: have thousand and thousands of machines and so there's a
Speaker 1: lot of choices of which she's a partner. On the
Speaker 1: other thing that you can do is actually you can
Speaker 1: be very clever on a test site, and when you're
Speaker 1: on a real project, you're kind of getting a unbiasample.
Speaker 1: You're just getting a random distribution of the things you see,
Speaker 1: just like driving around on a road. When you're on
Speaker 1: a test site, you can actually up sample the things
Speaker 1: you actually want and you can go and you can
Speaker 1: collect ten hours of data. There's representative of five thousand
Speaker 1: hours of random data, but which is particularly useful for
Speaker 1: things like safety situations. Right, you can actually create a
Speaker 1: much larger equivalent amount of data on a close course
Speaker 1: through like kind of structure testing. And so for example,
Speaker 1: safety scenarios where you have weird interactions with people doing
Speaker 1: things they shouldn't do. You don't wait to see that
Speaker 1: on an open site.
Speaker 2: What is one of those, like what is your nightmare
Speaker 2: human behavior scenario?
Speaker 1: In the field? Night very human behavior is a human
Speaker 1: is curious, they walk up to your machine, Your machine stops,
Speaker 1: and then they get really really close and they are
Speaker 1: now in a blind spot where you can't see that.
Speaker 1: But you still have to be smart enough to track
Speaker 1: them where they're in a hole in front of you
Speaker 1: while you're like thinking about digging, right, So occlusions from
Speaker 1: humans is probably a huge category which is called plex.
Speaker 2: Occlusions, meaning in your blind spot humans in a place
Speaker 2: where the machine can't see them.
Speaker 1: Yeah, or usually your number one priority is anything that
Speaker 1: touches on human safety. That's sacred. You never take any
Speaker 1: risks on that.
Speaker 2: What's something you haven't figured out yet?
Speaker 1: The things people do with excavators, Like we've seen them
Speaker 1: do bizarre like clearing of debris. We've seen them load
Speaker 1: a wheeloader with dirt. We've seen them bang tools to
Speaker 1: change them. We've seen them bang tools to change them.
Speaker 1: What's that one? Like a tool gets stuck and they
Speaker 1: like bang it on the ground in order to get
Speaker 1: it loose. They use their arm as a pivot point
Speaker 1: to turn when the wheels are like stuck in mud.
Speaker 2: It's kind of baller, Like it's pretty baller.
Speaker 1: Yeah, it's like I mean, like when you see the
Speaker 1: expert operators, they just show off and it's like it's
Speaker 1: increa like they're awesome. And so there's like all these
Speaker 1: like weird subtleties of how they'll use these tools in
Speaker 1: like really subtle ways, which like the dimensions of on
Speaker 1: the product side of the use cases that blew was away.
Speaker 1: We thought about the obvious ones, but as we started
Speaker 1: like really going deeper and like studying this, there's so
Speaker 1: much diversity and interesting things that you can do with
Speaker 1: these machines. It's quite powerful.
Speaker 2: I mean, presumably you don't have to do all those
Speaker 2: to have a product people will pay you for. Right,
Speaker 2: you can just have the like competent automaton that can
Speaker 2: dig a big hole just the way you want it.
Speaker 1: That's correct, and that there's a huge amount of work
Speaker 1: even in those areas. But there is this like tale
Speaker 1: that you go in you over time go and add
Speaker 1: more and more capability, and then I think it is
Speaker 1: just as softwareupdates and you get more. It's just like
Speaker 1: a you know, the product ends up being the digital
Speaker 1: operator which gets better over time, and so it's similar
Speaker 1: to human getting better.
Speaker 2: What's the business model?
Speaker 1: So we will go operator out next year. So that'll
Speaker 1: be our first, like fully operator list product that is
Speaker 1: in a spirit of like what this is meant to scale.
Speaker 2: As operator out means driver list means autonomous. Is that
Speaker 2: what it means?
Speaker 1: Yeah, nobody there, It's just like operator lists. Yeah, and
Speaker 1: so our product is the digital operators. So what I
Speaker 1: mean by this is that our customer is a general
Speaker 1: contractor or subcontractor, so it's the companies that already buy
Speaker 1: and manage these machines we are we sell them a
Speaker 1: retrofit of these machines, so we install an upfit that
Speaker 1: adds sensors and compute.
Speaker 2: So the contractor already owns the excavator.
Speaker 1: That's right, They've already buy a five hundred thousand dollars
Speaker 1: machine or three hundred thousand dollars machine, and so this
Speaker 1: is an upgrade that enables autonomy. And then our business
Speaker 1: model is selling labor, So we're effectively selling the labor
Speaker 1: and then a variety of digital services around it that
Speaker 1: operates as machine. And for the surface area of types
Speaker 1: of tasks that it's approved for, it's completely driverlests. And
Speaker 1: then that surface area increases over time with software updates,
Speaker 1: and for everything else, it can still be manually operated.
Speaker 2: So is the core product? Are they paying you by
Speaker 2: the hour for digging?
Speaker 1: So we're figuring it out, but it'll be something that
Speaker 1: is either by the hour, by the project by subscriptions.
Speaker 1: So it's effectively the way that today projects are forecasted
Speaker 1: and build by shifts, you know, for labor it's a
Speaker 1: parallel of that, but it becomes a huge win because
Speaker 1: you have complete flexibility. You can work ten hours a day,
Speaker 1: you can work twenty four hours a day. So there's
Speaker 1: all sorts of benefits that will operationally give you a
Speaker 1: lot of leeway on how you use it, what might
Speaker 1: go wrong, what might go wrong. I'll tell you the
Speaker 1: things that are like really challenging. They like we worry
Speaker 1: about it, we think about the nuance and diversity of
Speaker 1: things are high. As much as you want to just
Speaker 1: boil it down to a very simple dig and load
Speaker 1: sort of operation, there's always little corner cases. There's things
Speaker 1: you find in the ground, there's weird ways that trucks
Speaker 1: can interact with you, there's things people will do. There's
Speaker 1: varieties of machines, but there's like fifty kinds of excavator
Speaker 1: models and sizes and so forth. So I think the
Speaker 1: long tail is still challenging.
Speaker 2: Presumably there's you don't have to figure out every edge case,
Speaker 2: but you probably have to figure out a lot for
Speaker 2: your thing to be to work in a functional sense, right.
Speaker 1: Yeah, And so those in the meantime, you're not just
Speaker 1: digging and loading and got a reposition you got to
Speaker 1: organize the earth, You got to think about the sequencing,
Speaker 1: you got to deal with you know, daytime, night time rain,
Speaker 1: and so you have like these types of like really
Speaker 1: challenging diversities you have to think about and deal with.
Speaker 1: So I think all in all, it's still a complicated
Speaker 1: product area where there's a huge amount of diversity of
Speaker 1: the things that need to be done. But it's one
Speaker 1: of those where, like I personally think there's a handful
Speaker 1: of these holy grails of autonomy and physical industries that
Speaker 1: are like genuinely transformational opportunities for both impact positive impact
Speaker 1: to the country and the world, and also just kind
Speaker 1: of scale of industries that are like double digit percentages
Speaker 1: of GDP. Transportation is, without a doubt one of them,
Speaker 1: Construction is one of them, Culture is not far behind.
Speaker 1: You know, mining is a very very significant as well.
Speaker 1: Manufacturing is one of them. And so I think that
Speaker 1: we're going to see a wave over this next ten
Speaker 1: years in autonomy, but it's going to be tackling this
Speaker 1: like seventy five percent of the world's GDP that's physical
Speaker 1: and not digital, and there's a lot of work, like
Speaker 1: a lot of positive impact that can happen across these spaces.
Speaker 2: I mean, give me a little more on that one
Speaker 2: pick a time in the future, five years, ten years,
Speaker 2: not more than ten.
Speaker 1: So my personal belief is that this idea, like there's
Speaker 1: a lot of companies getting flooded for this, but the
Speaker 1: idea that like this giant brain for all of robotics,
Speaker 1: the foundation model for robotics, like, I personally do not
Speaker 1: believe that that's viable in the next ten years because
Speaker 1: you have such complexity in really understanding these verticals on
Speaker 1: the inputs, the hardware, the products, the neat use case,
Speaker 1: the customers, everything, that every single one of those have
Speaker 1: such complexity to get data that the idea to get
Speaker 1: enough data that bulldos is through a generalization problem. That's
Speaker 1: a long ways off. But I do think that word
Speaker 1: a perfect time for these vertical solutions where if you
Speaker 1: have a focused solution where you're trying to do construction,
Speaker 1: you're trying to do fulfillment in a warehouse, you're trying
Speaker 1: to do a very focused manufacturing solution, I think that's
Speaker 1: actually there's a giant stuff function and how powerful and
Speaker 1: male technologies are. But if you're trying to do a
Speaker 1: humanoid to do everything in a home for a consumer
Speaker 1: market too far away.
Speaker 2: Right now you're saying you basically you got to solve
Speaker 2: one problem at a time. Yeah, I want to go
Speaker 2: back to open road autonomy almost done. When do you
Speaker 2: think that most rides most people take in cars or
Speaker 2: trucks will be in driverless cars autonomous vehicles?
Speaker 1: Great question. There's a few pre requisites for most rides.
Speaker 1: So first, all the goofs is have to disappear and
Speaker 1: the thing just works everywhere that's on a trajectory of
Speaker 1: getting there because it's getting more and more efficient as
Speaker 1: the scales across the country and then the world. It
Speaker 1: has to go from ride healing to personal car ownership
Speaker 1: that is both cost down and versatility and everything that. Like,
Speaker 1: but that's gonna happen.
Speaker 2: In that universe. When do they take the steering wheel out?
Speaker 2: I just took a way mover the first time I
Speaker 2: was in San Francisco this summer.
Speaker 1: It's awesome, right, it was awesome.
Speaker 2: Yeah, And like the weirdest thing about it to me
Speaker 2: was the steering wheel. Yeah, right, Like if it was
Speaker 2: just a box that I got in that looked like
Speaker 2: a train car, someone I would have been like, oh sure,
Speaker 2: it's like the air train or whatever, but that there's
Speaker 2: a steering wheel and it's like a ghost is turning
Speaker 2: the steeringhell, Like what's the steering wheel doing there?
Speaker 1: So the steering wheels will disappear pretty quickly because ride
Speaker 1: sharing autonomous cars, yeah, have no use for it, and
Speaker 1: you need to point you know, there's like special cases.
Speaker 1: We need to recover them. But it's a wasted space, right,
Speaker 1: It's a wasted spot. So that'll happen soon. But the
Speaker 1: idea of really deep penetration, I think it's personal cars.
Speaker 1: It's beyond luxury, which is another generation to like actually
Speaker 1: make it be something that's affordable. Then you got to
Speaker 1: go through a buying cycle, which is like five to
Speaker 1: seven years, and so I think it starts to get
Speaker 1: serious penetrats in the back half of the thirties and
Speaker 1: it'll be the forties where like, okay, fifty percent of
Speaker 1: driving is kind of like autonomous. I think it's still
Speaker 1: twenty years off in my mind. For example, you bought
Speaker 1: a car today, it's going to be in circulation for
Speaker 1: the next twelve to fifteen years. It just takes a while.
Speaker 2: We'll be back in a minute with the lightning round. Okay,
Speaker 2: let's finish with the lightning round. If you could operate
Speaker 2: any machine, what would it be?
Speaker 1: Oh gosh, this is a fun one. If I could
Speaker 1: operate any machine at all in the world, not even
Speaker 1: in contry anything, Oh gosh, Okay, you know what, I
Speaker 1: would love to either operate one of the like Boring
Speaker 1: Company gigantic drill machines. Was like so astronomical or there's
Speaker 1: like my trucks that are so astronomically big that they
Speaker 1: just dwarfed any machine in the world, and it costs
Speaker 1: like five million dollars and just a skill that would
Speaker 1: be really really fun to try at some point.
Speaker 2: Just to be that high, just have that much momentum, right,
Speaker 2: that much mass at your disposal.
Speaker 1: Yeah, like literally the tire is like three stories tall.
Speaker 1: It's like it's wild, Like it's absurd.
Speaker 2: What's one thing you remember about immigrating from the Soviet
Speaker 2: Union to the US when you were six?
Speaker 1: I was six. Yeah, I was born in Moscow. We immigrated.
Speaker 1: I was super young. I remember we ended up having
Speaker 1: a pet stop in Europe where there's a standard path
Speaker 1: of going to be out of Venice in Rome, why
Speaker 1: your paperwork gets processed, and then we went to New York.
Speaker 1: I remember running around the rooftops of Venice with a
Speaker 1: friend of mine, causing a bunch of you know, trouble
Speaker 1: and running away and disappearing for long periods of time
Speaker 1: and having a boss for for whatever reason, running around
Speaker 1: the rooftops of Venice was in my was ingrained in
Speaker 1: my memory, which was a kind of very positive memory
Speaker 1: while my parents were going through a whole bunch of strusts.
Speaker 2: It sounds very free. Not to project East versus West
Speaker 2: language onto it, but it sounds very free.
Speaker 1: It was very freeing. It's It's funny. I just hit
Speaker 1: the age my dad was when they immigrated from the
Speaker 1: Soviet Union with two kids. My sister was one month old,
Speaker 1: zero money, almost no English, having a fresh start.
Speaker 2: So courageous, right, doesn't it seem so brave?
Speaker 1: Yeah? It does. And they were trying to leave for
Speaker 1: like ten years and couldn't leave. Yeah, it's kind of fascinating.
Speaker 1: So we're very fortunate to not have to go through
Speaker 1: something like that.
Speaker 2: Is that when you think about it that way, does
Speaker 2: that like put pressure on you? You think, oh my god,
Speaker 2: my parents did all this better? I better deliver.
Speaker 1: It's funny, Uh, a little bit. I mean you kind
Speaker 1: of have this I don't know, like adventurous spirit. I
Speaker 1: guess maybe it gets ingrained. I think of it now
Speaker 1: for my kids, where I'm like, okay, Like now they're
Speaker 1: in a really nice and comfortable environment growing up. How
Speaker 1: do you I convey that edge in a little bit
Speaker 1: of that spirit, Like, how.
Speaker 2: Do you keep them from going so.
Speaker 1: Yeah, it's something you want them to go through anything difficult,
Speaker 1: because it's not that I was, you know, for me,
Speaker 1: it was actually just an adventure my guards. It was difficult,
Speaker 1: But part of it is just, yeah, like conveying that
Speaker 1: spirit of being able to like be comfortable trying to
Speaker 1: tackle something new and being thrown in a completely different environment.
Speaker 1: It's hard to force ound or simulate that when you're
Speaker 1: just growing up in the Semptusco area. Right.
Speaker 2: I appreciate your time. Thanks for talking to me.
Speaker 1: It's a pleasure. This is a lot of fun. Thanks
Speaker 1: for having me.
Speaker 2: Boris Soffman is the co founder and CEO of Bedrock Robotics.
Speaker 2: Just a quick note that this is our last episode
Speaker 2: before a break of a couple of weeks and then
Speaker 2: we'll be back with more episodes. Please email us at
Speaker 2: problem at Pushkin dot Fm. We are always looking for
Speaker 2: new guests for the show. Today's show was produced by
Speaker 2: Trinamanino and Gabriel Hunter Chang, who was edited by Alexander
Speaker 2: Garretton and engineered by Sarah Brugueri. I'm Jacob Goldstein and
Speaker 2: we'll be back next week with another episode of What's
Speaker 2: Your Problem.