Speaker 1: Welcome to the Sentient Code, where intelligence is engineered, autonomy
Speaker 1: is emerging, and a line between human and machine grows thinner.
Speaker 1: Each episode, we decode the algorithms, explore the robotics, and
Speaker 1: examine the ideas shaping the future of artificial minds.
Speaker 2: Welcome back to the Deep Dive. We are doing something
Speaker 2: a little bit different today. Usually we look at a
Speaker 2: product launch or a new piece of software, something that
Speaker 2: might change your workflow next month.
Speaker 3: Right, the usual tech updates, Yeah, exactly.
Speaker 2: But today we are looking at something that changes the
Speaker 2: fundamental math of civilization. We're digging into a stack of reports,
Speaker 2: industry analysis, and some really wild operational data from and
Speaker 2: this is the important part. February twenty twenty six.
Speaker 3: Right now, It is happening right now.
Speaker 2: And the headline here isn't that robots will build robots
Speaker 2: someday that they already are.
Speaker 3: It sounds like science fiction, doesn't it. I mean, when
Speaker 3: you say that phrase out loud, robots building robots, it
Speaker 3: feels like we should be breaking down a movie script. Yeah,
Speaker 3: but we aren't. Now we're on We're talking about actual
Speaker 3: factory floors in California, Texas and Shenzen that are hum
Speaker 3: and along.
Speaker 2: Today exactly the core topic for you all listening today
Speaker 2: is recursive manufacturing. And to be super clear right up front,
Speaker 2: we aren't talking about like a robotic arm welding a
Speaker 2: car door. We've had that for forty years.
Speaker 3: That's old news, right, That is just standard industrial automation.
Speaker 3: What we are analyzing today is a massive transition. We
Speaker 3: are talking about humanoid robots, machines that have our form factor,
Speaker 3: that look and move.
Speaker 2: Like us, standing right there on the line.
Speaker 3: Yes, standing on the assembly line, using highly dexterous hands
Speaker 3: to wire, assemble, and package the very next generation of themselves.
Speaker 3: It is a closed loop production system.
Speaker 2: Closed loop. That's a phrase that keeps popping up in
Speaker 2: the research we're going through. It feels like the holy
Speaker 2: grail of industrial engineering.
Speaker 3: It absolutely is, yeah, because once you manage to close
Speaker 3: that loop, you fundamentally detach production growth from the limitations
Speaker 3: of the human workforce.
Speaker 2: Before we get into the heavy hitters like Tesla and
Speaker 2: Figure AI and the economic implications which are honestly mind bending.
Speaker 2: I want to clear up a definition. Sure, when we
Speaker 2: say robots building robots, my brain and probably your brain
Speaker 2: listening to this goes straight to the matrix or those
Speaker 2: self replicating space probes. Yeah, the Von Neumann probes.
Speaker 3: Right right, the classic Von Neumann probe. It's a great
Speaker 3: Sci Fi trope. A machine lands on an asteroid, It
Speaker 3: mines the raw iron, smelts, it builds a factory and
Speaker 3: replicates itself with literally zero human input.
Speaker 2: Right.
Speaker 3: I want to be very precise here. That is not
Speaker 3: what is occurring in twenty twenty six.
Speaker 2: Okay, so we aren't quite there yet. We don't have
Speaker 2: robots wandering out into the wilderness to build entirely new
Speaker 2: cities from scratch.
Speaker 3: No, that is autonomous self replication in an unstructured environment
Speaker 3: that involves mining, refining, complex chemical processing. That's decades away.
Speaker 2: So what are we seeing today?
Speaker 3: What we are seeing today is recursive manufacturing and strictly
Speaker 3: controlled settings. Imagine a highly structured factory environment. The lights
Speaker 3: are on, the floors are flat, the parts are delivered
Speaker 3: to specific bins by automated logistics systems.
Speaker 2: But the actual labor.
Speaker 3: Part, right, the labor, the actual act of snapping the
Speaker 3: plastic housing together, threading a delicate wire through a joint,
Speaker 3: testing the motor. That physical labor is shifting from human
Speaker 3: hands to robotic Hans.
Speaker 2: So the distinction is really the environment and the scope.
Speaker 2: It's not a robot mining or in a cave. It's
Speaker 2: a robot standing at a workstation doing what a human
Speaker 2: worker used to do just a couple of years.
Speaker 3: Ago, exactly. But the economic impact of that is almost
Speaker 3: exactly the same as the Sci Fi version. How so,
Speaker 3: because once you replace the human labor in the production
Speaker 3: of the labor force itself, you fundamentally change the entire
Speaker 3: economic equation. You stop being limited by how many people
Speaker 3: you can hire, or housing shortages in the area, or
Speaker 3: wage inflation.
Speaker 2: You just need materials.
Speaker 3: You start being limited only by how much silicon and
Speaker 3: steel and electricity you can buy.
Speaker 2: That's such a heavy concept, detaching production from the human population. Usually,
Speaker 2: I mean, if you want to double your factory output,
Speaker 2: you need to hire double the workers, and humans are scarce.
Speaker 3: Humans take eighteen years to reach working age, They need sleep,
Speaker 3: they get injured, they eventually need to retire, They.
Speaker 2: Have lives outside the factory floor.
Speaker 3: Precisely, but if the product, the robot can build the product.
Speaker 3: The only constraints left are raw materials and energy.
Speaker 2: Which brings us to the speed of all this. The
Speaker 2: sources we're looking at. Describe this theoretical framework called the
Speaker 2: triple exponential. This is something Elon Musk and a few
Speaker 2: other industry leaders have been harping on lately. Break this
Speaker 2: down for us. Why triple So.
Speaker 3: Most technology grows on a single exponential curve, you know
Speaker 3: More's law. For example, computer chips get faster and cheaper
Speaker 3: every two years. Right, But recursive robotics is writing three
Speaker 3: simultaneous waves that interlock with the each other. If you
Speaker 3: miss one, the whole system stalls out. But if you
Speaker 3: hit all three at the same time, you get this
Speaker 3: vertical takeoff.
Speaker 2: Okay, walk us through these three factors. What is the
Speaker 2: first one?
Speaker 3: The first one is AI software intelligence. In twenty twenty six.
Speaker 3: We aren't just running you know, standard hard coded logic.
Speaker 3: We are running massive foundation models. We're seeing these scaling
Speaker 3: laws play out in real time scaling laws.
Speaker 2: We hear that in the AI space constantly. That basically
Speaker 2: means if you dump more data and more compute into
Speaker 2: the system, it gets smarter at a very predictable rate.
Speaker 3: Right, yes, exactly, But specifically for robotics, it's about multimodal training.
Speaker 3: The AI isn't just reading text from the Internet anymore.
Speaker 3: It's processing live video force feedback from its fingertips. Proprioception.
Speaker 2: Wait, proprioception, what is that? Exactly? In this context, that's.
Speaker 3: The robot sense of where its limbs are in physical space.
Speaker 3: Just like you can close your eyes and still touch
Speaker 3: your nose. The robot knows exactly the angle and tension
Speaker 3: of every joint. Add spatial awareness to that, and the
Speaker 3: software is evolving to understand the physical world, not just
Speaker 3: the digital one.
Speaker 2: So factor one is the brain. The brain is getting smarter,
Speaker 2: way faster than we expected. What's factor two?
Speaker 3: Compute power. The brain needs a body, sure, but it
Speaker 3: also needs calories or in this case, watts and flops.
Speaker 3: We are seeing these companies, specifically Tesla and Figure, building
Speaker 3: massive in house supercomputer clusters.
Speaker 2: This is the massive infrastructure piece we keep hearing about.
Speaker 3: Right, because you can have the smartest AI architecture in
Speaker 3: the world, but if you don't have the next generation
Speaker 3: chips to run the inference in real time.
Speaker 2: So it can react fast enough.
Speaker 3: Exactly, so the robot can react to a falling screw
Speaker 3: in milliseconds. Without that compute it's useless. The compute density
Speaker 3: in these dedicated clusters is skyrocketing. They are literally building
Speaker 3: the physical capacity to think for millions of robots simultaneously.
Speaker 2: And the third factor in this triple exponential hardware dexterity.
Speaker 3: This is the physical machine itself, the actuators, the motors,
Speaker 3: the tactiles, answers on the hands.
Speaker 2: The muscles in the nerves exactly.
Speaker 3: Think back just a few years ago. Robots were incredibly clumsy.
Speaker 3: They could lift a heavy car chassis with ease, but
Speaker 3: they couldn't button a shirt. They couldn't handle a flexible
Speaker 3: floppy wire.
Speaker 2: They were strong, but dumb and stiff.
Speaker 3: Now we have custom actuators that offer human level precision.
Speaker 3: When you combine those three things, a smarter brain, a
Speaker 3: faster processor, and a highly dexterous body, you get the
Speaker 3: recursive exponential.
Speaker 2: It's a multiplicative effect. They don't just add up, they
Speaker 2: multiply each other.
Speaker 3: It creates a compounding loop. Better robots are able to
Speaker 3: build better factories, which in turn build even better robots.
Speaker 3: Must calls it pushing the margin. The goal is to
Speaker 3: push the robot into superhuman performance for economically valuable tasks.
Speaker 2: The timeline in our notes here is fascinating. It says
Speaker 2: we are currently right now, in early twenty twenty six,
Speaker 2: in a phase of partial integration.
Speaker 3: I think that's a fair assessment. We aren't one hundred
Speaker 3: percent automation yet. If you walk into these manufacturing facilities today,
Speaker 3: you still see people walking around.
Speaker 2: But the projection for a full loop where robots are
Speaker 2: the primary force building the robots is just a three
Speaker 2: to five year window. That feels incredibly fast to me.
Speaker 3: It's lightning fast in industrial terms. Usually retooling a major
Speaker 3: factory takes a decade. We're seeing physical literations happen in
Speaker 3: months now. The recursion is already active on the lines.
Speaker 3: Now it's just a game of percentages. How So, today
Speaker 3: maybe the robots do twenty percent of the assembly. Next
Speaker 3: year it'll be forty percent, then eighty percent.
Speaker 2: Let's get into some specific examples. Theories are great, but
Speaker 2: I want to know what's actually happening on the ground.
Speaker 2: We have a case study here on Figure AI. They
Speaker 2: seem to be the ones really pushing this recursive strategy explicitly.
Speaker 3: Figure is absolutely the poster child for this right now.
Speaker 3: They aren't being subtle about it at all. Their entire
Speaker 3: business model is built around closing this loop.
Speaker 2: They have a facility called BOTQ in California. What exactly
Speaker 2: goes on inside botq Q is.
Speaker 3: Designed for one thing, and that is scale. It's not
Speaker 3: a standard assembly plant where you have people standing at
Speaker 3: stations with screwdrivers and torque wrenches. It was built from
Speaker 3: the ground up to transition from making thousands of units
Speaker 3: a year to a highly aggressive, high volume output.
Speaker 2: Using the robots themselves as the primary workers.
Speaker 3: Exactly so.
Speaker 2: Brett Adcock, the CEO over at Figure has this roadmap
Speaker 2: involving the Figure O three.
Speaker 3: Model right, the Figure O three and specifically the Helix
Speaker 3: AI stack that runs it.
Speaker 2: Explain this Helix stack to us? Is that just their
Speaker 2: version of an operating system.
Speaker 3: Think of Helix as a collective intelligence. This is where
Speaker 3: the math gets really interesting. Every single time a figure
Speaker 3: robot moves, it's not just executing code, it's learning. This
Speaker 3: leads to what they call the data flywheel. This is
Speaker 3: easily the most critical concept for understanding figures massive valuation right.
Speaker 2: Now, the flywheel. We hear that term in business school
Speaker 2: all the time, but how does it apply to a
Speaker 2: physical robot assembling say a battery pack.
Speaker 3: Okay, let's play it out. Imagine a Figure O three
Speaker 3: robot on the line at BOTQ. It's tasked with wiring
Speaker 3: a subassembly. Let's say it struggles a little bit on
Speaker 3: its first try. Maybe it fumbles a tiny connector or
Speaker 3: the wire is bent in an unpredictable way. It tries,
Speaker 3: it adjusts its grip, it tries again, and eventually it
Speaker 3: gets it right.
Speaker 2: It figures the problem out.
Speaker 3: It figures it out. But here is the magic of
Speaker 3: the flywheel. That data the struggle, the micro corrections, the
Speaker 3: video feed, the exact force it felt on its fingertips.
Speaker 3: All of that is immediately uploaded to the central helix model.
Speaker 2: It's essentially recording the entire experience.
Speaker 3: Yes, and then that experience is used to train the
Speaker 3: next version of the model. That update is then pushed
Speaker 3: out to the entire fleet. Wow. So suddenly every single
Speaker 3: robot in the factory and every robot working out in
Speaker 3: the field knows exactly how to handle that specific connector
Speaker 3: perfectly on the first try.
Speaker 2: So if one robot learns something hard, they all learn
Speaker 2: it instantly, instantly.
Speaker 3: And here is where the recursive part kicks into high gear.
Speaker 3: The bitter they get it manufacturing the faster and cheaper
Speaker 3: figure can build more robots, which means.
Speaker 2: They can put more robots out in the world, collecting
Speaker 2: even more real.
Speaker 3: World data, which makes the central model smarter, which makes
Speaker 3: the manufacturing even better. Round and round, the flywheel goes
Speaker 3: gaining speed.
Speaker 2: That really explains the valuation numbers we are seeing. The
Speaker 2: notes here say Figure AI is valued at over thirty
Speaker 2: nine billion dollars. That's a massive number for a company
Speaker 2: that just a few years ago didn't even exist.
Speaker 3: Investors aren't really betting on the robot's figure is selling today,
Speaker 3: They are betting entirely on the flywheel. They see the
Speaker 3: direct correlation between this recursive strategy and exponential scaling. If
Speaker 3: you can make the robot that builds the robot, you
Speaker 3: essentially win the future manufacturing economy.
Speaker 2: And the vision here is scaling to billions of units,
Speaker 2: not millions billions.
Speaker 3: Ed Gock has been very clear on this point. He
Speaker 3: wants these things in factories and households and eventually helping
Speaker 3: with space colonization.
Speaker 2: Okay, let's pivot to the other giant in the room.
Speaker 2: You can't talk about manufacturing scale without talking about Tesla.
Speaker 3: Tesla's optimist program. It's a very different beast from Figure
Speaker 3: mostly because of their intense vertical integration.
Speaker 2: Right because figure is laser focused on just the robot.
Speaker 2: Tesla is already a car company, an energy company, and
Speaker 2: a massive AI company. What is the actual operational status
Speaker 2: of Optimists right now in February or twenty twenty six.
Speaker 3: We are looking closely at their Fremont pilot production lines.
Speaker 3: Right now they are assembling generation two and some early
Speaker 3: generation three prototypes. But if you look at the ground reports,
Speaker 3: the vibe there is quite different from a fully automated
Speaker 3: lights outline. It's very much human in the loop right now.
Speaker 2: So it's definitely not a ghost.
Speaker 3: Factory yet, No, not yet. You walk that line and
Speaker 3: you see teams of human engineers working right alongside the
Speaker 3: automated robotic processes. They're tweaking the actuators, adjusting tension in
Speaker 3: the wiring harnesses, calibrating the sensors by hand. It's a
Speaker 3: very close collaboration.
Speaker 2: Musk has said they are currently useful for simple tasks.
Speaker 3: That's the official line today, simple tasks now, but with
Speaker 3: a very aggressive trajectory toward complex autonomy by the end
Speaker 3: of twenty twenty six. But the hardware design itself is
Speaker 3: where Tesla really flexes its manufacturing muscles biomimicry.
Speaker 2: Right, We've seen a.
Speaker 3: Lot about that. Yes, Optimis is designed to mimic the
Speaker 3: human form biologically more than almost any other robot on
Speaker 3: the market. The specific motors they design are meant to
Speaker 3: act like biological muscles. The electronics are incredibly compact to
Speaker 3: fit inside the frame.
Speaker 2: Why is that so important? I mean, why not just
Speaker 2: build a robot that looks like a forklift or a
Speaker 2: robotic arm on wheels because.
Speaker 3: The world is already built for humans. The modern factory
Speaker 3: is built for humans. If you want a truly general
Speaker 3: purpose robot, it needs to be able to fit through
Speaker 3: a standard door frame. It needs to use standard hand tools,
Speaker 3: It needs to walk up stairs that were built for
Speaker 3: size ten shoes.
Speaker 2: Ah. I see.
Speaker 3: The whole design intent is to create a general purpose
Speaker 3: humanoid that can seamlessly slot into a world that was
Speaker 3: entirely built.
Speaker 2: For us, including the factory that builds the robot itself.
Speaker 3: Exactly. If the Tesla factory is already designed for people
Speaker 3: to walk around in, the robot needs to move and
Speaker 3: operate like a person.
Speaker 2: I want to ask about this thing in the notes
Speaker 2: called the Optimus Academy It sounds like a prep school
Speaker 2: from a sci fi novel, but the research says it's
Speaker 2: absolutely crucial for their data strategy.
Speaker 3: It sort of is a prep school. Honestly. This is
Speaker 3: their massive simulation engine.
Speaker 2: You see.
Speaker 3: You can't train a robot entirely in the real world.
Speaker 3: It's way too slow.
Speaker 2: And dangerous, I imagine, and very dangerous.
Speaker 3: If a robot loses its balance and falls over in
Speaker 3: the real world, you just broke a ten thousand dollars
Speaker 3: custom actuator. If it falls over in a simulation, you
Speaker 3: just reset the code and try again.
Speaker 2: So they essentially put them in the matrix to learn.
Speaker 3: Essentially, Yes, they have thousands upon thousands of virtual Optimist
Speaker 3: units running in these hyperreal simulations twenty four hours a day.
Speaker 3: They are practicing fine dexterity balance and complex object manipulation
Speaker 3: in a totally physics compliant virtual world.
Speaker 2: They call it self. Play in the documents, right.
Speaker 3: They play out these scenarios millions of times. Pick up
Speaker 3: the box, plug in the high voltage cable, walk over
Speaker 3: the uneven concrete floor. Then they take that brain the
Speaker 3: neural net weights they developed in the simulation, and they
Speaker 3: download it straight into the physical robot. This accelerates the
Speaker 3: learning curve massively without the risk of breaking expensive hardware.
Speaker 2: So you have the real world data coming from the
Speaker 2: Fremont pilot line combining with the massive simulation data from
Speaker 2: the Optimus academy.
Speaker 3: And that combination is what triggers the recursion tipping point.
Speaker 3: Once the internal fleets are capable enough to bootstrap their
Speaker 3: own output, the data looks tighten up significantly, becomes a
Speaker 3: blur of improvement.
Speaker 2: What's the actual production roadmap look like? When can I
Speaker 2: actually buy one to fold my laundry?
Speaker 3: Well, I wouldn't get your checkbook out just yet. Mid
Speaker 3: twenty twenty six is their target for repurposing lines for
Speaker 3: low volume internal use. Tesla wants to be its own,
Speaker 3: first and biggest customer.
Speaker 2: That makes total sense, eat your own dog food, as
Speaker 2: they say in.
Speaker 3: Software, or use your own robot. Then they are projecting
Speaker 3: ramping up to actual customer deliveries in late twenty twenty
Speaker 3: six or early twenty twenty seven. The volume targets they've
Speaker 3: set are staggering. Fremont is looking at fifty to one
Speaker 3: hundred thousand.
Speaker 2: Units initially, and the Giga Texas facility.
Speaker 3: Millions long term they are eyeing millions of units out
Speaker 3: of Texas. And this brings us to the cost factor.
Speaker 3: This is the part that blows my mind every single
Speaker 3: time I look at the economic church, the.
Speaker 2: Cost collapse phenomenon. Let's dig into that.
Speaker 3: As recursion scales up, the cost to produce each unit
Speaker 3: is projected to absolutely plummet. We are talking about a
Speaker 3: target range of twenty to thirty thousand dollars per robot.
Speaker 2: That is, I mean, that's comparable to a pretty cheap
Speaker 2: compact car.
Speaker 3: Actually it's cheaper than most new cars today. And remember
Speaker 3: a car sits idle in your driveway ninety percent of
Speaker 3: the time. This robot works twenty four to seven. The
Speaker 3: value proposition is just off the charts. The cost collapse
Speaker 3: happens because you are permanently removing human labor from the
Speaker 3: production equation.
Speaker 2: If the robot that is building the row doesn't draw salary,
Speaker 2: doesn't need health insurance, the cost of the new robot
Speaker 2: basically approaches the raw cost of the materials.
Speaker 3: And the energy to run the machines. That's it, that's
Speaker 3: the bottom line. If you take human wages completely out
Speaker 3: of the cost of goods sold, you are left with
Speaker 3: baseline commodity prices.
Speaker 2: We've talked a lot about California and Texas, but we
Speaker 2: really need to look east. The ecosystem in China is
Speaker 2: moving at an absolute breakneck pace.
Speaker 3: Relentless. That's the only word for the Chinese industrial sector
Speaker 3: right now. If the US companies are innovating heavily on
Speaker 3: the software models, China is innovating on the sheer scale
Speaker 3: of deployment.
Speaker 2: Let's talk about Unitree. They're a massive player here.
Speaker 3: Unitree has already deployed their G one humanoids on actual
Speaker 3: factory production lines in Shenzen. It isn't a controlled lab environment.
Speaker 3: This is Shenzen. It's the manufacturing capital of the world.
Speaker 2: And they are using this system called the Uniform LM
Speaker 2: X one zero.
Speaker 3: That's their proprietary embodied AI architecture. And the visual evidence
Speaker 3: we have from these factory floors is really strike. We
Speaker 3: see complex by manual manipulation, meaning the robot is using
Speaker 3: two hands together cooperatively to screw in tiny components, assemble subassemblies,
Speaker 3: and wire up actuators.
Speaker 2: Wiring is notoriously hard for robots, isn't it. It's floppy,
Speaker 2: it bends, it's unpredictable, it.
Speaker 3: Is incredibly difficult. A rigid metal part is easy for
Speaker 3: a robot to grab. A wire moves out of the way.
Speaker 3: Seeing a robot handle flexible cables with that level of
Speaker 3: precision is a major industry milestone. It signals a hard
Speaker 3: transition from cool tech demos to actual operational production.
Speaker 2: And what about their pricing.
Speaker 3: China always competes fiercely on cost. Currently, that level of
Speaker 3: capability is priced around ninety to one hundred thousand dollars,
Speaker 3: but they're scaling is pushing that number down extremely fast.
Speaker 3: They are actively projecting shipments of ten to twenty thousand
Speaker 3: units in twenty twenty six.
Speaker 2: Low I have to mention the Spring Festival showcases. We
Speaker 2: all saw online the videos of the robots doing backflips
Speaker 2: and kung fu. It felt a bit like a circus
Speaker 2: act to me.
Speaker 3: It's fantastic marketing, isn't it. But it serves a very
Speaker 3: real dual per pose. Yes, it looks incredibly cool on
Speaker 3: social media and gets the public excited about the tech,
Speaker 3: but it's also an extreme stress test.
Speaker 2: How so a backflip isn't building a car.
Speaker 3: Think about the physics of a backflip for a machine
Speaker 3: that heavy think about the impact on the metal joints
Speaker 3: when it lands, the dynamic balance required in mid air,
Speaker 3: the speed of the micro adjustments the computer has to make.
Speaker 3: If a robot can land a solid backflip, it proves
Speaker 3: the dynamic capabilities of their actuators and their balance algorithms.
Speaker 3: It proves the hardware can handle sudden, very high force
Speaker 3: impacts without shattering.
Speaker 2: Ah. So if it can do kung fu, it definitely
Speaker 2: has the range of motion and the durability to install
Speaker 2: a heavy dashboard in a car chassis all day long.
Speaker 3: Exactly. It's a flex, but it's a deeply technical flex.
Speaker 3: It tells the entire industry, Hey, our motors are strong
Speaker 3: enough and our control systems are fast enough to handle
Speaker 3: anything you throw at them.
Speaker 2: And it's not just unitry over there. The notes also
Speaker 2: mention engine AI.
Speaker 3: Right, emerging players like engine AI are showing off some
Speaker 3: really ultra precise handling. We've seen viral demonstrations of them
Speaker 3: manipulating bare circuit boards and highly delicate sensors.
Speaker 2: Things that would just snap in half if the robots
Speaker 2: squeezed even a tiny bit too.
Speaker 3: Hard exactly, tasks that were previously deemed way too human
Speaker 3: because they required a genuine gentle touch and fine motor
Speaker 3: skills are now being fully automated.
Speaker 2: It's fascinating to see how this tech bleeds into the
Speaker 2: broader industry beyond just the robot companies making robots. We
Speaker 2: see Agility Robotics, for example, partnering up with Toyota.
Speaker 3: That's a huge validation for the space. Agility's digit robot
Speaker 3: is deployed right now at Toyota Canada SUV plans. But
Speaker 3: look at what it's actually doing. It's not welding frames.
Speaker 3: It's doing logistics, bin handling, navigating the line.
Speaker 2: Just moving stuff around the factory.
Speaker 3: It sounds a bit boring compared to a backflip, but
Speaker 3: material handling is a massive chunk of all factory labor.
Speaker 3: If the robot can reliably bring the right parts to
Speaker 3: the line at exactly the right time, the humans or
Speaker 3: the other robots can focus entirely on the complexes. It's
Speaker 3: all about optimizing the flow of materials.
Speaker 2: And Boston Dynamics they finally retired their old hydraulic at
Speaker 2: lists and went fully electric the electric aatlysts.
Speaker 3: Yes, they've deployed it to Hyundai and deep Mind their
Speaker 3: primary focus right now is connectivity. They are deeply integrating
Speaker 3: these robots with MAS that's manufacturing execution systems and WMS,
Speaker 3: the warehouse management systems.
Speaker 2: So the robot isn't just a standalone physical worker, it's
Speaker 2: an active node in the factory's digital network.
Speaker 3: Precisely, it knows exactly what to build and where to
Speaker 3: go because the factory software is telling it directly over
Speaker 3: the network. It's not just looking at a printed checklist.
Speaker 3: It is physically plugged into the brain of the factory.
Speaker 2: What about heavy industry, We have a note here about
Speaker 2: zoom Lean.
Speaker 3: This one is genuinely impressive. Zoomlean has demonstrated their humanoids
Speaker 3: actively assembling heavy.
Speaker 2: Excavators, excavators like giant earth moving construction equipment, huge.
Speaker 3: Machines, and the cycle time for the specific assembly task
Speaker 3: was six minutes. It really shows the crazy versus tility
Speaker 3: of the humanoid form factor. It works on tiny, delicate
Speaker 3: circuit boards and it works on giant hydraulic arms. It's
Speaker 3: the exact same basic shape, two arms, two legs, adapting
Speaker 3: to completely different scales of physical work.
Speaker 2: So looking at the global shipment estimates across all these companies.
Speaker 3: We're looking at tens of thousands of units deployed in
Speaker 3: twenty twenty six, hundreds of thousand by twenty twenty seven,
Speaker 3: and millions shortly after that. The adoption curve is practically vertical.
Speaker 2: Right now, I want to spend the rest of our
Speaker 2: time here on the broader implications, because when you talk
Speaker 2: about millions of robots entering the global workforce, specifically robots
Speaker 2: that can build more robots, we are talking about a
Speaker 2: complete transformation of the global economic model.
Speaker 3: We are moving from linear production lines to recursive lines.
Speaker 3: I truly believe this is the biggest shift in manufacturing
Speaker 3: since the invention of the assembly line itself.
Speaker 2: Define the difference for the listener, what is a linear
Speaker 2: line versus a recursive line.
Speaker 3: A linear line is fundamentally labor capped. You have one
Speaker 3: hundred physical stations. You need one hundred people. If you
Speaker 3: want to run a night shift to double production, you
Speaker 3: need to find and hire another one hundred people. It
Speaker 3: is inherently wage bound. You have to pay all those people,
Speaker 3: and historically wages tend to go up.
Speaker 2: And a recursive line.
Speaker 3: The recursive line operates twenty four to seven. There is
Speaker 3: zero physical fatigue, there are no shift changes, no bathroom breaks,
Speaker 3: and critically, there is instant knowledge transfer.
Speaker 2: That's the software a piece coming back into play.
Speaker 1: Right.
Speaker 3: If you hire a brand new human worker, you have
Speaker 3: to train them. It takes weeks or months before they
Speaker 3: are fully up to speed. If you build a new robot,
Speaker 3: you just upload the latest software build. It starts its
Speaker 3: very first day on the job with the accumulated experience
Speaker 3: of every single robot that came before it.
Speaker 2: This directly leads to that cost collapse phenomenon we touched
Speaker 2: on earlier.
Speaker 3: It changes the very definition of cost in macroeconomics. As
Speaker 3: we said, if labor costs vanish from the equation, you
Speaker 3: enter an era of what economists are calling abundance.
Speaker 2: Abundance that's a really big It sounds almost utopian.
Speaker 3: Think about it from a purely economic standpoint. Why are
Speaker 3: houses so expensive to build? Labor? Why is infrastructure repair
Speaker 3: expensive labor? Why is deep earth mining expensive labor? And
Speaker 3: the danger associated with it? If you have robots constructing
Speaker 3: massive solar farms, running deep mining operations, and repairing highway bridges,
Speaker 3: and those robots only cost the price of raw materials,
Speaker 3: the fundamental cost of living could change completely.
Speaker 2: We could send them into highly hazardous environments without a
Speaker 2: second thought.
Speaker 3: Nuclear disaster cleanups, deep sea mining, space construction on the Moon,
Speaker 3: or Mars, environments where human presence is either far too
Speaker 3: dangerous or physically impossible. Suddenly these massive engineering projects become
Speaker 3: economically viable because you aren't risking human lives or paying
Speaker 3: hazard pay.
Speaker 2: But and there is always a big butt with these things.
Speaker 3: There is always a butt. You can't change the foundational
Speaker 3: bedrock of the global economy without some major cracks appearing.
Speaker 2: The human workforce. The GLOS global physical workforce is roughly
Speaker 2: two billion people right now, and.
Speaker 3: If the current scaling trend holds, humanoids have the potential
Speaker 3: to completely surpass that number by twenty thirty.
Speaker 2: That is a staggering amount of displacement. We are talking
Speaker 2: about the basic structure of the labor market. Just dissolving.
Speaker 3: It is a fundamental societal shift. We will see massive
Speaker 3: job displacement, that is an absolute certainty. We really can't
Speaker 3: pretend otherwise. But we will also see emerging roles in oversight,
Speaker 3: system design and fleet maintenance. The real question is will
Speaker 3: the new jobs appear as fast as the old manual
Speaker 3: jobs disappear.
Speaker 2: That's the multi trillion dollar question for the next decade.
Speaker 2: And it's not just about jobs, right. There are physical
Speaker 2: constraints to this growth. We can't just snap our fingers
Speaker 2: and wish billions of complex robots into existence.
Speaker 3: Resource limits are very real. These robots and the computers
Speaker 3: that train them eat a massive amount of energy. The
Speaker 3: power requirements for training these huge AI models and running
Speaker 3: the physical compute farms are staggering. We need more electricity globally,
Speaker 3: need to upgrade the grid significantly.
Speaker 2: And the physical supply chain.
Speaker 3: Rare earth elements are the major bottleneck, things like neodymium, dysprosium, urbium.
Speaker 3: These specific elements are absolutely essential for the high performance
Speaker 3: magnets using the robot's actuators. You simply can't build a compact,
Speaker 3: high torque robot.
Speaker 2: Motor without them, and the geopolitical map for those specific
Speaker 2: elements is pretty complicated.
Speaker 3: Complicated is putting it politely. China currently controls a vast
Speaker 3: majority of the processing capacity for rare earth minerals. This
Speaker 3: entire technology sector is the center of a very intense
Speaker 3: race for dominance between the US and China. It's not
Speaker 3: just about who employees the smartest AI engineers or who
Speaker 3: builds the best robot, it's about who actually controls the
Speaker 3: raw materials required to build them at scale.
Speaker 2: And then there's the issue of safety alignment.
Speaker 3: As physical autonomy increases, ensuring the robot does what you
Speaker 3: want it to do and strictly only what you want
Speaker 3: is a massive technical challenge. A bug in a digital
Speaker 3: chatbot writes a weird poem or gives bad advice. A
Speaker 3: bug in a recursive manufacturing robot could shut down an
Speaker 3: entire supply chain, break millions of dollars of equipment, or
Speaker 3: cause severe physical damage to the factory.
Speaker 2: So to try and wrap this all up twenty twenty six,
Speaker 2: you see this as the definitive pivot point.
Speaker 3: It really is. We have officially moved from the hype cycle,
Speaker 3: the cool tech videos, the lofty promises, and sci fi
Speaker 3: dreams to a very pragmatic, recursive reality. Factories worldwide are
Speaker 3: currently utilizing humanoids to handle the parts of their own lineage.
Speaker 3: The loop is closing as we speak.
Speaker 2: The production curves are going completely vertical.
Speaker 3: And as they do, they're going to redefine our economies.
Speaker 3: The future isn't a theoretical white paper anymore. You can
Speaker 3: book a flight to California or Texas or Shenzend today
Speaker 3: and watch it happening. It is unfolding in the assembly
Speaker 3: of individual robotic limbs right now.
Speaker 2: It's a fascinating, slightly terrifying, and incredibly exciting time to
Speaker 2: be alive. Before we go, though, just a thought for
Speaker 2: everyone listening to chew On. If the AI gets smart
Speaker 2: enough to assemble the robots, how long until the AI
Speaker 2: starts designing the hardware iterations themselves entirely bypassing the human engineers.
Speaker 3: That is the next exponential.
Speaker 2: We'll leave it there. That's the deep dive on recursive manufacturing.
Speaker 2: Thanks for listening to this one. We'll see you next time.