Imagine a factory, but not a factory like you've
ever seen before. Right. It's pitch black inside.
I mean, completely dark. There are no lights
on the ceiling because, well, nobody inside needs
to see. There's no air conditioning. No AC because
nobody needs to be comfortable. There are no
break rooms, no bathrooms, no parking lots outside.
Nothing. It's the ultimate efficiency engine,
the architecture of the machine age taken to
its logical conclusion. Exactly. It is a company
with absolutely positively zero humans on the
payroll. Not a single one. None. It is just a
fleet of autonomous agents. Some are, you know,
code running on servers. Some are robots moving
boxes on the floor. Now, here's the question
that keeps me up at night, and it's the hook
for today's deep dive. In that building, in that
dark, silent factory, how do you calculate the
wages? It sounds like a philosophical riddle,
doesn't it? It really does. Like if a tree falls
in the forest. But in 2026, and this is the key,
this isn't philosophy anymore. This is hard -nosed
accounting and this is business. It really, really
is. If you have a robot moving boxes and an AI
writing code. They're both working. But one is
burning calories or, well, joules lifting heavy
things, fighting gravity, friction and inertia.
It's dealing with the messy physical world. Right.
The other is just thinking, processing electrons.
So how do you compare those two values? How do
you write a paycheck for a robot? That is the
trillion dollar question. And honestly, up until
very recently, nobody had a good answer. No.
We were trying to apply human economics to machine
labor and the math. It just didn't work. We were
trying to use dollars to measure thermodynamics.
It's like trying to measure temperature with
a ruler. Well, today we are going to fix the
math. We are doing a deep dive into a brand new
academic paper that honestly just blew my mind.
It is titled Jewelwork Robotics, Thermodynamic
Wage Frameworks for Embodied AR. And before we
go any further, we need to set the stage properly.
This is the Read Multiplex podcast. We are bringing
you the absolute cutting edge of what is happening
in the Read Multiplex ecosystem. This isn't just,
you know, news. This is a glimpse into the future
that is being written right now. That is right.
And the author of this paper is someone we follow
very, very closely. Brian Romella. The chairman
of the Zero Human Company. The man himself. We've
covered Brian's work before. We did a deep dive
on his original jewel work concept a little while
back, which was, I mean, it was fascinating in
its own right. But I have to say, looking at
this new paper, this feels like a new peak in
brilliance from him. I agree. It's dense, it's
mathematical, but it's incredibly visionary.
It really is. You know, you look at the landscape
of AI development right now. You have the big
labs in San Francisco, the massive tech giants
in China, the European regulators. They are all
building the engines. Right. They are building
the LLMs, the vision models, the diffusion models.
Yeah. But Rolemo, he's doing something different.
He is flying solo on this. He's not building
the engine. He's building the economy. Precisely.
None of the big tech giants have really caught
up to his understanding of what a zero human
company actually is or how it functions operationally.
They're still thinking about AI as a tool for
humans. It's an assistant. Right. an assistant.
Romell is thinking about AI as an entity that
exists without humans. He is operating at a level
of clarity and speed that is just, frankly, unmatched
in the industry right now. While everyone else
is arguing about copyright or how to make the
chatbot sound more empathetic, he's writing the
tax code for the future of automation. That is
such a crucial distinction, and it highlights
why we do this show. If you want to know where
the ball is going, you look at what Romell is
writing. So our mission today is to unpack this
paper. We need to understand how we move from
paying for abstract thought, which was the original
jewel work concept, to paying for action for
robots moving in the real world. And that transition
is incredibly difficult. I mean, you can't overstate
it. It is the difference between a spreadsheet
and a construction site. Yeah. The physics are
completely different. The stakes are different.
So here is our roadmap for the next hour or so.
We are going to start by reviewing the concept
of the Zero Human Company and the original jewel
work to set the stage. A quick refresher. Exactly.
Then we're going to run headfirst into a problem
called Moravec's Paradox, which explains why
moving your arm is actually harder for a computer
than playing Grandmaster -level chess. It's such
a counterintuitive concept, and I can't wait
to get into that. It flips everything we think
we know about intelligence on its head. It really
does. Then things get historical. We're going
to see how Rommel connects 2026 robotics all
the way back to factory management in 1911. We're
talking about men with stopwatches and early
industrial theory. Which is just classic Rommel,
finding the answer in the past. It turns out
the past has the answer to the future. And finally,
we're going to break down the monster equation,
the JWR formula variable by variable. We are
going to do the math behind the money. It is
a comprehensive framework. It's dense, but it's
crucial. We're going to make sure everyone listening
can actually visualize how this wage is calculated.
It is. So let's dive in. Let's do it. Okay, so
before we get to the robots, we need to quickly
recap where we left off. We previously discussed
the original Joule work metric, or JW. For the
listeners who might have missed that deep dive
or just, you know, need a refresher, can you
give us the explain like I'm five? Sure. So in
the previous paper, Rommel established the fundamental
currency of the AI economy. The core idea was
that we need to move away from fiat currency.
Dollars, euros, yen. These are subject to inflation.
They're subject to human politics, central banks,
interest rates. They're soft. They're very soft.
They're based on faith. on collective agreement.
And if you have a machine economy, you want hard
money. You want something that can't be manipulated
by a Federal Reserve chairman waking up on the
wrong side of the bed. Exactly. You want money
grounded in physics, in thermodynamics. The laws
of the universe don't change. Right. The original
formula was deceptively simple. JW is E times
kappa times W. Okay, break that down for us one
more time. Just the basics. Sure. Dollars is
energy consumed. Pretty straightforward. How
much electricity did you pull from the grid?
Kappa. is a normalization coefficient, basically
a standardizing number to make the math work
across different systems, different chip architectures.
So an NVIDIA chip and a Google TPU can be compared
fairly. That's the idea. And doodle -allers is
the work output, the useful result. So in plain
English, that basically means how much energy
did you burn to produce a useful result? Precisely.
It was designed for what we call abstract AI.
Okay. Think of chat GPT or an algorithm analyzing
stock trends or a compiler turning code into
software. It lives on a server. Its body is just
silicon chips. It has no physical presence. None.
The energy it consumes is purely electricity
for computation. Yeah. And in that world, the
math is relatively clean. The more energy you
put in, generally, the more thought or compute
you get out. It's a linear relationship, mostly.
You feed the server electricity, it spits out
tokens. More electricity, more tokens. Mostly.
But now, in this new paper, Ravel has thrown
a wrench in the gears. He's moved from the server
room to the factory floor. We are talking about
embodied AI. And this is where the jewel work
robotics. framework or such a WR comes in, because
as the paper points out, you cannot just use
the old formula for a robot. Right. It breaks
immediately the moment you try. But why not?
I mean, this is what I was thinking at first.
Energy is energy, isn't it? A jewel is a jewel,
whether it turns a fan or lights a bulb or runs
a processor. Physics doesn't distinguish between
a smart jewel and a dumb jewel. In physics, yes,
a jewel is a jewel. You're absolutely right.
But in economics, no. And that's the key distinction.
Physical reality is expensive. When you move
from processing data to processing matter, listing
a box, walking across a room, navigating a cluttered
hallway, the energy dynamics change completely.
It's the difference between thinking about running
a marathon and actually running one. Exactly.
Perfect analogy. Thinking about it costs a few
calories of glucose in your brain, a tiny amount
of energy. Negligible. Doing it costs thousands
of calories. It's a massive energy expenditure.
And this brings us to that concept you mentioned
in the intro. Moravec's paradox. The paper cites
this extensively as the core reason why we need
a new equation. I love this paradox because it
makes me feel better about being bad at math.
Can you break it down? I think we all feel that
way. Yeah. It's one of the most fascinating things
in computer science. Hans Moravec identified
this back in 1988. He was looking at the progress
of AI and he noticed something weird. Okay. He
noticed that high -level reasoning things humans
find hard, like calculus, playing chess, analyzing
complex statistics, proving theorems, is actually
very easy for a computer. Computationally cheap.
Very cheap. requires very little computational
energy relative to the output. Right. My pocket
calculator from 1995 can do math better than
I can. A basic chess app on my phone can beat
a grandmaster. Those things are governed by strict
logic rules, which computers love. Exactly. We
solved intelligence in that sense decades ago.
But the things humans find easy walking, recognizing
a face, picking up a cup without crushing it,
tying your shoelaces, folding a towel. The things
a toddler can do. The things a toddler can master.
Those sensorimotor skills are unbelievably difficult
for AI. Computationally expensive. Why is that?
Why can a computer simulate a nuclear explosion
easier than it can fold a shirt? That just seems
so backwards. Evolution. That is the answer.
We have had millions and millions of years to
optimize our motor cortex. Walking is hardware
accelerated in our brains. It's baked in. I don't
think about walking. You don't even think about
it. You'll calculate the friction coefficient
of the floor before you take a step. You don't
consciously adjust the tension in your calf muscles
to maintain balance. You just walk. But a robot
does. A robot has to do it all from scratch,
every single millisecond. For a robot, walking
across a room involves calculating the friction
of the floor, the balance of its center of gravity,
processing all the visual data from its cameras
to avoid obstacles, sending the exact torque
required for each joint in the leg. It is a data
firehook. It requires massive amounts of computation
just to stay upright and massive amounts of physical
energy to drive the motors. So if you just use
the old jewel work formula. A robot doing simple
physical labor would look incredibly inefficient
compared to an AI doing complex math. Correct.
The robot would be burning thousands of joules
just to stand up and walk to a shelf. The AI
would burn a fraction of that to solve a complex
theorem. If you paid them strictly on the old
metric. The math breaks down. It does. The robot
would be overpaid for what looks like energy
waste. Or the system simply wouldn't account
for the difficulty of the physical world. You'd
have an accounting error that breaks the company.
It would be like paying a theoretical physicist
a million dollars a year and paying a construction
worker 10 cents because the physicist is thinking
harder. But if you need a building built, that
math doesn't work. You need the building. Exactly.
You have to value the physical actuation. You
have to value the work done against gravity.
So Rowemmel had to create a new system. a way
to value that physical struggle a way to say
hey moving this box is actually worth more than
writing that poem even if the poem seems smarter
he did he calls it jewel work robotics or rojwr
and what i love about this is that he didn't
throw away the old system he built a hybrid it's
like a layer cake it is the framework preserves
the cognitive jewel work for the brain of the
robot the planning and thinking part but it adds
a massive new layer for the body It's designed
specifically for these zero human companies where
you need to balance the books between your software
agents and your hardware agents. And to build
this new layer, Rommel didn't just look at code.
He looked at history. This is the part of the
paper that really surprised me. I expected citations
from 2024, 2025, maybe some obscure robotics
journals from MIT. But he's quoting guys from
1911. This is vintage Rommel brilliance. This
is why you read him. He bridges 2026 technology
with early 20th century industrial science. He
realizes that we have actually solved the problem
of physical efficiency before, just not for robots.
We solved it for humans. We did. You're talking
about Taylorism. Frederick Taylor, 1911. The
principles of scientific management. A man with
a stopwatch, the original efficiency guru. The
man with the stopwatch. Taylor was obsessed with
efficiency. He would stand in factories, steel
mills, bricklaying sites, and he would time every
single movement a worker made. Every single one.
He wanted to find the one best way to do a job.
He didn't want you to just shovel coal. He wanted
you to shovel coal with the exact perfect arc
to maximize the load and minimize the fatigue.
He broke it all down. I remember reading about
this in history class. It was, well, it was hated,
wasn't it? People did not like this. Oh, absolutely.
It was incredibly controversial. Workers felt
like they were being treated like machines. Dehumanized.
Completely. They were being micromanaged down
to the second. It led to strikes. It led to congressional
hearings. It was seen as the mechanization of
the human soul. But Rommel's insight here is
that what's controversial for humans is perfect
for machines. Exactly. Robots don't get offended
if you time them. Robots don't get stressed out
by efficiency quotas. They don't need dignity.
They need data. Taylorism is the native language
of robotics. It was a philosophy waiting for
the right species to arrive. That is a great
point. So applying Taylor's strict efficiency
rules to robots is actually ethically sound.
It's what they are built for. You're not hurting
its feelings. Precisely. And he goes even deeper.
He brings in the Gilbris, Frank, and Lillian
Gilbreth. The Gilbris, right. They were contemporaries
of Taylor, and they came up with something called
Thurbligs. Thurbligs. I love that word. It sounds
like a sci -fi creature from Star Trek. Captain,
the Thurbligs are attacking the warp core. It
does. But it's actually just Gilbreth spelled
backwards with the transposed. Oh, I never realized
that. That's clever. I've been saying that word
for years and never made that connection. Yes.
And a Thurblig is a fundamental unit of motion.
The Gilbreths realized that any complex task
like bricklaying or surgery or assembling a toaster
could be broken down into about 18 elemental
movements. Only 18 for everything. Roughly, yes.
That was their theory. What kind of movements?
Give us some examples. Search, grasp, hold, release,
inspect, assemble, transport loaded, transport
empty, plan, rest. Very basic fundamental actions.
So they would analyze a worker and say, You're
using too many thurblays to assemble that toaster.
Your search time is too long. Exactly. They were
looking for surplus thurblays. Wasted motion.
If you have to reach too far to grab a screwdriver,
that's a wasted transquart thurblay. If you have
to fumble to get a grip because the handle is
slippery, that's a wasted grasp. Thurblay. If
you have to look around to find the screw, that's
a wasted search, Thurblake. It's all about eliminating
waste. And Rommel is applying this to robots.
He is. He argues that just as ergonomics, which
was pioneered by people like Grandjean in 1980,
helped humans avoid physical strain, jewelwork
robotics helps robots avoid energy strain. Energy
strain. I like that. The framework isn't just
code. It is an economic philosophy rooted in
this physical motion economy. It rewards the
robot for reducing its Thurblakes. for being
more efficient in its movements. So instead of
a manager shouting, move faster, the code itself
incentivizes the robot to move smarter. Move
with less energy. Move with more purpose. Eliminate
the jitter. Eliminate the hesitation. If a robot
reaches for a box and misses slightly, then has
to correct, that's a wasted therblig. That's
energy burn with no value. The JWR system penalizes
that. It connects perfectly. So we have the modern
problem, Moravec's paradox, or why is moving
so hard, and the historical solution time motion
study, or how do we measure movement? Now let's
see how Rommel actually turns that into math.
Let's get to the equation. The paper calls this
the integrated equation, but I'm calling it the
monster. It is huge. When you first see it, it
has parentheses, inside brackets, inside equations.
It looks like something you'd see on the blackboard
in Good Will Hunting. It is complex, but every
piece serves a specific economic purpose. Let
me read out the full structure, and then we will
dismantle it piece by piece. Are you ready? Hit
me. GWR cognitive, Chespie times OMA times MM
times 10F plus SBU. Okay. That is a mouthful.
It is. Let's unpack this slowly. I heard JW cognitive
at the start. That's the brain, right? That's
the part we already know. Right. That is the
first component, the hybrid structure. JW cognitive
is the robot's thinking. Even a robot lifting
a box has to plan the path. It has to process
the image of the box. That computation is valued
using the standard Joule work formula we talked
about in the last episode. Okay, so the thinking
gets paid at the standard rate. That seems fair.
No changes there. Correct. But then we add the
body component. That's the physical times the
physical. Here, e -physical is the energy used
for actuation, the motor spinning, the hydraulics
pumping, and the sensing. Like the LIDAR lasers
firing or the cameras processing frames. And
this is usually a much bigger number than the
cognitive energy, right? Based on what we said
about Moravec's paradox. Orders of magnitude
bigger. Moving is expensive. A brain firing neurons
is cheap. A bicep curling a dumbbell is expensive.
Same for robots. A servo holding a 10 kilogram
weight against gravity consumes constant power.
Okay, so we add the brain value and the body
value. But then you mentioned these multipliers.
REM and MEM. These sound like the secret sauce.
This is where the new stuff really kicks in.
They are. These are the reality checks that adjust
the wage based on how well the robot handles
the physical world. Let's start with RM, the
Robotics Efficiency Multiplier. What does REM
do? REM is explicitly designed to mitigate Moravec's
paradox. It's a scalar value between 0 .1 and
1 .0. So it's a penalty. It reduces the wage.
It can't go above 1. Initially, yes. When a robot
first tries... A new, difficult, physical task.
Say, folding a fitted sheet, which is the nemesis
of all robots and humans. The ultimate test.
It is. The robot is going to be incredibly inefficient.
It will use way too much energy for the result.
So the REM starts low. It dampens the value.
So the robot doesn't get paid for flailing around.
Exactly. Just because you burned energy doesn't
mean you created value. That's a core principle
here. Yeah. But as the robot optimizes its sensor
fusion, as it learns the physics of the sheet,
the REM increases. It rewards the robot for learning
to move efficiently. So it's an incentive. It's
an incentive structure for machine learning.
It basically says we will pay you more as you
get better at understanding the world. That makes
sense. It's like a rookie wage versus a veteran
wage. The veteran gets paid the full rate because
they know what they're doing. They're not making
rookie mistakes. In a way, yes. A very good analogy.
And you have the MEM. the Motion Economy Multiplier.
This is the one derived directly from the Gilbreths
and their therbligs. And this ranges from 0 .5
to 2 .0, so this one can actually be a bonus.
It can double the wage. Yes. This is a performance
multiplier. It rewards minimal motion paths.
If a robot can pick up a box in one smooth, continuous
arc, like a ballet dancer or master craftsman,
it gets a high MEMS score. Above 1 .0. Right.
If it fidgets, readjusts its grip three times,
or takes a jagged, inefficient path, the MEM
drops below 1 .0 and penalizes the wage. I love
the idea of a robot getting a bonus for being
graceful, for having good form. It's not just
aesthetics. It's thermodynamics. Graceful movement
is usually the most energy -efficient movement.
Smooth curves use less acceleration and deceleration
than jagged lines. Stopping and starting is what
burns energy. Momentum is your friend. The mem
incentivizes the robot to find flow. So we have
the brain, the body, the experience level, which
is RM, and the gracefulness, which is mem. But
then there was that negative part, ROF. The overhead
factor. This sounded ominous. This is where the
cold, hard reality of business comes in. The
overhead factor is Ravel saying, you do not get
paid for downtime. You do not get paid for existing.
Ouch. What counts as downtime for a robot? I
mean, they don't take coffee breaks. The paper
defines three components to the overhead factor.
CTF, ITF, and TCF. Okay, let's break those down.
CTF. Charging time fraction. This is crucial.
It's two charge divided by the evaluation period,
which the paper sets as 15 minutes. The paper
is very specific. Charging energy is tracked.
It is accounted for in the total consumption,
but it is excluded from value attribution. So
the robot doesn't earn wages for sleeping. Exactly.
If a robot has a bad battery and has to charge
every 10 minutes, its CTF goes up, the overhead
factor goes up, and its total wage goes down.
It's an economic penalty for bad hardware. It
incentivizes the company to buy robots with better
batteries. Brutal, but fair. If I hired a guy
who slept for 45 minutes of every hour, I wouldn't
pay him for a full hour. What's ITF? Idle Time
Fraction. Time spent standing still, waiting
for instructions. Again, if you aren't working,
you aren't generating value. This incentivizes
the central management system to keep the robots
busy, to optimize the workflow. No standing around.
And TCF. Travel Cost Fraction. This is the cost
of getting from point A to point B. The formula
is a VST times 8A total. Basically, Rommel classifies
commuting as overhead. Oh, I feel that. Commuting
is the worst part of any job. It is. If the robot
has to walk 10 minutes to get to the box, that
walking time is a cost, not value. The value
is generated when it moves the box. Walking to
the box is just overhead. That is fascinating.
It forces the Zero Human Company to optimize
the layout of the warehouse itself. If you put
the charging stations too far away or if the
layout is a maze, your robots lose value walking
to them. Exactly. The JWR framework isn't just
measuring the robot. It's measuring the efficiency
of the entire facility. If your warehouse has
a bad layout, your JWR scores will plummet because
your TCF will be too high. It punishes bad architecture.
It's a system -wide check. And finally, there
was a little plus sign at the end. SB. The scalability
bonus. This is an additive term, like 0 .1. It's
a bonus for synergy. If two robots coordinate
perfectly to lift a heavy object that neither
could lift alone, they get a small bump in value.
It encourages swarm behavior. It encourages cooperation.
It rewards agents for working together rather
than competing for resources. It builds a collaborative
economy. Wow. It really covers everything. It's
not just... Did you do the job? It's how smart
were you? How smooth were you? How efficient
was your battery? How much time did you waste
walking? And did you play nice with others? It
is a holistic assessment of labor value. It turns
the robot's existence into a balance sheet. Every
action, every second is accounted for. I think
we need to see this in action. The math is great,
but let's make it real. The paper provides a
concrete example of a warehouse robot. Let's
walk through that. I want to hear the numbers.
Right. Let's imagine a standard humanoid robot
in a zero -human company. Let's call him Unit
734. His job is a manipulation task. Let's say
identifying a specific package on a shelf and
moving it to a conveyor belt. Okay, Unit 734
is ready. Step one, the brain. How much does
it cost to think about this? The robot has to
look at the shelf, recognize the box, and plan
its arm movement. The paper assigns a path planning
energy of 50 joules to this. 50 joules. Okay,
that sounds small. For context, what is that?
It is small. It's about the energy of a very
small LED light bulb running for a few seconds.
We apply the cognitive JW formula to this. $50
text joules times 22, that's the normalization
coefficient to the active emperor, times 0 .455,
the work output normalized. And the result? 45
JW. Okay, 45. Keep that number in mind. Now the
hard part, step two, the body. Now the heavy
lifting starts. To actually move its arm, fight
gravity, grip the box, and rotate its torso,
the robot consumes 400 joules. So almost 10 times
the energy of thinking. Correct. This is the
reality of the physical world. So we calculate
the base physical value, $400 times 2 times 0
.22, the physical work output. That gives us
160. So the body is generating 160. The brain
is generating 45. If we stop there, the robot
would look at it. It's doing great. It's earning
205 total. But we don't stop there. We have to
apply the reality checks. Step three. The multipliers.
Here we go. First, REMM, the robotics efficiency
multiplier. The paper sets REMM at 0 .5. This
implies it's a difficult task. Moravec's paradox
is kicking in. The robot isn't fully optimized
yet. Maybe the box is slippery or the shelf is
high. It's still a rookie. It's a rookie. So
that 160 drops to 80. Ouch. Cut in half. The
new guy tax. That's a big hit. But then mem.
The emotion economy multiplier. The paper says
the robot moved very efficiently. It didn't stutter.
It was smooth. It gets a mem of 1 .2. So that
80 bones back up to 96. Okay. Recovering some
value there thanks to good form. A 20 % bonus
for Grace. Now. the overhead. The robot spent
1 .5 minutes charging and some time idling during
this 15 minute cycle. The calculated overhead
factor, OF, is 0 .25. That's a 25 % tax on its
productive time. So we take 25 % off the physical
value. Right. So our 96 drops to 72. And the
final tally. What does Unit 734 take home for
this task? We take the cognitive wage, 45, plus
the adjusted physical wage, 72, plus a small
scalability bonus for not getting in anyone's
way, let's say, PO .1. The total is 117 .1 EIDFWR.
117 .1 okay so knowing that number What does
it mean? Is that a minimum wage, a high salary?
Can Unit 734 buy a nice virtual house with that?
The paper gives us a baseline for context. It
mentions that a standard baseline compensation
for an embodied agent is roughly 10 ,000 EDI
JAWR per 15 -minute interval. Wait, 10 ,000 per
15 minutes, and our single task was 117. Unit
734 is broke. It's not going to make rent. Well,
remember, a robot moves fast. It would be doing
many, many tasks in 15 minutes. But yes, the
baseline is set high. The paper notes this reflects
the fact that robots have a 5 to 10 times energy
premium over abstract AI. Living in the real
world is expensive, so the wage has to be sire
to cover that energy cost. If the robot only
earns 117 every 15 minutes, it won't be able
to pay for the electricity it consumes. It will
starve. That makes sense. You have to pay the
robot enough to buy the electricity it needs
to survive. If it's inefficient, its wages don't
cover its energy bill and it effectively dies.
It gets taken offline. Exactly. It's evolutionary
economics. Yeah. Survival of the fittest, most
efficient robot. Now, Rall didn't just write
this equation on a whiteboard and hope for the
best. He actually simulated it. He did. Section
5 of the paper covers simulation and validation.
He ran a simulation of a representative warehouse
robotic agent running for 1 ,000 operational
cycles. And he compared two groups, right? A
control group and a JWR group. Yes. The control
group was the unadjusted model. It didn't use
the JWR feedback loop. It just worked. It didn't
care about therbligs or overhead factors. It
just moved boxes, burning energy as it saw fit.
And what happened to it? It crashed and burned.
By cycle 150, the model had depleted its resources.
When you say depleted its resources, what does
that mean in the simulation? It means its cumulative
energy expenditure exceeded its cumulative value
generation. It went into energy debt. bankrupted
itself why escalating inefficiencies without
the feedback loop of the multipliers without
the pain of the overhead factor or the reward
of the mem the robot didn't optimize It wasted
motion. It idled too much. It didn't manage its
charging cycles efficiently. It effectively went
bankrupt in energy terms. It burned out. It was
like a worker who never sleeps and runs everywhere,
but never actually gets anything done. Just pure
waste. Exactly. And the JWR group. Tell me about
the JWR group. How did it do? The JWR group used
the framework. It had that algorithmic pressure
to increase its REM and MEM scores. It wanted
a higher wage, so to speak. The self -correction,
the drive to improve. Exactly. The paper even
includes the pseudocode logic for this. The robot
compares its actual energy usage to an optimal
energy target. If it's overspending, it adjusts
its behavior. It slows down to be smoother. It
calculates a better path. It learns. It learns
to be lean. And the result was a 30 % energy
reduction overall. The JWR robot reached sustainability.
and was still running strong at Cycle 600. It
found equilibrium. That is the difference between
a company that goes bust in a year and a company
that dominates the market for a decade. It's
the difference between waste and sustainability.
It is. And that's why this paper is so important.
It proves that accounting isn't just about recording
history. It's about shaping behavior. If you
measure the right things, like JWR does, you
get efficient robots. If you measure the wrong
things, or nothing at all, you get waste. This
brings us to the bigger picture, the implications
for these zero -human companies. Because Rommels
isn't just trying to save a few batteries. He's
building a corporate structure for a new kind
of entity. This is where we see the vision of
the zero -human company really taking shape.
This is the operating system. One question the
paper addresses, why go through all this trouble?
Why not just measure FLOPs, floating point operations
per second, like we do for computers? Or why
not just measure carbon footprint? Everyone talks
about carbon footprint. It's a great question,
and he tackles it head on. The paper argues that
FLOPs is useless for robots because it ignores
physical reality. You're going to have high FLOPs
thinking really hard while standing perfectly
still. You're burning brain power but moving
nothing. That's zero value in a warehouse. It
measures thought, not action. Exactly. And carbon
footprint. That seems like a good one. It's about
efficiency. It's an environmental metric, not
an economic one. It tells you if the work was
clean, but it doesn't tell you if the work was
variable. You can spend zero carbon to do absolutely
nothing. That's very green, but it's a terrible
business. It doesn't generate revenue. So JWR
is the Goldilocks metric, not too abstract, not
too narrow. It sits right in the middle. It has
the thermodynamic grounding, so it respects physics.
But it attaches economic value to the output.
It connects energy to profit. The paper also
mentioned something interesting about the corporate
structure, the idea of subsidization. This sounded
almost socialist for robots, which was a funny
thought. In a way, yes. In a zero -human company,
you might have a situation where the abstract
AI, the brains running the servers, is incredibly
profitable because it's so energy efficient it
costs pennies to run. But the embodied AI, the
robots, might struggle to break even because
moving is so expensive. So the brains subsidize
the brawn. Exactly. The JWR framework allows
the company to transparently see that transfer
of value. The server farm pays for the warehouse
floor. Because without the warehouse floor, the
product doesn't move and nobody makes money.
It creates an internal economy where the different
departments support each other based on energy
flows. It also creates a deflationary economic
model, doesn't it? It does. Think about the MIM,
the motion economy multiplier. The system inherently
rewards innovation. If a robot or the AI designing
the robot figures out how to do a task with 10
% less energy, its value goes up. But the cost
of the company goes down. Which drives down the
cost of goods for the consumer. Precisely. It
turns efficiency into a competitive sport for
the algorithms. And unlike human wages, which
tend to be sticky or go up, the energy cost per
task will continuously go down as the system
learns. It's a deflationary spiral by design.
But are there risks? We talked about Taylorism
earlier. Humans hated Taylorism because it could
be dangerous. If you were trying to shave seconds
off a task, you might cut corners on safety.
Does that apply to robots? Absolutely. The paper
has a section on ethical considerations. Romo
warns about excessive optimization. He's very
clear -eyed about the potential downsides. If
the MEM is too aggressive, if the bonus for being
graceful is too high. If the robot is obsessed
with a high MEM score, minimal motion, it might
prioritize speed over safety. We might swing
a heavy arm too fast to save momentum, risking
a collision with a wall or another robot just
to save a few jewels. So the framework needs
guardrails. You can't just let it run free. Yes.
You can't just let the equation run wild. You
need constraints. Safety protocols that override
the economic incentive. But on the flip side,
Romo points out that this system creates energy
equity. What does that mean? It means the valuation
is purely based on physics. Thermodynamics doesn't
care about your gender, your race, or who your
dad is. It removes all human bias from the compensation
model. A robot is paid exactly what it is worth
in energy terms. It's the ultimate meritocracy,
purely results -based. It's better or worse,
yes. And what about us, the humans? The paper
mentions workforce displacement. We can't talk
about automation without talking about jobs.
It does. And Romell is honest about this. He
doesn't sugarcoat it. In a zero -human company
context, a framework like JWR could accelerate
displacement. Because it makes the robots so
efficient, it quantifies their value so perfectly.
Exactly. If you have a system that mathematically
perfects the efficiency of labor, humans simply
cannot compete on cost. We are biological. We
need sleep. We need food. We have bad backs.
In a pure JWR calculation, a human is a very
inefficient machine. That is the scary part.
It is. But he advocates for hybrid human -AI
integration during transition periods. He suggests
that while robots are great at the how, the motion,
the lifting, humans are still needed for the
why, the intuition, the strategy, the edge cases.
We are the architects. They are the builders.
We set the goals. For now. For now. That hangs
in the air a bit. It does. Which brings us to
the end of our depth dive. It is a lot to digest.
This is a big... big concept. It really is. It
changes everything. But I want to circle back
to why we are talking about this. This isn't
just about robots moving boxes. This is about
solving the accounting problem for the physical
automation of the world. Brian Rome has basically
written the tax code for the 21st century economy.
And again, we have to emphasize this is the Read
Multiplex podcast. We are showcasing this because
Rome is truly ahead of the curve here. He's thinking
on a different level. No AI giant. not the ones
in Silicon Valley, not the ones in China, has
a framework this detailed for a zero human company?
They're not even asking these questions yet.
They are building the engines. He is building
the economy. And those are two very different
things. You can have a Ferrari engine, but if
you don't have a road system and gas stations,
you aren't going anywhere. Rommel is building
the roads. So if you find this fascinating, and
you should, because it's going to determine the
price of everything you buy in 10 years. You
need to support Brian's work. He is charting
this territory solo. He's not backed by a multibillion
dollar corporation. Absolutely. This is independent
research at its most vital. Now, before we go,
I want to leave you with one final thought. The
paper has a small line at the very end about
future work. It's easy to miss, almost a throwaway
line. Oh, I know the one you're talking about.
It's the one that really keeps you up at night.
It mentions blockchain -based tokenization for
interoperable JWR assets. This is the wild part.
This is the next leap. Imagine a future where
these robots aren't just earning points in a
corporate database. Imagine they are earning
actual crypto tokens based on their JWR score.
A robot earns tokens for efficient labor. It
holds those tokens in its own digital wallet.
It uses those tokens to pay for its own electricity
at the charging station. It uses those tokens
to pay for repairs. A maintenance bot comes over,
fixes its arm, and our robot transfers JWR tokens
to pay for the service. Or to pay other robots
to help it. Hey, I'll give you 50 JWR tokens
if you help me lift this heavy crate. A peer
-to -peer transaction. A completely autonomous
economy. Robots trading their own labor value
on a blockchain, completely independent of human
currency? The U .S. dollar could collapse and
the robots wouldn't even notice. The robot economy
doesn't just work for us. Eventually, it might
just work for itself. That is a provocative thought.
Something to mull over while you do your very
inefficient human laundry today. Don't calculate
your thurbligs while you fold socks. It'll just
depress you. Thanks for listening to the Read
Multiplex podcast. We'll see you in the future.