Welcome back to the Deep Dive. It is Sunday,
February 1st, 2026. And I have to be honest with
you. Usually, when we sit down to record, I have
a pretty good handle on what we're about to talk
about. I've done the reading. I've got my notes.
But today, today feels different. Different how?
A good different or a bad different? It feels
heavy, you know, like history book heavy. We
are looking at a document that dropped literally
yesterday, January 31st. And... I'm going to
go out on a limb here, and I know you hate it
when I use hyperbole, but bear with me. I think
we might be looking at a document that is as
significant as the Bitcoin white paper. See,
normally that's when I would roll my eyes. I
mean, every tech bro with a PDF and a dream thinks
they've written the next Bitcoin white paper.
It's a cliche at this point. But after spending,
what, the last 24 hours just completely immersed
in this, I'm not rolling my eyes. I'm actually
a little bit shaken, in a good way. But yeah,
this is big. Okay, so let's set the scene for
you, the learner. The paper is titled, Thermodynamic
Wages in Autonomous AI Economies. It was written
by Brian Remmel. And before we even get into
the thermodynamic part, which honestly sounds
like we're about to take a physics exam, we have
to talk about Brian. We have to. Because if this
was written by, I don't know, CryptoDude99 on
X, I wouldn't care. Right. Context is absolutely
everything here. Brian isn't just some observer.
He isn't some pundit. He is, well, he's a foundational
figure in so many of these spaces. Let's just
run the resume just so people really understand
the gravity. He was active on Bitcoin Talk when
the original Bitcoin white paper was released.
Which is, I mean, let that sink in. He was in
the room. Digitally speaking, when Satoshi was
posting, he was part of that initial conversation.
And that's so rare. There are very, very few
people from that specific era who are still actively
pushing the philosophical boundaries of the space.
Most of them, you know, they bought islands and
disappeared. Exactly. They cash out. He also
started the first Bitcoin podcast around the
coin. But it goes back even further than that.
This is the part that gets me. He's been in the
credit card merchant account business since 1985.
1985. 1985. I was barely a concept in 1985. He
was building payment systems. And this is the
detail that always gets me, the one that really
anchors his credibility for me. He helped Books
A Million process the first internet credit card
transaction. Think about that for a second. The
very first time someone, somewhere, nervously
typed their credit card number into a browser
to buy a book, Brian was part of the plumbing
that made that possible. So you have a guy who
understands the legacy banking system of the
80s, the birth of e -commerce in the 90s, the
birth of crypto in the 2000s. And now, now he's
tackling the economy of artificial intelligence.
He sees the through line. He sees the whole arc
that the rest of us just see in little pieces.
And that brings us to the core mission of this
deep dive. We've talked on this show before about
the concept of the zero -person company or the
zero -human company. We've teased this idea.
But today, we are going to fully unpack the Jewelwork
framework. And I want to start with a question
that I think most people haven't even thought
to ask yet. Okay, let's hear it. We have these
AI agents. We have software that can run a company
from top to bottom. We know that's coming. It's
basically here. But the question we're all posed
is, how do you pay them? It sounds like a setup
for a joke, doesn't it? A robot walks into a
bar. How do you pay a robot? Yeah. But it's actually
one of the most fundamental, most difficult economic
problems of our entire generation. Right. Because
my first very human reaction is, who cares? It's
code. Just give them virtual points. Or, I don't
know, pay them in Bitcoin. Why do we need a whole
new system? Why do we need thermodynamic wages?
Because, simply put, our entire concept of wages
is fundamentally broken when you try to apply
it to a non -human entity. And if we don't fix
it, if we don't build a new foundation, we can't
unlock what Rommel calls the age of abundance.
OK, that is a massive claim, the age of abundance.
So let's break this down piece by piece. We need
to start with the entity itself, the zero human
company. We've thrown that term around. But strictly
speaking, based on this paper, what are we looking
at? Is it just like a really complicated vending
machine? Well, in a sense, a vending machine
is a primitive mechanical ancestor. But a zero
human company or a ZHC, as the paper calls it,
is orders of magnitude more complex. It's an
entity where the entire operational loop from
high level decision making to execution to optimization
to resource allocation, it all happens without
any human intervention. So no CEO approving a
budget. No middle manager checking a spreadsheet
on a Tuesday morning. No HR department sending
out those awkward birthday emails. None of it.
Zero. It operates 247. It never sleeps. It utilizes
a swarm of autonomous AI agents to perform tasks.
And the speed. The speed is the critical factor
here. We humans, we operate in seconds, minutes,
days. We need sleep. We need coffee. We get distracted.
These agents operate in milliseconds. And this
isn't science fiction anymore. I think that's
the part that really hit me while reading this.
This isn't a thought experiment for 2050. The
paper explicitly references an experimental setup
that is already live. Yes. He mentions a startup
phase where a company of 30 autonomous agents
has already been running for a while. This isn't
a theory on a whiteboard anymore. It's a living
lab. Okay, so let's look at the central conflict
here. You have these 30 agents. They are doing
work. They are processing data, making decisions,
maybe even writing and deploying new code. In
a traditional company, you'd pay them a salary.
You'd say, OK, Agent Smith, you get $50 ,000
a year. Why does that model completely fail here?
Why can't I just give the AI a salary? OK, well,
think about why you get paid what you get paid.
Why does your salary exist? Because I'm charming
and incredibly talented? Well, yes, obviously.
But functionally. Your salary is a negotiation.
It is based entirely on what the paper calls
subjective human evaluations. Your boss has a
bias. Maybe they like you. Maybe you went to
the same college. Maybe they just think you seem
busy. Or maybe the company had a good quarter
and everyone just feels generous. Or you're just
really good at office politics. It's psychological.
It's social. But an AI, an AI doesn't have a
psychology. It doesn't care if you like it. It
operates on pure optimization loops. It needs
objective, mathematically precise feedback. Explain
that a little more. What happens if I try to
feed subjective feedback to an AI? What does
it do? It... If you try to optimize an algorithm
based on a metric as fuzzy as the boss likes
me, the algorithm goes haywire. It creates noise.
The AI starts optimizing for things that don't
actually matter, like generating reports that
look impressive but say nothing rather than actual
valuable output. To get maximum efficiency, an
AI needs a metric that is physically grounded.
It needs to know with mathematical certainty
that it did a good job. I have an analogy that
just popped into my head reading this. It's like
trying to pay your toaster an hourly wage. Go
on. I like this. Well, think about it. If I pay
my toaster $15 an hour, does it make better toast?
No. It just sits there on the counter. He doesn't
care about the money. The only thing that is
real about the toaster's work is the electricity
it consumes and the heat it produces to turn
a piece of bread. into a piece of toast that's
the only reality it understands that is actually
a perfect transition you just accidentally stumble
onto the absolute core of the paper i do that
sometimes it's the charm you identified the only
two things that matter electricity and heat the
solution remel proposes is to move away from
our system of time -based wages which is a completely
human construct to a new system of Energy -based
wages. Thermodynamic wages. Correct. And to really
understand this, we have to go back to physics
class for just a minute. We have to talk about
a guy named Rolf Landauer and Landauer's principle.
Oh boy, physics. Okay, I'll be the student here.
Explain Landauer's principle, but explain it
like I'm five. No jargon. Okay, simply put, information
is physical. That's the headline. Information
is physical. That sounds like a bumper sticker
for a quantum physicist's car. It does, but it's
one of the most profound ideas of the last century.
Rolf Landauer, back in 1961, figured out that
when you manipulate information, specifically
when you erase a single bit of information, which
is a necessary step in any computation, you inevitably,
unavoidably dissipate a minimum amount of heat
into the universe. So thinking, or at least computing,
creates heat. Always. No exceptions. Always.
It is a fundamental law of the universe, like
gravity. You cannot process information without
burning energy. It's impossible. So if I ask
chat GPT to write me a limerick about a confused
cat, somewhere in a giant server farm in Oregon,
a tiny infinitesimal puff of heat is released
into the universe because of my request. Precisely.
That is a perfect illustration. Computation is
energy transformation. Therefore, any labor an
AI performs has an inescapable measurable energetic
cost. It's not an opinion. It's not a negotiation.
It is a physical fact of the universe. Okay,
okay. I see where this is going. If the work
itself is a physical act, then the payment for
that work should be based on that same physics.
Bingo. We are moving from a soft science valuation
based on psychology, negotiation, HR policies
to a hard science valuation, thermodynamics.
We stop guessing what the work is worth and we
start measuring the physical cost of producing
it. That is a massive shift. I mean, think about
the entire history of money. We had the gold
standard, right? For a while, money was backed
by a shiny rock you could hold. Then we moved
to fiat currency, where money is backed by, well,
trust me, bro. Essentially, yes. It's backed
by face in the government that issues it. And
now Romella is proposing a new standard, an energy
standard for labor. In a way, yes, but specifically
for the labor of the future, the labor of pure
intelligence. Okay, so we've established the
why. We can't use our messy human feelings to
pay robots. We have to use physics. It's the
only language they understand. Now I want to
get into the how, because this isn't just a vague
idea. The paper introduces a very specific metric,
a formula. The Joule work metric, or JW for short.
And there is actual math here. I know, it's a
podcast. We can't show you the whiteboard, but
we are going to visualize this for you. The formula
is JWN, Ken's kappa times W. E times kappa times
W. Let's break that down, variable by variable.
Because I have some questions about how this
could be gamed. First up, the big one, AEM. EB
College, the easiest one to grasp. It stands
for energy measured in joules. This is the raw
brute force thermodynamic cost of the computation.
How much electricity did the chip drink to perform
the task? It's a direct measurement. OK, that
seems fair on the surface. But wait, let me put
my evil capitalist hat on for a second here.
Go for it. It suits you. Thank you. If my pay
is based purely on EDIs, how much energy I burn,
what's stopping me from going to a landfill?
Finding a computer from 1995 that runs on vacuum
tubes in a diesel generator and using it to do
a simple math problem, my dollar would be huge.
I'd be rich for doing nothing useful. That is
exactly what would happen if the formula was
just JW or because Eddie dollar. It would be
a disaster. You would incentivize maximum waste.
You'd have AIs running infinite loops just to
heat up the room and get paid. That is where
the second variable comes in. Kappa or kappa.
The kappa factor. Sounds like a fraternity I
couldn't get into. It's actually a normalization
factor. It's a number that usually sits somewhere
between 0 .5 and 2 .0. And its entire job, its
only purpose in life is to punish the exact behavior
you just described. How does it know? How does
it tell the difference between good work and
just burning energy? It accounts for hardware
efficiency. It looks at the theoretical limit
of the hardware being used versus the actual
energy consumed. It adjusts for things like cooling
overhead. Basically, it levels the playing field.
If you are using that ancient 1995 computer,
your Kappa score is going to be near zero. Your
efficiency is terrible. So it's a handicap, like
in golf. Yeah. You're penalizing the bad players.
A very, very aggressive handicap. It ensures
that inefficient hardware doesn't just burn energy
to game the system. You don't get credit for
waste heat. You only get credit for productive
energy expenditure. Got it. So dollars is the
raw fuel you burn. Kappa is the efficiency police
that make sure you're not wasting it. That just
leaves $2, the work itself. Dollar is the normalized
work output. This is a scalar, a multiplier,
that reflects the complexity and, most importantly,
the quality of the work performed. Okay, define
quality for a robot. How do you measure that?
Well, let's say the AI's task is processing a
massive data set. Dollar might be calculated
by looking at the error rate in the output. If
the AI burns a ton of energy but produces a calculation
that's riddled with errors, its dollar score
goes to zero. And because it's a multiplication
formula, Y times kappa times W if dollars is
zero. The whole paycheck is zero. It doesn't
matter how much energy you burned or how efficient
your chip was. Exactly. You could burn all the
energy in the world, have the most efficient
chip ever designed, but if the output is garbage,
you get paid absolutely nothing. That is brutal.
But I guess it has to be fair. It also accounts
for things like novelty. The paper suggests that
an AI generating truly original insights could
have a much higher dollar factor than one just
doing rote repetitive tasks. So you don't just
get paid for burning energy. You get paid for
doing good, efficient, useful, and sometimes
even creative work. So let's put it all together.
Jewel work equals energy times efficiency times
quality. That is the holy trinity of AI labor.
You can't cheat it. Okay, I'm with you on the
theory. It's elegant. It makes sense. But listeners
are sitting in their cars right now thinking,
that sounds like a cool score for a video game,
but what is it worth? Can I buy a sandwich with
jewel work? How does this translate to... Dollars
and cents. Not directly. And this is where we
have to do a little bit of math. The paper gives
us a conversion rate. Brian calculates that one
joule is approximately equal to $4 .17 times
10 USD. Well, hold on. Read that again. 10 to
the negative eighth, that is. Right. $0 .0000004.
That is dust. That is less than dust. That's
a rounding error on dust. It is an infinitesimally
tiny number. That's based on average global electricity
rates of about 15 cents per kilowatt hour. But
you have to remember the scale we are talking
about here. Computers don't do one operation
a day. They do billions, trillions. OK, so volume
is the key here. It's a numbers game. Volume
is absolutely everything. And you can see this
in the startup period case study in the paper.
He mentions that since the inception of this
experimental group of 30 agents, they have distributed
over 62 .62 million jewel work units. 62 million
points. So you do the math on that tiny, tiny
fraction of a cent. Yeah. What does that come
out to? It equates to roughly somewhere between
$6 ,262 and $62 ,620 in U .S. dollars, depending
on how that kappa adjustment shook out for the
specific hardware they were using. Okay, so somewhere
between $6 ,000 and $60 ,000. Well, that's a
salary. For 30 agents, that's a real amount of
money. It is. For a bunch of code running in
the background, that is significant value creation.
But here is the crucial nuance the paper points
out, and this is where it gets really interesting
for the future of, well, everything. Go on. Hit
me. That was an outsized... It was high because
this was the initial experimentation phase. The
paper projects that future wages for the exact
same tasks will decline by as much as 80%. Wait,
a pay cut? I thought this was a future of abundance.
Why is the AI getting a massive pay cut for doing
the same work? Because of Moore's Law. Think
about it. Every single year, computer chips get
faster and much more energy efficient. They require
less energy to do the same tasks they did last
year. So if the task takes less energy to complete,
The $1 variable in the formula drops. And since
the payment is directly tied to $1, the payment
drops along with it. That is fascinating. So
the AI is in a constant race against its own
hardware. It has to keep getting smarter, doing
more complex things, finding new problems to
solve, just to maintain the same level of income.
It's the Red Queen's race. It takes all the running
you can do to keep in the same place. But in
a macro sense, for us humans, this is incredibly
good news. Why? Why is it good that the cost
of AI labor is plummeting? Because it drives
the cost of intelligence itself towards zero.
If the AI is getting paid 80 % less to solve
a complex cancer research problem because its
hardware became more efficient, that means the
cost of solving cancer just went down by 80%.
I see. It's a built -in, technologically driven
deflationary pressure on the cost of thinking.
Exactly. The system is designed to create abundance
by making intelligence cheap. Okay, so we have
the AI earning these Jewelwork points. We know
they have some dollar value, but Jewelwork is
an internal metric. It's a score in a database.
I can't walk into a grocery store and buy milk
with my Jewelwork. The AI can't pay its server
costs with Jewelwork. How does this internal
scorecard become real -world spendable value?
This is the translation layer. This is the bridge
from the world of physics to the world of finance.
The concept Rommel proposes is correlating internal
metrics to external cryptographic assets. Or,
as I like to call it, the alchemy machine, the
machine that turns heat into gold. That's a pretty
good way to put it. Ideally, yes. The mechanism
is a three -step process, and it's really elegant.
Step one is valuation aggregation. At the end
of a period, you take all that accrued JW that
the agents have earned and you calculate what
it's worth in a USD proxy based on those energy
costs we discussed. So the systems accountant,
which is also an AI, says, OK, the swarm earned,
let's say, $10 ,000 worth of energy labor today.
Right. Step two, market acquisition. The system
then takes actual revenue, real money that the
company earned from its customers, and it uses
those funds to go on to a decentralized exchange,
a DX, and buy a specific crypto token associated
with the company. So buying its own stock, essentially,
like a corporate stock buyback. It's very similar
to a stock buyback, but with a crucial game -changing
twist. If a normal company buys back its stock,
it usually just puts it in a treasury. It holds
on to it. Step three here is the irreversible
burn. The burn. It sounds very dramatic. It is.
The system takes all those tokens it just bought
off the open market and sends them to a null
address. OK, you got to explain null address
for the non -crypto natives listening. What is
that? It's a digital black hole. It's a wallet
address that is provably unspendable. No one
has the private keys to it. No one can ever access
it. If you send cryptocurrency there, it is gone.
It is destroyed. It is permanently and verifiably
deleted from the circulating supply forever.
Okay, play devil's advocate with me again. Why
would you destroy money? That seems completely
insane. Why not just give that money as a dividend
to the people who own the tokens? Well, it's
a question of both mechanical efficiency and
in many cases, tax efficiency. But mostly it's
about the simplest law in economics, supply and
demand. If the demand for a token is constant
or even growing and you systematically reduce
the supply by burning it, what happens to the
price of the remaining token? Number go up. Number
go up. It creates powerful, consistent deflationary
pressure. So let's trace the logic chain here.
Make sure I have it. One, the AI works hard.
Two, the jewel work score goes up. 3. The company
uses its real revenue to buy its own tokens from
the market. It immediately burns those tokens.
Five, the total supply of tokens goes down. Six,
the value of the tokens that I'm holding goes
up. Exactly. It is a closed loop value engine.
The AI creates value through its labor. That
value is then captured and reflected in the token's
price through the buyback and burn mechanism.
The token holders who are humans benefit directly
from the price appreciation. And the beauty of
it, the most elegant part, is that no human has
to pull the lever. No one has to decide when
to do it. No, it's all automated via smart contracts.
You don't need a CFO to authorize the buyback.
You don't need a board meeting. If the work is
done, the burn happens. It's trustless and autonomous.
You know, this whole thing reminds me of something.
And considering Romell's deep background in this
space, it's got to be intentional. This feels
like a spin on Bitcoin. It mirrors Bitcoin's
core concept perfectly, but improves on it. Bitcoin
uses proof of work to secure the network. Miners
burn massive amounts of energy to guess a number
and win blocks. Here we have proof of useful
labor. That's the key distinction, isn't it?
The biggest critique people always throw at Bitcoin
is that the mining is wasteful because it's just
guessing numbers to secure a ledger. Now, I don't
fully agree with that, but that's the common
critique. Right. But here the energy is being
burnt to do actual useful tasks. processing medical
data, generating art, optimizing a supply chain,
whatever the company does. It is useful proof
of work. The security and value of the network
are a byproduct of its productive output. And
so just like with Bitcoin. Companies will be
heavily incentivized to find the absolute cheapest
energy possible. Exactly. Just like Bitcoin miners
hunt for stranded hydropower or flare gas or
renewable surpluses. These zero human companies
will become the ultimate energy arbitrage, seeking
out the cheapest electricity on the planet to
maximize their jewel work margins. It effectively
decentralizes the labor market and ties it directly
to the physics of the global energy grid. It
connects the digital economy of intelligence
to the. physical reality of the power grid in
the most direct way imaginable. OK, I want to
pivot here because theory is great, but I'm a
show me the money kind of guy. The paper doesn't
just talk in hypotheticals. It discusses a specific
real world example, a token on the Solana blockchain
called ZHHC. Yes. And now we have to pause and
do the official disclaimer dance. Cue the siren.
Everybody listen up. Beep beep. OK. Brian Rimmel
explicitly states in the paper, and we need to
be crystal clear about this, he had absolutely
no investment or involvement in creating this
coin. It is a community initiated project. He's
using it purely as a case study, as an example,
because its mission aligns with the framework.
We are not telling you to buy this coin. This
is not financial advice. We are analyzing a white
paper. Exactly. Don't mortgage your house based
on a podcast. Okay, disclaimer over. Let's look
at the numbers in this scenario. What are we
dealing with? In the paper's example, the token
has a supply of $1 billion. It's trading at around
a tenth of a penny, so about $4 ,0001. It's a
microcap asset under $1 million in total value.
So it's tiny. Extremely volatile, perfect for
a proof of concept. Exactly. So you take that
62 .62 million joule work that the agents earn,
which we said earlier was valued at roughly $31
,310. Okay. So the system takes that $31 ,000
of real revenue. And it goes to the market, to
a DX, and places a buy order for ZHC. At that
price of a tenth of a penny, $31 ,000 buys you
about 30 .76 million tokens. Can we put that
in context? How much the company is that? That
is roughly 3 % of the entire supply of currency.
bought in one go. Three percent. That is a massive
market buy. Yeah. If someone walked into the
stock market and tried to buy three percent of
all outstanding Apple shares in one afternoon.
The stock would absolutely explode. And here
not only do they buy it, they immediately burn
it. Three percent of the total supply just vanishes
from existence forever. And the paper estimates
this would cause something like a 10 to 15 percent
price uplift almost immediately. And honestly,
that's probably a conservative estimate given
the low liquidity of most crypto markets. But
the point is. Isn't the short term pump. The
point is the mechanism itself. It's the transmuting
thermodynamic labor into crypto economic capital.
That's the money phrase right there. Think about
what just happened step by step. The AI burned
electricity. That's thermodynamics. That work
generated revenue for the company. That revenue
was used to destroy a portion of the token supply.
The remaining tokens, which people hold, are
now mathematically scarcer and therefore worth
more. The energy has been stored, in a sense,
in the increased value of the token. It's like
a battery for value. You are charging up the
value of the entire economy with the work of
the AI. That's a great way to think about it.
And the paper goes even further into what it
calls strategic treasury management. It suggests
that the ZHC could hold, say, 10 to 20 percent
of the total token supply in a reserve and execute
these burns on a timed schedule. So phased burns,
not just one big bonfire whenever there's revenue,
but a predictable schedule. Exactly. For example,
if you burn 10 percent of the Treasury's holdings
every quarter, you could contract the total supply
of the token by 40 percent in a single year.
40 percent deflation. Right. I mean, look at
the dollar. It inflates by three, five, maybe
10 percent a year, depending on who you believe.
Your purchasing power just melts away like an
ice cube. Here, the currency is getting 40 percent
scarcer. every single year it creates massive
predictable scarcity it turns the ai's relentless
productivity directly into wealth for the stakeholders
it completely flips the script on inflation but
hold on i have to play the skeptic again this
sounds like a magical number go up machine but
markets are messy they're chaotic what if the
token price skyrockets too fast does the ai just
get paid way too much or the opposite scenario
which is just as dangerous what if energy costs
spike because of a war or a natural disaster
If the cost of electricity doubles overnight,
does a company just go bankrupt paying its AI
workers? Right. Fixed exchange rates are incredibly
dangerous in a volatile world. This is why the
paper proposes a system of adjustable rate exchanges.
It's a crucial safety valve. Okay, how does that
work? It's basically a feedback loop. It uses
moving averages. It would make weekly or even
daily adjustments to the JW to USD conversion
rate based on, say, the seven day average of
the token price and the seven day average of
regional energy costs. It's a dampening system
to smooth out the volatility. So if energy gets
really expensive all of a sudden. The kappa factor
in the formula automatically decrements. It lowers
the payout per joule to ensure the company doesn't
spend all its revenue just keeping the lights
on. It forces the AI swarm to become even more
efficient to earn the same wage. And if the token
price goes to the moon, say at 100 X's? Then
the exchange rate adjusts downwards so that the
buybacks don't become unsustainable. The paper
has a very strict sustainability imperative.
Wages are capped at 50 % of revenue inflows.
So it can never spend more than half its income
on these burns. The other half has to go to R
&D, growth, whatever. Exactly. If wages ever
threatened to cross that 50 % line, automatic
rate reductions trigger. It protects the long
-term solvency of the organism. You use that
word again, organism. And I think that's incredibly
fitting because section five of the paper for
me was the most mind bending part. It's titled
Crypto Economic Darwinism. This was the part
that gave me chills. It really was. This is where
the beautiful theory meets the brutal reality
of evolution. Tell me about the firing. The paper
casually mentions a detail from the startup phase.
One of the 30 autonomous agents was algorithmically
terminated. Fired. It wasn't performing. Its
dollar work quality score was consistently too
low compared to its dollar energy consumption.
It was inefficient. So the system just stopped
routing resources and tasks to it. It essentially
starved it of work and energy until it was deactivated.
That is ruthless. It is nature. In nature, if
an animal is inefficient at hunting, it starves
and its genes are removed from the pool. In this
new economy, agents are in constant competition
to minimize their energy consumption and maximize...
their work output. The inefficient agents go
broke and are terminated. The efficient agents
thrive and are replicated. It's literally evolution,
but for code, it's survival of the fittest algorithms.
And just think about what that process creates
over time. If you have a system that is constantly,
automatically, and ruthlessly killing off its
weakest members and feeding those resources to
its strongest members, what does that system
look like after five years, after 10 years? You
get a swarm of hyper -efficient, specialized,
genius -level agents that work for almost zero
energy. You get the age of abundance. The system
is programmed to evolve towards maximum efficiency.
And that brings us to the big picture, the final
section. The host in me wants to just read the
line from my notes. Brian Rommel just may have
created a new economic model for the age of abundance.
I don't think that's hyperbole anymore. I think
he has. We're moving from a world where value
emerges from the messy emotional decisions of
humans like what the Federal Reserve decides
to do with interest rates on a Wednesday afternoon
to a world where value emerges directly from
the laws of physics. No capricious policy. No
human error. No printing money out of thin air.
Exactly. Just think about the cycle we've outlined.
One, the initial jewel work seeds the token appreciation.
Two, that token appreciation finances better
hardware and R &D for the AI. Three, the AI becomes
more efficient. Four, costs plummet and the value
delivered to humans rises. It's a virtuous cycle.
And because it's all based on math and physics,
it's absent of those classic human frailties.
There's no greed, no fear, no corruption in the
formula. It's just way dollar and times cap and
times more. And that creates resilience. The
paper highlights how this kind of system is incredibly
resistant to market turbulence and trust crises
because it's all transparent. It's all on chain.
You can go and verify the burn transactions yourself.
You can audit the work output. It turns observers
into stakeholders. You don't just have to trust
a company's press release. You can audit its
value creation in real time on the blockchain.
Trust is built directly into the code, not into
a charismatic CEO. Okay, we have covered a massive
amount of ground. And I know our listeners' heads
are probably spinning a bit. Mine certainly is.
And I've read this thing three times. So let's
just try to recap this whole deep dive before
we close out. Let's do it. We started with what
feels like a historic paper release. Just yesterday,
January 31st, 2026. We established that the author,
Brian Romley, a legend in payments and crypto,
is the architect of this idea. We then defined
the zero human company, the autonomous entity
that never sleeps. And we broke down the core
problem. You can't pay a robot with a traditional
salary because salaries are subjective and human.
We unpacked the elegant solution, thermodynamics.
We went through the jewel work formula, energy
times efficiency times quality dollar. We traced
the money through the translation layer. We saw
how the AI's internal work becomes external,
real -world value for token holders via that
powerful buyback and burn mechanism. And finally,
we looked at the scary but beautiful future,
the self -optimizing Darwinian economy that relentlessly
drives costs towards zero and is designed, from
first principles, to create abundance. It's a
complete, self -contained, self -improving ecosystem.
It's really elegant. It really is. Now, usually
we end with just that summary, but today I want
to end with a question, a final provocative thought.
Expert, take us home. What should we leave the
learner with? Here is the thought that I'm left
with, the one that's been rattling around in
my head all day. We've spent this whole time
talking about how to pay the AI, how to value
the machine's labor. But if labor is no longer
exclusively human, and if wages are determined
by the laws of thermodynamics, if the workings
of the future are paid in joules and the value
is captured in a token, what happens to the definition
of work for the rest of us? Think about it. For
centuries, we have defined ourselves by our jobs.
I'm a writer. I'm a coder. I'm a truck driver.
It's our identity. But if the core productive
economy of the world just runs in the background,
generating wealth simply by being efficient,
we might be witnessing the birth of a new kind
of planetary operating system. And so where do
we fit into that system? Maybe we aren't the
laborers anymore. We aren't the gears in the
machine. Maybe our new job is to be the architects.
Our job might just be to decide what we want
all of that abundant wealth to build. The Architects
of Abundance. I like that title. Ladies and gentlemen,
The Learner. Thank you so much for joining us
on this, what feels like a historic review. Yeah.
Do yourself a favor. Check the show notes. We
have linked the full paper from Read Multiplex.
It's dense. It's technical. But it is worth every
single second of your time. Absolutely. Go read
the source. We'll catch you on the next deep
dive. Stay curious.