OpenAI and Enthropic are both moving
toward IPOs and most of the conversation
is going to collapse into one question.
Are these companies worth the numbers
people are putting on them? And I think
that in some ways is the least useful
place to start. I know we're all asking
the trillion dollar question, but I
think the better question is what are
public investors actually being asked to
believe? And I think the answer is
pretty simple. They're being asked to
believe that OpenAI and Enthropic can do
two things at the same time. One, they
can make intelligence cheap enough to
serve at massive scale. And two, they
can build the layer around that
intelligence fast enough that companies
rent the whole system instead of
building it themselves. That is the bet.
Cheap tokens and proprietary harnesses
equal a trillion dollars. If that sounds
abstract, I'm going to make it very
concrete. A token is raw intelligence,
right? It is the thing you buy by the
meter. A harness is everything that
turns that raw intelligence into work.
the files the models can see, the tools
it can use, the permissions it has, the
memory it keeps, the evals that check
the output, the routing between a cheap
model and an expensive model, and the
workflow that tells the system what done
needs. Codeex is a harness. Claude code
is a harness. Chat GPT is becoming a
harness, and inside companies, every
serious AI project is a harness project.
And that is why this IPO story matters.
The question is not just whether OpenAI
has better models. The question is
whether OpenAI and Enthropic can own the
work layer that sits above the models.
There's an analysis circulating today
that tried to estimate the notional API
value of the $200 AI plans from
Enthropic and OpenAI. I think it was by
semi analysis. The rough claim was that
a heavy Open AI user would be getting
$14,000 in value for a 200 buck plan and
a heavy Claude user would get $8,000 in
value for a 200 buck plan. And the
obvious reaction is these companies are
lighting money on fire. And maybe for
some users they are. But I think the
sharper read is that API prices are not
an internal cost. API prices are retail.
It includes markup. It includes margin.
It reflects the price charged to
developers, not necessarily the cost the
lab pays to serve the token internally.
So the question is not how much API
value did the user get. The question is
what did that usage actually cost OpenAI
or Anthropic to serve. And those are
very different questions. If the API
price includes 70 or 80% gross margins
and the internal cost is far below the
public sticker price and if the labs are
improving inference efficiency and model
routing and caching and batching and
distillation and chip utilization and
everything else that lets them squeeze
more intelligence out of the same
hardware, then the 200 buck plan may not
be as irrational as it looks from the
outside. It might be a subsidy. It could
also be a strategy. They may be letting
power users consume huge amounts of
intelligence while they race the cost
curve down underneath that usage. They
are effectively saying we can afford to
serve intelligence closer to cost now
because we believe the cost of serving
is going to keep falling. And this
changes the IPO frame because if you
think the models are hitting a wall and
token costs are going to stay high, the
business is much harder to run. But if
you think the labs can keep making
inference cheaper, then the story
becomes much more interesting. Open AI
and Anthropic are not only selling
intelligence, they're trying to make
intelligence abundant enough that the
real business moves somewhere else. And
that is the key turn. If tokens get
cheap, raw intelligence becomes way less
defensible. That doesn't mean
intelligence stops mattering. To be
clear, electricity matters, bandwidth
matters, compute matters. But once an
input becomes widely available, the
value often moves to what people build
around the input. So if intelligence
gets cheaper, the question becomes who
owns the layer that makes it useful and
that layer is the harness. This is where
the open AI and anthropic bet becomes
much much clearer. They do not want to
be just API companies forever. They do
not want to sell raw intelligence
forever because raw intelligence is
going to get compared and routed and
priced down and substituted. They want
to sell the work surface. They want to
sell the operating layer. They want to
sell the thing that makes the
intelligence useful before the customer
has to understand how any of it works.
Codeex is the cleanest example. Codeex
is not impressive only because the
underlying model is smart. It is
actually impressive because the model is
sitting inside a harness that
understands the job. It can see the repo
and edit files and run tests and inspect
errors and keep track of changes and use
the computer and move through the loop
of software and knowledge work. The
product is not just a model that knows
code. The product is a system that can
participate in general purpose knowledge
work. That is a huge difference. A model
gives you intelligence and a harness
gives you work. And the IPO question is
whether OpenAI and Enthropic can build
those harnesses faster than companies
can build their own. Because companies
have one enormous advantage the labs do
not have private context. Open AAI does
not know how your company works.
Anthropic does not know where the real
documents live. They do not know which
Salesforce fields matter to you. They
don't know which approval step is real
and which one everyone ignores. They
don't know who can approve the
exceptions. They don't know which
spreadsheet is a fake source of truth
and which one is the real source of
truth. They don't know the internal
history that explains why the workflow
is broken. The labs have models and they
have infrastructure and product talent
and usage data and they have speed.
Companies have context. That is a
powerful information asymmetry and the
whole fight is over which side can turn
its advantage into the better harness.
This is why the forward deployed
engineering move matters. The simplest
version is oh open AAI is becoming a
consulting company. I do think there's
something to that but it's not the
deepest point. The deeper point is that
forward deployed engineering is how the
labs try to overcome the context
problem. They cannot know your company
from the outside. So they send people
inside. They map the workflows. They
connect the tools. They learn which use
cases are real. They adapt the product
to the customer. They turn the generic
harness into a company specific harness.
And if that works, the customer is no
longer just buying tokens. The customer
is reorganizing work around the lab
system. That's much more valuable. It's
also much stickier because once your
workflow is rebuilt around open AIS or
Enthropics harness, switching gets
harder. Even if the model underneath is
replaceable, another model might be
cheaper. Another model might be better
for one task. An open model might be
good enough, but your process is now
wrapped around one company's way of
doing the work. That is the lockin. It's
not the model. So from a company's
perspective, the strategic question is
not should we use open AI or anthropic.
Of course, you should use them. The
question is, are we renting the harness
or are we owning the harness? Owning the
harness does not mean training a
frontier model. To be clear, almost no
company should do that. Owning the
harness means owning the layer that
decides which model gets used for which
job. It means owning the context, the
evals, the permissions, the workflow
definition, the review process and the
routing logic. It means open AI and
enthropic and Google and DeepSeek and
open source models are going to have to
compete to serve your work. If you own
the harness, the labs are suppliers. If
the lab owns the harness, the lab
becomes the operating layer. That is the
fork in the road. And this is also where
recursive self-improvement becomes more
practical than mystical. The dramatic
version of recursive self-improvement or
RSI is that AI improves AI, intelligence
explodes and everything changes. Well,
maybe. But for the IPO, the more
practical version is enough. If better
models help OpenAI and Enthropic improve
their own products faster, then
recursive self-improvement becomes an
iteration advantage. They can improve
code faster. They can improve eval
faster. They can tune routing faster.
They can optimize inference faster. They
can compress models faster. They can
make the harness better faster. And that
is what matters for the business. Not
just whether the model gets smarter in
the abstract, but whether the lab can
convert smarter models into cheaper
tokens and better harnesses faster than
customers can respond and build their
own. So the bullcase for OpenAI and
Anthropic becomes very clean in that
world. Open AAI and Enthropic can manage
token costs. They can compete with
open-source models on price over time.
They can use their scale to push down
the cost of inference. They can use
their models to improve their own
products. And they can build harnesses
so good that most companies decide not
to build their own. That's a real
thesis. And honestly, they have a shot.
After all, most companies are slow. Most
companies don't understand their own
workflows. Most companies can't write
down what done means. Most companies
won't build routing logic. Most
companies won't maintain evals. Most
companies will not create a clean
internal AI layer. they will just buy
the product that works. And if Codex is
a sign of where this is going, the labs
are getting very good at making products
that work. But the bare case is also
really clear. If companies learn to own
their harnesses and the labs become
suppliers of intelligence rather than
owners of the work layer, they may still
be huge companies. They may still make a
ton of money, but the valuation changes
because the most valuable layer is no
longer fully theirs. the company will
capture the workflow value in that
scenario and the lab is stuck with a
token margin. And if token prices keep
falling, that is a much less dominant
position to be in. And that's what I
would look for when the S1's are finally
released for anthropic and open AI. Not
just revenue, not just user growth, not
just cash burn, not just the valuation
number. I'm sure it will be in the
trillions. I would want to know whether
heavy users are getting cheaper to serve
over time. I would want to know whether
gross margin improves as usage grows. I
would want to know whether enterprise
customers are buying scalable software
or custom deployment labor. I would want
to know whether customers are building
real workflows inside the product. And I
would want to know whether forward
deployed engineering is a bridge to
product or a permanent requirement for
the product to work. Those are the
numbers that should tell you what kind
of business this actually is. But if
you're not an investor, the practical
question is even simpler. Are you
building your own harness or are you
letting someone else own it? By all
means, use the tools, use open AI, use
anthropic, use codeex, use cloud code,
use whatever works. But do not confuse
using AI with having an AI strategy. An
AI strategy is knowing what work should
run where. It's knowing which tasks need
a frontier model and which tasks need
very cheap, reliable intelligence. It's
owning the context. It's having eval.
It's having a review path. It's being
able to swap models without breaking a
workflow. That's the company version.
The individual version is the same thing
at a smaller scale. The valuable skill
is not prompting. Prompting is thin. Now
the valuable skill is harness building.
Can you take a recurring job and define
it clearly? Can you give the model the
right context? Can you connect the right
files and tools? Can you check the
output? Can you make the system better
next week? That is where the leverage is
because cheap intelligence is coming
either way. The question is who knows
how to use it. So the open AI and
anthropic IPOs are not just stories
about whether these companies are worth
a trillion dollars. They're the first
public test of a cleaner thesis. Can the
labs make tokens cheap enough and build
harnesses fast enough to own the work
layer of AI? Or will companies use
cheaper tokens to build their own
harnesses and keep more of the value
themselves? Sheep intelligence is the
input that makes the token economy
possible. The harness is the engine that
makes the token economy valuable. So,
whoever controls the harness has the
dominant position in the token economy
of the future. And that that is the
trillion dollar question I'm watching.
And yes, I do think we'll get clues to
that when those S1s leak, as they
inevitably will for Open AI and for
Anthropic. Stay tuned. And of course,
I'll be digging in as soon as we get
more information. Cheers.