The $1 Trillion Problem OpenAI Just Solved
146 segments
As you already know, I've been saying
the model itself is turning into a
swappable part. You just rent it, you
route it, and you move on. So, if models
keep getting cheaper, the big labs
should be stuck in a price war right now
watching their margin bleed out, right?
However, they did the opposite.
Instead of selling a model and walking
away, OpenAI and Anthropic reportedly
built teams that sit inside a customer's
business and ship a working system. The
industry borrowed the name from Palantir
and calls them forward deployed
engineers. So, what that means in plain
English is that they send their best
people into your office to build that
thing, not just to demo it. So, think
about how strange that is. The companies
that make the models decided the model
was the cheap part of the job. MIT
looked at the pile of company AI pilots
and demo and found something like 95% of
them produced no result you could
measure. Isn't that crazy? The model
worked fine, just [music] they just died
at the last mile, where somebody has to
put the thing inside the real job and
make it run on a Monday.
That old software deal where a company
hands you a login and wishes you luck is
dying with them right now. So, I'm
building a payroll and scheduling
accounting product right now. So, I'm
living this question, not theorizing
about it. The scariest thing was never
the intelligence. It was the person
willing to sit in your business until
the thing runs properly, which creates a
problem here because that is the one
thing you cannot check before you pay
for it.
Most of my revenue right now comes from
putting AI systems into real businesses,
and the demand [music] is real, like
people calling me real. But, a hot
market pulls in a crowd and not always
the good kind. There are teams who will
cash an owner's check and just disappear
once the demo looks impressive enough.
And the ugly part is that an owner
cannot inspect the system that does not
exist yet. So, the whole sales runs on a
slide deck and a confident voice. That
is not a small gap. That's just the gap
the 95% falls through. Now, obviously I
want to be fair because a course sells
you the exact same un-verifiable
promise. You buy the promise of a
result, only one or maybe two percent of
buyers ever get the result they want,
and nobody finds out which group they
landed in until the money is just gone,
right? I think that is why selling
courses started to feel bad to me. You
are handing someone a lottery ticket
[music] with your name printed on it,
right? Setting aside the work fixes that
though, because you own the outcome. It
runs or it does not, and both of you can
see which by Friday. So, you should only
sell what a client can inspect, and only
buy what you can watch running, which
leaves one question worth answering.
Which part of the business do you build
yourself into?
The labs go deep for a reason. Once you
are inside a customer's payroll data and
their weekly schedule, pulling you out
costs more than keeping you. Nobody
swaps out a six-month integration on a
Tuesday because a cheaper option just
showed up out of thin air. You know, my
take is that the boring operational core
is the safest place to stand as of now.
And that is why I go after payroll and
scheduling and accounting piece. The
cool, fresh stuff is for the other
geniuses who want it. I don't want it.
Think about your own business for a
second. You do not rip out the thing
that runs payroll and you do not wake up
excited to migrate your scheduling your
data again. Now, the labs won by going
one profession at a time. They started
with cybersecurity, then moved into
medicine, law, and whole lots of other
things right now. So, the lazy takeaway
that I want to kind of give out is
niching down, picking an industry, and
just be done with it. I do not fully buy
into [music] that. The trap is which
thing you niche. Is it the content
creation for roofers? I don't think it
makes sense to me. The content and
marketing are a stack of many different
skills. And honestly, most of us are
barely, barely scratching the surface of
any one of them, right? So, a bounded,
repeatable job is a different animal.
Payroll is payroll, onboarding is
onboarding, accounting is accounting,
scheduling is scheduling. These are
whole different things. And when the job
has clear edge and it repeats every
month, you can own it end to end. When
the job is a fuzzy pile of hand-creative
skills, you just simply cannot, and
neither can I.
So, it would be really bad for you to
watch where buyers are spending because
they spend in two places, and both of
them sit far away from the model, down
at the cheap infrastructure and the
routing, which was last week's whole
point, up inside the work itself, which
is this week's that I'm talking about.
The dangerous spot is the generic
middle. I use ChatGPT for my clients.
Just easier for your clients to do
without you every single month. That's
not something to brag about. So, my
whole take is, you know, you either go
big or go home, but with calculated risk
because your runway is real and one dumb
bet could end the game. So, sitting
inside the workflow only pays off when
the workflow is big enough to be worth
the wiring. A two-person shop doing five
jobs a week may not have the juice. Deep
integration ties your income to your
client's survival. Anyway, the model is
the cheap part now, and it gets [music]
cheaper every month. The boring seed you
built yourself into is the part that
nobody can copy. Think about that.
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This video discusses how AI model providers like OpenAI and Anthropic have shifted from simply selling models to offering deep, integrated solutions through 'forward deployed engineers.' The speaker argues that the real challenge of AI is the 'last mile' integration into actual business workflows, where 95% of AI pilots fail. Instead of chasing generic models or ephemeral trends, businesses should focus on building themselves into the 'boring operational core'—such as payroll, scheduling, or accounting—to create lasting, defensible value that clients cannot easily swap out.
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