You know, I was looking at the labor market numbers
this morning. It is February 2nd, 2026, by the
way. And there's a $10 trillion figure staring
back at me. Wow. That is the valuation of the
AI labor market. It feels massive. Kind of like
the entire world's been rewritten by code. It
definitely feels that way. But then you look
at the hard truth buried in those reports from
IBM and MIT. And that truth is just staggering.
Up to 95%. of AI implementations right now show
zero measurable return on investment. Zero. Zero.
That is a massive disconnect. You've got $10
trillion of hype on one side, and on the other,
business is just burning Pash with nothing to
show for it. So why? Why is there such a gap?
Well, the answer is actually pretty counterintuitive.
It turns out the secret isn't being exciting
or revolutionary. It's about being boring. Welcome
to the Deep Dive. I'm really glad you're here
with us. Today we are stripping away the hype.
We are not talking about how to build the next
chat GPT or, you know, the next sentient robot.
We are looking at the current landscape of 2026
to understand why most AI businesses crash and
burn and how to build one that actually works.
We are digging into a guide titled The Profitable
Pivot. And the core argument here is just fascinating.
It really is. We're going to unpack some concepts
that might honestly change how you see your own
work. We'll talk about the wrapper trap, why
treating AI like a smart intern is the only way
to survive, and why doing manual unsexy service
work is actually the shortcut to building a massive
software company. I love that. OK, let's start
with that 95 % failure rate. That is a terrifying
number if you're a founder or an investor. What
is going wrong? It usually starts with what we
call the wrapper trap. Even now, in 2026, you
see founders who think an AI business is just,
you know, slapping a user interface, a UI on
top of OpenAI or Anthropix models. OK. They create
a nice looking website that basically just passes
text back and forth to a big brain in the cloud.
Right. So you have a specialized chat bot, but
under the hood, it's just the same engine everyone
else has access to. Precisely. And the problem
is that doesn't solve a deep business problem.
It's a commodity. Think of it like this. Imagine
opening a restaurant that claims to have a secret
family recipe. Okay. But when you go into the
kitchen, you realize they're just microwaving
frozen meals from the grocery store. That's a
great analogy. It tastes fine. But anyone can
buy the frozen meal. There is no special sauce.
Exactly. There's no moat. And then you have the
corporate side of this failure, which is the
magic button fallacy. The magic button. I'm guessing
this means companies buying software expecting
it to fix their problems instantly. That's the
one. They buy the tool, they install it, and
they just wait for the profits to roll in. But
they don't change how they work. The MIT study
we looked at showed that only 5 % of AI pilots
actually make it to production. Only 5%. And
Deloitte found that only a tiny fraction of organizations
see real money coming back from these things.
But, wait, hold on. You do have winners in this
space. I mean, look at Klarna. They cut customer
service costs by 40%. Or, Intercom, resolving
millions of tickets, they're using the same underlying
tech as the failures. So what's the difference?
The difference is workflow. Klarna didn't just
buy a tool. They fundamentally changed their
internal processes. to accommodate the tool.
The failures happen when you try to jam a probabilistic
engine like an AI into a deterministic hole without
changing the shape of the work. That is a really
crucial distinction. So if the code isn't the
problem, is the issue actually us, the humans?
It's not a software bug. It's a workflow failure.
That makes so much sense. And it brings us to
the core of this deep dive, the three pillars
of a model that actually works. If the standard
model is broken, what replaces it? Well, the
first pillar is what we just touched on, integrating
into real workflows. You cannot just add AI as
another layer on top of your existing stack.
It creates friction. A ton of friction. You have
to redesign the flow. The source material mentions
a McKinsey study claiming that customizing workflows
is the number one success factor. But let's get
specific. I mean, customizing workflows sounds
like corporate speak. What does that actually
look like on the ground? Let's take a real estate
agency. In the old world, a lead comes in. and
a human agent has to read it, look up properties,
type an email, update a spreadsheet. It's slow.
Right. And boring. And prone to error if you
haven't had your coffee yet. Extremely boring.
Now, a successful AI integration using a tool
like N8n, for example, turns that into a digital
reflex. Pause there for a second. For people
who might not know, what is NN? Good catch. NAN
is basically a visual workflow builder. Imagine
a whiteboard on your computer. You drag and drop
little icons for different apps, Gmail, Slack,
a database, and you just draw lines between them
to connect them. OK, so it's the plumbing. Exactly.
It's the digital plumbing. So back to real estate.
The lid is captured. That's the trigger. The
system automatically fetches properties matching
the budget, say, within a 5 % variance. If a
match is found, it instantly executes three actions.
sends the email via Gmail, notifies the agent
on Slack, and updates the database. So it's not
just a chatbot answering questions. It's an engine
performing actions across different platforms.
It turns a manual tour into an automated reflex.
But, and this is huge, that brings us to the
second pillar, retraining the workforce. This
is where the mindset shift has to happen. Yes.
We've spent 30 years using deterministic software.
If you click Save, In Microsoft Word, it saves
every single time. It's binary. It works or it
doesn't. But AI is probabilistic. It might give
you a slightly different answer every time you
ask. Which is, I imagine, terrifying for a manager
who wants consistency. It is. That's why you
have to train your team to be what we call rigorous
editors. They have to stop trusting the screen
blindly. If they don't learn to audit the output,
they lose trust the moment the AI hallucinates
or just makes up a fact. I have to admit, I still
wrestle with this myself. I call it prompt grift.
I'll get a great result three times in a row,
so I stop checking. And then the fourth time,
it's completely off the rails and I don't touch
it until it's too late. It is so easy to get
lazy. We all do it. It's human nature to conserve
energy. That's why specific training prompts
are so valuable. The source suggests literally
giving your team a prompt that says, act as a
rigorous editor. Identify hallucinations, incorrect
facts, or tone mismatches. Explain why the AI
might have made that mistake. You're training
the human to grade the machine. Exactly. That
leads right into the third pillar, but I have
to ask, if the human is the grader, aren't we
just trading one job for another? I thought the
point was to do less work. That's the common
trap. Pillar three is owning operations. Think
of the smart intern analogy. AI is like a brilliant
intern from a top university. They're fast, they're
well read, but they're inexperienced. If you
don't watch them, they will make very strange,
confident mistakes. So the human role shifts
from doer to babysitter. Or operator. The operator's
job is updating prompts when the business logic
changes and handling the edge cases, those weird,
rare situations the AI hasn't seen before. Right.
If you don't have an operator, Your system is
like a Ferrari with a brick on the gas pedal
and no one at the steering wheel. That is a vivid
image. It raises a tough question, though. Does
this mean the dream of set it and forget it is
dead? For now, yes. You can't autopilot a car
with no driver. That is a hard pill to swallow
for people who wanted that easy passive income.
But it seems like this necessity for human involvement
is actually creating whole new business models.
It is. This is where we see the rise of service
-led AI. And this is probably the most important
shift happening in 2026. We usually think of
business as so binary. You're either a sauce
company software as a service, or you're an agency
selling hours. But you're saying that line is
blurring. Completely blurring. In fact, the most
successful companies are erasing that line. Even
the biggest startups, the ones coming out of
Y Combinator, are hiring roles like forward deployed
engineers. Forward deployed. That sounds like
military terminology. It does, doesn't it? Yeah.
But it's really just fancy talk for engineers
who go into the client's office and fix their
data manually. Look at a company like Harvey
AI. They're massive in the legal space. Now,
lawyers are notoriously resistant to new tech.
Right. They don't like changing how they work.
And they bill by the hour. Efficiency isn't always
their top goal. Sure. Exactly. So Harvey AI didn't
just hand over a login and say, here's your chat
bot. Good luck. They hand hold the deployment.
They send people in to ensure the model actually
works for that specific firm's messy data. They're
doing service work to make the software stick.
Because if they don't, the client churns. The
software just sits there. Unused and then it's
worthless spot -on. We're seeing the emergence
of the full stack automation partner. It's not
just building a bot It's a four -step cycle audit
the process build the automation train the staff
and then support it on a retainer That support
piece is key. That's recurring revenue and it's
sticky You aren't just a contractor you become
the AI officer That is a booming role right now.
Someone who sits between the CEO and the developers
to translate business needs into technical prompts.
Whoa! Imagine scaling that. You aren't just selling
a login. You are selling a whole new way for
a company to exist. You are. And that creates
a dependency that pure software can't match.
But wait a minute, why are investors suddenly
loving services again? We spent a decade hearing
services don't scale, revenue per employee is
too low, tech multiples are better. Why the U
-turn? Because in an AI world, services ensure
the software actually gets used. Services protect
the revenue. Without that service layer, the
churn rate is just too high. That makes perfect
sense. It's a moat. But there's another angle
here that I found really compelling. The idea
that starting with services is actually the fastest
way to build a product. This is the x -ray vision
concept. Break that down for us, because usually
people think you build the product, then you
sell it. OK, so everyone wants the passive income
dream, right? Build the software once, sell it
a million times while you sleep. But if you start
there, you're usually guessing at what people
need. You're building in a vacuum. Exactly. You
build as a solution in search of a problem. But
if you start as a service provider, let's say
you're manually managing SEO for 10 different
clients, you get x -revisioned into the market.
You see that client A, client B, and client C
all struggle with the exact same workflow step.
Maybe it's categorizing keywords or formatting
headers. That repetition. That is the signal.
That is the blueprint. Yes. You aren't writing
code based on a hunch anymore. You're writing
code based on the pain you felt doing it manually.
Yeah. Even Andreas and Horowitz A16Z, their reports
confirm this. The long -term AI winners often
start with a heavy service layer. So we should
essentially embrace the manual grind. Yes. The
grind is what generates the blueprint for the
software. I love that. It really validates the
hard work. Okay, let's get practical. We have
listeners who are employees, listeners who are
founders, and listeners running agencies. Let's
give them a roadmap. Start with the employee,
the intrapreneur. If you have a job right now,
do not quit yet. Use your job as a laboratory.
A laboratory, I like that. Look at your own day.
Find the tasks that drain you. The boring stuff.
Use tools like Zapier or NATU to automate just
your own desk. Once you save five or 10 hours
a week, you have a prototype. You go to your
boss and say, look, I made myself faster and
more accurate. You prove the value first. Exactly.
And then you get paid to learn. You're upgrading
your skills on the company dime. OK, what about
the new founder, the person starting from scratch
today? The biggest warning here is, do not be
the AI guy for everyone. The general is trapped.
I can automate anything for anyone. It's deadly.
You need to niche down hard. Be the AI automation
expert for dental clinics or inventory specialist
for local warehouses. How do you even find that
niche if you don't already have one? Use AI to
find it. You can literally ask ChatGPT. List
10 industries with high average customer value
and heavy manual admin tasks. It'll give you
things like legal intake or tax document labeling.
So the strategy is find a boring problem like
missed patient calls, build a specific solution
like a voice agent to book appointments, and
then charge a setup fee plus a retainer. Precisely.
You solve an expensive Boring problem. And finally,
for the existing agency owners, the marketing
firms, the design studios. For them, it is all
about silent margin expansion. That sounds profitable.
It is. You use AI internally to do the work 10
times faster. But, and here's the secret, you
keep your prices the same. So your delivery cost
just drops through the floor. And your profit
margin skyrockets. Plus, because a client already
trusts you, you can sell them trust upsells.
Sell them an AI training workshop. It's an easy
sell because the relationship is already there.
When you look at all these strategies, the employee,
the founder, the agency, what do they all have
in common? They all solve unsexy, boring problems
instead of chasing hype. Unsexy, boring, profitable.
That's the trifecta. Let's recap the big idea
here. We started with the failure of the wrapper
model. We talked about the need for a smart intern
mentality. Right. And the realization that the
winning business model isn't about the newest
shiny model from Google or OpenAI. It's about
customizing workflows, training humans to audit
the machines, and acting as the operator. The
money's in the gap. The gap between what AI can
do and what businesses actually do. That's the
best way to put it. Before we go, we always like
to leave you with something to do, not just something
to think about. Here's your challenge. Pick one
manual process in your daily life today. Just
one. Spend 30 minutes mapping it out on paper,
step by step. And then just ask yourself, how
can I help a human do this faster? That is where
it starts. The year is 2026. There are no true
experts yet. Everyone is figuring this out in
real time. If you are willing to do the unsexy
work, the future belongs to you. Couldn't have
said it better. Thanks for diving in with us.
We'll see you on the next one.