When you think about traditional business growth,
the answer was always just hiring more people.
You needed more sales reps, more accountants.
You just threw bodies at the problem. It was
the only way to scale, really. It was the bottleneck.
But that's changing. It's changing in a pretty
astonishing way. We're seeing data now where
a single role, what some are calling AI ops,
in a small, like 20 -person company, can recover
between 400 and 800 hours of team capacity. every
single week. Wow. Okay. So that just reframes
the entire cost of scale. That's like getting
five to 10 full -time employees from one strategic
person directing automation. Exactly. Welcome
to the Deep Dive. Today we're going way beyond
just productivity hacks. We're unpacking a framework
that reveals five really seismic shifts in how
businesses are creating value right now. And
these insights, they're coming straight from
the founders who are actually building these
new AI native companies. And our mission here
is to get to the bottom of this new paradox.
I mean, when anyone, and I really do mean anyone,
even a smart 12 -year -old, can build a sophisticated
app. Using plain language. Then what actually
becomes valuable? If building is easy, what's
hard? That's the question. That's the question.
So our roadmap today will cover how teams are
being restructured, how you defend yourself when
anyone can copy your features instantly, and
where your focus really needs to be. So we're
moving from a world that valued execution. To
one that values direction and distribution. Okay,
let's get into it. Shift one. Moving from org
charts to what you're calling leverage charts.
Yeah. The old model is all hierarchy departments.
And like we said, the default solution was always
hire more people. Right. Throwing bodies at the
problem. Yeah. That model is just, it's obsolete
now. So what's the new paradigm? It's nonlinear.
Instead of a whole department doing routine work,
you have one strategic leader. And that person
directs a team of AI agents and tools to deliver
the outcome. So finance isn't a team of six people
anymore. No. It's one person overseeing systems
that handle 98 % of the routine operations. The
human focus shifts entirely to strategy. Let's
make that concrete. What does this look like
for a sales team? Okay, so a traditional sales
team might need, what, 10 to 14 people? SDRs,
AEs, engineers. To hit their targets, yeah. With
this new leverage model, one elite closer, just
one person, backed by the right AI infrastructure,
can handle the revenue of that entire team. This
is that one -person sales machine idea, the infinite
SDR. Precisely. The AI agent does all the repetitive
tiring work, the outbound prospecting, personalizing
messages, qualifying leads. So the human closer
only steps in when a deal is actually ready.
When it's hot. And the AI is doing more than
just prospecting. It's optimizing their calendar.
It's drafting follow -ups based on the call.
And it can even provide real -time competitive
intel during the call. Exactly. The human brings
judgment, relationship skills. The AI brings
perfect, tireless execution. Which brings us
back to that AI ops role. Their whole job is
to find the repetitive work. And eliminate it.
And then train the rest of the team to become
these high -level directors of AI systems. Whoa.
I mean, just imagine scaling that. Taking that
400 to 800 hours a week of recovered time across
a company of, say, 100 people. It completely
changes what's possible. It redefines growth
itself overnight. So you have to stop asking,
who should I hire? and start asking, what can
we build to create leverage? So if AI is handling
all the execution, what is the new core skill?
What separates success from, you know, disappointment?
Directing the systems. It's having the vision
and judgment to guide the AI. And that leads
right into shift two, from being a doer to being
a director. It does, because this is where a
lot of people get stuck. They try chat GPT. you
know, for some generic blog post, and they walk
away thinking it's overhyped. Right. But the
problem isn't the AI. The problem is they're
using it like a simple tool, not directing it
like a team. They're treating it like a fancy
calculator instead of a thinking partner. You've
got it, which points to this massive shift in
how we need to spend our time. The time flip.
Yeah. The old split was maybe 90 % execution.
Doing the actual work in 10 % direction. Strategy,
planning. Right. Now, you have to flip that.
It has to be 80 % or 90 % direction. Designing
the system, setting strategy, quality control,
only 10 % to 20 % is execution. That's a huge
reversal. You mentioned a film director analogy.
I think it's the perfect analogy. A film director's
value isn't in holding the camera. No. It's their
vision. orchestrating all the resources, judging
the quality. The human's job is now to design
the world in which the AI can execute perfectly.
And honestly, I still wrestle with prompt drift
myself. Oh, yeah. You know, where you give the
AI a complex task and halfway through it just
kind of forgets the most important instruction.
It's a real thing. It's tough to consistently
provide all the right context, but that struggle,
that iteration. That is the new job. So how do
we get better at directing? You had a three -step
framework for this. Yeah, it's pretty straightforward.
First, you have to audit your time. For two weeks,
just track your actual execution versus direction
ratio. Be honest with yourself. Be ruthless.
Second, identify the opportunities. Find that
20 % of repetitive work that's eating 80 % of
your time. That's your first target. Okay. And
finally, learn to direct. Pick a couple of those
tasks, write really clear system prompts, give
the AI good examples, and just iterate. The goal
is to flip your ratio over, say, the next year
or 18 months. Okay, so we can build things instantly
with good direction. But if everyone can build
and copy things that quickly, how do you build
any kind of long -term competitive advantage?
By using your own unique data to build a moat
that makes your product smarter over time. Which
is shift three. From feature defenses to data
defenses. Exactly. The old feature race is just,
it's over. Competing on features is suicidal
now. It is. You used to get maybe a three to
six month head start with a new feature. But
now with AI coding tools like Cursor or Copilot,
a competitor can copy your feature in weeks.
The moat is gone. Instantly. So what actually
protects you? It's the private data your customers
generate. It's the unique industry data you collect.
These create learning loops. Like with ChatGPT,
it gets better for you because it remembers your
conversations. And no competitor can replicate
that specific personal feedback loop. That's
the moat. So how does a company that's not, you
know, open AI build one of these? You have to
be intentional about it. There's a three -step
framework. Step one is... You have to clean your
data. The unglamorous part. The most critical
part. Garbage in, garbage out. You have to fix
inaccuracies, validate everything. Okay, so your
data is clean. Step two. AI analysis. Use AI
to find the patterns that humans would just miss.
It can see correlations across years of data
that are invisible to us. So it discovers a proprietary
insight for you. It does. And then... Step three
is you close the loop with AI suggested next
steps. AI monitors data, sees a trend, recommends
an action, human approves it, the AI executes.
And the whole system gets smarter from the outcome.
That's the loop. The brutal reality is the company
with the best data and the fastest learning loop
is going to win. So. If AI can handle features
and data analysis, what does that mean for all
the repetitive internal stuff, the back office
functions? It means they are also ripe for near
total automation. We're talking up to 98%. And
that takes us to shift four, the autonomous back
office. For decades, we've staffed things like
finance, HR, and legal with full -time employees.
Right, assuming you needed human judgment for
every little thing. But if you actually look
at the work. Most of it isn't judgment. It's
just applying a policy to a request. It's pattern
-based execution. Which is exactly what AI is
built for. The new reality is 98 % automation
with just 2 % human oversight for the exceptions.
Let's go back to that finance example. The old
way is someone gets an invoice, they manually
type it in, manually check it. So much friction.
Now the invoice arrives, an AI extracts the data,
checks it against your expense policy and your
CRM. Instantly. And if it matches the rules?
It processes and reconciles it. Done. The work
of days now takes seconds. So the key to implementing
this is just having really clear documented policies.
The AI needs rules. It needs access to the data.
And it needs a clear point where it hands off
an exception to a human. The core principle seems
to be exceptions deserve people, patterns deserve
code. That's it. Automate the 98 % so your people
can focus their brilliance on... The 2 % that
really matters. Okay, so let's put it all together.
The product is easy to build. The data is your
defense. The back office is automated. What is
the one thing left that determines if you win
or lose? Distribution. Yeah. Your ability to
actually reach the customers who need what you've
built. And that's the final and maybe the most
important change. Shift five. From a development
advantage to a distribution advantage. For 30
years, the company with the smartest engineers
won. That was the game. But that advantage is
basically gone. It's been destroyed. I mean,
code quality still matters for stability, of
course, but it's not a competitive edge anymore.
When a kid can build your core feature set, code
has been competitively neutralized. So the entire
game shifts to distribution, reaching people,
building trust, converting attention. And that
distribution really comes in three main forms.
You've got your own audiences, like an email
list, that's the most valuable. Then there's
paid distribution ads, which are getting more
and more expensive. And finally, partnerships.
Right. Working with people who already have the
audience you need to reach. So there's a playbook
for this new world. There is. Step one. You have
to build distribution into your business model
from day one. You have to ask, how will we reach
customers before you write a line of code? Because
great distribution will beat a great product.
Every time. Every time. Step two. Attach your
brand to one clear problem. People buy clarity,
not a list of features. Be super clear about
the outcome you deliver. And step three. Pre
-sell before you build. Go get commitments or
at least real interest before you spend months
building something nobody actually wants. It
de -risks everything. Completely. And if you
don't have an audience, the shortcut is to partner
with creators in your space and share the revenue.
It's that simple. The meta lesson here is that
when anyone can build, the only edge left. is
who can reach that's the whole game now what
an incredible breakdown so to recap the big idea
here ai is fundamentally devaluing the work of
pure execution it is it's destroying the value
of building simple features success now demands
a total transformation into a strategic director
right you have to build data modes you have to
automate all the pattern -based work and you
have to master distribution it's a uh a complete
rewrite of business strategy And this isn't something
that's coming in a few years. It is happening
right now. The choice for you, the listener,
is pretty stark. Either lead this transformation
in your organization or you're going to be disrupted
by someone who does. The time to act is getting
very short. So here's the challenge for you this
week. Audit your business through the lens of
these five shifts. And then, and this is the
actionable part, pick just one high -impact area
to start with. Maybe it's defining your first
data loop, or maybe it's just doing that time
audit to justify hiring an AI ops role. Whatever
it is, start this week. Because every week you
wait, the gap gets wider and harder to close.
Thank you for this deep dive.