The AI gold rush is definitely on. Feels like
everyone's racing to stake their claim, doesn't
it? It really does. But what if the path, you
know, the one most people are on, leads not to
gold, but, well, nowhere fast? What if you're
carefully building this fragile Jenga tower,
you know, the kind that's just going to collapse
instead of a solid skyscraper? Oh, we've seen
that exact thing. It's what our sources are calling
the AI agent trap. It's this low margin, really
high stress cycle that just... burns people out.
But here's the good news, right? Our sources
show there's a clear way out. There's actually
a blueprint for something, well, much more solid
and rewarding. Welcome back to the Deep Dive.
Today, we're taking a close look at some really
fascinating research. It really challenges the
common way people think about building AI solutions.
It's titled, The AI Agent Business Model is a
Trap. Here's what works. Yeah. And it highlights
this really critical difference. Are you just
building these quick one -off AI agents like
individual automated tasks? Or are you aiming
higher, building something more integrated, more
fundamental, what they're calling an AI operating
system? So our mission today is to really explore
this shift. We'll look at why that common approach,
the agent approach, often fails. We'll dig into
what these operating systems actually are, how
you might build one both strategically and technically,
and probably the most important part for many
listeners, how they can actually unlock sustainable
recurring revenue for your business. Right. It's
about getting off that exhausting freelancer's
hamster wheel and stepping onto a path, a blueprint,
that could lead to maybe $10Ks plus a month consistently.
That's a total game changer. Buy it. OK, let's
dive in. OK, so let's really unpack this AI agent
trap that our sources describe. They paint a
pretty clear picture. And honestly, it's a bit
sobering looking at what's happening out there.
Yeah, exactly. They call it the freelancer's
hamster wheel. And it fits, right? You're just
constantly running. You pour weeks, maybe months
into building these complex AI agents for a client.
You land a one time gig, maybe get a few thousand
bucks. Client seems happy for a second and then
poof. Gone. And you're right back to scrambling
for the next project. It's exhausting. It is.
And that cycle is just brutal. Our sources point
to four specific fatal flaws that make this whole
model feel like it's built on quicksand. Just
fundamentally shaky for long term success. OK,
so first up, it's becoming a commodity. Basic
AI agents. you know, those sort of standard automated
tasks using AI. Everyone and their brother is
building them now, which naturally just drives
the prices down, down, down. It's that classic
race to the bottom. Nobody wins that. Exactly,
which leads right into the second big problem.
It's nearly always a one -time transaction. You
do the work, you deliver, you get paid once,
then you're back at zero, hunting again. Without
that predictable income, that recurring revenue,
it's just incredibly hard to build something
truly sustainable, isn't it? No doubt. Then there's
number three. What the sources call complexity
addiction. I mean, I'll admit, I've kind of fallen
into this trap myself sometimes. You build these
workflows that look super impressive, all these
moving parts, but really, they function like
some crazy Rube Goldberg machine. Spaghetti workflows,
they call them. Overly complicated, often inefficient.
But the real insight is, usually, a simpler,
more direct automation that often delivers more
consistent value. And it's way more robust. Less
likely to break. avoids that overengineering
trap. That's so true. And the biggest flaw, number
four, maybe the most fundamental, it solves the
wrong problem. It focuses on selling a tool,
you know, that specific AI agent instead of delivering
a core lasting business solution. Right. So if
we cut through all the hype around these individual
agents, what's the real root issue with just
selling those kinds of one -off things? It really
boils down to being a low -value one -time sale.
It just doesn't deliver that deep, long -term
business impact that really changes things for
a client. Okay, so if that's the trap, what's
the escape hatch? The sources bring in this idea
of an AI operating system, BEAT. Sounds kind
of big, doesn't it? Almost futuristic, like something
out of Star Trek, maybe? It does sound a bit
grand, but it's actually very practical when
you break it down. Think of it more like an AI
automation power cube. That's the term they use.
It's not just one single agent doing one task.
It's a combination, four integrated parts all
working together smoothly. And the crucial part
is it's designed to solve a massive recurring
business problem, not just automate a little
task here or there. Power cube. Okay, I like
that analogy. Makes you picture something powerful,
integrated, self -contained. So what are these
four components then? What makes up this system?
Okay, component A is the brain. These are your
sophisticated AI models, the parts that actually
analyze information, transform data, do the heavy
lifting of processing. That's the intelligence
core. Got it. Makes sense. And then the nervous
system, component B, what's its job? Ah, that's
the automation layer. They mention tools like
N8n, which, for listeners who don't know, is
a really flexible, low -code platform for connecting
apps and automating workflows. So this layer
acts like the central coordinator. It connects
everything, passes data between all the different
systems, and basically orchestrates the entire
flow to make sure everything talks to each other.
Okay. And the memory, component C, that's for
storing data, right? Like using Supabase or something
similar, keeping track of user insights, that
kind of thing. Exactly. Supabase provides that
robust, stable database. You need persistent
storage, a place to remember user interactions,
data points, progress. It's absolutely key. And
finally, component D is the body. This is the
user -facing part, the application layer. A friendly
front -end interface may be built with a tool
like Bolt .new, which lets you build web apps
quickly. Wow. Okay, picture that. The brain,
the nervous system, the memory, the body, all
working together totally seamlessly. It really
is like stacking intelligent Lego blocks, isn't
it? Data and logic combined to build something
a business just can't do without. Whoa. I mean,
imagine scaling that kind of integrated system,
handling like a billion queries a day. That's
just a whole different universe of impact compared
to a single agent. That's precisely the goal.
Right. Creating something so deeply embedded
in a business's core operations that they literally
cannot live without it. It becomes fundamental
to how they succeed. That's where you find that
sustainable, predictable, recurring revenue stream.
So just to be crystal clear then, how is this
PowerCube idea fundamentally different from just
selling a standalone AI agent? It's an integrated
multi -part system solving a core ongoing business
need, not just a single isolated task automation.
OK, this next part, for me, this work, it's really
practical where the rubber hits the road. Our
sources argue really strongly that success isn't
just about what you build. It's about who you
build it for. They emphasize this strategic framework
that starts with really zeroing in on your ideal
target customer. Right. This is step one. Choose
your target. And they have these goldmine criteria.
The advice is pretty blunt. Be a whale hunter.
Stop chasing the small fish who just don't have
the budget. And they list five key criteria for
finding that perfect customer. First, and maybe
most obviously, they have to have money. It sounds
basic, but it's often just as hard to sell a
$10 ,000 solution as a $1 ,000 one, so you might
as well aim high. What they're really saying
is you're not just looking for any client. You
need businesses with a clear, urgent problem,
yes, but also the resources and crucially the
mindset to invest in a proper long -term fix,
not just a cheap patch. Yeah, stop selling to
broke businesses. It's literally a quote from
the material. And it makes perfect sense. Second
criterion. They have to value their time immensely,
more than money, often. These are typically successful
business owners, but they're drowning, overwhelmed,
more cash than hours, basically. Third, they
need to be easy to find. You should pick a niche
where your potential customers gather, like specific
online communities, industry groups, maybe certain
types of events. Don't try to hunt in scattered.
hard to reach markets. Fourth, focus on businesses
with high margins. Think agencies, consulting
firms, maybe high end local services like specialized
contractors or medical practices. They understand
the value of something that boosts their existing
profitability and they actually have the budget
to pay for that value. And the fifth one, they
call it the ultimate cheat code. Target a growing
market. Think about it. If you own just 10 %
of a market that doubles every year, your business
grows almost automatically. You're riding a wave.
Okay, but finding them isn't the whole story,
right? The sources then move to step two, validate
customer resonance. And they stress this is absolutely
vital. So many people skip this. They really
do. You see builders hiding away for months,
coding in secret, then they launch and did crickets.
Nobody actually wants it. So our sources suggest
this really clever technique. The magic wand
method. Yeah, this is great. You find maybe 10
people who fit your ideal target profile, right?
From step one. Then you offer to work with them
for free just for a short time. The goal is to
get real testimonials, gather honest feedback,
and then you ask the magic question. Look, if
I could wave a magic wand and solve just one
of the 20 problems you're dealing with, the single
one that keeps you tossing and turning at night,
which problem would that be? And their answer,
that's your golden nugget. It forces them to
cut through the noise and pinpoint the single
most valuable, most urgent pain point they have.
This lets you rigorously check if there's real
demand before you commit to building the supply.
It's a huge de -risking step. So why is validating
that demand up front just so incredibly critical
before you even write a line of code? It stops
you from wasting months, maybe years, building
something nobody actually wants to buy. Plain
and simple. Okay. To make this whole AI operating
system idea less abstract, the sources give us
a really solid case study, the LAPS dashboard.
It's designed to tackle a super common, really
painful problem for service -based businesses,
converting leads effectively. Exactly. Most service
businesses suffer from this leaky sales funnel.
They spend good money, sometimes a lot of money,
generating leads, but then potential customers
just drop off at every stage from initial contact
to actually becoming a paying client. That's
where this LAPS framework fits in. Right. LAPS
leads, appointments, presentations, sales. That's
the standard flow. But the money ball insight
here, as they call it, that's the really interesting
part. They argue that the place with the most
leverage, almost always, is improving the conversion
rate specifically from a lead to an appointment.
Think about a typical funnel. Maybe you get 100
leads and that eventually results in, say, three
sales down the line. Right. But if you use an
AI optimized system focused just on boosting
that lead to appointment rate, you might double
it. Suddenly, those same hundred leads, they
turn into six sales. You haven't spent a single
extra dollar on getting more leads. Just by improving
that one specific conversion point, you've potentially
doubled the business's revenue. That is huge.
It really is significant. And the proposed solution
is pretty clever. Replace those boring static
contact us forms or maybe those downloadable
PDF lead magnets that nobody reads. Replace them
with an interactive personalized assessment dashboard.
Ah, okay. So instead of just passively collecting
info, this thing actively engages the user. It
gives them immediate value like a personalized
action plan based on their answers. It pushes
them towards taking the next step with a clear
call to action properly to book that appointment.
And crucially, it scales infinitely. Build it
once, serve potentially. thousands of customers.
Precisely. It turns that passive, easily ignored
interaction into something dynamic, a problem
-solving experience that feels tailored just
for them. So what is it about the LAPS dashboard
that makes it so effective at fixing that leaky
funnel problem? It delivers immediate personalized
value, which engages users deeply and drives
them directly towards booking appointments. All
right, now for the how -to. Our sources don't
just talk theory. They actually lay out the technical
bones, the architecture. They call it the assembly
manual for your first Power Cube. I love it.
And it's like they're assembling the Avengers
of tech tools, each playing a specific part.
Bolt .new is Iron Man handles the front end,
letting you build web apps just by describing
them in natural language. Super cool. NAN, that
workflow automation tool, is Nick Fury, the master
orchestrator, pulling the strings behind the
scenes, managing all the back end automation,
making sure all the systems communicate. Scoop
.js is Captain America, you know, the strong,
stable, reliable foundation. That's your database
holding all the persistent data securely. And
OpenAI, well, that's got to be Hulk, right? The
raw brute force intelligence providing that incredible
power for analysis, personalization, making sense
of the inputs. That's a pretty awesome lineup.
And the workflow they map out sounds really clean,
actually. User signs up on the Bolt front end.
That triggers a webhook over to N8n. N8n sends
the info to OpenAI for the analysis part. OpenAI
sends results back. N8n stores them in Supabase.
Bolt pulls from Supabase to display the results
to the user. And then N8n can trigger automated
email follow -ups based on those results. It's
a really neat, cohesive loop. It is cohesive.
And the material breaks down the actual build
process into clear phases, almost like assembling
IKEA furniture, but way more powerful, step by
step. Okay, phase one is the NERBA system. That's
setting up the N8n. Then phase two, the body.
Building the actual web application using Bolt.
And this is where that super prompt idea comes
in again, which sounds like a massive time saver.
Instead of building UI elements one by one, you
apparently give Bolt one big comprehensive prompt
describing the whole app, its function, layout,
goals, and the AI just builds a functional structure
for you in minutes. That sounds pretty amazing.
Yeah, total game changer for speed. Okay, phase
three, the memory. Getting Supabase ready. Configuring
the database, setting up the tables to store
user assessments, handling user login and authentication,
the data backbone, and phase four, the brains.
This is defining the AI processing logic itself.
And here's a crucial point I make. This isn't
about building one giant, super complex AI agent
that tries to do everything. Instead, it's about
using multiple, simpler, specialized AI models.
Maybe one focuses just on business analysis,
another on financial implications, a third on
strategic recommendations. Each has a narrow,
specific job. Ah, okay. So it's like maybe 90
% standardized structure and content, but then
that crucial 10 % is personalized by these focused
AI models. And that makes so much sense because
honestly, I still wrestle with prompt drift myself
sometimes, you know, where you give an AI the
same prompt later and the results get kind of
weird or less accurate. So breaking it down into
these smaller specialized AI jobs sounds way
more reliable, more predictable output. Exactly.
It really maximizes the impact while ensuring
scalable. consistent delivery, it makes the whole
system far more robust and predictable over the
long haul. Okay, so how exactly does using multiple
focused AI models improve reliability compared
to just one big complex AI agent? Each model
has a simpler specific task, which reduces overall
complexity and makes its output much more predictable
and consistent. Okay, so once you've built that
functional standard model, your first AI operating
system, the sources then suggest ways to upgrade
it. Think of it like adding premium features
to a car, making it a high -performance machine
that justifies a higher price point and delivers
even more value. Right, like adding dynamic and
interactive elements. Imagine the dashboard updating
scores in real time as a user answers questions.
Or maybe embedding personalized video messages
directly into the dashboard, tailored based on
their specific inputs. That's pretty slick, right?
Really elevates the whole experience. Definitely.
Then they talk about user experience optimization.
Things like progressive disclosure, revealing
information bit by bit, keeping the user engaged
and curious, rather than overwhelming them up
front. Maybe adding gamification elements, progress
bars, achievement badges to encourage people
to complete the assessment, and of course, mobile
optimization. That's just table stakes these
days. Absolutely non -negotiable. And finally,
they suggest building in smart conversion mechanics,
using subtle scarcity or urgency cues like countdown
timers for special offers, or embedding powerful
social proof like short video testimonials or
success stories from similar clients right there
within the dashboard experience itself. Little
nudges to drive action. And all this leads to
the big question, why this prints money? The
analogy they use is powerful. It's not like being
a house flipper, doing a quick project, selling
it, moving on. It's like being a commercial real
estate developer. You build a valuable asset
once your AI operating system, and then you collect
recurring rent from multiple. tenants, your clients.
That's it, exactly. You deliver way higher value
to each client. You get predictable recurring
revenue, which is the holy grail for any business.
And crucially, it's scalable delivery. You can
onboard more clients without linearly increasing
your own workload or costs. You position yourself
as the premium, indispensable solution in your
niche. And what about keeping those clients long
-term? Retention. They introduced this idea of
the sticky building. Your AI operations has become
so deeply woven into the client's daily workflow,
their processes, their data. It's like their
virtual office building. They simply can't imagine
operating without it. Yeah, the moving costs,
the pain and expense of trying to switch to something
else become incredibly high. They're locked in,
but in a good way. Plus, you keep adding value.
You keep upgrading the building, adding new features,
improving performance, maybe leveraging network
effects as more clients use it. It gets better
and better over time. It creates this really
powerful lock -in, yes. But it's a lock -in based
on genuine, ever -increasing value, not just
contractual obligation. So, boiling it down,
when we talk actual... What's the single biggest
difference in revenue generation between the
old AI agent model and this AI operating system
model? AI operating systems are built to generate
predictable, recurring revenue for many clients
over long periods, unlike the one -off payments
from isolated agent projects. Sponsor. So let's
pull this all together. What does this really
mean for you listening right now? The big idea,
I think, is a really profound shift, a fundamental
change in how we should approach building AI
solutions for businesses. Absolutely. The core
message is loud and clear. Stop selling those
one -off AI agents. They trap you on that exhausting
freelancer's hamster wheel. They're getting commoditized
fast, they provide only temporary value, and
ultimately they solve the wrong problem, both
for the client's long -term success and for your
own business's sustainability. Instead... The
path forward, according to this research, is
to build AI operating systems. These integrated
power cubes combining AI models, automation,
databases, user interfaces, all meticulously
designed to solve one huge recurring business
problem for a very specific high value type of
client. Right. And it requires that strategic
thinking up front, targeting the right clients,
rigorously validating the problem using techniques
like Magic Wand, and then leveraging powerful,
often no -code or low -code tools like Bolt,
NAN, and Supabase to build efficiently. It's
all geared towards creating massive, scalable
value that really moves the needle for businesses.
And that scalable value, as we've discussed,
is what translates directly into predictable,
recurring revenue for the builder. Often the
target is $10 ,000 a month or more. And you build
these sticky client relationships because your
solution becomes truly indispensable to their
daily operations. Yeah, but the sources are also
really clear. This isn't some kind of get rich
quick thing. It definitely isn't easy. It demands
that you learn the tech, develop real business
acumen, become obsessed with understanding your
customers deepest pains and just stick with it.
Persistence is key. True. But for those who are
willing to, you know, pay that. price of admission,
as they put it, the potential rewards are really
substantial. It's not just about the recurring
revenue or the scalable growth. It's about building
deep, integrated partnerships with clients where
you're delivering lasting, transformative impact.
And the sources suggest, maybe optimistically,
but maybe not, that your first $10 ,000 a month
could be just one well -designed, well -executed
system away. It really feels like a call to action.
Time to stop just building little tools and start
solving fundamental business problems at scale.
operating system revolution, it seems, has begun.
The only real question left is, will you be leading
it or watching it happen from the sidelines?
Thanks for joining us for this deep dive. Really
fascinating stuff. Until next time, keep exploring.
Out to your row, music.