So today, we're gonna skip the complexity, we're
diving into a really strange and frankly pretty
lucrative corner of the tech world. Yeah, it's
like, think about all those giant venture -backed
companies you read about, the ones building battleships.
We're not looking at those, we're looking at
the simple, hyper -efficient speed boats. And
that is the absolute core idea we're diving into.
These are small, really focused mobile apps,
sometimes built by just one person. And they're
making, what, $50 ,000 to sometimes $300 ,000
every single month. Every single month. And they
fly completely under the radar. Because they
only do one simple thing. But that focus, that's
what makes them ridiculously profitable. It just
flips the old scaling playbook on its head. Welcome
to the deep dive. Today we're digging into the
source material on these simple AI sauce models
to pull out the exact patterns of repeatable
stuff that makes them work. Our mission here
is to really understand how these founders are
building so much value with what looks like minimal
complexity. Exactly. So first we'll unpack why
those old barriers to entry, you know, needing
to be a coding genius or having tons of cash,
have just completely vanished. And then... We're
going to go deep on five specific real -world
examples. We'll break down the psychology, the
business case, and even the simple AI prompts
that are powering the whole thing. You know,
if you look back at the last, say, 20 years of
tech, starting a real software business... It
felt like you needed a PhD in computer science.
Oh, absolutely. I mean, just to get a prototype
running, you were talking months of coding school,
wrestling with syntax. Right. It was a nightmare.
It was this huge technical wall. It kept the
classic idea guy, the person who actually understands
the customer's problem, completely out of the
process. Totally. If you weren't a coder, you
had to, what, beg one to be your co -founder
or go out and try to find millions in funding.
But those walls have just, they've crumbled.
That technical barrier is, for the most part,
gone now. Right. With tools like Cursor or Replet,
all these AI developer assistants, you can basically
just describe what you want in plain English.
And it generates functional code. It's like a
language translator for engineering. It just
completely democratizes the whole process. The
person with the vision, the one who really gets
the market need, can finally be the builder.
And this is where the really fundamental shift
happens. This is what makes these little six
-figure businesses possible. AI isn't just a
helper tool for the engineer anymore. The AI
is the product. Yes. And the pattern is so simple.
All five examples we're going to look at follow
it perfectly. The user gives a little bit of
input. you know, photo, voice note, whatever.
Then the magic happens. The AI model crunches
it. And then you get the output. Which is the
valuable answer, the personalized result. Exactly.
And all the heavy lifting, the really complex
stuff, is being handled by these huge models
like GPT -4 or Claude or stable diffusion. So
these founders, they're not trying to build a
new brain from scratch. No way. They're just
connecting a very specific user to the powerful
brain that already exists. The source material
calls that connection the simple door. I love
that. The founder's main job is just to design
the easiest, most elegant door for people to
walk through to get to that AI power. They solve
that last mile problem. So if the core AI is
doing all that heavy lifting, what's the one
non -negotiable step a founder has to get right
to hit that $50 ,000 a month floor? They have
to design a door that instantly solves a specific,
painful problem for a really narrow group of
people. That distinction is everything. Yeah.
OK, let's jump into the real data. We're going
to look at these five businesses and see how
they did it. First up, we've got Flash Loop.
It's a viral video character creator, and it's
reliably pulling in about 50 grand a month just
by tapping into pure human vanity. The psychology
here is what the source calls the vanity loop.
Yeah. And it makes perfect sense. People love
seeing themselves in, you know, funny or surprising
situations. And more than that, they love the
social currency they get from sharing it. Right.
So what Flash Loop does is you give it a selfie,
and it just plops your face onto a character
in some funny, super shareable video. So you'd
be the star of a movie trailer. Or, the more
common one, your face is suddenly on a little
baby who's dancing around in a business suit.
It's ridiculous. And the technology behind this
has shifted. You don't even need pre -film templates
anymore. Nope. They use AI video tools like runway
or maybe cling AI to generate the whole scene
from scratch just from a text prompt Then they
use face swapping to put you in it So the prompt
is basically the entire product design pretty
much you'd write something super descriptive
like a cinematic shot of a cute chubby baby wearing
a business suit Hosting a podcast in a professional
studio and that just builds the viral growth
right into the product every time someone shares
their video on tik -tok It's a free ad for the
app. Exactly. OK, so from pure vanity, we're
going to pivot completely to utility and faith.
Next up is the Bible Note Taker. It's a niche
sermon recorder, quietly making $60 ,000 a month.
This is just a textbook example of finding a
recurring pain point in a very specific community.
People go to church every week. They hear a sermon
that really resonates. And then by Monday morning,
all the key lessons are just gone. evaporated
so the door is super simple you open the app
on Sunday hit record the app uses AI transcription
and they lean heavily on tools like open AI whisper
which is just It's phenomenal at turning speech,
even in a big, echoey room, into accurate text.
But the real money isn't just in the transcription.
It's in the synthesis. It doesn't just give you
a wall of text. No, it uses something like GPT
-4 to structure it all. The output is a bullet
point summary of the main lessons. It suggests
a prayer. And it gives you one actionable step
for the week. It's immediate, personalized value.
And the prompt is key here, too. It tells the
AI to act as a helpful spiritual assistant and
keep the tone encouraging and warm. And that
focus on trust and a weekly habit. It's perfect
for a subscription model. It's a need that the
big, general note -taking apps would never even
think about. OK, so let's move to an even bigger
financial pain point. AI Home Decor, the room
visualizer. This one's pulling in $100 ,000 a
month. Yeah, and this solves that universal,
very expensive problem. The fear of buying. The
risk of spending $2 ,000 on a sofa you end up
hating. Right. Or picking a paint color that
just ruins the room. It's paralyzing. So for
a $10 subscription, this app is basically cheap
insurance. You upload a photo of your room, and
you can instantly see it in any style you want.
Japanese minimalist, art deco, whatever. It just
removes that huge financial risk. And technically,
this all hinges on a very specific technology
called ControlNet. That's the secret sauce. OK,
let's unpack that for a second, because ControlNet's
precision is so important here. Normally, with
an AI image generator, the results are kind of
wild and unpredictable. Control net is different.
It acts like a digital stencil. It locks the
geometry. So the walls, the windows, the light
fixtures, they all stay in the exact same place.
Then the AI, like stable diffusion, just repaints
everything inside those lines. If you didn't
have that, you'd get a new room design with windows
on the ceiling. Exactly. So the prompt has to
command that. Something like, redesign this room,
but keep the window placement and room structure
exactly the same. You know, I have to admit,
even with these tools, getting that control just
right is a battle. I still wrestle with prompt
drift myself when I'm using control net. Yeah.
Yeah, you know, you try to change one small detail,
like the wood grain, and suddenly the whole structure
gets a little wonky. It takes real skill to get
that consistency. But when you nail it, apparently
you get to seven figures. OK, moving on. Our
fourth example taps into something we do all
day, every day. It's called MojiLab, a custom
sticker maker, also making 100K a month. This
one is just pure frequency plus social status.
We're on messaging apps constantly, and standard
emojis get old fast. So custom stickers of your
friends or your cat or yourself, that's like
high value currency in a group chat. It creates
this automatic viral loop. Right, because if
I send a sticker of my dog dressed as a pirate.
Everyone in the chat immediately asks, Wait,
how did you make that? And that question is all
the marketing you need. The tech behind it uses
powerful APIs like Deli 3 or Mid Journey. But
the real trick isn't just making the image. No,
it's making it look like a real usable sticker.
So the key detail on the prompt is all about
the packaging. It has to demand white background,
thick white border around the character, skicker
pack effect. Without that, it's just a picture.
With it, it's a product. OK, finally, we're turning
to the world of high -intent collectors. Vinyl
Snap, the collector's price guide. This one's
making $70 ,000 a month. This targets a really
specific high -stakes moment. You're a collector.
You're at a garage sale. You're holding a record.
And you need to know right now, is this a $5
common pressing, or is this a rare $500 first
edition? The seconds matter. They absolutely
do. So the door is your phone's camera, you scan
the album cover, and the app instantly identifies
the artist, the album, the specific catalog number.
Which is the critical detail for collectors.
Right. And then it estimates the condition and
gives you the current market value from a database
like Discogs. The technology here is AI vision.
We're talking tools like Google Cloud Vision
or GPT -4 Vision. They're trained to read that
complex, sometimes faded text on an album cover
with incredible accuracy. Whoa. Just, I mean,
stop and imagine scaling that accuracy to a billion
queries a day for every niche collector looking
for that hidden gem. The speed and precision
required to turn a phone photo into a verified
price in milliseconds. That's a phenomenal feat.
And that speed is the whole value proposition.
A regular Google search is just too slow, too
clunky for that moment. This app can pay for
itself with just one good find. So if we synthesize
all five of these businesses, from the vanity
videos all the way to the collector tools, what's
the one single defining characteristic that ensures
they're profitable? They all offer a near instantaneous
personalized answer to a problem that all the
existing general tools just solve poorly or slowly
or not at all. And that really brings us back
to the core idea we pulled from the source material.
These profitable AI Saws models are so effective
because they take a simple human problem. Boredom,
memory loss, fear. Exactly. Fear of commitment,
curiosity. And they use this advanced AI to provide
an immediate, valuable, and super -specific solution.
And that's the massive opportunity right now.
It's not about inventing the next GPT -4. That
takes billions of dollars. Right. The real economic
opportunity is in inventing that simple, focused
interface, that elegant door that lets normal
people apply all that existing AI power to their
own specific needs. We've only covered the first
five examples here. They show how that narrow
focus creates high returns and how the tech is
really accessible now. But part two of this research
is where it gets even more interesting. It actually
reveals the single top earner on the whole list.
An app making a confirmed $300 ,000 a month.
And it targets a totally different kind of anxiety.
And maybe more importantly, part two introduces
the master framework. Which is the checklist
these founders use to test if an idea is profitable
before they even write a single line of code.
Yeah. I mean, we've seen six -figure success
built around vinyl collectors and sermon notes.
The common thread is just finding that quiet
corner of the market. So here's the final thought
to chew on. If focusing on these quiet, specific
communities can yield these kinds of results,
what small, overlooked community problem could
you solve with AI vision or generation or transcription?
The real opportunity is in those spaces the big
companies are intentionally ignoring. We appreciate
you joining us for this deep dive into these
shockingly simple and highly profitable AI business
models. We'll catch you next time. Happy building.