In 2026, the gap between the top 1 % of AI users
and, well, everyone else is just widening like
crazy. And here's the thing. It's not about IQ.
It's not because they have some secret tool.
It's because 99 % of us are making the same basic
mistakes over and over again. Exactly. It's like
the difference between driving a supercar and
trying to tow a boat with, I don't know, a Toyota
Camry. Most people are just wondering why their
engine is smoking. Welcome back to the Deep Dive.
It is Tuesday, January 20th, 2026. Today we're
digging into a guide from Max Anne called The
Seven Deadly AI Mistakes. And, you know, there
is so much noise out there about AI right now.
Oh, it's deafening. But this one feels different.
It feels less like a how -to guide and more like
an intervention. Yeah, that's a great way to
put it. It's this uncomfortable truth about why,
with all this amazing tech, Most people's productivity
hasn't really changed. Hasn't budged at all.
So we're going to walk through these seven mistakes
from the chat GPT trap all the way to the lazy
fantasy and talk about how to fix them. Right.
And what I love here is that it cuts through
the hype. We're not talking about, you know,
is AI sentient? We're talking about why your
workflow is broken right now, today, and how
you can fix it. Okay, let's just dive right in.
Mistake number one. Max calls it the chat GPT
trap. The trap, yes. No, I have to play devil's
advocate for a second. Because for most people,
AI is ChatGPT. It is, yeah. It's the default.
It's the Google of this era. I open it, I pipe
what I need, and I get an answer. Why is that
a trap? It's a trap because of what the guide
calls the Swiss Army Knife fallacy. He compares
ChatGPT to a Toyota Camry. A Camry's a good car.
It's a great car. It's reliable. It gets you
from A to B. But would you ever use a Camry to
tow a boat? No, probably not. Or to win a drag
race? Of course not. But that's what people are
doing. They try to get it to write a novel or
make a photorealistic image. And the result is
fine. It's OK. It's mediocre. It's mediocre.
The top 1 % in 2026, they treat AI like a specialized
ecosystem. They have a whole garage full of cars,
not just one sedan. OK, so let's get specific
then. If I'm not supposed to default to chat
GPT for everything. What does a prose dashboard
even look like? For deep writing, where am I
going? If you're doing long -form writing essays,
reports, anything complex, the guide is very
clear. You should be using Clawed. Why Clawed?
What's the difference? It's the feel and the
context window. Clawed just has a more human,
less robotic tone. It can stay coherent over
really long documents. Yes. It actually remembers
what you said on page one when you're on page
50. Okay, so Claude for writing. What about research?
Because usually I just ask ChatGPT to look this
up for me. And that's so dangerous because of
hallucinations. Right. For real research, you
need to use perplexity. Because it's connected
to the live web. Exactly. Live web and... Crucially,
citations. You can see the sources. You're not
asking an AI to dream up a fact. You're asking
a research assistant to go find the facts. Okay,
visuals. I see people just tearing their hair
out trying to make slides. Don't ask a chatbot
to describe slides. You use Gamma .app. It builds
the layout, the design, everything you just edit.
And images. We've all seen those weird, waxy
-looking AI photos. Yep. For that, the guide
points to NanoBanana Pro for realistic textures.
And for video, it's a specific model, Kling AI.
Model 2 .6. So the big takeaway here is if you
think AI doesn't work for your problem, you're
probably just using the wrong tool. Precisely.
You're blaming the Camry because it can't pull
the boat. So why is that default to chat GPT
so hard for us to break? It's just convenience.
Convenience creates this massive blind spot to
specialized performance. We get lazy. Totally.
We want one window, one login. even if the results
are just okay. That makes perfect sense. Okay,
let's go to mistake number two. Yeah. This one
feels a little more technical, but I think it's
where people are wasting a lot of money. Confusing
models with tools. A money pit, yeah. Walk me
through this. To me, the app is the model. So
think of it this way. The model is the engine.
It's the big, powerful V8 under the hood. Okay.
The tool is the car around it, the steering wheel,
the dashboard, the nice leather seats. The mistake
is when you pay, say, $50 a month for a really
fancy dashboard, some new app that's just running
a free or super cheap engine underneath. So it's
basically a markup scheme. Often, yeah. The guy
calls them expensive wrappers. Can you give me
a concrete example? Sure. Image generation. Let's
say you want to use daily. Option A, you pay
$20 a month for ChatGPT+. You ask for a cat,
you get a cat. Right. And you have almost no
control. You get what you get. Option B. You
find a specialized tool that plugs into that
same deli engine. Maybe it's cheaper or has a
free tier. But this tool gives you aspect ratio
controls, brand kits, style presets. So it's
the same engine, but a better interface gives
you professional level control. Exactly. And
the warning is that a lot of these $50 a month
startups are just a pretty face on a model you
could use for pennies. So what's the fix? How
do we avoid getting ripped off? The guide mentions
Hugging Face and leaderboards. I mean, what even
is Hugging Face? Great name, right? It's basically
the main hub for the AI community. Think of it
like a GitHub, but for AI models. You go there,
you look for the OpenLLM leaderboard, and it
just ranks the best performing engines at that
exact moment. Once you see which engine is at
the top, you just find a tool that lets you use
it. A power user interface. Exactly. Like Recraft
or Leonardo .ai or OpenRouter. They let you swap
different engines in and out. You're paying for
the power, not for the fancy marketing. Does
the interface really change the output that much,
though? Yes, because good interfaces unlock the
model's full capabilities. The stuff that simple
chat windows hide from you. That brings us to
mistake number three. And this is one, honestly,
I see everywhere. One and done prompting. The
classic amateur move. Yeah, you type one prompt,
you get garbage back, and you just quit. You
say, see, this doesn't work. You know, I have
to be a little vulnerable here for a second.
Go for it. I still wrestle with this. I get this
prompt drift where I'll just sit there tweaking
one word, then another, hoping if I can just
find the perfect magic spell, it'll read my mind
on the first try. It's that desire for instant
magic. It is. And it's so hard to shake. But
the guide is really clear that's not how this
works. Max compares it to hiring a new employee.
Okay. Imagine you hire a junior assistant. You
just point to a desk and say, do marketing. And
then you walk away. That would be a complete
disaster. Right. Then you come back an hour later
and fire them because they didn't magically guess
your entire brand strategy. That's what we do
with AI every day. So what's the pro workflow?
If it's not one and done, what does it look like?
It's a fast four -step iteration cycle. Step
one, rough prompt. Write a sales email. 30 seconds
max. Don't overthink it. And gives you something
generic. Totally generic. Step two, fast evaluation.
Scan it for 10 seconds. Don't even read it all.
Just, okay, tone is wrong. Missed the price.
Step three, specific feedback. You tell it, make
it more casual. Add the $200 price. Shorten the
second paragraph. Another 30 seconds. And then
step four. Final polish. You take what it gives
back. And you manually tweak the last 10 % yourself.
You're collaborating with it. You're iterating.
The whole process takes maybe 90 seconds versus
30 minutes trying to craft one perfect prompt
that fails anyway. It's a huge shift. So why
do we expect magic instead of collaboration?
I think we forget that AI learns through feedback
just like a person. We treat it like a vending
machine, not a junior colleague. Let's move to
mistake four, drowning in news. I think we can
all relate to this one. Tutorial paralysis. Well,
this is the productivity killer for sure. It
really hits home. You know, reading 50 newsletters,
watching top 10 AI tools, videos. It feels productive.
You tell yourself, I'm learning. But you're not
doing. It's productive procrastination. You feel
busy. You get that little dopamine hit of finding
something new. But your actual output is zero.
So how do you stop? The FOMO is real. If I don't
read the news, I'll miss out on the next big
thing. The guide has a really simple fix for
this. It's about intent. Before you click on
anything, you have to write down one sentence.
What's the sentence? I want to use AI to outcome
so I can benefit. Okay. Give me an example. I
want to use AI to write my newsletter in 30 minutes
so I can spend more time with my clients. Right.
And then what? Then you look at the article or
the video and ask, does this help me write my
newsletter faster? If the answer is no, you close
the tab. It's just noise. And there's an 80 -20
rule in here for time management. Yeah. 80 %
of your time should be spent using AI, actually
doing work. Only 20 % learning about it. Most
people have that completely backward. The guide's
prescription is strict. A monthly deep dive,
a weekly check -in, but zero daily news. It's
an information detox. How do you know when you've
crossed that line into paralysis? When your input
of information exceeds your output of work. That's
the red flag. That is a tough mirror to look
into. We're going to take a quick break. When
we come back, we are tackling the biggest lie
people tell themselves. I'm not technical enough.
Stay with us. And we're back looking at the seven
deadly AI mistakes from Max Ann's guide. We are
up to mistake number five. The not technical
enough lie. This is a big one. It's that belief
that you need to know how to code, that you need
a CS degree to play in the space. I hear it all
the time. Oh, I'm not a tech person. Max uses
this great analogy. He compares this moment right
now to digital marketing in 2010. It's a perfect
comparison. Think back to 2010. If you knew how
to run a basic Google ad and set up a Facebook
business page, you are a wizard. You are a social
media guru. Exactly. Did you need to know how
to code the Facebook algorithm? No. You just
needed to know how to use the tool. And that's
exactly where we are with AI now. So what do
businesses actually need from people if it's
not coding? They need translators. They need
people who can identify use cases in a workflow.
People who can build simple prompt libraries
and set up no -code automations. The guide lists
some of the actual services non -technical people
are selling right now. And the prices are kind
of wild. Yeah, the numbers are real. AI readiness
assessments. Basically just auditing a company.
People are charging $2 ,000 to $5 ,000 for that.
For a prompt library creation. One to three grand.
For what is essentially a well -organized Google
Doc. So the value isn't writing code. The value
is in translating a business problem into a solution
that saves time or makes money. That's it. So
the barrier isn't a technical skill at all. No.
The barrier is simply the decision to start.
It's psychological. not technical. That's a perfect
lead -in to mistake number six, which is maybe
the most dangerous one. Thinking AI replaces
skills. The lazy fantasy. This is the one that'll
get you left behind. It's this idea that, oh,
AI does everything, so I don't need to learn
how to write or design or think strategically.
And it's so wrong. The concept here is that AI
raises the floor. It lets a beginner get to an
average mediocre result almost instantly. Right,
from zero to C plus in 10 seconds. But it does
not replace the ceiling. Experts still dominate.
And they dominate even more now. Why more? Because
of the judgment problem. If you don't know anything
about marketing and you ask an AI for a marketing
plan, how do you know if what it gives you is
brilliant or complete garbage? You don't. You
have no taste, no frame of reference. You're
flying blind. There's this moment in the guide
that just stopped me cold. Whoa. Imagine scaling
to a billion queries, but they're all just average
because no one has the taste to refine them.
Yeah. That one hits hard, doesn't it? Yeah. Just
this giant ocean of C -plus work. A world of
pure mediocrity because no one knows what an
A even looks like anymore. That's a scary thought.
It is. If you skip the fundamentals, you just
become a manager who can't evaluate their team's
work. You're just approving bad ideas because
you don't know any better. So the real strategy
is to learn the fundamentals manually to build
your taste and then use AI to scale your output.
Amplify your skill, don't replace it. You have
to be the editor -in -chief, not just the person
who pushes the print button. And what happens
if you skip learning the fundamentals? You become
a commodity. You produce the same average stuff
as everyone else and you become totally replaceable.
Okay, mistake number seven. The last one. Forgetting
AI is just a tool. This is the hammer analogy.
Right. If you hit your thumb with a hammer, you
don't stand there and blame the hammer. You figure
out you were holding it wrong. But with AI, we
love to blame the machine. It hallucinated. It
wrote a bad email. It didn't get what I meant.
We need responsibility framework. Right. If the
output is bad. It is not the AI's fault. It is
almost always one of four things. Not enough
context, unclear instructions, no iteration,
or you just picked the wrong tool for the job.
It puts the ownership right back on the user.
Completely. And this leads to what the guide
calls AI calibration. Calibration? What does
that mean in practice? It's knowing how much
effort a task actually deserves. If it's a low
-stakes task, like brainstorming party ideas,
a quick sloppy prompt is fine. Who cares? But
if it's a high -stakes client proposal, you need
to be meticulous. You need extensive quality
control. You can't treat them both the same.
So what's the ultimate mindset shift here? It's
shifting from blaming the machine to taking complete
ownership of the result. It's your fault, and
that's the only way to get better. So wrapping
all this up, what's the big idea after these
seven mistakes? The 1 % advantage isn't about
being a genius. It's about execution over passive
consumption. It's about doing. not just reading.
The guide closes with this very clear binary
choice, path one versus path two. Path one is
the 99%. Use chat GPT for everything, consume
endless news, and blame the tool when it doesn't
work. And path two. That's the 1%. You use the
right tool for the job, you limit the noise,
you iterate relentlessly, and most of all, you
own the results, good or bad. AI doesn't reward
curiosity alone. It rewards execution. That's
the line. That's the whole thing right there.
So I want to challenge everyone listening. Just
pick one of these mistakes. Maybe you're a one
and done prompter. Maybe you're drowning in newsletters.
Just fix one thing today. Unsubscribe from the
junk. Force yourself to iterate three times on
your next prompt. Just that one little change.
And just see what happens. Thanks for diving
in with us. We'll see you in 1%. See you there.