Okay, so let's unpack this. Welcome back to the
Deep Dive. Most professionals have their paid
AI subscriptions, right? They know the features.
They're using the tools. We call them AI literate.
They're efficient, yes. But they're missing out
on the biggest gains. The real transformation
happens when you stop seeing AI as just a tool
and, you know, start treating it like a 247 collaborator.
Today, we're analyzing a really crucial guide
that breaks down exactly what separates the top
performers from everyone else when it comes to
using these new systems. The mission for us and
for you is to understand the core difference
between just efficiency making old work a little
faster and true transformation, designing fundamentally
better work with AI at the center. We'll look
at these specific habits from AI breadcrumbs
to building swipe files that move you to that
highest level of mastery. And it's important
we define these terms because I think everyone
feels like they're already using AI pretty well.
Absolutely. And they might be, but maybe not
at the level for real compounding leverage. Exactly.
The source material suggests that working with
AI falls into roughly three clear levels and
most smart, busy professionals are kind of stuck
right at level two. So level one is the AI curious.
This is the experimental user. They use the free
tools. They play around with them maybe once
or twice a week. Right. It's optional. It's totally
optional. There's no compounding benefit to their
work because they haven't really committed to
a system. Then you get to level two, the AI literate.
Now, this user is genuinely skilled. They pay
for the subscriptions. They keep prompt libraries.
They know which model to use for what task. They're
invested. They're invested. But here's the kicker,
the fatal flaw. AI is still just a tool applied
inside their old existing workflows. So they're
doing the same things they always did, just maybe
20 % faster. They get efficiency, sure, but they're
missing the transformation that comes from redesigning
the process itself. And that leads us to level
three, the AI native. This user is a system builder.
workflow architect they approach a new project
and they redesign the process from day one starting
with the assumption that this intelligent collaborator
just exists 24 7. ai becomes a genuine force
multiplier their work compounds faster with less
effort because the process itself is just fundamentally
different it's built for collaboration what's
so fascinating here is that the jump from literate
to native It's not about writing better prompts.
It's not about getting more features. It is about
fundamentally rebuilding how work actually happens.
That's the real shift, isn't it? If we connect
this to the bigger picture, what is the core
difference in the mindset between level two and
level three? Level two asks how AI can help an
existing process. Level 3 asks how to design
a new process, assuming a 247 collaborator. That
reframing sets the stage perfectly for the first
habit. Yeah, this is the easiest habit to adopt,
but it has this outsized impact long term. The
main problem is that we treat AI chats as disposable,
one -off things. Right, you use it and you forget
it. Exactly. And I'll admit, I still wrestle
with prompt drift myself. Yeah. You spend 20
minutes getting the perfect sequence, the perfect
tone. And then three weeks later, you can't find
that exact thread. Oh, it's the worst feeling.
It's so frustrating. And that frustration is
the sign you're leaving performance on the table.
So the solution is actually pretty simple. We
call it leaving AI breadcrumbs. You have to anchor
your AI conversations directly to the work where
you're using the output. So instead of letting
those chat threads just live in isolation in
the app history, which is organized by time,
you create a unique URL for the conversation
and paste it right into your document. The core
productivity principle here is so clear it should
be obvious, really. Yeah. Organize your information
by where you will use it, not where you found
it. Okay, think about preparing a big presentation.
You've got a Google Doc for the project, right?
Sure. You just create two tabs. One is the final
outline, and the other is called AI breadcrumbs
or helpful hints. That hints tab is where you
anchor all those chat links. And the process
is so clean and quick. You ask the AI to optimize
a prompt or analyze something. The moment you
get a great result, you immediately copy that
unique URL. And paste it right in. And paste
it in your document. And you repeat this for
any refinement chat you do later. This just makes
sure you can always return to that exact conversational
context months later if you need to. And a pro
tip. Don't just paste the raw links. Add a little
bit of context next to it, something like Gemini
chat on brainstorming the Q4 outline or chat
GPT refining the talking points. Yeah, that context
is key. The rule of thumb is if the conversation
took you more than 10 minutes or you know you'll
need to reference it again, just anchor it immediately.
So this brings up a really important question
about the value of that context. Why is linking
the whole chat better than just copy the final
output into your document? The link retains the
full conversation so you can easily pick up the
thread where you left off, even weeks later.
That context is often the key. Okay, so breadcrumbs
solve the memory problem. But what about the
quality problem? How do we move beyond getting
consistently, you know, B or B plus work from
our collaborator? Exactly. That leads us right
into habit two. Building an AI swipe file system.
This one takes more effort than breadcrumbs,
for sure, but the payoff in quality is just...
Massive. But what exactly is a swipe file? Well,
historically, it was for like marketing copy.
But in this context, it's a curated library of
best in class examples in your field. So emails,
proposals, summaries, whatever you produce a
lot of. OK, so most people at level two, the
AI literate folks, they prompt the AI with basic
instructions. Yeah, like write me a business
proposal. Exactly. And they get a generic, competent
result. An AI native user, on the other hand,
opens their file system, finds the two or three.
best proposals they've ever made or seen and
gives those to the AI first. This is the power
move. They don't just use a generic prompt. They
use a really focused structure, something like
analyze these attached proposals, list the key
patterns, then apply those patterns to my draft
content below. And because the AI is learning
from your absolute best examples, the initial
draft it gives you is just. It's dramatically
stronger. Right. You save so much time on refinement
because the starting quality is already 90 %
of the way there. That's where the leverage is.
But it does require some discipline. The new
habit you have to develop is saving excellent
work immediately to the system whenever you find
it. Don't wait until you need it. Don't wait.
And you can start narrow. Maybe just two or three
repetitive things like project update emails
or strategy docs. Just build that initial repository.
And structure matters a ton for this to scale.
You have to organize your folders by use case,
so internal comms, client proposals, not by source
or date. This is the critical first step to get
your work library ready so the AI can learn from
your best stuff and replicate that quality over
and over. So what is the main outcome of training
your AI collaborator using this kind of structured
external swipe file library? It dramatically
improves output quality and saves time by letting
the AI replicate proven high standards consistently.
All right, now we move to habit three. And this
one is... arguably the hardest to maintain consistently.
A bit like going to the gym. The long -term impact
on your output is just massive. It's called AI
-first task planning. AI -first planning. So
that means mapping out exactly how and when you're
going to use AI before you even start a big project.
Yes. It gets rid of that decision fatigue that
stalls so many people mid -task. Exactly. You're
deliberately breaking down a big project into
small concrete tasks first. Then you go back
and you mark which tasks AI should help with.
And this is the key. You specify the best specialized
tool for that specific task. job. Okay. Let's
use newsletter creation as a real world example.
You start mapping. Task 1 .1, brain dump key
concepts is marked as manual. Right. Because
you need your human point of view. You have to
inject your own unique perspective. But then
task 1 .2, fact check the notes. is marked AI
-assisted with Notebook LM specifically because
it's known for better document grounding and
lower hallucination rates. And task 1 .3, turn
verified notes into a short brief. That's marked
as AI -assisted with Gemini because that model
has a bit of an edge in creative synthesized
writing. Yeah. This kind of intentional planning
gives you three big advantages. First, it cuts
decision fatigue. You never have to stop and
ask, should I use AI here? It's pre -decided.
Second, it increases quality and speed because
you're matching the right tool to the right work.
Right. And third, it creates reusable templates.
Once you build this map for a newsletter, you
just reuse it next month. That's compounding
leverage. The rule of thumb here is, if a project
is going to take more than an hour, just spend
five or ten minutes mapping. the steps and tagging
them first so if we connect this to the bigger
picture how does that specific tool assignment
prevent you from stalling mid -project you pre
-decide the tool and the task It avoids that
mid -task analysis paralysis about when or how
to bring AI in so you keep your momentum. This
last bonus habit really ties everything we've
talked about together. It solves one of the most
frustrating feelings for level 2 users. That
awful feeling where you wrote a perfect prompt
three weeks ago. It generated perfect output.
But today you try to recreate it and the result
is just... Nah. Yeah, it's just, okay, you lost
the magic. You lost the magic. The core concept
here is maintaining a prompts database. You save
those battle -tested prompts, the ones that leverage
your swipe files and give you great results,
into a central library. And it's not about quantity.
No, you don't need a thousand random prompts.
Yeah. You need maybe... 10 to 15 really reliable,
high leverage ones that work every single time.
And the key, again, is organization, the structure.
Your database shouldn't just be a list of text.
Right. It should have the category, like presentation
outlines, the exact prompt text that worked,
the context, so when and why to use it, and an
example output. Maybe even an AI breadcrumb,
a link back to that original conversation. Whoa.
That process of refining a perfect prompt, that
often takes, what, 15 minutes of back and forth?
At least. And imagine scaling that 15 -minute
time savings across your whole company or, you
know, across a billion queries a year globally.
That compounding value, that leverage, that's
what being AI native delivers. So for someone
listening who is ready to make this shift, what's
the simplest, lowest friction step to start building
this database this week? Just begin by saving
the prompts you use repeatedly. Things like rewriting
rough drafts or analyzing long documents. The
moment it gives you an excellent output, save
it. Use a breadcrumb to link to the success story.
So what does this all mean, really? The key difference
between AI literate and AI native is this profound
but subtle mindset shift. AI literate users try
to fit AI into their existing processes. They're
aiming for marginal efficiency. AI native users
redesign the entire process from the ground up
to leverage a 247 intelligent collaborator. And
this shift needs momentum, not perfection. You
have to start small. Right. This week, just commit
to leaving those AI breadcrumbs. Hyperlink your
chats into your documents. Next week, create
a simple swipe file. Just three folders with
a few high -quality examples. Then map out one
small project, AI first. The goal isn't immediate,
flawless implementation. It's just meaningful,
deliberate progress toward a truly AI -native
way of working. And this approach, it doesn't
just save time. It dramatically improves the
quality ceiling of your output. It helps you
produce better work than you ever could alone.
So if you want to achieve true professional transformation,
not just a little bit of efficiency, start asking
this question next time you plan a task. How
would I design this workflow differently if I
assumed an intelligent Intelligent Collaborator
was available and ready to help me 24 -7. That's
something to mull over.