WEBVTT

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In 2026, the gap between the top 1 % of AI users

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and, well, everyone else is just widening like

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crazy. And here's the thing. It's not about IQ.

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It's not because they have some secret tool.

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It's because 99 % of us are making the same basic

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mistakes over and over again. Exactly. It's like

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the difference between driving a supercar and

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trying to tow a boat with, I don't know, a Toyota

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Camry. Most people are just wondering why their

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engine is smoking. Welcome back to the Deep Dive.

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It is Tuesday, January 20th, 2026. Today we're

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digging into a guide from Max Anne called The

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Seven Deadly AI Mistakes. And, you know, there

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is so much noise out there about AI right now.

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Oh, it's deafening. But this one feels different.

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It feels less like a how -to guide and more like

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an intervention. Yeah, that's a great way to

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put it. It's this uncomfortable truth about why,

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with all this amazing tech, Most people's productivity

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hasn't really changed. Hasn't budged at all.

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So we're going to walk through these seven mistakes

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from the chat GPT trap all the way to the lazy

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fantasy and talk about how to fix them. Right.

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And what I love here is that it cuts through

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the hype. We're not talking about, you know,

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is AI sentient? We're talking about why your

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workflow is broken right now, today, and how

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you can fix it. Okay, let's just dive right in.

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Mistake number one. Max calls it the chat GPT

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trap. The trap, yes. No, I have to play devil's

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advocate for a second. Because for most people,

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AI is ChatGPT. It is, yeah. It's the default.

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It's the Google of this era. I open it, I pipe

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what I need, and I get an answer. Why is that

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a trap? It's a trap because of what the guide

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calls the Swiss Army Knife fallacy. He compares

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ChatGPT to a Toyota Camry. A Camry's a good car.

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It's a great car. It's reliable. It gets you

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from A to B. But would you ever use a Camry to

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tow a boat? No, probably not. Or to win a drag

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race? Of course not. But that's what people are

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doing. They try to get it to write a novel or

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make a photorealistic image. And the result is

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fine. It's OK. It's mediocre. It's mediocre.

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The top 1 % in 2026, they treat AI like a specialized

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ecosystem. They have a whole garage full of cars,

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not just one sedan. OK, so let's get specific

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then. If I'm not supposed to default to chat

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GPT for everything. What does a prose dashboard

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even look like? For deep writing, where am I

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going? If you're doing long -form writing essays,

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reports, anything complex, the guide is very

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clear. You should be using Clawed. Why Clawed?

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What's the difference? It's the feel and the

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context window. Clawed just has a more human,

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less robotic tone. It can stay coherent over

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really long documents. Yes. It actually remembers

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what you said on page one when you're on page

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50. Okay, so Claude for writing. What about research?

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Because usually I just ask ChatGPT to look this

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up for me. And that's so dangerous because of

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hallucinations. Right. For real research, you

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need to use perplexity. Because it's connected

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to the live web. Exactly. Live web and... Crucially,

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citations. You can see the sources. You're not

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asking an AI to dream up a fact. You're asking

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a research assistant to go find the facts. Okay,

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visuals. I see people just tearing their hair

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out trying to make slides. Don't ask a chatbot

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to describe slides. You use Gamma .app. It builds

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the layout, the design, everything you just edit.

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And images. We've all seen those weird, waxy

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-looking AI photos. Yep. For that, the guide

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points to NanoBanana Pro for realistic textures.

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And for video, it's a specific model, Kling AI.

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Model 2 .6. So the big takeaway here is if you

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think AI doesn't work for your problem, you're

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probably just using the wrong tool. Precisely.

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You're blaming the Camry because it can't pull

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the boat. So why is that default to chat GPT

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so hard for us to break? It's just convenience.

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Convenience creates this massive blind spot to

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specialized performance. We get lazy. Totally.

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We want one window, one login. even if the results

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are just okay. That makes perfect sense. Okay,

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let's go to mistake number two. Yeah. This one

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feels a little more technical, but I think it's

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where people are wasting a lot of money. Confusing

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models with tools. A money pit, yeah. Walk me

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through this. To me, the app is the model. So

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think of it this way. The model is the engine.

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It's the big, powerful V8 under the hood. Okay.

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The tool is the car around it, the steering wheel,

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the dashboard, the nice leather seats. The mistake

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is when you pay, say, $50 a month for a really

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fancy dashboard, some new app that's just running

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a free or super cheap engine underneath. So it's

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basically a markup scheme. Often, yeah. The guy

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calls them expensive wrappers. Can you give me

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a concrete example? Sure. Image generation. Let's

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say you want to use daily. Option A, you pay

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$20 a month for ChatGPT+. You ask for a cat,

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you get a cat. Right. And you have almost no

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control. You get what you get. Option B. You

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find a specialized tool that plugs into that

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same deli engine. Maybe it's cheaper or has a

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free tier. But this tool gives you aspect ratio

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controls, brand kits, style presets. So it's

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the same engine, but a better interface gives

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you professional level control. Exactly. And

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the warning is that a lot of these $50 a month

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startups are just a pretty face on a model you

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could use for pennies. So what's the fix? How

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do we avoid getting ripped off? The guide mentions

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Hugging Face and leaderboards. I mean, what even

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is Hugging Face? Great name, right? It's basically

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the main hub for the AI community. Think of it

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like a GitHub, but for AI models. You go there,

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you look for the OpenLLM leaderboard, and it

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just ranks the best performing engines at that

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exact moment. Once you see which engine is at

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the top, you just find a tool that lets you use

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it. A power user interface. Exactly. Like Recraft

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or Leonardo .ai or OpenRouter. They let you swap

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different engines in and out. You're paying for

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the power, not for the fancy marketing. Does

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the interface really change the output that much,

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though? Yes, because good interfaces unlock the

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model's full capabilities. The stuff that simple

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chat windows hide from you. That brings us to

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mistake number three. And this is one, honestly,

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I see everywhere. One and done prompting. The

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classic amateur move. Yeah, you type one prompt,

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you get garbage back, and you just quit. You

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say, see, this doesn't work. You know, I have

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to be a little vulnerable here for a second.

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Go for it. I still wrestle with this. I get this

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prompt drift where I'll just sit there tweaking

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one word, then another, hoping if I can just

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find the perfect magic spell, it'll read my mind

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on the first try. It's that desire for instant

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magic. It is. And it's so hard to shake. But

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the guide is really clear that's not how this

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works. Max compares it to hiring a new employee.

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Okay. Imagine you hire a junior assistant. You

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just point to a desk and say, do marketing. And

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then you walk away. That would be a complete

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disaster. Right. Then you come back an hour later

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and fire them because they didn't magically guess

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your entire brand strategy. That's what we do

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with AI every day. So what's the pro workflow?

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If it's not one and done, what does it look like?

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It's a fast four -step iteration cycle. Step

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one, rough prompt. Write a sales email. 30 seconds

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max. Don't overthink it. And gives you something

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generic. Totally generic. Step two, fast evaluation.

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Scan it for 10 seconds. Don't even read it all.

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Just, okay, tone is wrong. Missed the price.

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Step three, specific feedback. You tell it, make

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it more casual. Add the $200 price. Shorten the

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second paragraph. Another 30 seconds. And then

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step four. Final polish. You take what it gives

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back. And you manually tweak the last 10 % yourself.

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You're collaborating with it. You're iterating.

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The whole process takes maybe 90 seconds versus

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30 minutes trying to craft one perfect prompt

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that fails anyway. It's a huge shift. So why

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do we expect magic instead of collaboration?

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I think we forget that AI learns through feedback

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just like a person. We treat it like a vending

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machine, not a junior colleague. Let's move to

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mistake four, drowning in news. I think we can

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all relate to this one. Tutorial paralysis. Well,

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this is the productivity killer for sure. It

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really hits home. You know, reading 50 newsletters,

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watching top 10 AI tools, videos. It feels productive.

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You tell yourself, I'm learning. But you're not

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doing. It's productive procrastination. You feel

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busy. You get that little dopamine hit of finding

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something new. But your actual output is zero.

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So how do you stop? The FOMO is real. If I don't

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read the news, I'll miss out on the next big

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thing. The guide has a really simple fix for

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this. It's about intent. Before you click on

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anything, you have to write down one sentence.

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What's the sentence? I want to use AI to outcome

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so I can benefit. Okay. Give me an example. I

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want to use AI to write my newsletter in 30 minutes

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so I can spend more time with my clients. Right.

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And then what? Then you look at the article or

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the video and ask, does this help me write my

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newsletter faster? If the answer is no, you close

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the tab. It's just noise. And there's an 80 -20

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rule in here for time management. Yeah. 80 %

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of your time should be spent using AI, actually

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doing work. Only 20 % learning about it. Most

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people have that completely backward. The guide's

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prescription is strict. A monthly deep dive,

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a weekly check -in, but zero daily news. It's

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an information detox. How do you know when you've

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crossed that line into paralysis? When your input

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of information exceeds your output of work. That's

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the red flag. That is a tough mirror to look

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into. We're going to take a quick break. When

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we come back, we are tackling the biggest lie

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people tell themselves. I'm not technical enough.

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Stay with us. And we're back looking at the seven

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deadly AI mistakes from Max Ann's guide. We are

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up to mistake number five. The not technical

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enough lie. This is a big one. It's that belief

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that you need to know how to code, that you need

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a CS degree to play in the space. I hear it all

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the time. Oh, I'm not a tech person. Max uses

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this great analogy. He compares this moment right

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now to digital marketing in 2010. It's a perfect

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comparison. Think back to 2010. If you knew how

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to run a basic Google ad and set up a Facebook

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business page, you are a wizard. You are a social

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media guru. Exactly. Did you need to know how

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to code the Facebook algorithm? No. You just

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needed to know how to use the tool. And that's

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exactly where we are with AI now. So what do

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businesses actually need from people if it's

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not coding? They need translators. They need

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people who can identify use cases in a workflow.

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People who can build simple prompt libraries

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and set up no -code automations. The guide lists

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some of the actual services non -technical people

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are selling right now. And the prices are kind

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of wild. Yeah, the numbers are real. AI readiness

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assessments. Basically just auditing a company.

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People are charging $2 ,000 to $5 ,000 for that.

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For a prompt library creation. One to three grand.

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For what is essentially a well -organized Google

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Doc. So the value isn't writing code. The value

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is in translating a business problem into a solution

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that saves time or makes money. That's it. So

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the barrier isn't a technical skill at all. No.

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The barrier is simply the decision to start.

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It's psychological. not technical. That's a perfect

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lead -in to mistake number six, which is maybe

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the most dangerous one. Thinking AI replaces

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skills. The lazy fantasy. This is the one that'll

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get you left behind. It's this idea that, oh,

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AI does everything, so I don't need to learn

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how to write or design or think strategically.

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And it's so wrong. The concept here is that AI

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raises the floor. It lets a beginner get to an

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average mediocre result almost instantly. Right,

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from zero to C plus in 10 seconds. But it does

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not replace the ceiling. Experts still dominate.

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And they dominate even more now. Why more? Because

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of the judgment problem. If you don't know anything

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about marketing and you ask an AI for a marketing

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plan, how do you know if what it gives you is

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brilliant or complete garbage? You don't. You

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have no taste, no frame of reference. You're

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flying blind. There's this moment in the guide

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that just stopped me cold. Whoa. Imagine scaling

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to a billion queries, but they're all just average

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because no one has the taste to refine them.

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Yeah. That one hits hard, doesn't it? Yeah. Just

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this giant ocean of C -plus work. A world of

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pure mediocrity because no one knows what an

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A even looks like anymore. That's a scary thought.

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It is. If you skip the fundamentals, you just

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become a manager who can't evaluate their team's

00:12:15.090 --> 00:12:17.269
work. You're just approving bad ideas because

00:12:17.269 --> 00:12:18.850
you don't know any better. So the real strategy

00:12:18.850 --> 00:12:21.309
is to learn the fundamentals manually to build

00:12:21.309 --> 00:12:24.330
your taste and then use AI to scale your output.

00:12:24.649 --> 00:12:27.309
Amplify your skill, don't replace it. You have

00:12:27.309 --> 00:12:29.230
to be the editor -in -chief, not just the person

00:12:29.230 --> 00:12:31.210
who pushes the print button. And what happens

00:12:31.210 --> 00:12:34.129
if you skip learning the fundamentals? You become

00:12:34.129 --> 00:12:36.509
a commodity. You produce the same average stuff

00:12:36.509 --> 00:12:39.070
as everyone else and you become totally replaceable.

00:12:39.289 --> 00:12:42.769
Okay, mistake number seven. The last one. Forgetting

00:12:42.769 --> 00:12:46.129
AI is just a tool. This is the hammer analogy.

00:12:46.429 --> 00:12:48.990
Right. If you hit your thumb with a hammer, you

00:12:48.990 --> 00:12:51.169
don't stand there and blame the hammer. You figure

00:12:51.169 --> 00:12:54.210
out you were holding it wrong. But with AI, we

00:12:54.210 --> 00:12:57.409
love to blame the machine. It hallucinated. It

00:12:57.409 --> 00:12:59.750
wrote a bad email. It didn't get what I meant.

00:12:59.970 --> 00:13:03.350
We need responsibility framework. Right. If the

00:13:03.350 --> 00:13:06.500
output is bad. It is not the AI's fault. It is

00:13:06.500 --> 00:13:08.659
almost always one of four things. Not enough

00:13:08.659 --> 00:13:10.919
context, unclear instructions, no iteration,

00:13:11.220 --> 00:13:13.899
or you just picked the wrong tool for the job.

00:13:14.019 --> 00:13:15.320
It puts the ownership right back on the user.

00:13:15.500 --> 00:13:17.659
Completely. And this leads to what the guide

00:13:17.659 --> 00:13:20.980
calls AI calibration. Calibration? What does

00:13:20.980 --> 00:13:23.000
that mean in practice? It's knowing how much

00:13:23.000 --> 00:13:25.860
effort a task actually deserves. If it's a low

00:13:25.860 --> 00:13:28.220
-stakes task, like brainstorming party ideas,

00:13:28.480 --> 00:13:31.870
a quick sloppy prompt is fine. Who cares? But

00:13:31.870 --> 00:13:35.009
if it's a high -stakes client proposal, you need

00:13:35.009 --> 00:13:37.370
to be meticulous. You need extensive quality

00:13:37.370 --> 00:13:39.610
control. You can't treat them both the same.

00:13:39.789 --> 00:13:42.789
So what's the ultimate mindset shift here? It's

00:13:42.789 --> 00:13:45.450
shifting from blaming the machine to taking complete

00:13:45.450 --> 00:13:48.210
ownership of the result. It's your fault, and

00:13:48.210 --> 00:13:50.389
that's the only way to get better. So wrapping

00:13:50.389 --> 00:13:52.669
all this up, what's the big idea after these

00:13:52.669 --> 00:13:56.389
seven mistakes? The 1 % advantage isn't about

00:13:56.389 --> 00:13:59.509
being a genius. It's about execution over passive

00:13:59.509 --> 00:14:02.409
consumption. It's about doing. not just reading.

00:14:02.590 --> 00:14:05.269
The guide closes with this very clear binary

00:14:05.269 --> 00:14:08.309
choice, path one versus path two. Path one is

00:14:08.309 --> 00:14:12.149
the 99%. Use chat GPT for everything, consume

00:14:12.149 --> 00:14:14.669
endless news, and blame the tool when it doesn't

00:14:14.669 --> 00:14:17.389
work. And path two. That's the 1%. You use the

00:14:17.389 --> 00:14:19.230
right tool for the job, you limit the noise,

00:14:19.350 --> 00:14:22.169
you iterate relentlessly, and most of all, you

00:14:22.169 --> 00:14:25.080
own the results, good or bad. AI doesn't reward

00:14:25.080 --> 00:14:28.200
curiosity alone. It rewards execution. That's

00:14:28.200 --> 00:14:30.139
the line. That's the whole thing right there.

00:14:30.259 --> 00:14:32.139
So I want to challenge everyone listening. Just

00:14:32.139 --> 00:14:34.120
pick one of these mistakes. Maybe you're a one

00:14:34.120 --> 00:14:36.399
and done prompter. Maybe you're drowning in newsletters.

00:14:36.539 --> 00:14:41.440
Just fix one thing today. Unsubscribe from the

00:14:41.440 --> 00:14:44.220
junk. Force yourself to iterate three times on

00:14:44.220 --> 00:14:46.559
your next prompt. Just that one little change.

00:14:46.759 --> 00:14:48.259
And just see what happens. Thanks for diving

00:14:48.259 --> 00:14:49.779
in with us. We'll see you in 1%. See you there.