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Breakdown

Why now is the worst time to buy a generic AI course

By Shane Edward · September 10, 2026 · 7 min read
1

Ask one question before you buy any AI course

Before you spend a dollar on educational content, ask this: will this information still be useful in a year? In five weeks?

With AI, the honest answer is almost always no. The tools shift monthly. The workflows you paid to memorize get replaced by a single new feature drop. That's why a generic, broad AI course right now is one of the worst purchases you can make.

The exception: narrow, technical skills tied to a specific outcome — AI for paid ads, AI for a specific marketing motion, AI inside a specific platform. If it's a survey course promising to teach you 'AI' in general, skip it. AI itself will teach you AI, for free, faster than a curriculum can be updated.

2

What actually changed in the last 12 months

<20%
Small business AI adoption 12 months ago

A year ago, adoption looked totally different. Large businesses (250+ employees) were using AI in roughly the 36–37% range. Small businesses (50 or fewer) were under 20%. Why? It was slow, technical, and required real setup skill.

Nine months ago, the developer scare hit — teams realized one engineer using AI could do the work of three or four. Six months ago, Claude and Claude Code let you hand off a task, walk away from the computer, and come back to finished work. Today, models can build a working video game from a prompt.

The point isn't the timeline. The point is: a course written six months ago is already teaching you the slow version of something that's now one click.

3

Learn the fundamentals, not the features

Features change. Fundamentals don't. The single most important skill with AI is knowing when to use it, when to trust it, and what to use it for.

AI is best at the technical layer of anything digital — the API connections, the approvals, the config, the copy scaffolding, the debugging. When I built my content multiplier, AI walked me through every Meta and LinkedIn approval step. I didn't need to know the platforms. I needed to know what I wanted the system to do.

If your problem lives on a computer, AI can help you fix it. If your problem is a broken light bulb or an uncut lawn, it can't. Get clear on that line first.

4

Treat it like a sixth grader and amplify, don't replace

Two rules I use on every build:

Treat it like a sixth grader. Be explicit. Don't assume it knows the context of your business, your customer, or your goal. Spell it out. The more specific your input, the less garbage you get back.

Amplify, don't replace. AI should extend what you already do well, not stand in for the thinking. If you hand off the thinking, you get AI slop — generic output that reads like every other AI-generated thing on the internet. Your judgment is the moat. AI is the leverage.

Bad: 'Write me an email.'
Better: 'Write a 90-word follow-up email to a lead who booked a call but no-showed. Tone: direct, no fluff. Goal: rebook.'
5

Set expectations and limitations up front

AI has access to millions of data points. Ask it something with thousands of conflicting opinions attached — marketing advice, pricing strategy, hiring — and it will confidently hand you an answer that doesn't fit your scope.

This is where people get burned. They take the first output as truth, ship it, and wonder why it flopped.

Before you prompt, tell it what it is not allowed to do. Tell it the constraints. Tell it what 'done' looks like. Then read the output like an editor, not a customer. If it doesn't match the scope of what you're solving, throw it out and re-prompt.

6

Keep thinking for yourself

This is the one that gets lost in every AI course pitch. The people building reliable systems right now aren't the ones who outsourced their brain to a chatbot. They're the ones who spotted a real problem — a leaky funnel, a slow intake, a bottleneck — and used AI to fix it faster than they could alone.

Example: I kept seeing multi-step application funnels lose leads between the form, the qualification questions, the calendar, and the confirmation. Three or four redirects, three or four chances to drop off. I sat down, stayed up late a few nights, and built a single-page version where every step loads inline. No developer. No $20K quote. Just a problem I saw and AI as the leverage to solve it.

That's the whole game. See the problem yourself. Use AI to close the gap. Skip the certificate.

FAQ

Are all AI courses a waste of money?
No. Narrow, skill-specific courses tied to a clear outcome — AI for paid ads, AI inside a specific platform, AI for a specific workflow — can still be worth it. Broad 'learn AI' courses are the problem, because the surface area moves faster than the curriculum.
Does an AI certificate mean anything?
Not to anyone who actually uses AI. The people hiring for AI skills care about what you've built and shipped, not a badge. You can learn more in a weekend of building than you can from most certificate programs.
What should a total beginner do instead of buying a course?
Pick one real problem in your business. Open ChatGPT or Claude. Ask it to walk you through solving that problem step by step. Build the thing. You'll learn more fundamentals in one project than in ten hours of video lessons.
How do I know when to trust AI output?
Trust it more on technical tasks with a right answer — code, config, API steps, math. Trust it less on subjective calls — strategy, positioning, pricing, creative direction. Always read the output like an editor before you ship it.
What's the biggest mistake people make when they start with AI?
Trying to use it for everything at once instead of picking one bottleneck and solving it. Start narrow. Ship one working system. Then expand.