After taking some time off during the Thanksgiving break, I’ve been thinking about why so much AI education feels hollow.
Not bad, exactly. Not scammy. Just... hollow.
I believe the premise behind most approaches is fundamentally wrong. And the perfect summation of this is that the likes of OpenAI and Antropic aren’t publishing articles for the “100 Best Prompts for Productivity” as some would expect. They’re creating cookbooks and guides like this one to help you get the most out of the models.
They’ve made the right assumption that using AI is about knowing how to get the most out of the tools, not simply filling in the blanks as instructed.
Let me explain what I mean.
…And you get a template!
Scroll through LinkedIn for five minutes and you’ll find someone offering free AI prompt templates. Comment a keyword, get a PDF, unlock productivity secrets.
The structure always looks similar:
“Use this prompt to write emails 10x faster”
“Copy this template for better meeting summaries”
“Here’s the exact prompt I use for...”
The prompts probably work. You’ll get something out of it. They’re not lying.
But it’s like someone handing you a Mad Libs sheet and calling the final product creative writing.
Yes, you technically produced words. No, you didn’t actually learn anything about how to write.
The template got you to 60-70% of a result. Maybe even 80%. But there’s always a gap. And that gap is where the actual value lives.
The Hollow Gap
As I mentioned last week, AI isn’t like traditional software. When you learn Excel, the formulas work the same way every time. =SUM(A1:A10)does exactly one thing.
AI is a language. It’s malleable. Context-dependent. The same prompt produces wildly different results depending on what you’re actually trying to accomplish, how you’ve set up the conversation, and what unique constraints you’re working within.
Think about it this way: you wouldn’t copy someone’s LinkedIn post word-for-word and expect it to represent your voice, your perspective, your professional context. The structure might work. The message wouldn’t.
Prompts work the same way. There’s a mechanical component you can borrow. But the nuance - the part that makes it actually useful for your specific situation - can’t be templated.
And as we’ve played around with AI more, seen the possibilities, and gotten some value from it, our expectations have shifted.
Because everyone knows AI can do impressive things, we expect it to do impressive things for us. When it doesn’t, we feel like we’re failing. Or worse, we think AI is overhyped.
Neither is true. We’re just using the wrong learning model.
The Exhaustion Cycle
Here’s what I see happening:
Someone feels pressure to “learn AI.” Maybe their CEO made comments about people who aren’t using it. Maybe they’re building a career and watching the once-stable landscape shift under them. Maybe they’re just curious and want to understand what all the noise is about.
So they follow the people posting about prompts. They comment the magic words. They download the PDFs. They try a few things.
And they’re... underwhelmed (and overwhelmed for a different reason?)
Because someone else’s general-purpose solution doesn’t solve their specific problem. The broad approach recognizes a general pain point, but not their pain specifically.
So they try another template. Another course. Another “proven system.”
Same result. Different packaging. And another mailing list to be part of.
The exhaustion builds. The motivation fades. They conclude either that AI is overhyped or that they’re just not “technical enough” to get value from it.
Both conclusions are wrong.
Back to Basics
With all the change going on in the AI space, I’ve been going back to the question of “what’s not going to change in the next 10 years?”
It’s a bold question to try and answer in this case, but I think it’s a useful one.
Everything tactical will likely change. The specific prompts that work. The interfaces. The tools. The models themselves. What’s optimal today might be irrelevant by the New Year, even.
What doesn’t change are the principles underneath.
Understanding what these systems actually are. How they process information. What they’re genuinely good at and what they’re genuinely terrible at. Where to apply them and where to stay away. How to think about problems in ways that make AI useful.
This is the same pattern that exists in every other domain.
If you’re learning to write, you don’t just copy other people’s work. You learn about structure, voice, audience, and clarity. You develop instincts that transfer across any writing project.
If you’re learning to paint, you don’t just trace pre-drawn images. You learn about composition, color theory, and technique. You develop capabilities that transfer across any canvas.
AI should work the same way. But most of what’s out there is paint-by-numbers.
The Templated Learning Problem
I run an AI consulting business. I experiment with this stuff constantly - what works for clients, what doesn’t, what transfers across industries, what stays specific.
And I’ve noticed something about the courses and resources available.
The ones focused on “prompt engineering” are often teaching you to pay for experimental time. That’s literally what you’re doing… paying someone to guide you through trial and error that you could have done yourself.
That’s not inherently bad. Structure and guidance have value. But the thing they’re not teaching you is why any of it works. How the technology was built. How that architecture affects how you should use it. What’s happening under the hood that determines when to trust it and when to question it.
It still feels like magic. Pleasant magic, maybe. But magic.
And when the magic doesn’t work (when you hit a wall on something the template didn’t anticipate), you have no foundation to debug or gain confidence from.
What I Think Matters More
When I think about what actually prepares someone to be effective with AI long-term, it’s not always about the tactical stuff.
Yes, knowing how to architect solutions, build them in platforms, evaluate their performance, launch them, and enable others is still important.
But it’s knowing how to think about problems in ways that make AI useful that stands out to me. Knowing which models to use when. Understanding where AI insight is valuable and where human judgment should override. Knowing how to organize your thinking so AI can actually help - because prompting is really just organized thought, and most people can do that once they know what “organized” means in this context.
These are slower-moving concepts. They don’t feel as immediately actionable as “copy this prompt.”
But they’re the things that compound. They transfer across tools, across use cases, across whatever the next thing is. They put you ahead of the curve instead of perpetually catching up.
The models are going to keep getting better. They’ll require less prompt engineering. They’ll handle more context. The mechanical skills will matter less over time.
But knowing what you’re prompting for (knowing how to reason with these systems, how to evaluate their output, how to fit them into actual work) is going to matter more.
That’s the difference between hollow and substantial. Between consuming someone else’s shortcuts and developing your own capability.
The templates will keep coming. The PDFs will keep getting downloaded. And most people will keep feeling like something’s missing.
Because it is. The fundamentals were never there.
Going Forward
Connor put this all perfectly on a LinkedIn post of mine.
My bet is that people who think like solutions engineers will be ahead, and there’s an incredible opportunity to position yourself as one. I would imagine it’s going to gain the same momentum and popularity as the Forward Deployed Engineer.
P.S. I’m running a Lightning Lesson on Maven next week if you want to explore some of these ideas in this post live.
I’ll be launching a more hands-on course in January for those who want to go deeper.
Feel free to share it with your network to spread the word to those who might be on the lookout for more than PDFs of prompts.