AI Isn’t New. Your Discernment Is What Matters.
I’ve been writing code for over 40 years. Started in the eighties, back when you had to actually understand what was happening under the hood because there was no other option. No Stack Overflow. No “just Google it.” You figured it out or you didn’t.
And here’s what I keep telling people about AI: it’s not new. It’s just more of the same.
Code. That’s it. Code that, if you did it right and you’re lucky, will probably give you most of the answer you want. There’s just a lot more of it now.
The Fancy Autocomplete Thing
People love saying that AI is just a really fancy autocomplete. And they’re not wrong. But here’s the thing—you could say that any computer is just a watch and a calculator. Transistors. Simple logic gates. And with that foundation, we built everything you see around you.
So yeah, maybe AI really is that simple at the core. So are the computers we’ve had this whole time. Look what they can do.
(This is why I’ve been building MCP servers like a madman lately—it’s not magic, it’s infrastructure. Tools that make other tools more useful. Same game I’ve always played.)
Same Game, Next Level
What we’re experiencing right now isn’t a new game. It’s achievement unlocked in the same game I’ve been playing since the eighties. Same rules. Same principles. The token count went up, the processing power expanded, but the fundamentals haven’t changed.
Most people see AI as something completely different. I see it as progression. And that difference in perspective? That’s everything.
It’s like the tourist vs. explorer mindset I wrote about—tourists see AI as this exotic destination they need a guided tour for. Explorers see it as another interesting place to poke around and figure out how things work.
Discernment vs. Judgment
I’ve always been happy working with computers—whether it was 40 years ago or right now—because I’m the human with the discernment. I can tell whether what’s coming out of it is good or bad. I can see how to make it better.
Notice I said discernment, not judgment.
Judgment is quick. It’s a verdict. You pass judgment, you render judgment. It’s got finality to it, and it can be rash.
Discernment is different. It’s earned. It’s decades of tinkering, failing, solving problems, seeing what works and what doesn’t. It’s pattern recognition at scale. Discernment means you can actually see what matters—not just react to it.
Someone without decades in technology can still have strong opinions about AI. They can make snap judgments about what it will or won’t do. But that’s not discernment. That’s just noise.
(Claude and I actually dug into this distinction when I was drafting this post. I’d written “judgment” and caught myself—no, that wasn’t the right word. The conversation that followed got pretty interesting.)
Awareness and Accessibility
The real shift isn’t in what the technology can do. It’s awareness and accessibility.
Suddenly millions of people who’ve never thought about how to work with code or systems have a powerful tool in their hands. No barrier to entry anymore. And that’s both exciting and kind of the problem.
Awareness without discernment. Accessibility without understanding.
This is why I care about documentation and teaching. Not gatekeeping—the opposite. If more people develop actual discernment about these tools, we all win. But just having access to a thing doesn’t mean you understand the thing.
The Same Story Across the Ages
This isn’t new either. When machinery first came out, the Luddites had judgment—they saw machines and made quick calls. The early pioneers of electronic music had discernment—they understood what was actually possible and where the craft was going.
Same pattern every time new technology shows up. The difference between the people who get left behind and the ones who shape what comes next isn’t usually raw intelligence. It’s accumulated understanding. It’s discernment.
I wrote about the sacred principles of code recently—those aren’t AI-specific. They’re the same principles that have always mattered. Use your tools before coding. Never assume, always question. Write clear and obvious code. These apply whether you’re using a mainframe, a MacBook, or Claude.
What This Means for You
AI generates. Humans discern. That’s the relationship.
The tool isn’t going to tell you if its output is good. It’s not going to tell you where it fits into a real workflow. It’s not going to tell you what actually matters.
That’s your job. That’s always been your job.
And if you’ve been doing this work for decades—really doing it, not just watching from the sidelines—you already have what you need. You’ve been building discernment the whole time.
The game hasn’t changed. You just unlocked the next level.
Want to see discernment in action? Check out my collaborations archive where I document the actual back-and-forth of working with AI—the good, the weird, and the surprisingly insightful.
For a specific example: MCP Office Tools: When Documentation Becomes the Product — where rewriting a README turned into a live demonstration of everything this post is about.
