The Work I Couldn't Defend

AI gave me a polished website before I'd decided what I believed.

In July, I sat at my desk looking at the first version of The Abundance Trap website on a 52-inch Dell monitor.

Everything was big.

The colours were bold and the type looked considered. The homepage had a rhythm to it. Another page, which I call Rewind, stretched across six decades of progress and innovation. It showed how one convenience after another had removed some small difficulty from our lives.

I'd given Claude Code and Codex an early outline of the book, the notes I'd been accumulating, and articles I'd saved. I told them to organize the ideas, make them compelling, use these colours and fonts that suited the message, and build a homepage that explained the problem.

Then they did.

I felt a rush watching months of scattered thinking come back dressed as a finished product. I could see my ideas in it. I could see connections I'd been circling for a long time. The Rewind page arrived with roughly 120 examples of innovation, all laid out in a bright, coherent sequence.

AI had made something that looked close to good before I'd decided what good actually meant.

The first reaction was admiration, and the second took longer to arrive. I came back to the pages and began reading them rather than looking at them.

The pages contained my ingredients but not yet my reasoning. That mattered most on the homepage, where more was at stake. The AI had recognized a central distinction in the book: some friction blocks us, while other friction protects us or helps build capability. From there it jumped to a reasonable-sounding prescription. People should reintroduce friction into their lives.

I didn't believe that was the right conclusion.

I've spent twenty-five years in marketing and digital products. Telling people to add friction isn't much of a pitch. (People aren't generally shopping for new inconveniences.) In product work, friction usually means an unnecessary step or delay. A book that told readers to add more of it would sound like an argument for worse products and harder lives. I'd lose them before I could help them see the problem.

More importantly, the prescription had arrived before I'd worked out the answer I wanted the book to earn. So I removed it. The homepage would do a different job: help people notice what they might be giving up, what their children or students might be missing, and where a smoother path might hide a gap in how someone develops. The fuller response would come through the essays and the book.

I imagined someone inviting me onto a podcast to talk about the book. They might ask why one of those innovations belonged on the Rewind page, or what I meant by good friction, or what a parent was supposed to do after noticing it.

I needed to answer without wavering. If I couldn't defend the choice without returning to the model's notes and transcript, I had not made the choice. I had approved it.

The obvious diagnosis is bad prompting, which is convenient because the cure is more prompting.

Give the model better instructions. Write your position first. Use AI for critique rather than generation. All of that can improve the interaction.

But better instructions don't change the underlying problem. AI can produce the work faster than I can decide whether I stand behind it.

I use it every day, sometimes hundreds of times. It gives me genuine leverage and expands what I can attempt. Walking away wouldn't make me more principled, only slower.

The harder question isn't whether AI helps. It's which human work disappears in the process.

A four-university experiment with 1,176 first-year science students found that direct AI feedback improved immediate revisions more than peer feedback did. But when the students later worked without AI, the ones who'd first evaluated their own work did better than the direct-AI group.

The AI helped either way. Unfortunately for the fantasy of frictionless learning, making the student judge the work first was the useful part.

My website process changed the same way. On later pages, I gave the AI less room to decide what I believed. I connected the ideas, research, and logic first, then established the hierarchy of information. I decided what the reader needed to understand before I asked the tool to help express or implement it.

The two-wall comparison on the homepage was a good test of that.

One wall is a prison wall. It blocks and confines, and it's the kind of wall you tear down. The other is a climbing wall. It presents a difficulty that makes you stronger by asking you to engage with it. The current homepage expresses the distinction as "Some friction is a wall" and "Some friction is a workout."

Both are walls. Only one belongs in a self-improvement plan.

I kept that metaphor.

I didn't keep it because the AI had earned authorship of the book's argument, but because I understood why it worked. It helped me express a distinction I believed. I could decide where it belonged and what conclusion it didn't justify.

That's different from the instruction to reintroduce friction, which I removed because I couldn't stand behind the strategy it implied.

Authorship can't be measured by keystrokes. If keystrokes are the test, dictation is cheating and every presidential speechwriter is an accomplice. Generated code, generated sentences, generated layouts—I can use all of it and still own the result. What I need to own are the consequential choices inside it: the hierarchy, the connections, the reader's path, what stays and what gets rejected, and the conclusion.

AI can build the whole page without doing any of the hard human shit that makes it mine.

That's the trap hidden inside the impressive first preview. The work can look finished while the position inside it is still half-formed.

My 52-inch monitor made the homepage look enormous. (The monitor wasn't helping with perspective.) But its size and polish couldn't help me explain the book.

On a podcast, someone could ask why an example belonged, what I meant by good friction, or what I thought a parent should do next. The polished page couldn't answer for me.

The model can help me say what I believe. It can't decide what I believe.

Notes and sources

1. Huseyin Ates, Human-centered GenAI feedback design in higher education: a multisite experiment on direct, reflective, and hybrid approaches to scientific argumentation, International Journal of Educational Technology in Higher Education 23, article 38, July 16, 2026.