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 return as a finished-looking thing. I could see my ideas in it. I could see connections I had 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 had decided what good actually meant.

My ideas were there. My reasoning wasn't.

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.

My reasoning was missing from the homepage too, 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. 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 would lose them before I could help them see the problem.

More importantly, the prescription had arrived before I had 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.

Better prompting isn't enough.

This looks like a prompting problem.

Give the model better instructions. Write your position first, and use AI for critique rather than generation. All of those practices can change the quality of the interaction.

But speed creates pressure outside the interaction too.

Working this way takes deliberate thought, and thought takes time. Whether that time exists isn't always a personal decision.

For eight months, UC Berkeley researchers Xingqi Maggie Ye and Aruna Ranganathan watched what people did with generative AI inside a roughly 200-person American technology company. They found that broader access didn't simply let people finish the same work earlier. Employees expanded the scope of their tasks and ran more work streams at once. The pauses between tasks filled with more work.

The study covered only one technology company, so its findings may not apply to every workplace.

The mechanism is still recognizable. A time-saving tool enters the organization. For a while, the saved time looks like relief. Then it becomes available capacity, and soon that capacity is the new baseline.

Once that happens, choosing to use less AI carries a professional cost.

This is one reason I don't find "just use less AI" satisfying. AI gives me genuine leverage. I use it every day, sometimes hundreds of times. It expands what I can attempt with the time and capacity I have. Walking away from that wouldn't make me more principled, only slower.

How do I keep that leverage from deciding which human work disappears?

Good assistance leaves the judgment with you.

The difference isn't whether AI helps. It's what work the assistance leaves for the person.

Huseyin Ates recently compared four feedback designs among 1,176 first-year students in 48 science course sections at four universities:

• Peer feedback: students received feedback from classmates.
• Direct AI feedback: students received feedback from AI.
• Reflective AI feedback: students evaluated their own work before reading the AI's feedback.
• Hybrid feedback: students evaluated their own work and compared it with both peer and AI feedback.

Direct AI feedback improved students' immediate revisions more than peer feedback. But when students later completed a task without AI, those in the reflective and hybrid groups did better than students who had received direct AI feedback. The reflective and hybrid groups also acted on more of the feedback and showed more self-regulated learning.

The AI was useful in every assisted condition. What changed was the human's job. In the stronger designs, the student had to interpret the critique, compare it with what else they knew, and decide what mattered before owning the revision. The assistance handed the student one more judgment to make.

One writer described spending three months asking AI to generate scenes and hit word counts. The output felt productive, but the voice they had spent years developing disappeared. Their results improved when they stopped asking for scenes and began asking questions about a draft they had already written: Why isn't this scene working? Where am I avoiding the difficult choice?

"The tool didn't change, how I used it did," they wrote.

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 work I need to be able to defend

One of the ideas AI contributed to the homepage was a comparison between two walls.

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."

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. 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 those things for me.

That's the trap hidden inside the impressive first preview. The work can look finished before the person is finished forming the position it expresses.

My 52-inch monitor made the homepage look enormous. But its size and polish wouldn'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. Laura Counts, AI promised to free up workers' time. UC Berkeley Haas researchers found the opposite, University of California, February 26, 2026. The article reports on Xingqi Maggie Ye and Aruna Ranganathan's in-progress eight-month study at one roughly 200-person U.S. technology company.
2. 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.
3. Big-Training-8310, AI made me a worse writer for 3 months before it made me a better one, r/WritingWithAI, 2026. A pseudonymous first-person account, used here as testimony rather than prevalence evidence.