Months ago, I was invited to contribute to a real, published book. Not a blog post. Not a guest essay. A 432-page, ISBN-numbered book: AI Everywhere, Volume 1: How Women Are Changing the World with Artificial Intelligence, published in 2026 by Six Peas Press.
I said yes immediately. Then I wrote about the problem I cannot stop thinking about, the one I see in every AI product I audit.
Chapter 22: Rapid AI Prototyping Without Technical Debt. Seventeen pages. Four habits. Three copy-paste templates. And one pattern that is quietly eating the budgets of every team that builds with AI right now.
Here is what is in it, and why it matters.
The Problem Nobody Names Until It Is Too Late
In November 2025, I built a conversational prototype in Lovable in four hours. It worked. Two weeks later, I could not add a single feature because nothing was documented and everything was tangled. I could not even remember why I had written the prompt the way I had.
That is not a quirk of my process. That is the standard outcome.
GitClear analyzed 211 million changed lines of code in 2024 and found an 8-fold increase in duplicated code blocks. Code duplication is now ten times higher than it was two years ago. Google’s 2024 DORA report found that a 25% increase in AI usage leads to a 7.2% decrease in delivery stability. And technical debt already costs the US economy $2.41 trillion annually before AI made it faster to create.
The tools are not the problem. The habit of treating “prototype” as a permanent mode is the problem.
When a stakeholder sees a working interface, they assume the engineering underneath is done. That three-day build becomes the production system. A month later, developers are spending 40% of their time debugging instead of shipping.
The chapter opens with that pattern, because I wanted readers to recognize themselves before I offered a solution.