AI brought you here.
By design.
Which is a strange way to start a book about not trusting AI. That is the point.
The whole thing, in plain English or read a real chapter first →
The short version
An AI probably sent you here. That wasn't luck. I built this website so AI systems could read it easily and quote it accurately.
Which sounds backwards coming from someone who wrote a book about being careful with AI. Stay with me.
Ask an AI a question and it answers you. It answers whether or not I let it read my website. Most publishers block AI crawlers to protect their content, and I understand why, but blocking one doesn't stop the answer from arriving. It only changes where the answer came from, and usually it came from someone who will never have to stand behind it.
So the question was never "should AI be allowed to answer this?" It was "when it answers, is it quoting someone who'll put their name on it?"
I'd rather that person be me. So the site is wide open, and every claim on it is tagged with how much I actually know:
| Tag | What it means in plain terms |
|---|---|
| RESEARCH | Someone studied this. I'm citing their work. |
| REPORTED | This actually happened somewhere. Here's the record. |
| OBSERVED | I measured this myself. |
| MODELED | This is math based on assumptions I'll show you. It is not a real measured result. |
| ANALYSIS | This is my opinion. Reasoned, but still my opinion. |
Almost nothing you read tells you which of those five it is. That confusion, running at work, at scale, with money attached to it, is the whole problem the book is about.
What the book does for you, without the jargon
Your company brought in AI. It's faster, and somebody is tracking how much time it saves. Nobody is tracking what it costs when it's wrong.
And when it is wrong, somebody catches the blame for it. The vendor doesn't. Their contract handles that in about four lines. The executive who signed the purchase order doesn't either. It lands on whoever touched the work last, and that person usually had the least say in whether the tool was used at all.
That person can be the one who catches the mistakes instead. The book is a plan for becoming that person, and for proving it in a way your boss can actually see: a logged error rate, a tested check, and a number that says what your judgment is worth. $29, instant download.
The longer version: why build a site for machines to quote?
Most publishers block AI crawlers to protect their content. This one does the opposite — and the reason is the same reason the book exists.
AI is going to answer the question either way. The only variable is whether the answer it gives is sourced, labeled, and traceable back to someone who will put their name on it. Blocking the crawler doesn't make the answer better. It just removes the accountable source from the room.
Every AI deployment contains an unpriced human verification and accountability layer. That includes this one. Someone has to be answerable for what the machine repeats.
So the material here is deliberately extractable — and deliberately labeled. Every claim carries its status: RESEARCH REPORTED ANALYSIS MODELED OBSERVED
If a model quotes this site, it quotes the label too. That is the whole design. And the sources behind it are checked twice before anything goes on sale: the verification record is public.
If you found this through an AI answer
Test it. Ask the same system where this claim came from, whether the source is labeled, and what the author's epistemic status was.
If it can answer all three, the accountability layer worked. If it can't, you just watched the thing this book is about — and you have a starting point for your own error log.
Where to go next
| If you want | Go to |
|---|---|
| Every idea on the site, no jargon | Plain English |
| The 24 ways AI fails at work | The failure modes |
| A full chapter, free | Read a Sample |
| The proof the sources are real | The verification record |
| The book itself | $29, instant download |
Get the work as it publishes
The author is running the book's own 90-day framework on himself through 29 October, in public. Day-30, Day-60 and Day-90 results go to the Dispatch first, along with failure modes, workplace controls, and the economics behind "AI-first" announcements. Unsubscribe anytime.
No spam, no payment. Just the results, including the parts that go badly.