Where AI earns its place, and where it does not.
Longer pieces, not blog posts. I write these to be useful whether or not you ever hire me, and because the clearest way to show how I think is to think in public.
AI you actually own: why most implementations are rental
Most AI you could buy is rented, not owned: platform access, tools behind a login, systems only the builder can maintain. What ownership really means.
Pilot Purgatory: why AI projects don't ship
The most common and most expensive way for AI money to disappear: a pilot that demos brilliantly and never reaches production.
The Handoff Tax: why agencies lose quality on AI projects
The cost you pay when the senior who sold the work hands it to juniors, and why AI implementation punishes it hardest.
AI-Leveraged Delivery: one senior operator, a team's output
The model behind my work, and the shift in the leverage ratio that makes one senior person plus AI beat a staffed team.
The difference between using AI and implementing it
Most businesses are stuck at the tool-trial stage. Real implementation looks nothing like a ChatGPT subscription, and here is the line between the two.
Why one senior person now beats a team of fifteen
The economics of delivery have quietly inverted. What AI leverage actually changes about who should be doing your project, and why.
The three questions to ask before you spend a penny on AI
A short, honest filter for whether a process is worth automating at all, taken straight from how I run an audit.
What good AI implementation actually costs, honestly
Ranges, not vague promises. Where the money goes, what changes the price, and how to tell when a quote is inflated.
The AI projects I turn down, and why
Saying no is most of the job. The clearest signals that an AI idea is not worth your budget yet.
A quick call is where the thinking gets specific to your business.
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