Phantom adoption
The gap between the boardroom view of AI deployment and the daily reality on the floor is creating a new category of enterprise failure: tools that look adopted but deliver nothing.
Writing on product development, company building, and the AI industry.
All of my long-form thoughts on AI, programming, product development, and more, collected in chronological order.
AI formats these articles, after I write a draft. Thoughts and opinions are my own.
The disclosure penalty
When products announce a feature is AI-powered, they don't build trust — they trigger an evaluation reflex that reduces it. The most adopted AI features in history are the ones nobody thinks of as AI.
The one-user app
AI will not collapse software into one chat box. It will make software personal, letting people build small tools around edge cases too narrow for any product roadmap. The biggest software market may not be the next billion-user app. It may be the billion one-user apps.
The easy work was load-bearing
Easy tasks aren't just easy. They train new hires, pace veterans, and keep fundamental skills alive. When AI skims them off, the remaining human work becomes 100% hard cases — and most organisations aren't ready for a workforce that never gets an easy rep.
The competence penalty
Multiple studies show that workers who use AI are judged as lazier, less skilled, and more replaceable — even when their output is identical. This ancient cognitive bias is silently crippling AI adoption by driving the most productive behaviour underground.
Borrowed competence
AI makes you faster at your job today while quietly degrading your ability to know when the job was done wrong. The more you delegate, the less equipped you are to catch the mistakes that matter.
AI never flinches
Humans telegraph uncertainty through hesitation, hedging, and tone. AI delivers hallucinated nonsense with the same polished authority as correct answers. Organisations built to read confidence as competence have no antibodies for this.
The levelling trap
AI narrows the performance gap between junior and senior workers. That sounds like progress — until you realise nobody is building the expertise that made senior workers valuable in the first place.
New engine, old factory
The 5% of companies seeing real returns from AI spend 70% of their effort on process redesign and organisational change, not on the technology. Everyone else is repeating the same mistake factories made when they swapped steam engines for electric motors but kept the old floor plan.
Organisations run on workarounds
Your processes work because your people compensate for them. AI can't compensate — so every dysfunction your team has been quietly routing around becomes a blocking error the moment you deploy it.