The AI Tool Cemetery: What 14 Dead Subscriptions Taught Me About Choosing Tools
I audited a year of canceled AI subscriptions. The pattern in why they died is more useful than any review I have written.
I went through a year of bank statements and counted fourteen canceled AI subscriptions. Laid out in a spreadsheet, they stopped being individual decisions and became a dataset, and the dataset has a clear shape: almost none of them died because they were bad at their advertised job.
The first cluster of deaths was redundancy. Three transcription tools, two image generators, and a meeting summarizer all died of the same cause: an existing tool I already paid for added 80 percent of the feature. The lesson is uncomfortable for the startups and useful for the buyer: the bar for a new subscription is not being good, it is being good at something your current stack cannot fake.
The second cluster was friction death. Tools that required me to open them, as opposed to living inside where I already work, survived an average of six weeks. The pattern was so consistent that I now predict a tool's lifespan from where its icon sits: inside my editor or inbox means years, a separate tab means months.
The third cluster was the demo-to-daily gap. These were tools whose trial experience was a highlight reel: one perfect generation, one impressive demo file. In daily use, the usable-output rate halved, and I was paying subscription prices for demo-quality hit rates. I now test on my worst real task, not their best prepared one.
Fourteen graves taught me one heuristic I trust: a tool earns permanence by removing a step, not by adding a capability. Every survivor in my stack deletes work I used to do. Every corpse added something I could technically now do, and rarely did.