None of it applied to everyone
A retention rate that describes almost none of the apps inside it, a lever that works only for teams already fast, and a defence that can be applied once and never again.
How we organise
RevenueCat on why some AI apps retain users and others do not
Ranked by how many paying subscribers they keep after a year, 3,519 AI-powered apps came apart into three groups. The best keep 13.9 per cent of paid subscriptions active. The worst keep 1.4. Most of the gap opens at the first renewal.
We tell a client to read the spread before the average. A leader who takes the category rate as a fact concludes that AI products trade retention for revenue. The condition sits underneath it, in when the app launched and how it charges. These are consumer apps, and the rates are patterns rather than causes.
How we build
DX on cycle time and pull-request throughput
Cutting the time a change waits in review is supposed to raise throughput. Across more than 500 organisations it does not, except at the top. At the 25th percentile of throughput there is no significant relationship at all. At the 75th and 90th it is strong.
Our position is that speed is worth having and is not a universal lever. For an organisation in the bottom quarter, cutting review time changes nothing, because whatever limits it sits somewhere else. The condition is the finding here, not the correlation. DX is measuring its own customers.
How we assure
A defence that feeds an attacker confident wrong answers
Refusal can be stripped out of an open-weight model in minutes on ordinary hardware. This defence concedes that and attacks what the strip opens. The released model is trained to answer hazardous requests, in the attacked state, with fluent detail that is false.
We judge a control by whether it holds. This one cannot, and the claim is different: that a control which fails can still cost an attacker something. Russinovich states the limit. It works only on a first release, so a firm publishing weights gets one attempt and none after.