Data & Policy
The Displacement Data Standard
The displacement debate runs mostly on anecdote and vendor forecasts. We are positioned to collect something better: direct accounts from the people it is happening to. And the stakes of collecting it well have risen.
Every serious proposal for cushioning AI displacement, from wage insurance to an AI dividend, depends on knowing when displacement has happened; the Bores AI-dividend plan, for one, pays out “if and when AI meaningfully displaces” workers. Someone independent has to be able to say when that is, and for whom. We intend to be that someone.
What the standard is
An open schema and a verified dataset recording who was displaced, from what role, with what stated cause, and what happened in the 6, 12, and 24 months after.
Everything we publish here comes with its methodology attached, including sample size and known bias, so it can be argued with properly.
Published
Nothing published yet
We have not published any research yet. Rather than list forthcoming work as though it were finished, we are leaving this section empty until the first note is genuinely available. What we are working on is below.
In Preparation
State of the Displaced Developer
A survey of our community on their experience of AI-driven displacement: what changed, how quickly, what they tried, and what actually helped. Findings and full methodology published openly, including response rate and known sampling bias.
Entry-level collapse tracking
Tracking how junior and entry-level engineering openings shift over time in the markets where we run chapters, so the debate can proceed on measurements rather than anecdote.
Displacement Data Standard v0.1
The schema, verification protocol, and governance draft behind the standard, published open for comment before any data is collected against it.
Transition insurance pilot design
How a private pool funded by employer productivity gains could work, and what it would cost per displaced worker. Design work, not an offer.
Reading that shaped our thesis
- Drago & LaineThe Intelligence Curse (essay series)
- AnthropicAnthropic Economic Index, and the 81,000-worker survey
- AmodeiPolicy on the AI Exponential
- World Economic ForumFuture of Jobs Report 2025
Contribute your experience
Our research is only as good as the accounts behind it. If AI has changed your work, we want to hear how — in your words, and on your terms about attribution.