LO
Backend developer — how we know
The page itself gives the judgements. This one gives what they rest on: which technologies bear on the work, how the estimate moved since language models reached the public, and the method behind both.
Which technologies matter here#
Four separate signals. They are deliberately not added together — a job exposed to two technologies is not twice as exposed.
How it got here#
The index is not a static number. This is where it would have sat at each capability checkpoint since ChatGPT — reconstructed, and labelled as such.
—— this stretch contains a verified event- - - no event in this stretch — reconstruction only0 = no task exposed, 100 = every task exposed
● 2 verified events for this occupation, plotted at the date it happened — the parts of the line near a marker are anchored to something checkable.
It starts slightly above the frontend line — code completion and query tooling were already ordinary before 2022 — and ends well below it, and the gap between the two is the point of having both pages. The climb through 2024 is whole-service drafting becoming real. The early flattening is not difficulty: it is that being wrong here is expensive and often invisible for months, so more of the work is accountability — what must never happen when two requests arrive together, what an error message may reveal, what a migration does to data already written. A more capable model does not reach a position of accountability.
A flat line is not a forecast of safety. It says which tasks automation has reached so far — the occupations that moved least here are the ones where the constraint is physical or regulatory, and both of those can change.
Written about this#
These pieces argue from the same records this page holds, and each of their sections names what it rests on.
Method and sources#
- Assessment date
- 2026-09-12
- Basis of the task judgements
- 2 evidence-backed · 4 platform inference · 0 not enough evidence
- Verified events
- 2