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DevOps / platform / SRE engineer — 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
● 3 verified events for this occupation, plotted at the date it happened — the parts of the line near a marker are anchored to something checkable.
The climb is configuration: infrastructure code either applies cleanly or errors, and a machine-checkable outcome is what lets a model try, fail and retry unattended. It is also verbose and repetitive, so the volume drafted is large and the review is quick. The curve flattens at the on-call, which has no mechanism to move — an incident is by definition the failure nobody anticipated, and the call is about acceptable damage rather than a correct answer. Two things the height hides: faster configuration produces more configuration, so hours move from writing to untangling, which is invisible on a roadmap; and the change that reaches people here is services-per-engineer, which thins the rota without removing a single task.
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.
Method and sources#
- Assessment date
- 2026-09-14
- Basis of the task judgements
- 2 evidence-backed · 2 platform inference · 0 not enough evidence
- Verified events
- 4