LO
Management consultant — 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.
The second-steepest professional curve on this site after documentation, and for a structural reason rather than a judgement about quality: the deck's inputs are public documents, its output is a known format, and the steps between — summarise, compare, chart — are what models do most cheaply. Producing it was the junior pyramid's entire economic function. It flattens where the tasks have no artefact: finding the question behind the stated question, which depends on what is not said in the room; being the outside voice, which works because an accountable party can be dismissed; and moving three departments that do not report to each other. Read the height as the billing model rather than the advice — and note that every task that holds is one that does not scale.
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
- 2