LO
Quantitative analyst — 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
● 8 verified events for this occupation, plotted at the date it happened — the parts of the line near a marker are anchored to something checkable.
Starts at 34 because machine learning and automated backtesting were already inside many trading firms' research before this chart begins. It rises as language-model agents begin writing research code and proposing signals that firms test like human work, and stays in the middle because models still miscompute core risk metrics and rules in the US, EU and UK keep design, authorisation and validation with named people.
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
- 2026-09-30
- Basis of the task judgements
- 5 evidence-backed · 0 platform inference · 0 not enough evidence
- Verified events
- 10