LO
Welder — 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
Starts at 34 because robots already did most repeated welding on car bodies and production lines well before this chart begins. It rises slowly, and the rise is driven by robotics rather than language models: seam tracking, easier robot programming and collaborative welding cells bring automation to smaller shops, and shipyards build robots for large structural blocks. It stays in the lower half because out-of-position, site and repair welding, fitting up real parts and judging each joint are still done by welders.
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
- 2026-09-26
- Basis of the task judgements
- 2 evidence-backed · 4 platform inference · 0 not enough evidence
- Verified events
- 2