For decades, automation fears focused on factory workers while knowledge workers looked

The old automation map was easy to picture. Machines would arrive first for repetitive factory work, while people paid to write code, interpret accounts or draft legal documents would remain protected by education and judgement.

Generative AI has made that map look dated. Professional work already happens in the environment these systems understand best: text, code, spreadsheets and searchable documents.

What Happened

A coding assistant can be deployed to thousands of people overnight. A broadly capable factory robot must perceive a changing physical space, move safely around people, manipulate varied objects and remain economical through long shifts.

• In a widely cited study of US labour markets, Daron Acemoglu and Pascual Restrepo linked industrial robot adoption from 1990 to 2007 with lower employment and wages in the places most exposed.

• The International Federation of Robotics recorded 542,000 installations in 2024, more than twice the annual number a decade earlier.

• An ILO review published in April 2026 noted that older automation measures tended to place lower-skilled routine work at greatest risk, while newer capability measures put business, finance, computing and education among the most.

Key Details

This does not mean knowledge workers are being replaced at scale, or that factory automation has stalled. The evidence points to a narrower change in sequence.

• A 2026 Management Science study combined randomised trials at Microsoft, Accenture and a Fortune 100 company.

• Across 4,867 software developers, access to a coding assistant was associated with a 26.08 per cent increase in completed tasks.

• The August 2026 revision of Stanford’s Canaries working paper found no widespread economy-wide displacement.

Why It Matters

Parts of professional work are being compressed now, while general-purpose physical automation remains largely in pilots and narrow applications. Factory workers had good reasons to take automation seriously.

• However, employment for workers aged 22 to 25 in highly AI-exposed occupations was 19 per cent below where it would have been had it kept pace with less-exposed peers.

• A November 2025 working paper on financial analysts used FactSet’s release of its Mercury AI platform as a natural experiment.

What Reports Say

Coverage of the story so far points to:

• Analysts with access produced reports drawing on 40 per cent more distinct information sources, covering 34 per cent more topics and using 25 per cent more advanced analytical methods.

• Forecast errors rose by 59 per cent as the richer mix of information became harder to synthesise.

• Continued reporting by Silicon Canals as more details emerge