- Anthropic’s paper covers things like AI penetration and exposed occupations
- Observed AI coverage is far below its theoretical capability, the data shows
- Future research could explore how graduates are navigating employment
Anthropic has published a new research paper discussing how it will be collecting real-world data on AI’s impacts on the labor market – but this could be only the beginning.
The Claude maker notes that where the data could really come into play is among researchers and policymakers, who may wish to act upon those insights to protect future workforces from major displacement.
The paper explores data like theoretical versus observed AI penetration across job types, the most exposed occupations and differences in exposure levels.
Anthropic is researching which jobs are at risk from AI
Rather than strictly being a job loss warning scheme, Anthropic says the research could help companies identify areas where workers need upskilling support.
However, while all of this sounds particularly damning of AI, early data suggests that AI hasn’t actually caused any large-scale job losses despite the rapid adoption of chatbots and coding assistants. Anthropic says AI is more about augmenting human workers rather than fully replacing them.
One of the datasets shown in Anthropic’s post reveals not only the theoretical AI coverage across different occupations, but also the actual AI coverage. Management, business and finance, computer and math, life and social sciences, legal, arts and media, and office and admin are among the most likely to be affected, but the reality is that actual levels of AI penetration are several times lower.
That being said, we are starting to see some changes, with hiring slowing amid uncertainty around how AI can actually help companies, particularly among entry-level workers.
Looking ahead, Anthropic suggests further research into how graduates are navigating evolving hiring trends – more data and more context could suggest that they’re finding opportunities elsewhere despite one set of data showing that entry-level roles are slowing, for example.
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