Anthropic, makers of the Claude series of AI models, has released data into how its platform is being used to perform different job functions. The study, represented in a comparative graph, showcases the percentage of U.S. workers in various job categories versus the percentage of conversations on the AI platform Claude about those jobs. The results reveal stark contrasts in AI adoption and interest among sectors, highlighting both opportunities and gaps in the integration of this transformative technology.
High AI Usage Fields
The data underscores that AI usage—or at least interest in using AI—is heavily skewed toward certain white-collar professions. Notably:
- Computer and Mathematical is the biggest outlier, with questions around computing and mathematics representing 37 percent of all queries on Claude, while only 3.4 percent of the US populating being engaged in the sector. This shows how programmers have been the fastest adopters of AI. Also, Claude’s Sonnet 3.5 model is widely regardly to be extremely good at coding tasks, and this appears to be reflected in this usage.
- Office and Administrative Support was second, accounting for 12.2% of Claude conversations, compared to its workforce representation of 7.9%. This significant overrepresentation suggests a growing reliance on AI tools for administrative tasks like scheduling, data entry, and document drafting.
3. Arts, Design, Sports, Entertainment, and Media stands out with 10.3% of AI conversations, despite representing only 1.4% of U.S. workers. This overrepresentation indicates that creative fields are actively exploring AI tools for content generation, design assistance, and media production.
4. Education Instruction and Library accounts for 9.3% of Claude conversations, higher than its 5.8% workforce share. This suggests educators and librarians are increasingly using AI for tasks like personalized learning, research assistance, and administrative organization.
Moderate AI Usage Fields
Several fields show a balanced or slightly elevated use of AI relative to their workforce representation:
- General Management (6.9% conversations, 4.5% workforce) and Business and Financial Operations (6.6% conversations, 5.9% workforce) highlight a steady integration of AI for analytics, decision-making, and operational efficiency.
- Healthcare Practitioners and Technical Fields reflect a cautious but growing adoption, with 6.1% of conversations aligning closely with their 2.6% workforce representation.
Low AI Usage Fields
At the other end of the spectrum, jobs in labor-intensive and manual sectors are vastly underrepresented in AI conversations. These are also the jobs least likely to be replaced with AI.
- Transportation and material moving makes up for 9.1 percent of US jobs, but only 0.3 percent of AI queries were around this field.
- Farming, Fishing, and Forestry, while making up 0.3% of the workforce, accounts for a mere 0.1% of AI conversations. This disparity suggests that these industries have yet to widely adopt or explore AI applications.
- Building Grounds Cleaning and Maintenance and Protective Service also show minimal AI engagement (0.1% and 0.4% conversations, respectively) compared to their workforce size.
- Healthcare Support, a critical area, registers only 0.3% of conversations despite accounting for 4.7% of the workforce. This underrepresentation may highlight untapped potential for AI to streamline caregiving and support roles.
Insights and Implications
The data paints a picture of uneven AI adoption across industries:
- Fields like programming, education, media, and office work are leading in exploring AI’s capabilities, likely driven by the clear efficiency gains and creative possibilities AI offers.
- Manual labor and technical support roles are lagging, possibly due to a combination of limited awareness, cost barriers, or a perception that AI tools may not directly address their specific challenges.
- The significant underrepresentation of AI in healthcare support and farming reveals areas ripe for innovation, where AI could play a transformative role in improving productivity and service quality.
As AI continues to evolve, bridging these adoption gaps will be crucial. Tailored AI solutions for underrepresented fields could unlock enormous potential, democratizing access to the benefits of automation and innovation across the economy.