{"id":326,"date":"2026-06-29T08:12:12","date_gmt":"2026-06-29T00:12:12","guid":{"rendered":"https:\/\/aidashxp.com\/anthropic-economic-index-june-2026\/"},"modified":"2026-06-29T08:12:12","modified_gmt":"2026-06-29T00:12:12","slug":"anthropic-economic-index-june-2026","status":"publish","type":"post","link":"https:\/\/aidashxp.com\/en\/anthropic-economic-index-june-2026\/","title":{"rendered":"Anthropic Economic Index: 35% of users expect AI to take on most jobs within a year"},"content":{"rendered":"

On June 26, Anthropic released the sixth \"Economic Index\" report, which for the first time compared the questionnaire responses of approximately 9,700 users with actualClaude<\/a>Use data hooks. The most shocking numbers in the report:More than 35% of respondents expect AI to perform most or almost all of their work tasks within 12 months<\/strong>. This is more aggressive than most forecasts in the market.<\/p>\n\n\n\n

Automation goes beyond augmentation for the first time<\/h2>\n\n\n\n

A key turning point in the report: In APIs (professional workflows),Automation has surpassed enhancement as the dominant usage mode<\/strong>. By \u201cautomation\u201d I mean completely delegating tasks toClaude<\/a>Completed independently, while \"augmentation\" refers to humans and AI collaborating to complete tasks.<\/p>\n\n\n\n

This is no small change. In the use of AI in the past few years, \"AI is the co-pilot and humans are the captain\" has been a repeatedly emphasized safety narrative. But Anthropic\u2019s actual data shows thatIn production environments, people are already letting AI fly on its own<\/strong>.<\/p>\n\n\n\n

The experience gap is widening, not narrowing<\/h2>\n\n\n\n

The most actionable finding in the report is the gap between experienced users and novice users. experiencedClaude<\/a>userConsistently achieve better task completion results<\/strong>, because they view AI as a collaborative partner\u2014iterating, providing context, validating output, and determining which tasks are suitable for AI and which require human judgment. Newbies often try to entrust everything, and the success rate is even lower.<\/p>\n\n\n\n

More importantly,The gap is widening rather than narrowing<\/strong>. Experienced users learn AI faster than novices can catch up. This means that the earliest organizations to deploy AI not only have more AI workflows, but more importantly, their people have accumulated better AI judgment.<\/p>\n\n\n\n

About 49% of jobs: at least 1\/4 of the tasks have been completed by AI<\/h2>\n\n\n\n

Another core indicator tracked by the report is AI task penetration rate. About 49% of jobs have at least a quarter of their daily tasks passedClaude<\/a>Finish. This number has stabilized in recent reports, indicating that the breadth of AI task coverage may have entered a plateau period\u2014\u2014AI has already entered most of the jobs it can enter, and the next step is to deepen, not broaden<\/strong>.<\/p>\n\n\n\n

Classic optimism bias: 40% worry about being replaced in entry-level jobs, but think they are safe<\/h2>\n\n\n\n

Another interesting finding in the report: Respondents believe that entry-level jobs have a 40% chance of being affected by AI, but their own unemployment risk assessment is much lower.This is the classic optimism bias that appears again and again in every wave of automation.<\/strong>. The report points out that those who least underestimate the impact of AI tend to be those workers who are most exposed.<\/p>\n\n\n\n

Three suggestions for action for businesses<\/h2>\n\n\n\n