The AI expectations gap

來源原標題

The AI expectations gap

機構

Anthropic

原始標籤

Research

原始連結

https://www.anthropic.com/research/ai-expectations-gap

摘要原文

Economics The AI expectations gap Oct 16, 2026 Read in PDF Szymon Sacher, Tess Cotter, Charles I. Jones, Anton Korinek, Peter McCrory Key findings US adults expect AI capabilities to advance rapidly in the coming years; Anthropic staff expect even faster progress; 39% of US adults and 91% of staff expect that AI will be able to make a Nobel-level discovery by 2030. Anthropic staff expect much larger productivity gains from AI on the tasks it is well-suited for. Among US adults who use AI at work, 26% say a task takes a quarter of the time or less with AI, compared to 59% of Anthropic staff. When it comes to their own job, over half of Anthropic staff expect AI to be able to do at least 80% of their current work by 2030, compared with 5% of US workers. Among both US workers and Anthropic staff, people who expect AI to do more of their own work anticipate higher unemployment. While there are differences in beliefs about future capabilities, the perceived link between how AI might change one’s own job and broader unemployment is very similar for both groups. Introduction How will AI reshape the economy in the years ahead? AI capabilities are advancing rapidly, and the possibility that future AI models may be able to build their own successors (recursive self-improvement) could further accelerate the rate of progress. How quickly this might materialize and to what extent is unclear. Forecasts vary widely and are fiercely debated. What is the core source of disagreement about AI's future impact on the economy? Is it principally due to differences in beliefs about how quickly AI capabilities will improve? Or does it reflect disagreement about how quickly and in what form those capabilities translate to real-world adoption? Which views on the future path of AI are consistent with rapid economic growth? This note presents new survey evidence on these questions. In August 2026, we surveyed two groups: a sample of US adults that was nationally representative and Anthropic staff. 1 The questions covered, among other things, AI’s capabilities, how widely it will be used, how much time it saves, how autonomous it will be, and how difficult it will be for a displaced worker to find a job in a different occupation. 2 Anthropic staff build and use frontier AI every day. The second survey therefore shows how people closest to the technology anticipate its future, both in capabilities and in broader economic impact. We report the following main findings. First, the two groups largely agree about what AI can do today but disagree about the pace of future progress. Anthropic staff expect capabilities to advance faster, AI to be used more widely and with less human involvement, and the time it saves on a task to be much larger. For example, 91% of staff expect AI to be able to make a Nobel-level discovery by 2030, compared to 39% of US adults. Second, there are sharp differences concerning people's own jobs. On average, staff members expect AI to be able to do 75% of their current work by 2030, compared to 28% for US workers overall. Third, the two groups seem to reason about the labor market in a similar way: people who expect AI to do more of their own work in the future forecast higher unemployment. And, at the same level of expected AI automation of their own work, both groups forecast about the same unemployment rate. What people expect AI to do Capabilities We first investigate expectations about AI capabilities. We asked when AI will be able to do each of eight tasks as well as a skilled professional, from writing routine emails to making a Nobel-level scientific discovery. US adults and Anthropic staff largely agree on what AI can do today, particularly when it comes to more complex tasks. Both groups rank tasks in nearly the same order of difficulty (Figure 1, panel A). The groups disagree on the path of future AI progress. For example, around a quarter of US adults and Anthropic staff think that AI can design, conduct, and publish scientific studies today. But by next year, 9 in 10 staff expect this to be feasible for AI as compared to just 38% for the general public. By 2030, 39% of US adults expect AI to be able to make a Nobel-level discovery (panel C), compared with 91% of staff. What is perhaps the most striking difference, depending on the task, between 19 and 40% of US adults say AI will never be able to do a given task, compared with at most 2% of staff. In part, differences in views reflect differences in frequency of AI use. Among those who use AI at work regularly (about a fifth of respondents), 56% expect AI to be able to do at least seven of the eight named tasks by 2030. This is substantially higher than among all US adults (41%) and among those who have never used an AI tool (28%), but still well short of the staff view (96%). Figure 1: Share of respondents who expect AI to be able to complete the listed tasks as well as a skilled professional. Respondents were asked when AI will be able to complete these tasks, regardless of whether companies will actually use it. Panels show the share answering (A) already, (B) by 2027, and (C) by 2030. US adults’ responses (N=10,980) are weighted to be nationally representative, Anthropic staff responses are unweighted. Adoption and its obstacles The impact of AI on the economy is not solely determined by raw capabilities. People and businesses need to actually use AI, which takes time and experimentation. Of the work tasks AI will be able to do, US adults expect that 42% will be done with AI by 2030. Anthropic staff expect noticeably faster diffusion over the next few years, with AI used for 65% of all tasks for which it could be used. To understand what might hold back AI adoption at work, we asked US adults to choose up to three obstacles from a list of nine that they expect to be the biggest. The general public expects that people's preferences and rules, more than the technological constraints, may be what holds adoption back (Figure 2). “Customers preferring to deal with a person” was the obstacle selected most often, followed by “data privacy and security concerns,” “AI not being reliable enough,” and “laws, regulations or liability.” Figure 2: Obstacles to AI use at work in 2030 named by US adults. Respondents were told that businesses do not always use AI even when it is technically capable of doing a task, and were asked which of nine obstacles will be the biggest to AI being used at work in 2030. Each could choose up to three, so shares sum to more than 100%. We did not ask this question to Anthropic staff. Notably, US adults who use AI regularly at work rank data privacy and security concerns first (42% consider it among the top three obstacles, compared with 37% among the rest) and see customer preferences to deal with humans as slightly less important (37% vs. 42%). How autonomous AI will be The impact of AI on the labor market will in part depend on whether AI completes complex tasks autonomously or in collaboration with a human. The former tends to displace human workers. Alternatively, when AI complements human capabilities, workers become more productive. To get a sense for anticipated automation versus augmentation in 2030 among Anthropic staff and the general public, we asked how AI and humans will work together on various tasks ranging from email drafting to legal contracting by 2030. At Anthropic, the vast majority of staff say that by 2030 AI will either complete tasks alone or with a worker evaluating the work (Figure 3). By contrast, 4 in 10 US adults instead expect that humans will do most of the work themselves or with some AI help. Figure 3: Share of respondents who expect AI to do each task alone or with a worker checking. Respondents were asked to imagine that AI is useful for each of the five tasks and asked who will be doing them: AI alone; AI does most of it, with workers checking; workers do most of it, with AI helping; or workers alone.