These days, an average person following the news could be forgiven for thinking they are moments away from losing their job due to rapidly developing technologies in the world of artificial intelligence.
One reason is that proponents of these technologies often describe the future of work in bleak terms. For example, Dario Amodei, CEO of Anthropic, which developed the Claude generative AI (GenAI) tool, told Axios recently that AI has the potential to wipe out half of all entry-level white-collar jobs and drive the unemployment rate to between 10% and 20% within the next one to five years.
In short, we have entered an age of heightened fears about workers being replaced by automation. Although such job-loss fears among blue-collar and white-collar workers are by no means new, the emergence of increasingly complex AI technology, especially GenAI tools, has reshaped our perception of the types of workers that might be displaced.
Fortunately, there is no concrete reason to believe that the dire predictions currently making headlines will come to pass in the near future. In fact, recent evidence from the SHRM 2025 Automation/AI Survey suggests that although near-term automation displacement will still affect millions of individual workers, these effects are likely to be much more limited, complex, and nuanced than eye-catching headlines might suggest.
Technical vs. Nontechnical Barriers: What Protects Jobs?
To understand why the impact will be more limited and complex, it is important to remember that automation displacement in an organization can only occur when two types of barriers have been overcome: technical barriers and nontechnical barriers.
The first hurdle requires that a technology, such as GenAI, be able to complete a task that was formerly completed by a human worker. SHRM’s survey results suggest that this kind of task displacement is already quite common.
In fact, our analysis suggests that at least 50% of tasks are automated in 15.1% of U.S. wage and salary employment, which amounts to about 23.2 million jobs. The share of employment attaining this 50% task automation threshold varies significantly by occupational group, from a high of 32% of employment in the computer and mathematical group to a low of 7.3% in education and library jobs (see Figure 1).
Once at least half of a job’s tasks become automated, the likelihood of automation-driven displacement increases for two main reasons.
First, the technological leap required for a job to go from “mostly automated” to “fully automated” is likely to be relatively small.
Second, even without further technological change, a worker in a job that is at least 50% automated may face greater risk of displacement if the employer decides that the remaining nonautomated tasks in the worker’s job can be eliminated entirely or redistributed among other employees. For example, as GenAI tools become increasingly proficient at writing and reviewing computer programs, the need for dedicated human programmers is likely to decline in many settings.
Looking solely at technical considerations, the findings in Figure 1 suggest that slightly more than 15% of U.S. wage/salary workers face a high risk of job displacement. However, this conclusion ignores the importance of nontechnical barriers that can limit automation-driven job loss. These barriers may be able to shield jobs from displacement, even in cases where those jobs are already highly automated.
For example, modern commercial airliners typically fly with very little input from pilots, yet there has been no serious efforts to remove pilots from the cockpit. Beyond the legal and regulatory barriers that would prevent this, it seems likely that many passengers would recoil at the thought of boarding a plane that had no human pilot.