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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsAI exposure does not mean that a job will disappear. But Byron Auguste and Justin Heck argue that designing AI mainly to replace workers could weaken the job pathways that help millions of Americans turn skills gained at work into higher-paid roles. Their alternative is to use AI to complement workers and redesign jobs so people can keep learning.
What are the Turing Trap and the Talent Trap?
The Turing Trap: make AI imitate people
In an October 13, 2026, Fortune opinion article, Opportunity@Work leaders Byron Auguste and Justin Heck attribute the term “Turing Trap” to Erik Brynjolfsson in 2022. They use it to describe an approach to AI that treats human imitation and worker replacement as the main goals: if a machine can perform a task a person does, automate it.
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Auguste and Heck say that focus is too narrow. It can direct attention toward what AI can take over, rather than toward what people and AI might accomplish together. Their prescription is a choice about the purpose of deployment, not a claim that every automation decision has the same effect.
The Talent Trap: treat capability as fixed
The authors describe the “Talent Trap” as the assumption that valuable skills mainly come from formal education, that workers’ capabilities are largely fixed, and that employers must compete for a limited supply of already-qualified people. They argue instead that skills can grow through work and that employers should recognize and build on capabilities workers already have.
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Why gateway jobs matter to workers’ next steps
Gateway jobs are stepping stones: roles where workers can deepen skills and move into higher-paid work. If AI changes these jobs or the tasks people learn in them, the consequences could reach beyond current jobholders by affecting later opportunities. That is the authors’ risk argument, not evidence that AI exposure will necessarily eliminate a particular job or close a specific career path.
Auguste and Heck report that 10 million U.S. workers move each year from skills learned on the job into higher-paid, in-demand jobs, and that half of these workers are STARs—workers whose skills were acquired through alternative routes rather than the conventional education pathway implied by the acronym. They also report that roughly 75 Gateway occupations account for 62% of job-to-job changes leading to higher pay. That latter figure is attributed to the Fortune article; the Opportunity@Work and Brookings materials summarized alongside it do not independently confirm its method.
These pathways matter at the local level, too. Opportunity@Work and Brookings report that approximately 73% of U.S. workers live and work in the same county. That figure does not show that workers cannot move, but it underscores why local employers and workforce systems may be important to career access.
What the AI-exposure figures do—and do not—show
The Fortune article reports 18.6 million workers in the most AI-exposed Gateway jobs. Separately, Opportunity@Work and Brookings report that more than 15 million STARs work in jobs highly exposed to AI, including nearly 11 million in Gateway occupations. These are differently scoped figures and should not be added together or treated as interchangeable.
Opportunity@Work and Brookings also report that roughly 70% of tasks in customer service representative roles are already exposed to AI, and that nearly half of transitions from Gateway occupations to higher-paying Destination jobs are highly exposed. Exposure describes susceptibility of tasks or jobs to AI; it is not a forecast that all affected workers will lose employment. The cited figures do not establish a general causal estimate of jobs eliminated by AI.
Two strategies for AI and work
| Decision | Replacement-centered approach | Complementary, pathway-centered approach |
|---|---|---|
| Design goal | Measure which tasks AI can take over. | Ask whether people and AI can accomplish goals and learn together. |
| View of workers | Assume skills and talent are fixed and scarce. | Treat workers as learners whose skills can develop through work. |
| Deployment focus | Automate roles because their tasks are exposed. | Redesign work so AI supports tasks while workers build capability. |
| Career pathways | Risk removing Gateway steps without replacing their learning opportunities. | Preserve or create skill-based routes into higher-paying Destination jobs. |
This is the authors’ strategic contrast, not a controlled comparison showing that one approach reliably produces better productivity or employment outcomes. They present augmentation—supporting workers, accelerating skill building, improving matches, and opening pathways—as a possible result of intentional deployment, not a guaranteed one.
What employers can do in practice
The authors’ proposal puts significant weight on employer choices. A practical implementation can start with the work itself and then track whether the redesign preserves opportunities to learn:
- Involve frontline workers in workflow redesign. Ask which tasks are difficult, where AI assistance could help, and what expertise workers use to handle exceptions. Include the people doing the work in decisions about changing it.
- Map skills, not just credentials or job titles. Identify skills employees use and develop on the job, then consider where those capabilities could qualify them for other roles. The goal is to recognize demonstrated skills, not to assume that a credential is the only reliable signal.
- Build learning into daily work. When AI changes a workflow, make room for workers to develop the skills needed to use the system, check its output, and handle tasks it does not resolve. Training should connect to actual work rather than stand apart from it.
- Check what happens to the next step. If a role functions as a Gateway, assess whether the new workflow still gives workers a way to deepen skills and move into more advanced work. Where tasks change, consider how to maintain or create skill-based transitions into roles that reward those abilities.
- Coordinate locally where pathways cross employers. Workforce and regional economic development leaders can help connect employers’ skill needs with workers’ routes into higher-paid jobs, particularly where many people live and work in the same county.
What the argument does not prove
Auguste and Heck make a case for a high-road approach to AI adoption; the available figures do not prove that exposure inevitably causes displacement, or that worker participation and skills-based pathways will guarantee mobility. The statistics are U.S.-focused, and the 2026 Fortune piece is an opinion article by the authors, not a neutral forecast. The appropriate conclusion is that employers’ design choices may affect whether AI supports workers and preserves pathways—not that a particular employment outcome is certain.
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