AI exposure does not mean a job is destined to disappear. But when employers design AI mainly to replace workers—and treat skills as fixed and scarce—they risk weakening the job pathways people use to move into better-paid work. That is the warning from Opportunity@Work leaders Byron Auguste and Justin Heck, whose October 13, 2026, Fortune opinion article argues for AI that helps workers build on their skills instead.
What the Turing Trap and Talent Trap mean
Auguste and Heck use “Turing Trap” for an approach that prizes AI’s ability to imitate people and take over their work. Fortune attributes the term to economist Erik Brynjolfsson in 2022. The authors connect that approach to a second mistake: the “Talent Trap,” or the assumption that valuable talent is fixed, comes mainly from formal education, and is therefore a scarce resource employers must compete to hire.
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Their alternative is to treat workers as people whose skills can grow through experience, and to design AI and jobs so that technology can complement those skills. That is a strategic argument, not proof that every AI deployment will replace workers or that augmentation will automatically create better jobs.
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|---|---|---|
| What to measure | Which tasks AI can take over | Whether people and AI can accomplish goals and learn together |
| How to view workers | As a fixed pool of skills to hire | As working learners whose capabilities can develop |
| How to deploy AI | Automate exposed roles | Redesign work to use AI while developing worker capability |
| What happens to job pathways | Gateway roles may be removed without a replacement route | Skill-based transitions into higher-paid roles are preserved or created |
These are the authors’ contrasting goals, not results from a controlled comparison showing that one approach always produces better employment or productivity outcomes.
Why Gateway jobs matter to workers’ earnings
A Gateway job is a stepping stone: workers can build or demonstrate skills in it and then move into higher-paid, in-demand work. A Destination job is the higher-paying role at the other end of that transition. If AI changes a Gateway job, the consequences may reach beyond the worker currently doing it: the role may also be an entry point for people seeking to increase their earnings. That is a risk the authors identify, not evidence that a particular job loss has occurred.
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 upwardly mobile workers are STARs—workers Skilled Through Alternative Routes rather than relying solely on a bachelor’s degree. They also report that around 75 Gateway occupations account for 62% of job-to-job changes leading to higher pay. Those figures are attributed to the Fortune article; its 62% claim should not be read as a measure of all U.S. job changes.
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For household finances, the connection is practical: an entry or mid-career job can matter not only for its current paycheck but also for the skills, experience, and next-job access it provides. The authors’ concern is that removing such roles without creating other ways to gain experience could make it harder for some workers to reach higher-paid jobs.
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What the AI-exposure figures do—and do not—show
The numbers reported by Fortune and by Opportunity@Work and Brookings describe different populations and scopes. They should not be combined into a single count of workers who will lose jobs.
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| Reported figure | Scope and attribution | What it indicates |
|---|---|---|
| 18.6 million workers | Workers in the most AI-exposed Gateway jobs, as reported by Auguste and Heck in Fortune in 2026 | A count tied specifically to the article’s “most exposed Gateway jobs” framing |
| More than 15 million STARs; nearly 11 million in Gateway occupations | Opportunity@Work and Brookings, 2026 | STARs in highly AI-exposed jobs, and the subset in Gateway occupations |
| Nearly half | Opportunity@Work and Brookings, 2026 | The share of transitions from Gateway to higher-paying Destination jobs described as highly exposed to AI |
| Roughly 70% | Opportunity@Work and Brookings, 2026 | Tasks in customer service representative roles described as already exposed to AI |
| Approximately 73% | Opportunity@Work and Brookings, 2026 | The share of U.S. workers who live and work in the same county |
“Exposure” means susceptibility of tasks or jobs to AI; it is not a forecast that all affected workers will be laid off. The 18.6 million Fortune figure and the Opportunity@Work and Brookings counts have different stated scopes, so one cannot be substituted for another. The county statistic points to a local dimension of job mobility: opportunities to move into a new role may depend on what work exists nearby, not just on a worker’s willingness to change employers.
What a high-road AI strategy would require
Auguste and Heck argue that employers’ choices shape whether AI supports worker capability or narrows routes into better-paid work. A pathway-centered approach requires more than announcing a training program after automating tasks; it means involving workers in decisions about how jobs change and connecting learning to real opportunities.
- Involve frontline workers in workflow redesign. Ask the people doing the work where AI can assist, where human judgment remains important, and how responsibilities should change. Their knowledge can inform a deployment that supports the work rather than treating a job title as a bundle of tasks to eliminate.
- Build skills in the context of work. Pair AI use with opportunities to practice, receive feedback, and take on more complex responsibilities. The authors’ approach treats workers as learners whose capabilities can develop through work, rather than assuming that a credential alone captures their potential.
- Connect redesigned roles to career pathways. Identify which Gateway roles feed into higher-paying Destination jobs and make sure workers can still gain the experience those transitions require. If a role changes or disappears, plan how its useful learning and access to the next step will be replaced.
- Evaluate outcomes beyond task automation. Track whether workers are gaining skills, moving into roles that reward those skills, and finding opportunities to advance—not only whether AI can perform more tasks. These measures align with the authors’ proposed strategy; they are not a guarantee of improved outcomes.
The sources describe AI as potentially supporting workers, accelerating skill-building, improving matches between people and jobs, and opening new pathways. Those are possible benefits and organizational aims, not established effects that follow automatically from adopting AI.
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What employers and workers should not infer
- Exposure is not a layoff notice. A role’s tasks may be affected without the whole job disappearing, and the exposure figures do not establish how many jobs will be eliminated.
- Augmentation is not automatic. AI can support workers only if deployment choices, job design, and opportunities to learn make that possible.
- Credentials are not the only evidence of skill. The STAR figures highlight workers who gain skills through alternative routes, including experience at work; they do not imply that every employer already recognizes those skills.
- One pathway strategy will not fit every workplace. The argument is directed at U.S. employers and workforce leaders, and it does not establish outcomes for other countries or every industry.
For workers, the article is a reason to ask how an employer expects AI to change tasks, what training is attached to the change, and whether there is a credible route into the next role. It is not a basis for assuming a particular occupation or paycheck is secure or doomed. For leaders, the central choice is whether to judge AI only by the work it can take over or also by the capabilities and career paths it can help build.
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