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What will AI do to jobs? The answer depends partly on what employers ask AI to do. Byron Auguste and Justin Heck argue in a Fortune opinion article published October 13, 2026, that companies can create a “Turing Trap” by judging AI mainly on whether it can imitate and replace people. That can become a “Talent Trap”: treating workers’ abilities as fixed and eliminating the entry and middle steps through which people gain skills and reach better-paid jobs.
Their alternative is a pathway-centered strategy. Employers would use AI to help people learn, redesign workflows with frontline workers and preserve routes from skills-based jobs into higher-paying roles. This is a proposed management and workforce strategy, not proof that AI exposure inevitably causes layoffs or that augmentation automatically produces promotions.
What the Turing Trap and Talent Trap mean
The Turing Trap: measuring imitation instead of shared performance
Fortune attributes the term “Turing Trap” to Erik Brynjolfsson in 2022. In this framing, leaders make an avoidable choice when they define AI success as passing a human-versus-machine test: can the system perform a task well enough to take over the worker’s role? That benchmark steers investment toward substitution, even when the larger business objective could be achieved better by combining machine capabilities with human judgment, context and learning.
Auguste and Heck write that “The goals we set for AI matter deeply enough to create self-fulfilling prophecies.” If a company rewards headcount reduction, it is likely to build automation that removes tasks. If it rewards better service, faster learning and internal mobility, it can build tools that raise workers’ capability instead. They add that, given AI’s ability to deploy “the aggregated achievements of human intelligence,” the initials could stand for “Amplified Intention.”
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The Talent Trap: assuming capability is scarce and static
The paired error is to treat talent as a fixed pool produced mostly by formal education. Under that assumption, employers compete for credentialed candidates while overlooking people who acquired valuable skills through work. A pathway-centered view treats workers as “working learners”: their capabilities can expand when a job gives them practice, feedback and access to better tasks.
This distinction matters because removing a job can remove a learning stage, not just a paycheck. The authors call the stepping-stone roles that help workers deepen skills and move into higher-paid work “Gateway jobs.” Eliminating a Gateway can interrupt the sequence that leads to a Destination job, even if the immediate automation appears efficient.
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Why Gateway jobs matter in the U.S. labor market
The figures below are U.S.-focused and come from different publications and scopes. “Exposure” describes how susceptible tasks or occupations are to AI; it is not a forecast that every exposed worker will lose employment.
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| Finding | What it measures | Attribution and qualification |
|---|---|---|
| 10 million workers each year | People moving from skills learned on the job into higher-paid, in-demand U.S. jobs | Byron Auguste and Justin Heck, Fortune, 2026; the article says half are STARs |
| About 75 Gateway occupations | Occupations described as stepping stones; they account for 62% of job-to-job changes leading to higher pay | Fortune, 2026; the underlying method was not independently corroborated in the related Opportunity@Work and Brookings material |
| 18.6 million workers | Workers in the most AI-exposed Gateway jobs | Fortune, 2026; this is not interchangeable with the narrower or differently scoped counts below |
| More than 15 million STARs | STARs working in jobs highly exposed to AI | Opportunity@Work and Brookings, 2026 |
| Nearly 11 million STARs | STARs in Gateway occupations within that Opportunity@Work and Brookings framing | Opportunity@Work and Brookings, 2026 |
| Nearly half of pathways | Transitions from Gateway jobs to higher-paying Destination jobs that are highly exposed to AI | Opportunity@Work and Brookings, 2026 |
STARs—workers who are Skilled Through Alternative Routes—include people who developed expertise without a bachelor’s degree. The 10-million annual-transition figure and the STAR share are Fortune’s attributed claims; the Opportunity@Work and Brookings counts use related but different populations. They should not be added together or substituted for one another.
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One example illustrates why task exposure needs care: Opportunity@Work and Brookings report that roughly 70% of tasks in customer-service-representative roles are already exposed to AI. That statistic can justify redesigning the work, but it does not establish that 70% of jobs disappear.
Two competing ways to deploy AI
| Decision area | Replacement-centered approach | Complementary, pathway-centered approach |
|---|---|---|
| Design goal | Benchmark AI on tasks it can take over | Measure whether people and AI achieve goals and learn together |
| View of workers | Skills and talent are largely fixed | Workers can build capability through work |
| Deployment focus | Automate exposed roles | Redesign work so AI supports performance and skill development |
| Workforce pipeline | Risk removing Gateway steps | Maintain or create transitions into Destination jobs |
This is a normative contrast offered by the authors, not a controlled study showing that one model always delivers higher productivity or employment. Outcomes will depend on implementation, demand, training quality and how gains are shared.
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What a high-road AI adoption plan looks like
1. Map pathways before automating tasks
List the roles being considered for automation, the skills workers acquire in them and the higher-paid jobs those skills can lead to. Include internal moves and local labor-market options. Opportunity@Work and Brookings report that approximately 73% of U.S. workers live and work in the same county, so a regional pathway can matter as much as a company’s internal ladder.
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Workers see exceptions, customer needs and handoffs that process diagrams miss. Give them a formal role in identifying tasks AI can assist with, deciding where human review remains necessary and testing whether the new process actually improves results. This is especially important where automation changes every job’s skill mix; Walmart, quoted by Fortune, says “the skills of every single job will change due to AI.”
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3. Build skills in the context of paid work
Pair AI deployment with supervised practice, coaching and clear demonstrations of proficiency. A course detached from the workflow may not produce a usable transition. Define which new responsibilities—such as reviewing model outputs, handling unusual cases or improving prompts and procedures—count toward advancement.
4. Use AI to support transitions, not only selection
Employers can use skills-based records to identify workers whose existing abilities match adjacent roles, recommend learning tasks and make internal openings visible. The goal is to help a capable worker move from a Gateway role into a Destination role rather than discard the Gateway and recruit externally.
5. Track mobility and job quality alongside efficiency
Monitor training completion, internal moves, wage progression, retention, error rates and customer outcomes. A productivity gain that coincides with fewer routes to higher-paid work may be a warning sign, not an unqualified success.
What the evidence does—and does not—show
The available figures establish that many workers and job pathways are highly exposed to AI-related task change. They do not provide a general causal estimate of jobs eliminated by AI, nor do they prove that augmentation guarantees mobility. Exposure can lead to substitution, assistance, task reshaping or no immediate change, depending on the organization’s choices and the technology’s performance.
That uncertainty is the central management issue. A company that automates a Gateway role should be able to explain what replaces the learning opportunity, how affected workers can qualify for the next role and whether the new pathway is available in the communities where employees live. Without those answers, cost-saving automation can strand skilled workers even when the technology works as designed.
Quick Recap
Questions leaders should answer before approving an AI rollout
- Which tasks are changing, and which human decisions remain accountable?
- What skills will workers gain while using the system?
- Which Gateway-to-Destination transitions could weaken or disappear?
- How will workers participate in testing and correcting the workflow?
- What evidence will show that wages, advancement and job quality are improving?
- Are the reported exposure figures being treated as risk indicators rather than layoff forecasts?
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