AI-exposed tech jobs deliver bigger pay gains for senior staff

AI-exposed tech work pays more, but entry-level access is narrowing

Indeed Hiring Lab finds that higher advertised pay in technology work exposed to AI comes alongside a narrower route into that work for beginners. Employers setting salaries and professionals choosing skills therefore face two related questions: how much the work pays and who can get hired to do it. A market can reward experienced workers more while becoming harder to enter.

The research traces this uneven pattern across occupations, job titles, and career levels. Indeed is a hiring platform with a commercial stake in employers’ recruitment, so its analysis and recommendations come from a business that benefits from that activity. Its research places IT at the center of changes across industries and roles, and describes AI skills as especially valuable in technology positions.

The pay premium persists within the same job title

Understanding that value starts with the difference between occupational exposure and individual proficiency. Occupational exposure describes how directly AI affects the work in a job category; proficiency describes a person’s ability to use AI tools. Indeed classifies software development, IT systems and support, data and analytics, marketing, and banking and finance as highly exposed. Healthcare, food service, cleaning, and manufacturing fall into its less directly affected group.

Indeed compares these groups using advertised pay: the compensation employers publish when recruiting. Changes in that pay show how employers’ salary offers develop over time, while a pay premium describes the difference associated with AI exposure in a particular comparison. Hiring managers and applicants need both measures because growth within a group and a difference between groups answer separate questions.

At the occupational level, Indeed reports steady salary increases in the most affected roles since 2022. In its comparison beginning in 2021, advertised pay in the most-exposed roles grew 46%, compared with 25% in the least-exposed group. These percentages measure growth within each group over the comparison period. The premium estimates below measure the exposure-related difference under different comparisons.

The growth paths also changed during that period. Following ChatGPT’s launch, more- and less-exposed occupations initially recorded similar salary growth, then separated more clearly around 2024. Indeed links that separation to a recovery in hiring for more-exposed occupations, as employers reorganized work and competed for AI-related capabilities. In its account, changing hiring conditions help explain the widening pay difference.

Jack Kennedy, an economist at Indeed and the report’s author, puts the post-ChatGPT pay premium at 5.7%. He describes the research as covering four years following ChatGPT’s public release and says advertised wages in highly exposed occupations grew faster than those in less-exposed work. His interpretation challenges fears that AI will reduce knowledge-work employment and depress pay in the jobs that remain.

The premium persists when Indeed narrows the comparison to the same job title. Comparing data engineers with other data engineers, or truck drivers with other truck drivers, leaves a premium of around 4.7%. Comparing like titles reduces the influence of pay differences between occupations, so broad occupational categories explain only part of the result. Within those titles, however, career level still matters.

Senior tech pay grew 45%, compared with 2% at entry level

Indeed’s breakdown by career level shows where the strongest gains are concentrated. Between 2021 and mid-2026, advertised pay in more-exposed technology roles rose substantially at senior level, more moderately at midlevel, and very little at entry level. These figures measure salary growth over the period, separately from the exposure-related premiums.

The progression across all three levels makes experience central to understanding the occupational trend. Midlevel growth falls between the two extremes, showing a gradual increase across career stages. A technology team can therefore face quite different recruiting conditions at each level even when its overall occupational category shows strong pay growth.

Because these figures track advertised opportunities, they describe how employers’ offers have changed as they recruit. They measure a different outcome from raises received by a fixed group of employees. Individuals need to consider how their experience fits the jobs now available; employers need to compare current offers for the level they are hiring.

Kennedy’s account of hiring makes the entry-level result more consequential. “The data points to hiring restructuring rather than direct substitution,” he says, describing employers as narrowing the entry-level route into AI-adjacent roles, jobs involving AI tools, while concentrating hiring and pay gains among senior workers. He sees this as a shift in the experience profile of recruits. His explanation separates that restructuring from the idea that employers are financing senior raises through cuts to junior workers.

For beginners, this restructuring creates an access problem alongside limited salary growth. Pay growth concerns the compensation attached to available roles, while access concerns the chance to enter them. Entry-level candidates can therefore face weaker prospects even as their wider occupational group becomes better paid. The salary-growth percentage measures the first issue; changes in hiring describe the second.

For someone choosing AI training as a career investment, the practical question is which available roles that training could help them qualify for. A capability can become more valuable while employers become more selective about the experience required to use it in a paid role. Candidates should therefore compare training plans with the experience requirements of the jobs they hope to secure.

Employers appear to be rewarding oversight and expert judgment

Experience has a specific role in Kennedy’s explanation of the pay pattern. As AI takes on difficult technical tasks, employers need people who can assess its output and decide whether it is correct and usable. Indeed connects that responsibility with greater demand for soft skills, the human abilities used to communicate, collaborate, and exercise judgment, in technology hiring that has traditionally emphasized technical skills.

Kennedy describes the work employers appear willing to reward: “Employers appear willing to pay more for the ability to effectively oversee, integrate, and correct AI output,” he says. Oversight means supervising what AI produces, while verification means checking its correctness. Integration and correction put that responsibility into practice, because the output must fit the task and errors need attention.

These responsibilities help explain why familiarity with an AI tool and experience in a technology role can have different value. An experienced worker can judge whether an output meets the task’s requirements and decide what needs changing before use. In Kennedy’s interpretation, employers are paying for that judgment as they reorganize technical work. Generating an output is one part of a larger responsibility.

Kennedy connects this explanation directly to career level: “The fact that pay premia scale with seniority suggests that experience and expert judgement are particularly salient qualities. At entry level, the smaller pay premium for AI exposure points to the complementary effect being thinner.” Here, a complementary effect means AI increases the value of a person’s work by helping them perform it. He argues that this benefit is stronger where experience supports effective supervision and evaluation.

Rapid adoption adds pressure to the demand for these abilities. Indeed describes employers facing a skills gap while competing for AI-related capabilities, reorganizing work, and directing more hiring and pay gains toward experienced staff. The skills they seek influence both whom they recruit and what they offer. In Kennedy’s account, AI can increase the value of experienced judgment while employers reduce their intake of beginners.

Hiring plans need separate decisions on pay and access

For employers competing for that experience, Indeed recommends looking across industry boundaries. It argues that AI is changing demand and average pay for established IT and technology skills, and advises keeping compensation for highly exposed occupations aligned with other AI-adjacent industries over the next few years. The comparison therefore extends beyond what a company has historically paid for a familiar role. Employers elsewhere may now place a different value on the same capabilities.

These comparisons become more useful when employers separate roles by career level and responsibility. A hiring manager needs to identify how much supervision, evaluation, integration, and correction a position requires before judging the relevant salary market. Pay decisions can then reflect the experience being sought. Entry-level recruitment requires a separate decision about which opportunities the organization will make available to beginners.

Career communication should make those requirements equally clear. Employers and career advisers should specify whether an opportunity reflects demand for AI-related capabilities, demand for experienced workers, or openings suitable for beginners. Candidates can then assess both the skills they need to develop and the experience required to qualify. Precise role descriptions make that assessment possible before candidates invest in training or apply.

Key takeaways for leaders

• Pay premiums extend within job titles: Indeed finds an AI-exposure pay premium of around 4.7% when comparing the same job titles. Compensation managers need to benchmark offers against current demand for AI-related capabilities across industries.
• Seniority shapes pay and access: Advertised pay in AI-exposed technology roles rose 45% for senior positions, 12% for midlevel positions and 2% for entry-level positions between 2021 and mid-2026. Hiring managers need separate salary benchmarks and recruitment plans for each career level as entry-level access narrows.
• Expert judgment carries value: Indeed links stronger senior pay gains to demand for workers who can oversee, integrate and correct AI output. Technology hiring managers can make these responsibilities explicit in job descriptions and assess candidates’ ability to evaluate results.
• Pay and entry pathways require separate decisions: Employers recruiting experienced AI talent face a different challenge from employers creating opportunities for beginners. Workforce planners need to define which roles require established expertise and which offer a viable entry route before setting compensation and training requirements.