Key takeaways
Advertised pay in the jobs most exposed to AI has risen faster than pay in the least-exposed jobs. Indeed Hiring Lab's analysis of US postings with an advertised salary, published 17 September 2026, found pay in the most AI-exposed occupations up about 46% since 2021, against 25% in the least-exposed. That raw gap shrinks sharply once Indeed's models control for who is being hired. The post-ChatGPT premium is 5.7% after occupation mix, and a non-significant 2.4% with seniority mix held constant. The honest reading is that AI exposure has not cut advertised pay so far. The bigger change for a job seeker is who the postings are for, since the entry-level share of salaried postings in those occupations fell from 29% to 10%.
Is AI pushing pay down?
Not in the advertised salaries employers post. A common fear about generative AI at work is that it will make knowledge work worth less, and Indeed's posting data points the other way. Indeed Hiring Lab, in an analysis by Jack Kennedy, sorted occupations by how much of their required skill set generative AI could perform or reshape, then tracked advertised pay in each group before and after ChatGPT's release in late 2022. Its summary line reads, "Advertised pay is rising fastest in occupations most exposed to AI."
The most-exposed group includes software development, IT systems and support, data and analytics, marketing, and banking and finance. The least-exposed group includes nursing, personal care, food service, cleaning, and manufacturing. Since 2021, according to Indeed Hiring Lab, advertised pay in the most-exposed third of occupations climbed about 46%, against 25% in the least-exposed third. Indeed also reports that the two groups tracked closely for the first year after ChatGPT launched and that the gap opened around 2024.
For anyone in one of the exposed occupations, that is worth knowing, and Indeed's own appendix narrows it considerably.
Why does the AI pay gap shrink to 2.4%?
Indeed Hiring Lab's 46%-versus-25% comparison is raw growth in two groups of postings since 2021, and the mix of postings inside each group changed a great deal over that time. The appendix runs three difference-in-differences models, each isolating the extra advertised pay growth in exposed occupations after ChatGPT, over and above what less-exposed occupations and the wider market would predict.
| What the model holds constant | Post-ChatGPT premium for AI-exposed jobs | Statistically significant? |
|---|---|---|
| Occupation mix | 5.7% | Yes |
| The same job title over time | 4.7% | Yes |
| Seniority mix within each occupation | 2.4% | No, not at the 5% level |
Each row answers a slightly different question, and Indeed notes they are alternative cuts rather than steps in a sequence. Comparing data engineers against data engineers, the premium survives at 4.7%. Holding the share of senior postings constant inside each occupation leaves 2.4%, which the data cannot distinguish from zero.
Indeed adds a fair caveat of its own. If AI reshaping tasks is part of why postings tilted senior, then controlling for seniority "may tend to over-correct" by removing part of the effect being measured. The core premium is positive in all three models, and the largest is 5.7%. That is a far smaller claim than the raw 46%-versus-25% gap suggests, and none of the three shows a penalty for exposure in advertised pay.
What happened to entry-level postings in AI-exposed jobs?
They became a much smaller share of what gets posted with a salary. In the most AI-exposed occupations, Indeed Hiring Lab reports, the entry-level share of salaried postings fell from 29% to 10% between 2021 and 2026, while the senior share rose from 22% to 47%. The least-exposed occupations tilted too, by about a third as much.
This is the part of the study most relevant to someone early in a career, and it is easy to miss under the pay headline. Indeed says directly that "some of the rise reflects a shift toward fewer, more senior postings", which is why the premium narrows so sharply once seniority is held constant.
Indeed Hiring Lab separates two questions about seniority. Measured as cumulative growth since 2021, the pay gap between more- and less-exposed jobs widens with seniority, which its key findings summarize as "large for senior roles, moderate at mid-level, and negligible at entry level". Indeed treats that split as suggestive, because most of the underlying regression terms were not statistically significant. Measured only as the change since ChatGPT, against a 2022 baseline, Indeed finds that "the gap is far more even across levels".
For an entry-level candidate, advertised pay in exposed fields has held up. Indeed's index shows "only a modest 2-point gap" at entry level since 2021 and roughly 5 points at entry on the post-ChatGPT basis, though Indeed treats the seniority split as suggestive. What has shrunk is the share of salaried postings in those fields aimed at junior candidates at all. A new graduate reading a rising median in these occupations is reading a number that increasingly describes a more senior job.
What does advertised pay leave out?
Four things, and each one limits how far Indeed Hiring Lab's advertised-pay finding travels. First, advertised pay is what an employer writes in a posting, which is a different measure from what anyone accepts after an offer is negotiated. Second, Indeed's methodology states that "only job postings specifying annual salaries were included in the analysis." That population skews senior, and Indeed puts the senior share at about 37% by 2026, against about 14% across all US postings.
Third, pay says nothing about how many of these jobs exist. Indeed Hiring Lab notes that postings in the more-exposed occupations generally fell the most between 2022 and 2026, then saw the largest rebound over the past year, which Indeed reads as consistent with those skills becoming more valuable. Pay and posting volume move separately, so a candidate has to watch both.
Fourth, the exposure score measures how much generative AI could reshape a role's skills, not how much any employer has actually adopted it. The occupations at the top are knowledge-work sectors and the ones at the bottom are largely in-person jobs, and Indeed acknowledges that some of the gap may reflect those sectors' different dynamics.
What should you do with this when you negotiate?
Use current postings as your benchmark, and make the AI work in your background specific. Indeed's own advice is aimed at employers, telling them to "keep pay benchmarks up to date in AI-adjacent industries such as tech, marketing, and finance." The candidate's version of that advice is the same sentence pointed the other way. If advertised pay in your occupation has moved faster than general wages, a number you anchored on two years ago may be low, and a recruiter is unlikely to volunteer that.
Where a posting omits the range, Four-Leaf's guide to what to do when a job posting has no salary range covers finding the band from sibling postings and when to ask. The tools compared in the best salary negotiation tools for 2026 roundup cover comp data and coaching.
On the skills side, Four-Leaf's AI-Era Hiring Index of 3,502 postings at 16 AI-native and high-growth employers, captured in April 2026, found LLM or foundation-model experience listed in 57% of data-and-ML job descriptions. At those employers it reads as a baseline expectation, so saying "I use AI tools" adds little. Naming the system you built, the evaluation you ran, or the workflow you replaced gives a hiring manager something to price.
The harder moment is when a recruiter asks where your number came from. Four-Leaf's salary negotiation practice includes a voice scenario built for exactly that, in which an AI recruiter questions your data and pushes to see whether you retreat.
What is overrated
Two opposite readings of this data are both overrated. The first is the fear that AI is already crushing pay in knowledge work. Advertised salaries in the most exposed occupations have risen faster than in the least exposed, the premium is positive in all three models Indeed Hiring Lab ran, and nothing in this dataset shows a penalty.
The second is treating the 46% as the payoff for learning AI tools. The study compares whole occupations. It never measures an individual with AI skills against one without, and its three estimates of the post-ChatGPT premium run from 5.7% down to a non-significant 2.4%. Anyone quoting the 46% as a personal raise has read the chart and skipped the appendix.
A playbook for pricing yourself in an AI-exposed field
- Pull five to ten current postings for your title in your market that state a range, and benchmark against those rather than against a salary you remember.
- Check the seniority tag on every posting you use, because a median that is drifting senior will flatter an entry-level number.
- Rewrite your AI experience as outcomes, naming what you built, what you measured, and what changed.
- Decide your number before the first recruiter call, and write down the two or three postings it came from.
- Rehearse the answer to "where does that number come from?" out loud until it holds when challenged.
Where this is heading
Advertised pay in AI-exposed work has held up so far, and the premium is positive in every model Indeed Hiring Lab ran. The harder part for anyone starting out is getting in, because the salaried postings in these fields have tilted hard toward people who already have the experience. Candidates who win in that market are the ones who can prove what they can do and can say, with evidence, what it is worth.
Originally published on the Four-Leaf blog.
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