After a long period of job-hopping and record quits, a quieter pattern is emerging. More workers are staying in one place, even in tech — a change some call the Great Stay. It is less a flash of sudden loyalty than it is a wager about risk, opportunity, and timing.
What the Latest Data Says About Job Staying Trends
New survey results from Indeed reveal waning anxiety over layoffs and a reduced propensity to bolt. Some 31% of people say they are concerned their companies will cut staff, compared with 39% a year earlier. The proportion who would begin looking for a new job if their employer had layoffs that did not affect them declined to 41% from 70%.
AI is still a complicating, but not the only, factor. According to Indeed’s numbers, 26% of those professionals believe coworkers had been fired because of AI, and 35% say that some aspects of their jobs could realistically be automated. But on the whole, tech vacancies have softened — 19% lower for managerial positions, and a whopping 34% down for non-managers — making it harder to move externally.
Workday’s People Analytics says the market is active, but more competitive. Job requisitions in tech are up year over year, but roles are slow to fill and pipelines are clogged with candidates, of which more than half take upwards of a month to close, and around 40 applications per opening on average.
More general labor market signals are also supportive of that picture. The U.S. Bureau of Labor Statistics suggests the quits rate has pulled back toward pre-pandemic levels, and the Federal Reserve Bank of Atlanta tracks what it calls the Wage Growth Tracker to track job-switcher pay premiums — which are shrinking for many — effectively dialing back the financial incentive to jump ship.
AI Is a Motivator but Not the Cause of Staying Put
Experts warn against attributing the Great Stay entirely to fears of AI. Leaders in hiring and people analytics say the bigger force is uncertainty: geopolitics, funding shifts, cost controls, and uneven demand. In that kind of environment, even strong candidates see relocation as a coin flip.
Career coaches also point to the psychology at play. When bills, caregiving, and mortgages are on the horizon, there is a better-the-devil-you-know effect at work. Candidates are hearing stories of rescinded offers, studio closures, and last-minute pivots and are thinking: I need to hold my ground until the fog lifts.
Yet, at the same time, AI is being adopted so quickly that for many workers there’s plenty to stick around and build. Those influencing how AI is being applied inside their teams are generally more satisfied, as they are able to develop professionally and inform best practices — not to mention future-proof themselves against a move to another business.
Risk Calculus in a Tight Labor Market and Hiring
Even as the market has more openings to fill, the bar for winning them continues to rise. Hiring teams are more risk-averse, interview loops take longer, and assessments go deeper. A job search that used to take weeks can extend for months, with plenty of near-misses on the way.
That friction pushes employees toward internal mobility. If outside options are tight, a lateral move — or even just a stretch assignment — within the same organization can get you new skills without as much downside risk. Employers who emphasize internal career paths are good at hanging on to their most high-potential people.
Skills Transitions Driving the Trend Toward Staying
Analysis by Indeed highlights a mismatch: hiring managers look for skills like distributed computing, machine learning frameworks, model deployment, and site reliability engineering — but many résumés still undersell those abilities. The distance keeps candidates sitting on their hands as they learn new skills.
Pay signals reinforce that strategy. Several labor-market studies indicate that jobs requiring AI fluency can fetch hefty premiums, with some studies putting the uplift at 30% to 40%, depending on job function. Professionals are thus spending money on portfolio projects, certifications, and practicing with deployment rather than committing to a move.
The impact is not uniform at different levels. There does appear to be growth for senior contributors who can influence AI adoption or redesign workflow, but junior candidates whose work is tied to more specific tasks may find things strained. Early-career workers should therefore try to gain broader domain context at their current employer before jumping ship.
How to Make the Great Stay Work for Your Career
For workers, regard staying as an aggressive play, not a waiting room. Look for internal rotations, force time into AI work, and document measurable impact — less latency, costs saved, incidents averted — that travels well on a résumé.
For employers, retention is all about clear expectations and skill-building. Publish clear roadmaps, invest in manager training, and fund targeted skilling programs for things like MLOps, data governance, and SRE. When workers have a credible path to growth, the Great Stay becomes an asset instead of a silent disengagement.
The takeaway: this trend reflects less complacency than calculus. With the markets choppy and their skills in flux, many professionals are opting to invest more muscle in being valuable where they are — until they make a move only when the cards and the skills are squarely on their side.