Tech job hopping has cooled, but not necessarily because people are afraid of artificial intelligence. The “great stay” is being driven by a tighter hiring market, changing skill demands, compensation dynamics and an overall belief that the risk of changing jobs now carries more downside than upside.
How the Great Stay Outgrows AI Anxiety in Tech Jobs
AI nerves exist, but they’re not quite the whole story. Indeed’s latest data indicates that concern about layoffs is subsiding among tech workers, and many fewer say they would immediately begin job hunting if their company announced cuts. But a significant chunk — a quarter say their coworkers were laid off because of AI — still view AI as something to fear, with more than a third worried automation may reach their own jobs.
Paradoxically, the AI wave is equally an anchor. A lot of engineers would rather help figure out how to build the next system than just leap into the void. Indeed notes that the demand is strong for skills in distributed computing, machine learning frameworks, model deployment and site reliability engineering — areas where it’s paying off right now to stick around, upskill and ship.
Market Friction and the New Job-Change Risk Calculus
Even for candidates feeling confident, it’s a more challenging market. Workday’s people analytics team says tech requisitions are strong year over year but competition is fierce: roles tend to receive an average of 40 applications and more than half take longer than a month to fill. Long interview loops, capricious headcount approvals and frozen requisitions also increase the likelihood of getting left behind halfway through a process.
There is also the perceived risk of “last in, first out.” Against a backdrop of reorganizations and cost management, new hires might also fear being more exposed than entrenched colleagues. For others, hunkering down is about saving institutional capital and access to internal projects, as well as maintaining eligibility for severance protections if cuts do end up coming.
A Smaller Target in Hiring, but Not for Generalists
Indeed’s job-posting numbers indicate a retreat in broad tech hiring, with sharper drops in non-management jobs than leadership ones. That doesn’t mean demand is collapsing; it means that demand is more concentrated. Employers care about specific skills — SRE, MLOps, model evaluation, distributed systems — more than generalist titles.
Internal mobility isn’t always a safety valve. Workday has underscored how tech companies can be stagnant for employees whose opportunities to break into new teams or learn new skills may be limited. When the outside markets are hypercompetitive and inside ladders seem short, rational professionals wait on a move and invest in focused upskilling.
This is reflected in other labor market measurements. At different times, LinkedIn’s data on the workforce has indicated softening hiring in software versus elsewhere, and CompTIA has observed that tech unemployment tends to be lower than the national average — high demand but a narrower doorway. That imbalance encourages people to stay on until their competencies perfectly match what’s posted.
Compensation Clocks and Real-World Life Logistics
Money mechanics matter. Equity refresh cycles, annual bonuses and retention grants provide natural “hold” periods. Stepping away right before the vest can vaporize a year of upside. Some candidates are also concerned about probation periods or clawbacks if a project gets shelved.
There is a set of issues about life factors that raise the switching costs. Return-to-office policies, the realities of commuting, decisions about child rearing and housing all get in the way of a change. For workers on visas, portability can be convoluted — restarting sponsorship or green card timelines introduces risk. This friction, that is, isn’t about AI — it’s about stability.
AI Is Remolding Work, Not Just Eliminating Jobs
AI’s impact is uneven. Indeed sees tech management postings decline and traditional developer roles at risk as its AI-adjacent positions grow. The outcome is a rebalancing, with less generic orders and more specialized needs around model integration, data quality, latency, observability and cost control.
Many of the teams are navigating a tense middle ground between automation of routine tasks and heightened human-in-the-loop responsibility. This reality for mid-career staff makes sticking around logical, since they can influence where AI lands and acquire rare production skills.
Practical Playbooks for Employers and Tech Talent
Employers who need their movers to move have to limit their risk. Release a crystal-clear AI roadmap, define skills taxonomies and speed hiring cycles. Provide clear paths to advancement, SRE and MLOps training budgets, and safety nets like sign-on protections or severance guarantees if projects are canceled.
For candidates, precision beats volume. Calibrate resumes to the hard skills employers flag — distributed systems, model deployment, reliability, cost-aware architecture — and display outcome-driven examples. Establish evidence: Open-source contributions, public benchmarks, and small production proofs trump buzzwords.
The bottom line: Fear of AI accounts for only a portion of the great stay. The larger forces are market friction, skill specificity, timing of compensation and lifetime constraints. Tech workers aren’t frozen — they’re calculating. Until the trade-offs change, standing still might be the smartest career move in a noisy market.
