Tech sector hiring slowdown
The tech sector just took a pause. In April, companies across the industry shed 214,000 positions. That’s not surprising, considering the broader economic signals. Market volatility, geopolitical tension, and shifting fiscal priorities are pushing companies to be selective about where they invest. This isn’t retrenchment, it’s recalibration. Tech companies, used to scaling fast, are rethinking how they align talent with future-ready operations. They’re not hiring aggressively, and when departures happen, those roles aren’t quickly refilled. It’s a fundamental shift in workforce dynamics.
We’re seeing companies placing more pressure on precision hiring. You won’t see mass hiring unless there’s a clear, measurable return, especially in areas where AI plays a role. Leaders are less interested in adding headcount for scale and more focused on building systems that run smarter. That’s the mindset of most forward-looking firms right now. They’re waiting, watching, and tightening execution.
Across tech, unemployment rose to 3.5% in April, up from 3.1% in March. The BLS reports a net loss of 7,000 jobs in the tech sector that same month. Importantly, overall unemployment in the U.S. held steady at 4.2%, which means tech is diverging slightly from national trends. Also notable, job openings in the industry dropped 11% year-over-year, a signal that demand is softening but not disappearing.
Ger Doyle, ManpowerGroup’s U.S. Country Manager, captured the shift well: hiring confidence has dipped, job leavers are fewer, and finding a role is harder once you’re out. This kind of slowdown often precedes a realignment, not a collapse. Executives should see this as a chance to reevaluate workforce strategy, focus on return, not routine.
Accelerating demand for AI-related roles
If you’re in tech right now, AI isn’t optional, it’s the center of gravity. As companies stop and refocus hiring efforts, there’s one area where demand is doing the opposite: AI. The spike here isn’t gradual. It’s explosive. Database architects saw job demand jump 2,312%. Statisticians? Up 382%. When you see numbers like these, it’s clear, skills that support AI development and data structuring have become mission-critical.
This points to one thing: companies are moving fast to activate their hidden asset, data. Before you deploy AI, you need to clean, organize, and structure data across systems. That’s work that requires specialists. Once that groundwork is done, the task quickly moves to deploying flexible models and finding product-market fit for AI applications. That needs architects, ML engineers, and applied statisticians who can turn data into something useful.
Kye Mitchell, head of Experis North America, part of ManpowerGroup, put it plainly: the developer role is evolving. We’re not talking about coders building UI in isolation; we’re talking about engineers integrating AI to create value, directly tied to business outcomes. Yes, listings for traditional software developer roles dropped 13% month-over-month, but this isn’t a decline in need. It’s a shift toward more strategic, integrated thinking.
Here’s the point. AI is not “extra” anymore. It’s woven into product design, customer operations, and core infrastructure. If you’re not investing in AI talent right now, specifically people who understand how to use existing systems to generate new value, you’re already behind. The best are not waiting to catch up. They’re hiring ahead of the curve, building capability before the platform-level shift hits full force.
Shift from degree-based to skills-based hiring
Companies are shifting focus away from academic credentials. Competency matters more. When a business needs to move fast, it doesn’t have time to overemphasize where someone went to school. What matters is whether the person can do the job, now. That’s why about 50% of tech job postings in April didn’t require a four-year degree, according to CompTIA. This isn’t a trend. It’s a structural change.
The talent market is waking up. Executives are increasingly prioritizing skill sets directly tied to business value, things like advanced data analytics, prompt engineering, and programming languages like Rust. These are applied capabilities, the kind that generate output immediately. A recent study from Upwork found that 80% of corporate leaders plan to prioritize skills over degrees. Half also expect to increase freelance hiring to address gaps in AI and other hard-to-hire areas.
That shift is proving useful. The freelance market isn’t just filling gaps, it’s helping companies stay operationally flexible while strengthening innovation. If the right person for the job doesn’t have a degree but delivers impact faster, they earn the seat. That’s the direction hiring is moving. Results over résumés.
For leaders, this means internal capabilities need to evolve too. Upskilling your current team should be a priority. Build fluency in tools that support transformation, AI frameworks, next-gen programming environments, modern data stacks. The people already on your team can often adapt fastest. Don’t overlook that.
AI integration reshapes job roles rather than eliminating them
AI isn’t wiping out jobs, it’s changing them. Fast. Wherever AI is integrated, roles are being redefined. Repetitive, rules-based work? That’s moving to automation. But the remaining work, oversight, exception handling, strategic thinking, remains deeply human. That’s where demand is shifting.
As AI becomes more operational, the need for individuals who understand how to manage, monitor, and fine-tune these systems is growing. This means hiring people who can ask the right questions, control outputs, and ensure AI is delivering measurable business value. These roles require strong judgment, not just technical skills. Teams are starting to co-work with AI systems, not replace them.
Sarah Hoffman, Director of AI Research at AlphaSense, made it clear: as automation increases, humans will cover the spaces AI can’t, managing its decisions, correcting edge cases, and taking on the creative tasks machines don’t do well. That shift will accelerate over the next 12 months as more companies move from pilot projects to production-level AI.
Executives should factor this into org design. As AI takes over process work, you’ll need to reassign people to higher-leverage activities. It’s not a reduction. It’s a reallocation. Businesses that handle this shift with clarity and speed won’t just protect jobs, they’ll build stronger, more adaptive workforces equipped for what’s next. And in this transition, the blend of human intuition and machine precision will define who leads and who lags.
Evolving nature of traditional tech roles
Tech roles aren’t disappearing, they’re evolving. The title “software developer” doesn’t mean what it did five years ago. What companies are hiring now are problem solvers who apply technology for real impact, not just coders who build isolated features. This change aligns with business needs shifting from isolated sprints to continuous capability-building using advanced tools like AI.
Demand for traditional developer roles dropped 13% month-over-month, according to data from Experis. But this drop isn’t about a shrinking pool of opportunities, it’s about a redefinition of value. The market still needs developers, just not in the same shape or scope. Today’s developer must understand systems thinking, data integration, and AI workflows. Businesses are deprioritizing simple implementation work and focusing instead on developers who can align engineering decisions with strategy.
Kye Mitchell, head of Experis North America, noted that developers are increasingly expected to function as technology orchestrators, owning the broader technical architecture, not just what’s in the codebase. These are people who rethink workflows, automate intelligently, and bring AI to the operational level.
For executives, that means hiring and talent positioning should follow this shift. When evaluating tech teams, focus on those who can link technology to productivity, people who look beyond individual tasks and solve enterprise-wide problems. Tools have evolved. Teams need to evolve with them.
Cautious hiring stance amid economic uncertainty
Hiring is slowing, not stopping. The reason is simple: economic uncertainty makes companies careful. In April, U.S. employers added 177,000 jobs overall, but the tech industry moved cautiously. Business leaders are watching macroeconomic signals, tariffs, global policy shifts, and federal budgetary moves, and they’re choosing to be deliberate.
Rather than overextending, firms are concentrating on retention. They’re preserving institutional knowledge, focusing on internal productivity, and preparing for future shifts. This environment isn’t about aggressive growth, it’s about sustainability and efficiency. And it’s smart. Hiring too quickly under uncertainty can backfire; hiring intentionally doesn’t.
Ger Doyle, U.S. Country Manager at ManpowerGroup, pointed out that job market churn is down, fewer people are leaving roles, and candidates are having a harder time reentering once they exit. In short, the labor market has cooled. Real-time data shows job openings down 11% year-over-year. That’s not a signal to panic, it’s a moment to plan.
Some sectors are still expanding, healthcare, executive management, and logistics are showing steady increases. But even there, employers are being selective. For executives across any sector, the strategy now is simple: best-fit hires over headcount expansion. Focus on talent agility, not numbers. Build lean but adaptive teams who can shift priorities without friction. That’s the operating model that thrives when predictability is low.
Geographic variations in tech hiring trends
Tech hiring isn’t moving at the same pace everywhere. It’s becoming more regional and less centered on the usual players. In April, California still led with 26,280 tech job postings, that’s expected. But the story isn’t just about volume. It’s about relative momentum. Arizona, West Virginia, and Maryland were among the states with the highest month-over-month percentage growth in listings, according to CompTIA.
This shift matters. It suggests companies are exploring new markets, not just for cost benefits, but to access fresh pools of skilled talent. States traditionally outside the dominant tech corridors are building up credible ecosystems. They’re being noticed by firms looking to de-risk from expensive hiring centers and diversify location strategy.
For executives, this means rethinking location planning. Don’t assume talent is only concentrated in legacy hubs. Skilled professionals are operating in untapped regions, often with lower competition and higher retention potential. Expanding the hiring footprint isn’t just tactical, it can directly reduce fixed costs and improve long-term organizational stability.
If your team is still centralized around a few cities, it might be time to optimize. Evaluate where high-quality tech capabilities exist, even if those regions haven’t been on your radar before. The market has already shifted, so should your hiring model.
High value and demand for AI and data science skills
There’s no debate. AI and data science skills are the most valuable in the tech market today. Demand isn’t just high, it’s outpacing supply. Companies are investing heavily in roles that turn raw data into decisions and automation into outcomes. These roles aren’t future-facing, they’re deeply embedded in current go-to-market, operations, and R&D functions.
According to data from Indeed and other platforms, professionals with top-tier AI and generative AI (genAI) skills are earning up to 47% more than peers in other tech disciplines. This pay gap isn’t temporary, it reflects the strategic value these roles hold. Businesses are paying for impact. And that impact is being created by AI engineers, model trainers, and prompt engineers who understand both the tools and the business problems.
Julie Teigland, Global Vice Chair for Alliances and Ecosystems at Ernst & Young, spoke clearly on this: AI scientists and data scientists remain the two most in-demand roles across tech. The gap between open roles and qualified talent hasn’t closed, and likely won’t any time soon. Companies are chasing people who can design, scale, and tune systems that determine how decisions are made at speed and scale.
Leaders should act fast. That means rethinking compensation frameworks, fast-tracking internal AI training, and forming partnerships with universities or learning platforms that can strengthen the talent pipeline. Delayed action will result in a competitive disadvantage. The market already knows the value of these skills, and it’s pricing them accordingly. Your talent strategy should reflect that.
Recap
The shifts we’re seeing in tech hiring aren’t temporary reactions, they’re signals of structural realignment. AI isn’t coming, it’s here, and it’s changing the way companies hire, build, and operate. Skills now outweigh credentials. Roles are being reshaped, not removed. New regions are becoming talent hubs. The old playbook doesn’t apply anymore.
For decision-makers, this is the moment to get intentional. Rethink workforce planning. Prioritize capabilities over titles. Build teams that move fast, learn faster, and align with what the business needs next, not what it needed last year. Talent isn’t about volume, it’s about leverage.
Hire with precision. Train with urgency. Operate with clarity. The companies that act decisively on talent strategy now will be the ones leading the next growth cycle, not scrambling to catch up.