A new survey reported by The Times of Israel finds that the rapid adoption of artificial intelligence is pushing technology employers to hire for hardware roles at the expense of traditional software positions.

For HR and talent acquisition leaders, the headline finding is directional rather than incremental: the center of gravity in tech hiring is moving. Employers that built workforce plans around software engineering pipelines — computer science graduates, coding bootcamp alumni, iterative software release cycles — now face demand for skills tied to chips, servers, networking equipment and the physical infrastructure that AI workloads require.

What the survey signals

The Times of Israel's report frames the shift as a consequence of AI adoption itself. Training and running large AI models depends heavily on specialized hardware — processors, memory, data center capacity — and that dependency is reshaping which roles companies prioritize when headcount opens up.

The pattern carries direct implications for people-process decisions:

• Workforce planning. Companies scaling AI capabilities may need to rebalance requisition budgets between software and hardware engineering, supply chain and semiconductor-adjacent talent pools.
• Skills taxonomy. Job architecture built around software competencies may underweight hardware design, embedded systems, electrical engineering and thermal or power management skills that AI infrastructure demands.
• Sourcing strategy. Hardware talent pools are typically thinner and more geographically concentrated than software pools, which raises competition pressure and time-to-fill metrics for hardware requisitions.
• Internal mobility. Employers with large software workforces will need to assess how much retraining can convert existing engineers toward infrastructure-facing roles versus hiring externally.

The survey's framing also raises a retention question. If hiring demand tilts toward hardware, software engineers may see slower wage growth in some segments, while hardware specialists gain leverage — a dynamic compensation teams should model in their next salary review cycle.

Why the shift matters for recruiters

Software hiring has dominated tech talent strategies for more than a decade. University pipelines, employer branding, interview processes and assessment tools were all largely optimized for software candidates. A pivot toward hardware means those processes may not transfer cleanly.

Hardware hiring tends to run longer. Candidates with relevant experience in chip design, advanced packaging or data center engineering are scarce, and verification of hands-on skills differs from coding assessments. Talent acquisition teams that treat hardware requisitions like software requisitions risk extending time-to-hire precisely when AI roadmaps depend on that infrastructure arriving on schedule.

The geographic dimension matters as well. Hardware manufacturing and design clusters are concentrated in specific regions, and employers outside those clusters may need to weigh relocation packages, remote-eligible hybrid arrangements or partnerships with engineering programs to access candidate supply.

Questions employers should ask now

For CHROs reading the survey, the practical takeaway is a set of planning questions rather than a directive:

1. What share of our next 12 months of requisitions depends on hardware or infrastructure skills, and how does that compare with the prior two years?
2. Can our current assessment and interview processes evaluate hardware candidates credibly?
3. Where can reskilling move existing software talent into adjacent infrastructure roles, and where is external hiring the only realistic path?
4. Are compensation benchmarks for hardware roles current enough to support competitive offers?

Looking ahead

If the trend the survey identifies holds, employers that adjust their talent pipelines toward hardware-adjacent skills early will be better positioned as AI infrastructure investment continues to shape hiring priorities across the technology sector.