The “2026 State of Tech Talent Report” was recently published by Linux Foundation Research. “Over the past several years, the Linux Foundation has surveyed hiring and training stakeholders to capture the state of the technical talent market amid technological shifts and economic changes,” wrote authors Adrienn Lawson, Marco Gerosa, and Anna Hermansen in the report’s executive summary. “This year’s study is based on an online survey fielded in February 2026, which collected responses from 400 participants worldwide and examined the impact of AI, especially generative AI, on the IT talent market.”

The report’s central conclusion is both clear and timely: despite widespread concerns about AI-driven job displacement, the technology sector is facing a skills crisis rather than a jobs crisis.

“While AI is a net driver of job creation in IT, with a +31% net hiring effect expected for 2026, organizations are struggling with a major full-stack readiness problem,” wrote the authors. “Security concerns are also the #1 barrier to getting value from new technologies. To counter these challenges, organizations strongly prefer upskilling existing staff over external hiring, preserving institutional knowledge and boosting retention.”

“There is little doubt that AI is changing the IT job market,” the report added. “Adoption is widespread and organizations across industries and regions are integrating AI into core business functions.” This widespread adoption raises important questions. Where do organizations expect AI to deliver value? Is that value coming at the expense of technical jobs, or is it creating new ones? What skills are required to operationalize AI at scale, and how are organizations developing those capabilities?

Let me summarize the report’s key findings.

AI Creates Value Across the Enterprise

The survey found that organizations expect AI to deliver value across a broad range of activities.

Software development ranked first, cited by 55% of respondents, followed by IT infrastructure optimization (42%), customer support (38%), research and data analysis (36%), quality assurance (34%), and sales and marketing (29%). AI is also expected to contribute to systems maintenance, project management, human resources, network management, and supply chain operations.

The prominence of software development is particularly noteworthy because it has consistently ranked as the leading area where organizations expect AI to create value. The report notes that these findings are consistent with prior research documenting productivity gains from AI-assisted software development, as well as recent industry investments such as the Linux Foundation’s launch of the Agentic AI Foundation to support AI coding agents and related infrastructure.

Only 3% of respondents said they had no plans to use AI, down from 6% in 2025. AI adoption is rapidly becoming the norm rather than the exception.

AI Is Not Eliminating IT Jobs

Perhaps the report’s most surprising finding is that AI adoption continues to be associated with workforce growth rather than workforce reduction.

For several years, the Linux Foundation has tracked the impact of AI on technical hiring. Previous surveys, like last year’s 2025 State of Tech Talent, found that more organizations were increasing technical headcount because of AI than reducing it. The 2026 survey suggests that the positive hiring impact has been even stronger than expected.

Organizations reported a net hiring increase of 26% in 2025, exceeding the 21% previous expectation. Looking ahead, respondents expect a net hiring increase of 31% in 2026 and 22% in 2027. While growth is expected to moderate somewhat after 2026, the net effect remains positive throughout the forecast period.

These findings contrast sharply with media narratives that often attribute layoffs directly to AI.

“Our findings raise questions about the misalignment between our data and press reports attributing layoffs to AI,” notes the report. Macroeconomic conditions, post-pandemic workforce adjustments, and geopolitical uncertainties may be playing a much larger role in recent layoffs than AI itself.

The report also suggests that some displaced workers may be finding opportunities in smaller organizations and end-user enterprises that are expanding their technical workforces in order to deploy AI solutions throughout their operations.

AI Is Reshaping Technical Roles

Like many previous waves of technological innovation, AI appears to be reshaping jobs more than eliminating them.

The survey found positive hiring effects across every technical role examined. Demand was strongest for AI-specific positions, which showed a net hiring effect of 60%, followed by software development (28%), technical management (22%), IT operations (17%), quality assurance and testing (16%), and even entry-level technical positions (8%).

“This growth coexists with the reshaping of roles within the profession,” the report noted. Developers increasingly expect their jobs to place greater emphasis on architecture, systems integration, and AI-enabled decision making.

The broader lesson is that AI is changing the nature of technical work. Professionals who can effectively operate in AI-enabled environments are likely to remain in demand, while those who fail to adapt may face growing challenges.

The Skills Gap Is a Full-Stack Problem

One of the report’s most important findings is that AI readiness involves far more than learning how to use AI tools.

“The skills gap is not just about knowing how to use AI,” the authors wrote. “It is also about having the engineering knowledge required to deploy it.”

Organizations report significant understaffing across a range of technical domains. The largest shortages were found in AI and machine learning engineering (47%), cybersecurity and compliance (40%), FinOps and cost optimization (36%), platform engineering (34%), and cloud computing (19%).

These findings challenge a common assumption that AI readiness is primarily a matter of prompt engineering or learning how to use the latest AI applications.

Deploying AI in production environments requires robust platform engineering, cloud infrastructure, cybersecurity expertise, cost governance, operational monitoring, and data management capabilities. In other words, organizations need expertise across the full technology stack.

The report concludes that the major obstacle to successful AI deployment is not a shortage of AI specialists alone, but a broader shortage of the technical capabilities needed to support AI systems in production.

Security Has Become the Top Concern

The survey also found that security concerns have become the leading barrier to realizing value from AI.

When asked about obstacles to AI adoption, respondents ranked security concerns first (43%), followed by cost management challenges (36%), general skills shortages (34%), legacy systems limitations (30%), insufficient business knowledge (26%), and lack of infrastructure skills (21%).

The report warns that relatively few organizations appear fully prepared to manage the security risks introduced by increasingly autonomous AI systems.

Agentic AI systems can access files, interact with databases, invoke external services, and trigger business workflows. These capabilities create new forms of operational and security risks that traditional IT systems were not designed to address.

In addition, security expertise is itself one of the areas where organizations report the most significant talent shortages.

The Enduring Value of Institutional Knowledge

Given these talent shortages, organizations have a clear preference for how to respond.

Rather than relying primarily on external hiring, most organizations are focused on developing the skills of their existing workforce.

The leading strategy for addressing talent gaps is upskilling and cross-skilling current employees, cited by 57% of respondents. Other approaches include hiring new technical staff (49%), upskilling inexperienced employees (44%), developing junior talent (34%), and hiring consultants (22%).

A similar pattern emerged when organizations were asked how they planned to support AI initiatives. Upskilling existing employees ranked first, ahead of using AI-as-a-service platforms, building in-house AI capabilities, partnering with AI vendors, or purchasing proprietary solutions.

These findings underscore the enduring value of institutional knowledge. Organizations increasingly recognize that employees who understand their business processes, customers, systems, and culture often represent a more valuable long-term investment than recruiting new talent in a highly competitive labor market.

Conclusion

The 2026 State of Tech Talent Report presents a picture of a labor market that differs significantly from many popular narratives about AI.

Rather than eliminating technical jobs, AI appears to be increasing demand for technical talent. The challenge facing organizations is not a shortage of work but a shortage of the skills needed to deploy, secure, manage, and scale increasingly sophisticated AI systems.

Perhaps the report’s most important insight is that AI readiness is not primarily an AI problem. It is a full-stack capability problem. Organizations need expertise not only in AI and machine learning, but also in cybersecurity, cloud infrastructure, platform engineering, operational management, and business processes.

For hiring managers, the implications are clear. Building a workforce that can thrive in the AI era will require sustained investments in upskilling, continuous learning, certifications, and workforce development. Organizations that successfully combine AI adoption with the development of technical talent will likely be best positioned to capture the opportunities created by the next phase of the AI revolution.