IT hiring gains momentum as software jobs recover

Overall technology hiring strengthens

Technology hiring has increased for six straight months. The growth is not limited to technology companies. It is spreading across industries as businesses fund digital transformation and move AI projects from testing into production.

The CompTIA Tech Jobs Report shows this sustained rise in job postings through June. Seth Robinson, Vice President for Industry Research at CompTIA, said: “June’s employment data suggests that employers are ramping up their technology investments and hiring the talent needed to support them.” He added that even as some technology companies cut staff, other industries are accelerating digital transformation and moving “from AI experimentation to implementation.”

The sector data supports that view. According to Dice, technology job postings in finance and banking rose 47%. Manufacturing IT postings increased 27%. Insurance, aerospace, and defense recorded 23% growth. These figures matter because they show where technology investment is moving. Demand for technical workers increasingly comes from companies whose main business is not technology.

For executives, the key constraint is no longer access to technology itself. Cloud services, AI models, and software platforms are widely available. The harder problem is finding people who can integrate these tools with existing systems, data, security controls, and business processes. Buying an AI service is straightforward. Turning it into a reliable business capability requires technical skills and organizational knowledge.

This changes how companies should think about technology recruitment. Hiring plans should follow business systems and projects rather than broad job categories. A financial institution deploying AI, for example, needs people who understand software and data as well as security, regulation, and operational risk. Manufacturing companies need technical staff who can connect digital systems with production environments. Domain knowledge becomes more valuable as technology moves deeper into core operations.

The continued growth in postings therefore points to a wider change in the labor market. Technology companies may still announce layoffs, but those cuts do not describe total demand for technology skills. Businesses across other industries are building their own capabilities. For C-suite leaders, this means competition for qualified technical talent can remain strong even when headline employment news suggests weakness in the technology sector.

AI and machine learning roles experience a surge

AI and machine learning are the clearest growth areas in technology hiring. Dice reported that postings with AI/ML job titles increased 173% year over year from Q1 2025 to Q1 2026. These positions also carried median salaries 22% above the overall IT market.

The salary premium is significant. Companies are not simply adding AI to job descriptions. They are competing for a limited pool of people who can build, deploy, and manage AI systems. As businesses move from experiments to operational use, they need workers who can connect AI models to company data and applications, evaluate their output, and operate them securely.

Growth is also visible in adjacent technical functions. Dice reported hiring increases of 10% for data analysis, 4% for cybersecurity, and 1% for IT support. This broader pattern is important. Production AI depends on more than AI specialists. It requires usable data, secure infrastructure, reliable software, and operational support. Weakness in any of these areas can limit the return on AI investment.

The 173% increase also needs context. Percentage growth does not reveal the absolute number of available jobs, and rapid growth can start from a relatively small base. It should therefore not be read as evidence that AI roles dominate the entire IT labor market. The stronger conclusion is narrower: employer demand for explicitly AI-focused expertise is expanding much faster than demand in several established IT categories.

For executives, the main constraint is execution capacity. Funding AI software without the skills needed to deploy it creates little business value. Companies need to determine which capabilities must exist internally and which can come from vendors, cloud providers, or consulting partners. Roles tied to proprietary data, critical systems, security, and competitive differentiation are stronger candidates for internal development.

The 22% salary premium makes workforce design equally important. Hiring expensive AI specialists for work that existing software or external services can perform will raise costs without creating a durable advantage. The better approach is to reserve scarce AI expertise for problems where company-specific data, processes, or intellectual property matter.

AI hiring is therefore best treated as part of operating strategy. The data shows strong demand, but headcount alone is not the objective. The objective is to build enough technical capability to convert AI investment into reliable business systems.

Software development hiring shows early signs of recovery

Software development hiring remains below last year’s level, but current demand points to an early recovery. Dice reported that software development jobs were down 22% from 2025. That decline is substantial. Yet more recent posting data shows employers are again prioritizing developers and engineers.

CompTIA recorded about 49,000 postings for software developers and software engineers in June. That made software development the largest technology job category in its data. Systems engineers followed with 36,000 postings, tech support specialists with 27,000, data analysts with 21,000, and DevOps engineers with 19,000.

The important distinction is between the level of employment demand and its direction. Software development has not returned to its earlier strength. Recent increases instead show that demand is improving after a deep contraction. Executives should not interpret strong monthly posting volumes as evidence of a complete labor-market recovery.

There is also a business reason for continued developer demand. AI can automate parts of software creation, including code generation, testing, documentation, and debugging. But businesses still need engineers to define system requirements, make architecture decisions, integrate software with existing systems, manage security, and verify whether AI-generated code is suitable for production. AI changes the work developers perform. The data does not show that it eliminates the need for software engineering.

This has implications for workforce planning. Companies should focus less on the number of developers required for individual coding tasks and more on the capabilities needed across the software lifecycle. Engineers who can use AI tools while understanding architecture, security, data, and business requirements can support a broader range of work.

For C-suite leaders, the current market creates an opportunity to reassess software capacity before demand strengthens further. The 22% year-over-year decline shows that the market remains weak by historical standards. The 49,000 June postings show that employers are nevertheless hiring at meaningful scale. Both facts matter when setting compensation, recruiting plans, and internal development priorities.

The strongest strategy is therefore selective investment. Companies need software talent, but the required skill mix is changing. Hiring should target engineers who can translate new AI capabilities into secure and maintainable business systems.

Claude code’s release coincides with a rebound in software development postings

U.S. software development job postings increased 15% after Claude Code was released in late February 2025, according to Indeed Hiring Lab. Over the same period, overall job postings declined 7%. The difference suggests that software development demand began moving against the broader hiring trend.

Claude Code is an AI coding tool from Anthropic designed to help developers perform software engineering tasks. Its arrival provides useful context for the change in hiring patterns. Businesses are gaining access to tools that can accelerate parts of software development, while still increasing demand for people capable of directing, reviewing, integrating, and operating the resulting software.

The timing is significant, but it does not establish that Claude Code caused the hiring increase. The source article describes the product’s release as a catalyst, based on Indeed Hiring Lab data. Other factors could also influence software hiring over the same period. Executives should therefore treat the figures as evidence of a changing relationship between AI tools and developer demand, rather than proof that one product created the recovery.

The baseline also matters. Indeed Hiring Lab notes that the rebound began from a “low starting point.” Even after the 15% increase, software development job postings remained about 27.5% below their pre-pandemic level. Overall job postings, by comparison, have remained relatively stable since 2020.

These figures challenge a simple assumption that more capable coding tools will automatically reduce developer hiring. The observed period shows both trends occurring together: AI coding capabilities improved while software development postings increased. One reasonable business explanation is that lower development costs can make more software projects viable. Companies may also need experienced engineers to introduce AI tools safely into existing development processes.

Executives should therefore evaluate AI coding tools through output and economics rather than expected headcount reductions alone. Useful measures include development time, software quality, defect rates, security findings, deployment frequency, and the amount of engineering work completed per employee. These metrics provide a clearer basis for deciding whether AI changes staffing requirements.

The recovery is still incomplete. A 15% rise from a depressed base does not erase a 27.5% gap from pre-pandemic levels. But the direction is important. Current evidence indicates that AI-assisted development can expand at the same time as demand for software professionals. The strategic task is to determine which engineering skills become more valuable as AI handles a greater share of routine development work.

Software hiring is shifting toward senior and AI-Focused roles

The software development recovery is not evenly distributed. According to Indeed Hiring Lab data, 71% of the increase in software development job postings between May 2025 and May 2026 came from senior-level roles. Of the increase in postings, 37% mentioned AI in the job title.

This changes the meaning of the broader hiring rebound. Employers are not simply restoring the positions removed during the previous downturn. They are placing greater value on experienced professionals who can work with AI and take responsibility for complex systems. The emerging market therefore appears more selective than the software labor market that preceded it.

There is a practical reason for this preference. AI coding systems can increasingly generate code, assist with debugging, produce tests, and automate other routine development tasks. These tools can raise the output of experienced engineers. They do not remove the need for people to define requirements, make architecture decisions, assess security risks, validate generated code, and take responsibility for production systems. Those responsibilities tend to sit with more experienced employees.

The immediate economics can make senior-heavy hiring attractive. If experienced engineers equipped with AI tools can complete more work, companies may need fewer people for some tasks. But the longer-term constraint is talent development. Senior engineers normally acquire their judgment through years of progressively more difficult work. If companies sharply reduce junior recruitment, the number of people gaining that experience also falls.

That creates a workforce planning issue for executives. The effects may not appear in the next hiring cycle. They can emerge later as companies need technical leads, architects, engineering managers, and other experienced professionals but have developed too few people internally. Businesses could then become more dependent on external hiring for senior talent that many other employers are also seeking.

AI adoption should therefore change entry-level roles rather than automatically remove them. Junior engineers can spend less time on repetitive coding while developing skills in AI-assisted development, testing, code review, security, system integration, and data management. This requires companies to redesign early-career work and training around the tasks that remain valuable as automation improves.

The 37% share of increased postings that mention AI also requires careful interpretation. It demonstrates that AI skills are playing a meaningful role in new software demand, but it does not mean that 37% of all software development jobs are AI roles. Nor does a reference to AI in a title establish how extensively AI is used in the underlying position. Executives should distinguish between headline job titles and the capabilities their organizations actually require.

The management priority is to optimize for both current productivity and future capability. The 71% concentration in senior roles shows where employers see immediate value. It also identifies the risk. A company that hires experienced AI-capable engineers without maintaining a path for junior employees may improve near-term output while weakening its future supply of senior technical talent.

The strongest workforce strategy is therefore selective at both levels. Hire senior specialists where experience is essential, especially for architecture, security, critical systems, and AI implementation. At the same time, maintain enough early-career hiring and structured development to create the next generation of technical leaders. AI can change how that talent develops, but companies still need a deliberate process for developing it.

Key highlights

• Tech hiring is gaining momentum: Tech postings have risen for six straight months, with strong growth across finance, manufacturing, insurance, aerospace, and defense. Leaders should align hiring with digital transformation and AI implementation priorities.
• AI talent is driving demand: AI/ML job postings jumped 173% year over year from Q1 2025 to Q1 2026 and offer a 22% median salary premium. Executives should reserve scarce AI talent for work tied to proprietary data, critical systems, and competitive value.
• Software hiring is starting to recover: Software development jobs remain down 22% from 2025, but developers and engineers led CompTIA’s June technology postings at 49,000. Companies should use the recovery period to secure engineers with AI, architecture, security, and integration skills.
• AI coding tools are changing software economics: Software development postings rose 15% after Claude Code’s February 2025 release, even as overall postings fell 7%, according to Indeed Hiring Lab. Leaders should measure AI coding tools by productivity, quality, security, and delivery outcomes rather than assuming they will reduce headcount.
• Senior-heavy hiring creates a talent pipeline risk: Senior roles accounted for 71% of the increase in software development postings from May 2025 to May 2026, while 37% of the increase mentioned AI in job titles. Leaders should maintain entry-level development programs while using AI to redesign junior work around higher-value skills.