July tech hiring sends mixed signals

Mixed tech hiring points to a more selective labor market

July’s numbers show a split labor market. The information sector added 11,000 jobs, while professional and business services added 18,000. These gains came as broader national labor indicators weakened. The issue is not a collapse in demand for technology skills. It is a change in where companies are willing to hire.

Sneha Puri, Economist at Indeed Hiring Lab, said the gains in these tech-adjacent sectors were a positive signal in a slower hiring market. Indeed’s own data supports that view. Demand remains stronger in selected technical areas even as employers reduce hiring elsewhere.

For executives, national employment figures are therefore a poor proxy for the availability of specific technology skills. Aggregate hiring can slow while competition remains high for people who support priority programs. AI, software development, data, and infrastructure can follow a different cycle from general corporate recruitment.

The practical constraint is skills allocation. Companies are becoming more selective about which positions justify new headcount. Technology hiring is moving toward roles tied to clear operational or strategic needs. This gives employers room to control overall workforce growth while continuing to invest in capabilities that matter to their technology plans.

Software development hiring is shifting toward senior AI talent

Software development provides the clearest example of this shift. Indeed data cited by Sneha Puri shows that software development job postings have increased by nearly 15% since early 2025. Overall job postings fell 7% over the same period. Puri attributed much of the software development increase to senior, AI-focused hiring.

That 22-percentage-point gap matters. Companies are not simply increasing technology recruitment across the board. They are concentrating demand on experienced developers who can work with AI and help turn it into useful systems. This creates a more specialized hiring market even while total job availability declines.

The main constraint is qualified talent rather than the number of candidates in the broader labor market. Senior AI-focused developers need more than familiarity with AI tools. Businesses need people who can apply these technologies within existing software, data, security, and operational requirements. The source data does not establish which specific AI skills employers value most, but it clearly shows that hiring demand is moving toward senior, AI-oriented software roles.

C-suite leaders should respond by separating critical technology hiring from general workforce planning. A weaker labor market does not automatically make high-value AI talent easier to recruit. Companies can also reduce dependence on external hiring by developing AI skills among experienced software teams. The key is to direct investment toward capabilities connected to concrete business and technology priorities, rather than treating all software roles as equally scarce or strategically important.

AI adoption is sustaining demand for foundational tech roles

AI investment does not reduce the need for core technology staff. It increases the need for reliable support, infrastructure, and data capabilities. Seth Robinson, Vice President of Research at CompTIA, said demand remains strong in these foundational roles as companies integrate AI more deeply into their technology strategies.

This distinction matters. Deploying AI requires more than hiring AI specialists or buying new software. AI systems depend on accessible data, sufficient computing capacity, secure connections to existing applications, and ongoing operational support. Weakness in any of these areas can limit deployment, regardless of the quality of the AI technology itself.

For executives, the real constraint is execution capacity. An organization can approve an ambitious AI strategy but still struggle to put it into production if its data is fragmented, infrastructure cannot support new workloads, or technical teams lack the capacity to integrate and maintain AI systems.

Hiring plans should reflect these dependencies. Support, infrastructure, and data positions may not carry an AI title, but they can directly determine the speed and reliability of AI adoption. Companies should therefore assess AI workforce requirements across the full technology operation rather than measuring investment only by the number of dedicated AI specialists they employ.

This also explains why slower technology hiring can coexist with sustained investment in selected roles. Companies are controlling headcount while protecting capabilities required for priority projects. CompTIA’s analysis indicates that foundational technology work remains one of those priorities.

AI fluency is becoming a broader workforce requirement

More than half of employers are seeking workers with enough AI fluency to use the technology to help complete their tasks, according to Indeed data. At the same time, Indeed reports that the supply of workers with these capabilities remains limited. The immediate challenge is therefore not access to AI tools. It is having enough employees who can use them effectively.

AI fluency in this context means workers need enough practical knowledge to select appropriate tools, write useful instructions, evaluate outputs, and understand when human review is required. The precise requirements will vary by role and company, but the hiring data points to AI use moving beyond specialized technical teams.

That change has direct implications for workforce planning. Companies that rely only on external recruitment will compete for a limited pool of AI-capable workers. Internal training can expand that pool, especially among employees who already understand the organization’s customers, processes, data, and controls.

Executives should also avoid treating AI training as a simple software tutorial. Effective use requires clear rules for sensitive data, security, accuracy, intellectual property, and human accountability. Without those controls, higher adoption can introduce new operational risks instead of reliable productivity gains.

The opportunity is significant but specific. Organizations that build practical AI skills across relevant roles can increase their capacity to adopt the technology without creating a dedicated AI position for every use case. With more than half of employers already seeking AI fluency, developing these skills is becoming a workforce capability decision rather than a narrow technology initiative.

AI-focused roles are growing despite the broader hiring slowdown

AI-specific hiring is expanding at a much faster rate than the wider job market. A Dice analysis of 7 million job postings found that positions with “AI” in the job title increased 173% year over year in the first quarter. This growth stands out against the broader decline in job postings and reinforces a clear pattern: employers are concentrating hiring on AI capabilities even as they become more selective elsewhere.

The 173% increase also indicates that AI is becoming a more explicit job function. Companies are asking existing employees to use AI tools and they are recruiting for positions where AI expertise is central to the role. That requires deeper technical skills and creates a different talent challenge from general AI fluency.

Executives should read the 173% figure carefully. It measures growth in job postings with AI in the title, not the share of all jobs devoted to AI or the number of people ultimately hired. Rapid percentage growth can also come from a relatively small starting base. The Dice finding therefore demonstrates strong momentum, but it does not establish that AI roles dominate technology employment.

The larger signal is consistent with Indeed’s findings. Software development postings have risen nearly 15% since early 2025, driven mainly by senior, AI-focused hiring, according to Sneha Puri, Economist at Indeed Hiring Lab. Overall job postings fell 7% over the same period. Together, the Dice and Indeed data show that AI demand is moving against the direction of the broader hiring market.

The constraint for companies is access to people who can turn AI investment into production systems and measurable business results. Adding “AI” to a job title does not solve that problem. Leadership teams need to define the technical skills, business responsibilities, and expected outcomes of each role before competing for scarce talent.

This makes workforce design a strategic issue. Some needs will justify dedicated AI specialists. Others can be met by training experienced software, data, and business employees to work with AI. Companies that distinguish between these requirements can invest more precisely while the market for specialized AI talent continues to expand.

Key highlights

• Hiring is becoming more selective: July’s labor market was uneven, but information added 11,000 jobs and professional and business services added 18,000. Leaders should plan around demand for specific skills rather than broad employment trends.
• Senior AI talent remains a priority: Software development postings have risen nearly 15% since early 2025 while overall postings fell 7%, according to Indeed. Protect hiring and development budgets for senior technical talent tied to clear AI priorities.
• Core tech roles are critical to AI execution: AI deployment depends on support, infrastructure, and data teams. Leaders should assess these foundational capabilities before expanding AI programs.
• AI fluency is becoming a workforce requirement: More than half of employers want workers who can use AI to assist with their tasks, while qualified talent remains limited. Build practical AI skills internally instead of relying only on external recruitment.
• AI-specific hiring is accelerating: Dice found a 173% year-over-year increase in first-quarter postings with AI in the job title across its analysis of 7 million postings. Define which needs require dedicated specialists and which can be addressed by upskilling existing teams.