ACM CareerNews for Tuesday, July 21, 2026
ACM CareerNews is intended as an objective career news digest for busy IT professionals. Views expressed are not necessarily those of ACM. To send comments, please write to [email protected]
Volume 22, Issue 14, July 21, 2026
AI and Job Postings: From Destruction to Creation?
Indeed Hiring Lab, July 8
Software development job postings have rebounded over the past year, even as overall job postings continue to slowly decline. This is a surprising reversal after years of contraction in tech and other sectors highly exposed to AI-driven change. The findings are interesting, given the recent rise of agentic AI and vibe coding over the past 12 to 18 months. For example, Claude Code was introduced in late February, 2025. Since that date, the number of job postings for software developers published on Indeed in the U.S. has risen almost 15%, while job postings overall have declined by 7%. February 2025 was also when the term vibe coding was first coined.
The near-term rebound over the past year is striking. However, it is important to note that the rebound has a low starting point. Even after the recent rise, software development job postings remain about 27.5% below their pre-pandemic level, while overall job postings are essentially the same as in February 2020.The 2025-2026 software rebound is not unique to the U.S., either. With the exception of Germany and France, the share of all job postings that are software development jobs has been on the rise in most large, developed economies. Overall, nations that have been early adopters of AI agent tools have seen the greatest positive impact.
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Half of AI Job Cuts Will Be Reversed by 2027
Inc.com, June 17
Approximately half of the companies that cut workers for AI-related reasons will hire those roles back by 2027, according to Gartner. The reason is clear: the companies reversing course replaced jobs with AI that required human judgment and got information retrieval instead. Research into how AI is actually being used inside organizations shows that humans need to be in the loop when real judgment of tradeoffs is required. Just because AI can access everything employees know, it does not mean it can do everything people can do.
Quite simply, there are human gaps in AI technology. AI can categorize problems and retrieve policies at speeds no human can match. However, sitting with a frustrated customer, rebuilding trust after a systemic failure, and deciding in the moment that a person needs an exception are calls it has never made. The distance between those two categories is the same one that opens up when chatbots are given jobs that require having experienced something nuanced before and needing to draw on personal judgment about it. There is a profound difference between reading about surgery a thousand times and having done surgery a thousand times. The same goes for customer service when it comes to upset customers.
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IT Career Insurance: Skills That Survive Budget Cuts
Spiceworks, July 16
Global IT spending is primarily going to AI infrastructure, software, cloud services, and the data centers that support them. These investment flows are reshaping the job market. Companies are hiring where investment is required and freezing recruitment where work is becoming easier to automate or postpone. Increasingly, hiring decisions are tied to business priorities such as revenue, security, compliance, and operational efficiency. Employers are also becoming selective about where AI dollars go, and that is impacting how they hire for new AI roles.
The safest IT careers in 2026 align closely with business priorities. Cybersecurity continues to be a solid investment. Data breaches can be costly, regulatory oversight is increasing, and AI has created new vulnerabilities. The hiring landscape is evolving beyond just traditional network security roles. Companies are looking for experts in AI threat intelligence and cloud security. Organizations also seek the ability to analyze breaches rapidly and apply automation tools to accelerate threat mitigation. The pace of AI hiring is accelerating, and proficiency in AI is now a prerequisite. Candidates are now expected to smoothly integrate AI into company systems, create AI-based applications from the ground up, evaluate how well models work, and manage AI results effectively.
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AI Skills Now Listed in 73% of Tech Job Postings
CIO Dive, July 9
Demand for AI talent is fast outpacing hiring in other tech positions, according to a new analysis of more than 7 million tech job postings in the United States. Job postings for roles with AI in the title jumped 173% year-over-year in Q1 2026. Software development jobs, by contrast, dipped 22% in the same time frame. Tech hiring growth is uneven across different industries, with tech job postings in finance and banking jumping 47% year-over-year, compared to just 23% year-over-year in overall tech job postings.
CIOs are navigating a shifting tech hiring ecosystem as business priorities change. Workers with coveted AI skills, now at a premium in the market, are finding a home outside of big tech providers. Hiring demand has shifted away from the sectors that drove the 2021–2022 boom and toward newer industries and roles, many of which barely existed at scale two years ago. In just over two years, the share of tech job postings highlighting at least one AI skill jumped significantly, from 15% in January 2024 to 73% in May 2026. The overall findings align with the latest release of official labor data, which found unemployment for tech professions had dipped below 3% for the first time this year. According to recruiters, the boost in hiring can be largely attributed to ongoing AI efforts.
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How to Get Your Resume to the Top of the AI Pile
Mashable, June 30
Employers and HR directors are increasingly using AI to screen large piles of CVs, and that is forcing job candidates to figure out new ways to stand out from the pack. For now, AI screening is more likely among entry-to mid-level roles as compared to executive roles. It is also more commonly used by large, in-demand employers that receive high volumes of applications per vacancy. With that in mind, job candidates should understand that AI tools will increasingly be used do the first pass on resumes and applications, regardless of the role or employer.
For people trying to retrofit their resumes for AI and make them stand out, the best advice is to mirror the language of the job posting or job description. This means highlighting important keywords, quantifying experience whenever possible, and focusing on job-relevant information. The key here is tailoring the resume to the job posting, something that can take a lot of time and can be streamlined by using AI, so long as the applicant gives an LLM adequate information. For example, they can include their resume alongside a narrative of their accomplishments, as well as the job posting.
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Tech Jobs Numbers Show Positive Growth For Sector
IT Brew, July 13
Tech unemployment dropped in June, a potential sign that the industry is recovering after months of uncertainty. Overall, hiring in the sector has shifted across industries and roles, primarily in the financial services sector, which saw a 47% rise in job postings year-over-year, and AI and machine learning, which increased 173% in the same period. The current job market is a reflection of the ongoing AI boom. Organizations are embracing AI right now, and they know that they need skilled technology professionals to do so.
According to U.S. Bureau of Labor Statistics numbers, tech occupation unemployment, at 2.9%, is well below the national unemployment rate of 4.2%. It has been consistently dropping. In May, the rate was 3.1%, a decline from 3.5% the month before. The data suggests that employers are ramping up their technology investments and hiring the talent needed to support them. Even as some tech companies announce layoffs, employers in other industries are accelerating digital transformation initiatives and moving from AI experimentation to implementation.
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HR Professionals Can't Rely Totally on AI Tools to Evaluate Employees
HR Dive, July 15
When it comes to evaluating employee feedback, artificial intelligence models are better at working with easily categorized themes and less effective at handling nuance. A recent study examined the ability of AI to complete basic assignments, as well as its ability to demonstrate the judgment required to accurately interpret how employees experience work. The report found that when answers were clear and verifiable, AI models passed between 76% and 82% of the tasks. However, when it came to complex results that required the models to interpret and understand open-ended employee feedback, that percentage dropped to as low as 33%.
The study examined responses from AI models from OpenAI, Google, Anthropic and xAI across 84 employee listening tasks, and measured responses against criteria developed by psychologists and organizational behavior specialists. The research concluded that while existing AI models can handle objective work, they are unreliable when it comes to interpretation and synthesis. Organizations are already using AI to interpret employee feedback and generate recommendations that influence real decisions about people. The question is not whether these models can produce fluent answers. Rather, it is whether they understand what a good response looks like in the context of the workplace. The AI models studied for this report particularly struggled when it came to creating a cohesive accounting from multiple sources and ambiguous signals.
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AI Is Impacting Jobs But No One Can Agree How
BuiltIn.com, July 15
AI is expected to create enormous productivity gains, but economists do not always agree on what kind of impact it will have on the labor market. While most believe AI will eliminate jobs, the exact scope and speed of this displacement, which roles are most likely to be automated, and whether or not the technology will eventually create more jobs than it eliminates are all points of contention.
If there have been any clear conclusions from the dozens of studies on AI-related job loss published over the last couple of years, it is that it will have a disproportionate impact on recent college graduates applying for entry-level roles. Since the launch of ChatGPT in 2022, employment among early-career workers in AI-exposed occupations has dropped 16 percent, according to a Stanford University study. And as of March 2026, recent college graduates are now more likely to be unemployed than other Americans. Over the past year, Goldman Sachs economists estimate AI has reduced U.S. monthly payroll growth by roughly 16,000 jobs, hitting younger, less-experienced workers the hardest. However, that narrative is now being challenged. New data appears to suggest that companies investing heavily in artificial intelligence actually hire more employees.
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AI Didn’t Make Programming Easier. It Just Made It Differently Difficult
Communications of the ACM, July 14
AI-powered coding assistants are changing how people program, and that is having a significant impact on the types of skills that are required from programmers and developers. New AI tools function as external memory systems, offloading syntax recall, boilerplate generation, and API usage from human to machine memory. As memory demands lessen, reasoning, architectural comprehension, judgment, and code-structure awareness are becoming comparatively more important. Thus, knowing how to program is being fundamentally redefined, but not in ways that make programming easier.
Overall, there appear to be four major shifts taking place in programming work. The field is opening to new practitioners. The work is becoming differently difficult. Education is transforming. The role of the programmer is evolving from knowledge vessel to orchestrating agent. Together, these shifts suggest that AI is not eroding the cognitive substance of programming but relocating it. It is making different skills matter more and creating new forms of difficulty even as old barriers fall.
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Five Key Principles to Update CS Education in the Agentic Coding Era
Blog@CACM, July 14
Writing code has never been easier. Tools like Copilot and ChatGPT generate working code within seconds. But the real challenges of engineering have not disappeared. If anything, they have become the focus. This creates an educational risk. Students can become very productive very quickly, but without developing any depth. They can build systems they do not fully understand. The real question, then, is how computer science educators can update computer science education for the agentic coding era.
Before using AI, students should write code by themselves and they should struggle to understand what is happening before using AI. This principle is derived from a learning theory that emphasizes that people learn most effectively by actively constructing knowledge through the creation of meaningful artifacts. In this case, the principle is applied to the development of computer programs, a construct whose purpose is to support the learning of computer science concepts. If it is Python, students should write Python. If it is a Web server, they should build it. If it is a data structure, they should implement it. Then they should do the same thing with AI. This way, something interesting happens. Students can actually observe, not merely see, what the AI is doing, and so recognize patterns, spot mistakes, understand tradeoffs. Without trying first by themselves, AI is a black box; after experimenting on their own, it becomes a tool. At the end of the day, strong program comprehension opens up new pathways for teaching foundational programming concepts.
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