ACM CareerNews for Tuesday, April 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 8, April 21, 2026
The Demise of Software Engineering Jobs Has Been Greatly Exaggerated
CNN, April 8
Amidst fears that AI will one day replace software developer jobs entirely, the reality appears to be much different. In fact, AI may actually be expanding the range of job options available to jobseekers. Job openings for developers are actually growing. Instead of wiping out jobs, AI is shifting the tasks of developers. They are doing less routine coding work and devoting more of their schedule to overseeing AI-powered code-writing agents. Engineers are spending more time designing the structure of software and generating ideas.
The job of software engineer will look different in the future, but that does not mean it is going away. The best engineers are spending a significant amount of time with AI and using it to make their designs better. This has created a chaotic transition period, potentially hurting engineers who are reluctant to use AI or struggle to keep up with the technology. As proof of this overall trend, listings for software engineer jobs on Indeed are up 11% annually, a faster clip than postings overall. Companies are expanding their software budgets and increasing overall engineer headcount.
Click Here to View Full Article
New Data Shows Surprising Rebound in Tech Hiring
Yahoo Finance, March 13
For much of the past two years, the conversation around artificial intelligence has centered on one fear: that machines would soon start replacing white-collar workers at scale. But new labor market data suggests something very different may be happening. Instead of disappearing, many tech jobs appear to be coming back. According to a new report, job postings for software engineers are rapidly rising and are now up about 11% year over year.
Tech hiring appears to be rebounding, despite overall anxiety about AI. There has been a clear rebound in demand for software engineers even as companies pour massive investments into artificial intelligence. The broader job market has remained relatively steady as well. At the same time, companies are spending aggressively on AI infrastructure. AI capital expenditures have reached approximately 2% of U.S. GDP, or roughly $650 billion. Despite the narrative that AI will eliminate large numbers of jobs, the data does not support that view. The hiring trend comes as the U.S. prepares for a massive buildout of computing infrastructure. Almost 3,000 data centers are currently planned across the country.
Click Here to View Full Article
Employers Say They Struggle to Find Workers With the Right AI Skillset
HR Dive, April 17
Although artificial intelligence is rapidly changing the way companies do business, 53% of employers said their main challenge was finding graduates with the right AI skills. While 78% of higher education leaders think they are meeting employer expectations, only 28% of employers say universities are keeping up with AI-driven change. Meanwhile, only 14% of current graduates said they had achieved a high level of proficiency when it came to applying AI tools in a professional setting.
AI is changing entry-level roles amid a rapid decrease in the durability of skills, leaving workforce readiness at risk. The report on AI readiness was based on more than 2,700 survey responses from learners, higher education leaders, and employers in the U.S., UK, Brazil, Saudi Arabia, Vietnam, and Malaysia. A survey last year found that 83% of workers believed AI could perform most entry-level jobs as well as a person could. At the same time, some employers are using AI to replace workers instead of retraining them, with 31% of business leaders reporting that their organizations considered AI solutions before hiring for the role.
Click Here to View Full Article
Beating the AI Job Filters in a Tough Market
Spiceworks, April 9
In response to the growing number of applications that they receive for every open position, many organizations have turned to AI automation to sift through the backlog of resumes and narrow down the deluge of candidates. Thus, to stand out in the current market as a job seeker, you must adapt your search tactics in a world where potentially flawed AI algorithms act as gatekeepers to almost every job you apply to, and other candidates use AI to get ahead in the process.
According to a new survey, 82% of companies use AI to review resumes, while 40% employ AI chatbots to communicate with candidates. These AI-powered resume filtering tools are known as applicant tracking systems (ATS), and they are far from perfect. When screening for candidates based solely on keywords, companies run the risk of hiring people who are good at crafting resumes that pass through AI filters, instead of more qualified candidates who would actually excel at the job. Some companies take the use of AI recruiting tools a step further. In fact, nearly one-quarter (23%) of companies use AI bots to interview candidates via Zoom. While AI tools for screening candidates can be useful, they can present challenges for both hiring companies and job seekers.
Click Here to View Full Article
The 5 Stages of Career Growth and What It Takes to Reach the Next One
Entrepreneur.com, April 17
Careers tend to stall when people keep using strategies that worked in a previous stage of their careers. Just as consumer brands refine their positioning as they grow, IT professionals must evolve how they show up and build trust. For each career stage, it is important to define the steps required to reach the next one. Whether you are the leader of the company helping grow your team, or the employee pushing toward a greater personal brand, understanding these stages can unlock career growth for you or your team.
The first stage of a career is creating a new brand with no market awareness in order to pave the way for future career growth. This first stage includes individuals who are new to a company, function or are early in their careers. A common assumption here is that productivity automatically translates into advancement. The reality is that organizations reward visible impact. In this first stage, start by understanding the business and strategic priorities of your company. Study leadership communications and clarify how your work connects to broader business objectives. When you demonstrate context, you differentiate yourself from peers who operate in isolation. To move from Stage 1 to Stage 2, you must transition from being known to being trusted. To do that, first identify two to three projects that directly tie to enterprise goals and deliver them with measurable results. Then, proactively seek feedback. Ask your manager what exceeding expectations looks like and align your performance accordingly.
Click Here to View Full Article
Did AI Really Take Your Job, or Is it AI Washing?
BuiltIn.com, March 2
The impact that AI is having on the workplace continues to be debated. In 2025, employers announced more than 1.2 million job cuts, and AI was cited in nearly 55,000 of them, or 4.5 percent. After reading through quarterly earnings reports and investor calls, one might think that workers are already being replaced by AI tools that can do their jobs for them. But in reality, the current capabilities of AI often fall short of the disruption tech executives typically convey when justifying mass layoffs. Perhaps companies are simply using AI as a convenient cover to explain away underlying economic weakness, a practice known as AI washing.
The term AI washing was originally used to call out companies claiming to use artificial intelligence when they really were not. But lately, the term has shifted to describe when a company disingenuously blames AI to explain away job cuts and other unpopular business decisions, even when the full picture is far more complicated. Financially-driven layoffs are being confused with AI-driven layoffs. Many companies announcing AI-related layoffs do not have mature, vetted AI applications ready to fill those roles. Other experts have suggested AI layoffs could be premature, possibly misleading. Sixty percent of the more than 1,000 executives surveyed in another study said they made headcount reductions in anticipation of AI efficiencies. Another 29 percent reported hiring fewer people than normal. Only 2 percent said they had made large staff reductions as a result of actual AI implementation.
Click Here to View Full Article
Use AI to Polish Your Resume, Not Write It Into Oblivion
Dice Insights, April 14
Recruiters are increasingly using AI detection software and other tools to filter out resumes and cover letters entirely generated by AI tools. In large part, this is in response to mass produced application documents that lack personality, color and authenticity. While employers often get the blame, many tech job seekers are using AI in a way that leads to their rejection. In fact, more than one-third of workers say they rarely or only occasionally review AI-generated output before using it.
Experienced recruiters can often spot an AI-written resume in seconds, given that these resumes all use the same style. Resumes that are completely written by AI contain large sections of dense text instead of bullet points, black and white formatting, and few personal details. Also, the keywords seem mashed together, overused and lack context. In fact, modern applicant tracking systems (ATS) are shifting from simple keyword matching to semantic scoring to evaluate resumes. So, providing context and emotional intelligence that AI cannot is crucial. For instance, when a candidate claims to be a results-oriented team player with a strong work ethic, but offers no supporting examples, related skills or personal anecdotes, it is considered to be a red flag by recruiters.
Click Here to View Full Article
If You Lose Your Job to AI, It Is Even Harder to Bounce Back
Fast Company, April 9
Economists and academics are still not clear on how AI will change the jobs that are most vulnerable to its advances. However, new research from Goldman Sachs indicates that the workers whose jobs are hit hardest by AI will find it particularly difficult to secure a new job and suffer real economic setbacks in the aftermath. The report found that the workers who were most impacted by technological shifts struggled to recover and took a month longer to find a new job when compared to workers in other industries. If job displacement happens alongside a recession, those effects could be further amplified.
According to the Goldman Sachs report, workers displaced by technological shifts also saw a dip in their earnings potential, facing a loss of more than 3% even after they found a new job. During the decade after losing their job, those workers grew their earnings by 10 percentage points less than people who stayed employed and 5 percentage points less than those who lost jobs in other industries. Workers displaced by AI will not only deal with lost income but also broader challenges associated with their financial status. The scarring effects also spill over into broader economic outcomes, such as delayed homeownership.
Click Here to View Full Article
Teaching Programmers a Survival Mindset
Communications of the ACM, March 27
Two key trends are shaping the teaching of programming to the next generation of computing engineers at present. The first is the proliferation of artificial intelligence (AI) tools capable of automatically generating code. The second is the availability of ever more powerful computational resources. This combination has created a paradoxical situation: programming has never been more accessible thanks to AI, yet it has also never been easier to neglect programming efficiency and fundamental principles given the seemingly free nature of abundant hardware resources. Given this situation, it is relevant to ask whether institutions are adequately training young programmers and future computing engineers.
In many computer science classes, students already use AI-powered code assistants that suggest code fragments or even entire programs. The downside is that this technological convenience is fostering an increasing dependence on AI to solve programming tasks. It can leave students with only a superficial understanding of the algorithms being generated. This automation, along with powerful development environments, has contributed to reducing the perceived need to thoroughly understand what is really happening behind the scenes of the code being used. Becoming accustomed to over-provisioned resources has brought further concerns. Even while leveraging the benefits offered by AI in programming, an excessive dependence on AI-generated solutions and the over-provisioning of resources can undermine the proper development of computational, logical, and algorithmic thinking in future programmers or computing scientists.
Click Here to View Full Article
The Diffusion of GenAI Among STEM Students
Blog@CACM, April 15
The adoption of generative AI tools among science and engineering students is both rapid and stable, and these tools are becoming normalized across all stages of study. As students progress through their studies, they tend to use generative AI tools more and more frequently, for a wider range of purposes and in increasingly sophisticated ways. This corresponds to a popular theoretical model that explains how new technologies spread through a population over time. According to this model, individuals are categorized into five groups (innovators, early adopters, early majority, late majority, and laggards), based on when they tend to adopt innovations.
Early adopters are often characterized by their willingness to experiment with new technologies and explore their potential applications before these technologies become widely accepted. The early majority tends to adopt innovations once their usefulness has been demonstrated and their risks reduced, while the late majority is typically more cautious and adopts innovations only after they have become widely established. Laggards, as their name implies, are last to adopt innovations and usually exhibit resistance to change and a preference for tradition. Generally speaking, rapid growth in regular use, accompanied by convergence across groups, is one way to characterize technologies that are moving from early adoption to the early majority stage.
Click Here to View Full Article
Copyright 2026, ACM, Inc.