ACM CareerNews for Tuesday, December 2, 2025
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 21, Issue 23, December 2, 2025
The 10 Hottest IT Skills For 2026
CIO.com, November 27
AI continues to reshape the IT jobs market, especially as organizations prioritize candidates and employees with AI skills. The top four roles affected by AI-related restructuring include software engineers and developers, QA engineers, product managers, and project managers. Companies are now focusing their efforts and hiring budgets on professionals with skills in cybersecurity, data analytics and analysis, and building or managing AI teams. This reprioritization of IT roles has created a shift in the most in-demand IT skills that jobseekers will want to have on their resumes.
Organizations now expect candidates to have basic prompt engineering skills at a minimum, even for entry-level IT roles. And beyond that, they are looking for IT professionals who can help oversee, implement, secure, and manage AI tools and strategies. It is no surprise that AI is at the top of the list for one of the most in-demand skills based on growth in tech job postings since 2024. Companies are scrambling to adopt AI as it rapidly finds its way into every industry and career path. In 2024, there were just over 5 million job postings that required AI skills, and in 2025, that number grew by more than 4 million. So candidates, even for those working outside of tech, are now expected to have some level of AI skills, whether it is prompt engineering, natural language processing, or using AI for programming and coding.
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How AI Coding Is Creating Jobs
Morgan Stanley, October 29
According to Morgan Stanley Research, the rise of AI-powered coding tools is not eliminating jobs. Rather, it is creating new opportunities for developers and software companies alike. As enterprises build more complex applications and tackle long-standing technology debt, the demand for skilled developers will grow. As AI coding assistants and agents become standard tools in development workflows, the role of traditional software engineers is likely to shift to more complex applications. Developers are increasingly acting as curators, reviewers, integrators and problem-solvers.
The surge in AI-generated code is creating bottlenecks in other stages of the software-development lifecycle, potentially creating new career opportunities. Code review and testing are some examples. The volume of software increases significantly with AI coding, but higher volume could mean more bugs and more rework. Engineers have a lot more AI code to review and test. This challenge presents a new growth avenue for software providers, which are creating AI agents to work side by side with human developers. Software companies will deploy AI not only in coding, but also in other areas like testing, security, verification and deployment. Humans will remain in the loop for oversight, design and decision-making.
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Why Cybersecurity Jobs Are Likely To Resist AI Layoff Pressures
CRN.com, November 14
Even as AI-driven automation spreads to nearly all parts of the workforce, the field of cybersecurity is one of the few likely to be spared from major layoffs going forward. This is particularly the case for Security Operations Center (SOC) analysts, which are poised to remain in-demand even as they are expected to adopt AI in a massive way. For modern cybersecurity teams, any type of advantage you can give them, by using machines to help them defend their organizations, is only going to allow them to focus on different sets of problems. The bottom line is that there will always be a need for a significant number of cybersecurity professionals.
The notion that cybersecurity analyst jobs and other roles requiring security expertise might be at risk would have been unthinkable just a few years ago. Thus, the sudden shift to discussions around AI-driven redundancy for humans in the Security Operations Center is all the more startling. If you go back about two years ago, there was a constant narrative in the industry that there were a few million less cybersecurity professionals than actually needed. The solution to that problem is not more people in cybersecurity, filling all the open jobs. The solution is more automation and more AI. That, in turn, is leading to new openings in other areas.
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How LinkedIn Is Using AI to Improve Its Job-Search Features
Computerworld, November 26
LinkedIn is implementing AI features so job seekers can discover job matches, opportunities, and relevant connections much faster. The new tools make a job search more of a conversation, with less time spent scrolling through job descriptions. The goal is to make it possible to search for what is uniquely important to jobseekers, and not the same keywords everyone else is using. That means less wasted time and more relevant matches, as well as less time blasting out applications to companies.
The new AI tools from LinkedIn can establish the intent of a job seeker or recruiter. Those looking for work can type conversational questions into a search box and get more relevant listings. Based on their profile, a candidate might also receive listings for alternative careers. The hiring experience is also easier, as AI tools surface the most qualified candidates and those with interesting backgrounds. That creates real economic opportunity and efficiency with quality, not quantity, in the application and hiring process.
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Indeed's 2026 US Jobs & Hiring Trends Report
Indeed Hiring Lab, November 20
Heading into 2026, economic and labor market uncertainty is everywhere. The good news is that data from Indeed suggests that job openings are poised to stabilize. While the unemployment rate is likely to rise, it will not rise alarmingly so. Moreover, GDP growth looks to remain positive, if only barely so. However, there are a variety of factors that will drive the ultimate numbers up or down, including changes to immigration policy.
The Indeed Hiring Lab constructed consensus, upside, and downside scenarios that estimate the unemployment rate and the level of job openings at the end of 2026. These scenarios were informed by GDP growth forecasts from economists and financial professionals. Based on these GDP growth forecasts, it is then possible to use known historical relationships between economic growth and labor market performance to estimate both the unemployment rate and the number of job postings at year-end 2026. The consensus forecast for real GDP annual growth in 2026 is currently 1.8%, with the top 10 and bottom 10 forecasts averaging 2.5% and 0.9% growth, respectively. This then translates into estimated unemployment rates ranging from 4.1% to 4.8%, and job postings levels ranging from 6.8 million to 7.4 million at the end of next year. Interestingly, both the current unemployment rate and level of job postings are within this range and close to the consensus scenario. This indicates that, on average, big economic swings are not anticipated by the consensus in 2026.
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Always the Runner-Up? 6 Reasons You Are Not Getting the Offer
Dice Insights, November 20
Constantly being rejected for job openings can lead to disappointment, self-doubt, and skepticism about the job search in general. Unfortunately, many recruiters cut ties with all candidates once someone is hired, instead of considering runner-up candidates who almost got hired for other opportunities. Making it to the final round means you are a strong, qualified candidate. It usually takes just a few subtle changes to go from runner-up to first choice.
Hiring managers tend to choose candidates they like, connect with, and can see themselves working with. However, studies show that candidates may not be as likable as they think, causing them to take second place. In fact, 84 percent of job seekers think they are likable, but only about 33 percent actually are. Silver medalists often sound too scripted or come across as unnatural. Being negative or acting like a know-it-all can also turn hiring managers off. Likability comes from being genuine. So let your true personality show through transparency. Build trust and comfort by showing up as the person they would actually be working with, and not a stiff, rehearsed version of yourself.
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Survey: The AI Race Has Fostered Better Hiring Decisions and Mistrust
HR Dive, November 20
Seven in 10 hiring managers in the U.S. say AI helps them make faster and better hiring decisions with fewer recruiter resources. But almost as many (68%) say they are more involved with hiring than they were last year. One reason may be that to verify the authenticity of candidates, 39% said they are conducting more in-person interviews, suggesting they are trying to separate real talent from the fakes. Recruiters also have concerns. More than half have handed over most screening to AI or applicant tracking systems, and a similar amount say AI has improved hiring, mainly by saving time on screening and scheduling. Yet only 21% said they are very confident their systems are not rejecting qualified candidates.
For many of the key players in talent acquisition, the AI race has fostered mistrust and questions about authenticity. For instance, nearly half (49%) of U.S. job seekers apply to more positions just to get past automated filters, and 55% said they suspect AI is being used to evaluate their job applications without being told. On the other hand, 91% of U.S. hiring managers have caught or suspected AI-driven candidate misrepresentation, such as fake voices or backgrounds (32%) or AI scripts during job interviews (32%). Nearly one in 5 (18%) hiring managers reported having to deal with deep fakes. Candidates are doing whatever they can to break through the noise, while talent acquisition teams are drowning in so many applications, they are looking for ways to sort through what is real and what is not.
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US Workers Report a Major AI Trust Gap That Affects Their View of Companies
CIO Dive, November 25
Despite increasing adoption of AI tools at work, many U.S. employees remain uneasy about how AI may reshape the future of work. Notably, 74% of workers said being interviewed by an AI agent would change their perception of the company, with 37% saying it is impersonal and only 23% saying it is innovative. Although most workers said they are open to interacting with an AI interviewer, they still want human involvement and accountability.
By 2026, 1 in 3 companies say AI will run their hiring process. More than half already use AI in hiring, yet a similar amount also expressed concerns about AI screening out qualified candidates, introducing bias or lacking human oversight. In particular, AI interviewers can alienate applicants. Candidates may feel disrespected and confused, particularly when they expect to have a human interaction. Of particular concern, 59% of respondents said they believe AI is making bias worse, not better. In addition, more than half said they prefer humans rather than algorithms to review job applications, evaluate work performance and make decisions that affect careers.
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We Need to Teach the Inuit Mindset to Young Computing Engineers
Blog@CACM, November 26
Two key trends are shaping the teaching of programming to the next generation of computing engineers. 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 is also never been easier to neglect programming efficiency and fundamental principles. Given this situation, it could be useful to consider how different cultural and anthropological contexts might provide insights.
In order to understand the current situation, one useful anthropological context involves Inuit culture, which embodies optimized resource use and resilience under suddenly changing conditions. The Inuit are an indigenous people of the Arctic regions who have survived for generations in extremely hostile environments with scarce resources. The technological context involves rethinking the use of AI to tackle a major challenge of the coming decades, such as a planned mission to Mars. Both contexts help to underscore the need to rethink how programming is taught. The anthropological approach uses Inuit culture as an analogy to inspire how we teach programming. The technological approach involves developing software with AI to address a major technological challenge of the coming decades.
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Beyond Benchmarks: Responsible AI in Education Needs Learning Sciences
Communications of the ACM, November 24
Industry leaders are increasingly incorporating learning sciences into their approach to artificial intelligence (AI) in education. The growing dialogue between learning scientists, AI scientists, and developers is a welcome development. However, there needs to be greater understanding on how best to proceed both with the specific case of developing automated tutors and, more generally, when using AI in developing supports for human learning.
Learning sciences (LS) emerged in the 1980s from roots in cognitive science and AI, as well as other social sciences such as linguistics and anthropology. LS distinctively investigates the process of learning, in contrast to research on teaching, instructional design, policies, and institutions. LS also focuses on research in authentic educational settings, which contrasts with psychology or brain science research conducted in a laboratory. Learning scientists use and develop a variety of methods and theories, leading to a large body of knowledge. The field also has blurry lines with neighboring fields such as AI in education.
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