ACM CareerNews for Tuesday, February 3, 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 3, February 3, 2026
Tech Hiring in 2026: The Rise of the Specialist
The New Stack, January 5
For IT workers with experience in AI or machine learning, the 2026 job market looks bright. Approximately one-half (53%) of U.S. tech job postings in November required AI or machine learning skills. That is up from 48% of October job listings. In November 2024, that figure was just 29%. And most organizations plan to increase their investment in AI in 2026. In a recent survey, 84% of respondents said they plan to at least moderately increase resources for AI in 2026. As a result, the AI ecosystem grows by the day. In 2026, everyone needs AI expertise and everyone is building AI agents to automate tasks large and small. It all adds up to an unmistakable demand for AI skills.
In 2026, candidates will need skills that position themselves for a job market where AI is the main character. A new Dice report lists the skills that are growing fastest in popularity in tech job listings, year over year. The second most frequently advertised role in November, after software engineer, was data engineer, with data scientist the fifth most commonly sought. There is a huge shortage of qualified people for those roles. Data engineers, machine learning engineers, data scientists, and data analysts are the types of job categories that are in high demand now and are probably only going to grow going forward.
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What Will Tech Jobs Look Like in 2026?
Rest of World, January 22
In 2026, several top trends are driving the direction of the tech job market. Companies are laying off staff, insisting artificial intelligence (AI) will do more with less. Entry-level pathways appear to be narrowing, job titles are subdividing into new specialties, and workers are being asked to produce more output with fewer resources. Yet, at the same time, organizations have not found ways to deploy AI at scale and critical job roles remain difficult to fill.
The 2025 Emerging Technology Trends study from Deloitte noted that while 30% of surveyed organizations are exploring AI agent options and 38% are piloting solutions, only 14% have solutions that are ready to be deployed. A mere 11% are actively using these systems in production. Furthermore, 42% of organizations report they are still developing their AI agent strategy road map, with 35% having no formal strategy at all. Most workers are generally comfortable with predictable, rule-based robots. However, physical AI systems that learn and adapt introduce new uncertainties, and can lead to worries about job displacement. Experts predict, however, that most roles will evolve toward collaboration rather than replacement.
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Tech Hiring Intentions Are Down Despite Demand For Tech-Oriented Skills
CIO Dive, January 22
Tech hiring intentions are down at the beginning of 2026, but this is not due to a lack of demand. Rather, it is because employers are reconsidering how they plan to acquire IT talent. Tech employers in the U.S. reported a Net Employment Outlook (calculated by subtracting the percentage of employers anticipating staff reductions from those planning to hire) of 33% for Q1 2026. This represents a 10-point decline from last quarter and a 19-point drop year over year. To find the skills they need, employers are instead turning to skills training programs for current employees.
In general, tech companies are changing their hiring approach to obtain specialized skills. There is a move from broad, volume-based hiring to precision hiring. The competition for high-impact tech talent is as strong as ever. More than 90% of companies surveyed in a recent report said they planned to hire workers in 2026, with 44% saying they seek workers who can learn new tools and technologies quickly. Additionally, companies are hiring aggressively for certain functions tied to revenue and business transformation.
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4 New Roles Will Lead the Agentic AI Revolution
ZDNet, January 22
New jobs focused on the role of AI agents could offer substantially higher income and greater job security for IT professionals. At least four emerging job roles are emerging, all of them connected to the rise of AI agents. The four emerging roles are: AI leaders, agent operators, AI no-code creators, and workflow architects. However, these roles will not appear overnight. They will evolve from existing business, operations, and technology roles. The most in-demand skills require a blend of business expertise, AI literacy, and no-code configuration.
AI leaders are responsible for turning AI from a technical capability into business value, ensuring it is used responsibly and strategically. This role does not have a defined path and is attracting change agents focused on innovation. They oversee the application of AI in an organization, the definition and execution of a strategy to deploy AI agent use cases. They combine human and digital talent. Agent operators are essentially the human supervisors of AI agent workflows. They monitor execution, intervene when needed, and ensure accuracy, compliance, and business continuity. These roles typically emerge from the business and operations side, with a deep understanding of the workflows being automated and the outcomes those workflows must deliver.
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How to Find and Hire Scarce Specialized Tech Workers
Inc.com, January 30
A new report suggests that finding qualified technical talent remains difficult, and may even be getting harder. This, despite the fact that IT layoffs continue at high-profile tech companies, and AI appears to be eroding entry-level hiring in the tech sector. Fully three in four employers now report significant challenges in filling their IT needs. This is important because 71 percent of organizations are actively trying to hire workers to close up skills gaps in their workforce. Over the next five years, the core skills that recruiters seek out are expected to shift dramatically.
Common recruitment mistakes include publishing unclear job requirements and inconsistent candidate screening. This makes the tech talent hiring problem both persistent and increasingly complex. As a result, the technology landscape continues to advance faster than talent can keep up, widening long-standing skills gaps. Moreover, some of the key places where talented workers used to seek work are falling out of favor with candidates.
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LinkedIn Will Let You Show Off Your Vibe Coding Chops With a Certificate
Tech Crunch, January 28
LinkedIn will now allow users to display official certifications in AI skills, drawing on usage data from prominent AI apps. The integration includes the video and podcast editor Descript, coding apps Lovable and Replit, and AI agent building platform Relay.app. These platforms will use AI to assess your skills as you use them, and generate a certificate based on your usage patterns, product outcomes, and proficiency within the tools. Once earned, certifications will appear on your LinkedIn profile alongside other professional skills.
LinkedIn plans to add more partners to the program in the coming months, including Gamma, GitHub, and Zapier. The platform is also inviting companies to register interest in being a partner in the new verified skills program. More than 100 million professionals have already verified their identity on LinkedIn. Now, with the addition of verified skills, they can add an additional way to prove what they can actually do with vibe coding.
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Getting Out of the AI Recruiting Doom Loop
Spiceworks, January 23
Using AI in the job search and talent recruiting process may make both processes less efficient. While AI has become a familiar tool for recruiters in the hope of streamlining the process of finding IT talent, it is not working out as planned. Meanwhile, countless job seekers in IT and other industries continue to use AI applications to prepare resumes and cover letters, as well as to find job openings, but often to no avail.
Right now, the intensive use of AI by both recruiters and candidates is creating a counter-productive loop. First, job candidates use AI to enhance their resume and tailor it to the job description. Then, companies become flooded with a sea of sameness. All the resumes look qualified, and it becomes difficult to identify truly qualified candidates. As a result, companies apply more or tighter AI filtering, making it harder for candidates to get an interview. To get an edge, candidates apply to even more jobs, often using AI to do so at scale, flooding companies with yet more often unqualified applications to filter through. The result is a huge disconnect between needy employers and deserving candidates that may be genuinely qualified for a given role.
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Job Seekers Optimize Resumes Amid ATS Anxiety
HR Dive, January 29
Almost half of job seekers use resumes longer than one page and nearly one-third have resumes two pages or longer due to growing concerns over applicant tracking systems. Instead of trying to stand out among their peers, job seekers are just trying to survive the screening process, with 77% of candidates worried their resumes will be filtered out before even reaching a human reviewer. Due to high application volume, long job postings and a belief that ATS optimization matters more than fine-tuned storytelling, 68% of candidates said they spend less than 30 minutes tailoring a resume for each application.
The current hiring landscape has unclear standards that force many candidates to guess at what they are expected to do. Unfortunately, that means that confusion and anxiety continue to shape resume decisions. While job seekers know that resumes still matter, they are not convinced the hiring system works in their favor. Overall confidence in resume review is low, with 43% of job seekers saying they believe hiring managers only skim resumes, and 6% saying they believe resumes are read thoroughly. Job seekers know the rules are changing, but many are still unsure how to respond. The data shows people trying to balance speed, customization, and credibility in an increasingly automated process.
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Does AI Now Represent a Paradigm Shift?
Communications of the ACM, January 28
The arrival of ever more sophisticated AI models is leading to a paradigm shift within the tech world and changing the way we work. With the development of large language models (LLMs), organizations are now seeing significant utility from their embrace of AI. The vast amount of information encoded in these models and the capacity of the models to generate useful and comprehensive output sets the stage for a paradigm shift. The generality of these super-large models, the remarkable quantity of retrievable detail, and the capacity of these systems to synthesize responses to sophisticated requests represents a watershed moment in computing.
This new paradigm shift includes the use of AI to help write code. In vibe coding, for example, a programmer successively iterates with an LLM using natural descriptive language to cause it to generate a program satisfying the intent of the programmer. This is becoming a popular way to produce software. Given the remarkable scope of these models, it seems reasonable to imagine that some models might be specialized to check for errors in the output of other models. One could imagine training a model on software with identified bugs to increase the likelihood that mistakes could be detected. This paradigm shift will likely create new disciplines using these sophisticated, specialized models as tools for work. Just as webmasters grew out of the early world wide web, prompt engineering and other disciplines will emerge from the rapid evolution and proliferation of purpose-developed models.
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AI Tutored Me and I Learned Something
Blog@CACM, January 29
For some people, AI tutors will accelerate learning and make the process more enjoyable. However, there is still the question of whether AI tutors will only work for students who are both highly self-motivated and highly self-reflective. Moreover, there is still concern over whether or not it is reasonable for AI to play the role of a more knowledgeable other. Perhaps students should have a student collaborator or human mentor shaping their zone of proximal development.
Learning with an AI tutor does not imply that a student only uses generative AI to learn. The learning process might also include watching YouTube videos or scanning social media posts. Also, some open source packages have really good documentation, and this can speed up the process of learning with an AI tutor. As a general rule, students should make a practice of turning away from their tutor after almost every session and writing notes of what they have learned. AI can be very helpful for managing what learning scientists call cognitive load. It can also help to manage frustration levels that might otherwise cause students to quit the learning process. AI, for example, can help students make sense of error messages, or lead them them through a complex debugging process.
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