ACM CareerNews for Tuesday, January 6, 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 1, January 6, 2026
Top Highest-Paying Computer Science Careers to Explore in 2026
Analytics Insight, January 3
In 2026, AI-related computer science roles are expected to be in high demand. At the top of the list are roles such as artificial intelligence engineer, machine learning engineer, software architect, and data scientist. All of them offer high-paying career paths, as well as exposure to the fastest-growing areas of the tech sector. They also offer unique opportunities to leverage the new capabilities of artificial intelligence. Best of all, these computer science roles are in high demand by top companies in Silicon Valley as well as other tech hubs.
Artificial intelligence engineer is one of the fastest-growing computer science careers for new graduates to explore. AI engineers develop systems that learn from data and improve over time. Features such as voice search app recommendations and bank fraud detection systems depend on this work. These tools reduce manual effort and help businesses act faster. Since AI is now used across industries, this role continues to attract strong demand. Companies hiring include some of the biggest in Silicon Valley.
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Off-Beat Careers That Are the Future of Data
Towards Data Science, January 2
With the rise of artificial intelligence, the future of data extends beyond the traditional data analyst or data scientist roles. Data is everywhere around us and yet, many industries have not seen an influx of data professionals to their maximum potential. Right now, the technology, healthcare, finance, retail, and government sectors account for much of the hiring for data-related roles. But there are five fields where data can be used effectively, and where there is a limited quantity of quality data professionals in the workforce today.
Archaeology may not look like a modern data field at first glance, but in practice, archaeologists have always worked like analysts. They are constantly collecting fragmented evidence, looking for patterns across space and time, and constructing narratives grounded in data. What has changed in the last couple of decades is the introduction of digital data. Modern archaeology increasingly relies on high-resolution spatial data, remote sensing, and computational modeling. Excavation itself is no longer the starting point; it is often the last step, informed by extensive data analysis upstream. Dedicated data archaeologist titles are still rare, but hybrid roles are growing. These include GIS analysts embedded in archaeology or heritage teams, remote sensing specialists working with satellite imagery, and research data scientists in universities, museums, and cultural preservation institutes. Most of these roles sit at the intersection of archaeology, geography, and data science rather than inside corporate analytics teams.
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11 In-Demand Cloud Roles Companies Are Hiring For
CIO.com, January 1
The growth in cloud adoption has also sparked an increased demand for certain cloud computing roles. In short, organizations continue to invest heavily in cloud solutions heading into 2026, and that is leading to ramped-up demand for cloud roles that are related to business and strategic goals. These goals include improving employee productivity, accelerating adoption of AI and machine learning, and improving security and governance in the organization.
In order to support their cloud computing investments, companies are now hiring security architects at a rapid clip. These security architects are responsible for building, designing, and implementing security solutions in the organization to keep IT infrastructure secure. For security architects working in a cloud environment, the focus is on designing and implementing security solutions that protect cloud-based infrastructure, data, and applications. Skills required for this carer path include security architecture design, network security, security compliance and governance, incident response and forensics, data encryption, identity and access management, automation, and DevSecOps.
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2026 Tech Job Market Statistics and Outlook
Tech Target, November 5
With the exponential growth of AI and automation within the workplace, it is perhaps no surprise that many tech workers are now increasingly anxious about their future careers. Slow job growth within the broader economy and high-profile layoffs at top companies in Silicon Valley have not helped to quell the anxiety. The good news is that tech jobs in the U.S. are projected to grow at twice the rate of the overall workforce over the next decade. Moreover, due to the ongoing IT skills shortage, skills-first hiring is on the rise for remote tech roles.
Tech jobs in the U.S. are projected to grow much faster than average over the next decade. A new report projects tech job growth from 6.09 million in 2025 to 7.03 million in 2035. This includes 414% growth for data scientists and data analysts, 367% growth for cybersecurity analysts and engineers, 297% growth for software developers and engineers, and 220% growth for software QA and testers. According to November 2025 research from the Bureau of Labor Statistics (BLS), computer and IT occupations are expected to grow much faster than average from 2024 to 2034, with a projected 317,700 job openings annually. However, there has been a sharper decline in tech jobs over the past few years, with U.S. tech job postings down 36% from February 2020 to July 2025.
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5 of the Hottest Jobs in 2025
Fast Company, December 25
The demand for top AI talent boomed in 2025, and this led to increased demand for high-paying artificial intelligence, data science, and engineering jobs. In terms of the volume of overall postings, these jobs increased by 28% compared to 2024. That is a notable increase, given the lack of jobs growth in other sectors of the economy. The top five jobs in 2025 are all related to AI. These include data engineer, analytics engineer, AI full-stack engineer, and AI solutions consultant.
As the demand for AI increases, so does the demand for data engineers who can ensure AI models are fed the highest-quality data. As a result, the data engineer role saw 18% growth year-over-year. Growing even faster is the role of analytics engineer. This role is growing at a 25% rate. Analytics engineers ensure that companies can make sense of the data they have and use it to provide actionable insights. They organize data so it is easier to analyze, apply software engineering best practices to analytics code, and design and maintain data models. They also collaborate with other teams inside the organization to help turn these insights into better decisions. AI full-stack engineers can create complete AI applications. They can build the front-end user experience, the back-end infrastructure that powers the application, and embed AI as needed. In many ways, AI full-stack engineers are the next generation of full-stack engineers.
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AI Changed Work in 2025. What's Next?
BuiltIn.com, December 31
AI has started to reshape which tech roles are in-demand, where new job opportunities are found, and how workers collaborate with their new virtual colleagues. This has fundamentally changed how many workers now view the workplace. Workers are feeling more uneasy and burned out by AI, but are too afraid to leave their jobs. Entire career paths are being created and destroyed in real time. And while some roles are stagnating or disappearing altogether, others are becoming remarkably lucrative. The result is a labor market in flux, where both anxiety and opportunity exist side by side as AI adoption accelerates.
As businesses scramble to adopt AI, they have been reluctant to hire more people until they know how this technology will impact their staffing needs. This uncertainty, combined with the unknown impact of tariff policies, has led to a stagnant hiring market. Add in all the mass layoffs and dire job market predictions from top AI developers, and it is easy to see why workers are fearful about their professional future. This growing sense of anxiety has led some workers to hang on to their jobs for dear life, a phenomenon some analysts have dubbed job hugging. Just because they stay does not mean they are happy, though. Instead, the employees who feel burned out by staffing shortages or overwhelmed by the pressure to find a use case for AI are quiet cracking, a term used to describe a persistent feeling of workplace unhappiness that leads to disengagement, poor performance and an increased desire to quit.
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Godfather of AI Warns That It Will Replace Many More Jobs This Year
Futurism, December 31
According to computer scientist Geoffrey Hinton, winner of the Turing Award in 2018, AI will continue to improve at a breakneck pace next year, reaching a point where it will replace many jobs that were once thought to be irreplaceable. AI is already able to replace jobs in call centers, but it is going to be able to replace many other jobs, including those within the computer science profession. He predicts that it is only a matter of time until AI will effortlessly perform software engineering tasks that take a human a month to complete.
According to Hinton, AI is progressing even faster than originally anticipated. It has become better at doing things like reasoning and creating original content. AI is progressing so quickly, according to Hinton, that around every seven months it can complete tasks that took twice as long before. This is also true when it comes to software engineering tasks. At some point, there will be very few people needed for software engineering projects.
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Graduates Fear Lack of AI Skills Hurts Earning Potential
Dice Insights, December 10
A recent survey of recent college graduates highlights the widening skills gap, as well as the professional anxiety, that comes with trying to build a career in an AI-driven economy. According to the findings, 21% of recent graduates believe their degree is already outdated, given how quickly AI, automation, and data-driven technologies have reshaped employer expectations. An additional 22% say they would have chosen a different major had they understood the scale of impact on AI on workforce needs. This disconnect is now influencing how graduates approach the job search. The survey reveals that nearly two in five graduates lack confidence when applying for roles that mention AI or automation-related competencies.
When so many graduates already believe their degree is outdated, IT leaders should treat that as hard evidence that the skill cycle in technical roles has accelerated beyond what traditional education can keep up with. The takeaway is hard to ignore: too many traditional universities are still training for a pre-AI workforce. That does not mean degrees have lost their value. Rather, higher education must build durable fundamentals and, most importantly, the ability to learn quickly. However, it does signal that the half-life of technical skills is shrinking fast, and relying on a degree alone as proof of job readiness is no longer realistic. IT leaders need to assume that even smart, motivated new hires are entering the workforce underprepared for AI-driven environment. They also need to assume that continuous, employer-led learning is now a baseline requirement, not a bonus.
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AI Fatigue: Reflections on the Human Side of AI’s Rapid Advancement
Communications of the ACM, November 10
The constant effort to stay current on AI, by tracking breakthrough papers, integrating new models, and adapting to paradigm shifts, all while maintaining regular work responsibilities, can take its toll. While traditional approaches to professional burnout remain relevant, the current state of affairs may warrant additional steps. Organizations must develop sustainable practices that balance innovation with human capacity limits. This might include establishing AI update cycles that allow for proper integration periods, creating dedicated roles for knowledge synthesis and distribution, and fostering communities of practice that share the burden of staying current.
AI fatigue is the collective exhaustion experienced by individuals and organizations in response to the unrelenting pace of artificial intelligence advancement. It reflects the mental, emotional, and operational toll of trying to adapt to an unprecedented rate of change that has sustained for a relatively long period with no signs of slowing down. In some way, it is an acknowledgment that the pace of AI is massive and adapting to it has costs that we must all be clearly aware of. The relentless pace of AI advancements creates a treadmill-like environment where professionals struggle to keep up.
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It's Not the AI. It's Each of Us
Blog@CACM, January 2
AI learns from human data, reflecting our values, biases, and aspirations, amplifying both our strengths and our weaknesses. AI mirrors the society that builds and uses it. This creates enormous challenges for ensuring that AI serves human dignity, social well-being, and democracy. Better regulation and forward-thinking technical solutions, while essential, are not enough. The key issue is no longer what AI is capable of, but how it may shape us when we use it. As a result, new rules need to be developed for the wise and responsible use of AI.
Humans have limited time, information, and cognitive capacity. One consequence of this is the tendency to prefer certain, clearly framed outcomes (even negative ones) over uncertainty. In a world of information overload and rapid change, AI fits this pattern perfectly. It offers convenience (automation, speed, instant answers) and an illusion of certainty (confident outputs that are, however, probabilistic guesses). The danger is a slow erosion of curiosity, judgment, and responsibility when people stop questioning the suggestions of AI. It gets worse when people cannot easily distinguish what sounds right from what is right. The immediate threat is not a dramatic AI takeover, but a gradual slide into complacency, unless people consciously set limits and remain reflective about when and how we use AI.
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