ACM CareerNews for Tuesday, November 4, 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 21, November 4, 2025
What CIOs Want in Potential New Hires
Information Week, October 27
What was once standard advice on how to get started in technology careers has become less certain as AI is now leveraged across an expanding range of IT roles. Simply learning how to code may no longer be the answer. Candidates who can think critically, troubleshoot, and grow at the pace of technology stand out as great hires in the current landscape. With that in mind, CIOs should really think about hiring for potential and hiring for agility, not just for current skill sets in demand.
The reality of AI in the workplace means tech hires will need to demonstrate how artificial intelligence elevates their value to organizations, rather than replaces it. In short, if IT job candidates are not AI literate, they may not be marketable. Many students are struggling to land jobs as a result of this shift in demand. This includes students who were exceptional in their chosen field of study. If they cannot integrate AI or advanced data analysis in that field, it could be hard for them to find jobs.
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What Hiring Managers Really Want in 2025
Dice Insights, October 29
When applying to tech jobs, it is no longer just enough to showcase the knowledge of multiple programming languages. While coding still matters, and coding skills still need to be top notch. hiring managers now want to see other abilities and skills as well. In addition to being a great coder, you need to know how to use the tools of software development. A baseline knowledge of AI is also required, especially as organizations of all sizes now embrace AI as a key strategic catalyst.
Developers are figuring out how to make AI work for them, while hiring managers and team leads are learning how AI can help their teams build code faster. Today, candidates are very likely to be asked during a tech interview how they make AI work for them. The easiest way to do this is to point out that it is a great assistant. In short, candidates need to know how to use AI effectively to help them produce better code. While we are still a long way from AI simply building sophisticated apps from start to finish, we are very much at a point where they can be a great coding assistant. Reviewing AI-generated code and then spotting problems will likely put you far ahead of other people interviewing for the same job.
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More Job Posts Mention AI But Some Lack Clear Context
HR Dive, October 31
Mentions of artificial intelligence in U.S. job postings appear to be on the rise, according to Indeed. However, about a quarter of those postings lack clear context on its application as employers continue to figure out what role-related AI use means. As a result, slightly more than half of AI-related job descriptions mentioned building new AI tools or directly using AI models through prompts. Most posts, though, used general terms, such as AI or generative AI, rather than specific skills, such as ChatGPT.
Companies are starting to experiment with using AI to assist in solving business problems like hiring and logistics optimization. For employers, this suggests that now is a good time to experiment with AI and provide training so that workers can use these technologies effectively, responsibly, and ethically. Based on an analysis of job postings, the report found that AI use differed by occupation, with tech, management and creative roles driving AI use and development. Service and healthcare roles are using AI primarily in hiring processes. In addition, human resources and insurance stood out for their use of AI-powered platforms and tools, with each including it in more than 40% of postings mentioning AI, the report found.
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ChatGPT Is Redefining Employer Branding
BuiltIn.com, October 29
Establishing and maintaining an employer brand has never been easy, but it has been made even more complicated by the rising popularity of generative AI. Instead of checking a website or LinkedIn page, applicants are now conducting their entire job search within platforms such as ChatGPT, Gemini and Perplexity. Even those who do search for a company on Google are more likely to rely on AI Overview, as the majority of Google searches do not result in a website visit anymore. As a result, employers must be increasingly willing to navigate the world of AI chatbots in order to connect with top talent.
During the discovery phase of the applicant journey, visibility into different companies is increasingly shaped by large language models and the mountains of data they are trained on. When candidates ask the platforms about the top employers in their field, the models tend to surface large, well-known companies with a strong digital presence and leave out those that are less visible online, regardless of whether those organizations actually align with the skills, interests and values of the candidate. Even the companies that do get mentioned may not like the way LLMs portray them. Instead of directing job seekers to a career portal or social media accounts, the platforms often pull snippets of information from online forums, employee review sites and other third-party sources. And once a negative narrative takes hold, LLMs are likely to repeat and reinforce it over time, making it even harder for employers to regain control of their reputation. In short, LLMs now dictate both how companies get discovered and the first impression they make on job seekers.
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5 Ways Ambitious IT Pros Can Future-Proof Their Tech Careers in an Age of AI
ZDNet.com, October 25
After achieving a mid-tier management position, it may seem challenging to advance further in a career. While you might want to assume a senior role and even become a CIO, it could be challenging to find opportunities to expand in your current position. Making matters more complex, the increased use of AI could make it even harder to move into senior roles. However, there are five ways that ambitious IT professionals can ensure they have the capabilities to lead in the AI-enabled business of the future.
For job seekers, it is important to stay close to developments in the IT sector. Today, successful digital leaders need to focus on technology and business simultaneously. In the past, as you progressed in your career in IT, you were less involved in the technology, and the role became more about leadership and management. And while those leadership and management elements continue to be relevant and important, you will also need to stay closer to technology. One factor behind this shift in emphasis is the crucial role that AI now plays in all modern businesses. Successful IT chiefs are expected to be the expert resources for pioneering technology developments. The CIOs of the future will demonstrate how AI can fulfill some executive roles and responsibilities.
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Top Tips to Prepare for a Cybersecurity Job Interview
Silicon Republic, October 30
In rapidly evolving fields such as cybersecurity, job candidates can never just assume that an interview will maintain the status quo. The cybersecurity sector keeps moving with the times and year after year requires a whole new set of skills and experience to navigate. The good news is that there are several important steps you can take to prepare for a cybersecurity job interview.
Because organizations have such specific needs in regards to the treatment and maintenance of their data and networks, you need to be very clear about which aspect of cybersecurity you are most skilled at, the skills you currently possess and the division you wish to work in. That means skipping the AI-powered automatic applications and doing a deep dive on specific companies, their likely needs, and any open roles that would apply to you. Build yourself up from there, as the person with the ability to bolster cybersecurity teams based on what you know they require.
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Colleges Hope to AI-Proof Their Offerings as New Tech Changes Job Expectations
GBH News, October 29
AI is transforming the entire tech industry, including jobs in software development and coding. As a result, educational institutions are racing to keep up as artificial intelligence disrupts the entry-level job market. Openings are shrinking in fields most exposed to automation. To prepare students for this new reality, colleges are rethinking what and how they teach. For schools eager to prove their value to students, one answer is a brand-new curriculum focused on artificial intelligence to improve the chances of landing a job after graduation.
The shift in hiring mindset started last year, when postings for entry-level tech jobs began dropping fast. Employers increasingly want candidates with experience developing chatbots and machine-learning systems. From the perspective of educators, the hope is that offering new AI degrees will give students hands-on experience with a wide range of AI tools. Students really want to go into the workplace equipped. They really want to know the tools and be able to compete for positions in those fields.
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AI Is Killing the Job Market For Young Coders
SlashGear, October 28
It was only a few years ago that a degree in computer science was a golden ticket for young coders. Learn to code, and you could land a high-paying, stable job at one of the most valuable companies on the planet. That career path was a lifeline for a generation of college graduates and bootstrapping go-getters in the post-recession era that saw the ascendancy of big tech and the social web. But that lifeline has snapped. In the past several years, the job market for coders has contracted significantly.
The upheaval is felt across the spectrum, but it may feel worst for Generation Z, which had the misfortune of matriculating into the workforce with new coding degrees only to find their job prospects evaporated. Many companies are choosing to fill the gap with AI, according to a new study from researchers at Stanford University. Those most at risk are recent graduates. A study from Stanford researchers released in late August showed a 13% drop in employment for those in the most AI-exposed fields, chief among which is software development. The researchers analyzed payroll data to paint a bleak picture. In 2022, just before the release of ChatGPT, the job market for coding had peaked. In July 2025, it had slid by close to 20%. The conclusion the paper arrived at is that jobs are being automated away by AI rather than augmented by it.
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Generative AI For Computing Careers: A Sunny Take
Blog@CACM, October 30
Career prospects for computer science graduates are becoming increasingly uncertain, especially at a time when generative AI has risen in prominence. While many have predicted a gloomy outlook, it is time to take a more optimistic outlook. Admittedly, the skill of writing simple software modules is no longer useful. That has been easily automated away by AI. So, a student may be tempted to skip the foundations of software programming and software engineering and focus on prompt engineering or LLM fine-tuning. That would be a mistake if you want to build a meaningful, lasting career in the technology sector.
While plain code blocks can be AI-generated, it is still a human enterprise to create reasonably complex software by piecing together these code blocks while keeping the overall software readable and therefore maintainable. Based on an examination of large software packages generated by the latest AI tools, such software is often the epitome of spaghetti code. The interface design is not clean, the flow between code modules is unintuitive, and handling error cases is convoluted. If you change one specification, it may not be possible to revise the software. Maybe you can with that one change, but if the software has to last, such changes build up and that is where the spaghetti code problem comes into play. The fact is, we are far from trusting such code in production settings. So, for the foreseeable future, we will have humans verifying software, for its functionality, reliability, and security. And the process of translating ambiguous or imperfectly defined software specifications into something rigorous still remains a messy, human affair.
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The Silent Scientist: When Software Research Fails to Reach Its Audience
Communications of the ACM, October 10
The software research community often questions the relevance of its work. Some of this critical self-reflection is understandable. After all, significant resources go into software research, to improve collaboration or drive innovation. It is reasonable to critically question how many of these research findings will be implemented in the near or distant future. In fact, considerable resources have already been devoted to addressing this very question, and analyses have identified concrete examples showing how software research contributed to advancements in development tools and methods, such as configuration management and programming languages.
To appreciate the relevance of software research, it is important to consider how different topics resonate with different stakeholder groups. After all, what is impactful to one may be irrelevant to another. First, software research covers diverse content, which can be roughly divided into technical advancement and empirical understanding. It seeks technical improvements, such as methods to automatically find and fix bugs, and empirical insights, such as understanding what factors influence the productivity of software developers. Corresponding studies of the two types of research use entirely different research methodologies, with some focusing on technical evaluations without involving humans, while others include human participants as primary subjects. This alone can create varied perceptions of relevance, as some studies directly involve the people they aim to help. Additionally, measuring impact and implementing findings differ significantly. For technological progress, impact is measured through adoption or metrics on quality and performance. Findings on practitioner collaboration require integration into socio-technical processes, with impact measured through changes in behavior or improved satisfaction.
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