ACM CareerNews for Tuesday, April 8, 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 7, April 8, 2025
Generative AI Job Postings Nearly Tripled Year-Over-Year
CIO Dive, February 28
Demand for generative AI-related job roles soared over the past 12 months, according to a new study from Indeed. Jobs with generative AI terms in the role description, while still a small number, jumped 170% year-over-year. Management consulting jobs rose to the top, representing 12.4% of generative AI job titles. Machine learning engineers, software architects, data scientists and other core technology roles comprised the rest of the top 10.
Core IT professions related to AI, including data scientists and machine learning engineers, were already hard to come by prior to the enterprise adoption wave of recent years. The rush to deploy generative AI applications exacerbated the problem, putting pressure on talent attraction and development strategies. Companies are now grappling with looming talent gaps in machine learning, AI and generative AI. More than half of leaders said their organizations planned upskilling and training efforts in response.
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Messy Hiring Practices and Lack of On-the-Job Growth Drive Tech Developers Away
HR Dive, April 3
Despite the accelerated shift to AI by organizations, nearly three-quarters of technology developers say finding a job remains difficult. Almost all (97%) of developers use AI, and the heaviest users report that 48% of their code is AI-generated. Yet even with businesses clamoring for their skills, 74% of developers say landing a tech job is tough. On the other hand, 78% of tech leaders say finding skilled candidates is just as hard.
This disconnect in the supply and demand for talent is the result of how companies hire, evaluate, and retain developers. As a result, if companies are serious about hiring and retaining tech talent, they need to rethink how they attract, engage and upskill developers sooner rather than later. For HR professionals, this means thinking beyond just hiring and upskilling. It means thinking about workforce planning at a whole new level. To a certain degree, AI is helping. Many recruiters surveyed agreed that AI has improved hiring efficiency, allowing recruiters to take on more strategic roles. But far fewer recruiters agreed that AI has increased the quality of hires. Job candidates continue to complain about having to contend with ghost jobs that do not actually exist, never get filled, or vanish without explanation. They also reported long waits, unclear expectations, and resume filters that block out strong candidates.
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Closing the Experience Gap
Deloitte Insights, March 24
As tech workforce needs continue to evolve, organizations around the world are having difficulty finding the experienced talent they need. Often, the people they do hire are unprepared for the changing demands of the work. Two-thirds (66%) of managers and executives say that most recent hires were not fully prepared, and experience was the most common failing. Some organizations are responding to this perceived experience gap by raising experience requirements, especially for entry-level jobs.
Both entry-level workers and career-changers struggle to find jobs where they can acquire experience, even in sectors that are desperate for people. Recently hired workers can quickly find themselves under pressure, and many may be fired because they lack the experience needed. Alternatively, they may end up underemployed, or trapped in less-rewarding career trajectories that do not align with their education and training. Executives continue to rate critical talent shortages one of their greatest fears, while job-seeking workers report despair about their prospects. And yet neither side seems prepared to address it. The experience gap is not widely acknowledged as an important challenge for organizations, with just 48% of respondents saying it is very or critically important.
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ChatGPT Prompts to Boost Your Search For a Job in Technology
Dice Insights, March 31
Job seekers struggling to find opportunities or land interviews might be able to use ChatGPT to help them get past common obstacles. In short, using a large language model (LLM) can improve your chances of landing the right job with the right employer. Just keep in mind: the input or questions you enter into a generative AI tool determines the quality and usefulness of the output you receive. To optimize your results, it is important to understand that types of prompts for ChatGPT that are most effective.
The first set of prompts involves those that can be helpful in finding suitable opportunities that match your job qualifications. Instead of focusing on jobs with low demand in industries facing declines or slow growth, use a series of prompts to identify in-demand roles within your field in industries seeing significant hiring. You can ask ChatGPT to find current trends within a specific field and their impact on hiring. You can also use ChatGPT to find the top industries with the most job openings, or the top job roles with the highest demand in a certain field or sector. Once you have compiled a list of potential roles and industries, you can use prompts to compare your current skills with the key requirements and uncover major gaps.
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5 Ways to Escape Middle Management and Fast-Track Your Journey to the Top
ZDNet.com, March 23
If you are in a middle management position today and want to join the executive ranks in the future, it is time to start honing the right mix of soft skills and technical skills that will transform you into a future business leader. According to report from London Business School, successful leaders in 2025 will have a blend of special capabilities allowing them to build consensus, execute on business strategies, and deliver results. With that in mind, the article provides five tips for moving from middle management to the C-suite.
It can be difficult to make the leap from management to leadership. The best advice is to look around you and identify a great leader, or a role model you admire. Study how they work, what they do, and how they communicate. Learn from these role models, make them your mentor or coach, on either an informal or formal basis. Would-be senior executives can also hone their leadership skills by completing development courses. They can then put that knowledge into practice and see what works.
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Afraid of AI Taking Your Job? Make This Mental Change
Fast Company, March 31
Given that AI will soon be able to do a lot of traditional human work, it is time to start exploring new mental approaches for dealing with this seismic change happening in the workplace. After all, AI has already been able to demonstrate a capacity for writing code, summarizing and teaching information, predicting market moves, brainstorming, designing new products, managing projects, coaching, and much more. Using a shift in mindset can be a powerful way to deal with the anxiety and uncertainty surrounding AI.
In many ways, the rapid growth of AI has forced many workers to contemplate living with uncertainty. One good way to live with uncertainty is by first understanding what the worry, anxiety, and fear are there for. Your emotions, both positive and negative, can be useful sources of information. And they might even unlock solutions so that you get the outcome you desire. In other words, all of that anxiety and stress can serve a function: to help you understand and then control the future. Just keep in mind: no one can know what capabilities and impact AI will have in the future. There is a place for some upside or downside planning with AI, but that is different from the ongoing and repeated fears many have of AI taking their jobs. Worry, anxiety, and fear should not hold you back. And it should not be unpleasant. The information can help you obtain a greater sense of control over your career success.
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Your Next Job Interview May Be With an AI Recruiter
The San Francisco Standard, March 31
The job recruitment process may soon involve an AI recruiter. Some organizations are already experimenting with AI avatars to screen candidates. In some cases, this lifelike avatar might even carry out the job interview itself. While similar tools have been used in the past, few have been as lifelike as they are today, thanks in part to rapid advances in AI. While the concept of AI avatars interviewing humans for job openings might sound a bit dystopian, it should make the recruitment process better, faster, and cheaper.
AI recruiters can source relevant candidates and then invite them to complete an initial interview and an assessment (such as a coding exercise). After those are done, the AI recruiter sends the employer a summary of those tests and a video recording of the interview. But the AI does not make the final call on who gets hired. The assumption is that these AI tools can eventually automate 90% of the entire process, from interviewing candidates to sending reviews to companies. It can even be used to onboard new employees.
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My Startup Job Offers Keep Falling Short. Am I Asking For Too Much?
Sifted, April 2
If you want to join a startup, but are not being offered compensation packages that fit your needs, there are ways to overcome these challenges. The first step that you need to take is to realign your salary expectations. That is because many early-stage startups have lower base salaries than many people assume, even for highly-skilled candidates. It can also be helpful to sharpen your target focus. Right now, the market is competitive and company-driven with more candidates to select from, so you need to make sure you provide the best possible match.
A misalignment around salary expectations should never hold you back. This does not mean ruling out conversations that do not immediately match a specific number, but it is vital to set context early. Be upfront about your seniority, previous earnings and the type of scope you are looking for. Ask questions in the first or second conversation to help you understand the expected salary range on offer, and be forthcoming about your expected range in return, with an indication of how much flexibility you might have. At this stage, you are looking to discover more about the mission, ambition and opportunity, so give yourself enough space to assess whether compensation trade-offs make sense before entering hard negotiations.
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Discretionary Decision Making in AI-Driven Recruiting
Communications of the ACM, April 2
Many AI regulations now focus on assessing potentially biased outcomes of AI, such as those that might occur during the job recruitment process. But AI systems are embedded into social contexts and decision-making processes that are typically distributed across a range of human and machine agents. Bias and discrimination can occur anywhere in this human-machine network. Only focusing on potentially biased outcomes of an AI system will not fix the bias and discrimination problems that are integral to the whole human-machine network.
Addressing the issue of biased outcomes means focusing AI accountability approaches on practices and processes, rather than just machines or just humans. One way to see this in action is by looking at current job recruitment practices that emphasize AI-powered tools. Recruiting has become a frontier of AI-driven automation. AI recruiting tools support search for candidates on job platforms, candidate screening (such as video interviewing or technical interviews to test coding skills), crafting job descriptions, and integrating AI (for example, chatbots) into applicant tracking systems. Using these tools can also produce instances of discrimination, in terms of both gender and age. Even as particularly problematic tools are retired, issues of technology-mediated and AI-accelerated bias and discrimination persist. AI tools used in candidate assessments are prone to error, often disadvantage certain populations, or are based on pseudo-scientific constructs.
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The Last Solo Programmers
Blog@CACM, April 4
With each new step that helps automate code creation, the further away programmers move from the initial roots of understanding the core principles of each language, conceptualizing the solution, and then writing the actual code. With large language models (LLMs) trained in vast code libraries, programmers can now specialize in providing elaborate textual prompts that generate complete code solutions to a target problem. This may introduce some initial efficiencies. However, unless the problem is absolutely mundane, the produced code is often not 100% aligned with the intended task to solve. Someone needs to detect this and adjust it, and that person needs to be a programmer.
Until quite recently, programmers acquired their skills by starting with a mathematical and abstract problem-solving background. They then moved on to programming. First, they would write simple programs and then gradually move on to more complex ones, learning how to break down and compartmentalize larger problems along the way. This traditional approach had some obvious drawbacks, though, such as the need to retype code in every new code snippet.
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