After more than 30 years the cybersecurity field, Keith Jones recently realized that his role had changed, from being a single contributor to manager of a fairly large team. And this team was getting a lot accomplished \u2014 tasks that used to take up a huge chunk of his workday.<\/p>\n
No, his company hadn\u2019t hired a group of new employees to work under him. He simply accelerated his use of artificial intelligence tools. Now, instead of grinding through a lot of basic tasks, that work is done for him while he focuses on bigger-picture work.<\/p>\n
\u201cIt really feels like I have a team behind the scenes, but what I have is Claude [Anthropic\u2019s AI tool],\u201d says Jones, who currently works as a cybersecurity researcher. \u201cI\u2019ve been thinking for the last several months about how much differently I work now than I did a year ago, when I would sit and write all the low-level stuff before I could get to the 10% of the good stuff I really wanted to focus on. Now I can sit back and say, \u2018Give me three different ways to solve this problem.\u2019\u201d<\/p>\n
Most people working in the technology field, like Jones, have had to figure out how best to work with AI. The technology has come on strong, with many companies making its use mandatory and actively evaluating whether employees are faster and more efficient because of it. And while it is boosting productivity and taking over the burden of repetitive, manual tasks, it\u2019s also creating a new level of stress and a dizzying kind of mental exhaustion.<\/p>\n
So what can tech workers do about the heavier mental load that comes with using AI, on top of escalating worries about the safety of their own jobs? AI users and industry analysts say there are specific ways to ease some of those burdens and prepare for a changing job market.<\/p>\n
When it comes to working with LLM tools, a well-known issue is dealing with AI workslop and hallucinations. The slop is AI-generated output that is low-quality, buzzword-heavy, and generic. It also can refer to bloated, boilerplate code. Hallucinations are inaccurate or completely made-up results. AI routinely offers this messy or incorrect information with total confidence, giving users a false sense of security.<\/p>\n
Using this bad data can lead to anything from minor software bugs to severe liabilities. \u201cDon\u2019t believe the machine is infallible,\u201d says Craig Shue, computer science professor and department head at Worcester Polytechnic Institute (WPI). \u201cThat\u2019s when bugs will start working in. There\u2019s a lot of misinformation on the internet, and that could be what the LLM is grabbing and using.\u201d<\/p>\n
Craig Shue, computer science professor and department head at Worcester Polytechnic Institute\u00a0<\/p>\n
Let\u2019s face it: The great promise of AI is that it will take over repetitive, manual tasks, which will save you an incredible amount of time. What isn\u2019t talked about as much is that it also can create a new workload \u2014 one that can be exhausting in a whole new way.<\/p>\n
\u201cIs AI saving people time? The short answer is yes,\u201d says J.P. Gownder, vice president and principal analyst with Forrester Research. \u201cBut people also are being overwhelmed with overproduced things. Everyone wants to look busy and they\u2019re producing more, but not necessarily better. Managers have to push back on that or it\u2019s not really saving you time.\u201d<\/p>\n
In a multi-year study by Upwork, the largest online freelance marketplace, 77% of employees reported that AI had increased their workload. The report noted that a boost in productivity comes with a \u201csignificant emotional and relational cost,\u201d with 88% of workers who saw the highest productivity gains also feeling burned out. And IDC\u2019s Future of Work 2026 survey reported that 24% of IT workers report increased workload as a top AI concern.<\/p>\n
Using AI often necessitates a different kind of mental processing, changing what had been the natural pacing of your day and dramatically increasing context switching. Instead of simply building and testing, someone might be jumping back and forth between auditing, fact-checking, prompting, and re-prompting. To manage strain and protect your focus, new strategies are needed.<\/p>\n
Published this past March in the Harvard Business Review, a study by Boston Consulting Group and the University of California, Riverside, surveyed 1,500 workers and coined the term \u201cAI brain fry.\u201d The researchers found that juggling multiple AI tools causes decision fatigue and increases errors.<\/p>\n
With companies regularly using AI-based applicant tracking systems to filter resumes, and AI actively shifting job responsibilities and skills requirements, the strategy for how you apply for roles and handle interviews is changing.<\/p>\n
Leo Freitas, an analyst and research manager at IDC Research, says it\u2019s critical for job applicants to show potential employers what they can do that machines cannot. \u201cYou need demonstrable achievements,\u201d he adds. \u201cIt\u2019s good to show highly human skills.\u201d<\/p>\n