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The Most Valuable AI Skill? Knowing When Not to Use It
With AI assistants taking on more writing, analysis and problem-solving, workers across all industries will still need the brainpower, judgment and expertise to challenge an answer, explain a decision, and handle situations the technology gets wrong. Developing those capabilities requires deliberate practice, even when automation offers a faster route to a finished task —the challenge is deciding which work to delegate, and which work provides experience worth preserving. This means employees and managers must make that distinction part of AI adoption, alongside training in how to use the tools. Nick Gonios, vice president of learning transformation and company ambassador at Epignosis, the parent company of TalentLMS, says the difference rests on what the technology enables a worker to avoid. “The key difference is whether it is helping someone build on what they know or helping them avoid what they don’t know,” he says. “When it becomes a substitute for understanding the work itself,
Securing Critical Infrastructure Starts With Skilled Cybersecurity Pros
Since the current U.S. war with Iran started on Feb. 28, the most pressing security questions stemming from the conflict have focused on economic concerns — the closure of the Strait of Hormuz and its effect on global energy supplies and oil prices — as well as attacks on American military bases and personnel in the Middle East. While concerns over cybersecurity haven’t been raised to the same level, the war has increased scrutiny of vulnerabilities in U.S. critical infrastructure, especially as Iran is known to have an active offensive cyber program and has improved its capabilities for over a decade. A few months after the first shots were fired, reports surfaced that attackers associated with Iran’s government had begun targeting water treatment facilities in the Midwest and other locations. While nation-state groups are one concern, other reports have focused on ongoing U.S. critical infrastructure vulnerabilities and a lack of preparedness for what threat actors or cybercriminals
AI-Driven Activity Monitoring: What Tech Workers Need to Know
We all know that meeting or exceeding performance expectations is the best way to ensure your job security. But do you know how your employer is tracking and measuring your performance-related activities? Increasingly, employers are using a wide range of AI-powered tools and software platforms to track keystrokes, mouse movements, internal communications and calendar appointments as well as AI usage via prompts, tokens and tasks. In fact, 74% of U.S. employers now use online monitoring tools to track employee work activities. Potentially, the data could be factored into decisions about performance, promotions, raises and layoffs. But while companies have detailed visibility into activity and adoption rates, they have far less clarity on their impact on productivity, effectiveness and outcomes. Only 32% of employees report receiving formal workplace policies explaining online monitoring practices. Worse, taken in isolation, raw activity data can easily lead to incorrect conclusions abou
Cost Per Outcome: The FinOps Skill AI Agent Engineers Need
Engineers building enterprise AI agents increasingly need to explain the financial consequences of their technical decisions. Adding another reasoning step, retaining more context or delegating work to a second agent can improve results, but it can also increase spending without reducing the work employees must finish. Understanding that trade-off is becoming part of designing a useful system. The relevant skills extend beyond tracking token consumption to forecasting, allocating shared expenses and measuring cost per accepted outcome. Those capabilities will help engineers determine whether an agent delivers value—and give them a stronger basis for discussing architecture with finance and business leaders. Kevin Wade, director of FinOps at SHI International, says those decisions belong at the beginning of development. “Engineers should treat cost efficiency as a top-level design metric and first-party engineering concern, not something handed to FinOps after deployment,” he says. Lear
Platform Engineering vs. DevOps: Where Should Engineers Specialize in 2026 and Beyond?
For infrastructure engineers weighing their next career move, platform engineering presents a fundamental shift in perspective: How much of your career do you want to spend building internal tools, reusable features, and – well, platforms – that empower other developers to ship faster? Choosing where to specialize isn't just about picking a trending job title; it requires evaluating daily responsibilities, aligning with market demand, and deciding which class of technical problems genuinely excites you. In practice, the boundary between these disciplines is far more fluid than job descriptions suggest. Sai Joshitha Kathari, a senior site reliability engineer at Visa, points out that substantial functional overlap exists across DevOps, SRE, cloud, and platform engineering roles. Ultimately, the scope of technical ownership you carry matters significantly more than the label on your resume. Look at What the Team Owns Early in her career, Kathari spent her days configuring CI/CD pipelines