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Keeping Your Earning Power as AI Reshapes Tech Work
Years of experience can make a developer faster at writing code or an analyst more efficient at building queries, but as AI takes on more of that work, speed and technical familiarity may offer less leverage in a salary negotiation. The more valuable distinction is increasingly what professionals do with the answer: recognize a mistake, question an assumption or explain why a technically correct result would lead to a poor business decision. “Employers now pay more for people who can tell whether an AI-generated answer is right and explain what it means for the business,” explains Vishnu Shankar, chief data officer at workforce intelligence company Draup. Across six knowledge-work roles examined in Draup’s “The Execution Premium” report, the top 10% of earners make between two and 2.3 times as much as the bottom 10%, with the gap opening during midcareer, according to Shankar. Those differences highlight a question for professionals approaching their next career stage: Namely, whether
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