Duke University is redefining career services by blending alumni mentorship with AI-powered tools, helping students build confidence, find opportunities, and connect data with empathy.
Tech is simultaneously contracting and expanding as companies shed outdated roles but aggressively hire for AI, cybersecurity, and data-centric skills—reshaping the 2026 job market.
Companies pausing entry-level hiring to cut costs are unintentionally creating a long-term talent crisis that weakens innovation, leadership pipelines, and future competitiveness.
A data analyst overcame nine months of rejections and challenges to land a remote Data Engineering role, leveraging Interview Query’s frameworks, mock interviews, and AI tools to prepare strategically.
A new grad returned to the job market after a year off and secured a competitive data science role by using Interview Query’s structured prep, ML resources, and realistic interview practice.
Despite the AI boom, tech job postings have fallen over 30% below pre-pandemic levels as companies cut early-career roles, overcorrect post-COVID hiring, and struggle to balance AI adoption with long-term talent needs.
A data-driven look at how agentic AI is evolving from tool to teammate, transforming workflows, worker roles, and enterprise governance across the modern workplace.
AI engineers and insiders are increasingly avoiding automation for everyday tasks, exposing reliability gaps and highlighting why human judgment is becoming more essential—not less—in the future of work.
GPT-5.2’s improved reasoning, coding accuracy, and automation capabilities are reshaping how tech work gets done, and raising urgent questions about the future of junior roles and AI-driven workflows.
As trust in AI-driven recruiting erodes, tech workers are increasingly gaming automated hiring systems just to get their résumés seen by humans.
This guide breaks down how to become a business analyst by building the right skills, tools, portfolio projects, and interview readiness step by step.
New research shows a widening divide between corporate AI optimism and public distrust, driven by concerns over safety, fairness, worker impact, and who ultimately benefits from AI adoption.