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2026's Cautious Comeback: What Tech Execs Are Really Looking For
So far, the 2026 tech job market has been dominated by a cautious, uncertain pattern resulting in few hires and deep frustration for job seekers. Finally, market conditions appear to be improving or becoming more hopeful. New monthly job postings reached almost 300,000 in June, up from correction-era lows near 200,000. Plus, tech occupation employment across all industry sectors increased by 47,000 workers, dropping the unemployment rate to 2.9%, according to an analysis by CompTIA. What’s more, after holding back on hiring due to AI, major companies ranging from railroad giant CSX to Google parent Alphabet have recently told investors that they plan to hire to meet growth goals or seize on emerging technologies. Have we turned the corner? Not exactly. “We are in the process of turning the first corner, but there are more corners to turn,” noted Miloš Topić, vice president for Information Technology and chief digital officer at Grand Valley State University. Topić further explained tha
AI Talent Wars: Why Startups Now Pay Like Big Tech
The traditional startup employment bargain was straightforward: accept a salary below what an established technology company might offer, receive equity and hope that years of risk eventually produce a lucrative exit. The rise of AI is disrupting that bargain, with startups now competing with technology giants and heavily funded private companies for a limited number of engineers who have already demonstrated that they can move AI systems from experimentation into production. Those candidates often have substantial salaries and unvested equity at their current employers, making speculative stock options and a compelling mission insufficient recruiting tools. “Startups are looking for engineers that are shipping production AI systems,” says Samir Dutta, CEO and co-founder of Farsight. “This is a space that doesn't have nearly the same level of research or established best practices as traditional, deterministic software building, so it is naturally a very competitive market for talent.”
Quantum Computing: Creating the Next Cybersecurity Skills Race
While artificial intelligence is having its moment, there is another potential industry-changing technology that has gained momentum while avoiding the scrutiny that has followed the release of large language models and virtual chatbots. That technology is quantum computing. For years, quantum computing has mainly been relegated to the worlds of theoretical mathematics and physics. There is now, however, a growing sense that practical applications for quantum computing are moving closer to reality. In turn, the technology has the potential to upend multiple industries and deliver breakthroughs in areas such as medical and pharmaceutical research. Quantum computing also raises significant cybersecurity concerns. Before the annual RSA Conference in April, Google Research released a paper detailing new developments in post-quantum cryptography (PQC). Specifically, researchers updated their estimates of the number of quantum computing “resources” – qubits and gates – required to break the
How to Use AI Without Racking Up "Learning Debt"
AI tools can close a ticket, draft code or generate a cloud configuration in seconds. But when IT professionals use them to cross a knowledge gap without learning what sits beneath the answer, today’s productivity gain can become tomorrow’s liability. TalentLMS calls that accumulating backlog of missing skills “learning debt” and recently published a survey finding 41% of employees say they believe their roles are evolving faster than their employers’ ability to train them. Meanwhile, nearly six in 10 respondents said they use AI at least sometimes for tasks they were never trained to perform, and half use it to complete work they do not fully understand. The risk is easy to overlook because the assignment still gets completed. In the survey, 37% of employees said AI had made them appear more competent than they were, while 29% had delivered work they could not fully explain. Learn the Why Avoiding learning debt does not require abandoning AI. It requires treating the technology as a l
Survivor: C-Suite Edition — AI Is the Challenge
When executives talk about AI today, the conversation has shifted dramatically from a year ago. The question is no longer whether organizations should adopt AI, but whether leadership can manage it responsibly—and fast enough to remain competitive. That pressure is reaching the highest levels of the organization, with a recent Boston Consulting Group (BCG) survey finding roughly half of CEOs believe their own job security depends on executing AI successfully. With organizations pushing hard to move AI from experimentation phases into enterprise infrastructure integration, governance has become less about writing policies and more about giving executives confidence that AI systems are aligned with business goals, operating safely and producing measurable value. For CIOs and CISOs, that means AI governance is no longer simply a compliance exercise. It has become a strategic capability that determines whether organizations can scale AI—or whether they’ll be forced to slow deployments beca