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AI Adoption Is Outrunning Engineers' Trust
Engineers wary of recommendations built on incomplete context and unreliable telemetry want better operational data, continuous monitoring and evidence they can independently verify. Enterprise IT organizations are no longer debating whether to use AI, but rather which systems AI should influence, how much authority it should receive and whether engineers can trust its recommendations when production services are at stake. Data collected by Netscout from 950 IT professionals found 82% of organizations are discussing, testing, planning or running AI- and large language model-based IT projects. However, that activity has not translated into equivalent confidence among the engineers responsible for operational outcomes. When asked about obstacles to AI-driven IT operations, 22% said AI outputs could not be trusted, while 22.1% cited poor data quality or incomplete context. More than a quarter selected every listed operational challenge, including data silos, telemetry noise and mistrust.
Bug-Free Isn't Enough: The Real Anatomy of Software Quality
What is software quality? It’s one of those things that you recognize when you see it, but defining it can be a lot harder. One way to think about it is software with a lot of bugs is poor quality, so software without bugs must be good quality. Right? Well, how does it look? Users are a lot more forgiving of bugs in software if the software looks good. It turns out that there are quite a few factors that make up software quality. Here’s a list I’ve made of all the factors that I could think of. Factors involved in software quality Is it bug free? Does it look good? Does the software work and perform well? Does it cost a lot? Is it easy to add new features? Let’s look at these in detail. Is it bug free? Unless you go to extraordinary lengths to test, it’s impossible to guarantee that software is completely bug free. According to the book Code Complete by Steve McConnell, Microsoft had 10-20 bugs per 1,000 lines of code (LOC), which dropped to 0.5 defects in production code. Given that i
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