Nearly 1 in 5 workers laid off due to AI are asked to return, survey finds
Recent surveys reveal that many companies are reconsidering their decisions to replace human workers with artificial intelligence. According to research from SHL, nearly one in five US employees who lost their jobs to automation have been asked to return, while a separate study found that a third of organizations had to rehire staff after AI-related cuts. Experts suggest that employers moved too quickly to reduce headcount for immediate savings, only to realize that technology cannot easily replace human judgment, empathy, and contextual understanding of the business. However, bringing employees back is proving complicated because trust has been affected. Workers are returning with firmer demands, with many expecting significant pay increases, written guarantees of job security, and clear transparency regarding how AI will be used in their roles moving forward. This trend highlights a necessary shift in how businesses approach workforce planning. Rather than viewing technology purely as a tool for cutting costs, successful organizations are learning to balance the efficiency of automation with the irreplaceable experience of their human employees.
Securonix CPO on the limits of AI in security operations
In a recent interview, Simon Hunt, Chief Product Officer at Securonix, emphasizes that while artificial intelligence is a valuable tool for security teams, it cannot replace human judgment. He points out that AI is excellent at processing large volumes of data and identifying technical threats, such as a compromised account. However, it lacks the broader context needed to make sound business decisions. For example, AI does not inherently know the difference between locking an account on a quiet Sunday versus a critical Monday morning. Because of this gap in wisdom, Hunt envisions a future where security operations are heavily assisted by technology, but not entirely run by it. As automation takes over routine analytical work, human professionals will shift away from basic monitoring and focus instead on supervising the systems and handling complex exceptions. Furthermore, while these tools allow average criminals to launch stronger attacks, they also help defenders patch vulnerabilities much faster. Ultimately, organizations must maintain fundamental safety practices and rely on human oversight for final, high-stakes decisions regarding organizational risk.
Could modular pre-planned response capabilities offer something new to the business continuity toolbox?
Traditional business continuity plans often gather all response procedures into a single, comprehensive document. While well-intentioned, these large plans tend to become rigid and difficult to navigate during a fast-paced crisis. Because they frequently contain outdated information, experienced response teams often abandon them in real-time, choosing instead to adapt and formulate new strategies as the situation unfolds. Recognizing this flaw, the author suggests shifting away from massive, overarching plans toward a more flexible approach inspired by emergency services, such as mountain rescue teams. Instead of relying on a single manual, organizations should adopt modular pre-planned response capabilities. These are self-contained, heavily practiced response units designed to handle specific operational impacts. During an emergency, a leader can simply assess the situation and activate only the necessary modules. This strategy transitions organizations from a rigid, plan-centered mindset to an agile, capability-centered model. By focusing on flexible modules rather than static documents, businesses can respond to disruptions much more effectively, ensuring their teams rely on practical, well-rehearsed actions rather than cumbersome paperwork.
Cybersecurity Awareness Month Wrap-Up: Five New Threats and Steps to Take Now
The article closes Cybersecurity Awareness Month by highlighting five fast‑evolving threats that now shape everyday risk. It explains that attackers increasingly rely on impersonation powered by AI, using convincing messages, voices, and videos to trick employees into approving payments or sharing sensitive information. Identity‑based attacks are also rising, with criminals bypassing technical defenses by persuading help desks to reset passwords or enroll new MFA devices. The piece warns that prompt‑injection attacks can manipulate AI tools directly, especially when employees use unapproved “shadow AI” and unknowingly expose confidential data. Insider threats are another concern, including cases where foreign operatives secure legitimate remote IT jobs using fabricated identities. Vendor compromise remains a major risk, as organizations often inherit incidents from third‑party providers. To respond, the article recommends updating incident‑response plans, running realistic tabletop exercises, assessing AI and identity risks, establishing legal frameworks before an incident occurs, and reviewing critical vendors. Its steady message is that modern threats often look like normal business activity, and organizations must adapt quickly to keep pace.
NaaS hasn’t lived up to the hype. Is it still worth it?
The article explains why Network‑as‑a‑Service (NaaS) has not met early expectations, even though it still offers value in certain situations. Gartner once predicted rapid adoption, but uptake remains stuck around 1% to 5%, placing NaaS firmly in the “trough of disillusionment.” The piece shows that NaaS works well for organizations with aging infrastructure, limited staff, or a need for turnkey management — illustrated by a school that replaced its entire LAN through a vendor‑owned, fully managed deployment. However, the broader market faces obstacles. Consumption‑based pricing can be unpredictable, vendor lock‑in is a real concern, and ripping out functioning networks rarely makes financial sense. Analysts also point out that campus networking requires substantial on‑premises hardware, making it hard to deliver a true cloud‑like model. Definitions of NaaS vary widely, adding confusion and mismatched expectations. Surveys show interest in phased adoption, but fully mature deployments are rare. The steady message is that NaaS isn’t a failure — it’s simply a niche solution whose benefits depend heavily on an organization’s starting point and priorities.
From capacity, to the cost of intelligence
The article explores a major shift in the artificial intelligence industry: the focus is moving from simply buying enough hardware to figuring out the true cost of the intelligence it produces. While the price to run a single task has dropped dramatically, overall spending is soaring because companies are using AI far more heavily. The real cost of artificial intelligence is no longer just the price tag on a computer chip. Instead, it depends on the entire system working together efficiently, including how much power is used, how effectively the hardware is kept busy, and where the data is actually processed. This shift brings new challenges. For instance, as hardware becomes more powerful, it demands significantly more electricity. Finding enough power to run these advanced systems has become a bigger hurdle than securing the computer chips themselves. To build sustainable systems, the industry must prioritize complete efficiency across the entire setup, balancing cost, speed, and power, rather than just focusing on raw computing capacity.
Seven egregious failures of modern web tech
The article lays out seven ways modern web technology has let users down, arguing that the web has become slower, heavier, and more frustrating despite decades of innovation. It points out that websites now load layers of scripts, trackers, and ads before showing meaningful content, making even simple pages feel sluggish. Basic reliability has also slipped: pages freeze, buttons don’t respond, and forms break because they depend on fragile JavaScript frameworks. The author criticizes how often sites block content with pop‑ups, cookie banners, and sign‑in walls, turning routine browsing into a negotiation. Search results have become cluttered with SEO‑driven pages that bury useful information. The article also notes that privacy has eroded, with sites collecting far more data than users realize. Accessibility remains inconsistent, leaving many people struggling with poorly designed interfaces. Finally, the piece argues that developers often chase new tools instead of building stable, lightweight experiences. Its steady message is that the web’s problems are not technical limitations but choices — and better choices could make the web fast, simple, and trustworthy again.
Banks prepare for AI agents, but consumers hesitate to hand over control
European financial institutions are rapidly preparing for a future driven by AI agents, but their customers are not yet ready to hand over control. A recent survey by Signicat reveals a stark divide: while 79% of financial providers are prepared for AI-initiated applications, only a small fraction of consumers trust AI to act autonomously. Consumers are highly comfortable using AI as an assistant to research, compare products, or pre-fill forms. However, they draw a hard line when assistance turns into agency. Very few are willing to let AI submit applications, use their digital identity, or sign contracts on their behalf. The hesitation stems from valid concerns around security, fraud, and risk. Interestingly, digitally advanced markets like Norway and Sweden show the least comfort with autonomous AI. To bridge this trust gap, financial providers must move gradually, ensuring customers retain final approval over decisions. Ultimately, building confidence in this new landscape will require secure digital identities and continuous verification of both the customer and the AI agent acting for them.
When AI agents agree on the data but not what it means
The article explains that AI agents can work from the same data yet still reach conflicting conclusions because data alone rarely carries the business context needed to interpret it correctly. Enterprises have spent years centralizing records into data lakes and warehouses, but in the process stripped away the policies, processes, and semantics that originally gave those numbers meaning. As organizations begin using multiple agents across a workflow, this missing context becomes a coordination problem rather than a data problem. Each agent may perform its assigned task correctly, but without shared business semantics, their combined actions can produce harmful outcomes — such as payment‑term negotiations, liquidity management, and compliance checks unintentionally stalling vendor payments. The article shows how fragmented data architectures and disconnected context sources make these failures more likely as agents hand tasks off to one another. Its steady message is that enterprises must supply agents with consistent business meaning, not just raw data, because alignment on numbers is useless if agents don’t share an understanding of what those numbers represent.
Quantum computers are coming: Here are the problems they could tackle
The article explains that quantum computers are designed to solve a narrow set of problems that overwhelm even the fastest classical machines, especially those involving the behavior of complex molecules and materials. Because chemistry and materials science operate according to quantum mechanics, quantum computers could eventually simulate reactions and structures with far greater accuracy, opening doors to new medicines, cleaner industrial processes, better catalysts, and more efficient batteries. Researchers expect early, meaningful demonstrations once fault‑tolerant systems with a few dozen logical qubits arrive later this decade. The article also highlights the security implications: a sufficiently powerful quantum computer running Shor’s algorithm could break today’s widely used encryption, which is why governments are already pushing post‑quantum cryptography. Quantum systems may also help with certain optimization problems, though the article cautions that clear advantages are not guaranteed. Overall, it argues that quantum computers will not replace classical machines but will act as specialized partners, delivering breakthroughs in chemistry, materials, cryptography, and select optimization tasks as fault‑tolerant hardware matures through the late 2020s and 2030s.
