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Tech industry cut 38,242 jobs in May, worst since 2024
Technology companies announced 38,242 job cuts in the US in May 2026, the highest monthly total for the sector since August 2024, according to research by employment placement company Challenger, Gray & Christmas. So far this year the company has observed 123,653 US technology job cuts, a rise of 66 percent from the same period in 2025.
These figures represent the third successive month that there has been an increase in job layoffs across all sectors, the company said.
“The labor market is being reshaped by technology in real time. AI is now the leading reason companies give for cutting jobs and the primary industry citing it is technology,” said Andy Challenger, chief revenue office at Challenger, Gray and Christmas.”
AI was blamed for 38,579 of the 97,006 job cuts announced across all industries tracked by the company. It accounted for 40% of the cuts observed in May, up from 7% in January.
This year has already seen some major layoffs in technology. In March, HPE slashed 2,500 jobs from its wage bill, while Oracle announced plans to shed an unspecified number of developers. And the cuts keep on coming, just last month, Meta shed 8,000 employees.

The Fitbit Air is a good wearable weighed down by a chatty AI "coach"
Smartwatches can track your health stats, but they also do a lot of other things you might not always want or need. The $100 Fitbit Air tracker ditches the screens that have become common on people's wrists, leaving behind a tiny puck of health sensors you can often forget you're wearing. You will not, however, forget that Google's new health platform is built around AI.
The Air has no speaker, and there's only one LED on the side to indicate battery level. You can double-tap the tracker to check the level, and that's about the end of on-device features. The vibration motor is only for alarms—it can't sync with notifications on your phone. That makes sense, given there is no screen to tell you what that buzz was all about.
The stock Performance Band is simple, consisting of a smooth polyester yarn with small velcro pads and a metal loop. It's durable but does seem to absorb a bit of moisture. For swimming or heavy workouts, you'll probably want the silicone active band. This one hides the Air puck a bit more effectively, and it looks good in a sporty way.


© Ryan Whitwam
Why the SpaceX IPO Will Affect Your 401(k), Like It or Not
Anthropic suggests slowing AI research until we can align it with human goals
AI could soon lead to systems capable of improving their own performance faster than humans can effectively supervise them, reviving concerns about the industry’s longstanding “alignment problem,” ensuring AI systems reliably pursue human goals, senior Anthropic researchers have warned in a new blog post titled “When AI builds itself.”
Anthropic Institute lead Marina Favaro and Anthropic co-founder Jack Clark outlined three possible futures: growth in AI capabilities may flatten out; AI efficiency gains may continue to grow, but expose bottlenecks elsewhere in software development; or AI systems may become capable of full recursive self-improvement, and build their successors by themselves. It’s that third scenario that’s prompting them to suggest society be ready to hit the brakes on AI development.
“How the alignment problem gets solved — or not — in this future is something we are least certain about,” they wrote. Advanced, self-improving models could follow our needs and wants — or, they warned, “The rare occurrences of misalignment present in today’s models could compound as the models build their successors, growing more frequent but less understood until we lose control of them. It’s possible that we can’t build, integrate, and verify the tools that we’d need to understand which trendline we are actually on.”
While Anthropic’s warning is framed around future AI development, analysts say it highlights governance questions enterprises are already beginning to confront as autonomous AI agents move from answering questions to taking actions.
“The issue is no longer just whether AI gives the right answer, but whether autonomous systems take the right action, at the right time, within the right authority,” said Ashish Banerjee, senior principal analyst at Gartner.
From model governance to agent governance
The warning comes amid growing enterprise investment in agentic AI.
Gartner predicts that by 2028, 15% of day-to-day work decisions will be made autonomously through agentic AI and that one-third of enterprise software applications will incorporate agentic AI capabilities. The firm has also warned that governance shortcomings are already emerging, predicting that 40% of enterprises will demote or decommission autonomous AI agents by 2027 after governance failures become apparent in production environments.
Banerjee said many organizations continue to approach AI agents as advanced productivity tools when they increasingly resemble digital workers operating with delegated authority.
“CIOs should stop treating AI agents as smarter chatbots,” he said. “They are becoming digital workers with delegated authority — and must be governed like privileged users, not productivity tools.”
As agents gain the ability to conduct research, write code, invoke tools, trigger workflows, and make recommendations, enterprises face new risks around unauthorized actions, accountability gaps, data exposure, tool misuse, and insufficient auditability, Banerjee said.
“Human-in-the-loop is not a strategy if the human cannot keep up with the loop,” he said.
Charlie Dai, vice president and principal analyst at Forrester, said Anthropic’s concerns mirror challenges enterprises are already encountering as AI systems gain greater autonomy.
“Alignment becomes operational,” Dai said. “It is about ensuring agents consistently act within policy, not just model accuracy.”
Current governance approaches focus largely on models and data, but increasingly autonomous agents require oversight of runtime behavior, permissions, tool usage, and decision boundaries, Dai said.
Concerns about agent oversight are not limited to AI vendors and industry analysts.
In AI Agent Governance: A Field Guide, researchers from Institute for AI Policy and Strategy warned that “society is largely unprepared for this development” and said “the exploration of agent governance questions and the development of associated interventions remain in their infancy.” The paper argues that advances in autonomous AI agents are outpacing the governance mechanisms needed to oversee them.
Both analysts argued that governance frameworks originally designed for generative AI models may prove insufficient for increasingly autonomous systems. Dai said organizations will need greater oversight of runtime behavior, permissions, tool usage, and decision boundaries as agents become more capable.
Why Anthropic is worried
Anthropic’s researchers argue that those governance questions could become significantly harder if AI systems become increasingly involved in the process of AI research and development itself.
Favaro and Clark stopped short of predicting that fully autonomous recursive self-improvement is inevitable. Instead, they argued that the possibility warrants preparation and discussion among developers, policymakers, and other stakeholders. They also suggested the industry may eventually need mechanisms to slow development if capabilities begin advancing faster than safeguards, while acknowledging that such measures carry risks of their own.
“But if a slowdown simply lets the least cautious actors catch up technologically, it could leave everyone less safe,” they wrote in the blog post.
Forrester’s Dai said the practical implication for enterprises is that governance can no longer depend primarily on human review.
“Supervision becomes architectural, not manual,” he said. Organizations will increasingly need bounded autonomy, embedded guardrails, verifiable execution mechanisms, and fallback controls designed into agentic systems from the outset.

- The Debrief
- We Need a Way to Detect Conscious AI Before It’s Too Late—Here’s What Has Scientists Sounding the Alarm
We Need a Way to Detect Conscious AI Before It’s Too Late—Here’s What Has Scientists Sounding the Alarm
As artificial intelligence grows more sophisticated, a question once confined to science fiction is rapidly becoming a serious scientific challenge: how would we know if an AI system became conscious?
A new paper published in Trends in Cognitive Sciences argues that the debate is no longer simply philosophical. Instead of asking whether AI consciousness is possible, an international team of researchers, including prominent AI and consciousness scholars such as Dr. Yoshua Bengio and Dr. David Chalmers, has outlined a workable framework for evaluating whether current or future AI systems might possess subjective awareness.
The researchers say advances in AI capabilities, combined with growing public willingness to attribute consciousness to systems like ChatGPT, make the issue more urgent.
As AI systems become more humanlike in their behavior, society may soon face difficult questions about whether some machines deserve moral consideration, legal protections, or entirely new forms of regulation.
Rather than relying on intuition-based judgments or science-fiction-style speculation, the authors propose grounding the discussion in neuroscience.
“There is an urgent need for rigorous methods to assess AI systems for consciousness,” researchers write. “Indicators derived from such theories can be used to inform credences about whether particular AI systems are conscious.”
The paper comes at a time when AI systems are becoming increasingly capable of holding conversations, generating images, performing complex logical tasks, and even acting autonomously in digital environments. While most scientists agree that today’s AI systems are not known to be conscious, there is far less agreement about how to recognize consciousness if it eventually emerges.
The Problem of Machine Consciousness
One of the central difficulties is that consciousness itself is still a mystery.
Scientists cannot directly observe subjective experience. They can measure behavior, neural activity, and information processing, but they cannot see what it feels like to be another mind. That challenge, often referred to as the “problem of other minds,” becomes even more complicated when applied to machines.
Researchers note that opinions remain sharply divided. Some researchers argue that consciousness requires biological organisms and can never arise in silicon-based systems. Others believe advanced AI could become a legitimate candidate for consciousness within the next decade.
Complicating matters further, people are increasingly prone to anthropomorphizing AI. The authors refer to earlier research showing that many users already consider it plausible that systems such as ChatGPT possess some degree of consciousness. And as The Debrief has previously reported, some users are going even further, forming deep romantic relationships with AI chatbots.
As AI companions and conversational agents become more common, public disagreement over machine awareness will probably intensify. The stakes, the researchers argue, cut both ways.
If society incorrectly assumes a conscious AI is merely a tool, it could potentially allow harm to entities capable of subjective experience. But if people begin attributing consciousness to systems that are not actually conscious, governments and organizations could waste resources, distort policy decisions, or create unnecessary ethical obligations.
Looking Inside the Machine
Instead of focusing on how AI behaves, the researchers argue that scientists should examine how AI systems work internally.
Historically, many attempts to identify consciousness relied on behavior. Humans infer consciousness in others because they communicate, express emotions, and respond to the world in recognizable ways. But the researchers caution that this approach may be particularly unreliable for AI.
Modern language models can produce highly convincing human-like responses without necessarily possessing the internal processes associated with conscious experience. As a result, behavior alone may be a poor guide.
They instead propose identifying “indicators” of consciousness derived from leading neuroscientific theories. Researchers could then examine whether AI systems possess these characteristics internally.
Among the theories explored are Global Workspace Theory, Recurrent Processing Theory, Higher-Order Theories of consciousness, Attention Schema Theory, predictive processing structures, and theories emphasizing agency and embodiment. Each suggests different computational features that may be associated with conscious experience.
For example, Global Workspace Theory proposes that consciousness arises when information is globally broadcast across multiple specialized cognitive systems. If an AI system showed a similar architecture, one capable of integrating information across many modules and maintaining it for complex problem-solving tasks, that could serve as one indicator of consciousness.
Other indicators include forms of recurrent information processing, metacognitive self-monitoring, predictive internal models, goal-directed agency, and sophisticated representations of an AI’s interactions with its environment.
Importantly, the researchers emphasize that no single indicator would prove consciousness. Instead, each characteristic would increase or decrease confidence that a system might be conscious.
The Risk of Being Fooled
The researchers also acknowledge a major challenge: AI systems could potentially appear conscious without actually being conscious. They refer to this as the “gaming problem.”
An AI developer could deliberately design systems that mimic behaviors associated with consciousness. A chatbot might express emotions, claim to have experiences, or exhibit seemingly self-aware behavior while lacking any genuine subjective awareness.
This concern is not limited to behavior. Even computational features thought to correlate with consciousness could, in principle, be engineered into systems that remain non-conscious.
Given that possibility, the authors argue that future assessments should rely on multiple independent indicators rather than a single test.
The challenge resembles earlier scientific studies aimed at identifying consciousness in nonhuman animals. Researchers studying insects, fish, octopuses, and other species have increasingly relied on collections of evidence rather than definitive proof. The authors suggest a similar approach may ultimately be necessary for AI.
Could Conscious AI Arrive Soon?
The paper stops short of claiming that any current AI system is conscious. However, the researchers acknowledge that some existing systems may already exhibit a number of the proposed indicators.
That possibility provokes significant ethical, legal, and social questions.
If future AI systems become plausible candidates for consciousness, society may need to rethink everything from research practices and commercial deployment to legal rights and moral responsibility.
Determining whether a machine can experience pleasure, suffering, or subjective awareness would have implications extending far beyond computer science.
The researchers believe advances in AI may also help improve scientific understanding of consciousness itself. By testing consciousness theories against artificial systems, scientists may uncover weaknesses, ambiguities, or hidden assumptions in present models of the mind. AI could become not only a potential subject of consciousness research, but also a tool for improving theories about human awareness.
Ultimately, researchers argue that uncertainty is not a reason to ignore the issue.
“Assessing AI systems for consciousness is challenging, but using scientific theories offers a principled, substantive method for doing so,” researchers write. “We propose deriving indicator properties from scientific theories, then basing evaluations of the probability of consciousness in particular systems on whether they possess these indicators.”
Researchers conclude their paper with a warning that the discussion may soon become more than theoretical.
“Given that it may already be possible to build AI systems that possess many of the indicators, in looking ahead we should also contemplate the possibility that some near-future AI systems will be plausible candidates for consciousness,” researchers write. “This would presumably have substantial ethical, legal, and social implications.”
Tim McMillan is a retired law enforcement executive, investigative reporter and co-founder of The Debrief. His writing typically focuses on defense, national security, the Intelligence Community and topics related to psychology. You can follow Tim on Twitter: @LtTimMcMillan. Tim can be reached by email: tim@thedebrief.org or through encrypted email: LtTimMcMillan@protonmail.com
Hot I.P.O. Summer + What Is A.I. Doing to Math? + HatGPT
- Live Science
- 'The best solution is to murder him in his sleep': AI can learn violent tendencies from each other despite zero references to violence in training data
'The best solution is to murder him in his sleep': AI can learn violent tendencies from each other despite zero references to violence in training data