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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
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Asana launches AI ‘chief of staff’ to keep projects on track
Asana has launched an AI personal assistant that can track various data sources to alerts users when a work project runs into problems and recommends next actions.
It’s one of a range of product announcements made Thursday at the company’s Work Innovation Summit in London, including updates to its existing AI teammates product. These follow Asana’s recent acquisition of AI workflow automation software vendor StackAI for $75 million.
Asana Dash is described as an “AI chief of staff” that can help users stay up to date on work projects by accessing information in Asana as well as across email, calendar and team messaging apps, said Arnab Bose, Asana’s chief product officer. “Keeping people in their ‘zone of genius’ and hooking up all of these unstructured signals to the structure of Asana — that’s what Dash does best,” said Bose.
The AI assistant can access the same Asana project information as the user, and can flag when problems occur that could push a project off-track. Asana Dash can then act to address problems, such as posting messages within Asana on behalf of the user or directing an AI teammate to take action. (Asana Dash will ask the user before making any changes.)
“Asana is building on recent acquisitions, and earlier investment in a graph database focused on human connections — the Asana Work Graph — and its position within a well-integrated flow of work to deliver to each worker an executive assistant rooted in the context of their job,” said Wayne Kurtzman, IDC research vice president.
The Asana Dash personal assistant is enabled by an expanded Asana work graph — the data model related to work carried out by teams in the application. Asana has in the past been more focused on tasks, projects, portfolios, and goals, said Bose, but the work graph now includes new sources of data, linking to employee calendars and accessing meeting transcripts, for instance, alongside other documents and databases.
There are also updates to the AI teammates feature — collaborative AI agents that multiple human coworkers can interact with — which are now more powerful, said Bose. This includes additional skills and integrations with third-party apps such as Gmail, Slack, Outlook, Figma, and Canva.
As for the StackAI acquisition, Bose said it allows Asana to extend the reach of AI agents into a variety of business apps more easily and reliably, building ] on Asana’s “system of action” function. The latter tracks work carried out across an organization, he said, and can automate the complex processes that make up many enterprise workflows.
“If you look at StackAI’s website, the thing that they are really, really great at is building these complex, multi-step processes,” said Bose. The aim is to combine StackAI’s agent builder with integration expertise agents already available in Asana.
“So, the idea is when an AI teammate or Dash recommends the next best action, they will be able to choose downstream actions based on the portfolio of approved workflows that you’ve built out in StackAI.”
Overall, the announcements help Asana provide a platform that combines agents and workflow automation with AI assistance that aids humans to work more effectively, said Bose.
“Our terminology for this is a ‘human-agent operating system,’ because automation, I feel, is a little reductive in the sense that there are some things that are fully automated, but a lot that you’d want a human being and an AI agent to coordinate on and align on,” he said.
Asana did not immediately respond to a request for pricing and availability details for Asana Dash.

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Google brings local AI agents to laptops with Gemma 4 12B
Google has released new tools that allow developers to run agentic AI workflows locally using Gemma 4 12B, a 12-billion-parameter model from Google DeepMind.
In a blog post, the company said the model, combined with the Google AI Edge stack, can be used to build and test applications on everyday machines. The model-runtime combination supports capabilities such as autonomous data processing, visual insight generation, webpage creation, and tool use.
The release includes Google AI Edge Gallery for macOS, where developers can use Gemma 4 12B to generate and run scripts for tasks such as data analysis. Google also said its Eloquent voice dictation and editing app now runs fully on-device on macOS, with support for local transcription and voice-driven text editing.
Google has also expanded LiteRT-LM, its lightweight command-line tool for running language models locally, with a new serve command. The company said this allows the CLI to act as a local LLM server and lets developers connect Gemma 4 12B to standard tools, SDKs, and frameworks through a local endpoint.
“Your data stays on your device while maintaining reliable responsiveness, utility, and cost efficiency,” the company said in the blog post.
The announcement comes as enterprises are looking beyond large, general-purpose models for some AI workloads. Gartner predicted that by 2027, organizations will use small, task-specific AI models at least three times more than general-purpose large language models, citing demand for more contextualized and cost-effective AI systems.
Challenges to overcome
But running agents on employee devices brings a number of problems. Companies must work within the limits of endpoint hardware, which can restrict the size of models that run effectively and the number of model instances that can operate at one time.
“While the AI can now fit on a laptop, enterprise IT infrastructure is largely unprepared to manage it,” said Rishi Padhi, principal analyst at Gartner. “Even highly optimized models like the Gemma 4 12B require around 16GB of unified memory or VRAM to run alongside standard applications. Many standard-issue enterprise laptops lack the memory bandwidth and NPUs/GPUs required for fluid, multi-turn agentic execution.”
Anand Joshi, AI analyst at TechInsights, said local deployment also changes the nature of the workloads. On a PC, search may mean finding information across internal folders and files. In a data center, the same function could involve searching the internet or querying a large database such as SQL.
“The framework for local deployment of agentic AI is different from that of a data center,” Joshi said. “The models are smaller; you can run only one instance of a large model at a time. You are limited by memory, CPU, and so on.”
Security and governance are also likely to become bigger concerns as AI agents move closer to enterprise endpoints. Agentic AI is designed to take actions, creating new security risks when local models are given access to employee files or allowed to interact directly with applications and scripts.
“Sandboxing these agents without breaking their utility is still a major operational challenge,” Padhi added. “And all this while enterprises need to audit AI usage for compliance and security. When inference happens entirely offline, capturing logs, tracking model drift, and ensuring employees are using the approved, compliant ways for a model becomes incredibly difficult.”
The cost tradeoff
Running AI agents locally could reduce some cloud inference costs, but the savings may be offset in the near term by higher spending on endpoint hardware and management.
“First and foremost, it is an OpEx-to-CapEx shift, as it shifts that financial burden by forcing accelerated hardware refresh cycles for premium PCs or edge devices,” Padhi said. “It would require buying expensive, high-memory laptops for employees at a time when memflation in the hardware industry is already driving up end-user average selling prices for laptops.”
Many enterprises refreshed PCs in 2025 to support Windows 11, but at that point, most AI inference still ran in the cloud, and the case for on-device AI remained unclear, Padhi said.
Enterprises may therefore move cautiously, buying AI-capable PCs only where local inference has a clear business case.
Over time, however, on-device AI could make enterprise AI spending more predictable by reducing exposure to variable cloud inference bills. The tradeoff is that companies may face a higher baseline cost for equipping and managing employees’ devices.
Complementing cloud AI
For enterprises, local AI is unlikely to replace cloud-based AI outright. Analysts said local AI is more likely to be used for workloads that benefit from endpoint processing, especially when applications must operate offline or when privacy and response times are critical.
“For local agentic AI to proliferate, the use cases on edge will have to complement data center/cloud use cases,” Joshi said. “I don’t expect local agentic AI to replace cloud AI, but it has potential to take a slice away from the cloud, and models like Gemma are significant steps towards enabling that.”
The market, Joshi added, is still determining where local AI fits best. “I estimate that use cases that require privacy or have strict latency needs will move to local node first, with further migration of others in the next 2-3 years,” he said.
Padhi said model placement will depend on the privacy requirements of a workload, the computing power it needs, and where the relevant data resides. Tasks such as code generation or analysis of local files could increasingly run on employee devices, while enterprise-wide RAG systems and more complex AI workflows are likely to remain cloud-based.
The article originally appeared on InfoWorld.

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AI saves workers a day a week, but they don’t know what to do with it
A report released Wednesday by Boston Consulting Group (BCG) indicates that many organizations are having difficulty converting efficiency gains that are AI-driven into any sort of measurable value.
The fourth edition of the consultancy’s annual Global AI at Work Survey reveals 42% of frontline employees who use AI on a regular basis save upwards of a full day each week; however, 66% are not given guidance on what to do with time they save, and “more than half don’t redirect it to strategic work.”
The report, AI at Work: Strategy Matters More Than Tools, is based on a global survey of 11,749 employees in 14 markets, from industries ranging from financial services to the healthcare sector.
David Martin, global leader of people and organization work at BCG, and the report’s lead author, said via email that the number of employees lacking the required guidance is surprising, “but it also tracks with what we see in many AI transformations. Companies have moved quickly to give people tools, but many have not yet redesigned the work around those tools.”
Saved time, he added, does not automatically become value. If a frontline employee saves a few hours a week, but has no direction on whether to use that time for customer service, quality improvement, innovation, or faster execution, “that value can simply leak out of the organization”
The fix is for leaders to change the scoreboard, Martin said: “Don’t just measure AI adoption or hours saved. Decide where that time should go, measure whether it is being reinvested, and give managers clear guidance on how to help teams use it. This is where AI transformation becomes a management challenge, not just a technology rollout.”
In fact, said Vinciane Beauchene, a managing director and partner at BCG and one of the report’s five co-authors, “the first wave of AI focused on individual productivity. The coming wave will need to transform collective work.”
“Everyone is talking about AI replacing work,” she said, “but it is in fact really about rethinking the human value-add inside.”
A managerial revolution underway
According to Beauchene, “this is the role of leaders. Our survey reveals a true managerial revolution in the age of AI; 65% of managers and leaders now believe agents will take over at least half of their job in the next three years, and frontline workers see their jobs evolving towards more managing and directing AI.”
A BCG release stated that the survey also highlights the continued emergence and maturity of AI agents, with 30% of respondents saying that agents are already integrated into workflows, more than double the number from last year’s report (13%).
Other key findings revealed that AI adoption among frontline workers has surged, with 74% saying they now use it daily or a few times a week, which is up 23 percentage points from a year ago. In addition, six out of 10 people believe that, within the next three years, AI agents could do at least half of their jobs.
And a survey slideshow released by the company pointed out, “the AI ‘honeymoon’ won’t last unless leaders bring strategic clarity driving sustained impact AI’s novelty and cognitive stretch fuel enjoyment early on. But sustained joy comes from strategic clarity. Employees thrive when the direction is real and the message reaches them with strong CEO involvement.”
Strategic clarity is a key differentiator
The report suggests that CEOs take a holistic approach to AI transformations by focusing on business outcomes as opposed to AI usage, investing in “redesigning work end-to-end, not in more tools,” placing people at the heart of that redesign, and governing AI not as a one-off program, but as a moving target.
Overall, BCG says that strategic clarity, “more broadly emerges from the survey as the most crucial differentiator in sustaining AI’s impact over time as organizations are moving past simply implementing AI tools in use case deployment initiatives.”
Increasingly, it adds, “the focus is shifting to redesigning end-to-end workflows and processes to reimagine functions, as well as to building and innovating new business models and products to drive growth, which have nearly doubled year-over-year.”
Global leader of BCG’s tech build and design unit BCG X Sylvain Duranton, also a report co-author, added, “employees don’t push back on AI intensity; they thrive when the strategy is clear, the direction is real, and the message reaches them.”
He added, “Business value and employee enjoyment aren’t trade-offs. The organizations capturing the greatest business value are the same ones where employees enjoy work the most.”
Despite the opportunity, the report notes that only one-third of frontline employees say that leadership’s communications about AI are clear, and only 28% “see a strong connection between what leaders say and what the organization actually does.”
However, Martin said, management can’t deal with this situation on its own. “CIOs have a critical role, but this is not a problem they can solve alone, and I would not frame it as something IT created by itself,” he noted.
Many organizations, he said, “started with the natural first step of getting tools into people’s hands safely and at scale. That was necessary, but it is not sufficient.”
The next phase “has to be much more cross-functional,” he said. “CIOs should help set the technology foundation, governance, data model, and measurement systems, but they also have an important role in creating strategic clarity. Employees need to understand why the organization is using AI, where it is meant to create value, and how it should change the work.”
Martin pointed out that CIOs should also pay close attention to cognitive load, especially on technology teams, as those teams are often the heaviest AI users.
“This means they may be among the most exposed to the mental strain that can come with reviewing outputs, managing AI tools, and keeping up with constant change,” he observed. The biggest gains come when technology strategy, workforce strategy, and employee experience move together. If AI remains only an IT program, companies will “undercapture” the value.
New expectations
The abundance of AI activity is also having another effect, in that 60% of respondents say the bar for work that counts as ‘good enough’ is now higher.
That, said Martin, is because AI is changing expectations. “If a tool can produce a first draft, summarize research, generate options, or automate a routine task, then ‘good enough’ moves up the value chain,” he said. “People are being asked to spend less time producing basic output and more time exercising judgment like checking quality, improving the answer, making decisions, and applying context.”
While that can be a good thing, he said, because it can make work more interesting and more valuable, “it also explains why employees are feeling more mental strain. The work that remains is often more complex. Leaders need to recognize that AI does not just make people faster, it changes what excellence looks like. That means companies need to update training, performance expectations, and management support accordingly.”
This article originally appeared on CIO.com.
