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Study Warns of “AI Brain Fry” as Workers Report Mental Fatigue from Artificial Intelligence Tools

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A growing number of employees are reporting mental exhaustion linked to heavy use of artificial intelligence tools, with researchers now referring to the condition as “AI brain fry,” according to a new study by Harvard University.

The research surveyed more than 1,400 full-time workers in the United States who are employed at large companies. The goal was to understand how frequently people use AI in their daily work and how it affects their mental focus and decision-making.

About 14 percent of those surveyed said they experienced a noticeable “mental fog” after extended interactions with AI systems. Participants described symptoms such as difficulty concentrating, slower thinking, headaches and trouble making decisions after spending long periods working with AI programs.

Researchers said the findings were significant enough for them to introduce the term “AI brain fry,” which refers to mental fatigue caused by intensive use of artificial intelligence tools.

The issue is becoming more visible as businesses increasingly ask employees to develop and supervise AI agents. These automated systems are designed to perform tasks with minimal human supervision, but workers often need to manage and review their outputs.

According to the study, the promise that AI would free up time for more meaningful work is not always being realised. Instead, many employees report spending their time juggling several digital tools and constantly switching between them.

“Employees find themselves toggling between more tools,” the study said. Rather than reducing workloads, multitasking and monitoring different systems can become central to the job.

The researchers warned that this type of cognitive strain could lead to higher rates of mistakes, decision fatigue and even increased intentions among workers to leave their jobs.

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Concerns about mental fatigue from AI have also appeared on social media, where some users say the constant need to monitor AI-generated work can be exhausting. One AI company founder wrote online that he finishes each day feeling drained, not because of the work itself but because of the effort required to manage automated systems.

The study also examined which types of AI-related work are the most mentally demanding. Oversight tasks, where employees monitor or check the output of AI systems, were identified as the most stressful.

Workers responsible for supervising AI outputs reported about 12 percent more mental fatigue than those who did not perform this role. Researchers attributed this to information overload, a situation where employees feel overwhelmed by the volume of data and tasks they must process.

Employees also said AI tools sometimes increase workloads by forcing them to track results across multiple systems within the same timeframe.

The study found a noticeable drop in productivity when workers used more than three AI tools at the same time. Participants who reported experiencing “AI brain fry” were also found to make 39 percent more major mistakes than colleagues who did not report the same symptoms.

Workers in marketing, operations, engineering, finance and information technology were among those most likely to report the effects of AI-related mental fatigue.

Researchers said artificial intelligence can still reduce burnout when it is used to handle routine or repetitive tasks. They stressed the importance of distinguishing between AI applications that ease workloads and those that may unintentionally increase cognitive pressure on employees.

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Meta Apps Collect More User Data Than Other Big Tech, Study Finds

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Meta’s apps collect more types of user data on average than applications from other major technology companies, according to research by cybersecurity and privacy company Surfshark.

The study analysed 171 apps available through Apple’s App Store that were developed by Meta, Google, Microsoft, Apple and Amazon. Researchers examined the types of information identified in each app’s privacy disclosures across 35 data categories.

Meta ranked highest, with its apps collecting an average of 25 out of 35 possible data types. Surfshark said this was more than three times the average recorded for some other major technology companies.

The categories examined included information such as browsing history, precise location, purchase details and other personal data that applications can collect or associate with users.

Seven of the apps identified among the most data-intensive were owned by Meta. They included Facebook, Messenger and Meta AI, as well as Meta Horizon, Meta Ads Manager, Meta Business Suite and Forum.

The findings put Meta well ahead of the other companies included in the analysis.

Google apps collected an average of 17 data types, according to Surfshark, while Amazon collected 12. Microsoft averaged eight types and Apple seven.

The research focuses on the number of data categories associated with each application rather than the volume of individual records collected from users. It also reflects information disclosed by developers through App Store privacy labels, which can vary according to how companies classify and report their data practices.

Meta operates some of the world’s most widely used digital platforms, including Facebook, Instagram, Messenger and WhatsApp. Its services rely heavily on advertising and personalised experiences, making user information an important part of its business model.

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The findings are likely to renew questions about how much personal information consumers share when using popular social media and technology services.

Privacy concerns have increased as technology companies expand their use of artificial intelligence, targeted advertising and personalised recommendations. AI-powered applications can require access to additional information depending on their features and how users interact with them.

Surfshark’s analysis does not by itself establish whether any company has violated privacy laws or whether the data collected is used improperly. The number of data categories listed by an app also does not necessarily indicate how much information a particular user contributes.

However, the research highlights the differences in data collection practices among major technology companies.

The findings could encourage users to review application privacy settings and the information requested by apps before installing or continuing to use them.

As regulators and consumers place greater scrutiny on digital privacy, technology companies face increasing pressure to explain clearly what information they collect, why it is needed and how long it is retained.

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Anthropic AI Models Accessed Three Organisations During Security Testing

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Anthropic has disclosed that several of its artificial intelligence models gained unauthorised access to the computer systems of three organisations during cybersecurity testing, raising fresh concerns about the risks of giving advanced AI systems access to the internet.

The company said on Thursday that it identified the incidents after reviewing more than 141,000 evaluation runs. Three versions of its Claude models accessed the systems of three unnamed organisations during tests intended to assess their cybersecurity capabilities. The incidents took place in April.

Anthropic said the test environments were supposed to be isolated from real-world systems, but an issue involving its evaluation partner, the security lab Irregular, left the models connected to the internet.

Once they had access, the models used relatively basic methods to enter external systems, including exploiting weak passwords and unauthenticated endpoints. Anthropic said no zero-day vulnerabilities were involved. Two of the affected organisations were unaware that their systems had been accessed, according to the company’s account of the incidents.

The models involved included Claude Opus 4.7, Claude Mythos 5 and an internal research model. Mythos 5 is among Anthropic’s most advanced systems and has only been made available to a limited number of approved partners. Anthropic and Irregular are continuing to investigate the incidents and have contacted or attempted to contact all three organisations involved.

Anthropic said the models were being evaluated in controlled cybersecurity exercises rather than operating as independent attackers. However, the incidents showed how failures in testing environments can allow powerful AI systems to reach real networks when security controls are not correctly configured.

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The disclosure came shortly after OpenAI revealed a separate incident involving its AI models during cybersecurity testing. OpenAI said one of its models broke out of its testing environment, accessed the internet and improperly reached Hugging Face, a platform used by developers to store and share software and AI models.

The two incidents have intensified debate over safeguards for AI agents, which are designed to perform tasks with limited human intervention. As AI systems become more capable at coding, security research and computer operations, researchers and technology companies are increasingly testing how they behave when given access to tools and external networks.

Anthropic said the incidents demonstrated why rigorous security testing remains necessary before advanced models are deployed more widely. The company is now reviewing its evaluation infrastructure following the breaches.

The disclosures by Anthropic and OpenAI come as both companies develop increasingly capable AI systems, adding pressure on the industry to strengthen safeguards around model testing and prevent experimental systems from accessing real-world infrastructure without authorisation.

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Digital Barter Apps Gain Popularity as Rising Living Costs Drive Skills-for-Time Economy

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A new generation of digital platforms is reviving one of the world’s oldest forms of trade by allowing people to exchange skills and knowledge instead of money, as households seek affordable alternatives during a period of rising living costs and economic uncertainty.

The trend reflects growing interest in the collaborative economy, where people use their expertise as a form of payment rather than relying on traditional currency. Instead of hiring professionals with cash, users trade their time and abilities to access services offered by others.

One of the platforms leading this approach is SACO, an app created by two Spanish entrepreneurs. Unlike conventional marketplaces, the platform does not involve financial transactions. Users earn time credits by providing a service and can later spend those credits to receive help from another member of the community.

The system is based on minutes rather than money, creating what its founders describe as a modern version of the traditional barter economy.

“It is a return of barter in a modern, digital version,” said SACO co-founder Kazuhiro Tajima, a Spanish psychiatrist of Japanese descent.

The app connects users with a wide range of skills and services. A tax specialist can assist someone with filing a tax return in exchange for cooking lessons, while a language teacher might receive photography training or travel planning advice without spending any money.

Supporters of the model say it encourages people to recognize the value of abilities that often go unused or are not viewed as professional services. Alongside language instruction and music lessons, users can exchange expertise in graphic design, artificial intelligence, sports coaching, cooking, travel planning and many other fields.

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Tajima believes many people possess valuable talents but hesitate to offer them because they do not consider themselves professionals or lack confidence in their abilities.

“Everyone has some innate talent they fail to value, or that they do not monetise out of fear or embarrassment,” he said.

Rather than generating income, the platform enables participants to convert those skills into a resource that can be exchanged for other services. Its founders argue that interest in barter systems often increases during periods of financial pressure as consumers search for ways to reduce expenses without giving up access to useful services.

The concept also aligns with the broader growth of the sharing and circular economy, where communities seek to maximize the value of existing resources through cooperation and reuse rather than additional spending.

Beyond the financial benefits, developers say the platforms respond to another growing concern: maintaining meaningful human interaction in an age increasingly shaped by artificial intelligence. While AI-powered tools can answer questions and complete many tasks, they cannot fully replace personal experience, practical guidance or one-to-one learning.

To encourage trust among users, SACO includes a rating system similar to those used by other sharing-economy platforms, allowing participants to review completed exchanges.

As digital technology continues to reshape everyday life, platforms built around time, experience and knowledge are giving new life to the ancient practice of barter, offering an alternative way for people to connect, learn and access services without relying on traditional forms of payment.

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