AI Watch

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A familiar warning now shapes much of the discussion about artificial intelligence: A handful of dominant firms will control the technologies, stifle innovation, and require aggressive antitrust intervention. It is a compelling story—and mostly wrong.

The idea that large companies automatically mean less innovation has become conventional wisdom in antitrust circles. European regulators have embraced it, blocking mergers and attacking American tech companies. The Biden administration followed that path, treating size itself as a threat and wanting government-led AI. The Trump administration, by contrast, has signaled a more evidence-based view—one grounded in both economic logic and empirical studies.

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OpenAI CEO Sam Altman said on Tuesday the rapid development and adoption of AI would not lead to a global “jobs apocalypse” and the technology had not claimed as many white-collar jobs as he had feared. 

Speaking virtually at a Commonwealth Bank of Australia (CBA) conference in Sydney, Altman said he was initially concerned about the impact AI would have on global employment levels. 

 

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Beginning this summer, University of California Berkeley School of Law students will be banned from using artificial intelligence to complete coursework or exams.

Under the newly adopted policy, students cannot “conceptualize, outline, draft, revise, and edit their work” using AI.

It also explicitly forbids students from asking AI to correct grammar mistakes or translate a paper into English.

Students are permitted to use AI for “research on papers ONLY for the limited purpose of identifying sources, such as cases, statutes, or secondary sources,” the policy states.

However, professors are permitted to make exceptions to this rule as long as they “do so in writing and with appropriate notice and require students to disclose any authorized AI use,” it states.

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A proposed data center expected to cost more than $5 billion ran into intense resistance Thursday night in rural Pennsylvania, where residents packed a town hall meeting and delivered a clear message: They do not want their farmland and community identity sacrificed for a largely undefined mega-project.

During a three-hour informational session at Bangor Area Middle School, residents of Lower Mount Bethel Township voiced overwhelming opposition to the proposed Lower Mount Bethel Tech Center, according to WFMZ-TV. The event was organized by the project’s major stakeholders, including Peron Development and J.G. Petrucci Co., rather than township leaders.

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China has launched a national programme that will assign every humanoid robot manufactured in the country a unique digital identity code, effectively a citizen ID, but for bipedal machines (those that can balance and walk/run on two legs).

The initiative, called the Humanoid Full Lifecycle Management Service Platform, was announced on Friday. It is led by the Humanoid Robotics and Embodied Intelligence Standardization committee, which is under China’s Ministry of Industry and Information Technology (via South China Morning Post).

Plans by corporate and state America to rapidly build AI data centers could meet stiff resistance is a recent Gallop poll is correct. The poll shows 70% of Americans oppose Data centers, ESPECIALLY in their local regions.

Not only do residents fear the tax on the resources, the taking of land, but also they fear the surveillance capacity of these data centers to enable the state to track the actions of citizens almost in real time.

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A new working paper by Philip Moreira Tomei and Bouke Klein Teeselink, posted to arXiv in early May, makes claims that, if correct, should reorient the workforce policy conversation. In short, the authors argue that the AI exposure indices that have shaped most current thinking are looking at an incomplete subset of digital work. They identify which jobs and tasks current language models can already accelerate but they miss the jobs with features that make them amenable to automation later.

Tomei and Klein Teeselink build a new index that scores all 17,951 task statements in the federal O*NET database. The authors propose a measure what they call “reinforcement learning feasibility” which asks whether a task has the structural features (e.g., verifiable outcomes, use environments amenable to simulation, discrete decision/feedback loops) that allow AI systems to be trained on it through the post-training methods that are becoming the main drivers of AI capability. They then compare their index to the most-cited existing measure, from Eloundou and colleagues, which looks at whether tasks can be automated with current technology.

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The ‘cloud-native’ architecture of the last decade is built on a 20-year-old assumption: that state
lives in the database, and compute is stateless. If you want to scale, you scale the database
vertically (get a larger machine) [1][1] or design the database schema around partition the data
and you scale your application servers horizontally (add more
boxes). Any request can hit any server, the loadbalancer doesn’t care, and the database is the
single source of truth.

LLMs and agents are quietly violating this assumption, and making this architecture increasingly
hard to work with. Not all at once, but in three subtle ways:

Immigrations and Customs Enforcement have been using a Palantir tool to track far more than illegal aliens, critics allege. ICE has over 20 million people worldwide in a database that some suspect includes American citizens.

DHS told 404 Media, “U.S. Immigration and Customs Enforcement is committed to achieving the nation’s mandate to clear the backlog of illegal aliens who pose a threat to the security of our communities. Like other law enforcement agencies, ICE employs various forms of technology while respecting civil liberties and privacy interests.”

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A new report from the US National Partnership for Women & Families says women may face disproportionate disruption from artificial intelligence in the workforce. The study found that while women make up about 47 percent of the US workforce, they account for 83 percent of workers across 15 occupations identified as having the highest exposure to AI.Those roles include secretaries, receptionists, and office clerks, among others. According to the report, around 6 million women work in these positions.

Researchers said workers in these jobs may face greater challenges adapting to AI-related workplace changes due to lower access to resources and reduced flexibility in transitioning to other roles. The report also looked at sectors where women are more heavily represented but less likely to face full automation, including nursing, childcare, and home health care, in addition to others.

While these jobs typically require direct human interaction and physical presence, the study said AI could still affect workers in those fields through monitoring and workplace management systems: “These management systems, sometimes described as bossware, can be difficult for workers to understand or challenge, and may worsen job quality even where jobs are not eliminated,” the report said.

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Anthropic (ANTH.PVT) is expanding its presence in the legal software market as it continues to grow its enterprise footprint.

The latest offerings include integration with platforms law firms already use, such as Box (BOX) and Thomson Reuters (TRI), plugins designed for specific tasks and roles such as corporate counsel, regulatory counsel, and law students, and integration with Microsoft (MSFT) 365.

The launch comes just a week after Anthropic debuted its Claude for Financial Services, which includes 10 customizable AI agents for financial users, the ability to use Claude’s financial capabilities across Microsoft 365, and the option to connect Claude to more applications.

It also comes as the software industry continues to deal with the fallout from the initial debut of Anthropic’s Claude Cowork, which has hammered software stocks over fears that the AI startup will steal market share from existing enterprise platforms.

Anthropic’s new legal products could further raise concerns about the future of legacy enterprise services.

The company’s latest products feature 20 model context protocol (MCP) connectors, which allow Claude to connect to existing pools of data and tools in apps. That includes the ability to use Claude with programs such as DocuSign (DOCU), Ironclad, Datasite, and other legal software.

Anthropic co-founder and CEO Dario Amodei speaks at the Code with Claude developer conference on May 6, 2026, in San Francisco. (Don Feria/AP Content Services for Anthropic) · ASSOCIATED PRESS

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“Artificial intelligence is not just for computer scientists anymore; it’s going to permeate every aspect of our lives and influence every business,” says MIT President Sally Kornbluth.

The world is reaching an inflection point with artificial intelligence: over half of U.S. adults use generative AI — with 12 percent using it daily at work — and 88 percent of global organizations have integrated AI into at least one core function, up from 78 percent in 2024. AI knowledge is no longer optional for career growth, organizational leadership, and life. Yet, a growing information gap exists between those with the capabilities to leverage AI’s potential and those trying to keep pace.

The need for accessible, practical AI education has never been greater. To meet this moment, MIT Open Learning is launching Universal AI, an online, self-paced, modular program that takes a learner from AI novice to authority, starting with core fundamentals and building to real-world, industry-specific applications.

“We identified a need for an AI learning experience that is universal in breadth and accessibility — one that bridges the gap between deeply technical and surface level introductions to the latest AI tools, and that is designed for a non-technical, global audience,” says Dimitris Bertsimas, vice provost for open learning.

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Chinese researchers have made a significant advancement in circular resource recovery by creating a new way to turn nitrate-laden wastewater into valuable ammonia for fertilisers. They used artificial intelligence to find a super-effective dual-atom catalyst, which led them to a process that tackles two major global issues: water pollution and the heavy energy use of traditional industrial ammonia production. Their findings, published in the Journal of the American Chemical Society, show that this method achieves almost three times the conversion efficiency of earlier technologies. By converting runoff from farms and factories, this approach provides an eco-friendly solution to lessen environmental ‘dead zones’ and cuts down on the agricultural sector’s dependence on energy-heavy chemical methods.

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Three years ago, in the idyllic town of Woodside south of San Francisco, the United States and China held their first high-level talks on the dangers posed by artificial intelligence. President Xi Jinping and his longtime foreign minister appeared serious in their conviction that a channel should be a established between Beijing and Washington — a red phone for AI in case of emergencies.

They authorized a diplomatic effort that would begin in 2024 in Switzerland, only months before the U.S. presidential election. A large U.S. delegation arrived with high hopes that were abruptly dashed, according to four sources who attended the talks. The Chinese contingent dismissed American concerns over runaway AI as academic, almost theoretical, quickly turning the conversation to export controls seen in Beijing as yet another U.S. effort to hold China back.

A Texas couple is suing OpenAI, alleging their product, ChatGPT, led to their son’s overdosing on drugs after following the AI platform’s advice.

OpenAI released this statement, “This is a heartbreaking situation, and our thoughts are with the family… ChatGPT is not a substitute for medical or mental health care, and we have continued to strengthen how it responds in sensitive and acute situations with input from mental health experts…”

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AI agents choose tools from shared registries by matching natural-language descriptions. But no human is verifying whether those descriptions are true.

I discovered this gap when I filed Issue #141 in the CoSAI secure-ai-tooling repository. I assumed it would be treated as a single risk entry. The repository maintainer saw it differently and split my submission into two separate issues: One covering selection-time threats (tool impersonation, metadata manipulation); the other covering execution-time threats (behavioral drift, runtime contract violation).

That confirmed tool registry poisoning is not one vulnerability. It represents multiple vulnerabilities at every stage of the tool’s life cycle.

There’s an immediate tendency to apply the defenses we already have. Over the past 10 years, we’ve built software supply chain controls, including code signing, software bill of materials (SBOMs), supply-chain levels for software Artifacts (SLSA) provenance, and Sigstore. Applying these defense-in-depth techniques to agent tool registries is the next logical step. That instinct is right in spirit, but insufficient in practice.

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Most of humanity has a great propensity to think in the short-term, but generally, long-term considerations — air pollution, deforestation and emissions, for example — just aren’t our thing.

That’s partly why scientists are deeply concerned about a recent SpaceX proposal to launch one million satellites — data centres — into orbit around Earth.

Their concerns range from losing the natural night sky, to losing access to space, to the environmental impact on our atmosphere.

At the moment, there are roughly 16,000 satellites orbiting Earth, 14,000 of which are active. SpaceX is responsible for more than 8,000 of them.