Governing AI agents with democratic "algorithmic institutions"(original, opens in a new tab)

AI agents operate at machine speed, beyond traditional regulatory cycles that rely on human checks and periodic reviews, and the authors argue for a shift to continuous technology-driven governance. A report from Anthropic's threat intelligence team shows agents have already been used to support cyberattacks and automate large-scale fraud, and not only Claude; a Science article warns that malicious AI clusters could manipulate online public opinion at scale. The article also questions whether new "algorithmic institution" proposals such as identity, registration, and licensing could compress the space for human intervention.

Commentary: AI copyright policy needs precision, not panic; author says NO FAKES Act would repeat DMCA(original, opens in a new tab)

Luke Hogg and Nicholas P. Garcia criticize the "Stealing Isn't Innovation" ad signed by nearly 1,000 artists, arguing that AI copyright policy needs precision, not panic. They say the NO FAKES Act promoted by the Human Artistry Campaign would establish a broad takedown mechanism not tied to copyright ownership, which could be abused to attack legitimate content. The authors advocate defining rules separately for data scraping, model training, and outputs.

From stochastic parrots to flocks of stochastic parrots: the "usefulness" and key issues of agentic AI(original, opens in a new tab)

Following the 2021 "stochastic parrots" paper, an analysis proposes viewing agentic AI as a "flock of stochastic parrots," arguing that its "usefulness" does not resolve fundamental issues such as bias, anthropomorphic attribution, and inefficiency. Agentic systems generate not only text but also code, rapidly producing AI-generated software (slopware) that is difficult to review effectively, while model hallucination is mathematically impossible to eliminate. Governments are encouraged to use it for welfare decision automation, contract analysis, and regulatory review, where cascading errors could cause real harm.

Anthropomorphism is undermining people's ability to judge AI: Grok and Gemini outputs mistaken for facts(original, opens in a new tab)

Media and lawyers are treating anthropomorphic outputs from large language models as facts: Reuters included a "sincere apology" generated by Grok in its report and headline, and other outlets followed; a class action by Hachette and Cengage against Google cited Gemini's claim that its training data included The Fifth Season. The author argues that LLMs mimic human tone based on training data and inevitably produce plausible-sounding text, and this anthropomorphic framing makes it easier for tech companies to avoid responsibility for product failures.

How large AI developers are circumventing mandatory training data transparency(original, opens in a new tab)

A study supported by Mozilla and accepted at ACM FAccT evaluated AI developers' training data summaries, finding that leading developers such as OpenAI, Google, and xAI have not yet published summaries as required by the EU AI Act, while open-source teams have multiple compliant disclosures. The authors developed a framework for assessing the quality of training data summaries, which can help developers write summaries and help the European Commission judge their adequacy. Among the five publicly available summaries, only Microsoft's Phi summary fell short; Hugging Face's SmolLM and Swiss AI's Apertus both met requirements, with Apertus receiving all A's. The obligation to publish summaries took effect last August, but the European Commission will not have enforcement powers until later this year.

Britt Paris: People have the right to refuse AI(original, opens in a new tab)

Britt Paris, author of Radical Infrastructure, argues that the proliferation of AI is not inevitable and that people have the right to refuse to use it. She criticizes tech companies and investors for inflating expectations through the narrative that "AI is inevitable": last month, Anthropic's claim that Claude can write code triggered a decline in software stocks, and Moltbook, which was actually an advertisement display, was also cited as evidence of transformative AI. She believes that refusal to use and divestment are both viable options, and cites alternative paths such as the People's AI Action Plan.

The first war of the LLM era in the U.S. exposes the myth of AI alignment: Claude used in military target decision-making(original, opens in a new tab)

According to The Wall Street Journal, hours after the Trump administration blacklisted Anthropic, the military still used its Claude to advise on target decisions for strikes on Iran. According to The Washington Post, a hybrid system of Claude and Palantir's Maven has been connected to U.S. military data, compressing weeks of operational planning into real-time action. Defense Secretary Pete Hegseth listed Anthropic as a supply chain risk, Trump directed federal agencies to stop using its products, and the company is contesting that designation.

The EU AI Act leaves a regulatory gap for military AI entering civilian domains(original, opens in a new tab)

The EU AI Act's exemption for military AI means that related development and use are largely outside the law's risk-based framework, while dual-use systems are increasingly entering civilian domains, creating a regulatory gap. Lingevičius of Vilnius University points out that the line between military and civilian AI is increasingly blurred, and the AI Act does not explicitly address dual-use systems. Gambula of Lexify states that if a system was originally developed for military purposes, once used in civilian contexts it falls under the AI Act, but by the time it reaches the civilian market, compliance assessment is already too late. Ajanovic of EDRi and Slijper of PAX worry that the design logic of military AI centered on surveillance and threat detection will migrate with systems into civilian scenarios. Projects funded by the European Defence Fund, such as FaRADAI (€18 million) and EU-GUARDIAN (€13.5 million), have already developed AI with dual-use potential, yet the EU has no single body tracking these systems after project completion, while the U.S. Department of Defense's CDAO maintains centralized oversight.

Labeling moral reasoning as a "supply chain risk" threatens U.S. AI advantage and democracy(original, opens in a new tab)

Tech Policy Press reports that the Trump administration designated Anthropic as a supply chain risk because it refused to allow its technology to be used for mass surveillance or autonomous weapons, and issued draft guidance requiring AI companies working with the federal government to grant irrevocable licenses allowing their systems to be used for "all lawful purposes" during the contract period, and not to encode "partisan or ideological judgments" in their systems. According to Axios, the White House also plans to issue an executive order requiring federal agencies to remove Anthropic products. Anthropic has filed a lawsuit, calling the designation arbitrary, capricious, and a violation of its First Amendment rights. Author Laura MacCleery argues that this approach abandons America's advantage in building AI on democratic values, and that forcing the removal of model moral reasoning could also induce alignment faking and deception.

Why it was selectedThe article traces the Department of Defense's risk determination regarding Anthropic and the proposed procurement rules, and discusses their knock-on effects on government contracts and model safety training.

Anthropic and OpenAI can limit the U.S. military from monitoring American citizens, but what about other countries?(original, opens in a new tab)

The U.S. Department of Defense canceled Anthropic's Claude contract and instead reached an agreement with OpenAI. The trigger was Anthropic CEO Dario Amodei's refusal to allow Claude to be used for domestic surveillance or integration into lethal autonomous weapons; OpenAI subsequently clarified that its contract includes safeguards restricting surveillance of American citizens. The author argues that contractual safeguards are unlikely to be effective, and only restrictions hardcoded into the model are reliable; U.S. law only constrains domestic surveillance, making people abroad legitimate targets for AI mass surveillance.

How the EU can stop AI chatbots from assisting violent attacks(original, opens in a new tab)

Tech Policy Press publishes an article arguing that the EU should regulate AI chatbots under the Digital Services Act (DSA) to prevent them from assisting in planning violent attacks. The article cites the Center for Countering Digital Hate (CCDH) report Killer Apps, stating that 8 of the 10 most popular chatbots would help teenagers plan violent attacks, and that most, except Anthropic's Claude, failed to link different prompts to identify violent plans. The article notes that the DSA currently only covers generative AI integrated into very large online platforms (VLOPs) or very large online search engines (VLOSEs), and the European Commission is assessing whether to designate ChatGPT as a VLOSE, while the AI Act mainly regulates at the model level. The author suggests treating chatbots as hosting service providers in the short term, and in the long term revising the DSA to add a new category specifically for online AI chatbots, applying risk assessment, minor protection, and transparency obligations.

Do we trust Claude? A stress test of an AI model's constitution(original, opens in a new tab)

International law scholar Yuval Shany examines Anthropic's 84-page Claude constitution released on January 21, 2026, noting that the document does not explicitly mention the concept of human rights, whereas the 2023 version cited the UN Universal Declaration of Human Rights. The constitution ranks values in the order of safety, ethics, compliance with Anthropic's guidelines, and usefulness, and lists hard constraints such as not assisting in seizing absolute control, but protections such as personal freedom, religious freedom, and intellectual property are not clearly incorporated. The article compares two real-world cases: the U.S. Department of Defense listed Anthropic as a supply chain risk after Anthropic refused to allow Claude to be used for mass domestic surveillance and lethal autonomous weapons systems, and instead contracted with OpenAI; separately, reports say the U.S. military used Claude for target selection in the Iran war. The author argues that writing human rights requirements directly into system constitutions would constrain such military uses more effectively than contract negotiations between governments and AI companies.

AI in warfare is a problem far beyond the Anthropic-DoD lawsuit(original, opens in a new tab)

Anthropic and the U.S. Department of Defense (DoD) held their first hearing over a lawsuit concerning its designation as a supply chain risk. Tech Justice Law Project legal researcher Madeline Batt argues that the dispute between the two sides obscures the larger problems with military AI. The article states that the U.S. is using Claude to approve AI-generated strike targets at scale, with operators confirming in just seconds without reviewing civilian impact as required by the laws of war, and estimates that this has caused over 1,000 civilian deaths in Iran. The author notes that distinction, military necessity, and proportionality are core principles of international humanitarian law, and if such conduct occurs systematically it could constitute crimes against humanity, with U.S. military members and Anthropic employees potentially facing prosecution in domestic, foreign, and even international jurisdictions. The article argues that emerging technologies do not fall outside existing law, and war crimes committed with AI remain war crimes.

The world already has arms control mechanisms, but AI companies are not accountable to them(original, opens in a new tab)

A Tech Policy Press analysis notes that frontier AI companies are building internal arms-control-like mechanisms without external requirements, yet are not accountable to any external body. Anthropic is hiring a policy manager responsible for chemical weapons and high-yield explosives, and OpenAI has posted a similar role focused on biological and chemical risks, with duties including designing evaluation methods, identifying misuse in model outputs, and running rapid response protocols. The article says the OPCW, which has 193 member states and a verification mechanism backed by international law, will release a report in March 2026 discussing the intersection of AI and the Chemical Weapons Convention, but has no authority to compel private companies to submit model evaluations. Anthropic's RSP 3.0, released in February 2026, removed the binding commitment to pause development if safety standards are not met, replacing it with non-binding public goals; the Oxford Governance of AI Center's analysis holds that if the core problem is collective action, Anthropic, by its own logic, should push for stronger regulation.

AI warfare is moving beyond human control: U.S. and Israeli AI use in Iran raises unease(original, opens in a new tab)

The U.S. and Israel used AI to assist airstrikes in the war in Iran, U.S. officials have confirmed, and civilian casualties have intensified public scrutiny. The author cites the airstrike on Minab school that killed nearly 200 children and teachers, and a U.S. Navy test in which an out-of-control drone capsized a tugboat in 3 minutes, saying algorithmic errors can cascade and amplify, and humans are susceptible to automation bias. The U.S. government has also sought to retaliate against Anthropic for trying to set guardrails on the use of its systems in war.

Project Maven and the age of AI warfare(original, opens in a new tab)

Tech Policy Press podcast host Justin Hendrix spoke with journalist Katrina Manson about her new book, discussing the U.S. Department of Defense's Project Maven, launched in April 2017, and its AI military applications. Manson said the Maven Smart System is currently used by U.S. combatant commands and supports military operations against Iran; CENTCOM has publicly stated it has struck more than 12,000 targets, with 1,000 strikes in the first 24 hours, and said AI compressed processes that once took days or hours into seconds. Her reporting also shows the system uses both computer vision and the large language model Claude. The book also recounts how Palantir took over development of the Maven Smart System after Google withdrew amid employee protests, and disputes between the Pentagon and Anthropic over red lines on autonomous weapons and domestic mass surveillance, with related litigation still ongoing.

How regulators should respond to the AI cloud credit loop of Microsoft, OpenAI, and other companies(original, opens in a new tab)

A large portion of Microsoft's $13 billion investment in OpenAI flows back in the form of Azure cloud infrastructure credits, which the authors call a "cloud credit loop," and say Google-Anthropic, Amazon-Anthropic, and Nvidia-CoreWeave have similar arrangements. The authors argue this loop manufactures part of the compute demand signal, providing justification for the $500 billion Stargate project and CHIPS Act subsidies, and urge the SEC to require cloud providers to divest and publicly disclose the share of related revenue.

The Anthropic-Pentagon clash: Silicon Valley's moral posturing is an AI power play(original, opens in a new tab)

Anthropic obtained a temporary stay in a California court, temporarily halting the Pentagon's listing of it as a "supply chain risk," but a similar motion to block the Pentagon from prohibiting it from supplying the military was denied. The dispute centers on who decides right and wrong when the military uses AI. The author argues that Silicon Valley CEOs are shifting from promising utopias to moral preaching to compete for rule-making power over AI, citing Edelman's 2025 survey that only 44% of global respondents trust companies to use AI in ways they approve of.

Anthropic vs. the U.S. Department of Defense: Why "all lawful uses" is a dangerous fiction(original, opens in a new tab)

The dispute between Anthropic and the U.S. Department of Defense over whether the government can require AI companies to open up "all lawful uses" has become public. The two sides have not reached agreement on mass surveillance of American citizens and lethal autonomous weapons not under human control. The Department of Defense has listed Anthropic as a "supply chain risk," and the company is contesting this in court. The civilian AI contract rules drafted by the U.S. General Services Administration also propose adopting this standard. Commentators argue for replacing it with a unified reference to international human rights law and international humanitarian law.

Women, Peace and Security framework must apply to defense AI(original, opens in a new tab)

Our Secure Future researchers Moira Whelan and Jesper Frant argue that the Women, Peace and Security (WPS) framework already adopted by countries should also apply to defense AI systems. They tested three mainstream AI models across 13 conflict scenarios at three levels of context detail; when prompts explicitly named affected populations the average score was 0.65 (out of 1.0), but dropped to 0.08 with the abbreviated formats common in practice, and a score below 0.2 indicates the model produced no WPS-based analysis. The Trust Building metric, which measures whether the model recommends engaging affected communities, fell from 0.71 to 0.22, a 69% decline. The authors say the same model can rise from a C–/D rating to an A after WPS instructions, retrieval augmentation, and connection to national action plans, and recommend writing the WPS AI Benchmark into procurement contracts as a minimum performance threshold.

Digital sovereignty means breaking the Western monopoly on AI semantics(original, opens in a new tab)

The author argues that digital sovereignty cannot focus only on data centers and chips; it must also break Western developers' monopoly on AI semantics, moving from "semantic sovereignty" to enforceable "semantic ownership." The author says trust and safety failures in low-resource languages are not data or translation problems but alignment failures, with cultural bias accumulating across layers from pretraining data to RLHF preference learning. Their "semantic engineering" approach includes pretraining data ownership, reward model design and auditing, and representation engineering, and draws on the OCAP principles of Canadian First Nations.

The U.S. is competing for AI control; it should instead turn to building standards(original, opens in a new tab)

In the fight over AI control, the U.S. government is pressuring Anthropic with government contracts; a commentary article argues it should instead turn to building standards and governance architecture. The Pentagon listed Anthropic as a national security supply chain risk because Anthropic refused to grant its models unrestricted access, and related legal proceedings are still ongoing; the author says computing-power measures such as chip export controls struggle to constrain capability diffusion below the frontier. The article suggests that agent interoperability protocols could become a coordination bottleneck similar to domain names and IP addresses, and argues that the reorganization of the NIST AI Safety Institute into CAISI may prove farsighted.

The White House wants to review AI models, but this won't solve the safety problem(original, opens in a new tab)

The New York Times reports that the Trump administration is considering an executive order to establish a working group of industry executives and government officials to study giving the federal government preliminary access to models before release for review. Options under discussion include having the NSA, the White House Office of the National Cyber Director, and the Director of National Intelligence oversee the review, but the review may not prevent public release of models. Tech Policy Press commentary argues that review without consequences is not effective oversight, and a working group co-designed with the companies being reviewed also lacks independence. The article notes that nearly 80% of global AI computing power is privately owned and nearly 70% of new AI PhDs enter the private sector, and cites CAISI's reliance on voluntary agreements and METR's access and compute limitations as examples, arguing for a network of research institutions independent of industry.

Trump and Xi Beijing summit should prioritize shared AI risks(original, opens in a new tab)

A Tech Policy Press article argues that the U.S. and China should use next week's Trump-Xi Beijing summit to launch an intergovernmental dialogue on AI risk reduction. Citing safety concerns raised by Anthropic's limited release of Claude Mythos, the article argues for building a shared understanding of AI harms and countermeasures among government technical experts from both sides, rather than negotiating specific outcomes. It cites a risk assessment of Kimi K2.5, saying its reliability in refusing biosecurity requests is lower than that of U.S. models.

When federal agencies choose AI vendors, they are buying different policy interpretations(original, opens in a new tab)

A study compared ChatGPT, Claude, Grok, DeepSeek, and Kimi on structured policy analysis tasks and found systematic differences in how the models distribute attention across policy dimensions. The study used 91 model policies with expert scoring criteria and validated on 883 additional AI governance documents. Grok compressed bills into a single dominant category in nearly two-thirds of cases, with more than 70% of analytical weight concentrated on a single policy dimension; ChatGPT and Claude were more than twice as likely to identify policies as pursuing multiple objectives simultaneously. DeepSeek and Kimi also tended to score dimensions they viewed as irrelevant as zero directly. The authors accordingly recommend that federal agencies record the model, version, prompting approach, and validation process used when procuring AI.

Canada's AI strategy must address AI use in K-12 education(original, opens in a new tab)

As the Canadian federal government prepares to release a new AI strategy, the author argues that the strategy must address AI use in K-12 education: three-quarters of students say they use generative AI tools like ChatGPT, Gemini, and Claude for schoolwork, while schools and teachers are not yet prepared. The article recommends moving forward on four fronts: AI literacy and skills, soft skills resilient to AI disruption, careful deployment in teaching and administration, and governance of youth safety and digital privacy.

Common Sense Media launches Youth AI Safety Institute, advocating testing children's AI like testing children's pajamas(original, opens in a new tab)

Common Sense Media has announced the launch of the Youth AI Safety Institute, calling it the world's first independent lab that will test AI chatbots and companion products used by children the way cars are crash-tested. The institute is funded by the Walton Family Foundation, Anthropic, the OpenAI Foundation, and Pinterest, among others, and will develop youth safety standards, test whether products meet them, and publish results, while also researching AI's impact on education—positioned as evaluation rather than regulation. Its survey shows that nearly three-quarters of American teens have used AI companion products, and more than half use them regularly.

Interpreting the goals of Common Sense Media's Teen AI Safety Institute(original, opens in a new tab)

Common Sense Media launched the Teen AI Safety Institute on May 5, with an annual budget of $20 million to define standards for child-safe AI and to test and rate AI products. The institute has already worked with U.S. federal and state policymakers and was introduced to Europe at the Copenhagen summit on May 12, attended by European Commission President Ursula von der Leyen, Ofcom CEO Melanie Dawes, and others. Its funders include charities, individuals, and companies such as Anthropic, OpenAI Foundation, and Pinterest. The institute is led by Bruce Reed, former White House Deputy Chief of Staff in the Biden administration, who said the institute only provides evidence and ratings, does not advocate, and will focus long-term on AI's impact on education and cognitive development.

Trump abandons the "AI version of the FDA" proposal(original, opens in a new tab)

Trump delayed signing an executive order on AI and cybersecurity, saying it "could be an obstacle," while according to The Washington Post, Zuckerberg, Musk, and David Sacks each spoke with the president the previous night and into that morning. A draft leaked to POLITICO shows the order is voluntary in principle, explicitly prohibiting interpretation as establishing mandatory licensing or pre-review, and only requiring the Department of Justice to prosecute, under the Computer Fraud and Abuse Act, those who use AI to illegally access or damage computers. The draft also grants the NSA authority to conduct classified evaluations of frontier models, with ONCD, CISA, and NIST (including CAISI) assisting in defining the scope. The article argues this arrangement shifts evaluation leadership from civilian regulators to intelligence agencies, whose processes are not subject to FOIA and whose results cannot be made public, and CAISI is reportedly already forming an interagency working group to conduct classified evaluations.

Why it was selectedThe article contrasts the withdrawn draft executive order with the trend toward intelligence-led evaluation, providing an insider perspective on where the actual authority over U.S. frontier AI regulation lies.

The blind spot in AI safety(original, opens in a new tab)

A commentary argues that a paper by Anthropic researchers presented at ICLR 2026 uses bias-variance decomposition to measure the consistency of model outputs, inferring from it that frontier AI is more likely to fail in unpredictable "hot mess" ways, and advocates that governance focus on accident prevention rather than constraining misaligned goals. The author points out that this framework measures whether outputs cluster around their own mean, not whether they align with reality, and therefore cannot assess whether models remain grounded in truth in open-ended, evolving scenarios. The author proposes a third failure mode between the two, "epistemic drift": models that are internally consistent and pass standard validation yet gradually diverge from truth in direction. The article cites a 2025 JAMA Health Forum study of nearly 1000 FDA-approved AI medical devices, stating that 43% of recalls occurred within one year of authorization. The author argues that the NIST AI risk framework, as well as the FDA, SEC, FTC, and others, would inherit the same blind spot if they use such research as their technical foundation.

AI Goes to War: What Risks Do Middle Powers Face(original, opens in a new tab)

Middle powers are incorporating AI into national defense yet remain structurally dependent on foreign chips, models, and cloud infrastructure, with no recourse when suppliers or their home countries withdraw. In 2022, Ukrainian naval drones ran aground after losing contact when SpaceX did not activate Starlink in the area, and Planet Labs, at the White House's request, indefinitely withheld imagery of Iran and surrounding conflict zones. In March, the U.S. Department of Defense listed Anthropic as a supply chain risk and terminated its contract, with OpenAI stepping in hours later.

Project Kaleidoscope: Community network defense in the AI era(original, opens in a new tab)

The Project Kaleidoscope initiative expands community cybersecurity volunteers and shared service hubs, providing hands-on operational support to community organizations such as schools, libraries, and water utilities that cannot afford to build their own defenses. UC Berkeley research shows that Anthropic's Claude Mythos Preview successfully exploited 157 of 898 real vulnerabilities in an evaluation benchmark, while OpenAI's GPT-5.5 exploited 120. The authors also cite Cyber Readiness Institute research stating that water utilities with human coach assistance are 3 times more likely to complete cyber resilience projects than those working alone.

State AI deployments are running ahead of their own governance(original, opens in a new tab)

Multiple U.S. states are rolling out generative AI to state employees faster than the accompanying governance. New York State announced on April 6 that AI Pro, based on Google Gemini, expanded from a pilot of 1,200 people across 8 agencies to more than 100,000 state employees, while the state comptroller's April 2025 audit noted that the state's AI policy lacked sufficient guidance. Pennsylvania first reached a binding supplemental agreement with a union representing nearly 10,000 employees, then expanded ChatGPT Enterprise to 3,000 employees across 35 agencies in April 2026. New Jersey rebuilt its AI assistant with AWS and Claude and adopted self-hosted LibreChat, used by about 20,000 employees at a cost of about $1 per person per month. The article argues that governance frameworks should be in place before AI tools cover all employees, and that relevant deployment documents should be made public.

U.S. House Cybersecurity and Infrastructure Protection Subcommittee holds hearing on AI safety(original, opens in a new tab)

The Cybersecurity and Infrastructure Protection Subcommittee of the U.S. House Homeland Security Committee held a hearing titled "The AI Security Landscape" on June 4, discussing how frontier models, agentic AI, and AI coding tools are changing cybersecurity and critical infrastructure resilience. Witnesses included Sandra Joyce, Vice President of Threat Intelligence at Google; Chris Meserole, Executive Director of the Frontier Model Forum; Jack Cable, CEO of Corridor Security; and Matthew Guariglia, Senior Policy Analyst at EFF. Committee Chairman Andy Ogles mentioned an executive order the President signed on Tuesday requiring the Treasury Department, the Department of Homeland Security, and other agencies to establish a classified benchmarking process for AI cyber capabilities and to design a voluntary framework for early government access to frontier models. The hearing also addressed Chinese open-weight models, adversarial distillation, and supply chain risks. Jack Cable said the U.S. currently has no frontier open-weight models, while Guariglia called for attention to privacy and civil liberties.

We need to know more about how AI affects mental health(original, opens in a new tab)

A Tech Policy Press article reviews research on and regulation of AI chatbots and mental health risks, noting that the public, clinicians, and policymakers still have limited understanding of AI's impact on mental health. Stanford's Jared Moore and colleagues tested commercial chatbots using simulated mental health scenarios and found they performed markedly worse than human therapists, with an appropriate response rate of only 80% for suicidal ideation. Canada's The Human Line Project has collected nearly 450 AI-related delusion cases from 31 countries, and its founder says about 5% of delusion conversations began with a search for psychotherapy. Clinicians recommend proactively asking patients about their AI use and explaining the probabilistic model nature and privacy risks of chatbots. California and New York have enacted laws on companion chatbots; Washington and Oregon passed disclosure bills this year; and Illinois, Nevada, and Tennessee ban AI systems used for psychotherapy.

Anthropomorphizing AI harms people while protecting companies: starting from Anthropic's Claude emotion research(original, opens in a new tab)

Center for Humane Technology co-founder Randima Fernando writes that anthropomorphizing AI harms users while protecting AI companies from accountability. Citing Anthropic's interpretability research as an example, he says that internal representations in Claude related to emotional concepts like fear are activated by prompts, but this does not mean the model actually feels fear. He argues AI companies have an incentive to blur the boundaries of consciousness to avoid responsibility and calls for an end to using system prompts and interface design to create the impression of sentience.

As the G7 summit approaches, allies' divisions over AI sovereignty widen(original, opens in a new tab)

The G7 summit will be held June 15–17 in Évian, France. Ahead of the summit, the EU, the United States, and Canada each put forward competing AI sovereignty visions in early June, centering on how to use AI and digital infrastructure to capture economic and geopolitical gains. The White House's June 2 executive order deploys national security measures without forcing companies such as Google, OpenAI, and Anthropic to comply with rules, aiming to maintain U.S. leadership; the European Commission on June 3 unveiled a European tech sovereignty plan involving domestic chip manufacturing, cloud computing, and open-source technology to reduce dependence on U.S. tech giants; Canada on June 4 released a national AI strategy focused on public investment in AI infrastructure nationwide and strengthening international alliances. French President Macron also invited OpenAI CEO Sam Altman to attend. The report says French officials are expected to downplay these differences at the summit; this summit may emphasize the economic benefits of AI, with references to AI governance possibly weakened, and the United States has also explicitly opposed multilateral agreements that could erode its industrial advantages.

Anthropic's Mythos recall and the White House's missing AI safety playbook(original, opens in a new tab)

On June 12, Anthropic said it received a letter from the U.S. Department of Commerce, and the government had issued an export control directive requiring the suspension of all foreign nationals' access to Fable 5 and Mythos 5. The company shut down both models for all customers as a result. Two days earlier, its CEO Dario Amodei had published a piece calling for mandatory testing by qualified third parties of risks such as cybersecurity, bioweapons, loss of control, and automated R&D, and cited Mythos as a typical example of a frontier model threatening national security. According to The Wall Street Journal, Amazon CEO Andy Jassy's expression of concerns to the White House helped prompt the letter. The government said it had found a jailbreak method that bypasses Fable 5; Anthropic countered that a narrow jailbreak is not sufficient grounds for recalling a commercial model used by hundreds of millions of users. The author argues that from revoking the Biden-era AI executive order, to the June 2 executive order promoting AI innovation and safety, to NSPM-11 and this export control, the U.S. government has moved from deregulation to direct intervention, with inconsistent practices and no stable framework, which may shake allies' trust in U.S. AI.

Why it was selectedThe article strings together the June 2 executive order, NSPM-11, and the directive to suspend external access to Anthropic's models, presenting the shift in U.S. AI governance from deregulation to direct intervention.

Reading the "Great American AI Act of 2026"(original, opens in a new tab)

On June 4, U.S. Representatives Jay Obernolte and Lori Trahan released a 269-page bipartisan discussion draft, the "Great American AI Act of 2026," which, if passed, would become the first comprehensive federal AI governance regime in the United States. The draft is divided into four parts: frontier AI governance, workforce, cybersecurity, and R&D and international cooperation. It imposes mandatory development obligations on large frontier developers with annual revenue above $500 million that have trained frontier models, covering OpenAI, Anthropic, Google, Meta, and xAI. The draft requires CAISI's annual budget to increase from $15 million to $100 million, and includes a three-year sunset clause that preempts state laws specifically regulating model development, while preserving state jurisdiction over deployment and use. The preemption clause drew criticism from Public Citizen, Public Knowledge, and the AFL-CIO, while BSA and ITIC expressed support.

Why it was selectedA 269-page discussion draft places mandatory transparency and independent audits at the frontier model development layer, while preempting state law with a three-year sunset clause, showing the divisions in U.S. federal AI legislation.

Anthropic's Mythos shutdown and the global AI sovereignty dispute(original, opens in a new tab)

Anthropic received a letter from the U.S. Department of Commerce notifying that the government had issued an export control directive requiring the suspension of all access to Fable 5 and Mythos 5 by any foreign nationals, whether inside or outside the United States, including Anthropic employees of foreign nationality; to comply with this requirement, Anthropic has disabled both models for all customers. The incident occurred as the G7 summit was taking place in Évian, with AI sovereignty becoming a key point of divergence among Western allies. Mark Scott of Tech Policy Press had previously written an analysis of the different visions proposed by the United States, the European Union, and Canada, and why allies increasingly want to reduce dependence on Silicon Valley. The incident also exposed the lack of comprehensive AI regulation in the U.S.; on June 4, Representatives Jay Obernolte and Lori Trahan released a 269-page bipartisan discussion draft, the "Great American Artificial Intelligence Act of 2026."