AI News: Tech Giants Face New York City Council Over Safety Rules

Nic Reeve14:00 05.10.20265 min read

Google, Meta, OpenAI and Anthropic representatives are appearing before New York City lawmakers on October 5, 2026, for a rare hearing focused on artificial-intelligence safety, accountability and possible local regulations. The proceeding is the latest development in the city’s push to question major technology companies directly, making it a major moment in AI news.

Why is the New York City Council holding the hearing?

The Council is examining whether fast-moving AI products create risks that existing safeguards do not adequately address. The October 5 hearing brings all 51 Council members together and is being conducted by the Committee of the Whole, allowing lawmakers to question companies in a single public session.

Event date: October 5, 2026.
Venue: New York City Council hearing in New York City.
Participants: Representatives of Anthropic, OpenAI, Google and Meta.
Format: Public testimony under oath.
Scope: AI safety risks, consumer protection and possible legislative safeguards.

According to the New York City Council, the hearing is intended to examine “the potential dangers of AI technology” and gather company input on legislative responses. Council Speaker Julie Menin announced the session on September 28, after earlier letters sought testimony from leading AI companies.

Which technology companies agreed to testify?

Anthropic, OpenAI, Google and Meta agreed to send representatives. Meta committed a senior executive before the other three companies confirmed participation after the Council warned that subpoenas could be used to compel testimony.

Anthropic: The company behind the Claude family of AI models.
OpenAI: The creator of ChatGPT and other generative-AI systems.
Google: The Alphabet company developing Gemini and related AI services.
Meta: The owner of Facebook, Instagram and WhatsApp, which is developing its own AI products.

According to Council announcements reported on September 28, OpenAI and Google agreed to appear on the Sunday before the hearing. Reporting published by CBS News on October 4 identified executives from the four companies as scheduled witnesses.

Why did subpoenas become part of the dispute?

The companies did not all accept the invitations at the same time. Speaker Menin initially requested participation from senior leaders, then warned that the Council could use its subpoena authority. OpenAI, Google and Anthropic later agreed to send representatives, while Meta had already committed to attend.

September 16: The Council announced plans for an October 5 hearing and requested participation from major AI leaders.
September 25: The Council unveiled proposed legislative measures and reiterated that attendance was expected.
September 28: The Council said OpenAI, Google, Anthropic and Meta would testify under oath.
October 5: The public hearing is scheduled to take place.

According to PoliticsNY, OpenAI, Google and Anthropic agreed to appear after lawmakers threatened subpoenas. The New York City Council separately announced that Elon Musk’s AI company, SpaceXAI, faced a subpoena after it did not confirm participation.

What will lawmakers ask the companies?

The Council has not published a complete question list, but its announcements identify several areas of concern. Lawmakers are expected to focus on how companies test models, respond to harmful outputs and protect New Yorkers from misuse.

How companies identify and reduce safety failures before releasing AI systems.
What protections exist for children and other vulnerable users.
How platforms respond to self-harm content, fraud, impersonation and manipulated media.
Whether companies provide enough transparency about model limits and incidents.
What city governments can regulate without conflicting with state or federal law.

The hearing follows reports of tens of thousands of AI-related safety incidents cited in recent coverage, although the public material gathered for the hearing does not establish a single official incident count. The Council’s stated purpose is broader: to assess risks and consider safeguards for residents.

What legislation is New York City considering?

The Council has linked the hearing to a package of proposals aimed at reducing potential harm from AI systems. The measures remain proposals, not enacted city law, and the public hearing is intended to inform debate over their contents and enforceability.

Rules addressing the safety and accountability of AI developers.
Possible requirements for companies to disclose risks or testing practices.
Consumer protections for people affected by automated decisions or generated content.
Local responses to harmful or deceptive uses of generative AI.

According to the Council’s September 25 announcement, the proposed bills were scheduled for discussion at the October 5 Committee of the Whole hearing. The Council has also questioned whether protections at the state and federal levels are sufficient for New Yorkers.

What makes the hearing unusual?

The proceeding combines an unusually large group of lawmakers with testimony under oath from several leading AI companies. According to the Council, it is the first time major AI firms have been set to provide public testimony under oath before the body following recent incident reports.

51 Council members: The full City Council is expected to participate.
Four companies: Anthropic, OpenAI, Google and Meta have confirmed attendance.
Under oath: The format gives testimony a more formal legal setting than a private briefing.
Local focus: Questions will center on risks experienced by people in New York City.

Fortune reported on September 25 that the hearing would be a Committee of the Whole, a format the Council had not used since 2022. The same report said several requested chief executives were unlikely to appear personally, meaning company representatives may answer questions instead.

What happens after the testimony?

The Council can use the testimony to revise proposed bills, request further records or pursue additional hearings. The hearing itself does not automatically create new rules. Any measure would still need to move through the Council’s legislative process and comply with higher-level law.

Lawmakers may compare company safety policies and public commitments.
The Council may seek documents or additional testimony.
Proposed legislation could be amended after the hearing.
Companies may face continuing scrutiny over AI-related incidents in New York.
The subpoena dispute could shape future dealings between City Hall and technology firms.

The hearing also places pressure on companies to explain their safeguards in a public forum. Their answers may influence how New York City approaches AI oversight while broader debates continue in Albany and Washington.

Sources

AI & Tech

AInews: How Sovereign Open-Weight Models Became the New Tech Frontier

On August 4, 2026, a new U.S. federal AI governance framework and a wave of recent open-weight model releases showed how sovereign, open-weight AI has moved to the technology frontier, a shift widely tracked under the banner of AInews in policy and developer circles. Why is sovereign, open-weight AI suddenly at the frontier? Governments and firms now treat control over model weights and infrastructure as strategic, responding to security, cost and IP concerns while exploiting a flood of large open-weight releases from Asia, Europe and the United States. The frontier has moved fast in mid-2026. Several developments converged in weeks, not years: On August 4, 2026 , the White House briefed a federal AI governance framework that exempts open-weight models from security review , while subjecting closed frontier systems to a 30‑day evaluation window. According to a July 21, 2026 analysis of Moonshot AI’s Kimi K3, open-weight models publish trained parameters for anyone to download and run, even if code and training data remain closed. A July 20, 2026 European policy paper defined “sovereign capability” as intellectual and operational control, infrastructure independence from non‑EU providers, and verifiable reproducibility of training and safety methods. The Sovereign AI Index published on August 18, 2026 reported that most national model projects rely on fine‑tuning foreign open-weight bases on local data to embed language and culture at lower cost. An August 28, 2026 business analysis described a “third way” for firms: building domain models on their own rights‑cleared data to avoid dependence on external providers while protecting proprietary information. These strands combine into a clear frontier: open-weight models as the common substrate, sovereign infrastructure and data as the differentiator. What recent open-weight releases are shaping this frontier? Between early July and early August 2026, model labs and telecoms released multi‑hundred‑billion‑parameter open-weight systems, giving governments and enterprises new options to self‑host high‑end language models. Key releases create the technical base for sovereign deployments: On July 27, 2026 , Moonshot AI’s Kimi K3 mixture‑of‑experts model went live with 2.8 trillion total parameters and 104 billion active per token , with full weights downloadable on Hugging Face. A July 21, 2026 report called Kimi K3 the largest open-weight model built to date, noting that weights would be published by July 27 so governments or firms with sufficient hardware could run it “without paying a single cent per token.” An open-source release tracker on July 20, 2026 listed nine notable models whose weights were downloadable by July 27, including: Hy3 – 295 billion parameters, 21 billion active, with a permissive open-weight license. Inkling – 975 billion parameters, 41 billion active, also permissive open-weight. Solar Open 2 – 250 billion parameters, 15 billion active, under a custom open license. Laguna S 2.1 – 118 billion parameters, 8 billion active, using the OpenMDW‑1.1 license. A July 31, 2026 release summary counted 11 open-weight models shipped in July 2026 , led by Kimi K3 and including compact and realtime systems such as MOSS‑VL‑Realtime and Laguna S 2.1. An August 19, 2026 benchmark showed GLM‑5.3’s weights will be publicly released under an MIT license after safety audits, extending the open-weight pool with another high‑performing system. These releases kept capabilities near the frontier while lowering the entry barrier for any actor able to procure compute. How are governments using open-weight models to pursue AI sovereignty? Governments are backing domestic foundation model projects and regulatory carve‑outs that favour self‑hosted or locally built systems, viewing open weights as a route to national control over critical AI infrastructure. Recent moves show how policy and engineering align: The South Korean Ministry of Science and ICT is running a Sovereign AI Foundation Model project , described on August 1, 2026 as a government‑backed competition to build large models using “entirely domestic South Korean technology and data.” No frozen weights from foreign models are allowed. The same report noted that SK Telecom released A.X K2 , a 688‑billion‑parameter open-weight model, on July 29, 2026, followed two days later by LG AI Research’s K‑EXAONE 2.0 , a 750‑billion‑parameter model. The European Futurium platform on July 20, 2026 laid out three criteria for classifying a system as sovereign and open in Europe: IP, architecture and governance under European jurisdiction. Compilation, fine‑tuning and deployment on European, multi‑provider infrastructure, without hard dependencies on non‑EU APIs. Transparent training, dataset lineage and safety alignment open to independent audit. The Sovereign AI Index published on August 18, 2026 observed that most national foundation model efforts fine‑tune foreign open-weight bases on local data to encode national languages, cultural context and sector knowledge at a fraction of full training cost. An August 20, 2026 insight from the same index reported that as of mid‑2026, Meta’s Llama remains the most used base model for tracked sovereign AI projects, with France’s Mistral and Google’s Gemma tied for second. Policy and procurement choices are turning open-weight availability into a lever of geopolitical and industrial strategy. How are companies building their own sovereign AI stacks? Enterprises are combining downloadable weights with proprietary data and self‑hosted infrastructure to reduce reliance on external providers, following a “third way” between public APIs and full in‑house training. Recent reporting highlights this corporate approach: An August 28, 2026 analysis of Thomson Reuters’ strategy described how firms with “unique, rights‑cleared data” can build specialized AI models to protect IP while cutting dependence on third‑party providers. The same piece argued that this method brings AI sovereignty to the firm level, not just the nation, by keeping both the model and data inside controlled environments. A July 24, 2026 technical guide introduced the term “weight sovereignty” as legal and operational control over a model artifact. Under this model, organizations can download, inspect, fine‑tune, quantize and run open-weight systems on hardware they own, and switch models later without rewriting their products. The guide explained that open weights alone do not provide “context sovereignty,” which refers to controlling the institutional knowledge a model accesses via embeddings and retrieval. To combine both forms of control, the guide recommended running an open-weight model endpoint entirely inside a firm’s own perimeter and keeping the knowledge layer that feeds it within the same boundary. For large enterprises, the attraction is clear. They get frontier performance while keeping strategic data and operations in‑house. What does the U.S. federal framework mean for open-weight AI? The U.S. framework outlined in early August 2026 creates lighter regulatory friction for open-weight models, signaling that self‑hosted, downloadable systems will face fewer federal hurdles than closed frontier services. Key elements highlighted in an August 11, 2026 policy brief include: The White House framework exempts open-weight models from federal security review, even when they reach frontier‑level capabilities. Closed frontier models face a 30‑day evaluation window under federal oversight before deployment or major updates. The brief argued that this asymmetry “may have more practical impact than anything else” in the document, because it makes self‑hosted AI based on downloadable weights the lower‑friction path for many organizations. The driver behind this structure is the assumption that actors with operational control over their own deployments can manage risks locally, reducing the need for central pre‑authorization. The framework does not remove safety obligations, but it places more responsibility on deployers while granting them more freedom in system choice and architecture. Where does India’s new sovereign AI stack fit into the trend? India’s technology sector is building its own sovereign AI layers on top of open-weight models, aiming to serve domestic enterprises and public institutions with locally governed tools and agents. A late‑August 2026 report described the launch of Artha , a sovereign AI stack from Indian firm Gnani: Artha is designed as an end‑to‑end stack for Indian enterprises and public institutions, incorporating models and orchestration tools. Two components, Evon v3.3 and Plexus, form part of the stack, supporting both foundational capabilities and downstream agents. The approach mirrors sovereign AI agendas in other countries but targets sectoral deployments such as contact centers, financial services and government applications. Artha illustrates how open-weight availability enables regional players to craft localized AI ecosystems under domestic governance. What challenges remain for achieving true technological sovereignty with open weights? Open-weight systems lower barriers to self‑hosting, but analysts warn they are not enough by themselves to deliver full technological sovereignty, which also depends on data, infrastructure, and transparent methods. Recent commentary points to several obstacles: A July 21, 2026 article argued that open-weight systems “alone do not solve the issue of technological sovereignty,” because they release parameters but not necessarily training data, code or full methodology. The same piece cited the Open Source Initiative’s 2025 definition that genuinely open-source models must publish code and data, not just weights. The Sovereign AI Index found that three‑fifths of disclosed foreign bases used in national projects are American, with Meta’s Llama as the most common base, which raises dependency questions. European policy thinkers stressed that without infrastructure independence from non‑EU cloud and API providers, countries may gain model access but not operational control. Analysts also warned that opaque dataset lineage and safety alignment can undermine auditability, even when weights are downloadable. Open weights are a powerful tool. They are not a complete solution. What happens next in the race for sovereign, open-weight AI? The next phase is likely to feature larger domestic foundation projects, more permissive open-weight licenses, and firm‑level strategies that treat AI infrastructure as a core asset rather than a rented service. Several trends are already visible: South Korea’s elimination‑style Sovereign AI competition will test whether fully domestic technology stacks can match performance built on foreign open weights. European initiatives will try to move from dependency on American bases such as Llama to home‑grown architectures governed under EU law. Enterprises following the Thomson Reuters model are likely to expand internal AI teams and infrastructure budgets to keep both weights and data in‑house. Labs promising open-weight releases, such as the MIT‑licensed GLM‑5.3 after its safety review, will broaden the technical menu for sovereign projects. Policy frameworks that distinguish between open-weight and closed frontier systems, as in the U.S. brief, may be replicated in other jurisdictions. The frontier is no longer defined only by raw model scale. It is defined by who controls the weights, the infrastructure and the knowledge that models read.

Nic Reeve·14:00 08.09.2026

AI & Tech

Angie Nixon’s ICE ‘slave catcher’ remarks ignite Florida Senate race furor

Florida Democratic Senate candidate Angie Nixon is facing intense criticism after a conservative outlet highlighted past comments in which she labeled U.S. Immigration and Customs Enforcement (ICE) officers “modern‑day slave catchers” and called federal immigration enforcement “state‑sanctioned violence.” The remarks have thrust Nixon’s abolitionist stance on ICE to the center of Florida’s high‑stakes 2026 Senate race. Background: a democratic socialist challenger in a deep‑red state Nixon, a state representative from Jacksonville and member of the Democratic Socialists of America, shocked political observers in August by winning the Democratic U.S. Senate primary in Republican‑dominated Florida. Her platform includes ending “mass incarceration,” “demilitarizing policing,” abolishing ICE, halting deportations and creating a pathway to citizenship for undocumented immigrants. According to reporting by the Washington Times , Nixon has used starkly confrontational language about immigration enforcement throughout her political rise, portraying ICE agents as “THUGS” and “kidnappers” and likening detention centers to “modern day concentration camps.” A separate compilation of her statements by Breitbart News emphasized her description of ICE officers as “modern‑day slave catchers” and her characterization of enforcement actions as “state‑sanctioned violence.” Nixon’s criticism of ICE and detention facilities Nixon’s rhetoric predates her Senate bid and is rooted in opposition to a major expansion of immigration detention in Florida. In 2025, she drew attention when she described planned ICE holding facilities in the Everglades and at Camp Blanding in Clay County as “modern day concentration camps” that echo the history of southern slavery. She argued that the proposed facilities — including a large detention complex along Alligator Alley — would “disappear” migrants and reprise “the worst chapters in our history.” In interviews, Nixon has tied her criticism to the racial history of the region, citing stories of enslaved children being fed to alligators as a symbol of past brutality and warning that contemporary detention policies reproduce patterns of dehumanization. She has also described current immigrant detention centers more broadly as “makeshift concentration camps” fueled by xenophobia. From ‘weaponized paramilitary force’ to ‘modern‑day slave catchers’ Nixon has expanded her critique of ICE beyond detention to the agency’s broader role in immigration enforcement. In recent media appearances, she claimed Republicans are “literally trying to kill” Black Americans and argued that ICE has been transformed into a “weaponized paramilitary force” designed to “terrorize us.” She contends that, after being created in the post‑9/11 reorganization of homeland security, ICE initially targeted Muslims and has since “morphed into this agency that goes after immigrants, that demonizes immigrants.” Her remarks highlighted by the Washington Times and Breitbart push this argument even further. Nixon called ICE officers “modern‑day slave catchers,” explicitly linking immigration enforcement to antebellum fugitive slave patrols. She has argued that the agency’s operations amount to “immigrant abduction” and warned that “Florida’s extremist Republican leaders have empowered ICE to conduct its largest immigrant abduction operations here,” framing the issue as both a humanitarian and economic threat to the state. Concerns about racial profiling and deadly encounters Nixon’s criticism is not limited to policy; she has repeatedly suggested that ICE agents racially profile and pose a lethal risk to Black communities. In a podcast interview cited by Mediaite, she said, “We’re Black, [ICE is] gonna profile us, too,” adding that agents “can’t tell the difference between an African‑American…or someone from Jamaica or Nigeria,” and warning that they “are going to escalate things and they are going to shoot and kill us.” She has referenced several incidents in which ICE officers shot individuals during enforcement operations, including a deadly encounter in Houston and another in Maine, as evidence that what she calls “state‑sanctioned violence” is already occurring. In public statements, Nixon argues that the cumulative effect of ICE actions in communities across the country is “fear, chaos, and death.” Push to end local ICE cooperation Nixon’s rhetoric underpins concrete policy demands. Earlier in August, she joined immigration advocates at a Miami news conference to urge the city commission to terminate its 287(g) agreement with ICE, which allows local law enforcement to collaborate with federal agents in identifying and detaining undocumented immigrants. She has described “mass incarceration and mass deportation” as “moral failures” and reiterated her commitment to abolishing ICE altogether. Her campaign website frames large‑scale enforcement operations as “immigrant abduction” and warns that Florida’s partnership with ICE could have severe humanitarian and economic consequences, particularly in sectors reliant on immigrant labor. ICE and conservative response: ‘equal opportunity deporter’ Nixon’s comparison of ICE agents to slave catchers has sparked a sharp backlash from conservatives and current and former immigration officials. Former acting ICE director Tom Homan, responding to her claims in a televised interview, called her allegation that ICE racially profiles Black people “ridiculous” and insisted, “We don’t arrest people based on the color of their skin.” Homan described himself as an “equal opportunity deporter,” arguing that ICE targets individuals based on immigration status and criminal records, not race. Conservative media have portrayed Nixon’s comments as extreme and anti‑law‑enforcement. Fox News highlighted her depiction of ICE as a “weaponized paramilitary force,” framing it as part of a broader narrative that Republicans are “literally trying to kill” Black Americans. Breitbart and the Washington Times emphasized her “modern‑day slave catchers” remark and her calls to abolish ICE, presenting them as indicative of a radical agenda out of step with Florida’s political mainstream. Historical analogies fuel polarizing immigration debate Nixon’s comparison of ICE agents to slave catchers taps into a broader debate among activists and scholars about the historical roots of contemporary enforcement. Some opinion writers have argued that 19th‑century slave catchers and modern ICE agents share “militarized tactics” and a focus on capturing targeted populations for detention and removal, drawing parallels between the Fugitive Slave Law of 1850 and post‑2002 immigration enforcement practices. Nixon’s language echoes these critiques, casting ICE’s presence in communities — including the use of unmarked vehicles and surprise raids — as reminiscent of historical “kidnappings.” Supporters of Nixon’s approach say such analogies are necessary to highlight what they see as systemic abuses and human rights concerns in immigration policy. Critics argue that equating federal officers with slave catchers is inflammatory and dismisses the agency’s stated mission of targeting serious offenders and enforcing immigration law. The clash illustrates how historical memory and racial justice rhetoric are increasingly shaping the political struggle over border security and deportation policy in the 2026 election cycle.

Nic Reeve·14:30 23.08.2026

AI & Tech

Anthropic Gives Claude Cowork Shared Memory with Chat for Persistent Context

Anthropic is rolling out a major upgrade to its AI assistant, giving Claude Cowork the ability to seamlessly reuse information it learns in regular chat. The company has merged the memory systems behind Claude’s chat interface and its Cowork desktop agent, so details you share in one surface can now automatically be used in the other. One Shared Memory Across Chat and Cowork Previously, Claude’s long‑term memory was largely confined to chat sessions and was synthesized periodically, meaning it could take up to a day before information carried over into new conversations. Cowork, which runs complex, multistep jobs on a user’s desktop or in the cloud, relied on its own background memory file and prompt stitching to simulate continuity. With the August 25 update, Anthropic has combined these mechanisms into a single, shared memory system that serves both chat and Cowork. Anthropic describes the change simply: the same memory now powers both Claude chat and Cowork. When users hand a task to Cowork—such as drafting reports, updating spreadsheets, or coordinating project documents—the context Claude has accumulated over months of chats is immediately available. Likewise, any new facts or preferences learned during Cowork runs are written back into the shared memory and become available in subsequent chat sessions. Real‑Time Memory, Not Just End‑of‑Chat Summaries Another important shift is how Claude updates memory. Instead of waiting to summarize an entire conversation once it ends, Claude now adds topics to memory in real time as users chat. This means that if a user mentions that a project deadline moved to September, that update can be reflected in memory almost immediately and show up in the very next interaction—whether in chat or Cowork—without requiring a manual “remember this” command. Anthropic’s support materials explain that when Cowork runs in the cloud, what Claude remembers from previous chats is automatically available, and what emerges during Cowork tasks feeds back into chat memory. Behind the scenes, each Cowork prompt is assembled from the user’s immediate request, their global instructions, and a relevant slice of the shared memory, allowing the AI to behave as if it has persistent awareness of roles, projects, and preferences. What Users Gain: Less Repetition, More Continuity The practical effect for users is that they no longer need to repeatedly brief Claude on who they are, what they are working on, or how they like to work every time they switch between chat and Cowork. Anthropic and independent commentators highlight several common scenarios: Persistent project context: Ongoing details such as quarterly goals, client names, and current project status can be retained across weeks or months and recalled in both chat and Cowork. Stable roles and preferences: If a user identifies themselves as an investment analyst, a teacher, or a particular type of creator, Claude can remember that role and tailor responses accordingly, even when individual chats are short or focused on different tasks. Cross‑device consistency: The shared memory applies across web, desktop, and mobile experiences, so moving from a browser chat to the Cowork desktop agent no longer breaks context. Tech industry observers note that this update positions Claude more directly as an AI “teammate” that can track medium‑ and long‑term workstreams instead of acting purely as a session‑bound chatbot. Transparency and User Control Over Memory The shared memory system arrives alongside a push for greater user control. Anthropic now surfaces everything Claude remembers in a dedicated Topics view within memory settings, where users can inspect, edit, or delete individual entries. Memory is stored as discrete, categorized entries rather than a single opaque summary, making it easier to remove outdated or inaccurate information. Users can also pause memory or reset it entirely if they no longer wish Claude to retain prior context. In addition, Anthropic provides guidance on importing and exporting memory, so the information Claude stores about a user is not locked in and can in principle be backed up or moved. Handling Sensitive Topics Anthropic has emphasized that the system is designed to minimize the capture of highly sensitive information by default. Topics such as health data, beliefs, and other potentially sensitive categories are excluded from memory unless users explicitly opt in via an “Include sensitive topics in memory” setting. For business customers, team or enterprise administrators can centrally control whether memory is enabled at all, and may choose more restrictive policies depending on corporate governance requirements. External reporting indicates that memory generation is turned on by default for free, Pro, and Max plans, while Cowork itself is not available on free accounts. For organizations that want to keep different workstreams separated, Anthropic has indicated that the only way to maintain fully separate memories for chat and Cowork is to use different accounts, since the new system treats them as a single unified space. Availability and Limitations The new shared memory capability began rolling out on August 25, 2026, across Claude’s web, desktop, and mobile experiences, as well as Cowork running in the cloud. Earlier in the year, memory support was limited to chat surfaces, and some third‑party analyses noted that Cowork lacked access to that long‑term context. Anthropic’s latest release notes and help center now explicitly state that memory works across both chat and Cowork when the latter runs in the cloud environment. There are still technical constraints. Cowork’s use of memory depends on cloud execution rather than purely local processing, and incognito or memory‑disabled sessions remain stateless by design. As with other AI systems, Anthropic cautions that Claude’s memory is selective: it prioritizes high‑level preferences and recurring topics rather than storing every detail of every conversation. A Step Toward More Personalized AI Workflows By unifying memory between Claude chat and Cowork, Anthropic is betting that users will value a more personalized and continuous AI experience, particularly for complex, ongoing work. The update reduces friction for individuals juggling multiple projects and gives enterprises a clearer path to building AI‑augmented workflows that persist over time. At the same time, the company is attempting to balance convenience with privacy and security by giving users fine‑grained controls and limiting sensitive data retention by default.

Nic Reeve·16:00 26.08.2026

AI News: Tech Giants Face New York City Council Over Safety Rules

Why is the New York City Council holding the hearing?

Which technology companies agreed to testify?

Why did subpoenas become part of the dispute?

What will lawmakers ask the companies?

What legislation is New York City considering?

What makes the hearing unusual?

What happens after the testimony?

Sources

Related Articles

AInews: How Sovereign Open-Weight Models Became the New Tech Frontier

Angie Nixon’s ICE ‘slave catcher’ remarks ignite Florida Senate race furor

Anthropic Gives Claude Cowork Shared Memory with Chat for Persistent Context