Anthropic's 5% fund and AI bill, Claude tool trips Instagram, Disney hires Character.AI

Why is Anthropic giving 5% of its shares to a Public Benefit Fund?

Anthropic is setting up the Anthropic Public Benefit Fund and, once shareholders approve, will give it 5% of its shares. The board can add 2% more for every 50% rise in the company’s value above a set baseline.

• The fund is a wholly owned Anthropic LLC, not a 501(c)(3) foundation. That keeps it flexible: large grants to nonprofits or direct payments to specific groups of people.
• The company’s example is AI’s effect on jobs. It has already committed $200M to studying and easing it, but writes that “bolder approaches may be needed”.
• Fund shares cannot be used for lobbying or political funding, in the US or abroad.
• Counting the co-founders’ pledge (80% of their stakes) and the employee donation match (about half the staff take part), about 20% of Anthropic’s shares are meant to go to public causes.
• Valuation thresholds and governance rules come “in the coming months”, along with a report on its mission as a public benefit corporation.

Why it matters: It flips the usual mechanism: where tech companies give executives growing stock grants as the valuation rises, Anthropic sends that growing pool to a fund. For now it is a promise with no baseline, governing body or payout rules.

In the background, the WSJ is writing about a private trust that “hangs over Anthropic’s IPO”.

Sources: Anthropic, Investing in the public benefit · @AnthropicAI on X · CNBC

Anthropic’s own bill: a judge could halt a model over a risk of 50+ deaths

Politico reported, and Anthropic confirmed, that the company is showing lawmakers the full text of a frontier model safety bill (working names: AEGIS Act or Advanced AI Accountability Act of 2026). The government could block a model only over an imminent risk of more than 50 deaths or serious injuries, or over $1B in damage.

• Scope: models trained with more than 10^25 operations, including fine-tuning and RL.
• Large developers (over $500M in AI revenue or over $1B in AI development spend) must:
  • publish safety frameworks;
  • issue risk reports every 180 days, including on internal-only models;
  • be assessed every 180 days by a registered independent evaluator.
• The evaluator is paid by the lab but regardless of the result, and has 72 hours to report an “imminent threat” to the Commerce Department.
• Only the federal court in the District of Columbia could halt a model, on Commerce’s request, with a 45-day order. No other federal body could pause a model.
• The risk must come from one of a few listed paths: bioweapons, loss of control, offensive cyber operations, or automated AI research that amplifies these.
• The bill would preempt some state law (such as California’s) on disclosure, evaluation and incident reporting. As recently as June, Anthropic wrote that without strong federal law, states should keep full freedom.
• On 18 October, federal affairs lead Anthony Cimino promised revisions:
  • states keep a role in evaluator standards;
  • the thresholds are “worth rethinking”;
  • stricter incident reporting and protection against “shopping for a friendly evaluator” are in the works.

Why it matters: It is the first time a frontier lab has written the full text of a law meant to regulate it. Critics focus on the very high bar for intervention and the state preemption, while supporters point to mandatory outside audits, which federal law does not have today.

Sources: Politico · Reuters via Investing.com · AI Policy Landscape, bill breakdown

How did a Claude tool get the No Kings Instagram account suspended?

Meta says the No Kings Coalition’s Instagram account was suspended on 17 October, the day of nationwide pre-midterm protests, because a Claude-based tool was browsing profiles, posts and hashtags at about four times the usual rate, in sharp bursts.

• The trigger was “reading” from the account, not posting.
• This corrects Meta’s first version on Saturday, which blamed unusually frequent posting and commenting.
• Automated review restored the profile after about an hour, but admins only got full access back at 17:33 New York time.
• The coalition says it was down for 90 minutes during nearly 3,000 events across all 50 states.
• Meta did not say which Claude tool it was, who ran it or what exactly it did. Meta apologised.

Meta spokesperson Andy Stone: “sudden, numerous, sequential requests in a short period of time resembled automated bot traffic rather than a typical human”.

Why it matters: A textbook case of a new problem: an agent acting for a person, on their account, looks exactly like a bot to anti-bot systems. Anyone giving an agent a browser logged in to a social account should expect this, and here the lockout came at the worst possible moment.

Sources: Business Insider · The Straits Times (Bloomberg) · The Guardian

Disney takes Character.AI’s people: $180M in pay, $20M for the bankrupt company

Character.AI’s Chapter 11 filings in Delaware show Disney hiring 66 of its 86 employees, including former CEO Karandeep Anand, now Disney’s first CTO. For the company itself Disney pays only $20M.

• Disney puts the hired staff’s annual pay at about $60M, plus a $30M retention pool. In total that is about $180M over three years.
• The $20M buys a non-exclusive licence to the training, safety and serving code, plus 9 patents.
• The app, brand, user data, chats and trained models stay in the bankruptcy estate.
• It is a stalking-horse bid, so someone can still outbid it. If nobody does by the end of November, the app is to be shut down.
• Character.AI cites high costs and the risk of lawsuits and regulation, including a case after a 14-year-old’s suicide and child-safety investigations.

Why it matters: Another acqui-hire in the style of Inflection or Windsurf, this time through a bankruptcy court: the value is in the people and the code, while the consumer product with legal baggage is left behind. For the AI companion market, it signals that users’ data and conversations can end up at auction.

Sources: TechCrunch · PPC Land, term sheet · Bondoro, case summary

Codex: full limit reset, better caching and message editing

As part of “28 days of quality-of-life improvements”, OpenAI’s Tibo Sottiaux announced a full Codex limit reset for all users on 17 October (day 13). Overnight (day 14) came editing earlier messages in a thread in the desktop app.

• Day 12: OpenAI improved “effective usage” in Codex and Work through:
  • context reuse across subagents;
  • keeping context cached longer;
  • better request routing.
• No limit multiplier was given for the day 12 changes.
• The reset was announced on 17 October at about 18:20 CEST, and Tibo posted “Done” at 23:12 CEST.
• This morning Tibo added that OpenAI and OpenCode restored access for OpenAI API customers hit by the key leak and will refund credits used during the attack.
• He also pointed to Daybreak Blue access, a more permissive model setup for defensive cybersecurity work.

Why it matters: In practice, the reset is a good moment for heavy Codex jobs, but it does not change the Pro plan limit cut announced from 30 October. The caching fixes are the only change that could lastingly stretch real working time on the same limit.

Sources: Tibo, day 13 · Tibo, day 14 · Tibo on OpenCode and Daybreak Blue · OpenAI forum

Cloudflare will rate Verified bots by how they behave

Cloudflare launched an internal system that continuously checks whether bots on its Verified list respect robots.txt and back off after an HTTP 429 (“too many requests”). The result is a “trust band”, not a one-off verdict.

• Lasting patterns count, not single slips. Consistently backing off raises trust, and ignoring the signal lowers it.
• Cloudflare recalls that in 2025 it removed Perplexity’s bot from the Verified list for evading blocks.
• Plans: publishing the scores in Cloudflare Radar, and letting customers set the trust threshold a bot must meet to get through.
• Bot identity rests on Web Bot Auth signatures. The new system answers the second question: “can we trust you?”

Why it matters: Together with item 3, the direction is clear: proving identity is no longer enough, and platforms are starting to judge agents by behaviour. Anyone building crawlers or browsing agents should handle 429 and Retry-After now, because it will soon show up in a public score.

Sources: Cloudflare Blog, Continuous accountability for Verified bots

Which six models leave GitHub Copilot today?

From 19 October, Copilot retires Gemini 3.7 Flash, GPT-5.5, GPT-5.4, GPT-5.4 mini, GPT-5 mini and Grok 4.5 across all modes: chat, inline edits, ask/agent and code completions.

• Recommended replacements: Gemini 3.8 Flash, GPT-5.6 Sol, GPT-5.6 Luna and Grok 4.6.
• On Business and Enterprise plans the alternatives switch on automatically, unless model policies block them.
• Models hard-coded in workflows, config files and automations will stop working.

Why it matters: Short model lifecycles (GPT-5.5 retired after a few months) are now normal. Every script with a hard-coded model name is technical debt.

Sources: GitHub Changelog

NVIDIA and AI4Bharat: 2.8T tokens in 22 Indian languages

NVIDIA, IIT Madras and the AI4Bharat lab announced Nemotron-pretraining-Indic-v1, a synthetic pretraining corpus of about 2.8 trillion tokens in all 22 constitutional languages of India. The full recipe for generating it is open from day one.

• Size: 2,601B tokens in native scripts and 224.6B in romanised versions (12 languages), 34 language–script configurations in total.
• Source: English documents from 15 Nemotron-CC-v2 subsets (web, maths, code, Q&A), translated whole-document by Gemma-4-31B-it.
• The same model grades quality on an MQM rubric. Texts scoring at least 75/100 stay, and weaker ones are repaired, which recovered 438B tokens.
• For 10 rarer languages, the Indic Translate model (Bodhan AI) is used. Manipuri goes through Bengali script before conversion to Meitei Mayek.
• Adding 10B Indic tokens to Nemotron 3.5 Super VL raised scores on five benchmarks by 4.98–9.82 percentage points.
• The corpus itself is “coming soon”. The pipeline (a NeMo Data Designer plugin) and technical reports are already out.

Why it matters: The same recipe works for any mid-resource language: whole-document translation, model grading, repair of weak passages and provenance for every record. The authors note that in 12 languages the translator is also the judge, and translated text does not replace native writing.

Sources: Hugging Face Blog, Scaling Indic Pretraining · Report on HF

Agents write papers that look like science but aren’t

Two new benchmarks show the same gap: agents deliver something that looks like a result, but break the rules along the way or make things up.

• Into the Unknown: scientists from US Department of Energy national labs gave agents unpublished follow-up directions from their own work.
  • Fields: quantum computing, plasma physics, computational chemistry, materials and scientific ML.
  • Nine frontier agents wrote 15 papers each, and none of the 135 met all requirements.
  • 114 failed the integrity check, most often for describing experiments never run or giving numbers that contradict the logged data.
• BizBench: 50 multi-step tasks built on real procedures at a large payments company, with 789 documents and 601 tools, including conflicting and outdated policies.
  • Strict success rate (goal reached with no violations along the way):
    • Claude Opus 5: 64.0%
    • GPT-6 Sol: 61.3%

Why it matters: In payments or science the end result is not enough: what counts is whether rules were broken and whether anything was invented along the way. It is an argument for judging agents by their execution traces, not by how convincing the output sounds.

Sources: Into the Unknown (HF Papers) · BizBench (HF Papers)

World wants to sell banks “proof of human”, and Zoom gets a Human badge

World (the project co-founded by Sam Altman) launched a World ID suite for banking and finance on 18 October, and the World for Zoom integration is generally available. After a one-time Orb check and a live selfie, a call participant gets a visible “Human” badge on their video tile.

• Design partners include Bain Capital, VanEck, OpenExchange, Outtake and Pantera Capital.
• The target is deepfake fraud on video calls and impersonation of customers and advisers.

Why it matters: As agents and deepfakes become indistinguishable from people, demand grows for cryptographic proof of being human. But it requires an iris scan in one private system, which raises questions about privacy and concentrated control over identity.

Sources: World, announcement · The Block · Fortune