OpenAI released GPT-6.1 Terra in the API and is rolling it out in ChatGPT Work and Codex. The model focuses on visual reasoning, computer use and writing. Standard API prices start at $1 per million input tokens, $0.05 for cached input and $5 for output, half the published standard rates of GPT-5.6 Terra and GPT-6.1 Sol. Long-context and processing surcharges apply; performance comparisons are OpenAI’s evaluations.
Anthropic filed a public S-1 registration statement for a proposed Nasdaq listing under ANT. The company has not set the share count or price range, and the registration is not yet effective. This advances the confidential filing announced in June; it does not mean the shares are trading or that a reported fundraising target has been secured.
Meta launched concurrent offerings of $12.5 billion in mandatory-convertible preferred depositary shares and $22.5 billion in Class A shares tied to forward-sale agreements. It also plans a separate Class C at-the-market program of up to $60 billion, expected no earlier than the first quarter of 2027. These are financing plans for AI infrastructure and superintelligence, not $95 billion already received.
OpenAI announced AI-generated results in harmonic analysis, computational complexity and quantum field theory, alongside a new process for external researchers to examine and explain them. Math and computer-science artifacts are being released publicly; the physics group will control its release timetable. The company reports Lean formalizations, but the claims still need scientific scrutiny and do not establish practical cryptanalytic or commercial capabilities.
SpaceX reached a binding commitment for an approximately $40 billion GPU and equipment finance lease with an Apollo-managed entity. The plan covers Nvidia Vera Rubin hardware for terrestrial compute. Closing remains conditional and is expected before year-end; lease schedules are expected to begin as equipment arrives in the first half of 2027. The announcement is a commitment, not installed capacity.
Nevis announced a $70 million Series B led by EQT Ventures, with Charles Schwab, Sequoia Capital, ICONIQ, QuantumLight and Ribbit Capital participating. Its software combines adviser data and workflows with agents for administrative work. The round brings disclosed total funding to $110 million; the company release does not state a valuation.
Anthropic is donating the Agent Skills standard, name, domain and GitHub organization to the Linux Foundation’s Agentic AI Foundation. Skills package task instructions and supporting material for agents. Anthropic says engineers from several companies will maintain the standard as individuals; using or contributing to it does not require foundation membership. Versioned releases and security guidance are proposed next steps.
Ailytics raised $7.4 million in a Series A led by Tin Men Capital, with SEEDS and Safie among the participants. The Singapore company applies vision AI to existing industrial CCTV and plans expansion across Asia-Pacific, the US and Gulf markets. Its deployment counts and claimed safety improvements are company-reported rather than independently measured results.
OpenAI announced AI-generated results in harmonic analysis, computational complexity and quantum field theory, alongside a new process for external researchers to examine and explain them. Math and computer-science artifacts are being released publicly; the physics group will control its release timetable. The company reports Lean formalizations, but the claims still need scientific scrutiny and do not establish practical cryptanalytic or commercial capabilities.
OpenAI released aggregated data linking consumer ChatGPT work-related use to 165 activity categories across nearly 100 countries, covering quarterly periods from early 2025 through the third quarter of 2026. Enterprise conversations and Codex use are excluded. The data describes sampled use, not productivity gains, job displacement or the whole AI market.
METTA combines within-episode neural memory with compact summaries across episodes, according to its preprint abstract. The authors report adaptation results on held-out reinforcement-learning tasks. This is research on meta-RL agents, rather than evidence that a general-purpose language-model assistant has acquired reliable long-term memory.
The authors propose choosing which partially generated solutions to continue by directly optimizing pass-at-k. Their abstract reports tests across open-weight reasoning models. The method tries to retain promising but varied reasoning paths, rather than optimizing quality and diversity separately; these are preprint claims, not independently reproduced results.
