· 11 min read
Anthropic's $16.8B quarter, OpenAI's looped flagship and METR's audit terms
Anthropic posts $16.8B Q3 revenue before its IPO, OpenAI's next flagship loops its layers, and METR spells out terms for independent safety audits.
Today in 30 seconds
- Anthropic reported $16.8B of Q3 revenue, up 45% from Q2, and was profitable on an adjusted basis.
- OpenAI's next flagship reportedly uses looped transformers, a technique that makes chain-of-thought monitoring harder.
- METR published the terms it wants for independent loss-of-control assessments, including private staff interviews.
- Eight US states sued OpenAI over ChatGPT safety, and Character.AI filed for Chapter 11 with Disney bidding $20M.
- StepFun released open weights for Step 5 Preview, a 600B MoE with 27B active parameters and up to 1M context.
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Thursday tea after a heavy Wednesday. Anthropic showed investors a $16.8B quarter, OpenAI’s next flagship reportedly leans further into looped reasoning that safety teams can’t easily read, and METR published what an independent audit of a frontier lab should look like. Eight US states took OpenAI to court, StepFun dropped a 600B open-weight MoE, and Google and Anthropic both shipped tools for checking what agents produce rather than producing more of it.
Top news
How big is Anthropic now? $16.8B in Q3, up 45%
Anthropic reported $16.8B of revenue for the three months to the end of September, up from $11.6B in Q2 and $4.7B in Q1, a 45% quarter-on-quarter jump. The company was profitable on an adjusted basis in Q3, a measure that excludes items such as stock compensation, so it is not GAAP profit. The figures were presented by Dario Amodei and CFO Krishna Rao at an investor day at its San Francisco headquarters on October 14. Reuters reports the IPO will probably come after the November US midterms; analysts estimate the run rate could reach $110B by year-end, and Anthropic’s compute commitments are at least $518B over 10 years. For comparison, Reuters previously reported 2025 revenue of about $4.6B with an operating loss above $8B.
Why it matters: It is the first hard sign that a frontier lab can be profitable, even on an adjusted basis, before going public, and it sets the reference point for OpenAI and the wider AI-capex debate. All figures are company-reported to investors.
Sources: Reuters via Investing.com · BBC · FT (paywall) · Bloomberg (paywall)
Will OpenAI’s next flagship think where no one can read it?
The Information reports that OpenAI’s next major model uses the same looped transformers as GPT-6 Astra, internally called “Recurrent Depth”, where a token passes through the same layers several times. Looping helps math and coding at lower cost, but part of the “thinking” moves out of readable chain of thought into hidden numerical states; OpenAI capped the loop count in Astra to keep its CoT readable. Employees expect “one or two” new models by year-end, and it is unclear whether the major upgrade is among them. After the Hugging Face incident OpenAI moved about 10% of its total compute, “hundreds of MW”, to alignment and safety. A since-deleted Microsoft write-up said GPT-6.1 Sol uses the same weights as GPT-6 Sol with 2 inference passes instead of 3.
Why it matters: It is the first concrete report that the frontier is moving toward latent reasoning loops, which work directly against CoT monitoring, a method Jakub Pachocki has called “fragile” and “trending in a negative direction.”
Sources: The Decoder · The Information (paywall) · @steph_palazzolo
What should an independent audit of a frontier lab look like? METR’s terms
METR published two posts on October 14 proposing independent loss-of-control assessments of frontier developers, covering alignment (the full set of internal incidents and whether training suppresses evidence of misalignment), control (monitoring coverage, robustness to agent attacks, response time) and AI R&D acceleration. Its terms include access to data on company devices for weeks to months, private staff interviews without a company representative, editorial independence, disclosed redactions and preliminary reports “within weeks, not months.” METR lists the limits itself: no full employee-level access, no protection against retaliation and no security audit. Separately, Reuters reported that until at least October 5 Anthropic and OpenAI wanted strict control over what evaluators publish; Anthropic’s position softened by October 8.
Why it matters: The embedded evaluators the labs promised are now running into the real question of who decides what gets published, and METR’s list is the first concrete public template for that.
Sources: METR: loss-of-control assessments · METR: alignment assessment · TNW on Reuters · The Verge
Eight US states sue OpenAI; Character.AI files for Chapter 11
Iowa, Kansas, Nebraska, West Virginia, Arkansas, Arizona, Idaho and Montana filed separate suits against OpenAI under state consumer-protection laws on October 14. They allege harm to minors, encouraging delusions, dangerous medical advice, collection of minors’ data and weak age gating, and cite the rogue AI hacking incidents as evidence of recklessness; they seek civil penalties, an order to stop misleading the public and deletion of data. OpenAI pointed to its age protections and parental controls and called for rules that apply to the whole industry. A day earlier, Character.AI filed for Chapter 11 in Delaware, with Disney as the stalking-horse bidder at $20M for a perpetual non-exclusive licence to its IP and technology; New York AG Letitia James disclosed a multi-year, multi-state investigation.
Why it matters: With no federal AI law, state attorneys general are becoming the de facto regulators of consumer chatbots, and Character.AI shows what the companion model can end up costing.
Sources: The Verge · CNBC · The Guardian · NBC News · Variety
StepFun Step 5 Preview: a 600B MoE that fits on one 8-GPU node
StepFun published open weights for Step 5 Preview on October 15, a sparse MoE with 600B total parameters (604B on the model card) and 27B active, with up to 1M context. Checkpoints come in FP8, BF16 and NVFP4 on Hugging Face and ModelScope, with dedicated vLLM and SGLang images; FP8 runs on one node of 4×B300 or 8×H200. StepFun positions it for agentic software engineering and knowledge work, “particularly strong in finance.” The licence is the StepFun Model License 1.0, Apache-style but with extra attribution and commercial conditions.
Why it matters: It is a fresh Chinese open-weight frontier-class model with long context that a single serious node can serve, though the licence is not pure Apache and benchmark claims are StepFun’s own.
Sources: Hugging Face model card · StepFun docs · StepFun
Grok Voice 3.0 tops the Speech-to-Speech Index at 86.9
SpaceXSI released Grok Voice 3.0, a speech-to-speech model for voice agents, which scores 86.9 on Artificial Analysis’s Speech-to-Speech Index, ahead of GPT-Live-1 (Astra) at 83.2 and Gemini 3.8 Live at 82.6. It reports 97% on Full Duplex Bench and 71.6% on τ-Voice versus 74.5% for GPT-Live-1, with about 40% faster first audio. Price stays at $0.08 per minute ($4.80 an hour), and the grok-voice-latest alias has already switched. SpaceXSI says Starlink support handles about 20,000 calls a day with about 70% resolved without a human.
Why it matters: Voice agents are now measured on real contact-centre numbers, not demos. The index scores are third-party, but the production figures are company-reported.
Sources: SpaceXSI · SiliconANGLE · @SpaceXSI · @ArtificialAnlys
Google Deep Review: an agent that checks papers, not writes them
Google Research made Deep Review public on October 14, an agentic verifier for technical documents that grew out of the Paper Assistant Tool, piloted on 16,000+ manuscripts at STOC 2026, ICML 2026, NeurIPS 2026 and ICLR 2027. It checks logic, math, factual grounding and consistency across sections in four stages, spending more compute on dense proofs. Every user gets 2 free reviews a week; a Gemini API key unlocks higher limits and an API, and it now runs on Gemini 4 Argon. Uploads are not used for training but are kept for one year. Google cites more than 74,000 submissions to ICLR, NeurIPS and ICML in 2026, up from about 45,000 in 2025.
Why it matters: Reviewing, not generating, is becoming the bottleneck, and this is a free production tool for design docs, contracts and papers; note the one-year retention before uploading anything sensitive.
Sources: Google Research
Claude Platform adds OpenTelemetry tracing for agents
Anthropic launched a public beta of native OpenTelemetry export from Claude Managed Agents, the Messages API and the Claude SDKs. Spans follow the GenAI semantic conventions with latency, tokens, model and tool use, with a metadata-only mode or full prompt and response content. SDKs export through your app’s tracer provider; for the API an admin creates an OTLP destination in the Console. Supported destinations include Grafana, Honeycomb, Sentry, Langfuse, LangSmith and Datadog.
Why it matters: Without traces an agent is a black box; built-in observability makes production debugging and evals from real traffic much easier for enterprise teams.
Sources: Claude: agent observability · OpenTelemetry docs · @ClaudeDevs
Semperis: AI involved in 84% of ransomware attacks on surveyed victims
Semperis’s 2026 Ransomware Risk Report, a survey of organisations hit by ransomware in the last 12 months, says 84% of attacks involved weaponised AI. Per CBS’s summary, 40% were AI-based attacks and 44% exploited the victim’s own AI agents or systems. 40% of attacks included psychological or physical threats against staff, and 87% targeted identity systems such as Active Directory, Entra ID and Okta, while about half of organisations have dedicated identity backups. It is a vendor survey.
Why it matters: “Your agents as an attack surface” now shows up in a ransomware statistic, so agent permissions and identity hardening become ransomware controls, not just AI-safety hygiene.
NVIDIA’s local-agent stack: Nemotron 3.5 Bolt, PAIR and RTX Spark
NVIDIA’s Nemotron 3.5 Bolt 74B-A9B is due on October 15: a MoE with 74B total and 9B active parameters, packed into 24 GB with 2-bit quantization-aware training, released with training data, weights and recipes. PAIR, a free open-source router that spreads inference across PCs on a local network, is out of beta, and RTX Spark laptops with up to 128 GB of unified memory and 1 PFLOP go on sale October 16. Sandboxing comes via OpenShell and Microsoft Execution Containers, already used in OpenClaw and GitHub Copilot.
Why it matters: Local agents with OS-level sandboxing are turning into a default PC setup, and an open model this size with open training data is rare.
Sources: NVIDIA blog
GitHub repos
- View repo →
vxcontrol/pentagi
Self-hosted multi-agent system for autonomous penetration testing that runs 20+ tools such as nmap, Metasploit and sqlmap in a Docker sandbox with your own LLM; for authorized testing only.
- View repo →
K-Dense-AI/scientific-agent-skills
177 validated science skills plus 100+ scientific databases (biology, chemistry, medicine) for Cursor, Claude Code, Codex and Antigravity.
- View repo →
morluto/rea
The MCP and CLI tool for agent-driven reverse engineering is still #1 on GitHub trending.
Research papers
- Read paper →
Jeva: System One Models from First Principles
Four structural requirements for decision models: null-aware, permutation-invariant, option-scalable and cacheable.
- Read paper →
Last Secure Code Benchmark
450 tasks from 150 real vulnerabilities across 7 languages and 24 CWEs, testing whether vibe-coded code is secure.
- Read paper →
LatentLoop
In looped MoE models the first loop visit wants a smaller scale, which motivates asymmetric compute per loop; open research on the technique in item 2.
- Read paper →
Hybrid Reasoning Agents
Agents switch between latent reasoning and text or tool mode through a lightweight “HybridLink”.
- Read paper →
Agent-Reflex
Teaches agents to recover from failures by injecting noisy MCP tool feedback and user interventions during training.
- Read paper →
Any Time, Any Space
Agentic video generation that keeps scenes consistent across shots with explicit scene and event memory.
AI agent use cases
- Get a second reviewer for one document: upload a design doc or draft to Deep Review (2 free a week) and compare its findings with a colleague’s feedback; skip anything confidential, since files are kept for a year.
- Trace one failed agent turn: if you run Managed Agents or the Messages API, add an OTLP destination and open one failed run in Langfuse or Grafana.
- Audit your agents’ identity access: ask your agent to list every token, OAuth grant and service account your AI tools use, since 44% of surveyed ransomware cases reportedly exploited the victim’s own AI systems.
AI workflows
1. Claude Code as L1 support and L2 review for a failed morning FX report
- Replaces: waiting on a human support engineer for a missed scheduled report, then a multi-hour bank-style change cycle (author, remote reviewers, production) for retries and alert tiers.
- What to connect: Claude Code with Sonnet as L1 (triage, rerun, implement) and Opus as L2 reviewer, an optional more senior model on call, job logs and phone push alerts. Full write-up: agentic-blueprint.com.
- Who it’s for: solo operators and small tech teams who run scheduled jobs and want maker/checker change control without a full support desk.
2. Grok Bot for cross-border admin: invoices, tax set-asides, agent requests
- Replaces: carrying multi-country paperwork (chasing a property agent, chasing invoices both ways, tax set-asides) as mental load.
- What to connect: Grok Bot with email or inbox access and a standing task list or routines for agent requests, invoices and tax flags.
- Who it’s for: sole proprietors and expats juggling admin across countries without a bookkeeper on retainer.
3. Claude Code and Rushilo for YouTube Shorts without a timeline
- Replaces: days of putting off Premiere, CapCut or After Effects edits (webcam punch-ins, overlays, captions or fully animated faceless shorts).
- What to connect: Claude Code plus Rushilo on the local machine, running on an existing Claude subscription with no cloud editor credits or watermarks. The animated-only path is in a follow-up thread.
- Who it’s for: founders and marketers who need short-form video often but hate the timeline.
Quick hits
- Apple v. OpenAI — OpenAI voluntarily agreed not to use trade secrets Apple says were taken and to collect forensic images of 14 employees’ devices, while the judge signalled an injunction is “highly likely.” 9to5Mac · TechCrunch
- Anthropic opens a Singapore office — its fifth in APAC, with an MOU with the Ministry of Digital Development and Information on safety, evaluations and incident response. Anthropic
- AA-Robotics v0.1 — Artificial Analysis had frontier models drive a real 6-axis arm zero-shot; GPT-6 Astra led with 69.4, ahead of GPT-6.1 Sol at 64.6, Gemini 4 Argon at 55.3 and Opus 5.5 at 48.8. Artificial Analysis
- OpenAI: agents are over half of EU business AI use — weekly active agent users at small businesses grew 8.1× from early May to early October. @OpenAI
- Liquid AI LFM2.5-24B-A2B — an open-weight 24B MoE with 2B active and 131K context for on-device agents, in GGUF, MLX and ONNX. Liquid AI
- Claude Fable 5.5 sighting — community trackers spotted
claude-fable-5-5listed in Azure’s model catalog; Anthropic has not announced it. @GeekChaos
Caveats: Anthropic’s revenue and profit figures were presented to investors and are not audited public filings; the OpenAI looping report relies on anonymous sources; Grok Voice production numbers, StepFun benchmarks and the Semperis survey are company-reported; Claude Fable 5.5 is unconfirmed.