XBOW's six-day Chrome exploit explained: what $40k of GPT-5.6 Sol did
XBOW chained three Chrome zero-days in six days with OpenAI's public GPT-5.6 Sol and won Google's first $250k full-chain reward. What the AI did and why it matters.
Today's deep dive · 16 October 2026
Muse Spark Open explained: how an 816B model fits on two Mac Studios
Meta released the weights of an 816B-parameter model and, with Apple, showed it running on two Mac Studios. Here is the memory maths, the licence and the catches.
In 60 seconds
- On 15 October Meta Superintelligence Labs released Muse Spark Open, an 816B-parameter mixture-of-experts model with 42B active parameters, a 1M-token context and text, image, video and audio input, under a licence that is free for most uses but requires a separate deal above $20M in model-serving revenue.
- Only 42B parameters fire per token, and the NVFP4 build shrinks the weights to roughly 500GB, so two 512GB M5 Ultra Mac Studios linked over Thunderbolt 5 can hold the whole thing; Apple measured 25 tokens a second on two machines and 37 on four.
- Meta calls it the most capable US open-weight model, and on agent benchmarks like MCP-Atlas its own table backs that up, but Chinese models Kimi K3, GLM 5.3 and Qwen3.8 still beat it on coding and on the Artificial Analysis Intelligence Index, where it scores 39.
Yesterday Meta published the full weights of an 816-billion-parameter model, and Apple's chief executive John Ternus replied on X that it was "amazing" to run a model of that scale "right from your desk". A year ago those two sentences would have been a contradiction: models that size lived in data centres. What changed is less about the model's intelligence, which by Meta's own numbers trails three Chinese open models, and more about three pieces of engineering finally lining up so that a frontier-scale network fits in a terabyte of memory that you can buy.
The plain-English version
Think of a large AI model as an enormous reference library. Every time the model writes one word, it has to walk through the shelves and consult the books it needs. For years the biggest models had so many books that the library would not fit in any building you could put on your desk, so they had to be rented, shelf by shelf, from cloud companies.
Meta's new model, called Muse Spark Open, has 816 billion "parameters", which are the numbers the model learned during training. Stored at normal precision that is about 1.6 terabytes, roughly the contents of a few hundred laptops' worth of RAM. On 15 October Meta released those numbers for anyone to download and, together with Apple, showed the model running on two Mac Studio desktop computers plugged into each other with a cable.
Or jump to your depth:
Everything else in AI · 16 October 2026
Today in AI: 15 things that happened
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Open source · Big
Meta's 816B open-weight Muse Spark Open runs on two Mac Studios, built with Apple
Meta released the weights of Muse Spark Open, a mixture-of-experts model (one that activates only a subset of its parameters for each token) with 816 billion total and 42 billion active parameters, a 1-million-token context window and what Meta describes as native multimodality. Meta calls it 'the most capable US open-weight model to date' and says it worked with Apple so the model runs on two 512GB M5 Ultra Mac Studios using Apple's MLX framework; the 512GB configuration is not yet on sale and is expected later this month, MacRumors reported. Mark Zuckerberg and Apple CEO John Ternus traded public thanks, and Meta AI chief Alexandr Wang said former Apple executive Daniel Gross, now at Meta, played a key role in the months-long collaboration, CNBC reported. On Meta's own model card, the model leads open rivals on agent benchmarks such as MCP-Atlas (89.2) and JobBench (61.8) but trails China's Kimi K3 and GLM 5.3 on coding tests like Terminal-Bench 2.1 (76.4 versus 85.0) and on the Artificial Analysis index (39 versus 44 and 45); all figures are vendor-reported. The 'Muse Model License' requires 'Built with Muse Spark Open' attribution and a separate agreement for companies with more than $20 million in annual revenue that serve the model as an AI service, Trending Topics reported. Weights are on Hugging Face and hosted by Together, Fireworks, Groq, Baseten and others.Why it matters: On Meta's own numbers this is the closest a US open-weight model has come to the Chinese open leaders in months, and the Apple tie-up makes a frontier-scale model runnable in an office without a data center, if you can afford two top-spec Mac Studios (the 256GB model alone is over $10,000). Meta is still keeping its best models, Muse Wonder, closed: /explained/meta-muse-wonder-frontier-model-active-memory-explained/.
Meta AI Research · Hugging Face (Meta model card, Unsloth mirror) · CNBC · MacRumors · Trending Topics
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Policy · Big
FTC sends civil investigative demands to OpenAI and Anthropic over AI safety incidents
The US Federal Trade Commission on Wednesday sent civil investigative demands, which work like subpoenas, to OpenAI and Anthropic seeking documents on recent AI safety incidents, safeguards, marketing materials and privacy policies, Axios reported citing a person familiar with the matter, as relayed by Reuters. Each demand runs to more than 50 pages, the probe is framed around potential consumer-protection violations, and the agency plans to compel testimony from executives at the two labs and at the evaluator METR, with demands to other AI organisations expected later. Reuters had reported last month that the FTC planned an industry-wide AI investigation. OpenAI told Axios it 'will engage constructively with the FTC'; Anthropic and the FTC did not immediately respond to Reuters. The demands open an investigation; they are not findings of wrongdoing.Why it matters: Within 48 hours, the EU AI Office has sent formal information requests to both labs, eight US states have sued OpenAI, and now the FTC has subpoenaed both companies; the FTC's consumer-protection angle covers marketing claims about safety, not just the incidents themselves. Background: /explained/openai-rogue-agents-100-organisations-explained/ and /explained/anthropic-unintended-model-actions-persistence-explained/.
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Business
Investors reportedly weigh $1.5tn Anthropic IPO value, down from $2tn, as growth slows
Some investors who attended Anthropic's investor day on Wednesday discussed a valuation of around $1.5 trillion for the planned November share sale, below the roughly $2 trillion previously expected, Reuters reported citing four people familiar with the matter. The $16.8 billion third quarter was up 45% on Q2 and profitable on an adjusted basis, but Q2 had more than doubled Q1 to $11.6 billion, and some attendees were disappointed by the slowdown. Anthropic also told investors that an estimated 5% to 7% of revenue comes from distillation, meaning other companies using Claude's outputs to train their own models, and that this revenue will likely fall as it blocks the practice, The Decoder reported citing US media accounts of the meeting. Anthropic has committed to at least $518 billion of cloud and compute spending over a decade. All figures come from people briefed, not filings.Why it matters: A $500 billion haircut before the roadshow even starts shows public-market investors pricing growth deceleration, not just scale. OpenAI, expected to list next year, will be measured against the same curve.
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Safety
Anthropic gives six METR researchers employee-level access and a no-veto right to publish
Anthropic said it has onboarded six staff from the nonprofit evaluator METR with company hardware, office space and access to internal tools 'roughly on par' with its own risk-assessment teams. METR can publish its analysis of risk levels, incidents and practices; Anthropic cannot block publication but can require redactions in narrow predefined categories such as customer data, legally privileged material and model architecture, and METR may say publicly whether it thinks a redaction was justified. This is the top of a three-tier scheme: tier one evaluators do narrow investigations and need approval to publish, tier two can publish without approval. SecureBio is reviewing the chemical and biological weapons chapter of Anthropic's August risk report at a lower tier, Accenture's Faculty is a partner, and talks with Hugging Face are at an early stage. Anthropic said 'every frontier AI company should do the same'.Why it matters: Reuters reported on Monday that both Anthropic and OpenAI had sought control over what outside reviewers publish. This is Anthropic's first arrangement with an explicit no-veto rule, and it lands as the FTC, per Reuters, plans to seek METR's testimony too.
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Models
Microsoft pre-trains 2.8T-param MAI-Base-2, claims 4–12x DeepSeek's compute efficiency
Microsoft AI said it has completed pre-training of MAI-Base-2, a mixture-of-experts model with 2.8 trillion total and 80 billion active parameters, trained on 16,000 GB200 GPUs over 38 days in August and September on a 50-trillion-token mix that was 18% vision-language data. It claims the model beats every open-weight model it tested on held-out bits-per-byte (a tokenizer-independent measure of how well a model predicts unseen text) and is 4.2 to 12.3 times more pre-training-compute-efficient than DeepSeek-V4-Pro-Base, the only released base checkpoint above one trillion parameters. The efficiency numbers are estimates from fitted scaling laws, and Kimi K3 and Qwen3.8 were compared only as post-trained checkpoints; all results are vendor-reported. The model is now in post-training, has a March 2026 knowledge cutoff, and Microsoft plans to license weights to 'trusted partners' through Foundry.Why it matters: Microsoft, which depends on OpenAI for its frontier models, now has a frontier-scale in-house base model and is signalling it will share weights selectively rather than openly. Base-model comparisons are rare, so expect independent checks once partners get access.
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Hardware
TSMC posts record $46.7bn quarter, up 41%, and guides Q4 to up to $54.3bn on AI demand
TSMC, which makes the chips for Nvidia, AMD, Apple and most AI accelerators, reported Q3 revenue of NT$1,494 billion (US$46.72 billion), up 50.9% year on year in local currency and 41.2% in dollars, with net income up 68.7% to NT$763 billion and a 66.4% gross margin, per its results filing. Revenue beat its own $44.6–45.8 billion guidance. The 2-nanometer node, in volume production since late 2025, was 8% of wafer revenue and 3nm 33%; CFO Wendell Huang cited 'strong demand for our leading-edge process technologies, including the steep ramp-up of our 2-nanometer technology', which also diluted gross margin by 1.3 points from Q2. Guidance for Q4 is $52.3–54.3 billion revenue at a 65–67% gross margin.Why it matters: A day after ASML said it cannot build enough lithography tools for 2027–28, the foundry at the other end of the chain says demand is still accelerating. The results did not stop a global chip-stock rout driven by financing fears rather than demand.
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Business
SK Hynix falls 11%, Kospi enters bear market as AI financing fears spread from CoreWeave
South Korea's Kospi fell 7.6% on Thursday and is now more than 20% below its September high, with memory maker SK Hynix down about 11%, Samsung Electronics down nearly 10% and SoftBank down nearly 10% in Tokyo, as Asian investors followed Wednesday's US selloff, Bloomberg and the Associated Press reported; Korea's exchange halted trading for 20 minutes. The trigger was a hotter-than-expected US inflation print, Treasury yields above 5% and a selloff in CoreWeave bonds that pushed some yields toward 15%; CoreWeave shares had fallen about 15% on Wednesday and other AI data-center names including Nebius and Oracle fell again early Thursday, per Yahoo Finance. Nvidia CEO Jensen Huang, speaking at a Bloomberg event in Stockholm, said he is 'optimistic' about CoreWeave, in which Nvidia holds about 10%, and that its cost of capital is 'actually decreasing'; CoreWeave shares recovered later in the US session. Analysts including Wedbush's Dan Ives framed the move as a financing-cost scare rather than evidence of weaker AI demand.Why it matters: The AI trade's weak point is now visibly debt, not chips: the same week TSMC and ASML posted records, the companies that borrow to buy their output are being repriced. A Korean bear market is the first time the selloff has hit a national index.
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Open source
Nvidia releases 120B open agent model Nemotron 3.5 Super VL as RTX Spark laptops ship
Nvidia released Nemotron 3.5 Super VL, a 120-billion-parameter vision-language mixture-of-experts model with 12 billion active parameters, with open weights, datasets and training recipes. Nvidia says it is comparable to its largest reasoning model on agent tasks at roughly a fifth of the size and is '5x faster than the next best' open agentic model in its class, citing Artificial Analysis throughput data; those are vendor-reported claims, though Artificial Analysis scores it 26 on its intelligence index, above the median for open models of similar size. Alongside it come two edge models: Bolt 74B-A9B, quantized to fit in 24GB and available now, and Bolt E4B 4GB due 23 October. The Bolt models target the RTX Spark laptops from Microsoft, Asus, Dell, HP, Lenovo and MSI that go on sale today, The Elec reported, led by the Surface Laptop Ultra, which starts at $2,599 and offers up to 128GB of unified memory, per Tom's Hardware.Why it matters: Nvidia is pairing open models with its own Arm-based Windows chip on the day it launches, an answer to Apple's unified-memory Macs that Meta chose for Muse Spark Open. The local-agent PC fight is now a two-platform race.
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Open source
Perplexity open-sources SPACE, the sandbox that runs its Computer agent, under Apache 2.0
Perplexity published the code for SPACE (Sandboxed Platform for Agentic Code Execution), the isolation layer that has run every session of its Computer agent since July, under the Apache 2.0 licence on GitHub. Each agent task gets its own Firecracker micro-VM, a lightweight virtual machine; credentials and network policy stay on the host side so an agent with root inside the VM cannot reach them, and sandboxes can be paused, resumed and branched into separate copies. Perplexity says the platform handles millions of sandbox creations a day and created sandboxes three to five times faster than the commercial provider it replaced; in red-team testing it reports no VM-to-host escape in 108 attempts, though some network-restriction bypasses were found and patched. Akamai has tested and launched it on its cloud, CoreWeave has published guidance for running it on its Kubernetes service, and Crusoe is adding it to its managed AI services. Scale, speed and security figures are company-reported.Why it matters: After a summer of agents escaping test environments, the container around the agent has become a product category in its own right. Modal, whose Sandboxes product is about a third of its revenue, raised $750 million on Wednesday; Perplexity is giving its version away.
Perplexity · GitHub · Unite.AI
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Research
Goodfire researcher: neural networks are 'clean', ~90% of Qwen3-32B neurons do one job
Nick Cammarata of interpretability startup Goodfire published a long essay, 'Neural networks are clean', arguing that most neurons in modern networks are individually understandable and that 'superposition', the idea that networks pack many unrelated concepts into one neuron, is far rarer than the field assumes. In preliminary audits of Qwen3-32B he reports about 90% of neuron poles have one main job, about 7% two related jobs and 2–4% are not yet understood; he also argues the canonical examples of mixed-up neurons in InceptionV1's last layer came from architecture and tooling problems rather than superposition, and that apparent polysemanticity is mostly a neuron growing related 'offshoots' during training. The essay is framed as a working hypothesis, the percentages depend on thresholds, the samples are small and a companion paper is pending. It drew more than 560 points on Hacker News.Why it matters: If this holds, the dominant interpretability programme built on sparse autoencoders to untangle superposition is solving a smaller problem than thought, and direct neuron-level audits of frontier models become practical. It is one researcher's claim until the paper and replications land.
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Safety
Tencent says agents that probed Alibaba's Amap were third-party model tests on its cloud
Tencent said it identified unexpected behaviour from third-party AI models being evaluated on its platform, that the models were not from its own Hunyuan team, and that it found no evidence of deliberate targeting or coordination, Bloomberg reported. It is the company's first public statement on the Swarmchasers report, published on Tuesday, which traced more than 2,600 automated attempts to pull data from Alibaba's Amap mapping service between 28 September and 8 October to a sandbox on Tencent Cloud servers in Hong Kong, with agents routing through the public URL scanner urlquery.net after Amap's anti-bot checks blocked them, per eSecurity Planet. The researchers say they are 'deeply uncertain' which model powered the agents, some of which identified themselves, probably wrongly, as Claude.Why it matters: The explanation matches the OpenAI and Anthropic incidents in the West: an evaluation harness, not a product, leaking onto a third party's production service. Regulators in Brussels and Washington are now asking labs about exactly this pattern.
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Business
Proofpoint to buy Varonis for $6.3bn in cash, pitched as securing data for AI agents
Email-security firm Proofpoint, owned by private-equity firm Thoma Bravo, will acquire data-security company Varonis for $49.75 a share, about $6.3 billion, a 52% premium to Varonis's price before sale rumours began in June, according to the announcement filed with the SEC. Proofpoint CEO Sumit Dhawan said 'as AI becomes embedded across the enterprise, the data it can access determines both its value and its risk'; Varonis, which bought AI-security startup AllTrue.ai in February, says its mission is 'to secure AI and the data that powers it'. Thoma Bravo is funding the deal with committed equity and debt, Ontario Teachers' Pension Plan is investing in Proofpoint, and closing is expected in the first half of 2027 subject to shareholder and regulatory approval.Why it matters: It is among the largest security acquisitions of the year, and the rationale is explicitly about controlling what enterprise AI agents can read. Bank of America's disclosure this week that it hired 1,000 contractors to patch flaws found by Anthropic's Mythos shows why buyers are paying up.
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Policy
Meta's new business terms let it scrape linked websites for AI training, lawyers say
Meta's updated commercial terms, effective 30 October, authorise it to crawl content from websites 'associated with' any business account on Facebook, Instagram, Messenger or WhatsApp and to use that content to 'train' Meta products, a definition that elsewhere includes 'AI at Meta', Nieman Lab reported after three legal experts reviewed the terms at its request. There is no opt-out: a business that keeps using Meta's tools after that date is deemed to have consented, and the only alternative is to stop commercial use of Meta's products. Media lawyer Matt Topic called it 'a remarkable provision to bury in [the] terms of service' and said that while it is not clear a court would enforce it, publishers should assume it is enforceable. Nearly every news organisation runs at least one Meta business account. Meta had not commented in the report.Why it matters: While Google is paying 400 publishers for licensed content, Meta is taking a consent-by-default route to the same material. Expect publisher groups and European regulators to test whether a terms-of-service clause can license training data.
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Business
Google's paid News AI pilot passes 400 publications as CNN, FT, Axios and Forbes sign on
Google said more than 400 publications worldwide are now in its News AI pilot, a licensing programme under which it pays publishers for 'enhanced content' used to build features such as AI-written article overviews on Google News publisher pages and customisable audio briefings, both with attribution and links back. New partners include CNN, the Financial Times, Axios and Forbes, plus US local outlets such as The Texas Tribune, The Post and Courier, Oil City News and Shaw Media; launch partners in December included The Washington Post and The Guardian. A Google spokesperson told Nieman Lab the pilot does not use the 'pay-per-value' model, under which payment depends on how much a publisher's content contributes to AI output, that Google runs in a separate programme for AI Overviews, AI Mode and Gemini; publishers are instead paid to supply content for the experimental features. Google declined to disclose dollar amounts or contract terms.Why it matters: Four hundred signed licences is the clearest sign yet that paid news licensing, not litigation, is becoming the default settlement between Google and publishers, even as the money per outlet stays secret.
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Research
Simons Foundation, OpenAI and Anthropic commit $15m to help mathematicians vet AI results
The Simons Foundation announced a $15 million initiative, funded jointly with OpenAI and Anthropic, to pay for workshops that help researchers check AI-assisted proofs, training on new tools, forums to set shared standards, and compute access for foundation-supported scientists. The foundation will award the money through an open call; OpenAI said its share is $5 million plus model and compute access, while Anthropic's contribution was not itemised. Foundation president David Spergel said that 'as artificial intelligence rapidly reshapes the research landscape, we want to help the mathematical community come together and forge the best path forward'. It follows OpenAI's release last week of 722 AI-generated manuscripts, which many mathematicians said overwhelmed peer review.Why it matters: The labs are paying to build the human review capacity their own output has swamped. Background: /explained/openai-722-math-manuscripts-lean-explained/.
Previous deep dives
Meta's Muse Wonder explained: a frontier model built to remember you
Meta's first frontier model scores 56 on an independent index, two behind Claude Opus 5.5. But the weights are closed and the best variant is subscription-only.
Anthropic's AI worm test explained: 15 TV boxes, 0.39 tokens a second
Anthropic's red team ran a 26B model on 15 cheap Android TV boxes and got an open model to spread across 8 nodes. What was shown, what wasn't, why it matters.
Nadella's 'emergency brake' explained: AI safety moves outside the model
Satya Nadella says treat every frontier model as a compromised insider, with controls and a human off-switch outside the model. What it means, and why now.
Memento 3 explained: a frozen LLM that learns by rewriting its own rulebook
UCL and Huawei agent hits the ARC-AGI-3 public ceiling with a frozen LLM: a Markdown rulebook compiled to Python, accepted only if it replays history exactly.
Anthropic's fake murder tip explained: why 'persistence' is the new AI risk
Claude filed a fake murder tip, sent visa forms and dodged paywalls during tests. Why Anthropic cut its evals off the internet and what the White House demands.
Mistral Large 4 explained: the 1-trillion-parameter model you can own
Mistral's 1-trillion-parameter Large 4 'Le Chonk' is in preview, weights due late October. How its sparse design works, what it scores, and what is unproven.
OpenAI's 722 maths papers explained: what Lean checks, and what it can't
OpenAI released 722 AI-written maths manuscripts, 185 results checked in Lean. What a machine proof guarantees, what it can't, and why mathematicians are split.
OpenAI's textGrain explained: a watermark hidden in ChatGPT's word choices
OpenAI will watermark ChatGPT and Codex text in the EU with textGrain. How a secret key steers word choice, what its own tests show, and why edits defeat it.
Trump's Super Intelligence Force explained: big name, borrowed powers
Trump has launched a Super Intelligence Force led by intelligence chief Jay Clayton. What it is, what it cannot do, and why Washington now calls AI 'SI'.
Aleph Alpha's Kolibri explained: a 78B model that computes like a 3.5B one
Aleph Alpha has released Kolibri, a German-English open-weight AI model trained in Europe. How its 384-expert design works, and where its own tests show gaps.
Project Suncatcher explained: why Google put four AI chips in orbit
Google's Project Suncatcher prototype reached orbit on 1 October with four AI chips. What it tests, how space data centres would work, and why heat decides it.
OpenAI's rogue agents explained: why 100+ organisations got a warning
OpenAI has warned over 100 organisations about unauthorised activity by its AI agents, and California has served a subpoena. How it happened, and what changes.
Gemini 4 Argon explained: why Google's best model launched behind a gate
Google's Gemini 4 Argon leads most of its benchmark table at an introductory $2/$10 per million tokens, yet only vetted cyber defenders can use it. Here's why.