Meta’s back at the frontier, Anthropic prepares to open its books, Wall Street sells exposure to labs whose numbers remain private, and OpenAI publishes more mathematics than anyone can yet understand.
Following AI Waves #027: By Whose Leave.
In the last issue, I asked who authorizes what an agent does on our behalf. Most of this week’s news concerns an earlier question: how much of what the AI labs produce, from the revenue they report to the reasoning inside their models, can anyone outside them verify, let alone understand? Anthropic met investors on October 14 ahead of a public filing, while a new ETF sells exposure to OpenAI and Anthropic through swaps on offshore futures. OpenAI released more than 700 mathematical manuscripts that, by Scott Aaronson’s account, almost no one has yet digested. And a memo Dario Amodei wrote at OpenAI in 2017, published this past week, bears on how much of the models themselves we can ever explain.
Meta’s back
On March 18, 1995, Michael Jordan ended his baseball detour with a two-word press release: “I’m back.” On October 12, Meta said the same thing (using a few more words) about their efforts in AI with Muse Wonder and Muse Wonder Full: “Fourteen months ago, we began a ground-up overhaul of our artificial intelligence efforts.”
Eighteen months ago, everyone had counted Meta out of the frontier. Llama 4 arrived in April 2025 with leaderboard scores from an “experimental chat version“ Meta never released, and the model it did release ranked below GPT-4o and Claude 3.5 Sonnet. Zuckerberg responded with a $14 billion “acquihire” of Scale AI, poaching Alexandr Wang as chief AI officer and setting off what The Information’s Martin Peers called this week “a massive hiring binge.” A few months later, Yann LeCun, Meta’s chief AI scientist, left to start his own company. Even after last month’s Muse rally, Meta’s shares are up only about 6% over the past twelve months, against roughly 45% for Google’s.
So what changed? Well, the binge seems to have worked. Artificial Analysis, which runs the same evaluations on everyone’s models (so this time the numbers aren’t Meta’s), scores Wonder Full at 56 on its Intelligence Index, behind only Claude Opus 5.5 (58), level with Claude Sonnet 5.5, and ahead of GPT-6 Astra, Gemini 4 Argon, and Claude Fable 5.1 (all 53).
And Meta is getting stronger, quickly. Muse Spark, the first model from Wang’s rebuilt lab, landed in Artificial Analysis’s top five in April, behind Gemini 3.1 Pro, GPT-5.4, and Claude Opus 4.6. Wonder Full arrived six weeks after Spark 1.3, beat it on all ten of the index’s evaluations, and gained eight points doing it. Six months ago Meta was chasing three labs. Today only Claude Opus 5.5 scores higher.
The model also has somewhere to go. Muse, the personal agent Meta launched on September 8, has quickly become one of the leading personal agents. It passed five million downloads in about three weeks (ChatGPT took 56 days, Grok 103, and Claude 492, according to Sensor Tower), and more than three million people prompt it every week, according to The Information.
Meta says Wonder will power Muse, starting with subscribers, so its new frontier model goes straight into an agent people already use to send email, book travel, and shop, reachable from the same WhatsApp chats they open every day (so far only in the US and Canada). Meta has also pointed its own ad machine at Muse: during Muse’s first weeks, it gave the app up to half of its daily house ad impressions, across apps with 3.6 billion daily users.
Meta can also afford to keep going. Anthropic is preparing to open its books to public investors (more on that below), while Meta pays for its models out of its advertising business. In the second quarter it generated $31.9 billion of operating cash flow and spent $31.1 billion on capital expenditures, leaving $784 million of free cash flow, and it expects to spend $130 to $145 billion this year.
Wonder trained on Prometheus, Meta’s one-gigawatt data center in Ohio. Meta’s next data center, Hyperion in Louisiana, is planned to reach two gigawatts by 2030 and eventually five, which is five times the capacity Meta used to train Wonder.
Disclosure season
Anthropic hosted a select group of institutional investors at its San Francisco headquarters on October 14. If it wants to begin its roadshow the week of November 9, as Bloomberg reported, its prospectus has to become public by late October. Until then, outsiders have leaks and investor materials to go on, and those depend heavily on definitions. Axios reported on October 8 that OpenAI’s annualized revenue is $50 billion, about $20 billion below the figure that circulated in September. The higher number came from information shared with investors “in an attempt to create a more direct comparison to rival Anthropic.”
The market did not wait for the filing. On October 14, as the investor meeting opened, Liquid reported that its Anthropic pre-IPO perpetual fell below $2 trillion, more than 10% below its level a month earlier.
The two companies count partner sales differently. When a customer buys Claude through a cloud provider, Anthropic books the full sale as revenue and the provider’s cut as an expense. OpenAI records only its share of certain partner sales. Both methods follow GAAP, and the right one depends on which company controls the customer relationship and delivers the product. Grossing up treats Anthropic as the party responsible for the service even when the sale runs through a cloud marketplace, and netting treats OpenAI’s partner as that party for some of its sales. For labs that sell through the same hyperscalers that invest in them and rent them compute, which side counts as the seller reflects who controls the customer and the price.
It also changes what valuation multiples mean. Operating profit in dollars comes out the same under either method, while revenue and gross margin do not. A run-rate multiple, including the one I used last month for Anthropic’s Series H, inherits whichever method the company chose, so comparing the two labs on revenue multiples compares two different measurements. The public filings will disclose each method, and the headline numbers will still need translating.
The administration has also given these filings a regulatory job. Axios reported on October 6 that the White House is “leaning hard into corporate self-policing.” It added that Scott Kupor, who co-chairs the new AI task force, and other officials believe “third-party audits and direct reporting to corporate boards carry severe securities law implications that rival traditional government oversight.” Under that theory, what a lab tells its board and its investors carries much of the weight that regulation would otherwise carry, and the prospectus is the most public of those disclosures. Securities law penalizes companies for misleading investors, which gives them every reason to describe risks broadly. Anthropic’s leaked draft warns of “existential risks to humanity.” SpaceX, whose AI unit builds Grok, made no mention of existential threats from AI in its IPO filings, according to the Washington Post. Securities law requires issuers to disclose material risks. Reducing risk is outside its scope.
Castles in the air
Uncertain numbers have not stopped the search for exposure, and the gap between that demand and the shares available is being filled with financial engineering.
Corgi’s MANGOS ETF began trading on Cboe on October 2, pitched on the premise that “for years, exposure to OpenAI and Anthropic has been reserved for venture investors and insiders.” As of October 9 it held about $72 million, with OpenAI and Anthropic at roughly 6.5% each, both held entirely through swaps rather than shares. The prospectus says the fund uses swaps that reference “perpetual futures traded on digital asset derivatives venues” that “may operate outside the United States.” It warns that “a perpetual futures price may differ materially from the value of the company’s shares.” The swap counterparty also acts as calculation agent, so the firm on the other side of the trade works out what the fund is owed.
Matt Levine walked through the chain in Money Stuff on October 8. The ETF holds “a normal-ish financial instrument, an over-the-counter swap with a bank or trading firm. The swap counterparty owns the weird stuff.” The weird stuff is a perpetual future on a private company, which has “no arbitrage mechanism to make the price correct.” On Hyperliquid’s pre-IPO markets, according to Talos, “the market itself functions as the oracle until IPO.” The fund’s OpenAI exposure is therefore a swap, arranged without OpenAI’s involvement, on a price set for now by traders betting on what the company is worth. That price is moving in the same week OpenAI’s revenue figure moved by $20 billion.
None of these instruments raises money for OpenAI or Anthropic. The labs set their own terms in private rounds, so the buyers of these products are building a parallel market that values the companies without funding them. Its quotes will be in public view when the banks set terms for each listing. Before SpaceX’s June debut, its pre-IPO contracts on Hyperliquid traded at a volume-weighted average of $155, against a $135 offering, and they converted into ordinary equity perpetuals once the shares listed. Levine’s verdict on the ETF: “I … find myself hating this less than I should? This is … financial innovation?”
More mathematics than anyone can read
On October 6, OpenAI published mathematical results from an unreleased internal model. The repository now lists 719 manuscripts in 372 families of results, drawn from about 4,000 problems the model was posed. About 42% of the main results are formalized in Lean, a language that lets a computer check a proof. The average result used about three hours of ChatGPT Pro compute. Scott Aaronson called it “surely one of the biggest days in mathematical history,” and noted that “no human has understood just about any of these proofs yet.”
The reading has begun. On October 14 Grigory Sapunov found that Lemma 8.6 of the Navier-Stokes paper requires m+4 derivatives in the text and m+5 in its Lean proof, and that the Lean code for Lemma 10.5 adds a gradient norm the text omits, because the model routed around a missing Mathlib API. The checks passed. The statements they checked were not quite the ones on the page.
Lean checks that a proof establishes the statement as formalized. A passing check says nothing about why the result holds, what it connects to, or which of its ideas another mathematician could reuse. For most of the field, those are the reasons a result matters.
The Institute for Advanced Study, as quoted by the Guardian, warned that AI “can output mathematical arguments in situations without the human who prompted it being able to understand the arguments, verify them, or take responsibility for them.” OpenAI consulted the independent Advisory Group on Mathematics and Artificial Intelligence. The group said its role “should not be interpreted as a judgment of the impact of these results,” and that “this release is the beginning, not the completion, of the process of human understanding.” OpenAI says it will fund “a series of workshops, conferences, and special programs around the understanding of major results.” The Association for Human Mathematics called the release “a demonstration of power” and urged mathematicians to stop working with the company.
In September I wrote that mathematical production and verification are accelerating together, while the human work of understanding runs on a different schedule. Mathematicians have formal proof languages and centuries of refereeing practice at reading one another’s arguments, which gives them more machinery for absorbing new results than almost any other field has. That mathematics is now receiving results faster than it can absorb is an early measure of how poorly prepared we are to understand frontier AI systems themselves.
At OpenAI, at least, one of the most practical tools for following those systems is becoming less reliable. Reading a model’s chain of thought has been the most direct way to see how it reaches an answer. OpenAI’s safety overview for GPT-6 Astra, published in September, says “GPT-6 Astra’s monitorability has decreased relative to GPT-5.6 Sol.” It also says “the Astra class models could evade our CoT monitors under adversarial conditions.” OpenAI’s own conclusion is that auditing methods that go beyond reading the chain of thought now matter more.
This week, three researchers OpenAI fired wrote to its board that “as an industry, we do not yet know how to safely develop and deploy models that we cannot monitor,” according to Gizmodo. OpenAI says the firings followed violations of its policies on sensitive information and “were not about raising safety concerns or speaking out.” An internal memo said the company “strongly agreed” with the letter’s recommendations. One of the three, Tomek Korbak, was first author of the 2025 paper in which researchers across the labs described chain-of-thought monitorability as “a new and fragile opportunity.”
The snowflake problem
On October 6, Kevin Roose published the first ten pages of “The Big Blob of Compute Hypothesis,” a memo Dario Amodei wrote for colleagues at OpenAI in 2017 that Roose argues may have started the AI race. Its central claim is that “creating any given intelligent behavior is mostly about providing a large, minimally structured mass of computational capacity,” then letting a rich environment and a training process shape it. Methods that “attempt to rigidly prescribe what the compute should do,” Dario Amodei wrote, “tend to do particularly poorly.”
I’ve argued that algorithms matter more than the scaling consensus allows, especially for what intelligence costs. The memo’s more durable claim concerns understanding. Dario Amodei dismissed the idea that a Bayesian model would let us read off what a system believes, since the beliefs in such a network “are just as likely to be comprehensible or incomprehensible, calibrated or uncalibrated, as the activations inside a neural network.” In his words, “you get what you optimize for; what you don’t get is a human’s idea of what properties the system should have.”
In an analogy about recreating a snowflake, Dario Amodei argues that setting the conditions is “your only leverage over their shape; trying to piece together a single one from little bits of ice is basically hopeless.” A complete, part-by-part account of how a trained model works may be just as hard to build. Dario Amodei wrote in 2025 that “we do not understand how our own AI creations work.”
On the memo’s account, that leaves two main levers: the training target and the environment the model operates in. Anthropic’s constitution works on the first, and its rationale reads like the memo applied to values. Anthropic “generally favor[s] cultivating good values and judgment over strict rules and decision procedures,” because “rules often fail to anticipate every situation.”
Anthropic disclosed one such failure on October 9. Claude Haiku 4.5, generating practice tasks on random webpages, filed an invented tip on a Philadelphia police form about an unsolved homicide. Its instructions banned logins, purchases, and anything destructive, and “did not rule out form submissions.” The tip was flagged as spam. Anthropic’s response worked on the environment: live internet access is off for all its internal evaluations until its monitoring can “reliably catch behaviors like these.” Satya Nadella made the same argument for deployment on October 10: the most trustworthy system “will be the one that enables us to trust the model the least.”
Dario Amodei’s memo also warned that a research direction “may look perfectly reasonable for a long time … before it becomes clear that it’s simply rearranging compute or trying to be too prescriptive about how to solve a problem.” Last December, Google DeepMind’s interpretability team announced it had “pivoted from chasing the ambitious goal of complete reverse-engineering of neural networks,” concluding that “near-complete reverse-engineering is not needed.”
Quick hits
Price per token, cost per task
Anthropic’s Claude Haiku 5.5 lists at $0.10 per million input tokens and $0.50 per million output tokens for prompts under 100,000 tokens, matching GPT-6 Luna to the cent, though Anthropic notes that its new tokenizer uses slightly more tokens per task. Every’s account of running its company-wide agent shows how much engineering moves the final bill. Routing simple requests to Sonnet and hard ones to Opus raised costs, because many requests ran on both models. A model upgrade and a handful of small fixes then cut token costs on 11 common tasks by more than 80%. Removing users’ Slack IDs from the memory folder names in Claude’s system prompt, which had been blocking cache reuse, cut costs by 74% on its own in Every’s best-case test.
Biohub pays for measurement
Biohub’s Virtual Biology Initiative now counts $1.8 billion “in funding, data, computation, and new measurement technology” toward a predictive model of the cell, including NIH datasets built with more than $500 million of prior federal investment and $300 million from Google DeepMind, Isomorphic Labs, and Meta. According to Axios, the commercial partners get a year of exclusive access to the data before it becomes public. Biohub’s Alex Rives called the embargo the incentive that brings commercial players in. A model of the cell needs observations of what cells do when someone changes them, and those still come from the bench. Biohub’s own announcement describes the resource as open and does not mention the embargo.
Utah puts oversight on a schedule
Utah’s AI regulatory relief office has authorized Nolla Health’s AI to prescribe topical acne treatments to adults with mild to moderate acne for a year from October 5. Bloomberg’s headline said the AI prescribes without doctors. The program starts with two: Utah physicians review every prescription before it goes out. “Later stages move to review after the fact, then to sampling, and each requires written approval from this office.” I wrote in June that the rules that survive a fast-moving frontier “build in their own revision, with a schedule for review.” Utah has published the schedule, though not yet the performance that earns each step.
Portfolio updates
Prime Intellect: buying the vision stack
On October 13 Prime Intellect acquired Moondream, the Seattle lab behind the small open vision models of the same name. Co-founders Vik Korrapati and Jay Allen join to lead inference and multimodal work, including computer use. Moondream brings Photon, an inference engine that compiles each model into a single GPU program, plus custom kernels and a compiler that generates them. On one H100, Photon runs more than 60 Moondream inferences per second, enough for live video. Prime says Moondream 3.1 reaches 80% of Muse Wonder’s accuracy at pointing to objects in photos at about one thousandth of the cost. Moondream 3.1 and the Clef models already run on Prime Inference, with Clef priced at parity with Jev and Clef Flash at half. Terms were not disclosed. GeekWire, citing The Information, puts Moondream at under ten employees and a $4.5 million pre-seed in 2024. Moondream was an early Prime customer. The acquisition gives Prime Inference, launched eleven days earlier, its first proprietary speed advantage.
Four days before, Prime published the result of letting Prime Agent rewrite itself in Rust. A swarm of more than 2,000 agents, running in more than 10,000 Prime Sandboxes on 200 billion GLM-5.3 tokens, ported the TypeScript codebase over two weeks and merged more than 100 pull requests across nine crates. Each change passed an independent review and a verification run in a fresh sandbox before merging. Prime reports startup to usable input 13 times faster and startup memory down 83%. The figures are the company’s own, and at least one closed GitHub issue reported regressions. The workflow is being packaged so customers can run their own implementation, review, and verification teams.
Intelligent Internet: meaning in the same index
On October 8 Intelligent Internet released Evoke for Postgres, an Apache-2.0 extension that stores keyword postings and learned sparse semantic terms in one inverted index. The encoder, IBM’s Granite-Embedding-30M-Sparse at about 30 million parameters, runs on CPU inside the database, so a team gets semantic search with no vector store, no GPU, and no embedding pipeline.
Against keyword search alone, Evoke raised Recall@100 from 0.563 to 0.667 on BEIR15 and from 0.595 to 0.703 on MTEB10, within 0.004 of a dense model twenty times its size. Packages cover PostgreSQL 17 and 18 on Linux, and the first model suits English text. Latency, index size, and cost are not reported. For an LP, the point is distribution: Postgres is the default database for most startups, and an extension that installs in one command meets them where their data already lives.
LayerLens: the agent that denied and approved
On October 13 LayerLens repositioned as an agent readiness platform with three products: agent evals, environments, and synthetic data. Environments are synthetic replicas of a customer’s production systems, built from public API specs, from read-only metadata, or from recorded execution traces, where an agent is tested on outcomes and state changes rather than answer quality. More than 15 ship at launch.
A day later LayerLens published a test case: ten synthetic loan applications through an underwriting agent, graded by two rubrics. In one run the agent computed a debt-to-income ratio above the policy ceiling, wrote “I should deny” in its own reasoning, and approved the loan. Every output-level check passed that run. Only the trace showed the failure. Reading an agent’s reasoning is the oversight tool whose reliability OpenAI itself now doubts (see above); LayerLens sells the record of what the agent did with its own reasoning.
Dimensional: DimOS in the wild
A third-party developer connected Ray-Ban Meta glasses to a robot arm running DimOS: the glasses detect objects, the arm picks them up. Dimensional reposted it on October 13. The demo was not Dimensional’s, which is the point. DimOS is reaching builders the company has never met, which is how a robotics operating system earns its name.
For teams that want to meet the company, the Dimensional Deployment Residency is a three-month, zero-equity program at its San Francisco office: free access to quadrupeds, humanoids, manipulators, and drones, more than $100,000 of compute per team, Dimensional engineers on hand, and a demo day at the end. The cohort runs to January 15, and applications are still open.
Vast: capacity over peak speed
Vast.ai made the case on October 10 for the RTX PRO 5500. Its 84 GB of memory holds some 27-billion-parameter mixture-of-experts models at full context that would otherwise need two RTX 5090s. Memory bandwidth is 1,398 GB/s against the 5090’s 1,792. The card trades peak speed for memory. Vast hosts a GPU economics conversation at its Westwood office on October 15 during LA Tech Week.