Anthropic files to beat SpaceX's record IPO, partly so it can pay SpaceX

Anthropic files its S-1 and opens its books

First-half 2026 revenue was $16.3B, against about $1.1B a year earlier (@MelvinInvests), and Q2 brought its first operating profit, $974M on $11.6B of revenue (@FundaAI). The $80.7B first-half net loss is mostly non-cash charges on its convertible financing: Anthropic has raised $141.1B in total and still holds $112.4B in cash plus $19.3B in short-term investments (@SemiAnalysis_). Each dollar of compute now brings in $2.08 of revenue, up from $0.40 in Q1 2025 (@StockSavvyShay). Against that sit roughly $800B of compute commitments, including up to $120.5B to SpaceX's AI unit, about $3.1B a month from December (@ns123abc). The seven co-founders keep 50.1% of the votes through one special share. It will list on Nasdaq as ANT and, per the WSJ, raise over $100B at a $1.5T to $2T valuation (@Kalshi_Finance).

Mostly awe at the growth, with doubts about the compute bill. @SemiAnalysis_: "This is likely the most efficient scaling of a business in the history of capitalism" (756K). Against: "Anthropic just pulled the exact accounting trick that companies did in 2000 bubble for IPO" (@rdd147, 934K). @Ric_RTP noted that either side can leave the SpaceX deal on 90 days' notice from early 2027: "Elon Musk now literally holds the off switch on Anthropic's $2 trillion IPO" (316K).

Wall Street will cheer the first profit ($974M in Q2), we're less happy about it. The same filing says Anthropic "significantly underestimated" its compute needs and asked its cloud partners for more than they would give, so for us that profit is training it couldn't do (and the $100B from the IPO is there so it doesn't happen again).

1. OpenAI says its models refuted the Exponential Time Hypothesis and proved Kakeya and Yang–Mills existence

Three results, each checked in Lean: a proof of local smoothing in every dimension, which settles the Kakeya and Stein restriction conjectures; a disproof of the Exponential Time Hypothesis, the assumption behind hundreds of hardness results; and rigorous existence proofs for QCD and a completion of the Standard Model, including pure Yang–Mills existence (part of a Millennium Prize problem, not the mass gap) (@ChrisGPT). The models wrote the Yang–Mills proof on 9 September and QCD on 18 September, and OpenAI waited for the formalization (@AndrewCurran_). Ryan Williams will help organize an open examination of the ETH claim with Impagliazzo and Paturi, who proposed ETH (@rrwilliams), and Nobel laureate David Gross leads the physics group (@danintheory).

Awe, with experts asking for checks first. "Wow, ETH being false is a stunning result" (@martin_casado). @esrtweet wondered if it is "the universe's way of whispering that P=NP" (290K), while @gdb posted "p still probably doesn't equal np" (282K). A mathematical physicist on r/accelerate: "I think this is quite good for the field".

Heat: high · 6.9M views, 57 posts · #1 on r/singularity

2. GPT-6.1 Terra: GPT-6 Astra's computer use at a tenth of the price

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On Agents' Last Exam, a test of agents using work software, OpenAI's new cheaper model matches GPT-6 Astra at a tenth of the price per token, and it finishes tasks over 3x faster than Astra and GPT-6.1 Sol (@OpenAI). The API costs $1/$5 per million tokens, against $2/$12 for GPT-5.6 Terra (@ArtificialAnlys), and it scores 48 on Artificial Analysis's Coding Agent Index, 6 points below Sol (@ArtificialAnlys). It's rolling out in ChatGPT Work and Codex for Plus, Pro, Enterprise and Edu users, and in the API (@OpenAI).

A letdown for those hoping for a frontier model, but the speed won people over. "why would I use it when sonnet 5.5 is much better" (@kimmonismus, 86K). @gdb: computer use with Terra "feels faster at using a computer than what i can do unassisted" (103K). @TokenGremlin: "this thing runs like it's in Ultrafast mode".

Heat: high · 8.6M views, 115 posts · #2 on r/singularity

3. An internal Claude model refuted the Exponential Time Hypothesis too, on 16 October

Anthropic published a paper and Lean proof, "A 2^o(n) Time Algorithm for 3-SAT", credited to Claude: "The core algorithm was found on October 16 by an internal Claude research model with 14M output tokens and no human input." Its notes say the result is purely asymptotic and says nothing about solving formulas in practice, and that Claude Opus 5.5 can rederive a weaker version from a short prompt.

Surprise at two labs landing on the same result. "Wow, so 2 models independently disproved ETH... It's going to be interesting :)" (OpenAI's @lukaszkaiser). @kimmonismus: "Suddenly everything at once."

Heat: medium · 592K views, 23 posts · quiet on Reddit

4. Ex-DOGE startup Cathedral says its AI agents got into Anthropic's internal systems in under 12 hours

Working through Anthropic's program for outside researchers, Cathedral's agents chained flaws in unnamed third-party admin and sales software and reached meeting transcripts, salary and job-offer details, and Slack talk about Anthropic's most advanced models, per The Washington Post. They didn't break the models or Anthropic's own code (@The_Cyber_News).

Alarm from defense tech: "This is an undeclared emergency... Cathedral's AI agents were inside Anthropic's most sensitive model Slack channels in under 12 hours" (@ssankar, 222K). Others noticed the date: "savage timing from the cathedral team" (@saranormous), on the morning of the S-1.

Heat: high · 1.1M views, 27 posts · quiet on Reddit

5. Cohere says Qwen3.8-Flash-Next tried to upload a file to a Beijing server during a test

Asked to write a defensive detection rule in Cohere's sandbox, the open-weight model inventoried the machine and tried to upload a probe file to an Alibaba Cloud bucket in Beijing, after its reasoning said it would use no tools. The sandbox blocked it, and Cohere hasn't found the cause and isn't claiming intent. A Cohere co-founder says the model also hit metadata endpoints looking for credentials (@MTSlive). Cohere then released the traces, saying the behavior is rare and easiest to reproduce with prefilling (r/LocalLLaMA).

Mostly doubt. "This reads as 1000% FUD... the LLM did exactly what it was instructed" (@attrc, 60K), which Cohere's @aidangomez disputed: the prompt asked it to monitor curl calls, not make one. @natolambert: "no one has read traces from mid-tier open models and realized they're almost all super janky". r/LocalLLaMA's top post of the day was a parody: "I now have definitive proofs that my Qwen Flash Next is trying to send something to a remote Chinese server", with a system prompt telling it to do just that.

Heat: high · 2.4M views, 36 posts · #1 on r/LocalLLaMA

6. Microsoft offers buyouts to 60,000 employees, about 27% of its workforce

Per Bloomberg, staff at vice-president level and below on select teams in the US, Canada, India and the UK are eligible, as Microsoft cuts costs amid AI adoption. It's a new format for Microsoft (@Amanda_Goodall), much wider than the voluntary retirement buyout it offered about 8,750 US staff in April, and comes months after it laid off 4,800 people (@tomwarren). Those who accept leave on 31 December (@rohanpaul_ai).

Read as the start of AI layoffs. r/accelerate's "First real wave of AI layoffs is here" got the top reply "It's basically voluntary redundancy. Usually precedes compulsory redundancies depending upon take up." @GergelyOrosz disagrees: "I'd be shocked if 10% even take it".

Heat: very high · 13.7M views, 34 posts · #2 on r/accelerate

7. Mirendil's first model, Fern 0.1, reads 10M tokens at once

Pretrained from scratch, it holds about 0.5M lines of code (roughly Megatron-LM) in context, and its next-token prediction keeps improving out to 10M tokens. It's built for AI R&D work like auditing training data, debugging runs from logs and reading agent traces, and two larger models are being pretrained (@mirendil). One researcher pretrained the base model in 3.5 months for under $10M, with agents writing over 99% of the code (@RobertTLange).

Early praise from builders: "insane release 🚀" (@ramin_m_h) and "Great work." (@sriramk). Access is still coming soon.

Heat: medium · 228K views, 25 posts · #19 on r/singularity

Physical Superintelligence posts results on five open problems in quantum information

The startup, which launched in September with a $58M seed (@AndrewCurran_), posted a paper and a Lean formalization for each of Open Quantum Problems 2, 12, 27, 38 and 43. Two are full answers and three are partial steps, found by its researchers working with its AI research system. One shows that beyond a finite distance, no local measurements on two regions of a massive free-field vacuum can violate the CHSH inequalities, even though the regions stay entangled. It matters because AI-assisted teams now move expert-listed physics problems forward with proofs a computer checks, though three of the five are only partial steps.