Even the A100, older than ChatGPT, still rents above its depreciation schedule

Nothing big in the last 24 hours. Here's what led.

GPUs rent far above their depreciation schedules, and Musk warns of stranded compute

a16z's charts show GPUs still renting above a standard 6-year depreciation schedule: H200 +143%, B200 +111%, H100 +54%, and even the six-year-old A100 +9%, with most of the gap opening after April, when token demand took off. Its explanation: token demand on OpenRouter is up about 27x since last November, from 6T to 165T a week, and open-weight models now handle about 72% of it (@a16z). Then Musk, answering a post about a neolab that locked in GPUs early and can now resell them at rents up 2 to 3x (@Yuchenj_UW): "I think we're going to see a lot of stranded compute in partially complete data centers" (@elonmusk, 1.1M).

Split between scarcity and a coming glut. "Two sentence horror: For rent: GB300s, never used." (@MillionInt), and "Why would there be 'stranded compute' and partially-complete data centers if there's endless AI demand?" (@BenjaminDEKR). @GavinSBaker explains the prices with the shift to open models: "$10 in Claude revenue requires $1 of inference compute. $10 of open-weight revenue requires $3 to $7".

Heat: high · 4.8M views, 46 posts · quiet on Reddit

1. OpenAI's ban of the man who took apart his Samsung TV turns into a backlash

On Friday, @log4j said OpenAI had banned his account because his "local reverse engineering" tripped its cyber checks ("daybreak"). That week he had taken apart his Samsung TV and found that it classifies what its mic hears, with dog-barking and baby-crying counters, and borrows a soundbar's mic when it has none (@log4j, 2.1M). On Saturday the ban spread across X, along with user reports that Claude now stops reverse-engineering work done with REA, an open-source tool (@Pirat_Nation).

Mostly anger at OpenAI: "You aren't allowed to stop your tv from spying on your family. You have to ask for permission" (@yacineMTB, 677K). On r/LocalLLaMA the top reply was "Time to move to the Chinese models", and another answered that Chinese models are "now purposely pulling there cyber and IT training".

Heat: high · 1.5M views, 26 posts · #1 on r/LocalLLaMA

2. [rumour] OpenAI wins Replit's coding spend from Anthropic with a 30%+ discount

Per The Information, OpenAI took most of Replit's AI spend, a deal worth more than $100M a year, by cutting its price by over 30%. Claude stays available in Replit, but OpenAI is now the default. Rippling has moved coding work to OpenAI too (@kimmonismus).

@kimmonismus sees OpenAI "potentially deepening its own losses", but notes that "Anthropic's renewed lead on OpenRouter suggests better models can quickly win users back". @MazdakSafaei: "Loyalty to a lab is basically gone."

Heat: low · 63K views, 4 posts · quiet on Reddit

3. OpenAI's Tibo and Meta's Alexandr Wang trade shots over Dots and Muse

Tibo asked for help to "scale dots to 1B users" (@thsottiaux, 1.1M). When Wang replied, Tibo shot back: "Step 1: Quit posting butts and go buy containers. Step 2: ??? Step 3: Celebrate your 12 users". Wang answered "ahem, is the implication you have under 12 users?" (@alexandr_wang), then "someone should check in on tibo" (@alexandr_wang). Muse reportedly had 10.2M US downloads in its first 40 days, against 3.7M for ChatGPT (@StockSavvyShay).

Most think Wang won: "LMFAOOO tibo got cooked here😭" (@ns123abc, 207K), and "This is literally the first time I've ever seen Tibo reply like this 💀" (@hakmgpt, 119K). Under Tibo's reply, users compared the agents: "12 users? Lmao muse is so much better than dot" (@NayteThaGreat).

Heat: high · 2.1M views, 15 posts · quiet on Reddit

4. ISTA squeezes DeepSeek V4.1 Flash to about 2 bits per weight

ISTA's lab released GGUFs of DeepSeek V4.1 Flash 1017 (763B parameters, 6 of 384 experts active per token) at 1.75 to 2.25 bits per weight, made with its GSQ and RCO methods. The backbones are 121 to 155 GB, plus a shared 111 GB Engram file.

Interest, with doubt about the benchmarks: "Too good to be true?" asks why the 2-bit version matches the base model, since the benchmarks are "saturated (90%+, and AIME25 is 100 for every version), so they probably can't show the degradation".

Heat: low · no X posts yet · #6 on r/LocalLLaMA

Recursive's agent "factory" writes open-source libraries with a few messages from its owner

In Recursive's case study, published on 23 October, teams of Claude Opus 5.5, Sonnet 5.5 and GPT-6 Astra agents (planner, engineers, reviewers) wrote libraries from scratch with no dependencies. Its C# image library beats ImageSharp on 27 of 29 tasks and its PDF library beats PdfPig on all 17 rows, by the agents' own measurements, and the owner's messages per library fell from 112 to 11. It matters because the human's part shrank to a few messages per library, while outside test suites still judge the code.

Rewriting its own harness barely helps a model on new games

Jens Lehmann and Sahar Vahdati had frontier models write a full harness for ARC-AGI-3 games, play five development games and rewrite their whole codebase twice. Across 27 lineages, two rewrites raised scores on 20 held-out games by only 2.80 points, and the 95% interval crosses zero. It matters because rewrites swing scores both ways (one nearly doubled, one halved): models can't yet tell which of their own changes really help.