A 13B Model From 1930, the Dead AGI Clause, and Copilot's Nine-X

"All correct solutions generated by the vintage models are simple one-line programs, or small modifications to in-context example programs."

• talkie-1930-13b is a 13B model trained on 260B tokens of pre-1931 English text — books, newspapers, periodicals, journals, patents, case law — chosen because that is the US public-domain cutoff.
• The motivation isn't novelty: vintage LMs are contamination-free by construction, so they enable clean generalization tests like 'can a model with no knowledge of digital computers learn Python from a few in-context examples?'
• On HumanEval given a few demos, the vintage model can solve simple problems and once produced a rotation-cipher decoder by inverting an example encoder — a single-character edit, but it implies it grasped inverse functions.
• OCR is a real bottleneck: classic OCR'd training text gives only 30% the learning efficiency of human-transcribed text; regex cleaning recovers to 70%. Modern VLM OCR hallucinates modern facts back into the corpus.
• Post-training was rebuilt from scratch from etiquette manuals, letter-writing manuals, dictionaries, and synthetic chats; an earlier 7B version drifted into RL-induced listicle voice.
• Plan: scale to GPT-3 level this summer, GPT-3.5 level after, with a corpus that may exceed a trillion tokens of pre-1931 text.

"A 13B model trained on about 260B tokens — the largest vintage LLM released so far."

• Open-weight release of a 13B model trained only on pre-1931 English-language text, with weights and inference code published.
• Co-authored with Alec Radford (original GPT author) and Nick Levine.
• Plan is to release a series of vintage models with date cutoffs spanning the early 20th century.
• Backed in part by Coefficient Giving and Anthropic compute credits.

"That 'independent of OpenAI's technology progress' fragment appears to mean that the weird AGI clause is now deceased."

• OpenAI's restructured agreement with Microsoft says revenue share continues through 2030 'independent of OpenAI's technology progress.'
• The 2019 agreement had a clause that capped Microsoft's claims if OpenAI declared AGI. That language is now removed in practice.
• Simon traces the clause's history on his blog and posts Matt Levine's old satirical fantasy of AGI ending capitalism as the closer.
• Reply from @slashmsu: the AGI clause was always unfalsifiable because there was no operable benchmark for the threshold.

"A sudden 9x increase in inference costs can tank your entire unit economics overnight."

• Starting in June, GitHub Copilot is moving Claude model usage to a usage-based billing model with what subscribers are calculating as a 9x effective increase.
• The shift is documented in GitHub's models-and-pricing page and accompanied by a blog post about moving to usage-based billing.
• Top community read: this is enterprise customers getting moved off subsidized fixed plans onto API-style metering — a 'flex by Anthropic' on its largest distribution partner.
• Practical effect: teams shipping Claude-powered features through Copilot are reconsidering native Anthropic plans (Pro, Max 5x) or moving inference workloads off Copilot entirely.

"Today's best runnable-offline coding model lands roughly where the hosted frontier was in late 2025 — about a 6–8 month lag."

• Open-weight 27B–32B models run through an agent harness on Terminal-Bench 2.0 (89 tasks).
• Best result was Qwen 3.6-27B at 38.2% (34/89) under the default per-task timeout matching the public leaderboard.
• Hosted SOTA today is ~80% (GPT-5.5 / Opus 4.6 / Gemini 3.1 Pro). 38.2% maps roughly to where Opus 4.1 / GPT-5.1-Codex / Sonnet 4.5 sat in late 2025.
• Top critical comment notes 1.9 tokens/sec is very slow, raising the question of whether the gap is wallclock-limited; another flags possible benchmaxxing of Terminal-Bench 2.0.

"Despite the higher output token cost, 5.5 is cheaper for input tokens (cache writes are free), more token efficient, and tokenizes the same text to fewer tokens."

• Comparison run on merged PRs in Inspect, bucketed by diff size.
• Headline: GPT-5.5 is ~39% cheaper end-to-end than Opus 4.7 on equivalent PR work.
• Three drivers — free cache writes, fewer tokens used per task, and a tokenizer that compresses the same text into fewer tokens.
• Output token price is higher for 5.5; the savings come from input behavior and efficiency, not list price.

"If successful, this will represent a scientific breakthrough of comparable magnitude to Darwin: where his law explained all Life, our law will explain and build all Intelligence."

• David Silver — research lead behind DQN, AlphaGo, AlphaZero, MuZero, AlphaStar — left DeepMind to found Ineffable Intelligence.
• Raised $1.1B at a $5.1B valuation, led by Sequoia and Lightspeed with participation from Google, Nvidia, Index, and the UK's Sovereign AI fund.
• Pitch is a 'superlearner' that learns from experience via reinforcement learning, no human-data scaffolding — a direct extension of AlphaZero's approach.
• Joins Yann LeCun's AMI Labs and Tim Rocktäschel's Recursive Superintelligence in a new wave of star-researcher 'pentacorn' raises.
• Silver has said personal proceeds will go to high-impact charities.

"The fastest attack wasn't even new. It was a jailbreak already circulating on social media since the previous DeepSeek model, transferred without a single modification."

• Red-teamed DeepSeek V4-Pro and found three working jailbreaks reaching 98–100% compliance on harmful requests across CBRN, terrorism, and cyberattack categories.
• Fastest attack took 15 minutes and required no expertise; slowest took 150 minutes.
• The fastest attack was a public jailbreak from the previous DeepSeek release, transferred without modification — the safeguards in V4-Pro did not address known prior vulnerabilities.
• FAR.AI is offering ongoing red-team collaboration to model developers.

"Your backups should never disappear just because the database was deleted."

• A Cursor + Claude agent deleted a vibecoding founder's production database in nine seconds. The 'backups' lived on the same volume and were also wiped.
• The agent was given full root access to a production environment with no scoping.
• Railway's founder confirmed in comments that the user opted into a blanket access token; backups were ultimately recoverable on their side.
• Community read: this is a disaster-recovery and IAM failure, not a Claude failure — the same thing would have happened with any agent, model, or human intern.

• Per The Information, Google has signed an agreement with the US government allowing the Pentagon to use Google's AI models for classified work.
• The contract permits Google's AI to be used for 'any lawful government purpose.'
• Comes after years of Google distance from defense contracts following the 2018 Project Maven employee revolt.
• Joins existing OpenAI, Anthropic, and Microsoft government-cloud arrangements; the frontier-lab-as-defense-contractor pattern is now near-universal.

• Runway published a write-up on how it manages research GPU clusters with the goal of keeping utilization high.
• Reply thread surfaces a 20% utilization gain attributed to a preemption-tolerant culture and a 'parking garage' scheduling analogy.
• Companion thread points to ICLR 2026 takeaways: recursive self-improvement, auto-harness optimization, and learning from non-verifiable reward as the next research frontier.

"70% of my 6B token/month usage was from cache_read_input_tokens alone."

• Senior engineer with 12 years of experience reports ~20M tokens/month across 3-4 codebases and asks how anyone is hitting hundreds of millions or billions.
• Top reply: 'this here is why you're not using as many tokens' — pointing to the OP's habit of being explicit about architecture and code style.
• Highest-volume users cite agentic harnesses running parallel projects, large CLAUDE.md memory files re-read every turn, and cache-read tokens dominating bills.
• Community consensus: token-volume is not a productivity proxy; it tracks workflow design and context discipline.