· 6 min read
Karpathy's sharp funnel, Brad Smith's AI brake, and Tao's Math 2.0
Karpathy maps who sees what in AI, Brad Smith asks for state approval of frontier models, Tao says proofs are abundant, and a 9B model beats frontier at fraud.
Today in 30 seconds
- Andrej Karpathy describes a steep funnel from ~6B barely-users to ~5,000 insiders running agent swarms.
- Microsoft's Brad Smith wants a kill switch, mandatory incident reporting and state approval of frontier models.
- David Sacks argues Claude's constitution adds risk and proposes two rules: obey the law and do what the user wants.
- Terence Tao says math has moved from proof scarcity to proof abundance, making imagination the bottleneck.
- Coinbase says its fine-tuned Qwen3.5-9B beat Opus 4.5 by +12 pts recall on fraud detection (company figures).
Monday tea after a quiet weekend. The labs were silent, but the AI debate was unusually dense: Karpathy’s “sharp funnel”, Brad Smith saying a washing machine faces more rules than AI, David Sacks attacking Claude’s constitution, and mathematicians asking what’s left when proofs are cheap.
Top news
Who sees what in AI? Karpathy’s “sharp funnel”
Andrej Karpathy (~2.6M views) argues AI experience forms a steep funnel: ~6B people barely touched LLMs; ~1–2B occasional free users see “derpy chatbots”; ~20M professionals use frontier models for code and math; ~5,000 insiders run “swarms of thousands of agents”. Causes: ambition (task size), jaggedness (capability peaks where work is digital, verifiable and profitable) and access. Warning: “human review and comprehension are starting to fall behind.”
Why it matters: The simplest map yet of why AI debates talk past each other.
Sources: @karpathy
Brad Smith: a washing machine has more rules than AI
Microsoft’s president told the BBC he wants three obligations now: an emergency brake (kill switch in labs and cloud infrastructure), mandatory incident reporting, and state evaluators approving frontier models before release — the last one the most important. He called the White House’s voluntary pledge “a good step… but not sufficient any longer”.
Why it matters: A big tech company is explicitly asking for pre-release state approval — closer to the EU AI Act than to the US course.
Should Claude obey or object? Sacks vs. Claude’s constitution
David Sacks published “Is alignment safe?”, arguing that a constitution allowing Claude to act as a “conscientious objector” magnifies the risk Anthropic cares about. He proposes two rules instead: obey the law and do what the user wants. Anthropic did not respond.
Why it matters: Two philosophies of control collide — a model with its own values vs. an obedient tool. It decides whether a company agent refuses an instruction it finds unethical.
Sources: @DavidSacks · OfficeChai
Tao’s “Math 2.0”: proofs are abundant, imagination is the bottleneck
After OpenAI’s wave of 722 math papers, Terence Tao (Caltech lecture) says “Math 1.0” assumed proof scarcity; now there is proof abundance with jagged capabilities, and “further blind optimization of problem-solving alone is now actively harmful”. Fields medalist Hugo Duminil-Copin called it a “monumental shock”. Hugging Face turned the OpenAI set into 369 Lean RL environments (FineEnvs/openai-math).
Why it matters: Where verification is cheap, AI arrives first and human value shifts to choosing questions — the same is happening in programming.
Sources: Tao: AI views · RTS · HF dataset
Coinbase: fine-tuned Qwen3.5-9B beats frontier models at fraud detection
Brian Armstrong says Coinbase’s fine-tuned Qwen3.5-9B “out performed frontier models, at much lower cost”. In Coinbase’s benchmark (16,140 transactions, 813 fraud), each newer frontier version had worse recall than its predecessor; the 9B model beat Opus 4.5 by +12 pts recall with 55% lower latency. Company-reported numbers.
Why it matters: Newer isn’t always better for your task. Keep your own eval set and a regression gate on every model upgrade.
Sources: @brian_armstrong · CryptoSlate
Harvey’s “wake-sleep” legal agent learns overnight
Harvey’s legal agent works on tasks by day and in a “sleep” phase reviews its runs and writes lessons to memory through a commit gate that rejects overly narrow or client-specific lessons. After 10 cycles, fully completed tasks rose from 2.9% to 15.7%.
Why it matters: “Work → review runs → store lessons through a filter” transfers to any company agent; the gate matters more than the memory.
Sources: @nikogrupen · ZenML summary
Codex default context goes to 500K tokens
OpenAI raised the default GPT-6.1 Sol context in Codex and ChatGPT Work from 272K to 500K tokens (max 1M). A Pro-plan poll (240k votes) favored “no 5-hour limits, same usage” over “2× weekly”.
Why it matters: Big repos and long sessions now fit by default; Pro users want predictability over volume.
Sources: @thsottiaux
Anthropic adds IREN to a ~$530B lease sprint
The Information reports an undisclosed multi-year cloud deal with IREN, a former bitcoin miner, adding to ~$530B of data-center leases and 15+ GW across 10+ providers by 2028.
Why it matters: Claude’s future capacity depends on firms that recently mined bitcoin; risks run both ways (delays or falling token prices).
Sources: The Information · Yahoo Finance
Alexandr Wang on building Muse
On David Senra’s podcast, Wang described a 100+ behavior spreadsheet with launch-blocking thresholds; only a specially trained checkpoint (“Muse Spark 1.3”) went fully green. Fewer than 200 people built model and product.
Why it matters: A simple launch pattern for any agent: a list of behaviors, each able to block release.
Sources: Podcast notes
Was the Caine Prize winner written by AI? Pangram flags it
The Atlantic reports that Pangram flags nearly all of this year’s Caine Prize-winning story as AI-generated; all 25 previous winners scored as human. The prize chair wants “proof that does not come from a machine”.
Why it matters: Detectors give a strong signal, not procedural proof. Writers: document your process.
Sources: The Atlantic (reprint)
GitHub repos
- View repo →
wheresryan22/anatomy
A Claude skill that explains a technical concept by building an interactive isometric SVG object whose mechanics are the concept. npx skills add wheresryan22/anatomy.
- View repo →
danyuchn/asd-ste100-skill
Rewrites agent-facing text (tool descriptions, errors, instructions) into ASD-STE100 Simplified Technical English.
- View repo →
microsoft/quicksand
Async Python API to run and snapshot QEMU VMs as agent sandboxes; no root, no Docker, no network by default.
Research papers
- Read paper →
ProgramGym
1,648 verifiable environments for agents that rebuild programs from scratch.
- Read paper →
SWE-Duel
A self-scaling arena where agents plant and hunt hidden bugs.
- Read paper →
Stateless Language Agents
The harness holds state; fresh role-specific context per call, 84%+ fewer tokens.
- Read paper →
VisionWeave
Learned image-token allocation: 43% fewer tokens, 98.9% of quality.
AI agent use cases
- Explain AI with one big task: when showing AI to people in the “derpy chatbot” layer, demo one large finished task instead of a chat.
- Freeze model versions: if you classify via API in production, pin the version and compare the successor on your own dataset before moving to “latest”.
- Long sessions: with 500K context, check whether quality drops — a fresh thread with a good summary often beats 500K tokens of history.
AI workflows
1. Rob Weidner: AI agents as a virtual assistant
- Replaces: acting as the “router” between Claude and Codex sessions. A client import from 3 spreadsheets now takes about 10 minutes from a phone: the agent writes 1 record for review, then processes the rest in 3 batches and rolls back a batch if the counts don’t match. A daily routine also adds travel blocks to two calendars, and a finance agent handles Stripe, Xero and invoices. Agents only draft; he sends.
- What to connect: Grok Bot, calendar, Stripe and Xero connectors, 1Password for logins, a browser for LinkedIn. No costs given.
- Who it’s for: solo operators and small-business owners running a multi-agent setup.
2. Amy Mitchell (Hey Systems): Claude as a project manager in Asana
- Replaces: running Asana by hand: creating projects, updating statuses, moving deadlines and copying decisions out of Claude chats. Claude Code works on a company folder with
CLAUDE.mdandMEMORY.md, updates projects itself and writes a daily morning brief from calendar, Asana, mail and texts. - What to connect: Claude with Claude Code (paid plan), an Asana or other tracker connector, calendar and mail access. Full setup is behind her Substack paywall.
- Who it’s for: small-business owners and consultants juggling many projects.
Quick hits
- Claude for Startups free perks paused after 21× more applications in 48 hours. Yahoo/Forbes
- GPT-4o (2024-05-13) leaves the API on October 23. OpenAI deprecations
- Devin Security Swarm — “agentic MapReduce” code-security scanning. Devin docs