This week
Suggest a nameObligation Marketing for AI Builders
The 7-Rung LadderThe 8-Type TaxonomyWrapper or WinnerThe Sample ColumnToday
9 November 2026
This framing cuts to a real tension: if models handle coding, optimization, debugging, and systems design, what's left to teach? Willison is respected, this will resonate with builders and educators alike. The substance depends on whether it's diagnosis or prescription, but either way it's pointing at a structural shift in what skills matter. Worth reading to figure out what parts of your team's hiring and onboarding you should rethink.
Reasoning models are the frontier for agentic systems, and thought injection is a novel attack surface that nobody's defending yet. Success rates up to 50% on operational objectives means this isn't theoretical. For anyone shipping agents with reasoning models, this is a critical threat to model before deployment. Expect guard layers to emerge fast.
This is the first credible demo of AI agents doing genuine scientific research at the frontier, not just optimizing known problems. The key is the formal verification loop: agents propose, Lean proves, the wiki accumulates knowledge, and better agents build on it. For builders exploring multi-agent systems and automated research, this shows what the architecture should look like. For investors, it's proof that AI can accelerate domains where correctness is verifiable.
This is the economics of agentic systems shifting in real time. Once an LLM has reasoned through a problem class, you can bake the reasoning into cheap, deterministic CPU code. For builders: audit your agent pipelines for repeated reasoning patterns and cache them. For investors: this is why LLM inference cost curves are going to hit a wall, and why hybrid systems win.
This is a clean architectural win for multi-agent inference. Small teams in parallel get the diversity of many agents without the coordination overhead of one large team. On formal proofs and exploit development, it beats GPT-class models at a fraction of the cost. The economics matter: if you're running verification workloads or theorem proving, parallel teams should be your baseline. The method generalizes beyond those domains.
arXiv cs.CL
arXiv cs.CL
arXiv cs.AI
arXiv cs.AI
arXiv cs.AI
arXiv cs.LG
arXiv cs.CL
arXiv cs.LG
arXiv cs.CL
arXiv cs.LG
arXiv cs.AI
arXiv cs.CL
arXiv cs.LG
TechCrunch AI
arXiv cs.AI
arXiv cs.CL
arXiv cs.CL
arXiv cs.AI
Coming soon. In the meantime, the Tuesday Brief is free.
Get the free brief