Anthropic ships Claude Opus 4.7 with 1M context window
1M-token context arrives in the flagship tier, closing the spec gap with Gemini and shifting the long-context economics for agentic workflows.
500M+
developers worldwide building on AI tools and APIs
GPT-4 class
models now run on consumer hardware thanks to open weights
$200B+
invested in AI infrastructure globally in 2025
78%
Fortune 500 companies running at least one production AI workload
Today
All briefs → Latest briefs
OpenAI Codex lands Microsoft, Stripe as enterprise design partners
Coding-agent product moves from limited preview to named enterprise rollouts, with usage-based pricing and SOC 2 attestation as the procurement unlock.
EU AI Act foundation-model rules finalized, compliance window opens
Final implementing acts published; 12-month compliance window starts for general-purpose model providers. Training-data disclosure is the binding constraint.
Cursor acquires Windsurf in $2.1B all-stock deal
The two leading AI-native IDEs combine, consolidating the coding-tool layer that VS Code's Copilot integration left to startups.
Meta releases Llama 4.5 open weights with 600B sparse MoE
First open-weight release to land near flagship-tier evals; 17B active parameters keep single-node inference viable on H200 boxes.
The White House AI executive orders: what changed, what didn't, what's still moving
The Biden administration's October 2023 AI executive order set a broad framework; subsequent actions and the administration transition have selectively advanced, stalled, or reversed different elements.
Analysis
All analysis → Recent long-form
Weekly digest: May 12-17, 2026
This week's signal across models, tools, applied AI, and policy. Claude Opus 4.7 ships, Cursor-Windsurf closes, EU Act compliance window advances, and the enterprise RAG conversation gets more honest.
The coding-agent procurement cycle
Why enterprise rollouts of AI coding tools are running 9-12 months from pilot to seat-license, and what that timeline tells you about the next leg of the category.
The open vs. closed model debate is the wrong frame
Framing the AI model landscape as 'open source vs. proprietary' obscures the question that actually matters: who controls the training data, and what are the implications of that control.
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