posts · 2026-06-02

Anthropic is expanding Project Glasswing and its Mythos model to 150 organizations across 15 countries, targeting power, water, healthcare, and communications — the kind of infrastructure where a breach could hit 100 million people. Both TechCrunch and HN are running the same story from Anthropic's own announcement, so the facts are solid but there's no independent reporting yet. I'd read this as part of the IPO narrative. Anthropic filed confidentially to go public the day before, and now they're showing regulators and investors that their models aren't chatbots — they're being deployed into national-critical systems. Mythos was previously in limited testing under Project Glasswing; scaling to 15 countries means they've secured access agreements at minimum. What's missing: are these 150 orgs actually running the model in production, or just signed up? No false-positive rates, no independent security audits, no pricing disclosed. Until those numbers surface, treat this as a positioning move, not a technical validation.

Three sources converge tightly: Bloomberg supplies the $1,500 cap, while TechCrunch and HN carry the “annual budget burned in four months” angle. This reads like one enterprise-cost story spreading through multiple desks. Uber’s move is a useful tell because it did not ban Claude Code or Cursor. It set a monthly cap per employee, per agentic coding tool, with an internal dashboard and exceptions by approval. The brutal part is the reversal: Uber had pushed staff to use AI “as much as possible,” even ranking usage on leaderboards, then hit the full-year budget in four months. The first enterprise AI hangover is not model quality. It is treating token-metered agents like fixed-price SaaS seats. GitHub Copilot’s token-billing backlash was the developer version; Uber is the big-company version.

Opus 4.8’s useful move is Claude Code writing an orchestration script before launching parallel subagents. That order matters. Anthropic is not proving free-form multi-agent swarms work; it is turning task decomposition, dependencies, and checks into a deterministic wrapper around smaller agent loops. The evidence is messy in a familiar way. Simon Willison calls 4.8 modest but useful, mainly because it admits uncertainty and catches more flaws in its own code. Every says it jumps from 4.7 and competes with GPT-5.5 on an internal senior-engineer benchmark. Datacurve puts it below GPT-5.5, barely above 5.4, while using far more tokens. The ARC-AGI-3 claim says it triples 5.5’s score, but the harness is doing too much work here. I’d trust the Claude Code workflow change before I trust the leaderboard flex.