AI-agents
5 articles
- Anthropic's $2 Trillion IPO Rests on a Profitability Metric That Excludes Its Largest Costs
Anthropic's >80% "gross margin" and $559M "adjusted operating profit" exclude partner revenue sharing, training costs, and stock-based compensation. The S-1 discloses $518B in take-or-pay compute obligations, 80% non-cancelable, with named counterparties including Broadcom ($161B), Google ($111B), and Amazon ($110B). The safety slowdown is a capital preservation maneuver timed to the IPO window.
- The AI Safety Narrative as a Synthetic Moat
Frontier AI labs face rapid algorithmic depreciation and escalating compute costs. Safety regulation can create a synthetic moat by imposing fixed compliance costs that favor incumbents over open-weight competitors.
- How AI Safety Became a Rent-Seeking Institution
AI safety has increasingly become an institutional apparatus optimized for funding, prestige, regulatory influence, and adoption rather than binding constraint. Material AI risks arise from deployed socio-technical systems, requiring layered governance across compute, security, incentives, organizations, and institutions.
- The Intelligence-Explosion Debate Uses the Wrong Model
The key variable is feedback-loop generation time, not loop gain. The central question is whether AI removes bottlenecks faster than new ones emerge.
- The Agentic Extinction of Middle Management: Why AI Agents Are Replacing the Coordination Layer
The 2026 restructuring wave is hitting middle management, not just entry-level roles. The reason is structural: coordination work is repetitive, rule-governed, and embedded in software systems, making it a natural target for agentic AI.