OpenAI
10 articles
- The Mechanical Repricing of Concentrated Tech Valuations
The U.S. equity market is in a mechanical repricing cycle. The convergence of the 2027 earnings-growth cliff, the discount-rate regime shift, and the AI capex circularity will compress concentrated tech valuations by 10–15% within the November 2026 – May 2027 window. The 20%+ tail is gated by the AI capex → revenue conversion.
- 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 Duplication Tax: The U.S.-China Tech Split Is a Permanent Infrastructure Cost
Export controls and data-sovereignty laws have divided the technology economy into two non-interoperable production systems. Multinationals now face a permanent cost premium from bifurcating infrastructure, products, data systems, and compliance operations.
- The Zero-Click Consumer Insight Void Is a Value-Capture Fight
Agentic commerce relocates consumer behavioral data into three owned chokepoints: the agent platform, merchant, and payment rail. Their owners determine access, pricing, and value capture.
- 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 Two-Layer Constraint on AI Infrastructure
Power and permitting cap data center capacity. Credit markets determine which sponsors can finance projects within that physical ceiling. Hyperscaler balance sheets and guarantees provide the cheapest credit enhancement, concentrating the buildout among a few large platforms.
- The AI Empirical Grounding Bottleneck
Open-weight foundation models have collapsed the intelligence generation layer to near-zero marginal cost. The structural bottleneck is migrating to the verification layer — where search engines, financial data aggregators, and content publishers are converting their historical indexing moats into paid API tollbooths. This is not a single monopoly rent; it is a fragmented patchwork of metered, unevenly-priced access gates whose integration complexity is the real constraint.
- The Indian Engineering Bifurcation: Why the AI Buildout Is Reshuffling Talent Arbitrage
The U.S. AI infrastructure buildout is not a uniform win or loss for Indian engineering talent. The traditional IT-services labor arbitrage model is structurally shrinking, while high-value engineering work is expanding directly in India through GCCs, frontier AI labs, and chip design centers.
- The AI Implementation Tax: Where Enterprise AI Dollars Actually Go
Enterprises spent $684 billion on AI in 2025, and $547 billion produced no measurable result. The money is going to the wrong places — idle compute, tool sprawl, consulting markup — while actual return drivers get starved.