Data reveals an extreme power law where model labs OpenAI and Anthropic capture nearly all AI startup revenue, and their share is growing. This indicates value is accruing to the foundational layer, posing an existential threat to the long-term viability of application-focused startups.

Proprietary labs argue against 'distillation' (using their model outputs for training) while they have built their own models on vast amounts of copyrighted data. This opposition is an anti-competitive tactic, as model outputs are not copyrightable and distillation helps smaller, open players to compete.

Anthropic has surpassed OpenAI's revenue growth while maintaining training costs at a quarter of OpenAI's. This combination of accelerated growth and superior cost efficiency presents a significant competitive threat, a rare dynamic where a competitor is both faster and more efficient.

Anthropic's misuse report exposes a significant industry practice: major companies like Alibaba and Xiaomi use fraudulent accounts to extract reasoning traces from rival models to train their own. More surprisingly, firms like Moonshot routed live customer queries directly to Claude, effectively using a competitor's model as their own backend service.

The battle against AI model distillation is not a niche issue. Anthropic is shutting down millions of accounts per week attempting to distill their models, revealing a highly organized and distributed effort by competitors. This frames the problem as a major cybersecurity and national security challenge, not just a terms-of-service violation.

Meta and Microsoft have reduced employee usage of Anthropic's Claude Code in favor of proprietary tools like MuseCode to control soaring inference costs. While homegrown models may not fully match frontier capabilities yet, enterprise cost controls and data security concerns push companies to mandate internal tools once performance becomes adequate.

Anthropic's quarterly revenue reached $16.8 billion, more than tripling its full 2025 revenue, yet valuation expectations fell sharply. Because investors priced in perpetual exponential compounding rather than a one-time adoption spike, a drop from 140% to 45% quarterly growth triggered market alarm.

Developers are using Anthropic's popular Claude Code "harness" with cheaper, rival models. While Anthropic cannot officially ban this without risking a community revolt, it has quietly banned subscribers and suspended other users, creating reputational risk and portraying the company as a closed ecosystem against developer wishes.

API providers like Anthropic struggle to differentiate between users distilling models for competitive purposes and those conducting large-scale evaluations. Both activities generate similar high-volume, repetitive API calls, creating a detection challenge that also raises user privacy concerns.