DeepSeek's 10 trillion USD grand strategy (via @bookwormengr)

kept by eddie may 26

DeepSeek's strategy to generate substantial revenue does not rely on coding or multimodal models, but on an undisclosed approach outlined in the piece.

Anthropic Accuses 3 Chinese Companies of Harvesting Its Data - The New York Times
Anthropic publicly accused three Chinese AI startups of using around 24,000 fake accounts to scrape data for training rival chatbots.
also on Moonshot, MiniMax, DeepSeek, #competition, #business-strategy, #ai
Anthropic blocks third-party use of Claude Code subscriptions
Anthropic blocks third-party tools like Opencode from using Claude Code subscriptions, sparking user backlash and migration to alternatives.
also on GLM, #business-strategy
Toward Recursive Self-Improvement: How GLM Built Its Own Inference Infrastructure
GLM developed a production inference service on over 100,000 domestically made AI accelerators to run its GLM-5.3‑Flash model and achieved significant performance gains through aggressive memory optimizations.
also on GLM, #ai
9 theses on AI | Sarthak Munshi
AI progress is constrained by long-task reliability, labor reallocation, cost inefficiencies of general APIs, the declining value of raw coding skills, inadequate benchmark testing, the limits of formal verification without strong specs, memory-bound local hardware advantages, the shift from data to environment-driven training, and the rising competitiveness of US open-weight models.
also on Moonshot, #startups, #ai
DSHR's Blog: Distilling The Moat
The article argues that AI companies’ claimed moat from expensive model training is shallow because model distillation allows competitors to cheaply replicate capabilities via repeated API queries.
also on MiniMax, DeepSeek, #competition, #ai