TL;DR

  • DeepSeek’s January 2025 release of R1 shook markets - a frontier-grade reasoning model trained for a reported $6M, a fraction of US lab budgets
  • The app shot to #1 on Apple’s App Store inside days, and the open weights forced an industry-wide rethink of what training really costs
  • Subsequent releases (V3 and beyond) cemented DeepSeek as a serious competitor in the open-source and cost-efficient AI category
  • The story is less “China caught up” and more “the cost floor moved” - implications for closed-model pricing, GPU demand, and open-weight strategy
  • Worth understanding as the moment that made cheap, capable, open models a credible default rather than a curiosity

Overview

In January 2025, a Chinese AI lab most people had never heard of dropped a frontier-grade reasoning model for a reported $6 million and watched it hit the top of the Apple App Store inside days. DeepSeek R1 did not just impress researchers - it shook equity markets, forced a hard look at what US labs were actually spending their billions on, and made cheap, capable, open-weight models a credible default rather than an interesting curiosity.

Venture capitalist Marc Andreessen called it a “groundbreaking achievement”. The lab claims its models rival industry leaders like ChatGPT at a fraction of the cost - and the $6 million training figure, compared to the billions spent by US AI companies, made that claim very hard to dismiss.

DeepSeek had already shipped its V3 base model in late December 2024, but it was the R1 reasoning release in January 2025 that broke into the mainstream. The lab has continued iterating since - V3.1, V3.2, and further reasoning-tuned variants - and has cemented its place as one of the most credible open-weight competitors to the US frontier labs.

YouTube

[2025-01-27] OpenAI is Done, China Won (Deepseek Explained)

[2025-01-26] I Did 5 DeepSeek-R1 Experiments | Better Than OpenAI o1?