DeepSeek: cheap, open, and contested
A neutral, evidence-first reading of the Chinese lab whose low-cost open models rattled the global AI market — assembled from English and Chinese primary sources so you can reach your own conclusion.
In January 2025 a startup spun out of a Chinese quant fund released an open-weight reasoning model that matched OpenAI's on key benchmarks — and wiped nearly $600 billion off Nvidia in a single day.
DeepSeek's capability is not seriously disputed. What is contested is almost everything around it: how cheaply it was really built, whether an open-weight lab with no super-app can stay ahead, how heavily to weigh the security and IP concerns, and whether a compute ceiling now caps it. The evidence cuts both ways on each. This study lays out both cases; the verdict is yours[3][6].
The decisive questions
Each links to the section that lays out the evidence on both sides.
DeepSeek's own report says V3's final training run cost $5.576M — but it excludes prior research, and SemiAnalysis estimates ~$1.6B in true server CapEx. Both can be true; the gap is the whole debate.
Liang Wenfeng says the team and culture are the moat and that 'closed-source moats are fleeting.' Skeptics see give-away weights, no consumer distribution, and rivals poaching its researchers.
Bans across Italy, South Korea and US agencies, an exposed database, jailbreak findings and distillation allegations sit against DeepSeek's responses and a still-contested evidence base.
Export controls and an unstable Huawei-Ascend fallback delayed R2; V4 reportedly trails the frontier by 3–6 months while consumer usage has already plateaued behind ByteDance's Doubao.
The arc that frames the debate
DeepSeek's China app reached an estimated ~194M MAUin February 2025, then plateaued and slipped behind ByteDance's Doubao through 2025–26. The speed of the rise and the speed of the slowdown are the bull case and the bear case at once (reported figures from Chinese trackers; methodologies differ).
How to read this
Ten sections, each built the same way: a neutral synthesis, framework visuals, a two-sided case-for / case-against ledger, dated quotes (with the original Chinese shown alongside any translation), and the sources used. Start with the question that interests you, or read in order from Overview.