Deepseek

The open-weight model conversation in 2023 was about whether the open ecosystem could keep up with the frontier labs at all. The conversation in 2024 was about how big the gap was. The conversation in 2026 has changed shape: on most benchmarks that matter to most production workloads, the open-weight ecosystem has either closed or substantially narrowed the gap, and the strategic question is no longer “can we use open models” but “which open model fits this workload best.” ...

Two years ago the way to make a model better was to train a bigger one. By the start of 2026 that recipe has stopped being the most interesting answer. The frontier has moved to a different lever - letting the model think for longer at inference time, generating intermediate reasoning, and only then producing the final answer. The category has a name now (reasoning models) and a family of products built around it. The interesting questions are no longer whether the trick works, because it clearly does, but when to reach for one, where it lands in production, and what the costs actually look like once the demo glow wears off. ...

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. ...