DeepSeek-R1 1st Anniversary: The End of 'Compute Superstition' and the Dawn of the Reasoning Era

In January 2025, DeepSeek-R1 burst onto the scene. Today, one year later, as we look back from 2026, this event—known in the industry as the "DeepSeek Shock"—has had an impact far beyond that of a single open-source model. It was not just a model, but a major turning point in the global artificial intelligence roadmap.

1. Breaking the "Compute Equation": The Technical Legacy of R1

Before R1, the industry generally believed in "Compute is Justice," thinking that leaps in reasoning capability depended on astronomical cluster sizes. DeepSeek-R1 spent a year proving three core propositions:

• Democratization of Reinforcement Learning (RL): R1 showed the world for the first time that large-scale reinforcement learning (especially its innovative GRPO algorithm) could enable models to spontaneously generate "reflection, error correction, and verification" Chains of Thought (CoT). This "spark of thinking" is no longer the privilege of closed laboratories.
• Transparency of Reasoning Processes: Unlike some vendors who hide their thinking paths, R1 fully demonstrated how the model thinks. Over the past year, countless developers have used R1's Reasoning Traces to distill smaller models, triggering an explosion of "small but strong" reasoning models in the open-source community.
• Extreme Squeezing of Computational Efficiency: R1 proved that under constrained hardware conditions, it is entirely possible to achieve $10\times$ or even higher training efficiency improvements through algorithmic refactoring.

2. Changing the Landscape: From "Arms Race" to "Efficiency Race"

Over the past year, DeepSeek-R1 has forced global tech giants to re-examine their strategies:

• Awakening of Open Source Power: R1's success directly pushed vendors like Meta and Mistral to radically open source their reasoning domains, breaking the long-standing monopoly of high-performance reasoning models by closed-source vendors.
• Breaking the Cost Curve: R1's extremely low API pricing strategy triggered a global "price war" for large models in 2025, forcing Silicon Valley vendors to optimize their inference costs.
• Return of Architectural Innovation: The industry no longer solely discusses "parameter count" but turns to "model architecture optimization"—this is the fundamental reason why technologies like MHC and DSA, which deepseekv4.app is focusing on, are gaining such high attention.

3. From R1 to V4: Extension and Evolution of Logic

If R1 was a "surprise attack" by DeepSeek in the reasoning domain, then the upcoming DeepSeek-V4 is a "full-scale trench warfare." Judging from current technical intelligence, V4 is inheriting two major spiritual legacies of R1:

1. Native Reasoning Integration: V4 is no longer an external reasoning module but deeply internalizes R1's thinking ability into the model's base layer.
2. Decoupling of Knowledge and Logic: Through the Engram system, V4 attempts to solve the memory pressure R1 faced when handling ultra-large-scale background knowledge, realizing "having a brain (logic) and also a bookshelf (knowledge)."

4. Conclusion

The year of DeepSeek-R1 was a turning point for the AI industry to return to rationality. It tells us: the depth of algorithms can compensate for the thickness of compute, and the breadth of open source can dissolve the height of closed source.

For readers of deepseekv4.app, R1's anniversary is not an end, but the prelude to DeepSeek-V4 formally taking the baton and opening the next "intelligence dividend period."