Parts: 1. DeepSeek-MOE 2. DeepSeek-V2 3. DeepSeekMath 4. DeepSeek-Prover-V1.5 5. DeepSeek-V3 6. DeepSeek-R1 7. Implications for the AI Industry and the World Recently, the announcement of DeepSeek-R1 shook the AI world, as an open source project managed to match the performance of OpenAI's state-of-the-art API, o1, within months of its release. The market reacted vehemently to this news, with Nvidia's stock dropping 18% in a singe day. AI researchers, engineers, and commentators alike took to Twitter/X to share their thoughts on DeepSeek-R1's implications for the AI industry and the United States, with many asserting that the age of American AI had come and gone in a flash, with China now firmly taking the lead. But were these takes correct? In order to dissect the true implications for the world going forward, we first need to understand DeepSeek-R1 on a fundamental level - what is it, what does it do, how does it work, and what are the key innovations that it introduced. To do this effectively, we are going to start at the beginning of DeepSeek's major papers and work our way forward in time, tracing out the researchers' reasoning and how they arrived at the final design for DeepSeek-R1. This final design included two key components: 1. An efficient mixture of experts language model base 2. Reinforcement learning-tuned chain of thought capabilities In this blog series, we will explore two separate but related series of papers in order to deeply understand the two key components of DeepSeek-R1. First, we will trace the evolution of the mixture of experts architecture from DeepSeek-MOE to DeepSeek-V3, their newest state-of-the-art language model. We will then turn our attention to reinforcement learning-tuned chain of thought, beginning with the seminal DeepSeekMath paper and working our way forward to the current AI darling - DeepSeek-R1. With this strong foundational knowledge of the theoretical underpinnings of DeepSeek-R1, we will be able to separate the hype from the noise. In light of what we've learned from these paper deep dives, this blog series will conclude with an analysis of the implications of DeepSeek-R1 from several perspectives: 1. Technological progress 2. AI market dynamics 3. Geopolitical risks By the end of this series, you will have a clear, evidence-based understanding of DeepSeek-R1—what makes it powerful, where it stands relative to its competitors, and what its long-term impact might be. As the AI landscape continues to shift at an unprecedented pace, cutting through speculation and focusing on the fundamentals will be key to making sense of the road ahead. Let’s dive in.