Author: BUBBLE, BlockBeats
In January 2025, the emergence of DeepSeek R1 caused a stir in the AI world, and it also truly transformed the Crypto AI ecosystem. In the past cycle, Crypto AI mainly revolved around AI Agents, but DeepSeek R1 and its open-source strategy have completely changed the game rules: extremely low training costs, breakthrough adaptive training methods, and the vision of decentralizing the AI industry is no longer just talk, but a tangible reality. This transformation has far-reaching implications. The total market capitalization of the Crypto AI market has shrunk significantly, and many AI tokens have experienced a 70% correction, but is this really a crisis? Or does it mean a thorough reshuffle of Crypto AI? Is DeepSeek the "terminator" that shatters the Crypto AI narrative, or the "disruptor" that accelerates its entry into the era of practical application?
The Rapid Growth of DeepSeek
The development of DeepSeek can be traced back to 2021. At that time, the quantitative trading-focused hedge fund Phantasm began to massively recruit AI talents, which was not common for quantitative companies to shift to AI. The recruits were mostly AI researchers exploring frontier directions, including large language models (LLMs) and text-to-image models. Although there were rumors that Phantasm's transformation was to better utilize the company's idle GPU resources, the main reason was likely to seize the high ground of frontier AI technologies such as large models.
By the end of 2022, Phantasm had attracted more and more top AI talents, mainly from Tsinghua University and Peking University students. Inspired by ChatGPT, Phantasm CEO Liang Wenfeng decided to venture into the field of general artificial intelligence and established DeepSeek in early 2023. However, the rapid rise of AI companies such as Zhishitu, Yuezhi'anmian, and Baichuan Intelligence made it extremely difficult for DeepSeek, a pure research institution without a star founder, to raise funds independently. Therefore, Phantasm chose to spin off DeepSeek and fully fund its development, despite the high risk. This decision allowed DeepSeek to focus on technological breakthroughs without the pressure of profitability or valuation from investors, and the team of young innovators could charge ahead in a fertile land, making DeepSeek more like a research institute than a company.
Just like OpenAI in its early days, no one would have thought that a company researching robot hands playing Rubik's cubes would eventually develop ChatGPT, and no one could have imagined how Phantasm, a quantitative trading company, would use DeepSeek to burst the current AI bubble, with the former taking 7 years and the latter only 2 years. In November 2023, DeepSeek released its LLM with 67 billion parameters, with performance close to GPT-4. In May 2024, DeepSeek-V2 was launched, and in December of the same year, DeepSeek-V3 performed on par with GPT-4o and Claude 3.5 Sonnet in benchmark tests. DeepSeek's rapid technological leaps were not due to the company's financial strength or high academic credentials, but rather the "ChatGPT impact on the world's AI industry" after a technological singularity, with small and large singularities accelerating in any soil that can satisfy the imagination, until the next critical singularity appears.
Finally, in January 2025, DeepSeek accelerated through the singularity, using the first generation of large models with reasoning capabilities, DeepSeek-R1, to open the door with training costs far lower than ChatGPT-O1 and outstanding performance.
Using Open Source to Distribute the Key to the Stargate to the World
The day after the release and open-sourcing of DeepSeek R1, former US President Trump officially announced the start of a $500 billion "Stargate" program at a White House press conference. A joint venture called Stargate, formed by OpenAI, SoftBank, Oracle, and investment firm MGX, will build new AI infrastructure for OpenAI in the United States.
This level of investment is even comparable to the "Manhattan Project," with the apparent intention of using algorithmic stacking to push closed-source AI to new heights and monopolize the AI market to ensure the leading position of the US domestic AI industry. However, the plan's release did not anticipate that just a few days later, this open-source model from across the ocean would directly refuse to open the door, not only bringing a hammer to smash the wall, but also distributing hammers to others.
As an open-source model that can match the top closed-source models, DeepSeek's new training architecture has triggered a chain reaction, making it difficult for closed-source AI to move forward, and closed-source models that cannot keep up with DeepSeek R1 will be directly eliminated by the capital market. Even Marc Andreessen, the founder of A16z and an investor in OpenAI, publicly stated that more attention should be paid to open-source AI rather than closed-source AI. In the industry, whether supporting the potential of AGI or supporting AI only as an upgrade to the SaaS industry, it is widely recognized that the drawbacks of closed-source are far greater than open-source, be it black boxes, industry monopolies, information security, or capital control of attention, any of which is a highly dangerous direction of development.
Although some industry insiders have doubts about the large data set required for V3's Mixture of Experts (MoE) technology and the high hardware resource requirements for R1's reinforcement learning (RL) method, which are suspected of using distilled models from OpenAI and exaggerating the number of training chips, this does not diminish the industry-restructuring impact it has brought.
The open-sourcing of DeepSeek R1 has broken the commercial logic of OpenAI's closed-source large models, using the logic of self-evolution to avoid the massive investment in computing power and data annotation required by the traditional paradigm. Although the model training is still a black box, the cost of the black box has been greatly reduced.
At the AI hardware level, DeepSeek's open-sourcing of V3 directly challenges Nvidia's market dominance. Nvidia's GPU moat is largely due to its underlying parallel computing platform and programming model CUDA, as well as its extensive ecosystem and sufficient developers, making the learning cost too high for using non-Nvidia chips for training, while the high purchase thresholds and political restrictions have led to a fragmentation of global AI development.
For us, in the short term, the AI sector in the US stock market has shrunk significantly, and the total market capitalization of Crypto AI is almost severed, with the market entering a bear market. But in the long run, the most recognized AI industry is moving towards an open-source, transparent, and decentralized development path. From any perspective, the integration of Crypto and AI will become more and more seamless.
The Redemption of Crypto AI, Forge Ahead! Forge Ahead! Advance by Any Means Necessary
In this round of Crypto AI bubble bursting, many AI concept Tokens have undergone a 70% correction, and the Crypto AI market has shrunk significantly. Some jokingly say, "You can train a large model for $5.5 million now, so why buy Crypto AI?" It is true that Crypto is a capital-driven market, not a product-driven one, and 90% of AI tokens have no real meaning.
However, with the improvement of the crypto market regulatory system, the crypto market is still the most suitable soil for the entrepreneurship of small and medium-sized AI companies. The 1/100 large model cost and training methods brought by DeepSeek, compared to ChatGPT O1, will bring ecosystem growth that is orders of magnitude greater than the current market.
Here is the English translation:To put it directly, what DeepSeek brings to crypto is a decentralized training model, which may make Depin-type projects more reasonable, making the training process and information feeding more transparent, and the value reward mechanism for data contributors more reasonable, making the settlement between the supply and demand of model training easier. The development of the surrounding ecosystem of the AI industry, which is more than a hundred times larger, has also made the industrial richness of the downstream of Crypto AI more complete. As long as one of the competitive and creative products in the market really breaks through, external capital will naturally flow back to Crypto.
Many Crypto AI projects have already integrated DeepSeek or are updating their architecture, including ElizaOS, Argo, Myshell, Build, Hyperbolic, Nillion Network, infraX, and some of these projects have directly optimized their products through DeepSeek.
Myshell
Myshell has integrated V3 and R1, and even the image generation model Janus-Pro, into its chatbot and application plugin production process. As a project that has always insisted on polishing its products in the blockchain, and has even gained a reputation in the Web2AI products but is reluctant to issue tokens, the open-sourcing of DeepSeek will bring good news to Myshell users in terms of cost, and the lower cost will bring more Agent developers to the already well-developed Myshell product.
Argo
Argo's developer Sam Gao has integrated DeepSeek R1 as a standard into Argo's important functions from the initial product design stage. As a workflow system, Argo has assigned the original workflow generation work to DeepSeek R1. Due to the nature of the Workflow, the Token consumption and context information volume will be extremely large, with an average of >=10k Tokens. Argo has also integrated CoT (Chain-of-Thought) into its Workflow thinking process. After the open-sourcing of DeepSeek, not only has the cost of the workflow product been reduced, but users' privacy and security can also be guaranteed by local deployment of LLM in Argo.
Argo has integrated its CoT (Chain-of-Thought) model training logic into the Agent Workflow production process even before the release of DeepSeek R1. Especially for tasks such as meme trading and market trend analysis, Argo has customized its workflow using Graph-of-Thought (GoT), which is a novel approach that constructs reasoning as a graph with nodes representing "LLM thoughts" and edges representing the dependencies between these thoughts.
Hyperbolic
Hyperbolic Labs has also announced that it will host the DeepSeek-R1 model on its GPU platform, allowing users to rent Hyperblic GPU resources to run the DeepSeek-R1 model locally or in a designated data center without having to send sensitive data to DeepSeek's servers. This approach not only ensures data privacy, but also allows users to take advantage of DeepSeek's outstanding reasoning performance at a lower cost through Hyperbolic's decentralized computing network, which will be a competitive solution for startups, individual entrepreneurs, or simply efficient AI users.
This round of bubble burst has indeed dealt a heavy blow to the Crypto AI market, with many AI Tokens losing their speculative value. But essentially, DeepSeek is not destroying Crypto AI, but forcing the market to evolve faster. After DeepSeek R1, the future of Crypto AI will no longer rely solely on speculation, but will need to be reconstructed around the directions of decentralized AI computing, economic incentives for model training, fair distribution of AI resources, and practical products. The real challenge is whether Crypto can use the technological revolution brought by DeepSeek to build a truly valuable AI ecosystem, rather than just creating concepts and speculation.
This is not the end, but the evolution. Crypto AI needs to move forward faster and more aggressively. / Accelerate
