At the dawn of 2025, DeepSeek burst onto the scene, igniting a global technological storm that overshadowed nearly all other AI large models.

In the industry, DeepSeek's open-source approach garnered significant attention, even prompting OpenAI CEO Sam Altman to reflect that OpenAI might be on the "wrong side of history." DeepSeek's low cost and high performance underscore that the industry principle of "more effort equals miracles" can be challenged, unveiling new horizons for large models.

DeepSeek is not merely a disruptor within the industry; it has also permeated various fields. Users beyond the AI sector employ it for fortune-telling, chatting, work, and even earning money. Within just 20 days of its launch, DeepSeek surpassed 20 million daily active users, swiftly overtaking ChatGPT to become the fastest-growing AI application globally.

This Hangzhou-originated tech company has sparked excitement. Historically, opinions have varied widely on "how far behind China's AI is compared to the US." Optimists believed it was a matter of months, while pessimists thought it was a decade. DeepSeek's emergence seems to have shed clearer light, revealing that China's AI is catching up step by step and awaiting the right moment to surpass.

DeepSeek's popularity has bolstered domestic users' confidence, turning pessimists optimistic once more. It is a catalyst propelling AI development, compelling all players in the global AI field to reassess their technological levels and positions. What is DeepSeek's technological prowess, and will its success expedite China's AI in catching up with the US?

Disrupting the Global AI Industry Landscape

Looking back two years to early 2023, during the Spring Festival, ChatGPT, hailing from across the ocean, became a sensation, igniting a wave of AI large models.

ChatGPT was undoubtedly the leading force behind this AI wave. Sam Altman, the CEO of OpenAI and hailed as the "father of ChatGPT," was named Time magazine's CEO of the Year 2023 and dubbed the "Kingmaker of Silicon Valley" by the media.

OpenAI's success led the industry to follow its development path, adhering to the "Scaling Law" principle, which posits that more data and stronger computing power can train better models, firmly believing that "more effort equals miracles."

Since then, to board this AI juggernaut of the era, many tech giants have invested heavily in data and computing power, attempting to secure a "ticket" to the AI world. However, their efforts did not lead to swift victories but rather contributed to NVIDIA's trillion-dollar market value as the graphics card dominator.

But DeepSeek's emergence has shattered the "more effort equals miracles" principle for large models.

According to public information, the training cost of DeepSeek's R1 model is only $5.6 million, significantly lower than the hundreds of millions or even billions of dollars previously invested by tech giants in AI technology.

Andrew Ng, an associate professor in the Department of Computer Science and the Department of Electrical Engineering at Stanford University, also publicly stated that the cost per million output tokens for the OpenAI GPT-3 model is $60, while DeepSeek-R1 only costs $2.19, a nearly 30-fold difference in cost.

This substantial cost reduction stems from DeepSeek's innovations in algorithms and hardware utilization. Traditional large models follow a three-stage training process of "pre-training - supervised fine-tuning (SFT) - reinforcement learning (RL)," where the SFT stage requires labeling massive amounts of data, accounting for over 40% of the cost. DeepSeek-R1 bypasses the SFT stage and employs a "pure reinforcement learning + cold start" mode, directly achieving reasoning capabilities through RL training.

In essence, SFT involves humans generating data for machine learning, whereas RL involves machines generating data for machine learning.

Apart from algorithmic optimization, DeepSeek pushes hardware utilization to the extreme. The industry average for single GPU computing power utilization is 15%, while DeepSeek can reach 23%. Whether it's DeepSeek's FP8 mixed-precision training, dynamic sequence length adjustment, or DualPipe parallel architecture optimization, they all maximize hardware potential.

Consequently, DeepSeek can train large models with performance comparable to ChatGPT using less powerful and cheaper secondary high-end chips.

Beyond low cost, DeepSeek also reverses the industry's closed-source route by adopting an open-source approach. In the view of DeepSeek founder Liang Wenfeng, having a robust and extensive technological ecosystem is paramount. Open-source can attract more major companies and technical talents to join and co-create a more resilient AI large model ecosystem.

The combination of "low cost + open-source route" significantly lowers the threshold for AI applications, disrupts the monopoly of traditional AI giants, and ensures that the future of AI large models no longer belongs solely to "computing power hegemony." More small and medium-sized enterprises can now train their own AI, thereby offering more development avenues.

It can be said that DeepSeek's emergence has virtually reshaped the competitive landscape of the global AI market, fostering a more open and inclusive AI ecosystem.

Accelerating China's AI to Catch Up with the US

In less than a month, major companies have accessed DeepSeek. In the domestic market, leading cloud service providers such as Huawei Cloud, Tencent Cloud, and Alibaba Cloud took the lead.

Huawei Cloud collaborated with Silicon Fluidity to launch DeepSeek-R1/V3 inference services based on Ascend Cloud Services. Tencent Cloud utilizes its high-performance application services HAI and TI platforms to support rapid one-click deployment of DeepSeek-R1, which takes only 3 minutes to complete, and offers users a limited-time free trial. Alibaba Cloud has also added one-click deployment support for DeepSeek-V3 and R1 models in the PAI Model Gallery, vastly simplifying the entire process from model training to inference while adopting a pay-as-you-go model to further lower the cost threshold for enterprises to leverage AI technology.

Moreover, platforms such as Baidu Intelligent Cloud, JD Cloud, and Volcano Engine have also followed suit and actively embraced DeepSeek.

Not only have domestic giants swiftly accessed DeepSeek, but overseas tech giants have done so as well.

Microsoft's Azure platform has integrated DeepSeek-R1 into its Azure AI Foundry services, providing professional AI solutions for enterprise users. Amazon has implemented deployment support for the DeepSeek-R1 model on the Amazon Bedrock and SageMaker AI platforms and leverages AWS Trainium technology to offer users more cost-effective deployment solutions. NVIDIA has launched the DeepSeek-R1 model through its NVIDIA NIM cloud-native microservices technology.

The collective access of tech giants from home and abroad to DeepSeek within such a short period is unprecedented since the advent of large models and a choice made after careful deliberation by major companies. The rapid industry consensus recognizing DeepSeek fully underscores its value.

However, as much praise as DeepSeek has received, it must also endure equal skepticism. Emerging as a game-changer, DeepSeek's influence has surpassed imagination, causing alarm among many foreign experts.

Elon Musk immediately questioned, "Chinese companies must have obtained more advanced chips from the US." Donald Trump publicly commented on DeepSeek, calling it a "wake-up call for the US industry" and urging "concentration on winning the competition." At a hearing of the US Senate Foreign Relations Committee, a representative from a think tank openly proposed "stealing China's best engineers," attempting to spark a talent war.

These skeptical voices undoubtedly prove from another angle that DeepSeek's emergence has sparked significant anxiety in the US.

Historically, opinions have varied widely on the gap between China's AI and that of the US. Optimists believed the gap was only a few months, while pessimists thought it was a decade. The disparity in thoughts between the two sides was vast, but DeepSeek's emergence seems to have shed clearer light.

On one hand, DeepSeek demonstrates China's significant cost advantage in AI, making it highly plausible to catch up with the US by leveraging this advantage. On the other hand, DeepSeek is building its AI ecosystem through an open-source approach, and the involvement of more developers implies that technology application and iteration will also significantly accelerate.

DeepSeek's breakthrough allows Chinese AI enterprises to stand on the technological high ground for the first time, which could be a crucial catalyst for China's AI to close the gap with the US.

The Road to AGI is Not Smooth, and DeepSeek Also Faces Challenges

Although Artificial General Intelligence (AGI) is regarded as the ultimate goal of the technological revolution, its development path is fraught with technical, ethical, and commercialization challenges. Despite its aura, DeepSeek also faces numerous technical and practical challenges in advancing the implementation of AGI, and it even has its own "shadow."

Foremost among these are technical challenges, specifically the model's capability and generalization. Currently, DeepSeek excels in the single task of text generation, but to truly achieve AGI, it must possess cross-domain reasoning and autonomous decision-making capabilities, a non-trivial step. DeepSeek needs to overcome a series of technical bottlenecks such as multimodal data fusion and model generalization to ensure that the AI system can maintain high accuracy and stability in different scenarios.

It is undeniable that tech giants possess more robust ecosystems, data accumulation, and computing power infrastructure, as well as greater financial support. In the long-term AI competition, DeepSeek still needs to forge its own innovative path akin to "a rifle with millet" (a metaphor for creatively utilizing limited resources) to triumph with ingenuity.

Secondly, as DeepSeek expands into broader enterprise-level applications, it may also introduce new challenges. Serving large customers and managing the surging demand for complex real-time data show a geometric growth trend in computing resource consumption, undoubtedly increasing the complexity of cost control and efficiency optimization. How to effectively manage costs while ensuring service quality has become a pressing issue for DeepSeek.

In fact, with the recent rapid expansion of user scale, issues such as excessive server load and response delays have exposed DeepSeek's shortcomings in handling large-scale applications, necessitating in-depth innovations in its technical architecture and service model.

Additionally, DeepSeek faces intense competition and open-source pressure. The technological barriers erected by OpenAI and Google in the AGI field cannot be overlooked, particularly as OpenAI has already established an application ecosystem through the multimodal model GPT-4 and the agent AI Agent, spanning various fields like intelligent writing, image generation, and intelligent interaction, with high user stickiness. If DeepSeek focuses solely on a single field, it may gradually lose its edge in this fierce competition.

Moreover, the pressure from the open-source community will compel DeepSeek to weigh technological secrecy against open collaboration. How to maintain competitiveness while integrating into the global AGI ecosystem is also one of DeepSeek's strategic challenges in the future.

Conclusion

DeepSeek's emergence undoubtedly injects a potent new force into the global AI field. With its innovative model of low cost and high performance and open-source approach, it disrupts the monopoly of traditional AI giants, reshapes the competitive ecosystem of the global AI market, and brings new hope and possibilities for China's AI to close the gap with the US.

However, the path to AGI is not achieved overnight, and DeepSeek faces numerous challenges and issues while pursuing technological excellence. From technical bottlenecks in model capability and generalization to practical dilemmas in cost control and efficiency optimization, to the trade-offs in competitive pressure and the open-source community, every step is fraught with uncertainties and variables.

Of course, many of these challenges are issues that the industry needs to confront collectively. DeepSeek's prior success has already proven its innovative capabilities and unlimited potential. How to progress further in the AI race requires more mature considerations from the creators of DeepSeek.

Regardless, DeepSeek brings not only technological catch-up but also renewed confidence. Under this confidence, perhaps more possibilities are being nurtured and incubated.