DeepSeek's Achievements: What OpenAI Couldn't Do

Introduction

DeepSeek-R1 has undoubtedly sparked a new wave of enthusiasm in the market. Not only are related stocks soaring, but some individuals have even developed courses and software related to DeepSeek, attempting to profit from the trend. While these phenomena may seem chaotic and carry certain risks, they undeniably reflect the public's curiosity and passion for DeepSeek.

Previously, I analyzed the significance of DeepSeek-R1's emergence, but today I want to delve deeper into the underlying opportunities it presents, specifically the potential to drive the widespread adoption and prosperity of AI applications.

Strategic Insights

At the strategic level, I have consistently emphasized the importance of continuous investment to enhance performance. When technology reaches a certain stage, performance optimization and energy efficiency should become the focus to reduce costs and improve competitiveness.

DeepSeek has caused such a sensation because it has trained the DeepSeek-R1 model, which rivals the performance of OpenAI's o1 model, at a cost far lower than that of OpenAI, Meta, Anthropic, and other major American AI companies. This has given people hope that China's tech industry can break through the containment by the United States.

Moreover, some experts recently believed that the Scaling Law was about to become ineffective. As AI models grow in size, it becomes increasingly difficult to obtain high-quality data, and the marginal effect of performance improvement gradually weakens. Additionally, the sharp increase in computational power demand for AI models also brings serious energy consumption and environmental issues. This has led many to believe that DeepSeek's approach has a good chance of reaching the top of AI models.

However, I agree more with Huang Renxun's view that the Scaling Law is still valid. Increasing investment in funding and computational power can continue to enhance model performance, and the ceiling for this improvement is definitely much higher than that of performance optimization and energy efficiency. In other words, once we have optimized all the details that can be optimized, further performance enhancement can only rely on increased investment.

Therefore, in the long run, relying solely on performance optimization may not be able to keep pace with competitors who continuously invest to improve performance. I have discussed this in detail in my previous articles and live classes in the Tech Training Camp, so I won't elaborate further here.

The True Value of DeepSeek

So, I believe that while we need to maintain a calm perspective on DeepSeek's cutting-edge competitiveness, its actual value may be underestimated.

OpenAI and other leading AI companies have invested substantial resources in training and optimizing models but have failed to address application issues and develop application markets to support the development of these models. High operating costs, complex computational processes, and data security and privacy concerns have led to the companies' continuous need for high levels of financing, which also limits their further expansion and application in the AI field.

Can DeepSeek solve this problem? This requires us to carefully examine the delicate balance between open-source and closed-source models, as well as between performance improvement and market application.

Open-Source Strategy

On the one hand, DeepSeek's open-source approach is different from other models.

Traditionally, open-source means making the code fully public, allowing anyone to freely use, modify, and distribute it, with the open-source provider not profiting. However, in the AI field, open-source is not just about code openness but more importantly about model training and optimization.

DeepSeek makes its model structure public and provides well-trained and optimized open-source models, which not only lowers the barrier for users but also ensures model performance. Additionally, DeepSeek continuously collects user feedback and data through online services to further optimize model performance. In the future, it may even adjust model parameters in real-time based on user usage, providing more efficient and personalized services.

In the future, similar to Meta, DeepSeek's open-source strategy will attract developers and researchers from around the world to participate, forming a larger collaborative ecosystem. This cooperation model will greatly promote the innovation and application development of AI technology. At the same time, DeepSeek will gain more technical support and business opportunities from this collaboration, achieving a win-win situation.

Democratizing AI Applications

On the other hand, DeepSeek is expected to address the issue of the democratization of AI applications.

Currently, many companies developing AI applications have achieved significant revenue, indicating that AI technology is already mature enough. For example, Palantir, whose stock price has recently surged, has significantly improved operational efficiency by building its own AI platform. Not only did its fourth-quarter revenue reach $800 million, far exceeding market expectations, but its user base also increased by 43%.

However, these successes still seem to belong only to large software companies, with limited opportunities for entrepreneurs and startups.

The emergence of DeepSeek has broken this deadlock. Through innovative architecture and training methods, DeepSeek has successfully reduced the development and usage costs of AI models, enabling more people to try and use AI technology. This approach will not only promote the widespread adoption of AI technology but also help discover new application scenarios and demands.

Currently, many enterprises have already developed low-cost applications using DeepSeek's open-source models, further proving the feasibility and commercial value of the DeepSeek model.

In the future, with the emergence of low-cost, high-performance AI solutions, more and more people will start using AI technology, and new demands and application scenarios will continue to emerge, driving the development of the entire AI industry.

Conclusion

In summary, DeepSeek will promote some new trends in the current AI industry, namely that the development of general-purpose technology has matured, and the development of supporting technologies as well as the application and commercialization of technology will become more important.

In the future, with the development of multi-modal technology and the continuous expansion of application scenarios, AI technology will play a more important role in more fields, providing more development opportunities and space for emerging AI companies like DeepSeek. This is also the content that I will discuss in future articles, and I welcome entrepreneurs and tech entrepreneurs to continue to pay attention.