THE GREAT CATCH-UP

However, there are reasons to believe that this is not one of those instances, and it could be equally true that the opposite might happen.

1. THE UN鞋也不能一举天下

While DeepSeek continues to assert that its cost-efficiency gains have preserved its value over time, there are several reasons to doubt this claim. According to reports, the costs associated with training and deploying the company’s models have not declined as dramatically as feared. Specifically, the total investment required to build and run these models has remained relatively stable, with no substantial reductions. This challenge highlights the complexities of modern AI development and the difficulty of accurately measuring change over such timescales.

2. Lateral Acceleration Awards and Infrastructure Costs

Another issue hails from analysts who claim that DeepSeek has access to a large number of high-end Nvidia chips, though not all of the highest specifications. Analysts such as Semianalysis estimate that the cost of running these chips has surpassed $500 million, leaving little room for cost savings. Dario Amodei, the CEO of Anthropic, argues that the "scaling curve" concept in AI suggests that investment in more advanced models is actually required to reduce costs. He explains that the long-term value of high-performance AI far outweighs any short-term savings, making it imperative for companies to reinvest their savings into continued innovation and development.

3. Market Penetrations and Profitability

While there is mounting evidence that DeepSeek has not significantly eroded infrastructure investments, the profitability gains it may have yet to be fully realized. The company’s ability to provide efficient models without unduly scaling costs implies a potential opportunity. However, the transition from Alibaba, the industry’s best AI model at the time, to OpenAI in the frontier AI model race highlights the transitional nature of the competition. The "great catch-up" is just the beginning of a multi-faceted battle for dominance in the AI space.

4. Existence Uncertainty

The scientific consensus remains that DeepSeek currently holds the exclusive position in the frontier deep learning universe, according to sources. However, the speculative nature of the claim complicates its validation and acceptance by the broader research community. Nevertheless, the company’s potential to redefine the boundaries of capabilities in AI is significant. Furthermore, proposed investments in infrastructure and chips by government and private companies that benefit from DeepSeek’s advancements could presume a favorable trajectory for its continued growth.

5. Future Challenges and Scalability

DeepSeek’s initial momentum has been driven by its success in the frontier AI market, but its scalability to end-users and applications is not yet apparent. The company’s growing influence in cross-industry adoption, however, suggests a potential strategy that could be repeatedly tested. Nonetheless, the potential for further challenges, such as regulatory scrutiny or economic competition, remains discretionary. As the AI landscape evolves, the potential impact of DeepSeek on the industry could beMatrix-like, with firmism and foresight playing pivotal roles.

6. Implications of the Scientific Findings

The evidence surrounding DeepSeek’s cost-efficiency gains and regulatory challenges are not without consideration. Furthermore, the initial DEMO cut-offs within China invalidate a substantial portion of DeepSeek’s cap-ex profile, an oversight that undermines its explicit and sustained demonstration of value. Given these ambiguities, it is increasingly uncertain whether DeepSeek’s claims will materialize or reverse. This uncertainty underscores the importance of continued scrutiny and validation of such claims before they are accepted as fact. Ultimately, the implications of these findings could far outweigh the short-term gains claimed, placing the company at heightened risk of disappointment.