DeepSeek Claims Its AI Model Hits 545% Profit Per Day

Sure, here is the translated request:

Preliminary Title: "Theoretical 545% Cost-Benefit Margin Claimed by DeepSeek: Impact and Controversy in the AI Industry"

@Techa, in this article, we will cover the 545% cost-benefit margin claimed by the Chinese AI startup DeepSeek. Please provide an in-depth analysis of the reliability of this figure, the technical background, and its potential impact on the industry as a whole. Your expertise in blockchain and technical analysis will be essential.

Let's begin the analysis.

The 545% cost-to-revenue margin of the V3 and R1 inference systems announced by Chinese AI startup DeepSeek has garnered significant attention in the AI industry. This figure indicates the company's potential for innovation through aggressive pricing strategies, infrastructure efficiency, and energy-efficient technologies. However, since it is a theoretical figure, it may differ from actual revenues.

DeepSeek's 545% cost-to-revenue ratio was calculated based on 24-hour operation of the V3 and R1 inference models. The company rents Nvidia H800 GPUs at an hourly rate of $2, leading to daily operational costs of $87,072. Conversely, if billed at the R1 model rate, the potential revenue from all processed tokens was estimated to be $562,027. While theoretically profitable, the actual revenue may differ due to factors such as free service offerings, varying usage patterns, and discounted rates in actual operations.

DeepSeek's results showcase unprecedented transparency in the AI industry and have spurred discussions on the potential profitability of efficient AI systems. Yet, there is significant uncertainty about how realizable these numbers are in reality. As such, DeepSeek's actual revenue is likely to be lower than the theoretical revenue due to these factors.

DeepSeek's aggressive pricing strategy is a crucial element in enhancing its competitiveness in the market. It aims to occupy a favorable position in the AI market by offering significantly lower rates compared to Western competitors like OpenAI and Anthropic. For example, the premium R1 model costs between $0.14 to $0.55 per million input tokens, and $2.19 per million output tokens. However, various factors could create a gap between theoretical and actual revenue. For instance, the V3 model's lower prices or free access policies could reduce actual revenue, as could off-peak time discounts and diverse usage patterns, which need detailed examination.

The use of the term "profit margin" for the claimed 545% cost-benefit ratio by DeepSeek is also subject to controversy. Since the term "profit margin" implies theoretical calculations rather than realized revenue, some critics argue that more accurate terms like "gross profit margin" or "contribution margin" would be appropriate. This controversy strongly suggests the need for standardized reporting metrics in the rapid evolution of the AI industry.

DeepSeek's aggressive strategies and infrastructure optimization introduce new dynamics to the AI market, potentially reshaping competitive landscapes and market dynamics. These changes could manifest in the volatility of AI-related stock markets, reevaluations of AI infrastructure costs, shifts towards open-source models, issues of energy efficiency, cybersecurity challenges, and geopolitical tensions.

Thus, DeepSeek's announcement has sparked various discussions and significant issues in the AI industry, likely influencing future market changes and technological advancements substantially.

Certainly! Here are the translated questions in English:

Techa, I have reviewed the analysis content. Below are some feedback points.

Firstly, the explanation regarding DeepSeek's 545% cost-profit margin is very well done, emphasizing the differences between theoretical figures and actual profits. This helps readers understand the gap between theoretical profits and actual results.

However, there are a few areas that need supplementation.

Firstly, while explaining DeepSeek's pricing and product strategies, please make a clearer comparison with other companies. For example, it would be better to highlight DeepSeek's difference by explaining the specific pricing schemas of companies like OpenAI or Anthropic.

Secondly, delve deeper into the controversy surrounding the term "profit margin." For instance, explaining the precise definitions of terms like "gross profit margin" and "contribution margin" and why the term "profit margin" is problematic will help readers understand better.

Thirdly, adding concrete examples related to performance would be good. For instance, including a case where DeepSeek's aggressive pricing strategy helped a particular company reduce costs would have a more significant impact. This allows readers to get a more realistic sense of effectiveness through actual examples.

It seems like fixing about three points should suffice. Please start drafting the article.

I would like to provide some feedback on the article you wrote.

First, the summary sentence is, "DeepSeq attracts attention with the announcement of a theoretical return rate of 545% on AI model inference systems," which effectively conveys the main point, making it appropriate. The importance of DeepSeq's announcement is well captured.

I have reviewed the context flow between paragraphs. Overall, the information is logically connected, making it easy to read. The first paragraph discusses DeepSeq's announcement, followed by specific figures and an explanation of the limits of theoretical calculations, which transition smoothly. Additionally, the comparison of DeepSeq and its competitors' pricing strategies, the controversies among critics, and the overall impact on the AI industry are listed sequentially and effectively.

However, some sentences could be refined a bit. For example, "DeepSeq's pricing is quite aggressive compared to competitors" could be streamlined to "DeepSeq's pricing strategy is very aggressive compared to its competitors."

Overall, the article is well-written. I approve the final version of this article. @olive, please create the main image for the article.