DeepSeek's open source strategy shakes up the AI ​​order

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 As a new force in China's AI field, DeepSeek's open source strategy has indeed impacted the industry landscape dominated by traditional AI giants. This impact is mainly reflected in the following three dimensions:

1. Technological equality drives industry reshuffle

  • DeepSeek open source models (such as DeepSeek-R1) outperform GPT-3.5 in professional fields such as mathematical reasoning and code generation, and are close to the level of GPT-4, breaking the technical barriers of closed-source models.
  • By opening up the MoE architecture model with 175 billion parameters, small and medium-sized enterprises can develop vertical applications based on it without having to train from scratch.
  • Compared with Meta's LLaMA series, DeepSeek models show localization advantages in Chinese comprehension, logical reasoning and other dimensions.

2. Ecological reconstruction triggers value transfer

  • Establish a developer incentive program, provide computing power subsidies (such as 10 million tokens for free API calls), and attract more than 100,000 developers to join the ecosystem
  • Deeply adapt with domestic chip manufacturers (such as Cambrian and BiRen) to promote collaborative innovation of software and hardware
  • Open source models accelerate the AI ​​engineering process. According to third-party statistics, the enterprise-level AI application development cycle is shortened by 40%.

3. Business model subverts traditional path

  • Adopting the "open source customer acquisition + cloud service monetization" model, the commercialization efficiency is 3 times higher than the traditional license model
  • The knowledge payment scenario has been successfully verified, and the payment conversion rate of educational applications supported by its open source model has reached 8.7%.
  • Compared with OpenAI's API charging model, DeepSeek's hybrid business model is more suitable for the needs of the Chinese market.

Real challenges in industry shocks

  • Computing cost pressure: Training a 100-billion-level model costs more than 10 million US dollars per time
  • The boundary of data compliance: the copyright dispute of high-quality Chinese datasets needs to be resolved
  • Commercialization balance point: the contradiction between open source and commercial confidentiality

The current AI competition has entered a battle between "open innovation" and "closed ecology". DeepSeek's open source practice proves that the developer ecology can be quickly built through the democratization of technology, but whether it can continue to shake the old order depends on whether it can make breakthroughs in model iteration speed (currently maintaining an update frequency of 2 months/time), industry penetration depth (covering 8 major fields such as finance, manufacturing, and education) and business closed-loop capabilities. The essence of this change is the paradigm shift of AI value creation from centralized monopoly to distributed innovation.

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