A closed discussion about DeepSeek (Part 3)

Impact on the secondary market

(1) Short-term impact on the US AI market

  • DeepSeek is causing turbulence in the short term , especially in the USA.
  • Stock prices are affected because demand for pre-training is growing more slowly and post-training and inference scaling have not yet scaled fast enough.

(2) Influence on hardware and chip manufacturers

  • DeepSeek uses FP8 instead of FP16 , which allows for optimized use of limited computing resources .
  • Last week, DeepSeek had a big impact in North America : Mark Zuckerberg raised the forecast for Meta’s investments. NVIDIA and TSMC fell , while Broadcom emerged as the only winner .

(3) Impact on NVIDIA and market structure

  • Analysts fear gaps (“air pockets”) in NVIDIA’s transition from H to B cards , which could put pressure on the share price in the short term.
  • Nevertheless, this could represent an opportunity for investors in the long term .
  • DeepSeek’s low training costs ensure short-term market reactions (e.g. to NVIDIA’s share price).
  • In the long term, AI will remain a growth market , especially if CUDA remains the preferred standard – then the need for high-performance hardware will also remain high.

Open Source vs. Closed Source: “If the capabilities are similar, it’s a challenge for closed source”

(1) DeepSeek as a catalyst for the open vs. closed source debate

  • DeepSeek is attracting attention because it is exacerbating the conflict between open and closed source models.
  • OpenAI and others may hold back their best models to protect their technological leadership.
  • But after DeepSeek’s disclosure, other companies may be forced to disclose more as well.

(2) Impact on tech companies and cloud providers

  • Amazon & Co. have not made any changes yet , they continue to follow their existing plans.
  • Open and closed source models currently coexist , especially because cloud providers support both.
  • Universities and small labs are likely to choose DeepSeek because it is freely accessible – but there is no direct competition from major cloud providers.
  • DeepSeek is not yet as mature as Anthropic in areas such as tool use and security , which would be important for long-term acceptance in Western markets.

(3) Open source as a potential threat to closed source models

  • Open source puts price pressure on the entire market.
  • When open source models achieve 95% of the performance of closed source models , the question arises whether expensive closed source models are still competitive.
  • If open source and closed source become technologically equivalent, this would pose a major threat to closed source approaches.

DeepSeeks importance for China: “The vision is more important than the technology”

(1) DeepSeek proves China’s AI progress

  • DeepSeek’s success shows that China is not two years behind the US in AI, but only 3-9 months – or in some areas even further ahead.
  • Historically, China has often broken through technologies where it was blocked by the US – and AI could be a similar example.

(2) International perception and technological leadership

  • DeepSeek is not a product of chance , but the result of continuous development.
  • The success of R1 has raised awareness of China’s AI advances in the United States , especially at the highest levels of decision-making.

(3) China’s advantage: efficient use of limited computing power

  • China can compensate for its lack of computing capacity through engineering.
  • Chinese AI teams must find ways to remain competitive with fewer computing resources – this could influence the future dynamics of the AI ​​race.

(4) The next big breakthrough: Reasoning and infinite context lengths

  • China is currently reproducing existing technologies , but the crucial race is to see who can develop the next big reasoning model.
  • Infinite reasoning could be the next milestone.

(5) Why vision is more important than technology

  • The difference between AI labs is not in the technology, but in their long-term vision.
  • Technology alone is not enough – the crucial question is what vision an AI lab pursues.
  • Ultimately, the vision is more important than the technology.
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