DeepSeek's Breakthrough Revelation

China's AI Uprising: How DeepSeek Shook Silicon Valley

In 2024, the Chinese AI company DeepSeek released the DeepSeek-R1 model, achieving GPT-4 Turbo's inference performance at just 1/8 of the training cost, and announced that the model was entirely trained on domestic Ascend 910B chips. DeepSeek open-sourced its entire model lineup (including DeepSeek-MoE, DeepSeek-R1), completely tearing open the "technological black box" of closed-source models and sparking global developer excitement.

This breakthrough directly caused NVIDIA's stock to plummet 12% in a single day, vaporizing over $100 billion in market value. The community quipped: "DeepSeek is the true 'OpenAI', and OpenAI should rename itself 'CloseAI'."

Technical Breakthrough: DeepSeek leveraged MoE (Mixture of Experts) architecture and quantization compression techniques to achieve highly efficient training with limited computing power.
Ecosystem Insight: Chinese AI teams demonstrated that through algorithm innovation and decentralized resource integration, they can circumvent hardware blockades.
Open Source Power: After open-sourcing, the community contributed 30% of optimization code (such as quantization compression, multilingual extensions), feeding back to model performance and creating a "developer-enterprise" win-win flywheel.
Cost Revolution: Open-source models reduce inference costs to 1/10 of closed-source APIs (e.g., DeepSeek-R1 local deployment costs only $0.005 per thousand tokens), directly threatening OpenAI's business model.

Industry Pain Points: Three Core Contradictions

1. Geopolitics and Chip Blockades: Unequal Computing Resource Distribution

US Chip Policy Containment: By restricting high-end GPU exports from NVIDIA, AMD, and others to regions like China (such as H100, A100 chip embargoes), artificially creating a computing power gap and suppressing AI R&D capabilities in other countries.
Chinese Enterprises' Breakthrough and Cost: Despite Chinese teams (like DeepSeek) achieving breakthroughs through algorithm optimization and domestic chips (like Huawei Ascend) - for example, the DeepSeek-MoE-16B model achieving GPT-4 level with just 1/10 the computing power - over-reliance on centralized computing clusters still results in high costs (single training cost exceeding millions of dollars).
Global Computing Monopoly: NVIDIA dominates 90% of the global AI chip market, raising industry barriers through pricing power, marginalizing small and medium enterprises and developers.

2. Centralized AI Service Cost and Access Barriers

High Usage Costs: OpenAI's ChatGPT API costs $0.06 per thousand tokens, Anthropic's Claude3 is even higher at $0.25 per thousand tokens, with frequent price increases.
Account Banning and Access Restrictions: Centralized platforms mass-ban accounts under the guise of "safety reviews" (such as ChatGPT banning Chinese VPN users), hindering AI service democratization.
Model Opacity: API black-boxing prevents developers from fine-tuning models, suppressing innovation (like optimizing for niche languages).

3. Data Privacy and Innovation Suppression

Data Sovereignty Crisis: User data is used to train closed-source models, creating a "data-model" monopoly loop (e.g., Meta using social data to train LLaMA).
Long-Tail Demand Neglect: AI services for non-English languages and vertical industries (like agriculture, traditional crafts) are underinvested due to low commercial value.
Compliance Risks: Cross-border data flow faces conflicts with regulations like GDPR and China's Data Security Law, dramatically increasing enterprise compliance costs.

EveryAI's Answer: Breaking Monopolies, Constructing a Decentralized AI Future

DeepSeek's victory proves that only decentralization and community building can break monopolies. EveryAI will go even further:

Computing Network: Aggregate global idle devices, constructing a "grassroots AWS", reducing costs by 70%.
Data Sovereignty: Your data always remains local, with AI models serving you, not platforms.
Open Battlefield: Developers can freely deploy models, directly competing with OpenAI and Claude.

We believe the future of AI belongs not to any country or giant, but to every contributor.

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Last updated 1 month ago

China's AI Uprising: How DeepSeek Shook Silicon Valley

Technical Breakthrough: DeepSeek leveraged MoE (Mixture of Experts) architecture and quantization compression techniques to achieve highly efficient training with limited computing power.

Ecosystem Insight: Chinese AI teams demonstrated that through algorithm innovation and decentralized resource integration, they can circumvent hardware blockades.

Open Source Power: After open-sourcing, the community contributed 30% of optimization code (such as quantization compression, multilingual extensions), feeding back to model performance and creating a "developer-enterprise" win-win flywheel.

Cost Revolution: Open-source models reduce inference costs to 1/10 of closed-source APIs (e.g., DeepSeek-R1 local deployment costs only $0.005 per thousand tokens), directly threatening OpenAI's business model.

Industry Pain Points: Three Core Contradictions

1. Geopolitics and Chip Blockades: Unequal Computing Resource Distribution

US Chip Policy Containment: By restricting high-end GPU exports from NVIDIA, AMD, and others to regions like China (such as H100, A100 chip embargoes), artificially creating a computing power gap and suppressing AI R&D capabilities in other countries.

Chinese Enterprises' Breakthrough and Cost: Despite Chinese teams (like DeepSeek) achieving breakthroughs through algorithm optimization and domestic chips (like Huawei Ascend) - for example, the DeepSeek-MoE-16B model achieving GPT-4 level with just 1/10 the computing power - over-reliance on centralized computing clusters still results in high costs (single training cost exceeding millions of dollars).

Global Computing Monopoly: NVIDIA dominates 90% of the global AI chip market, raising industry barriers through pricing power, marginalizing small and medium enterprises and developers.

2. Centralized AI Service Cost and Access Barriers

High Usage Costs: OpenAI's ChatGPT API costs $0.06 per thousand tokens, Anthropic's Claude3 is even higher at $0.25 per thousand tokens, with frequent price increases.

Account Banning and Access Restrictions: Centralized platforms mass-ban accounts under the guise of "safety reviews" (such as ChatGPT banning Chinese VPN users), hindering AI service democratization.

Model Opacity: API black-boxing prevents developers from fine-tuning models, suppressing innovation (like optimizing for niche languages).

3. Data Privacy and Innovation Suppression

Data Sovereignty Crisis: User data is used to train closed-source models, creating a "data-model" monopoly loop (e.g., Meta using social data to train LLaMA).

Long-Tail Demand Neglect: AI services for non-English languages and vertical industries (like agriculture, traditional crafts) are underinvested due to low commercial value.

Compliance Risks: Cross-border data flow faces conflicts with regulations like GDPR and China's Data Security Law, dramatically increasing enterprise compliance costs.

EveryAI's Answer: Breaking Monopolies, Constructing a Decentralized AI Future

DeepSeek's victory proves that only decentralization and community building can break monopolies. EveryAI will go even further:

Computing Network: Aggregate global idle devices, constructing a "grassroots AWS", reducing costs by 70%.

Data Sovereignty: Your data always remains local, with AI models serving you, not platforms.

Open Battlefield: Developers can freely deploy models, directly competing with OpenAI and Claude.

We believe the future of AI belongs not to any country or giant, but to every contributor.