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.

PreviousWelcome to EveryAI NextAbout EveryAI

Last updated 4 months 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.