Prompt: "Why can't the Chinese Academy of Sciences produce the DeepSeek model? Please analyze and compare from the perspectives of the background, positioning, personnel team, management, and business model of both the Chinese Academy of Sciences and DeepSeek, and draw conclusions and suggestions."
Analysis of the Differences in Achievements in Artificial Intelligence Between the Chinese Academy of Sciences and DeepSeek#
The differences in achievements in artificial intelligence between the Chinese Academy of Sciences (CAS) and DeepSeek are essentially the result of two different innovation ecosystems. The following is a systematic analysis of this issue:
1. Divergence in Core Goals and Value Orientation#
Chinese Academy of Sciences:#
- Positioning: Focused on national strategic needs, concentrating on basic science, frontier technology exploration, and major scientific research tasks (such as quantum computing and aerospace engineering), pursuing long-term value in academic influence and technological breakthroughs.
- Achievement Measurement Standards: Publication of papers, number of patents, national awards, rather than direct market share or commercialization revenue.
- Case: The achievements of CAS in the AI field are mostly concentrated in theoretical algorithms (such as deep learning framework optimization) and interdisciplinary research in brain science, but few directly commercializable products are launched.
DeepSeek:#
- Positioning: Driven by market demand and commercial competition, pursuing rapid technological iteration and product implementation, directly providing solutions to enterprises or consumers.
- Achievement Measurement Standards: User growth, revenue scale, product performance indicators (such as model accuracy and response speed).
- Case: DeepSeek V3 may quickly enhance the model's practicality in specific scenarios (such as customer service and content generation) through optimized training data, engineering parameter tuning, and user feedback loops.
Key Differences:#
The R&D logic of CAS is "technology-driven" (from theory to experiment), while DeepSeek is "demand-driven" (from market to technology). The different goals lead to fundamental differences in resource allocation and priority setting.
2. Dynamic Balance of Team Structure and Talent Flow#
CAS Team Characteristics:#
- Composition: Mainly composed of researchers and postdoctoral fellows with academic backgrounds, skilled in theoretical modeling and experimental validation, but with limited engineering experience.
- Incentive Mechanism: Promotion and academic honors (such as academician selection) depend on papers and national projects, rather than product implementation.
- Limitations: Lacks the composite talents required for commercialization (such as product managers and operations engineers), making it difficult to form a "research-development-operation" closed loop.
DeepSeek Team Characteristics:#
- Composition: High integration of technical and commercialization teams, with a high proportion of engineers, most of whom have industrial experience (such as backgrounds in BAT).
- Incentive Mechanism: Equity and performance bonuses are directly linked to product market performance, promoting rapid trial and error and iteration.
- Advantages: Attracts top talent through high salaries and flexible mechanisms, even "poaching" young research backbones from CAS.
Key Differences:#
The "academic promotion system" of CAS and the "market competition system" of enterprises create a talent diversion. Many AI talents cultivated by CAS ultimately flow to enterprises, weakening its sustained R&D capabilities.
3. Efficiency Comparison of Management Models and Resource Allocation#
CAS Management Model:#
- Decision-Making Process: Requires approval from multiple academic committees, long project cycles (often measured in 3-5 years), and low flexibility.
- Resource Allocation: Funding is mostly used for large-scale infrastructure (such as supercomputing centers) and long-term basic research, with limited investment in short-term high-risk applications.
- Risk Tolerance: Tends to support projects with high success rates, with insufficient support for "trial-and-error" innovations.
DeepSeek Management Model:#
- Decision-Making Process: Flat structure, allowing the CEO or CTO to make direct decisions and quickly adjust technical directions (such as shifting from NLP to multimodal).
- Resource Allocation: Concentrates resources on core products (such as the V3 model), rapidly optimizing through A/B testing and user feedback.
- Risk Tolerance: Allows a certain proportion of failed projects, encouraging exploration of new directions (such as the metaverse and AI-generated content).
Key Differences:#
The "planned scientific research" of CAS and the "agile development" model of enterprises show a generational gap in efficiency, especially in AI, which heavily relies on data iteration.
4. Disconnection in Business Models and Technology Transfer#
CAS Technology Transfer Bottlenecks:#
- Mechanism Defects: The transfer of scientific research results must go through technology transfer centers or derivative enterprises, with complex processes and unclear benefit distribution (such as intellectual property ownership issues).
- Market Sensitivity: Researchers have insufficient understanding of actual business scenario needs, leading to results that are "well-received but not commercially viable."
- Case: Although CAS has developed AI chips (such as "Cambricon"), the initial commercialization process was slow due to insufficient ecological adaptation.
DeepSeek's Commercialization Advantages:#
- Closed-Loop Capability: Fully controllable from demand insight, model training to customer delivery, allowing for rapid response to market changes.
- Profit Model: Direct monetization through API call charges and customized solutions, creating a positive cycle of funding back to R&D.
Key Differences:#
CAS has a disconnection in the "technology-market" link, while DeepSeek's business model naturally requires seamless integration of technology and market.
5. Ability to Acquire Data and Computing Resources#
CAS Challenges:#
- Data Sources: Academic research often relies on public datasets (such as ImageNet), lacking large-scale private data from real-world scenarios.
- Computing Costs: Although it has supercomputing centers, they must serve multiple disciplines, limiting resources allocated to AI models.
DeepSeek Advantages:#
- Data Accumulation: Obtains vertical domain data (such as finance and healthcare) through commercial partnerships, and can continuously optimize models using user feedback data.
- Computing Investment: Can invest in proprietary GPU clusters and even collaborate with cloud vendors for elastic computing expansion.
Key Differences:#
Training large AI models relies on the dual barriers of "data + computing power," and commercial companies have a resource advantage in this regard.
6. Conclusions and Suggestions#
Conclusions:#
The inability of CAS to launch a model similar to DeepSeek V3 is fundamentally a result of its misalignment in positioning, mechanisms, and market demand. Its strengths lie in basic research, while DeepSeek's competitiveness stems from market-oriented agile innovation. The two are not a matter of "superiority or inferiority," but rather a division of labor in different links of the innovation chain.
Suggestions:#
- Establish a Dual-Track System of "Frontier Exploration - Technology Transfer":
- Separate basic research institutes (focusing on long-term theoretical breakthroughs) and industrial innovation centers (collaborating with enterprises to develop application products).
- Reform Incentive Mechanisms:
- Allow researchers to engage in entrepreneurship or hold equity in derivative companies to enhance the motivation for technology transfer.
- Strengthen Data Ecosystem Cooperation:
- Collaborate with industry giants to build data-sharing platforms (such as in healthcare and transportation) to address the shortfall of scenario-based data.
- Introduce Enterprise Management Thinking:
- Pilot a "project manager responsibility system" in specific projects (such as large model R&D) to shorten decision-making chains.
Final Direction:#
CAS does not need to replicate DeepSeek's path but should leverage its strengths in basic research to become a "technical source" in the AI field, while innovating mechanisms to unblock transfer bottlenecks and form a complementary ecosystem with commercial companies.
Although the response given by DeepSeek may not be entirely credible, the points expressed still highlight significant issues. Like all new technologies, the birth, development, and transformation of AGI require suitable soil, where talent, technology, management, and capital are all indispensable. For DeepSeek, without the astonishing performance returns of tens of billions in quantitative investment from Huansquare, it would not have the confidence to venture into AGI; without these more than 100 young and talented individuals, DeepSeek could not have developed V3 in a short time; without an extremely flat management model, it would not have been able to attract and retain a group of talented young people. In summary, without so many "ifs," there would be no groundbreaking DeepSeek today. The reason CAS cannot produce a large model like DeepSeek V3 is likely not a matter of technology or financial resources, but rather indicates that when top research institutions lack mature commercial transformation mechanisms, they often struggle to become market winners, thus still needing to leverage market entities.