Report Reading | Dylan Chiang

Report Reading: Generative Artificial Intelligence Application Development Report (2025)

Fri, 26 Dec 2025 00:00:00 +0000

Report information

Title: Generative Artificial Intelligence Application Development Report (2025)
Issuing agency: China Internet Network Information Center (CNNIC)
Release Date: October 2025
Original link: [Generative Artificial Intelligence Application Development Report (2025)] ( ) (Note: This is CNNIC’s typical reporting path, please refer to the official website for details)
Core Content: Based on the framework of “User Popularization-Industrial Development-Typical Applications-Development Environment”, analyze the development status and future prospects of generative AI.

Comprehensive overview and summary of the report

This report records in detail the explosive growth of generative artificial intelligence (GenAI) in 2025, especially the rapid popularity and technological iteration of the Chinese market.

1. Development characteristics: domestic achievements and efficiency breakthroughs

Scale Explosion: As of June 2025, the number of GenAI users in China reached 515 million, with a penetration rate of 36.5%.
The Rise of DeepSeek: DeepSeek-R1 achieves excellent performance at less than 1/10 the cost of similar models, breaking the “computing power determinism” and topping the application list in 140 countries around the world.
Technical Trends: Logical reasoning capabilities have been significantly improved, multi-modal (Venture Video, Tusheng Audio) has developed by leaps and bounds, reasoning costs have been significantly reduced, and lightweight models (device-side AI) empower more terminal devices.

2. User popularity

Market structure: Doubao and DeepSeek occupy the leading position in the market.
Application motivation: Mainly used for answering questions (80.9%), text processing (36.0%) and audio and video generation (33.0%).
Group Characteristics: Young and middle-aged users are the main force, with users aged 19 and under accounting for 33.8%, showing the young generation’s high acceptance of AI technology.

3. Typical application scenarios

Industry and Agriculture: From smart irrigation, precision agriculture to industrial robots (such as UBTECH Walker S1) in production line training, AI is reshaping the labor structure.
Life Services:
- Smart Search: Shift from “finding links” to “getting answers”, Search as a Service.
- Content Creation: Sora and Keling AI bring short drama and advertising production into the era of “second-level generation”.
- Office Assistant: AI code generation (more than 30% of new code is generated by AI) and intelligent document processing become the norm.

4. Development environment and prospects

Policy Support: Establish an artificial intelligence security governance framework and promote “artificial intelligence +” actions.
Future Trends: Agent has autonomous decision-making and execution capabilities, Embodied Intelligence allows robots to enter the physical world and interact, Scientific Research (AI for Science) accelerates lunar research and weather prediction.

My understanding

This report is not only a accumulation of technical data, but also reveals the paradigm shift of AI technology from “concept laboratory” to “full employee productivity”.

1. Implications for personal research fields (political work and assistant behavior)

The report pointed out that 80.9% of users’ primary need is to “answer questions”, and in the office assistant scenario, AI’s understanding and polishing capabilities have become the core. For my study of Legislative Assistants’ Use of Generative AI, this provides strong external validity:

Efficiency Liberated: Political assistants are faced with the collection of data on a large number of public issues and the preparation of press releases. The high usage of AI search and document processing indicates that there will be an “automated revolution” in political work processes.
Decision Assistance: The “improvement in reasoning capabilities” mentioned in the report means that the assistant will not only use AI to write drafts in the future, but may also use AI to conduct policy impact assessment and voter sentiment analysis.

2. Inspiration for the development of AI in Taiwan and cross-strait

Lessons from cost advantages: The success of DeepSeek shows that Taiwan, with limited computing resources, should focus on algorithm optimization and deep cultivation of specific vertical fields (Vertical AI) instead of simply pursuing parameter scale.
Complementarity of application scenarios: Mainland China has a large scale of industrial and agricultural applications, while Taiwan has advantages in semiconductor end-side AI and high-quality service trade. Both sides of the Taiwan Strait face common challenges in AI governance (such as copyright, data privacy), and there is room for cross-regional dialogue.

3. Thoughts on the direction of academic research

From “use” to “collaboration”: Future academic research should not only focus on whether assistants use AI, but should study how the “human-machine collaboration model” changes the power structure in the political field.
Update of Technology Acceptance Model: The traditional TAM model may not be enough to explain the changes brought about by Agentic workflow. Research needs to focus on users’ psychological boundaries and ethical trust in AI “autonomy”.

Conclusion

The “2025 Report” marks that AI has entered the “full-scenario penetration” stage from a single breakthrough. As researchers, we need to pay close attention to the dynamic balance between the “efficiency dividend” and “ethical risks” brought by AI, especially in the highly sensitive field of political work.

Report reading: Microsoft AI Diffusion Report (2025)

Fri, 26 Dec 2025 00:00:00 +0000

Report information

Title: Microsoft AI Diffusion Report: Mapping Global AI Adoption and Innovation
Published by: Microsoft Research
Release Date: October 2025
Original link:
Core content: Track the global adoption status, infrastructure distribution, narrowing of the technology frontier and challenges faced by AI as the fastest spreading technology in history.

Comprehensive overview and summary of the report

This Microsoft report focuses on the “diffusion” dynamics of AI technology on a global scale, revealing the contradictory current situation of technology popularization and resource concentration coexisting.

1. The fastest technological diffusion in history

Breakthrough Velocity: AI has attracted more than 1.2 billion users in less than three years. Its diffusion rate is far faster than any previous general purpose technology (GPT), such as the Internet, personal computers or smartphones.
Adoption rate differentiation: Although the adoption rate is fast, there are significant differences between the Global North vs. Global South. The adoption rate in northern countries is approximately 2 times that of the South.

2. Frontier trends in infrastructure and models

Concentration of computing power and data: 86% of the world’s data center capacity is concentrated in the United States and China. This extreme concentration of infrastructure creates challenges for the democratization of AI.
Frontier Narrowing: Although the United States (such as OpenAI’s GPT-5 and other benchmarks) still maintains the lead, the performance gap of latecomers is rapidly narrowing. The report states that China is estimated to be less than 6 months behind on the technological frontier.
Model Distribution: The world’s top 200 models are only concentrated in 7 countries (the United States, China, France, South Korea, the United Kingdom, Canada, and Israel), showing the high threshold for technology research and development.

3. Diffusion Leaders

Non-R&D adoption leaders: Countries such as the United Arab Emirates (59.4%), Singapore (58.6%), and Norway (45.3%) stand out.
Success Factors: These countries have proven that even without developing underlying models, they can maintain global leadership in AI adoption through strong education systems, friendly policy environments, and digital infrastructure.

4. Key barriers: language and infrastructure

Language inequality: Adoption rates are significantly lower in low-resource language areas (e.g. Malawi, Laos). The language tolerance of AI models has become a key bottleneck for popularization.
Infrastructure constraints: About half of the world’s population (4 billion people) still lack the basic conditions (electricity, network and broadband) required to use AI.

My understanding

Microsoft’s global perspective provides a broader context for understanding AI’s place in politics, bipartisanship, and academia.

1. Implications for personal research fields (political work and assistant behavior)

The report mentioned that the rapid spread of AI indicates that the “digital generation gap” in the political field will be quickly compressed:

Global knowledge equality and challenges: If 1.2 billion users are using AI, political assistants are no longer a question of “whether to use them”, but a question of how to maintain “political uniqueness” during use.
Skills first: The cases of the United Arab Emirates and Singapore illustrate that “skills use” and “policy guidance” have a more direct impact on the application side than “model development”. This supports the hypothesis in my research that focuses on assistants’ personal technology preferences and environmental support.

2. Inspiration for the development of AI in Taiwan and cross-strait

Strategic opportunities for frontier narrowing: When the frontier technology gap narrows to within 6 months, Taiwan, as the core of the global supply chain, will have a greater say in hardware support in the “inference” stage.
Learn from the “Adoption Leader” model: Taiwan should become a world-leading “AI efficient adoption zone” through regulatory innovation and education transformation, like Singapore or Norway, despite the lack of local ultra-large-scale underlying models.

3. Thoughts on the direction of academic research

Focus on the imbalance of “AI diffusion”: Research should not be limited to advanced regions, but should focus on whether AI has exacerbated the gap between disadvantaged groups (or small parties, resource-poor politicians) and resource concentrators.
Language specificity research: For Taiwan, which uses traditional Chinese, we should study how “language specificity” affects the model’s understanding and expression accuracy in the local political context.

Conclusion

If CNNIC’s report shows the “depth” of China’s AI applications, then Microsoft’s report shows the “breadth” and “imbalance” on a global scale. The future of AI depends not only on who has the strongest model, but also on who can harness this power the fastest and fairest.

Report reading: OpenRouter State of AI (2025)

Wed, 10 Dec 2025 00:00:00 +0000

Report information

Title: State of AI | OpenRouter (Empirical Study)
Publishing Authority: OpenRouter
Published: December 5, 2024 (reported observation period spans 2024-2025)
Original link:
Data Foundation: Real interactive metadata based on OpenRouter unified inference layer 100 trillion (Trillion) tokens, covering 300+ models and 60+ providers around the world.

Report Key Insights (Abstract & Empirical Findings)

OpenRouter’s report avoids traditional subjective evaluations and instead starts from the “real behavior” of large-scale production environments, revealing in-depth patterns in the following dimensions:

1. Paradigm shift of Agentic Inference

Absolute dominance of inference models: The report points out that since the release of the o1 class model at the end of 2024, AI usage has shifted from “content generation” to “multi-step reasoning.” By 2025, more than 50% of total token traffic will flow to inference optimization models (led by xAI’s Grok Code Fast, followed by the Gemini 2.5 series and DeepSeek R1).
Tool-Calling Trend: Data display tool calling is no longer an option for developers, but a default for high-value workflows. The Claude 3.5/3.7 series dominated in the early days, and then Grok and GLM 4.5 quickly entered the market, reflecting that action through planning is the moat for future models.

2. Dynamic balance between open source and closed source (Market Equilibrium)

30% “Open Source Ceiling”: Although closed source models still account for 70% of the market share (focusing on regulated enterprise-level workflows), open source/weighted open models (OSS) have stabilized at around 30%.
The Rise of China’s Open Source Model: The release of DeepSeek V3 and Qwen 3 Coder directly led to a surge in usage. Especially in the field of code assistance (Programming), the Chinese open source model briefly accounted for more than half of OSS code tasks in mid-2025.

3. Model family usage profiles (Provider Profiles)

The report reveals users’ “cognitive division of labor” towards different model brands:

Anthropic (Claude): Extreme focus on Programming and Technology (80%+). Users consider Claude their go-to choice for complex reasoning and engineering.
Google (Gemini): The most diverse performance, covering translation, science, law and general knowledge, showing the characteristics of “digital encyclopedia/information engine”.
OpenAI (GPT): It has undergone a transformation from early science and general knowledge to deeper developer workflow and productivity tools. Its positioning is between Claude’s professionalism and Google’s diversity.
DeepSeek: shows an amazing consumer-heavy, with more than 2/3 of the traffic coming from creativity, entertainment and role-playing.

4. Retention Analysis: Cinderella’s “Glass Slipper Effect”

This is the most interesting finding in the report. The study observed:

Foundational Cohorts: Early users (such as first-month users of Gemini 2.5 or Claude 4 Sonnet) have retention rates of up to 40% after 5 months, much higher than those of subsequent users.
First solution advantage: When a model is the first to solve a specific problem (such as a complex Tool-use or logic difficulty), the user group will have a strong path dependence (Cognitive inertia). This is the “glass shoe” effect: once adapted, a powerful locking effect will occur.

5. Cost and Demand Elasticity (Jevons Paradox)

Price Inelastic: Interestingly, a 10% price drop only drives 0.5-0.7% usage growth. This shows that “quality and trust” are far more important than price, and top companies are willing to pay a premium for stability.
Jevons Paradox: Although the price elasticity is low at the macro level, in the field of “Efficient Giants” (such as Gemini Flash or DeepSeek), low cost does induce larger Token consumption (users start to perform more iterations and longer context queries).

My understanding: academic and practical inspiration behind the data

This “empirical” report elevates the discussion of AI from “whether it is easy to use” to “how to deploy it systematically.”

1. Implications for personal research fields (political work and assistant behavior)

Inference Dividends and Agentization: More than 50% of the models in the report turned to inference, validating my observation that assistants in political work are leveraging AI to handle more “judgmental” tasks. Future research should shift from “whether assistants use AI” to “how assistants guide public opinion through AI’s agentic workflow.”
Scenario Adaptation (Glass Slipper): For political assistants, the first model that can accurately craft their own tone or analyze the sensitivities of a constituency will create loyalties that are extremely difficult to break. This explains why some offices are stuck on older versions of GPT or specific models.

2. Inspiration for the development of AI in Taiwan and cross-strait

Export advantages of open source models: The explosive growth of DeepSeek and Qwen on OpenRouter proves that “open source orientation” is the main driver of Chinese models going global. Taiwan can boldly integrate these “Efficient Giants” on the application side and use cost savings for front-end scene optimization.
The necessity of multi-model architecture: Since no single model can dominate all portraits (DeepSeek for Roleplay, Claude for Code), Taiwanese companies and think tanks should adopt “Multi-model Stack” to achieve the best balance between cost and performance.

3. Thoughts on the direction of academic research

Focus on the bias of agent inference: When AI begins to autonomously call tools and plan paths (Agentic Inference), its bias will no longer just be “saying the wrong thing”, but “doing the wrong thing”. This is an extremely critical new topic in political and legal studies.
Application of Retention Rate Research: We should study how to shorten the journey for users to find the “glass slipper” model to increase the success rate of digital transformation.

Conclusion

OpenRouter’s data reveals a cruel but hopeful truth: In the great era of AI, the emergence of inference models such as o1 has not capped competition, but has opened up a new battlefield of “multi-step operation” and “scene adaptation”**. The future does not belong to the person with the biggest model, but to the person who can accurately find the “glass slipper”.