Looking back at 2025, the narrative logic of the technology world underwent a fundamental shift.
If the past was defined by the rapid rise of large models, this year was defined by the comprehensive implementation of data intelligence. From the sudden rise of DeepSeek to the scaled application of Agents, behind every keyword lies another restructuring of technology and business.
As a builder of the technology ecosystem, MatrixOrigin combines industry trends with frontline practice to summarize 10 key terms across four dimensions for 2025.
01 Model Evolution: From General Knowledge to Logic
AI is beginning to gain stronger reasoning capability and physical perception, while the model ecosystem is forming a new structure with one leading player, multiple strong players, and edge-cloud collaboration.
- DeepSeek & China Innovation
- Definition: A phenomenal presence in 2025. DeepSeek and the domestic model ecosystem it represents have entered the world's first tier in reasoning and coding capabilities, marking China's innovation as a core force in the global AI landscape.
- In-depth interpretation: The rise of domestic models is not only a victory of algorithms, but also the result of coordinated evolution among underlying compute power and data infrastructure. It proves that even with limited compute power, world-class models can still be trained through extreme software-engineering optimization and high-quality data governance.
- Reasoning Models
- Definition: Represented by OpenAI o1 and DeepSeek R1, reasoning models significantly improve AI's logical reasoning ability in complex tasks such as mathematics, coding, and scientific research through reinforcement learning and chain-of-thought techniques.
- In-depth interpretation: Reasoning models allow AI to move beyond parroting and begin to acquire slow-thinking capability. At the same time, this raises higher requirements for the logical rigor and accuracy of data. Only high-quality data with rigorous logic can train models with rigorous logic.
- World Models
- Definition: These models enable AI not only to understand language, but also to understand physical laws. Releases such as Sora 2.0 and Genie 3 have provided the "brain" for autonomous driving and embodied intelligence.
- In-depth interpretation: Building world models requires massive amounts of video and sensor data. How to efficiently store, retrieve, and process these multimodal spatiotemporal data assets has become a new challenge for data infrastructure.
02 Data Foundation: From Storage and Compute to Governance
Data has become the blood of AI, and infrastructure is accelerating toward AI-Native and multimodal capabilities.
- Multimodal Data Governance
- Definition: 80% of enterprise data, including PDFs, images, videos, and logs, is unstructured dark data. In 2025, governing this heterogeneous data became the top task for enterprise IT.
- In-depth interpretation: Governance is the prerequisite for application. Through a hyper-converged architecture, multimodal data can be semantically parsed, cleaned, and chunked, turning it into knowledge assets that AI can understand. This is the only path to breaking enterprise data silos.
- Agentic RAG
- Definition: The ultimate form of RAG technology. Retrieval is no longer a one-time, static dictionary lookup. Instead, an Agent autonomously plans the path: decompose the question -> perform multi-step retrieval -> self-reflect -> retrieve supplementary information. This gives AI the ability to dig deeper like a human researcher.
- In-depth interpretation: Agentic RAG turns a single user question into dozens of database interactions in the background. This places strict requirements on underlying data infrastructure: it must support extremely low-latency, high-concurrency hybrid retrieval, otherwise AI response speed becomes unacceptable.
- Synthetic Data
- Definition: As high-quality human data becomes scarce, AI-generated data used to train AI has become mainstream.
- In-depth interpretation: Synthetic data is reshaping the data supply chain. But when using synthetic data, avoiding model collapse and ensuring data diversity and lack of bias have become new topics in data engineering.
- NL2SQL (Natural Language to SQL)
- Definition: NL2SQL allows business users to query databases and generate reports directly through natural-language conversations without learning code. In 2025, as reasoning model capabilities improved, NL2SQL accuracy crossed the industrial-grade threshold and became the standard interaction paradigm for enterprise data analysis.
- In-depth interpretation: "Everyone is a data analyst" is no longer just a slogan. But the difficulty of NL2SQL implementation lies not in the model, but in how AI-friendly the database is. The underlying database needs AI-friendly metadata management capabilities and must proactively provide Agents with clear schema and business-logic context to ensure that AI-generated SQL is both accurate and efficient.
03 Application Implementation: From Conversation to Action
AI is moving out of the chat box and into production processes, becoming a new quality productive force with execution capability.
- AI Agent
- Definition: An intelligent system with autonomous perception, planning, decision-making, and execution capabilities. 2025 is called the first year of Agents, and Agents have begun to replace some manual processes in enterprises.
- In-depth interpretation: The core of an Agent is not only its brain, but also memory. Building a unified data foundation with both short-term and long-term memory capabilities, enabling Agents to call business data in real time and accumulate experience, is key to moving from demo to production.
- Embodied AI
- Definition: AI brain + robot body. In 2025, humanoid robots began entering factories and homes to perform dexterous operations.
- In-depth interpretation: Embodied intelligence is the intersection of the physical world and the digital world. It generates enormous and real-time data volumes, placing extremely high requirements on consistent synchronization between edge computing and cloud data.
- Vibe Coding
- Definition: Collins' word of the year. It refers to a new development model in which developers describe intent in natural language, and AI automatically generates code and completes deployment.
- In-depth interpretation: Lowering the programming threshold means an explosion in the number of applications. In the future, data modeling and business-logic orchestration will replace code writing as developers' core competitiveness.
Closing
These 10 keywords outline the double helix of technological evolution in 2025. One strand is the continuous breakthrough of model capabilities, and the other is the continuous strengthening of the data foundation.
When the bubble fades, what remains are the builders who truly create value for industry.
Looking toward 2026, MatrixOrigin will continue to stay true to its original commitment, refine AI-native data infrastructure, and work with every ecosystem partner to move through cycles and foresee the future.