If we compare AI to a digital organism, what capabilities does it need?
- Brain: Thinking and understanding — LLM + Reasoning
- Memory: Storing and recalling — Long Context + RAG
- Hands: Executing and operating — Agent + Tool Learning
- Nerves: Connecting and communicating — MCP
- Body: Perceiving and existing — Multi-Modal + On-Device
- Team: Collaborating and dividing work — Multi-Agent
- Foundation: Supporting and running — AI Infra
These seven capabilities form the complete picture of AI technology in 2026.
Brain: LLM + Reasoning#
From “Fast Thinking” to “Slow Thinking”#
Large Language Models (LLMs) are the “brain” of AI, responsible for understanding and generating language. GPT-4, Claude, and Gemini are all typical LLMs.
Early LLMs were like “intuitive thinkers” — answering immediately when asked, fast but error-prone. This is similar to human “fast thinking” (System 1).
Since 2024, Reasoning has become a new focus. AI began learning “slow thinking” (System 2): when encountering complex problems, it first decomposes, analyzes, and verifies before giving an answer. OpenAI’s o1 and o3 series are representatives of this approach.
Why Does It Matter?#
Imagine you ask AI: “Help me plan a trip to Japan.”
- Fast thinking: Directly gives an itinerary, possibly missing key factors like visas and budget
- Slow thinking: First clarifies your time, budget, and preferences, then gradually plans transportation, accommodation, and attractions, finally checking feasibility
Reasoning enables AI to evolve from a “chatbot” to a “problem solver.”
Representative Products#
| Product | Features |
|---|---|
| OpenAI o1/o3 | Reasoning models trained with reinforcement learning, excelling at math, programming, and scientific problems |
| Claude | Long context + reasoning capabilities, suitable for complex analysis tasks |
| DeepSeek R1 | Open-source reasoning model with high cost-effectiveness |
Future Trends#
Reasoning capability is transitioning from a “premium feature” to a “standard offering.” Future AI will handle more complex multi-step tasks, not just answer questions.
Memory: Long Context + RAG#
AI’s “Short-term Memory” and “Long-term Knowledge Base”#
AI needs to remember information to provide personalized services. There are two mainstream approaches:
Long Context: Equivalent to AI’s “short-term memory.” The amount of text a model can process at once has expanded from thousands to hundreds of thousands or even millions of words. You can “feed” an entire book or codebase to AI for one-time understanding.
RAG (Retrieval-Augmented Generation): Equivalent to AI’s “long-term knowledge base.” When specific information is needed, AI first retrieves relevant content from an external database, then generates an answer based on the retrieved results. This is like humans consulting materials before answering questions.
Analogy#
| Scenario | Long Context | RAG |
|---|---|---|
| Exam | Open-book exam, bring the whole book | Closed-book exam, but can check the library |
| Chat | Remember all previous conversation content | Look up your history when needed |
| Enterprise App | Load all documents at once | Retrieve from enterprise knowledge base on demand |
Representative Products#
- Long Context: Claude (200K tokens), Gemini (1M+ tokens)
- RAG: Various enterprise knowledge bases, intelligent customer service systems
Future Trends#
Long Context and RAG are not replacements but complements. Future AI systems will flexibly combine both: important information in context, massive knowledge retrieved via RAG.
Hands: Agent + Tool Learning#
From “Chatting” to “Doing”#
Early AI could only “chat” — you ask, it answers. The emergence of Agents enables AI to “do things”: call tools, execute tasks, and complete goals.
An Agent is an AI system capable of autonomous planning, execution, and reflection. Give it a goal (“help me book a flight to Shanghai”), and it will automatically decompose tasks, call tools, and handle exceptions.
Tool Learning is the core capability of Agents. AI learns to use various tools: search engines, databases, APIs, and even operating systems.
Analogy#
- LLM: A knowledgeable person with no physical capabilities
- Agent: That person now has tools and can actually do things
Representative Products#
| Product | Function |
|---|---|
| Claude Code | Programming Agent that can write code, run tests, and fix bugs |
| Manus | General-purpose Agent that can complete web browsing, data analysis, and other tasks |
| AutoGPT | Early open-source Agent capable of autonomous planning and task execution |
Future Trends#
Agents are moving from “demo-level” to “production-level.” Future Agents will be more reliable, safer, and capable of handling more complex real-world tasks.
Nerves: MCP#
AI’s “Universal Interface”#
MCP (Model Context Protocol) is an open protocol launched by Anthropic in late 2024, dubbed “USB for AI.”
Before MCP, every AI application needed to develop separate interfaces to connect to external tools. This is like needing a dedicated charger for every new device you buy.
MCP provides a unified standard: developers only need to implement once according to the MCP protocol, and AI can automatically discover and use that tool. This greatly reduces the cost of AI connecting to the external world.
Analogy#
- Without MCP: Each AI application needs to write separate interfaces for each tool, N applications × M tools = N×M interfaces
- With MCP: Applications and tools both follow the same protocol, N applications + M tools = N+M adapters
Representative Products#
- Claude Desktop: One of the first AI applications to support MCP
- Various MCP Servers: MCP adapters for GitHub, Google Drive, databases, and other tools
Future Trends#
MCP is becoming the de facto standard for AI tool connectivity. In the future, most AI applications and tools will support MCP, forming a rich ecosystem.
Body: Multi-Modal + On-Device#
Multi-sensory Perception + Local Deployment#
Multi-Modal: AI no longer only understands text, but also images, audio, and video. GPT-4V and Gemini are both multi-modal models. You can show AI a photo and have it analyze the content, or give it an audio clip for transcription or analysis.
On-Device: AI models run on local devices (phones, computers) rather than in the cloud. This brings three major benefits: privacy protection (data stays on device), low latency (no network transmission needed), and offline availability.
Analogy#
- Multi-Modal: AI goes from “only hearing” to “hearing, seeing, and speaking”
- On-Device: AI goes from “living in the cloud” to “living in your phone”
Representative Products#
| Product | Features |
|---|---|
| GPT-4V / Gemini | Multi-modal understanding, supports image-text mixed input |
| Apple Intelligence | On-device AI, privacy-first |
| Xiaomi, Huawei Phone AI | Locally running intelligent assistants |
Future Trends#
Multi-modal is becoming standard, and on-device AI is rapidly developing as chip performance improves. Future AI assistants will “live” in your devices, responding anytime while protecting privacy.
Team: Multi-Agent#
Professional Division of Labor, Collaborative Completion#
A single Agent has limited capabilities. Multi-Agent systems enable multiple AI “experts” to collaborate on complex tasks.
Imagine a software development team: product manager, frontend engineer, backend engineer, and QA engineer. Each role focuses on their domain while collaborating to complete the project.
Multi-Agent systems are similar: one Agent plans, one executes, one reviews, and one tests. They work together to complete complex tasks that a single Agent cannot handle.
Analogy#
- Single Agent: One person handles all the work
- Multi-Agent: A team divides and collaborates
Representative Products#
| Product | Function |
|---|---|
| MetaGPT | Multi-Agent software development team, capable of completing the full process from requirements to code |
| AutoGen | Open-source multi-Agent framework from Microsoft |
| CrewAI | Simplifies multi-Agent system construction |
Future Trends#
Multi-Agent is a key direction for handling complex tasks. More “AI teams” will emerge in the future, each optimized for specific domains.
Foundation: AI Infra#
The Cornerstone Supporting Everything#
AI Infra (AI Infrastructure) is the underlying technology supporting AI operations, including:
- Compute: GPUs, TPUs, NPUs, and other specialized chips
- Frameworks: PyTorch, TensorFlow, JAX, and other training and inference frameworks
- Cloud Services: AWS, Azure, Alibaba Cloud, and other AI cloud platforms
- Inference Optimization: Model compression, quantization, distillation, and other techniques to make models run faster and more efficiently
Analogy#
If AI applications are cars, AI Infra is the roads, gas stations, and traffic systems. Without good infrastructure, even the best cars can’t run.
Representative Products/Technologies#
| Category | Representatives |
|---|---|
| Chips | NVIDIA H100, AMD MI300, Huawei Ascend |
| Frameworks | PyTorch, TensorFlow, JAX |
| Cloud Platforms | AWS Bedrock, Azure AI, Alibaba Cloud PAI |
| Inference Optimization | vLLM, TensorRT, ONNX Runtime |
Future Trends#
AI Infra is developing toward “more efficient, cheaper, and easier to use.” Specialized chip performance continues to improve, inference costs keep dropping, making AI capabilities more accessible.
Summary#
| Capability | Technology | Core Value |
|---|---|---|
| Brain | LLM + Reasoning | Understanding and reasoning, from fast thinking to slow thinking |
| Memory | Long Context + RAG | Remembering information, short-term memory + long-term knowledge base |
| Hands | Agent + Tool Learning | Executing tasks, from chatting to doing |
| Nerves | MCP | Connecting tools, AI’s universal interface |
| Body | Multi-Modal + On-Device | Perceiving the world, multi-modal + localization |
| Team | Multi-Agent | Collaborative division of labor, handling complex tasks |
| Foundation | AI Infra | Supporting operations, compute + frameworks + cloud services |
These seven capabilities work together, enabling AI to evolve from “chatbot” to true “digital assistant.” In 2026, we stand at the eve of an AI capability explosion.