Why Large Models Are Redefining Software: The Four AI Tech Drivers

Recent rapid AI development highlights the importance of cloud service platforms in AI application development. The AIAgent architecture introduces a new paradigm, bringing technological innovation and a strong competitive edge. Enterprises must decide whether to train industry‑specific large models, create their own ChatGPT moments, and support deployment within existing IT and organizational capabilities.

Four Major Technical Drivers of Large Models Reshaping Software

Driver 1: Everything Can Be Embedded – Universal Machine‑Intelligence Representation

Embedding converts text, images, audio, and video into computable tokens, enabling multimodal large models and fundamentally impacting the AI wave.

Driver 2: Transformer Architecture and Scaling Laws – Unified Model Architecture

Unlike classic models lacking generality, the Transformer has proven to be the most effective large‑model architecture; increasing parameters, data, and compute significantly boosts intelligence, following a deterministic evolution.

Driver 3: From Intelligent Moore’s Law to Scenario Moore’s Law – Large Models as General Productivity Engines

Compute advances drive model intelligence and scenario generalization, often exceeding Moore’s law, allowing models to naturally acquire problem‑solving abilities without extensive programming.

Driver 4: LLM OS – Abstracting a General AI Compute Architecture for Native AI Applications

LLM OS treats the large model as a core processing unit, providing a theoretical compute architecture that has been implemented industry‑wide and demonstrates the capability to solve generic problems.

Key Challenges for Deploying AI‑Native Applications in Enterprise Production

Security: data security, model interaction security, application security;

Professionalism & Collaboration: enabling large models to understand and solve complex business problems;

Responsibility: defining accountability when AI assists or replaces humans.

To address these, enterprises should focus on five aspects:

Enterprise Vocabulary : Load domain‑specific terminology to eliminate ambiguity and improve accuracy.

Domain Knowledge Base : Attach an external knowledge base for real‑time data and knowledge updates, ensuring timeliness and relevance.

Scenario Paradigms : Use Huawei Cloud’s seven Agent paradigms (conversation, content understanding, perception, central decision, knowledge query, design generation, data analysis) to simplify development of varied business scenarios.

Model Gateway : Provide unified routing, format translation, failover mechanisms, and observability for multiple models to enhance availability and performance.

Observability & Control : Establish metrics for inference performance, cost, and feedback to continuously evaluate result quality.