AI Technology Evolution and Industry Practice from 2022 to 2026

1. From Chat Interfaces to General-Purpose AI Infrastructure

At the end of 2022, conversational generative models became available to the public, and AI moved quickly from a specialized research field into consumer and enterprise use. Compared with traditional search, question answering, or content generation tools, these systems can understand user intent in context and complete tasks such as summarizing, rewriting, explaining, and programming assistance through continuous dialogue. More importantly, users no longer need to learn complex operating instructions. They can describe their goals directly in natural language.

This shift had three notable effects.

First, the entry point for AI products changed. Earlier software typically required users to understand menus, forms, and fixed workflows. Generative AI is closer to an intent-driven interaction. Users state the task goal, constraints, and output format, and the system attempts to produce the result.

Second, the criteria for evaluating models expanded from single-point accuracy to overall task completion. Enterprises no longer only ask whether a model can answer one question. They also examine whether it can read materials, extract information, generate content, call tools, and stay consistent across multiple turns of interaction.

Third, AI became a general-purpose technology foundation. It is used not only for chat but is increasingly embedded in office work, R&D, customer service, education, finance, manufacturing, and content production. This forms a new software architecture that combines model capabilities with business processes.

1. Open Weights Drive the Expansion of the Model Ecosystem

In 2023, the weights of some research-oriented large models were made public. Developers could then run inference, fine-tune, and evaluate these models in local environments. Practices around lightweight models, instruction tuning, parameter-efficient training, and domain adaptation grew quickly, and an open-weight ecosystem took shape.

This stage mainly delivered value in the following areas.

First, it lowered the barrier to model research and application experimentation. Enterprises and developers no longer depend entirely on remote interfaces. They can choose local deployment based on data sensitivity, deployment environment, and business requirements.

Second, it advanced model compression. Through quantization, pruning, knowledge distillation, and low-rank adaptation, larger models can be reduced to a size suitable for edge devices or ordinary servers. Smaller models often change in capability, stability, or context handling, so parameter count alone cannot determine model quality.

Third, it shifted model adaptation from retraining toward incremental optimization. In most enterprise scenarios, training a foundation model from scratch is costly. A more practical approach is to use retrieval augmentation, instruction tuning, domain data adaptation, or tool calling so that a general model serves specific business needs better.

Open models do not remove engineering costs. Enterprises still need to handle weight provenance, license boundaries, data compliance, vulnerability fixes, inference resources, and version maintenance. In practice, model selection should be supported by a model registry, version management, risk classification, and offline evaluation.

1. Chinese Large Models Enter a Period of Intensive Development

In 2023, the Chinese market entered a phase of concentrated large model launches and rapid experimentation. Domestic technology companies such as Baidu began releasing generative AI services for the public and enterprises, and many industry players started building their own models or industry-specific models.

The main feature of this period is that the number of models grew quickly, while competition still centered on core capabilities and adaptation to the Chinese language. Most systems used dense models based on attention mechanisms and improved performance by scaling training data, increasing compute, and optimizing instruction data. Quality of Chinese-language corpora, knowledge coverage, content safety, and integration with local business processes became important points of differentiation.

For enterprises, a larger number of models does not make selection easier. Focus on the following metrics:

• Task accuracy: whether the model can reliably complete the target business task, not just produce correct results occasionally.
• Factual reliability: whether the model proactively states uncertainty when knowledge is incomplete or information conflicts.
• Context capacity: whether it can handle long documents, multi-turn dialogue, and complex business rules.
• Tool-calling ability: whether it can call databases, search, calculation, or business systems in the agreed format.
• Security and compliance: whether it provides sensitive content detection, access control, auditing, and data isolation.
• Cost and latency: whether response time and resource consumption are acceptable under real concurrency.

1. Native Multimodality Becomes a Focus of Model Evolution

After 2023, multimodal capabilities gradually shifted from add-on features to core capabilities. Early approaches typically added an image encoder to a language model, then used a connection layer to convert visual information into representations the text model can understand. Later, more systems processed text, images, audio, and video together during training, allowing the model to build associations across modalities within a unified framework.

The significance of native multimodality is that a model no longer only looks at an image and describes it. It can combine text, visuals, sound, and changes over time for integrated reasoning. In smart manufacturing, for example, a system can analyze equipment images, maintenance records, and sensor data at the same time. In education, it can evaluate a student based on question images, the handwriting process, and spoken answers. In customer service, it can process text, screenshots, and call recordings together.

Multimodal applications also face clearer challenges. Data formats, sampling rates, semantic granularity, and noise characteristics differ across modalities. When building multimodal systems, enterprises need to define data annotation standards, time alignment rules, privacy handling procedures, and cross-modal evaluation sets in advance. This prevents teams from focusing on demo effects while overlooking real accuracy.

1. Video Generation Pushes Development Toward World Simulation

Around 2024, video generation made clear progress. Systems can generate short videos with continuous motion and camera changes from text descriptions. Compared with static image generation, video tasks must address spatial consistency and temporal consistency at once. This includes preserving character identity, object motion patterns, lighting changes, shot transitions, and physical plausibility.

This progress moves AI from generating content toward simulating environments. If a model can predict future states from historical frames, or generate environmental changes that follow physical patterns from action instructions, it could support virtual training, industrial simulation, game development, film previsualization, and robot control.

When using video generation, enterprises should pay attention to copyright, likeness rights, material sources, detection of false content, and traceability of outputs. For content published to the public, keep generation logs, human review results, and material licensing information, and define cl