From Hot Debate to Practice: An Analysis of the Current State of AI Development in 2025

The field of artificial intelligence is undergoing a significant transformation from theory to practice. Building scalable AI products has become the core of competition for enterprises. The recently released 2025 AI Status Report "Builder's Handbook" delves into the entire process of AI products from conception to large-scale operation.

This report is based on research conducted with 300 executives from software companies and in-depth interviews with industry leaders, providing enterprises with a strategic guide to transform AI advantages into sustained competitiveness. Here are five key findings from the report:

1. AI Product Strategy Matures

Compared to companies that only integrate AI into existing products, AI-native companies are more adept at marketizing their products. Data shows that 47% of AI-native enterprises have achieved key scale and validated market demand, while only 13% of companies with AI-integrated products have reached this level.

AI-native developers primarily focus on intelligent agent workflows (AI systems capable of autonomously executing multi-step tasks) and vertical applications. Nearly 80% of AI-native developers are laying out plans in these areas.

In terms of technology selection, multi-model architectures have become mainstream to optimize performance, control costs, and adapt to different application scenarios. Surveys show that, on average, each company uses 2.8 AI models in customer-facing products.

2. The Evolution of AI Pricing Models

AI is changing the product pricing strategies of businesses. Many companies are adopting hybrid pricing models that add usage-based charges on top of a base subscription fee. Some enterprises are also exploring pricing models that are entirely based on actual usage or customer revenue.

Although many companies still offer AI features for free, more than one-third (37%) of businesses plan to adjust their pricing strategies in the coming year to make prices more aligned with the value customers receive and actual usage.

3. AI Talent Strategy Becomes a Key Differentiator

AI is not only a technical challenge but also an organizational challenge. Top teams are forming cross-functional teams composed of AI engineers, machine learning engineers, data scientists, and AI product managers.

In the future, most companies expect that 20-30% of their engineering teams will focus on AI, with this proportion possibly reaching 37% in high-growth companies. However, talent acquisition remains a significant challenge. The average hiring cycle for AI and machine learning engineers exceeds 70 days, making it the longest among all AI-related positions.

There are differing opinions on the recruitment progress. While some recruiters believe that progress is going smoothly, 54% of respondents indicate that the progress is lagging, primarily due to a lack of qualified talent reserves.

4. AI Budget Grows Significantly

Companies adopting AI technology are allocating 10%-20% of their R&D budgets to the AI field, and by 2025, companies across all revenue ranges are showing a continuous growth trend. This highlights that AI has become the core driving force of product strategy.

As the scale of AI products expands, the cost structure is also changing. In the early stages, human resource costs (including recruitment and training) dominate. However, as the products mature, costs for cloud services, model inference, and compliance regulation will become the main expenditures.

5. Uneven Expansion of Internal AI Applications in Enterprises

Although most surveyed companies provide internal AI tool access to about 70% of their employees, only about half actually use these tools regularly. Large, established companies face greater challenges in encouraging employees to use AI.

High adoption rate enterprises (with over 50% of employees using AI tools) deploy AI in an average of more than seven internal scenarios, including programming assistants (usage rate 77%), content generation (65%), and document search (57%). The improvement in work efficiency in these areas ranges from 15% to 30%.

The AI tool ecosystem is gradually maturing

The report also investigated the AI technology frameworks, libraries, and platforms actually used by enterprises. The results show that although the AI tool ecosystem remains relatively fragmented, it is gradually maturing. Commonly used tools include various frameworks, cloud services, development tools, and specialized libraries, covering all aspects of AI development and application.

Overall, the development of AI in 2025 shows a clear trend from concept to practice and from experimentation to scaling. Enterprises are actively exploring AI strategies, talent development, cost control, and application scenarios to gain an advantageous position in this technological revolution.