AI Investment Logic Shifts from Model Training to Application Deployment
The World Artificial Intelligence Conference (WAIC) has seen a significant shift in focus for the venture capital market regarding artificial intelligence investments. Two years ago, the conference was characterized by a "race for armaments" among large model manufacturers, with companies competing on model parameters and setting new financing records. However, at the 2026 WAIC, investors in the primary market are now prioritizing keywords such as inference costs, mass production delivery, and product launch timelines.
Interviews conducted at the conference reveal a fundamental change in AI investment logic. The core of project valuation has moved from model parameters to the application and implementation of AI technologies. Consequently, long-term capital and industrial ecosystems are accelerating their entry into the market, aiming to facilitate the deployment of AI applications. This transition signifies a maturation of the AI investment landscape, moving beyond theoretical capabilities to practical, market-ready solutions.
AI investment has pivoted from a focus on foundational model development to the practical deployment and commercialization of AI applications. This evolution reflects a maturing market where the value proposition is increasingly tied to tangible outcomes, such as reduced inference costs and scalable delivery, rather than solely on the scale of model parameters. The influx of long-term capital and the development of industrial ecosystems suggest a strategic shift towards supporting the entire value chain of AI implementation. This trend indicates a growing emphasis on real-world utility and market adoption, potentially leading to more sustainable growth and a clearer path to profitability for AI ventures in the coming decade.
AI-generated to prompt reflection — not editorial opinion, not advice, not a statement of fact. How this works.