While big companies are locked in a fierce arms race over "trillion-parameter" models, Sohu has chosen a more "clear-headed" path.

At the 2026 Sohu Technology Annual Forum, CEO Zhang Chaoyang clearly outlined the company's AI strategy: instead of chasing the prestige of being in the "first-tier" large model group, Sohu will focus its limited resources on practical applications that can be implemented and bring returns. In short, Sohu won't be the one building models, but the one using models most intelligently.

How to implement this? Sohu's AI applications focus on two practical directions:

✅ Efficiency and cost reduction: Using mature large models from leading vendors to empower core processes such as content production, review, and distribution, making workflows lighter and improving productivity;

✅ Content neutrality: Maintaining an objective stance in algorithmic recommendations and intelligent content generation, avoiding excessive pursuit of traffic, and safeguarding the credibility of the media platform.

The essence of this strategy is a "precision positioning" for mid-sized tech companies under resource constraints — rather than competing in computing power and parameters, it focuses on vertical scenarios, transforming general capabilities into business value. For example, semantic understanding in news and information, intelligent video editing, and personalized user interaction responses are all "small but beautiful" entry points that Sohu is refining.

But being pragmatic doesn't mean it's easy. This path also faces dual challenges: on one hand, relying on external large models may lead to risks such as being constrained by others' technology iteration cycles and difficulty in building data loops; on the other hand, in a public opinion environment where everyone is talking about AI, how to make users perceive Sohu's differentiated value instead of being seen as "another content platform that just uses APIs" is a question that needs continuous answers.

For the industry, Sohu's choice may offer a new reference: as large models enter deeper waters, "knowing how to use" may be more challenging than "knowing how to build" in the long run.