In 2026, as generative AI reshapes the tech industry landscape, while most internet giants are competing to invest in billion-parameter models, Sohu has chosen a more practical path. At the recent Sohu Technology Annual Forum, CEO Zhang Chaoyang clearly stated that Sohu is not entering the "first-tier" development of large models, but rather focusing on "rational application based on its own business foundation." This statement is both a calm response to the industry's overenthusiasm and reflects the survival wisdom of mid-sized tech companies in the AI era.
Strategic Trade-offs: Why "Not Joining the First Tier"?
Zhang Chaoyang's judgment is based on three real-world considerations:
| Consideration Dimension | Specific Logic | Industry Comparison |
|---|---|---|
| Financial Barriers | Training billion-parameter models requires hundreds of millions of dollars in computing power investment, with short iteration cycles and high sunk costs. | Baidu, Alibaba, and Tencent all spend over ten billion yuan annually on AI, making it difficult for Sohu to match at the same level. |
| Technical Barriers | Developing large models requires top algorithm teams, massive high-quality data, and engineering capabilities, which cannot be quickly compensated. | Top manufacturers have already built a "data-compute-talent" loop, and the window for later entrants to catch up is narrowing. |
| Commercial Return | The monetization path for general-purpose large models is still unclear, while vertical scenario applications can already generate immediate value. | Sohu chooses to "use others' models and do its own business," reducing the cost of trial and error. |
This strategy is not "giving up," but rather a precise positioning: seeking the optimal solution of "model capability × business scenario" outside the competition of computing power and parameters.
Implementation Path: Efficiency First, Content King
Sohu's AI applications focus on two directions:
- Efficiency and Cost Reduction:
- Programming Assistance: Introducing code generation tools to improve R&D efficiency and shorten product iteration cycles;
- Operational Automation: Using AI to handle repetitive tasks such as content review, user feedback, and data analysis, freeing up human resources to focus on creativity and strategy;
- Cost Optimization: Using intelligent scheduling to reduce server resource consumption and improve the output per unit of computing power.
- Content Restraint:
- Maintaining Neutrality: Clearly labeling AI-generated content to avoid misleading users by "machines pretending to be humans";
- Avoiding Chaos Risks: Not blindly pursuing "automatic writing" or "mass production," prioritizing information accuracy and value orientation;
- Human-Machine Collaboration: Journalists and editors remain the main decision-makers for content, with AI serving only as an auxiliary tool for material organization and fact-checking.
Zhang Chaoyang emphasized specifically: "The core value of a content platform is trust. Sacrificing neutrality for short-term traffic would harm long-term brand assets." This position is particularly precious in a content ecosystem where "clickbait headlines," "plagiarism," and "false information" are frequent.
Industry Insights: The "Rational Survival Rules" for Mid-Sized Companies
Sohu's choice provides a reference model for companies that have not entered the "first tier" of large model development:
- Not Competing on Parameters, but on Scenarios: Leave general capabilities to leading companies, and focus on deep optimization in vertical scenarios;
- Not Re-inventing the Wheel, but Using the Wheel: Reuse mature capabilities through API calls and model fine-tuning to reduce R&D risks;
- Not Chasing Trends, but Upholding Principles: In high-sensitivity fields like content, finance, and healthcare, place "compliance" and "trustworthiness" before "innovation."
This "pragmatism" strategy may be the optimal solution for mid-sized tech companies under resource constraints: instead of running in a red ocean, they should focus on niche areas.
Challenges and Outlook
Certainly, this path also faces challenges:
- Technology Dependency Risk: If the underlying model provider changes its strategy or increases prices, Sohu's application layer capabilities might become dependent on others;
- Differentiation Challenges: When multiple companies use the same model, how to build unique user value?
- Content Boundary Management: Finding a balance between "restraint" and "innovation" requires continuous mechanism design and value alignment.
