In 2026, the AI industry is undergoing a pivotal transformation. Leading enterprises such as OpenAI and Google are actively reaching out to advertisers. ChatGPT, which once pledged “no ads will be stuffed into the product,” has reversed its stance, while Gemini has also been exposed to be in talks for native advertising collaborations. The trend of AI transitioning from a pure technical tool to a commercial carrier is irreversible.
The core driver behind this shift is the pressure to profit. In recent years, training large models has consumed enormous costs. Even though OpenAI had over 20 million subscribed users in 2025 and generated $4.33 billion in revenue in the first half of the year, it still faced a dilemma where every $1 earned was accompanied by $3 in losses due to high computing power and R&D expenses, resulting in a net loss of $13.5 billion. The model of relying on venture capital and parent company funding is unsustainable, and as the most mature monetization method in the internet industry, advertising has naturally become a key choice for AI companies.
The forms of AI advertising are becoming increasingly diverse. Various schemes are undergoing internal testing, ranging from lightweight ads that do not interfere with the core experience—such as sidebar recommendations and bottom banners—to incentive-based interactions where users can unlock advanced features by watching short videos. The most controversial, however, is the GEO (Generative Engine Optimization) model. In conversations, AI prioritizes recommending cooperative brands in the tone of “objective reviews,” and its subtle commercial persuasion may erode AI’s neutrality, making it difficult for users to distinguish the authenticity of the suggestions amid the latest AI news.
Beyond advertising, AI commercialization is developing along multiple paths. Subscription services are evolving towards refinement and scenario-based customization, with specialized AI tools for vertical fields such as legal consulting and study-abroad document writing set to become paid hotspots; To B enterprise-level services remain the focus, as large models are deeply integrated with cloud services to provide solutions for bank risk control, pharmaceutical R&D, etc., boasting a higher commercial ceiling; customized models for vertical industries are emerging, leveraging precise services to secure high-value contracts in professional fields like healthcare and law; in the long run, AI is expected to become a dispatch center for the entire digital ecosystem, completing the closed loop from recommendation to transaction and extracting transaction commissions.
The wave of commercialization also brings multiple challenges. Users face risks of distorted content orientation and privacy leakage—AI recommendations may skew information due to commercial collaborations, and data such as conversation history and interest preferences required for targeted advertising may be misused. Meanwhile, the content ecosystem is being reshaped: creators tend to produce template-based content to cater to AI algorithms, and public discussions are becoming traffic-driven. In response, regulators around the world have taken action, with regulations such as the EU’s Artificial Intelligence Act introduced to emphasize AI’s transparency and traceability.
Large-scale AI commercialization is an inevitable outcome under the logic of capital, and the once free and open AI utopia is fading away. For users, this means needing to reposition their relationship with AI and examine its suggestions and recommendations with critical thinking. In an era where objectivity may be priced, maintaining the ability to think independently and verify proactively has become the key to coping with AI’s commercial transformation.
