The AI field has witnessed another major development—Open AI announced the acquisition of the product experimentation platform Statsig in an all-stock transaction worth approximately $1.1 billion. This acquisition not only brings Statsig founder Vijay Raji on board as the Chief Technology Officer of the applications division but is also seen as a key signal of Open AI’s shift from technical research to productization.
As a leader in generative AI, Open AI has seen significant revenue growth in recent years, with annual revenue exceeding $10 billion as of June 2025, primarily relying on Chat GPT membership subscriptions and API services. However, high model training costs and talent investments led to an annual loss of over $5 billion exposed in August 2025. CEO Sam Altman has repeatedly expressed intentions to go public, but the core consideration for capital markets is a sustainable profit model. While the current revenue structure can sustain operations, its growth potential and profit margins are insufficient to support its goal of becoming a top global tech company.
Changes in the industry landscape have accelerated Open AI’s strategic adjustments. The latest news from Google ‘Nano Banana’ project demonstrated strong productization capabilities—quickly transforming the Gemini model into a market-recognized product. The project team adopted reverse thinking, starting from user pain points and employing a “Minimum Lovable Product” strategy for rapid iteration. Its success confirms that the core of AI competition has shifted from model parameters to product experience. Google’s engineering execution capabilities and organizational strengths pose a direct challenge to Open AI’s research-oriented DNA.
The core value of Statsig lies in its data-driven product development methodology. The platform offers A/B testing, feature flags, and real-time decision systems, helping companies validate product optimization directions through experimentation. For example, A/B testing compares user feedback for different feature versions, feature flags support gradual rollouts and quick rollbacks, and real-time decision systems dynamically adjust user experiences. This scientific toolchain addresses the challenge of quickly validating new ideas in complex products, and Raji’s experience leading billion-user product development at Meta makes him an ideal candidate to strengthen Open AI’s productization capabilities.
The deeper logic behind Open AI’s acquisition is “trading money for time.” As an existing Statsig customer, Open AI’s engineering team has extensively used the platform and recognized that merely being an external client cannot fully integrate a rapid experimentation culture. Through the acquisition, OpenAI not only gains ownership of the tools but also brings the entire team into its system, driving a shift in product development thinking from research-oriented to user demand-driven. This transformation signals that AI industry competition has entered the “product experience race” phase, where application stability, feature relevance, and interaction fluency become critical as model performance gaps narrow.
This strategic upgrade may trigger industry chain reactions. When AI products themselves are uncertain, a scientific experimental validation system becomes essential. Open AI’s move may prompt other giants to re-evaluate their productization processes and increase investment in experimentation platforms and teams. Infrastructure AI application development efficiency is becoming a new competitive focus.
