OpenAI acquires Statsig, names Raji CTO for Applications

The deal at a glance

OpenAI is expanding its product development engine by acquiring analytics and experimentation startup Statsig for around $1.1 billion. At the same time, Vijaye Raji, founder and CEO of Statsig, has been named OpenAI's first Chief Technology Officer for Applications. In practical terms, this doubles down on OpenAI’s ambition to turn cutting-edge research into reliable, user-facing features at scale, from consumer chat tools to enterprise offerings.

What happened

The transaction signals a strategic shift: OpenAI will absorb Statsig’s platform—known for A/B testing, feature flagging, and real-time decisioning—into its own infrastructure. That integration aims to speed up development cycles and enable more rapid, reliable experimentation on AI models and features. While the deal reportedly values Statsig at about $1.1 billion, the move isn’t just about money; it’s about tightening the feedback loop between product testing and deployment.

Who is Vijaye Raji?

Raji brings two decades of experience from Silicon Valley’s tech big leagues. He spent ten years at Microsoft as a Principal Software Design Engineer working on core products like Windows and Visual Studio. He then joined Facebook (now Meta), where he led engineering for Messenger for Windows, Marketplace, and Groups Commerce, eventually becoming VP and Head of Entertainment and head of Facebook Seattle. His track record includes scaling consumer and business products to millions of users — a skill OpenAI is counting on as it pushes further into enterprise markets and global deployments.

Raji will report to Fidji Simo, CEO of Applications, reflecting OpenAI’s intent to connect its research breakthroughs directly to product outcomes. In practice, that means turning research labs into tools that users can actually adopt widely and safely.

Why Statsig matters for OpenAI

Statsig has built a toolkit that helps developers validate feature changes with specific user segments, control rollout speeds, and quantify the impact of those changes on key metrics. In an AI context, this means more controlled experimentation around how new features affect user experience, safety, latency, and model behavior.

The argument for in-house experimentation is straightforward: when you’re iterating on models that power everyday assistants and enterprise tools, you want fast, trustworthy feedback. Statsig’s platform is designed to provide verifiable results even amid complex experiments, which is critical for navigating safety and reliability concerns that accompany AI deployments.

How the deal will unfold operationally

For now, Statsig will continue to operate as an independent unit based in Seattle, serving a roster of clients that includes Notion, Brex, and Microsoft, while its entire team joins OpenAI. In practice, you can imagine a bridge that lets OpenAI borrow the best parts of Statsig’s toolset without breaking the existing customer relationships.

From a product-development standpoint, the integration aims to embed A/B testing, feature flags, and real-time decisioning directly into OpenAI’s product pipeline. This could shorten feedback loops for new features and improve the predictability of model updates in production, a welcome shift for teams racing to demonstrate real-world value to enterprise buyers and consumer users alike.

What this means for the AI landscape

• Data-driven product engineering gains prominence: OpenAI is signaling a move beyond pure research toward optimized user experiences and enterprise-grade deployment.
• A talent grab amid the AI talent war: Acquiring Raji and his team adds product analytics and experimentation expertise that could be hard to source in a tight labor market.
• A real-world deployment spine for AI systems: The combination of Statsig’s testing framework with OpenAI’s deployment capabilities could enable faster, safer iteration on features that affect real users.

The broader industry is watching this as a proof of concept that AI breakthroughs are only as valuable as the practical tools that bring them to customers. The speed at which OpenAI can test, learn, and apply updates will likely influence how quickly competitors move to scale their own products and enterprise offerings.

Potential caveats and what to watch

• Safety and governance remain central: Speed must be matched with safeguards since rapid experimentation on AI systems can produce unexpected results.
• Independent unit status could affect integration timelines: While Statsig will remain a separate unit, the degree of integration into OpenAI’s product roadmap will determine how swiftly the benefits materialize.
• Customer experience remains the north star: The real-world value comes from how well these tools translate into smoother user interactions, better reliability, and clearer data-driven outcomes for Notion, Brex, Microsoft, and others.

What’s next

Expect a multi-quarter rhythm of integrated product updates, guided by an in-house analytics backbone that draws from Statsig’s platform. If the in-flight plans hold, we may see faster feature experimentation across OpenAI’s consumer tools and enterprise offerings, with a stronger emphasis on measurable impact, safety, and governance as the groundwork for broader adoption.

Final thoughts

This isn’t a stunt. OpenAI’s move to bring Statsig in-house plus appointing a veteran technologist to lead Applications signals a maturation path: build, test, learn, and deploy at scale, with a data-backed feedback loop that makes product improvements more predictable and the AI more usable for everyday users.

Sources

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