Google has paid $2.4 billion in licensing fees to access Windsurf’s AI code-generation technology and has hired key members of its team—including CEO Varun Mohan and co-founder Douglas Chen—to join its DeepMind division. The deal does not involve equity or control over Windsurf, which will continue as an independent startup with a license to serve enterprise clients
How OpenAI’s $3B Takeover Fell Apart
Windsurf was previously in exclusive talks with OpenAI for a potential $3 billion acquisition. However, that deal collapsed—reportedly due to Microsoft’s stake in OpenAI and concerns about sharing IP—leaving room for Google to step in with this non-exclusive licensing and hiring approach
Why the Move Matters
- This “reverse acqui‑hire” reflects Google’s strategic focus on agentic coding, where AI not only assists but autonomously writes, tests, and improves code—particularly within the Gemini project at DeepMind
- It highlights a broader trend: tech giants like Microsoft, Amazon, and Meta are increasingly securing talent via licensing hires rather than full acquisitions—often to sidestep antitrust scrutiny
Windsurf After the Deal
Despite the senior team’s departure, Windsurf remains intact with roughly 250 employees and a refreshed leadership: Jeff Wang becomes interim CEO, and Graham Moreno is now president. They plan to continue product development for enterprise customers The Economic Times
The Wider Tech and Regulatory Landscape
- The agreement underscores the AI arms race and how major companies are battling to outmaneuver rivals through talent.
- Regulatory bodies are increasingly scrutinizing such licensing‑plus‑talent deals—like those involving Character.AI, Inflection AI, and Adept—as potential ways to evade merger review
The Bottom Line
Google’s decision to bring in Windsurf’s C‑suite and R&D leaders is a calculated move to strengthen its DeepMind-powered Gemini project and bolster its AI coding capabilities. By structuring this as a licensing deal—not full acquisition—it gains strategic advantage without triggering regulatory alarms. It also sends a strong message: in the AI era, hiring elite engineering talent can be as powerful—and possibly more flexible—than buying companies outright.
