As AI-powered tools continue to integrate into enterprise software stacks, the rapid growth of the AI coding platform Windsurf is emerging as a compelling case study. At the recent VB Transform 2025 conference, CEO and co-founder Varun Mohan shared insights on how Windsurf's integrated development environment (IDE) has exceeded one million developers within just four months of its launch. Even more impressive, the platform now generates over half of the code committed by its user base.
The session, moderated by VentureBeat CEO Matt Marshall, began with a significant disclaimer: Mohan was unable to comment on the widely speculated acquisition of Windsurf by OpenAI. This topic has gained traction following a Wall Street Journal report highlighting a standoff between OpenAI and Microsoft regarding the terms of the deal. According to the WSJ, OpenAI is interested in acquiring Windsurf while retaining its intellectual property, a move that could potentially reshape the landscape of enterprise AI coding.
Windsurf’s IDE is designed around a concept known as the “mind-meld” loop—a shared project state between humans and AI that facilitates comprehensive coding workflows rather than simple autocomplete suggestions. This innovative setup allows agents to execute multi-file refactors, generate test suites, and even implement UI changes when a pull request is initiated. Mohan emphasized that effective coding assistance must go beyond mere code generation, stating, “Only about 20 to 30% of a developer’s time is spent writing code. The remaining time is dedicated to debugging, reviewing, and testing.” To genuinely assist developers, an AI system must have access to all relevant data sources, he explained.
Recently, Windsurf has embedded a browser within its IDE, enabling agents to test changes, read logs, and interact with live interfaces much like a human engineer. This feature is crucial for enhancing the platform's usability and functionality.
As AI begins to play an active role in enterprise development cycles, Windsurf's commitment to built-in security has become paramount. “We utilize a hybrid model for enterprise deployments—none of the personalized data is stored outside the user’s tenant. Security is critical, especially with features like our integrated browser agent,” Mohan noted. These security measures have made Windsurf a viable choice for regulated industries, with its agents deployed on extensive codebases at notable firms such as JPMorgan Chase and Morgan Stanley.
Mohan highlighted that as AI becomes more accessible across various roles, security will increasingly be a determining factor for productivity. “If everyone at a company is going to contribute to technology in some capacity, the missing piece is security. You don’t want a one-off system built by a non-technical user to disrupt another service,” he stated.
Internally, Windsurf operates with small teams of three to four engineers, each concentrating on testing specific product hypotheses. Mohan explained, “There’s a notion that one-person billion-dollar companies are the future. However, more people facilitate faster growth and better product development. The secret lies in organizing into small, focused teams that can test hypotheses concurrently.” This approach has empowered the company to iterate swiftly in a dynamic environment where foundational AI models and user needs evolve rapidly.
Windsurf's most significant optimization at an enterprise scale is not merely faster token generation or smaller models; it is relevance. “At scale, the most critical optimization is personalization. A deep understanding of the codebase enables the agent to make maintainable, large-scale changes that align with user intent,” Mohan explained. Rather than depending solely on general-purpose code generation, Windsurf's system learns the structure, style, and preferences of each customer’s tech stack.
Looking ahead, Windsurf is committed to designing its platform to remain adaptable as the capabilities of underlying AI models expand. “Every significant advancement in foundational models necessitates a major product reevaluation. As agents grow more capable, our responsibility is to construct a platform that can effectively manage and orchestrate multiple agents,” Mohan said. The company is also developing an open protocol that will allow enterprises to integrate any large language model (LLM)—including on-premises models—into Windsurf’s agent framework, ensuring flexibility and minimizing vendor lock-in.
Windsurf ensures transparency into its performance through built-in analytics. “We provide insights on ROI through metrics—such as the percentage of code generated by the assistant—which can be directly tied to internal engineering performance,” Mohan remarked. This approach empowers platform teams to link agentic productivity with business outcomes, justifying further investment in the technology.
When asked how Windsurf plans to differentiate itself from competitors like OpenAI, Microsoft, and Google, Mohan emphasized the importance of internal velocity. “The challenge lies not in visibility but in executing the right strategy swiftly. The risk is either moving too slowly or overextending and missing near-term relevance,” he noted. He also dismissed the notion that established companies are destined to fail, asserting, “There’s no inherent reason legacy firms like Salesforce can’t evolve to become AI-native. The true limitation is their pace of innovation, not their capabilities.”
Whether Windsurf becomes part of OpenAI's future or continues to operate independently, the company’s impressive traction with enterprise customers and its commitment to grounding AI in secure, measurable workflows make it a notable player as agentic development enters the mainstream.
