How Windsurf is Shaping the Future of Software Engineering with AI

The world of software engineering is rapidly evolving, and AI-powered tools are at the forefront of this transformation. One standout player in this space is Windsurf, an AI-driven coding assistant designed to supercharge developer productivity and redefine how software is built. In a recent deep dive interview with Vun Moan, co-founder and CEO of Windsurf, we explore the engineering challenges behind building such a tool, how Windsurf tests and improves its AI models, and the broader implications for the software engineering profession.

The Origin Story: From Infrastructure to AI-Powered IDEs

Windsurf’s journey began nearly four years ago, initially focusing on GPU virtualization for complex workloads in autonomous vehicles and robotics. However, the emergence of large language models (LLMs) like GPT-3 shifted their vision. Recognizing that generalized AI models could simplify and improve software development, the team pivoted to build an AI-powered development environment.

They initially developed Kodium, an autocomplete tool integrated with popular IDEs, and trained their own models to overcome the limitations of early open-source AI models, which lacked essential capabilities like "fill-in-the-middle" coding. This functionality allows the AI to generate code snippets in the middle of existing code, an essential feature that distinguishes coding from general text generation.

Realizing that traditional IDEs like Visual Studio Code lacked the flexibility needed for advanced AI-driven workflows, Windsurf forked the open-source foundation of VS Code and built their own product, integrating deeply with JetBrains IDEs via plugins. This approach allowed them to innovate while respecting developers’ existing workflows.

Rigorous Testing: Bringing Autonomous Vehicle Expertise to AI

Testing AI models for coding assistance is a complex challenge, especially given the non-deterministic nature of machine learning outputs. Drawing on their autonomous vehicle background, Windsurf developed sophisticated simulation and evaluation suites that test models across multiple dimensions:

• End-to-end task success: Does the model complete the coding task correctly?
• Retrieval accuracy: Does it identify the correct parts of the codebase to modify?
• Edit precision: Are the changes optimal and free from unnecessary steps?

They utilize open-source repositories and historical commits as ground truth to evaluate whether AI-generated edits match intended changes. This multi-layered testing framework allows Windsurf to quickly assess model performance across tens of thousands of scenarios, far exceeding traditional manual evaluations.

Building Custom Models for Coding Challenges

Off-the-shelf language models are not always optimized for coding tasks. Windsurf found that popular models lacked critical features such as fill-in-the-middle completion and struggled with code tokenization nuances. Their engineering team developed proprietary models and training recipes tailored specifically for code, enabling better understanding of syntax, context, and developer intent.

Additionally, Windsurf leverages a knowledge graph built from commit histories and code relationships, enhancing the AI’s ability to predict related code changes and improving precision in large codebases.

Fine-Tuning vs. Retrieval: What Matters Most?

Windsurf experimented with fine-tuning AI models on specific company codebases to personalize suggestions. While fine-tuning provided some improvements, the team discovered that enhancing retrieval systems—how the AI finds relevant code snippets—had a far bigger impact on performance. They built infrastructure allowing companies to self-host models and fine-tune them efficiently while balancing GPU usage and minimizing latency.

Overcoming Latency and Scaling Challenges

Latency is critical in developer tools—delays of even tens of milliseconds can reduce user engagement. Windsurf invests heavily in optimizing GPU utilization, batching requests, and speculative decoding to deliver code suggestions in near real-time. They also strategically place data centers to minimize network delays, although local internet congestion (e.g., in India) remains a challenge beyond their control.

Advanced Code Indexing and Search

Searching vast codebases efficiently requires a hybrid approach. Windsurf combines:

• Embedding-based search: Captures semantic meaning but can be lossy.
• Keyword-based search: Precise but brittle against typos or variations.
• Knowledge graph and AST-based retrieval: Understands code dependencies and structure.

By fusing these techniques and performing additional computation at query time, Windsurf achieves high recall and precision necessary for AI assistants to function effectively on large and complex projects.

Balancing Present Needs and Long-Term Vision

Windsurf actively manages a “split brain” approach—balancing rapid development of user-facing features with long-term foundational projects. About half of their engineering team works on innovations that haven’t yet shipped, ensuring they stay ahead of evolving developer needs and technology capabilities.

Embracing failure is part of their culture. Many early features, like an AI-powered code review tool, didn’t meet expectations but provided valuable learning. This iterative mindset fuels continuous improvement.

Empowering Developers and Non-Developers Alike

Interestingly, Windsurf has enabled even non-developers within their company—such as partnership leads—to build and deploy simple internal apps, replacing expensive third-party SaaS tools. While complex, stateful enterprise software (like Workday or Salesforce) is unlikely to be replaced soon, simple business tools and stateless apps are ripe for AI-assisted internal development.

Changing the Software Engineering Landscape

Contrary to fears that AI will reduce the number of software engineers, Windsurf’s CEO believes AI will increase the return on investment of developers, allowing companies to build more and better products. AI tools reduce mental fatigue by automating repetitive, low-level tasks and enabling engineers to focus on problem-solving and creative work.

Developers now feel more empowered to explore unfamiliar codebases and rely on AI as a first stop for suggestions, dramatically shifting workflows. However, deep technical skills remain essential, especially for understanding complex systems and debugging.

Infrastructure and Compliance

To support enterprise customers, Windsurf has built scalable, secure infrastructure and achieved FedRAMP High compliance—the only AI software assistant to do so. They manage GPU resources intelligently to balance inference and fine-tuning workloads, ensuring reliability and performance at scale.

The Road Ahead: Models, MCP, and Developer Experience

Windsurf continues to evolve alongside AI capabilities and emerging standards like Microsoft’s Model Context Protocol (MCP). While MCP promises to democratize access to internal systems, challenges remain around security, granularity of access, and workflow integration.

Windsurf also maintains a shared language server backend that supports multiple IDEs, reducing duplication and speeding development of new features.

Final Thoughts

The Windsurf story is a compelling example of how AI is transforming software engineering from the inside out. Through rigorous engineering, thoughtful product design, and embracing both failures and successes, Windsurf is pushing the boundaries of what developer tools can achieve.

As AI-generated code becomes the norm, the role of software engineers will evolve but remain vital—shifting toward higher-level problem-solving, collaboration, and innovation. Tools like Windsurf will be key enablers in this exciting future.

If you're interested in diving deeper into AI coding tools and their engineering challenges, check out the Pragmatic Engineer Deep Dives podcast series for more insightful conversations.