Saurabh Misra, an alumnus of the Indian Institute of Technology and Carnegie Mellon University, set out to solve one of software’s most expensive hidden problems: slow code. As founder and CEO of Codeflash, he created what he calls the world’s first automated code performance optimizer an AI-powered system that turns inefficient Python into high‑performance applications. This breakthrough is reshaping how teams approach software efficiency.
From his early days building performance tools at NVIDIA to leading AI work at major tech companies, Misra’s journey shows how one engineer’s conviction became an industry-changing solution. In this interview, he shares the insights, challenges, and impact behind his technology.
Q: Can you share your journey from completing your Electronics Engineering degree at IIT to founding Codeflash and introducing the first automated code performance optimizer?
A: My path from IIT to founding Codeflash was shaped by witnessing the same performance crisis at every company I joined. At NVIDIA, Meta, and other technology leaders, I saw enormous waste from inefficient code. The pattern was consistent: brilliant engineers shipping great features, but code running slower than it could. I realized someone needed to solve this systematically.
Q: What inspired you to tackle code performance optimization?
A: I’ve always loved making code run fast. But optimization is hard and manual, so it often gets skipped in favor of shipping features. That leaves every company with a lot of slow code. When large language models like GPT‑4 started getting better at code optimization, it clicked: if we could automate optimization with LLMs, we could solve slow code at its root. The industry needed a system that could instantly find and implement the fastest version of code.
Q: How does Codeflash address the inefficiencies you observed in developers’ workflows?
A: Today, developers focus on features and fixes. It’s impractical to hand‑optimize every new change. Finding opportunities is hard, and fixing each one takes time. AI coding agents compound this because their output is typically slower than expert code. Teams then deal with performance only when problems hit production.
Codeflash addresses the root cause by automatically finding the most optimized version of code before it ships. As a result, products are faster, customers are happier, and developers move faster because they deliver high‑quality software from the start.
Q: How did your experience building performance tools at NVIDIA and working on generative AI at Meta shape the design and capabilities of Codeflash?
A: At NVIDIA, I learned how to build tools that engineers actually use to solve performance problems. At Meta, working with AI systems, I saw how even the most sophisticated algorithms could be optimized for dramatic improvements. These experiences taught me that performance optimization needed to be automatic, accurate, and integrated directly into development workflows, not a separate manual process. Otherwise, optimization opportunities tend to get skipped.
Q: How have real-world implementations with companies like Roboflow and Unstructured shaped Codeflash’s development and influenced your approach to solving performance optimization challenges?
A: Working with customers like Roboflow showed us that optimization opportunities exist everywhere, even in well-written code. Their feedback helped us understand that developers need not just faster code, but confidence that optimizations won’t break functionality and are easy to merge in. This led us to build sophisticated correctness verification that gives teams complete confidence in automated optimizations.
Q: Could you describe the measurable impact Codeflash has delivered for clients like Roboflow? What performance improvements have they realized?
A: For Roboflow’s Yolov8n models, which are deployed on millions of devices, Codeflash’s automated optimizations improved the model performance from 80 fps to 100 fps, a 25% speed increase. For that model, high performance is essential since a faster model leads to fewer latencies and more responsiveness for vision tasks.
Q: How has Codeflash’s global customer base influenced your product roadmap and market strategy?
A: Our global customer base revealed that code performance problems are universal, but optimization approaches vary by region and industry. This diversity has made Codeflash more robust and adaptable. We’ve learned that whether you’re a startup in Brazil or an enterprise in Europe, the core need is the same: automatically making code run faster without breaking functionality.
Q: In your view, how is the rise of AI-powered coding assistants changing the landscape of software development, and why is automated performance optimization more critical than ever?
A: AI coding assistants are accelerating software development, but they’re also generating more inefficient code. AI models optimize for correctness, not performance, which means the volume of slow code is exploding. This creates an urgent need for automated optimization. We’re moving toward a future where every piece of software, whether written by humans or AI, must be automatically optimized for peak performance.
Q: What were the key challenges in introducing a completely new category of technology to the market, and how did you establish credibility with early customers?
A: It’s never easy. From my personal background and experience, I have always had the conviction that the world needs automated code performance optimization. But early on, the biggest challenge was convincing developers that automated optimization could be both safe and effective. Most had bad experiences with LLM tools that broke their code. We established credibility by demonstrating our correctness verification system and delivering measurable results from day one. What really worked for us was to optimize open source projects and show the project maintainers optimization results on their code base. This really helped us establish credibility with early customers.
Q: Looking ahead, how do you plan to scale Codeflash’s technology and expand its applications across different programming languages or cloud environments?
A: We’re focused on becoming the performance optimization layer for all software development. This means expanding beyond Python to other languages, integrating deeper into development workflows, and eventually optimizing entire cloud architectures automatically. The goal is to make performance optimization automatic and hands-free, so that every software system in the future will always run at peak performance.
Q: As a thought leader speaking at conferences like AI Native Dev Con and Shift Miami, what key industry trends do you believe will define the future of AI-driven software development?
A: The future of software development is continuous optimization. Just as we now have continuous integration and deployment, performance optimization will become an automatic part of every development workflow. AI will write more code, but AI will also optimize all that code automatically. This transformation will unlock trillions in economic value by making all software fundamentally more efficient.
As software shifts to an AI-assisted future, the mandate is clear: ship faster without sacrificing performance. Misra’s vision for Codeflash makes that possible by baking optimization into the development lifecycle, catching inefficiencies before release, safeguarding correctness, and turning performance into a default, not a fire drill. If this approach scales across languages and stacks, continuous optimization will sit alongside CI/CD as a core discipline, and developers will deliver responsive, efficient software by design rather than repair.
