In the competitive landscape of AI-powered developer tools, Michael Truell has positioned his company, Anysphere, and its flagship product, Cursor, as a critical integrator in a field dominated by foundational model builders. As co-founder and CEO, he navigates the complex dynamic of relying on APIs from the very companies he competes with, like OpenAI and Anthropic. In our conversation, we explored his strategy for turning this dependency into a strength, focusing on creating a superior, end-to-end user experience. We touched upon the growing pains of a necessary pricing model shift driven by intense user engagement, his vision for making teams the central focus of product development, and the ambitious goal of evolving Cursor into an agent capable of tackling software development tasks that currently consume weeks of human effort.
You described competing AI coding tools as a “concept car” versus Cursor’s “production automobile.” Could you elaborate on this? Please share a specific anecdote where Cursor’s end-to-end integration and UX solved a developer’s problem in a way a standalone model couldn’t.
That analogy really gets to the heart of what we do. An LLM, no matter how powerful, is just an engine. It’s an incredible piece of technology, but you can’t get to work with just an engine. You need the whole car: the chassis, the steering, the user interface, the safety features. That’s what we build. We take the best intelligence from multiple providers, combine it with our own product-specific models, and wrap it all in a seamless tool built for the developer. Think about a common but frustrating scenario: a developer is hunting down a subtle bug. A standalone model might offer a code snippet, but that’s out of context. With Cursor, the AI understands the entire repository. It doesn’t just suggest a fix; it can trace dependencies across files, understand the build process, and present the solution directly within the developer’s workflow. It’s the difference between being handed a part and being given a fully equipped garage with a master mechanic.
Regarding the pricing shift to a consumption model, you cited users doing “hours of work” in the tool. What key metrics first signaled this behavioral change? Beyond spend controls, what concrete steps is your enterprise team taking to help large teams forecast and manage these new costs effectively?
The shift in user behavior was truly dramatic, and it was the primary driver for our pricing change. When we first started, the data showed people were using Cursor for quick, discrete tasks, like asking for the syntax of a JavaScript function. The engagement patterns were short bursts. Suddenly, we started seeing session times and query complexity explode. Users weren’t just asking questions anymore; they were engaging in long, iterative dialogues with the AI, essentially pair-programming for hours at a time. This change from a simple utility to a deep work partner meant the old all-inclusive subscription was no longer sustainable. To address the new cost structure for our larger clients, we built out a whole team dedicated to enterprise engineering. Their focus is on giving companies the same kind of financial visibility they expect from cloud providers. We’re building granular spend controls, billing groups to map costs to specific projects, and detailed dashboards so an engineering manager can see exactly how their team is leveraging the tool and manage the budget without any surprises.
You’ve stated your in-house models generate a massive amount of code. How do these product-specific models work in tandem with the general-purpose APIs you use from competitors? Could you walk me through a common coding task where your own model provides a tangible advantage for the user?
It’s a symbiotic relationship. We leverage the massive, general-purpose models from providers like OpenAI and Anthropic for their broad world knowledge and powerful reasoning capabilities. They are phenomenal. But we pair that broad intelligence with our own highly specialized, in-house models. These models are trained specifically on the tasks and context of software development within the Cursor environment. Take a task like refactoring a piece of legacy code. A general model can give you a solid, modernized version of a function in isolation. Our product-specific model, however, understands the surrounding architecture of the project. It knows your team’s coding conventions, it sees the other files that call that function, and it can perform the refactoring with a much higher degree of contextual accuracy, reducing the need for manual cleanup. That’s why our models end up generating so much code—they’re finely tuned for the actual production work happening inside the editor.
You want Cursor to handle complex agentic tasks, such as fixing bugs that take “weeks of someone’s time.” What are the top two or three technical milestones your team must achieve to make this a reality? Describe what the first version of this end-to-end feature might look like.
That’s our north star: turning Cursor into a true agent that can take on significant, end-to-end tasks. The first major technical hurdle is achieving deep environmental awareness. The AI needs to not just read the code, but understand the entire development loop: how to run the test suite, how to interpret compiler errors, and how to spin up a local server. The second milestone is iterative problem-solving. A human developer doesn’t fix a complex bug in one shot; they form a hypothesis, test it, observe the result, and refine their approach, sometimes running the code thousands of times. We need to empower the AI to do the same. The first version of this won’t be a magic “fix bug” button. It will likely be a collaborative agent. You’ll describe the bug, and Cursor will respond with a multi-step plan, maybe saying, “First, I’ll write a failing test to reproduce the issue. Then, I’ll analyze the call stack. Does that sound right?” It will then execute those steps, showing its work and allowing the developer to guide it, making the process transparent and controllable.
You mentioned a strategic shift to focusing on “teams as the atomic unit.” Besides the code review product, what does this look like in practice for your roadmap? Describe a specific pain point that engineering teams face today that you are planning to solve with a new team-centric feature.
Exactly. We’re expanding our aperture from the individual coder to the entire engineering team and its lifecycle. Our AI-powered code review, which analyzes every single pull request, was the first big step. The next logical area is tackling institutional knowledge and collaboration bottlenecks. A massive pain point for any growing team is knowledge transfer. A senior engineer leaves, or a new person joins, and there’s a huge gap. We envision a feature where Cursor acts as an AI team lead. It would be able to analyze the entire codebase, the pull request history, and internal documentation to answer complex, team-specific questions. A new developer could ask, “What is our standard procedure for database migrations?” or “Who is the expert on our authentication service?” and get an instant, accurate answer synthesized from the team’s collective work. This moves us from just helping write code to actively helping teams operate more effectively as a whole.
What is your forecast for the future of AI in software development?
I believe we are on the cusp of a fundamental paradigm shift in how software is created. In the next few years, the line between writing code and specifying intent will blur almost completely. Developers will transition from being manual laborers, typing out every line and chasing down every semicolon, to becoming architects and directors. Their primary role will be to provide high-level guidance, business context, and creative problem-solving to sophisticated AI agents that handle the end-to-end implementation, from initial code to debugging and deployment. The focus will move from the craft of coding to the art of building products, dramatically accelerating the pace of innovation for everyone.
