Building the future of software creation with AI-driven IDEs

Build your own IDE when the form factor of programming is about to change radically.

Existing editors have limited extensibility; owning the full stack lets you evolve the UI and interaction model as AI capabilities advance.

Why: If you believe programming will flow through models and UIs will change, you need control over the entire application surface.

"the extensibility that existing coding environments have is so, so, so limited... you necessarily need to have control over the entire application"

The future of programming moves from code to intent specification.

Instead of writing formal languages like TypeScript or Go, engineers will describe logic in concise English-like pseudocode and point at what they want changed.

Why: AI models are becoming powerful enough to translate high-level human intent into executable software, making the process more productive and accessible.

"Our goal with Cursor is to invent a new type of programming, a very different way to build software... a world kind of after code"

Professional engineers remain in the driver’s seat; AI augments rather than replaces them.

Humans retain complete control over every decision and can make changes quickly without waiting for slow background agents.

Why: Precision and rapid iteration loops are critical for high-quality software; chat-bot style interfaces lack the necessary precision.

"we're very opinionated that that path goes through kind of existing professional engineers... the human is still being in the driver's seat"

Dogfood relentlessly and ship only what you personally find useful.

The founding team used Cursor as their daily driver within five weeks of the first line of code and iterated in public from day one.

Why: Being your own end-user instills realism about current AI limits and keeps product quality high.

"we had the benefit of doing that because we were the end user spark of our product... we never wanted to ship anything that wasn't useful to us"

Exponential growth feels slow at first; patience plus continuous improvement compounds.

Cursor’s revenue curve was steady exponential month-over-month; early absolute numbers felt small even though the rate was historically fast.

Why: Compounding improvements in product quality and word-of-mouth drive exponential adoption once product-market fit is achieved.

"an exponential, to begin with, feels fairly slow when the numbers are really low... the growth has been fairly just consistent on an exponential"

Focus almost all early energy on product excellence, defer sales and marketing.

The team spent months building and polishing the editor while letting traditional go-to-market fires burn.

Why: In markets with very high ceilings, product quality creates stronger distribution than early sales or marketing spend.

"just working on the product and building a product that you like... some of the normal things that people would maybe reach for in building the company early on, we really let those fires burn for a long time"

Complement foundation models with custom, task-specific models.

Cursor trains its own small, fast models for autocomplete and diff prediction while still using GPT/Claude/Gemini for high-level reasoning.

Why: Specialized models can hit latency and cost targets (300 ms, millions of calls) that large general models cannot, and they plug gaps where foundation models are weak.

"every magic moment in Cursor involves a custom model in some way... picking your spots carefully, not trying to reinvent the wheel"

Use an ensemble of models, routing each sub-task to the best tool.

Fast cheap models fill in details after a large model sketches high-level changes; mini-search models retrieve relevant context for big models.

Why: Optimizes for both quality and speed/cost, leveraging the strengths of each layer in the stack.

"we take the sketches of the changes that these models are suggesting... these smaller specialty, incredibly fast models... turn those high level changes into full code diffs"