## Overview Cursor is an AI-powered code editor founded by four MIT graduates (including Amon, the speaker) who all graduated in 2022. The company represents a compelling case study in LLMOps, demonstrating how to build a production AI system that combines frontier models from major labs with custom-trained models, all while maintaining a lean team of 30 people and achieving $100 million in revenue. The product launched in 2023, and this interview captures insights from the company's rapid growth and technical evolution. ## Origin and Problem Identification The founders were early users of GitHub Copilot when it launched in summer 2021, recognizing it as "the first language model based product that actually worked." However, they observed a critical gap: while the underlying models were improving rapidly in research, the product experience remained largely static—simply swapping in newer OpenAI models without reimagining the interface. Their thesis was that if language models were going to produce 90-95% of software, the interface needed fundamental reinvention, not just better autocomplete. A key insight came from understanding that Copilot's success was partially due to Microsoft's ownership of both VS Code and GitHub, which allowed them to introduce the "ghost text" UX pattern for autocomplete. This replaced the older "intellisense bubble" approach and dramatically improved usefulness. The founders realized that to push the boundaries of AI-assisted coding, they needed to own the IDE itself. ## Technical Architecture and Model Strategy Cursor employs a pragmatic hybrid approach to model deployment that reflects mature LLMOps thinking: **Frontier Model Usage**: The company primarily uses Claude Sonnet as the main frontier model, with options for users to select other models like GPT-4, O1, O3, and DeepSeek's R1 reasoning model. These are accessed via API calls and handle the complex reasoning tasks that require the most capable models. **Custom Model Development**: Cursor has trained approximately 50 model variants, with around 10 actively served in production. These custom models handle specific use cases where frontier models are suboptimal: - **Low-latency requirements**: For features that need to feel instantaneous (like autocomplete and next-edit prediction), the latency and cost of frontier model APIs is prohibitive. Custom models provide the speed necessary for a responsive user experience. - **Unique data distribution**: The company identified that pre-training data (primarily code from GitHub) captures code states at commit time, but not the edit patterns between commits. Cursor's proprietary data on how users actually make edits represents an out-of-distribution advantage that improves model performance for next-edit prediction. - **Embedding models**: The company trains custom embedding models for codebase indexing, noting that the best researchers at major labs focus on advancing reasoning capabilities rather than embeddings, leaving room for specialized improvement. The speaker emphasizes being "pragmatic" about model choices, warning against the 2023-2024 trend of companies raising large sums and spending heavily on GPUs before achieving product-market fit. Instead, Cursor advocates using "the best tool for the job," which often means calling an API rather than training custom models prematurely. ## Key Features and Experimentation Process Cursor's development process is characterized by extensive experimentation, with the speaker noting that for every shipped feature, there are approximately 10 failed experiments. Key production features include: **Command K (Instructed Edit)**: A core feature that allows users to invoke instructed edits with a keyboard shortcut, representing a new UX paradigm for AI-assisted code modification. **Codebase Indexing**: Enables users to ask questions about their entire codebase, likely leveraging custom embedding models and retrieval systems. **Cursor Tab (formerly Copilot++)**: Perhaps the most technically interesting feature from an LLMOps perspective. Unlike traditional autocomplete that predicts what comes next after the cursor, Cursor Tab predicts the user's next edit to existing code. The development history is instructive: - Initial attempts in summer 2023 using prompted models "completely failed" - Two subsequent attempts also failed - Success came in January 2024 when they had accumulated enough user data (approximately 30,000 daily active users) to train models specifically for next-edit prediction - The feature launched as an addition on top of Copilot, then gradually improved to become "substantially better than Copilot at both autocomplete and this thing of predicting your next edit" This pattern demonstrates a key LLMOps principle: some features require sufficient user data before they become viable, and companies should be prepared to revisit failed experiments as data accumulates. ## Data Flywheel and Competitive Advantages The speaker discusses competitive positioning with notable candor, acknowledging that Cursor doesn't yet have strong moats but is building toward them: **Current Advantages**: - Distribution advantage relative to all competitors except GitHub Copilot - Data advantages from user edit patterns, though Copilot still has more users - Technical co-founders who are heavy users of their own product, enabling rapid iteration **Future Stickiness Strategy**: The company plans to build enterprise stickiness by specializing models on individual codebases. When thousands of developers at a company use Cursor daily, the accumulated edit data can be used to improve models specifically for that company (without cross-contamination with other customers' data). This creates a flywheel where increased usage leads to better performance, making migration less attractive. The speaker notes that growth currently takes priority over stickiness, reflecting a strategic choice about when to invest in defensive capabilities. ## Deployment and Release Philosophy Cursor takes an aggressive approach to releasing features, shipping "half-finished things" that competitors refuse to release. The rationale is that real-world feedback enables rapid improvement. The first version of Cursor Tab "sucked," but releasing it allowed the team to observe user reactions and iterate quickly. This philosophy requires careful consideration of user experience and trust, but has enabled faster learning cycles. The company releases features "as soon as something shows signs of usefulness to the team," leveraging their position as power users of their own product. ## Infrastructure and Team Considerations The company maintained an extremely lean structure during its growth phase—just four technical co-founders for the first year, reaching 30 employees by the time of this interview. Early hiring came from MIT connections and, notably, from power users of the product who demonstrated high code quality through analytics. This represents an unusual recruiting channel enabled by the nature of developer tools. The team culture emphasizes curiosity about the future of programming, with conversations at dinner focusing on speculation about model capabilities and UI/UX possibilities. The speaker estimates the product is "2-3% done," suggesting significant room for continued development. ## Monetization Approach The path to $100 million in revenue came without a traditional sales team (sales hiring only began 1-2 months before the interview, with 3 salespeople). Revenue comes from a mix of individual developers and teams, with individual developers representing a significant portion. Two factors enabled this unusual self-serve growth for developer tools (a category historically known for poor monetization): the normalization of paying $20/month for AI tools (established by ChatGPT and Copilot), and word-of-mouth driven by product quality. ## Future Direction and Industry Perspective The speaker offers a nuanced view on the future of AI coding tools. Rather than believing in a pure "prompt to code" future, they envision a spectrum: - Simple CRUD applications may become fully prompt-driven - AI co-workers may handle routine bug fixes and tickets - High-value work will remain human-driven but at a higher level of abstraction, focusing on taste and judgment A notable observation is that AI colleagues may not be the optimal interface pattern. The overhead of communicating with human colleagues (implicit knowledge, back-and-forth iterations, ideas that are hard to articulate) suggests that truly instant, responsive AI tools that iterate in real-time may be more effective than autonomous agents that work independently. The company aims to make it possible for anyone to produce sophisticated software like Figma, but acknowledges this is a long-term vision requiring continued advancement in both models and interfaces.