In-depth interview with Cursor founder: How AI reshapes the future and practice of programming

Cursor is an integrated development environment driven by artificial intelligence. It supports writing or modifying code through natural language instructions, and integrates multiple large language models (such as GPT-4, Claude, DeepSeek-R1, etc.), providing code generation, intelligent completion, error repair and other functions.

Recently, Michael, the founder of Cursor, shared his profound insights on how AI technology will revolutionize the software development industry. From the grand blueprint of the "post-code era", to the founding intention and development strategy of Cursor, to the outlook on future engineer skills, Michael described a new programming paradigm driven by AI that is more efficient and intelligent.

1. Post-code era: Cursor's grand vision and new programming paradigm

Michael pointed out that Cursor's goal is to create a completely new way of programming and fundamentally change the way software is built.

• •  Core concept : Future programming will change from the tedious "how to implement" to the higher-level "what you want". Developers only need to clearly describe the functions and appearance of the software, and AI will assist in completing most of the underlying implementation.
• •  Beyond traditional coding :
• • Different from current text editing that relies on formal programming languages ​​such as TypeScript, Go, and C++.
• • It is also different from simple “chatbot-style” command interactions.
• • Cursor envisions a future where programming interfaces will be “weirder”, where the representation of code logic will be closer to natural language or pseudocode, but humans will still have precise control and editing capabilities.
• •  Future Engineer Skills :
• •  “Taste” : Intuition and judgment in building the “right” product will become increasingly important, including visual design and more core “logical design” capabilities. Engineers will become more like “logical designers”.
• •  Reduce “caution” : As AI capabilities increase, engineers may be able to pay less attention to every detail of the underlying implementation (although “Vibe Coding” still has its limitations and AI has not yet reached a level of complete reliability).

2. The birth and growth of Cursor: a strategic shift from mechanical engineering to AI programming

The creation of Cursor was not smooth sailing. Behind it was the team's deep insight into the potential of AI and a key strategic adjustment.

• •  Source of inspiration :
• • The early beta version of GitHub Copilot demonstrated the huge potential of AI in the field of programming and was the team’s first experience with a truly practical AI product.
• • The Scaling Law paper published by OpenAI and other organizations reveals that even without theoretical breakthroughs, AI performance can continue to improve simply by expanding the scale of models and data.
• •  Initial attempt : The team initially chose a seemingly “unpopular” field – AI-assisted mechanical engineering.
• •  Reasons for failure : lack of domain knowledge (founder is not a mechanical engineer), 3D model data is difficult to obtain and process, and the team has limited enthusiasm for this field.
• •  Strategic shift : After the setback in the mechanical engineering project, the team re-examined the field of AI programming and believed that the existing tools were not ambitious enough and failed to fully tap the potential of AI. This prompted them to make up their minds to develop Cursor.
• •  Key learning : Even when a market appears crowded (as with Copilot), there are still huge opportunities if existing players are found to be not “ambitious” enough or their approaches are flawed.
• •  Rapid iteration and user feedback :
• • The initial version of Cursor was handwritten from scratch, and the team started using the self-developed editor full-time within about 5 weeks.
• • The product was launched in about 3 months, and the user interest and feedback that far exceeded expectations prompted the team to switch the underlying Cursor to be built based on VS Code to meet user needs for stability and scalability.
• •  Growth secrets :
• • Continuous product polishing and obsessive pursuit of the ultimate goal.
• • Product-driven growth: In the early days, there was almost no investment in sales and marketing, and the focus was on making the product good.
• • Deep understanding of interdisciplinary challenges: Cursor’s development lies between traditional software companies and basic model companies. It not only aims to create an excellent product experience, but also to make continuous breakthroughs in model science.

3. Cursor's technical core: IDE strategic selection and in-depth development of self-developed models

Michael elaborated on why Cursor chose to build a complete IDE instead of a plugin, and why it invested in its own model.

• •  Why choose to build a complete IDE?
• •  Control and future form : We firmly believe that the interactive interface and form of programming will undergo tremendous changes. The scalability of existing IDEs is not enough to support this change, so we need to control the entire application.
• •  Human-led : Ensure that human developers are always in the driver’s seat and have full control over the software.
• •  Deeply integrate AI : Deeply integrate AI capabilities into every aspect of the development process, rather than just being an auxiliary tool.
• •  Future Outlook : The concept of IDE will continue to evolve, and in the future it may be possible to seamlessly integrate AI agent tasks that run autonomously in the background and human-computer collaboration in the foreground.
• •  Challenges and responses of AI-assisted development :
• • Avoid becoming an “AI engineering manager” who is tired of reviewing large amounts of low-quality AI-generated code.
• •  【actionable】Cursor suggestion : Break the task into small pieces, and review and adjust after the AI ​​completes a small part, forming a fast iteration cycle, rather than handing large tasks to the AI ​​at one time.
• •  The importance of self-developed models (Counterintuitive Learning) :
• •  Original intention : We did not initially plan to develop our own model, as we thought we could rely on the existing excellent basic models.
• •  Transformation : In practice, it is found that in order to achieve the ultimate product experience (such as speed, cost, and specific task effects), self-developed or deeply customized models are crucial.
• •  Application scenarios :
• •  Code autocomplete : It requires extremely high speed (response within 300 milliseconds) and low cost, and focuses on predicting code snippets (diffs) rather than general text. Cursor has trained a special model for this purpose.
• •  Enable large-scale base models :
• • Input: Conduct “mini Google searches” in the code base through self-developed models to provide the most relevant context for the large model.
• • Output: Large models generate high-level logical ideas, and then smaller, faster specialized models combine reasoning techniques to convert these ideas into complete code diffs.
• •  “Ensemble of Models” : Using multiple models to work together, leveraging their respective strengths (as described by OpenAI Kevin Weil), and customizing and fine-tuning open source models (such as Llama).

4. Moat and market structure in the AI ​​era

Against the backdrop of the rapid development of AI technology, Michael has unique insights into Cursor's long-term competitiveness and future market landscape.

• •  Moat concept :
• • The ceiling in the AI ​​field is extremely high, there is still huge room for disruptive innovation, and traditional moats can be easily crossed.
• • More like a consumer product market: Continuously delivering the best products is key, rather than relying on the lock-in effect of the enterprise market.
• • Analogous to the search engine market in the late 1990s or the history of computer development in the 1970s-1990s.
• • Having a user distribution channel helps improve the product through data feedback.
• •  Market potential and competition :
• • The market size of AI programming tools far exceeds that of traditional developer tools.
• • A dominant general-purpose software construction tool may emerge in the future, which will be a huge “generational” business opportunity.
• • While there will be tools that serve specific market segments or specific parts of the development process, general-purpose platforms have the greatest potential.
• •  Benchmarking Microsoft Copilot :
• • Structural reasons: When innovation potential is huge and user switching costs are low, the market is not friendly to existing giants.
• • Historical reasons: loss of core team members in Copilot’s early days and challenges of coordination within a large organization.

5. Tips for developers on using Cursor and career development

Michael provides valuable advice to Cursor users and developers in general.

• •  【 actionable 】Cursor usage tips :
• •  Develop “model sense” : understand the complexity of the tasks that the current model can handle and the level of instruction clarity required.
• •  Task decomposition : Break down large tasks into small pieces, take small steps, and iterate frequently.
• •  Be bold in trial and error (in a safe environment) : In non-critical scenarios such as personal projects, be bold in trying the limits of AI and you may find that it has capabilities beyond your expectations.
• •  Continuous learning : After a new model is released, recalibrate your understanding of the model’s capabilities.
• •  Impact of AI on engineers at different experience levels :
• •  Junior engineers : May be overly dependent on AI and need to be cautious.
• •  Senior engineers : May underestimate the capabilities of AI and stick to existing workflows.
• • People at the “mid- to senior-level but not top-level architect” level (Michael jokes) may be in the best position to better balance the use of AI.

6. Cursor’s Talent Outlook and Team Building: Building a World-Class Team

Michael emphasized that talent is one of Cursor's most core assets.

• •  Recruitment philosophy :
• • Patiently seek out world-class talent, focusing on intellectual curiosity, experimental spirit, honesty and inner strength.
• •  【actionable】 Unique interview process : Candidates will conduct a two-day on-site work test project and complete a simulated real project with the team. This has proven to be very effective in assessing candidate capabilities and attracting talent, and has a certain degree of scalability.
• • In the early days, we paid too much attention to candidates from prestigious universities and young candidates, but later we found that experienced and senior talents are equally valuable.
• • The current team consists of approximately 60 people, with engineers, researchers and designers accounting for a large proportion.
• •  Dealing with the rapid changes and noise in the AI ​​industry :
• • Recruit team members who are mentally mature and not overly demanding of external recognition.
• • Leadership leads by example.
• • The team has gradually built up an “immune system” against industry noise and can distinguish truly important technological advances.

7. Looking to the future: How AI will reshape software development

Michael believes that we are in the midst of a technological transformation that will last for decades.

• •  Depth and breadth of change : AI’s impact on society will be more profound than the Internet, comparable to the invention of computers.
• •  Breakthroughs in multiple fields : A series of independent problems need to be solved, including model capabilities (understanding different data types, faster, cheaper, smarter, and interacting with the real world) and human-computer interaction experience.
• •  Key Force : Companies that focus on automation and augmentation of specific areas of knowledge work (such as Cursor for programming) will become the core force driving technological progress and creating user value, and may grow into huge commercial entities.
• •  Demand for engineers remains strong : Although AI will greatly improve development efficiency, software demand is extremely elastic. Currently, it is still expensive to build many seemingly simple software. If the cost can be greatly reduced, it will release a large amount of new software demand, and engineers will be able to build more and more powerful tools.

Michael's sharing not only reveals the thinking behind Cursor, but also shows us the exciting future of software development in the AI ​​era. This is not only a technological innovation, but also a rethinking of creativity, collaboration and value definition.