It All Started with Simple Curiosity About Programming
It all started with simple curiosity about programming.
One winter break, a decision to build a mobile game together with a brother — the confusion and excitement of facing Objective-C code for the first time — became the original experience that would later drive an AI coding revolution.
Cursor's founders were reading PG (Paul Graham) essays since middle school, drawing inspiration from YouTube and social media while taking on a long series of projects. Mobile games, robot dog development, bold forays into cutting-edge AI research — all of those experiences crystallized into a vision for how they would redefine future coding and transform software development as a whole. Initially they worked on projects focused on CAD and mechanical engineering, but gradually experienced the realities of market response and technical limitations firsthand, forcing a decisive change of direction. Cursor was founded to overturn conventional thinking about code editing and provide a new coding experience powered by AI.
This article explains in detail, with rich anecdotes, the founders' journey, the lessons learned through trial and error, and the future of coding that AI is reshaping. For technologists, entrepreneurs, and anyone taking on the challenges of the future, this article offers major inspiration and encouragement.
- From robot dog to revolution: how a high school student's encounter with AI led to everything
- 3.5 years of persisting without giving up: the trial and error behind Cursor's birth
- This is what programming looks like in the future: the inside story of Cursor's AI coding revolution
- Summary: Cursor's trajectory as an AI-era lesson for every developer
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From Robot Dog to Revolution: How a High School Student's Encounter with AI Led to Everything
The founders first touched programming during winter break, working with a brother to build a mobile game. At the time, they searched online for "how to make a game" and downloaded Xcode. For a complete beginner, nothing made sense at first — it was a trial-and-error process. The brother quickly surrendered to the difficulty and changed course, but the founder became captivated by programming through the challenge. Eventually, buying an Objective-C book and diving seriously into mobile game development followed.
From there, the passion for programming spread in many directions. Interest in AI emerged in childhood too — encountering essays by entrepreneurs and thinkers, particularly PG (Paul Graham), Sam, and YC essays, seeing those voices speaking passionately about the intersection of technology and entrepreneurship was deeply energizing. By high school, programming had grown beyond a hobby into a vehicle for commercial challenge, with the constant feeling that something big lay ahead.
The founders, extending from their mobile game ideas, one day embarked on a "robot dog" project. The innovative idea was to teach a robot to learn using the same approach as training a dog with treats — positive and negative feedback that mirrors how a human would teach. This wasn't just a playful experiment; it became an important proving ground for understanding practical technological development and AI algorithms. They dove into genetic algorithms, methods for evolving neural networks, and a technique called reinforcement learning (RL), entering a period of intensive experimentation.
This process produced important learnings. Specifically, they faced the challenge of dealing with very limited training data and noisy human feedback — and found paths to resolution through real robot experimentation.
The key learning points from these early projects:
- Programming has a high initial barrier, but persisting through it deepens understanding
- The feedback gained from failure becomes the foundation for future success
- Hands-on practice brings technical insights that theory alone could never reveal
These experiences would become important guiding principles for overcoming technical challenges and pursuing new possibilities in the advancing age of AI. Through the robot dog project, the founders didn't just practice traditional programming — they took on the challenge of building their own small neural network library while working on real hardware integration in the field.
These technical experiments were also valuable opportunities to learn not just theory but how things work in the real world — opportunities that fundamentally changed how the founders thought. Through failure and repetition, they viscerally understood that technological evolution is never linear — it involves wandering, course corrections, and constant recalibration. Conducting empirical experiments on how to extract useful information from small, noisy datasets and apply it directly to a robot — overcoming countless attempts and failures — accumulated as invaluable hands-on experience for the Cursor development project that followed.
This early AI and robotics experimentation was not merely youthful play or tinkering — it was a formative process that cultivated a large vision for the future. Through difficulty and effort, the founders challenged the limits of their own technical knowledge, deepened their expertise in the face of problems that conventional knowledge alone couldn't solve, and chose to build their own distinctive capabilities. This laid the foundation for the major projects that followed. Where many technologists stumble and give up, the founders treated failure not as something to fear, but as the next step toward success. This was the moment they built their foundation as entrepreneurs — the source of the flexible thinking and practical technical pursuit that defines the AI era.
3.5 Years of Persisting Without Giving Up: The Trial and Error Behind Cursor's Birth
From 2019 to 2022, the founders worked on many projects while always carrying in their hearts a passion for "the future of code." In the early days, they developed auto-completion tools for CAD, SolidWorks, and Fusion 360 focused on mechanical engineering, aiming to improve productivity in mechanical design workflows. The market at the time was considered "boring" and "untapped," and deliberately challenging a steady domain was a way of exploring the possibility of transforming existing technology more innovatively with AI. However, no matter how compelling the idea, countless barriers existed between making a prototype and seeing it actually used by real users.
The first major wall was data collection. CAD data exists in many different formats and is not standardized, so converting scattered data from across the internet into a single common format required enormous effort. The difficulty of exporting data from cloud-based CAD systems also significantly impeded project progress. Through repeated testing, they had to focus on parameter tuning to improve model accuracy and on developing a real-time code editing integration mechanism, all while feeling their way forward. Initially they started from a completely self-built code editor — a fully custom editor set up from scratch — but its functionality was limited and couldn't fully meet user requirements.
Nevertheless, grounded in the belief of "why do we care so much about the future of code?", the founders continuously reassessed their direction and experienced multiple pivots. For example, early on they also took on an end-to-end encrypted messaging system focused on security. While Signal and WhatsApp had demonstrated strong encryption, they couldn't hide metadata like communication patterns, which posed significant risks for journalists and whistleblowers. Technically superior, but with scalability problems, real-world market adoption was limited.
The founders then turned their attention to "AI-powered coding support." With competitors already deploying products like GitHub AI agents, there were initial doubts about whether they could compete. But they were convinced that existing products still had significant room for improvement, and committed to channeling all their energy into building a new experience from the user's perspective. Building a system where raw code and AI interact, continuously suggesting optimal code candidates, involved a complexity and technical challenge that resisted easy solutions. In the early stages, in particular, building their own custom model involved enormous training and infrastructure costs, and they faced a difficult dilemma between actual product development and operational reality. Looking back at the CodeX paper, they had speculated that training costs cited as hundreds of thousands of dollars might not actually be so high — and they were constantly experimenting with the balance between investing in their own model versus leveraging existing models.
The early editor functionality also clearly showed a gap between the actual development environment and user needs. Feedback from users made it clear that achieving a production-quality editor — not just code completion, but the editor itself — required integration with mature platforms like VS Code. This led Cursor to make a sharp pivot from building its own editor to leveraging a powerful open-source editor foundation, rapidly improving practicality in a short time. This change in technical direction was an inevitable response to rapidly evolving market needs, revisiting relationships with existing users along the way.
The Cursor development team was very small — just four people at founding — but their passion and technical ability earned genuine market recognition, and they gradually succeeded in expanding their user base. In particular, adoption of Cursor's coding tools spread within YC, and the effects eventually showed up in the numbers. User count grew slowly through parts of 2025, then expanded rapidly. The dramatic growth was recognized as a "compound effect" from accumulated product improvements, with the product reaching week-over-week growth rates exceeding 10%.
Through this process, the founders faced not just technical walls but the realities of market response and the diverse demands of users. They learned that technical excellence alone doesn't guarantee market acceptance — user feedback around "ease of use" and "coding experience" in practice played a major role in shaping the product's evolution. As a result, meticulous improvements were made not just to basic editor functionality, but to the depth of AI's involvement in code prediction and auto-completion.
Looking back on everything, it's clear that Cursor's founding journey was anything but smooth. Through many projects that ended at the prototype stage or failed to find market acceptance as envisioned, the founders made relentless efforts to genuinely transform "the future of coding" and open up what technology makes possible. Facing the enormous wall of an already-established competitor like GitHub AI, they drew on years of failure and setback as fuel, and achieved significant success by building a distinctive AI model while also leveraging high-performance external models.
This Is What Programming Looks Like in the Future: Cursor's AI Coding Revolution
Modern software development is transforming from mere code input into collaborative work with AI. Cursor's founders hold deep, unwavering beliefs about the future of coding, and have poured everything into product development in pursuit of that vision. Their view is that over the next five to ten years, traditional hand-typed code will give way to AI receiving instructions and completing or generating code, with software development as a whole being dramatically automated.
In the future coding environment, programmers will no longer be mere input operators — they will become true partners collaborating with AI. New systems will have built-in ability to predict what users need and what code is optimal, providing appropriate code snippets and overall design flow informed by the user's intent. This will dramatically improve development speed and will undoubtedly bring major changes to traditional approaches to programming education and training. The skills technologists need will also shift. While foundational programming knowledge, algorithms, and a general understanding of computer science will remain important, the ability to communicate with AI and to read and modify AI-generated code will be indispensable skills for the era ahead.
Furthermore, with AI automatically understanding code context in a real code editor and suggesting optimal revisions and completions, errors will decrease and development efficiency will improve dramatically. The evolution from Cursor's early product produced changes that actual users describe as feeling "immediately useful." Word spread among users, and adoption rates grew rapidly especially in development communities like YC. Technologists shared perspectives on the innovation and practicality via Twitter and other social media, with many engineers captivated by the new coding experience.
In future software development, the prediction is that AI will go beyond just code generation to understand the entire codebase, handling bug detection, security checks, and performance optimization as well. This evolution — with AI taking over many of the roles previously held by developers — will lead developers toward an environment where they can focus more on creative problem-solving. This is expected to promote engagement with advanced algorithms and creative design challenges, accelerating technological innovation further.
Even in this era of transformation, foundational knowledge remains crucial: basic mathematics, computational theory, and core programming concepts. No matter how advanced AI becomes, these are the skills engineers will always need — cultivating flexible thinking and problem-solving ability. Even as AI generates code, thoroughly understanding the basics of programming remains necessary to understand and improve what AI produces. The founders themselves, actively engaged in development today, emphasize the importance of foundational study in technology and mathematics, and advise students and young programmers to "learn by doing what you're interested in."
In this way, as technology evolves at an accelerating pace, the new coding environment is not static — it is a dynamic world that continues to evolve every day. The future that Cursor's founders describe is not merely a dream — it is grounded in active ongoing projects and real results. They continue to listen to user voices, improve technology, and enhance their product, and as a result product growth has accelerated at a remarkable pace. In future coding, co-creation with AI will dramatically improve the traditionally labor-intensive process of writing code, with the potential to shift development itself toward a more enjoyable, creative activity.
Summary: Cursor's Trajectory as an AI-Era Lesson for Every Developer
This article traced the remarkable journey of Cursor's founders — from first encounters with programming, through robot dog and CAD experiments, to the realization of a new AI-powered coding experience — and explored in detail the possibilities for the future of software development. Cursor's work represents both the growth of technologists and the ability to adapt precisely to the AI era. Going forward, Cursor's evolution signals not just a technological innovation, but a coming change to how developers work itself.
For technologists, but also for anyone taking on the challenges of startups and innovation, the many experiences and insights described in this article offer significant inspiration.
The future of software development continues to carry the potential to bring dramatic changes to our lives and the way we work. The new frontiers that AI opens will overturn conventional wisdom and evolve toward an environment where everyone can work more creatively. Cursor's challenge and success is a symbolic example of this — a strong message for riding the wave of technological innovation to come. Going forward, with eyes fixed on the possibilities that expand before us, we keep moving ahead toward a future where technology and human wisdom continue to fuse.
Reference: https://www.youtube.com/watch?v=TrXi3naD6Og
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