Lessons from Michael Truell

On the ultimate goal for Cursor: "Our aim with Cursor is to continue to lead this shift, by building a magical tool that will one day write all the world's software." [1]
On the founding vision: "We were obsessed with AI's potential to change software development. But existing tools like GitHub Copilot weren't pushing the limits. We realized AI should not just assist coding—it should be the foundation of how developers work." [1]
On the future of programming languages: "We think it looks less like English and more like higher-level programming languages with better abstractions.” [1]
On the long-term mission: "We want to eventually automate programming and replace it with something much better.” [1]
On a "world after code": "Our goal with Cursor is to invent sort of a new type of programming, a very different way to build software, that's kind of just distilled down into you describing the intent to the computer for what you want in the most concise way possible." [2]
On the abstraction of code: "A world after code... looks like a world where you have a representation of the logic of your software that does look more like English... it won't be the impenetrable millions of lines of code, it'll instead be something that's much terser, and easier to understand, easier to navigate." [2][3]
On the magnitude of AI's impact: "I think it's going to be more consequential than the internet... it's going to take a while, and I think it's going to be a multi-decade thing." [2]
On defensibility in the AI space: "I think that there are ways to build in inertia and traditional moats, but... we're in a space where it is incumbent on us to continue to try to build the best thing." [2]
On maintaining human agency: "We care about giving humans control over all of the decisions in the end tool that they're building." [2][4]
On the most impactful individuals in the AI era: "I think people who... work on automating and augmenting a particular area of knowledge work, build both the technology under the surface... and the product experience for that, I think those folks will be really, really, really consequential." [2][3]
On the evolving skillset of engineers: "I think taste will be increasingly important... being an engineer will start to feel like being a logic designer, and really, it will be about specifying your intent for how exactly you want everything to work." [2][5]
A warning against "vibe coding": "One of the issues also with vibe coding... is, you can create stuff, but a lot of it the AI making decisions that are unwieldy and you don't have control over." [2][4]
On replacing coding: “The goal with the company is to replace coding with something that's much better... We think that over the next 5 to 10 years it will be possible to invent a new way to build software that's higher level and more productive.” [5]
On the impracticality of "vibe coding" for professional projects: “The vibe coding style of things is definitely not something that we recommend if you're going to have the code stay around for a really long time.” [5]
On the future of software logic: “If we were to get to a place where you don't have to pay attention to the code as much... that written down version of the logic of the software is going to have to get higher level.” [5]
On the irreplaceability of "taste": “One thing that will be irreplaceable is taste... defining what do you actually want to build.” [5][6]
On the dual nature of LLMs: “One way in which you can view LLMs is [to] interface with them like a human, like a helper. Another way... is they're kind of an advanced compiler or interpreter technology.” [5]
On the future of computing: “Things that you want to happen on computers will then just kind of be able to happen.” [2]
On interacting with AI models effectively: "I would bias less toward, trying in one go to tell the model, 'Hey, here's exactly what I want you to do'... Instead, I would chop things up into bits." [3]
On discovering AI's limitations: "I would encourage people to explicitly try to fall on their face, and try to discover the limits of what these models can do by being ambitious in a safe environment." [3]
On the bottlenecks for superhuman AI: “There are a bunch of bottlenecks to agents being human level... If you have 10 million lines of code, that's maybe 100 million tokens... Both having a model that can actually ingest that [and] one that actually pays attention effectively to that context window is tricky.” [2]
On their hiring process: “The last step of our hiring process is a two-day on-site where you come and you just work on a project with us... That gets at energy and excitement and passion for the problem space.” [2]
On his personal mission: "I'm building Cursor to automate coding." [7][8]
On the early focus of the company: "We had this overriding fear of focusing on anything other than making the product better." [8]
On startup advice: "The meta advice is like be really skeptical of taking advice. and kind of just think through the details of what you're doing." [8]
On the opportunity in AI for coding: "There was going to be an opportunity for all of coding to change in the next 5 years. and for all of software development to flow through models it felt like no one working on the space at the time. was really taking that seriously." [9][10]
On continuous improvement: "The Cursor of today should feel obsolete in a year." [3]
On user-centricity: "We're not just building for ourselves. We're constantly listening to our users, understanding their pain points, and evolving Cursor to meet their needs." [3]
On the beauty of the current moment in software: "Something beautiful is happening to code." [3]
On the importance of speed for developer tools: "We knew developers would abandon a tool that slowed them down, so Cursor is built for speed, with ultra-fast AI responses." [3]

Key Learnings from Michael Truell's Journey

Distilled lessons from Truell's experiences building Anysphere and Cursor, offering valuable advice for entrepreneurs and engineers.

The Criticality of Founder-Market Fit: Truell's first venture with his co-founders was a co-pilot for mechanical engineers. They pivoted to developer tools because they lacked passion for the initial market and faced significant data challenges. This underscores the importance of working on problems you are genuinely passionate about. [1]
Leverage Existing Tools for Inspiration: The emergence of GitHub Copilot was a pivotal moment, validating the potential of AI in coding and providing a clear benchmark for Truell and his team to innovate beyond. [1][11]
Own the Primary Interface for Maximum Impact: The decision to build a full-fledged editor, rather than a simple extension, was a strategic one. It granted them complete control over the user experience, which they deemed essential to fundamentally change how developers work. [2][3]
Pragmatism Trumps Ideology in Execution: While their initial impulse was to build a code editor from the ground up, they pragmatically chose to fork the open-source VS Code. This allowed them to accelerate development and concentrate on their unique AI-native features. [8]
Recognize the Limitations of "Vibe Coding": Truell cautions that for professional developers building and maintaining large, complex codebases, a pure "vibe coding" approach where the developer doesn't understand the underlying code is not sustainable. [2][5]
Cultivate a "Macro-Optimist, Micro-Pessimist" Culture: Anysphere seeks out team members who are deeply optimistic about the long-term vision but are also meticulously detail-oriented and perpetually dissatisfied with the current state of the product. [11]
Question Conventional Startup Dogma: Truell advises founders to be skeptical of one-size-fits-all advice. He notes that for every piece of conventional wisdom, there are successful companies that did the opposite, emphasizing the need for context-specific thinking. [8]
The Power of a Complementary Founding Team: He attributes a large part of Cursor's success to the strength and complementary skill sets of his three co-founders, a dynamic he considers a significant asset. [1]
Hiring Too Slowly Can Be as Detrimental as Hiring Too Fast: In retrospect, Truell believes their initial fear of distraction led them to be overly cautious in hiring. He learned that finding top talent is a time-consuming process that is crucial to fuel growth. [5][12]
The Future of Engineering is "Taste" and Logic Design: As AI automates the more mechanical aspects of writing code, the most valuable skill for an engineer will become "taste"—the ability to define the desired functionality and user experience at a higher level of abstraction. [2][5]
In AI, Continuous Innovation is the Ultimate Moat: Truell posits that in a rapidly evolving field like AI, traditional business moats such as customer lock-in are less effective than the relentless pursuit of building the best product. [2][3]
View AI's Transformation as a Multi-Decade Marathon: He advocates for a pragmatic perspective on AI, seeing its impact as a gradual but profound transformation that will unfold over decades, cautioning against both the hype of an overnight revolution and the cynicism of dismissal. [2]
Prioritize Human Control in AI Systems: A foundational principle of Cursor is to empower developers with AI tools that augment their abilities while ensuring they remain in full control of the final output. [2][3]
Focus on Reinventing a Specific Domain of Knowledge Work: A key element of their strategy was to select a single domain—software development—and dedicate themselves to completely reimagining its workflow through the lens of AI. [1]
A Grand Vision Can Be a Valid Starting Point in AI: In a field advancing as quickly as artificial intelligence, Truell suggests that starting with a broad, ambitious vision—a "solution in search of a problem"—can be a powerful motivator and guide. [1]
"Dogfooding" as a Catalyst for Rapid Improvement: The Anysphere team's practice of using Cursor for their own daily development work has created an invaluable, high-speed feedback loop, accelerating the product's refinement. [12]
Redefine "Greatness" in Your Hiring Criteria: Truell learned that the most impactful hires often came from unconventional backgrounds, prompting a shift away from relying solely on traditional markers of talent like prestigious degrees. [5]
Deconstruct Complex Problems for AI Collaboration: To achieve the best results from AI coding assistants, it is more effective to break down large, complex tasks into smaller, manageable, and iterative steps. [3]
Embrace Ambitious Experimentation to Understand AI's Boundaries: He encourages a mindset of fearless experimentation, suggesting that developers should "try to fall on their face" in a safe environment to truly grasp the current capabilities and limitations of AI models. [3]
A Compelling Long-Term Vision as a Guiding Star: From its inception, Anysphere's goal was not merely to build a better coding tool but to fundamentally alter the paradigm of software creation. This long-term vision has been instrumental in shaping their strategy and product decisions. [2][5]

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Lessons from Michael Truell

Key Learnings from Michael Truell's Journey

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