Michael Truell on the Future of AI and Software Development with Cursor

ursor, the fastest-growing AI code editor, has achieved a staggering $300 million in annual recurring revenue (ARR) just two years after its launch.

Truell, with a decade of AI experience and a background in computer science and math from MIT, offered profound perspectives on what lies beyond traditional coding, the evolution of software engineering, and actionable advice for navigating the AI future.

The Vision: What Comes After Code?

Truell envisions a seismic shift in how software is built, moving away from traditional coding languages like TypeScript or Python toward a new paradigm he calls “programming after code.”

This future involves developers describing their intent in concise, human-readable formats—think pseudocode or English-like logic statements—rather than wrestling with complex syntax.

Cursor aims to pioneer this transformation by creating tools that abstract away low-level coding details, enabling developers to focus on high-level logic and design.

Unlike some who predict software development will either remain unchanged or devolve into chatbot-style interactions, Truell believes the future lies in a hybrid model.

“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,” he explains.

He argues that chatbots lack the precision needed for full human control, while static coding practices fail to leverage AI’s advancing capabilities.

“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,” Truell predicts.

Cursor is building a system where developers edit software logic in a navigable, human-editable format, maintaining control while harnessing AI’s power for productivity.

Key Skill for the Future: Taste

As coding becomes more abstract, Truell emphasizes that taste—the ability to intuitively discern what software should do and how it should work—will be a critical skill. Beyond visual design, taste in software development involves specifying the logic and functionality of a program with clarity and precision.

Engineers will transition from writing code to designing logic, focusing on the “what” rather than the “how.” “

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,” Truell notes.

This shift reduces the need for meticulousness in syntax and increases the importance of understanding user needs and system behavior.

Truell contrasts this with “vibe coding,” where developers rely heavily on AI without understanding underlying details, often leading to unwieldy software.

“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,” he warns.

Cursor’s goal is to empower developers to maintain control over software logic, ensuring that taste-driven design results in robust, intentional outcomes.

Cursor’s Origin Story: From Mechanical Engineering to AI Coding

Cursor’s journey began with a misstep. Initially, Truell and his co-founders explored automating mechanical engineering, believing coding was too competitive. However, their lack of domain expertise and the scarcity of relevant data led them to pivot.

Inspired by the transformative potential of GitHub’s Copilot and OpenAI’s scaling research, they realized AI could revolutionize software development. Disappointed by the lack of ambition in existing tools, they launched Cursor to redefine programming.

The early days were marked by rapid prototyping. Within five weeks, the team built a hand-rolled editor from scratch, abandoning existing platforms like VS Code. After a brief beta period, Cursor launched publicly just three months from its first line of code.

Unexpectedly, it attracted a flood of interest, prompting a switch to a VS Code-based architecture based on user feedback. This “build in public” approach fueled Cursor’s meteoric rise to $100 million ARR in under two years.

Why an IDE? Keeping Humans in the Driver’s Seat

Cursor chose to build an integrated development environment (IDE) rather than a standalone model or a fully autonomous AI agent. Truell believes programming will evolve significantly, requiring a flexible platform that can adapt to new paradigms.

“We care about giving humans control over all of the decisions in the end tool that they're building,” he emphasizes. Unlike plugins, which are constrained by existing editors, an IDE offers full control over the user experience.

Cursor’s approach ensures humans remain in charge, avoiding the pitfalls of end-to-end automation where AI makes unguided decisions.

Truell also sees the IDE evolving to integrate various workflows, from background tasks like bug fixes to foreground collaboration. “I think the IDE is will totally change over time... we just mostly think of an IDE as the place where you are building software,” he says.

This flexibility positions Cursor to redefine the IDE as the central hub for software creation, adapting to both current and future needs.

Counterintuitive Lessons: Building Custom Models

One of the most surprising lessons for Truell was the necessity of developing custom AI models. Initially, Anysphere planned to rely on foundation models like GPT or Sonnet, assuming model development was redundant.

However, they discovered that custom models were essential for specific use cases, such as Cursor’s autocomplete feature, which predicts multi-file code changes within 300 milliseconds.

“Every magic moment in Cursor involves a custom model in some way... that was definitely counterintuitive, and surprising,” Truell reveals.

These models, often built on open-source foundations like Llama, enhance speed, cost-efficiency, and quality, complementing larger models for tasks requiring high-level reasoning.

This “ensemble of models” approach—using specialized models for niche tasks and foundation models for complex reasoning—has become a cornerstone of Cursor’s stack, challenging the notion that AI coding tools are mere “GPT wrappers.”

Defensibility in AI: Building the Best, Consistently

Truell views defensibility in AI as less about traditional moats like lock-in and more about relentless innovation. “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,” he asserts.

He compares the AI coding market to the search engine wars of the late 1990s, where high ceilings for improvement rewarded those who consistently built the best products. Cursor’s strategy is to stay ahead through continuous R&D, leveraging economies of scale to push technological boundaries.

“I think that there will be one company... that builds the general tool that builds almost all the world’s software, and that will be a very, very generationally big business,” Truell predicts, while noting niche players can thrive by addressing specific segments.

Tips for Using Cursor Effectively

For new Cursor users, Truell offers two key tips:

Chop Tasks into Smaller Pieces: “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,” he advises. Break tasks into smaller, iterative steps—specify a bit, review the output, and refine—to maximize control and quality.

Push the Limits Safely: “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,” Truell suggests. Experiment ambitiously on side projects to build an intuitive “taste” for the AI’s capabilities and limitations.

Hiring and Staying Focused

Cursor’s success hinges on its team, which Truell describes as a blend of curious, honest, and level-headed engineers, researchers, and designers. Early hiring mistakes—focusing too narrowly on young, credentialed candidates—gave way to a broader approach, prioritizing diverse experience and seniority.

“Getting the right group of people into the company was the thing that maybe more than anything else, apart from building the product, we really, really fussed over,” he reflects.

A unique two-day work test, where candidates tackle a mock project in Cursor’s codebase, has proven effective in assessing fit and sparking excitement.

To stay focused amidst AI’s rapid advancements, Truell emphasizes hiring individuals who prioritize high-quality work over external hype.

“Hiring people with the right disposition, people who are less focused on external validation, more focused on building something really great... can get you through a lot,” he notes. By fostering a culture of intellectual honesty and leading by example, Cursor avoids distractions from AI news cycles.

The Bigger Picture: AI’s Multi-Decade Impact

Truell believes AI’s impact will surpass that of the internet, unfolding over decades. “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,” he predicts.

He rejects both the hype of instant transformation and the dismissal of AI as snake oil, advocating for a pragmatic view.

The future requires solving diverse problems, from improving model capabilities to designing intuitive human-AI interfaces.

“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,” Truell emphasizes.