The rise of Cursor: The $300M ARR AI tool that engineers can’t stop using | Michael Truell

Name: The rise of Cursor: The $300M ARR AI tool that engineers can’t stop using | Michael Truell
Uploaded: 2025-05-01T11:00:20.000Z
Duration: 2 h 22 min 14 s

What Comes After Code?

Vision for Cursor and Software Development

The goal of Cursor is to create a new type of programming that moves beyond traditional coding, focusing on specifying intent rather than writing code.

The development process has unexpectedly involved significant custom model creation, contrary to initial expectations.

Michael Tru highlights the rapid growth of Cursor, achieving $100 million ARR in just 20 months and $300 million ARR within two years.

Michael's background includes extensive experience in AI research at MIT and Google, contributing to his insights on the future of software engineering.

The discussion emphasizes the transformative potential of AI in software development, suggesting a shift towards higher-level programming methods.

Insights from Building Cursor

Michael reflects on hiring practices, noting that they initially hired too slowly despite rapid company growth.

He envisions a future where building software involves defining how it should work and look in a more concise manner.

The aim is to develop a method for creating software that is more productive and accessible as technology matures.

Future of Software Development: Beyond Traditional Coding

Diverging Visions of Software Development

There are two main perspectives on the future of software development: one believes it will remain similar to current practices using formal programming languages like TypeScript and Go, while another envisions a more conversational approach where users interact with bots to build applications.

The speaker critiques the chatbot-style vision for lacking precision, arguing that human control over software design requires more than simple text commands.

The Evolution Towards Human-Readable Logic

A future without traditional coding is envisioned, where software logic is represented in a more human-readable format, akin to pseudo code rather than complex lines of code.

This new representation would allow for easier navigation and editing by humans, moving away from the overwhelming complexity of millions of lines of code.

Transitioning from Code to Intent

The anticipated shift involves moving away from thinking in terms of specific programming languages (like JavaScript or Python) towards expressing software logic in English-like sentences.

Professionals will still play a crucial role in this evolution; engineers will maintain control over software aspects while being able to make rapid changes without lengthy delays.

Skills for the Post-Code Era

As we transition into this new era, skills such as "taste" will become increasingly valuable. This includes not just visual aesthetics but also understanding how software functions logically.

Engineering roles may evolve into "logic designers," focusing on specifying intent rather than detailing every technical aspect under the hood.

Balancing Carefulness and Creativity

While carefulness is currently essential for engineers due to potential pitfalls when relying on AI tools, there’s an expectation that future engineering will require less meticulousness and more emphasis on creative taste.

How to Maintain Control in AI-Driven Development?

The Challenge of Understanding Code

Discusses the difficulty users face in maintaining control over software details when they lack understanding of the underlying code.

Highlights that current solutions do not allow creators to have complete control, leading to reliance on AI decisions that may be unwieldy.

Defining 'Taste' in Development

Explains 'taste' as having the right vision for what should be built, emphasizing effortless translation from idea to execution.

Suggests a shift away from labor-intensive processes towards more intuitive design and development tools.

The Origin Story of Cursor

Initial Problem-Solving Approach

Describes how Cursor began as a solution-seeking venture, reflecting on future advancements in AI technology.

Key Moments of Inspiration

Shares excitement about using Git Copilot, marking it as a pivotal moment due to its utility compared to previous AI products.

Notes the impact of scaling papers from OpenAI that indicated ongoing improvements in AI capabilities without needing new ideas.

Shifting Focus Towards Programming

Discusses an initial exploration into various knowledge work areas and their potential transformations with advancing tech.

Reflects on the need for better models and tools as programming evolves alongside AI advancements.

Challenges Faced During Development

Early Missteps in Mechanical Engineering

Details early efforts focused on automating mechanical engineering despite lacking expertise in the field.

Realization and Pivoting Strategy

The Future of AI in Software Development

Ambition in the AI Space

The speaker emphasizes the importance of being ambitious about future developments in software creation, particularly with AI models, which inspired the founding of Kersia.

Contrary to common advice to target "boring" industries, the speaker advocates for pursuing opportunities in popular fields like AI and coding apps, highlighting that perceived saturation can still harbor significant potential.

Identifying Opportunities

The discussion revolves around recognizing flaws or lack of ambition in existing products as indicators of opportunity. Even if a market seems crowded, there may be room for innovation.

The high ceiling for growth and development within AI is noted as unique compared to other software areas, suggesting ongoing demand for advancements.

Choosing Development Paths

Various approaches to integrating AI into development tools are discussed: building an IDE (Integrated Development Environment), creating fully autonomous AI agents, or focusing on superior coding models.

The decision to pursue an IDE was influenced by a desire to maintain human control over programming decisions rather than allowing complete automation by AI.

Realism About Technology

Acknowledging current technological limitations is crucial; while excitement exists about future capabilities, it's important not to overestimate what AI can achieve today.

The product development process involved rigorous testing and usage by the team themselves ("dogfooding"), ensuring that only useful features were released.

Human-Centric Approach

Emphasizing human involvement in programming decisions helps avoid pitfalls associated with over-reliance on AI. This approach also reflects personal values regarding user control.

The belief that programming will evolve necessitates full control over applications rather than relying on plugins within existing environments.

Future Integration of AI Tools

Speculation about future roles for AI engineers suggests a blend between background tasks handled autonomously and foreground tasks requiring direct human interaction.

Flexibility is key; users should seamlessly transition between using automated assistance and engaging directly with their work when necessary.

Evolution of Programming Interfaces

The Evolution of AI in Software Development

Changing Perspectives on Integrated Development Environments (IDEs)

The concept of IDEs is evolving, with a focus on integrating various tools like Slack and issue trackers to enhance the software development experience.

As AI becomes more prevalent, engineers may find themselves acting as managers overseeing numerous less experienced team members, leading to challenges in quality control and productivity.

Challenges of Managing AI-Assisted Teams

Many engineering managers express concerns about the quality of work produced by junior developers, which can lead to overwhelming workloads and frequent one-on-one meetings.

Successful customers utilizing AI tools often adopt conservative approaches, focusing on features like next action prediction while carefully scoping tasks for AI assistance.

Effective Use of AI Tools

There are two primary patterns for leveraging AI: either specifying tasks upfront for the AI to complete or breaking down tasks into smaller segments for iterative review and refinement.

Users who effectively chop up their tasks tend to have better experiences with these tools, making the process feel less burdensome.

Building Cursor: From Concept to Launch

The development team behind Cursor was initially cautious about releasing their product but eventually decided to share it publicly after achieving a functional prototype.

The first version of Cursor was built from scratch before transitioning to a base model similar to VS Code, allowing for rapid development and integration of essential features.

Rapid Growth and User Feedback

After launching Cursor within three months, the team was surprised by the immediate interest and feedback from users, which significantly influenced further development decisions.

Growth Journey and Product Development Insights

Initial Growth Experience

The growth journey began slowly, leading to feelings of impatience among the team. Despite initial perceptions, the overall speed of growth surprised them as it became consistent over time.

Month-over-month growth was exponential but felt slow at first due to low starting numbers. This gradual increase did not initially feel like a rapid success.

Product-Centric Approach

The team's focus on building a product they loved led to organic user interest and word-of-mouth promotion. Their dedication to product quality was prioritized over other business aspects.

Early efforts were concentrated on product development rather than traditional sales and marketing strategies, allowing for a strong foundation in product quality.

Challenges in Product Development

Strategic focus and prioritization in product development proved challenging, especially with numerous potential directions available for the team.

The company operates in a unique space between traditional software companies and foundation model companies, requiring excellence in both user experience and scientific model development.

Importance of Quality Science

Balancing high-quality user experiences with robust scientific models is crucial yet complex. Achieving this balance is essential for long-term success.

While some concepts may seem straightforward, executing them effectively presents significant challenges that require careful consideration.

Introduction of One Schema's New Product

Overview of One Schema

Andrew Luo introduces One Schema's new data intake product aimed at automating manual work associated with importing CSV and Excel files.

Features of File Feeds 2.0

The newly launched File Feeds 2.0 utilizes AI to simplify the transformation process for nontechnical teams, supporting various file integrations such as SFTP and S3.

Addressing Data Quality Issues

One Schema addresses common issues related to bad data entries by validating files before they enter systems, thus preventing outages caused by erroneous records.

Counterintuitive Lessons from Building AI Products

Unexpected Model Development Involvement

Initially, the team did not anticipate engaging in their own model development due to resource constraints; however, they found it necessary as their understanding evolved.

Realization About Existing Models

They recognized that existing models could be leveraged more effectively rather than replicating foundational training processes already established by others.

Understanding Model Development and Use Cases

The Evolution of Model Usage

The development of models has been gradual, initially focusing on specific use cases where existing foundation models were not suitable.

Emphasis is placed on identifying weaknesses in foundation models rather than trying to replicate their strengths, allowing for complementary innovations.

Unique Aspects of Cursor's Models

Cursor utilizes both major generative models and its own proprietary models to enhance user experience.

A key example is the autocomplete feature in coding, which requires a different approach compared to text writing due to the predictability of code changes.

Autocomplete Functionality in Coding

Cursor’s autocomplete system predicts upcoming actions across multiple files, requiring speed (completion within 300 milliseconds) and cost efficiency due to high model usage.

The focus is on training models specifically for predicting code diffs—changes made in a codebase—rather than generic text completions.

Integration with Foundation Models

Proprietary models assist in searching relevant parts of a codebase before inputting data into larger foundation models like Sonnet or Gemini.

After receiving suggestions from these large models, smaller specialized models refine the output into complete code snippets, enhancing quality and speed.

Ensemble Approach to Model Utilization

This method mirrors strategies used by other organizations like OpenAI, leveraging various model strengths while maintaining cost-effectiveness.

Defensibility and Competition in AI

The Role of Custom Models

Open-source models are prevalent, but there is also a need to work with closed-source providers for tuning. This raises questions about the long-term defensibility of AI companies.

Founders and investors consider custom models as a potential moat in the competitive landscape of AI.

Building Inertia and Long-Term Strategy

The speaker emphasizes that building inertia is crucial, suggesting that continuous improvement is necessary to maintain competitiveness against emerging players.

Historical parallels are drawn with markets like search engines in the late '90s and personal computers, indicating that high ceilings exist for innovation and value creation.

Market Dynamics and User Feedback

In markets like search engines, distribution helps improve products through user feedback, which is similarly applicable in the current AI market.

The speaker notes that many opportunities for innovation still exist, implying a long journey ahead for advancements in AI technology.

Consumer-Like Tendencies in AI Development

A consumer-oriented approach may be more effective than creating lock-in strategies seen in traditional enterprise software like Salesforce.

Companies should focus on generating ongoing value through significant investments rather than running out of useful innovations quickly.

Future Market Landscape

There’s speculation about whether one company will dominate or if multiple solutions will coexist; the market size suggests room for various players.

Unlike past fragmented IDE markets, the potential applications for knowledge workers are vast, indicating a much larger opportunity space.

Automation and Knowledge Work Transformation

The automation of busy work within knowledge sectors presents significant growth potential compared to previous developer tools.

The speaker believes that while there may be niche players, one dominant company could emerge to create comprehensive software solutions across industries.

Conclusion on Market Potential

Microsoft's Co-Pilot: A Missed Opportunity?

The Initial Promise of Co-Pilot

Microsoft was at the forefront with Co-Pilot, which initially seemed like a groundbreaking product that could lead to significant business opportunities.

Despite its potential, there are concerns about why Co-Pilot has not met expectations, suggesting both historical and structural reasons for its underperformance.

Market Dynamics Affecting Incumbents

The market is not particularly favorable to established companies; innovation often thrives in environments where switching costs are low and competition is fierce.

In markets where products can be easily bundled or commoditized, incumbents may struggle to justify investment in innovative solutions.

Challenges Within Microsoft

Coordination issues among various departments have hindered the development of Co-Pilot, as key personnel from the initial team have moved on to other projects.

Tips for New Users of Cursor

For new users of Cursor, understanding the capabilities and limitations of AI models is crucial for success. This includes knowing how much detail to provide when specifying tasks.

Users should avoid trying to accomplish large tasks in one go; instead, breaking down tasks into smaller components can yield better results.

Experimentation and Learning

Developers are encouraged to experiment with AI tools in safe environments (like side projects), allowing them to discover the limits and capabilities of these models without risking professional work.

Building an intuitive understanding of what AI can do will help users leverage these tools more effectively over time.

Benefits Across Experience Levels

Understanding AI Adoption in Engineering

The Spectrum of AI Utilization Among Engineers

Junior engineers tend to overly rely on AI tools for all tasks, which is premature given the current limitations of these technologies in collaborative environments.

Senior engineers often undervalue the potential of AI, sticking to traditional workflows instead of exploring new efficiencies that AI can offer.

There are notable exceptions where senior engineers actively push boundaries and adopt new technologies, but this is not the norm.

The contrasting approaches between junior and senior engineers illustrate a spectrum of expectations regarding AI's capabilities—some expect too much while others expect too little.

Insights on Hiring Practices

Reflecting on his early experiences, the speaker notes that much valuable knowledge in hiring is tacit and often learned through trial and error.

A key focus was assembling a world-class team with intellectual curiosity and honesty; this was deemed crucial for company strategy and product development.

The speaker emphasizes the importance of patience in hiring, suggesting that many companies hire too quickly without ensuring cultural fit or capability.

Initially, they struggled with recruitment strategies but eventually found success by targeting high-caliber candidates over extended periods.

Lessons Learned from Recruitment Challenges

Early biases towards candidates from prestigious schools led to overlooking excellent talent who did not fit conventional profiles; diversity in experience proved beneficial.

Evolving their interview process included developing tailored questions and practical work tests to better assess candidate suitability.

Interview Process Insights

Understanding Candidate Engagement

The speaker emphasizes the importance of learning about candidates' interests and engaging them in conversations, even when they are not actively seeking opportunities.

A two-day work test has proven effective, allowing candidates to complete a real project, showcasing their skills without being overly time-consuming for the team.

The Value of Real Projects

This approach enables candidates to demonstrate their capabilities through an end-to-end project experience, which helps assess both technical skills and cultural fit within the company.

The two-day test is particularly beneficial during early stages when the product may not be fully developed; it allows potential hires to connect with the team and understand its dynamics.

Interview Structure and Company Growth

The interview process is described as formative rather than traditional; it provides insight into how well candidates mesh with existing team members over shared meals and collaborative work.

Candidates are given assignments that mimic actual tasks within the company's codebase, fostering a realistic working environment during interviews.

Company Size and Team Composition

Currently, the company consists of around 60 employees, primarily engineers. This small size contrasts with typical software companies that have larger operational teams.

The focus remains on building a strong team capable of enhancing product quality and customer service while maintaining lean operations.

Navigating AI Industry Challenges

With rapid developments in AI, staying focused amidst distractions is crucial. Hiring individuals who prioritize quality work over external validation is essential for maintaining productivity.

Emphasizing hiring practices that attract level-headed individuals can help mitigate distractions from industry noise while fostering a culture centered on high-quality output.

Organizational Strategies for Success

The speaker notes that while processes are necessary for growth, hiring people with desirable traits can often compensate for less structured environments.

AI Evolution and Its Impact on Business

The Rapid Development of AI Technologies

The speaker reflects on the significant changes in AI technologies since late 2021, highlighting the emergence of models like GFD3, DALL-E, and Stable Diffusion.

Despite numerous advancements in various AI modalities, only a few have had a substantial impact on business operations.

The speaker notes that while there has been extensive research in deep learning, many ideas lack longevity or practical application.

Misunderstandings About AI's Future

There is a common misconception regarding the pace of AI development; some believe it will happen rapidly while others dismiss it as hype.

The speaker argues that we are amidst a transformative technology shift that could surpass the internet's impact and will unfold over decades.

Challenges Ahead for AI Integration

Achieving meaningful progress in AI requires addressing multiple independent challenges related to data understanding and real-world applications.

Collaboration between humans and machines is essential for creating effective user experiences with emerging technologies.

Importance of Knowledge Work Automation

Companies focusing on automating specific areas of knowledge work will play crucial roles in advancing technology and building successful businesses.

Successful companies will integrate technology development with user experience design to create impactful products.

Hiring Needs and Industry Dynamics

The speaker discusses current hiring needs within their organization, emphasizing openness to diverse roles beyond traditional expectations.

They highlight the urgency to develop superior products while expanding market presence amid competition from less innovative tools.

The Future of Engineering: Demand and Evolution

The Role of Engineers in the Future

The discussion begins with a question about the increasing need for engineers across companies versus the potential for automated systems to take over engineering tasks.

Emphasis is placed on the importance of human involvement in engineering, suggesting that professionals will always be necessary to guide and control technology development.

There is a lasting demand for software, highlighting how current software development is labor-intensive and costly, which may limit innovation.

If costs could be significantly reduced, there would be an explosion of new tools and applications available for use, indicating a vast untapped potential in software development.

Personal experience from working in biotechnology illustrates the high demand for tailored internal tools that existing off-the-shelf solutions fail to meet.

Challenges and Opportunities in Software Development

Despite advancements, significant friction remains in computer work processes; there's more demand for software than can currently be produced efficiently.

Current production costs are likened to making blockbuster movies even for simple productivity software, underscoring inefficiencies in the industry.

Successful teams must adapt continuously while improving existing products; this requires innovative thinking and reflection on past successes.

Learning from History

Companies that have successfully navigated technological changes are rare but serve as valuable examples of innovation and adaptation.

Reflecting on historical examples can provide insights into what strategies have worked or failed in tech evolution.

Engaging with Potential Candidates

Information is provided on how interested individuals can reach out regarding job opportunities at Kers.com, emphasizing openness to connect with prospective candidates.