The Paradigm Shift in AI Programming

The AI programming field is undergoing a profound paradigm shift, with the rise of Cursor serving as a powerful testament to this trend. From the reflections of its founder, we gain insight into how AI programming tools are reshaping development processes and the key to standing out in competition by continuously delivering exceptional products.

Founded by AnySphere co-founder and CEO Michael Truell, Cursor is not only one of the fastest-growing AI programming products today but also an early form of the “post-code era.”

With a team of 60, Cursor achieved an annual recurring revenue of $100 million just 20 months after its launch, growing to $300 million within two years, making it one of the fastest-growing development tools in history. This achievement is supported not only by improved code generation capabilities but also by a complete reconstruction and redefinition of the software development process.

Michael, a tech person with a decade of experience in AI, studied mathematics and computer science at MIT and later worked in research engineering at Google. He has a deep understanding of both AI technology pathways and business history.

In a conversation with overseas tech blogger Lenny, he clearly outlined a future that differs from mainstream assumptions: code will not be completely replaced, but it will no longer be the primary output of humans. Instead, people will express their ideas about software functions and behaviors in a manner close to natural language, with systems responsible for translating these intentions into executable program logic.

He pointed out that the two mainstream assumptions about the future of AI programming are flawed. One assumes that development methods will largely remain the same, continuing to rely on languages like TypeScript, Go, and Rust to build programs; the other believes that the entire development process can be completed solely through conversations with chatbots.

Diverse Development Methods Coexisting

Discussing the starting point of Cursor, Michael recalled two key moments:

The first was their initial exposure to the internal testing version of GitHub Copilot. This was their first experience with a truly practical AI development tool that significantly enhanced work efficiency.

The second moment was their study of a series of Scaling Law papers published by OpenAI and other research institutions. These papers made them realize that even without new algorithms, as long as model parameters and data scales are continuously expanded, AI will continue to evolve.

By the end of 2021 and early 2022, they firmly concluded that the era of AI products had truly arrived. However, unlike most entrepreneurs who focused on “building large models,” Michael and his team attempted to think backward from the perspective of knowledge work, considering how various specific work scenarios would evolve under AI enhancement.

At that time, they chose a seemingly niche direction—mechanical engineering. They believed this field had little competition and a clear problem space, so they began automating CAD tools. However, they quickly realized they lacked sufficient passion for mechanical engineering and data corpus, making development challenging.

Ultimately, they decided to return to the field they were most familiar with: programming. Although there were already products like Copilot and CodeWhisperer in the market, they believed no one had truly pushed the vision to its limits. Despite being one of the hottest and most competitive areas, they judged that the “ceiling” was high enough to support a breakthrough product company. They abandoned the strategy of “avoiding hot zones” and chose to “delve deep into hot zones.”

One of Cursor’s core decisions was not to create a plugin but to build a complete IDE. They believed that the existing IDE and editor architectures could not adapt to future development methods and human-computer interaction logic.

“We want to have control over the entire interface and redefine the interaction interface between developers and systems.” This was not only to achieve a more natural control granularity but also to build a system base that could truly support the next generation of programming paradigms.

Michael also believes that future development methods will coexist in multiple forms. Sometimes AI acts as an assistant, completing tasks in Slack or issue trackers; other times, it interacts at the IDE frontend; it may also run certain processes in the background while iterating control at the frontend. These are not contradictory as long as users can flexibly switch between full automation and manual control, which constitutes a qualified system.

Regarding the current industry trend of “agent hype,” he expressed a reserved attitude. Completely handing tasks over to AI could turn developers into “engineering managers” who must constantly review, approve, and modify outputs from a group of “very dumb interns.” “We do not believe in that path. The most effective way is to break tasks down into multiple steps, allowing AI to complete them step by step while humans remain in control.”

Cursor’s early version was developed entirely from scratch, without relying on any existing editors. Initially, they spent just five weeks building a usable prototype, quickly replacing their original development tools. The entire process from writing code from scratch to going live took only three months. The unexpectedly positive user feedback after launch prompted them to iterate rapidly, ultimately finding a balance between performance, experience, and development speed, and then restructuring based on the VS Code framework.

However, Michael believes that true success is not about the speed of the initial version but rather the continuous optimization that follows. He admits, “The initial three-month version was not very usable; the key is that we maintained a persistent improvement rhythm.” This rhythm of continuous optimization ultimately formed Cursor’s very stable growth trajectory. Although there was no obvious feeling of “takeoff” in the early stages, the cumulative effect of the exponential curve eventually exploded after multiple iterations.

Running in the Right Direction Every Day

While Cursor’s explosion may seem to stem from a key feature or product decision, Michael Truell states that the real secret is quite simple: “Running in the right direction every day.”

This may sound ordinary, but it is extremely difficult to maintain. Every decision and every detail of iteration is made from the user’s perspective, constantly getting closer to actual scenarios, continuously simplifying and optimizing. They never rely on a one-time hit but firmly believe that product value must withstand the test of continuous use and real feedback.

In line with this philosophy is the technical path chosen behind Cursor. Michael mentioned that the team initially had no intention of training their own models when building Cursor. In his view, there were already sufficiently powerful open-source and commercial base models available, and investing computational power, funds, and manpower to build new models from scratch was not only costly but also diverted attention from their true focus: building useful tools and solving specific problems.

However, as the product went deeper into iteration, they gradually realized that existing base models, while powerful, could not meet the critical scenarios in Cursor. Most of these models were trained for general dialogue, question answering, or text tasks, lacking a native understanding of issues like “multi-file structured code editing.”

Thus, they began internal attempts to develop their own models. Initially, it was due to a specific function requiring extremely low latency, and the existing model’s invocation was not feasible; after trying to train their own, they found the results exceeded expectations. Since then, self-developed models have gradually become a core component of Cursor, supporting key functions and becoming an important direction for team recruitment.

A key feature of Cursor is the prediction of “next editing actions.” This is difficult to achieve in writing but highly feasible in coding scenarios due to the strong contextual coherence of programs—once a developer modifies a function or file, the subsequent operations are often predictable.

Cursor’s model is based on this contextual logic, inferring which files, locations, and structures the user is likely to modify next, providing completion suggestions with almost imperceptible latency. This is not just token-level completion but structured code snippet-level prediction, relying entirely on self-developed models trained specifically for this scenario rather than general base models.

In a reality where model invocation costs are extremely high, such self-developed models can significantly lower the product’s usage threshold. To achieve this, the models must possess two characteristics: fast response and low cost.

Cursor requires that every completion inference must be completed within 300 milliseconds and that there should not be excessive resource consumption during prolonged continuous use. This hard constraint necessitates that they control the design and deployment of the models themselves.

In addition to handling core interaction functions, Cursor’s self-developed models also take on another important task—acting as “orchestrators” to assist in invoking large models. For example, when the codebase is large, large models struggle to know which files, modules, and contexts to focus on.

Cursor’s model first conducts a search and synthesis, extracting relevant information from the entire codebase, and then feeds it to the main model. This is akin to building a specialized “information feeding pipeline” for large models like GPT, Claude, and Gemini, enhancing their performance.

At the output end of the model, these sketch-style code modification suggestions are first processed and rewritten by Cursor’s self-developed models, transforming them into truly executable, structured patches.

This collaborative system architecture, where multiple models work together, is what OpenAI refers to as “model integration.” Michael is not fixated on building models from scratch but pragmatically chooses existing open-source models as a starting point, such as LLaMA.

In certain scenarios, they also collaborate with closed-source vendors to fine-tune model parameters to adapt to specific tasks. He emphasizes that what matters is not whether the underlying structure of the model is controlled by them but whether they can obtain operational training and customization rights to serve the actual needs of the product.

As the technical system continues to improve, another question gradually emerges: where is Cursor’s moat in this rapidly evolving race? Michael’s answer is very clear. He does not believe that “product binding” and “contract locking” can build true long-term defenses.

Unlike traditional B2B software, the barriers in the AI tools market change dramatically, with low user trial costs and high acceptance of new tools. He candidly states that this is not a market favorable to traditional giants; rather, it encourages new companies to continuously experiment, iterate quickly, and compete for user choice.

From this perspective, the moat that Cursor can rely on is not model control or data monopoly but the ability to “continuously build the best products.”

This industry resembles the search engine boom of the 1990s or the early PC industry, where every improvement can yield significant returns. Competitive barriers arise from the “deep inertia” formed by continuous iteration and the differences in team organizational capabilities and product refinement systems.

Michael presents a core viewpoint: when a market still has a large number of unmet needs and many technical structures that can be optimized, continuous research and development itself is the biggest moat. It does not rely on binding users but rather on its own continuous evolution to gain cumulative advantages in time and quality.

He emphasizes that this “evolutionary moat” does not exclude competition nor does it imply that there can only be one winner in the market. However, under the proposition of “building a globally universal software construction platform,” it is indeed possible for a single company to emerge as a massive entity.

While multiple products may coexist in the future, if the question is “who can handle the largest-scale code logic translation tasks globally,” then ultimately only one company may remain. The reason is not that other companies are doing poorly, but that users will naturally gravitate towards the most universal, stable, and contextually understanding platform. In this field, product quality and evolution speed determine market concentration.

He further points out that one cannot judge this round of technological evolution’s pattern based on the fragmentation experience of the traditional IDE market. The IDE market of the 2010s saw “no one making big money” because the capabilities of editors at that time were close to their limits, and the parts that could be optimized were merely basic functions like syntax highlighting, debugger integration, and quick navigation. However, today, developer tools are at a new paradigm starting point, where the goal is no longer to optimize an editor but to reshape the entire workflow and expression structure of knowledge workers.

The essence of AI programming tools is not to replace code but to enhance human instruction expression capabilities and compress the path from idea to implementation. This represents a much larger market than traditional development tools and a future channel with platform attributes. In this channel, whoever can provide the smoothest, most reliable, and most contextually understanding programming experience has the opportunity to become synonymous with the next generation of “software construction infrastructure.”

When Lenny mentioned Microsoft Copilot, he also raised a typical current issue: do the companies that entered the market first possess the ability to lead continuously? Michael acknowledged that Copilot was a source of inspiration for the entire industry, especially when the initial version was released, bringing an unprecedented development interaction method.

However, he believes Microsoft has not truly maintained its initial momentum, which is due to both historical reasons and structural challenges. The core team that initially developed Copilot experienced frequent personnel changes, making it difficult to form a unified direction within a large organization, and the product path could easily be diluted by internal power struggles and process complexities.

More fundamentally, this market is not friendly to incumbents. It does not rely on integration and binding like enterprise-level CRM or ERP systems, nor does it have the strong user stickiness of “switching costs.” User choice is entirely based on experience differences, which determines that “product strength” rather than “sales ability” will be the decisive factor. In such a dynamic, open, and high-frequency trial-and-error market, the companies that can truly win are those entrepreneurial teams that can iterate their products weekly, improve monthly, and continuously strive for technical limits.

The sense of direction and product rhythm that Cursor currently exhibits is precisely a response formed in this context. It does not rely on “closure” but rather on the simple, clear, yet extremely challenging mission of “continuously building the best development tools in the world,” attracting developers’ active choice.

How to Use Cursor Correctly?

In building an AI IDE platform aimed at global developers, Michael Truell is most concerned not with the limits of model capabilities but with how users understand and make the best use of these capabilities.

When asked what advice he would give if he could sit next to every first-time Cursor user, he did not explain features or operational tips but emphasized the establishment of a mindset—an instinctive judgment of what the model can and cannot do.

He candidly admitted that the current Cursor product does not do enough to guide users in understanding the boundaries of the model. Without clear prompt tracks and interactive feedback mechanisms, many users easily fall into two extremes: either placing too high expectations on the model and trying to solve complex problems with a single prompt, or completely giving up after an unsatisfactory first result.

His suggested approach is task decomposition, gradually progressing through “small prompts – small generations,” engaging in continuous two-way interaction with AI to achieve more stable and higher-quality results.

Another suggestion is more strategic. He encourages users to “go all out” in side projects without business pressure, attempting to push AI capabilities to their limits.

Without affecting mainline work, through a whole set of experimental projects, developers can feel how much the model can truly accomplish and where the boundaries of failure lie. This “wrestling-style exploration” can help developers build a more accurate intuition and give them more confidence when facing formal projects in the future.

As model versions continue to update, such as the release of GPT-4.0 or Claude iterations, this judgment also needs to be updated accordingly. He hopes that future Cursor products can incorporate a guiding mechanism so that users do not have to explore the model’s “temperament” and boundaries each time. However, for now, this remains a skill that users need to accumulate subjectively.

Regarding another frequently asked question—whether such tools are more suitable for junior engineers or senior engineers—Michael provided precise classifications. He pointed out that junior developers often tend to “completely rely on AI,” trying to use it to complete the entire development process, while senior engineers may underestimate AI due to their rich experience, failing to fully explore its potential. The former’s problem is “too much reliance,” while the latter’s issue is “too little exploration.”

He also emphasized that certain senior technical teams within companies, especially architect-level talents focused on Developer Experience, are actually the most proactive adopters of such tools. They understand system complexity and focus on tool efficiency, often achieving the best results in AI programming scenarios.

In his view, the ideal user profile is neither a beginner nor a seasoned veteran with fixed processes but rather those “senior yet not rigid” mid-level engineers—who possess system understanding while remaining curious and open to new methods.

How to Build a World-Class Team?

When asked what advice he would give himself if he could return to the year Cursor was founded, Michael chose a non-technical answer—recruitment. He repeatedly emphasized that “finding the right people” is the second most important task after the product itself.

Especially in the early stages, building a world-class engineering and research team is not only a guarantee of product quality but also a decisive factor for organizational focus, rhythm, and culture. The talent he seeks must possess technical curiosity, willingness to experiment, and the ability to maintain calm judgment in a turbulent environment.

He recalled that Cursor went through many twists and turns in the recruitment process. Initially, they placed too much emphasis on “high-profile resumes,” leaning towards hiring young people from prestigious schools with standard success paths. However, they ultimately realized that truly suitable talents often do not fit these traditional templates. Instead, those with slightly later career stages, highly matched experience, and mature technical judgment are often the key forces driving the team’s leap.

In the recruitment process, they gradually established a set of effective methods. The core is a two-day “work test” system, where candidates need to complete a task closely related to a real project within a specified time while working with the team.

This process seems cumbersome, but in practice, it is not only scalable but also significantly improves the accuracy of team judgment. It assesses candidates’ coding abilities, communication skills, thinking styles, and hands-on capabilities, and even helps candidates determine whether they are willing to work long-term with this team.

The “collaborative interview” mechanism has gradually evolved into a part of Cursor’s team culture. They view the recruitment process as a two-way selection rather than a one-way evaluation. When the company is not widely recognized in the market and the product is not mature, the team itself is the most important attraction.

He admits that many early employees joined due to one or multiple collaborative experiences rather than judgments based on salary or valuation. Today, this system is still retained and applied to every new candidate. Cursor’s team size currently remains around 60, which is considered streamlined in many SaaS companies.

Michael pointed out that they intentionally maintained this lean configuration, especially being restrained in expanding non-technical positions. He acknowledges that they will certainly expand the team in the future to enhance customer support and operational capabilities, but for now, they remain a highly engineering, research, and design-driven company.

When discussing how to maintain focus in the rapidly changing pace of the AI industry, Michael does not rely on complex organizational systems.

He believes that the foundation of organizational culture lies in recruitment itself. If they can hire rational, focused individuals who are not swayed by trending emotions, the team will naturally have a good sense of rhythm. He admits that Cursor still has room for improvement, but overall, they have achieved good results in guiding a culture that “only focuses on creating excellent products.”

Many companies attempt to solve problems through processes and organizational design that could actually be avoided by “finding the right people” in advance. Their development process is extremely simple, and the reason it can succeed is that team members generally possess self-discipline and a spirit of collaboration. He particularly emphasizes a shared psychological trait: an “immunity” to external noise.

This immunity is not inherently present but is gradually formed through long-term industry experience. As early as 2021 and 2022, the Cursor team was already exploring AI programming directions. At that time, GPT-3 did not yet have the Instruct version, DALL·E and Stable Diffusion had not been made public, and the entire generative AI industry was still in its technical infancy.

They experienced the explosion of image generation, the popularization of dialogue models, the release of GPT-4, the evolution of multimodal architectures, and the rise of video generation… but among these seemingly bustling technological trends, very few had a substantial impact on the product.

This ability to discern between “structural innovation” and “surface noise” has become an important psychological foundation for maintaining their focus. He compares this approach to the evolution of deep learning research over the past decade: while countless new papers are published every year, it is the very few elegant and fundamental structural breakthroughs that truly drive AI forward.

Looking back at the entire technological paradigm shift, Michael believes that the current development of AI is at a profoundly pivotal moment.

The outside world often falls into two extremes: some believe that the AI revolution is about to arrive, almost overnight overturning everything; others view it as hype, a bubble, and not worth considering. His judgment is that AI will become a paradigm shift more profound than personal computing, but this process will be a “multi-decade” continuous evolution. I/O to iO, Jony Ive will drive a new design movement—AI is rewriting computing paradigms and hardware definitions, and it is also the new battleground after large models.

This evolution does not rely on a single system or a specific technological route but consists of independent solutions to numerous segmented problems. Some are scientific issues, such as how models can understand more data types, run faster, and learn more efficiently; some are interaction issues, such as how humans collaborate with AI, how to define authority boundaries, and how to establish trust mechanisms; some are application issues, such as how models can truly change real work processes and provide controllable outputs in uncertainty.

In this evolution, he believes a class of key enterprises will emerge—AI tool companies focused on specific knowledge work scenarios. These companies will deeply integrate base models and may also develop core modules independently while building the most suitable human-computer collaboration experience. They will not merely be “model callers” but will refine technology and product structures to the extreme, thereby growing into new-generation platform enterprises. Such companies will not only enhance user efficiency but may also become the main force driving the evolution of AI technology.

Michael hopes that Cursor can become one of these companies, and he also looks forward to seeing a group of equally focused, solid, and technically deep AI entrepreneurs emerge in more knowledge work fields such as design, law, and marketing. The future does not belong to speculators but to those builders who truly deconstruct problems, reshape tools, and understand the relationship between people and technology.

He also pointed out that the two most important things for Cursor in 2025 are to create the best product in the industry and to promote it on a large scale. He describes the current state as a “land grab”: the vast majority of people in the market have not yet encountered such tools or are still using slowly updated alternatives. Therefore, they are increasing investments in market, sales, and customer support while continuously seeking excellent talents who can push the product boundaries from a technical level.

When discussing the impact of AI on engineering positions, Michael’s response is quite calm. He does not believe that engineers will be quickly replaced; on the contrary, he thinks engineers will be more important than ever in an AI-driven future.

In the short term, programming methods will undergo significant changes, but it is hard to imagine that software development will suddenly become a process where “just inputting requirements will lead the system to complete everything automatically.” AI can indeed liberate humans from low-level tedious implementations, but core decisions regarding direction, intent, and structural design must still be controlled by professional developers.

This judgment also implies that as software construction efficiency dramatically increases, the elasticity of demand will be thoroughly released. In other words, software itself will become easier to build, costs will significantly decrease, ultimately leading to an expansion of the entire market scale. More problems can be modeled, more processes can be systematized, and more organizations will attempt to customize their internal tools rather than accept generic solutions.

He illustrates this with a personal experience. In a biotechnology company he participated in early on, the team urgently needed to build a tool system that matched internal processes, but the available solutions on the market were not suitable, and the efficiency of self-development was very limited, resulting in most needs being shelved.

Such scenarios are still common across various industries, indicating that the barriers to software development remain high. If one day, making software is as simple as moving files or editing slides, what will be released is a whole new application era.

Finally, he emphasizes that AI will not reduce the number of engineers; rather, it will change the structure of engineering positions. Those who are good at collaborating with AI, understand system logic, and possess product intuition will play a larger role in the new generation of work systems.