After Code: Cursor's CEO on Logic Design, Custom Models and Why One Winner Will Take the Market

Truell says Cursor's goal is to invent a new type of programming in which a person distills their intent for how software should work and look into the most concise form possible. He contrasts this with two popular visions. One says software building will keep looking like text editing in TypeScript, Go or Rust; the other says you will simply talk to a chatbot, as if addressing an engineering department over Slack.

He thinks both are wrong. Chat lacks precision: if humans are to keep complete control, they need a way to gesture at exactly what should change, not type "change this about my app" into a detached text box. And the world where nothing changes ignores how much better the technology will get.

What comes after code, in his telling, is a representation of the software's logic that reads more like English, an evolution of programming languages toward pseudocode. It would be terser, easier to navigate, and editable at a high level. Truell stresses that this path runs through existing professional engineers, keeps the human in the driver's seat, and preserves a fast iteration loop rather than handing work to something slow in the background.

Asked which skills grow in value, Truell answers taste, but not only visual taste. Visuals are well served by existing tools, while the logic of how software works is still best expressed as code or a working prototype. He expects being an engineer to feel more like being a logic designer: specifying the whats, with less attention to how things work under the hood.

He admits we are far from that today, pointing to the memes about apps built by trusting AI too much. But he expects a future in which engineers can be less careful, an extremely important skill now, and lean more on taste. By taste he means having the right idea for what should be built and being able to translate it effortlessly into something a computer can run.

On vibe coding, Truell sees it as related but flawed. Generating lots of code without understanding the details quickly leaves you stuck once the project is big enough that you can't change it. The open problem, he says, is giving people continued control over all the details even when they don't understand the code.

Truell describes Cursor as a solution searching for a problem. Two things pushed the founders toward AI products: the first beta of GitHub's Copilot, the first AI tool they found genuinely useful, and the scaling-law papers showing models would improve simply by scaling up models and data. At the turn of 2021 and 2022 they began an exercise in imagining how areas of knowledge work would change as AI matured.

Their misstep was assuming coding would be too crowded, so they spent about four months building tools for mechanical engineers. None of the cofounders were mechanical engineers, there was little 3D-model data online, and they concluded they would need to build their own models. Eventually they realized they weren't excited about the field.

Looking back at programming, they saw that despite plenty of time passing little had changed, and the teams in the space didn't seem ambitious enough about how software creation would flow through these models. Lenny draws the lesson that apparent late arrival is no obstacle if incumbents lack ambition. Truell agrees, adding that the ceiling in AI is unusually high, so leapfrogs remain possible.

Truell explains why Cursor chose an IDE over a pure model or a fully end-to-end agent. Those who chose the model route envisioned AI making all the decisions, while Cursor wants humans to control every decision in what they build. The team also dogfooded intensely, never shipping anything not useful to themselves, which instilled realism about what AI can't yet do.

He argues that owning the whole application, rather than writing a plugin, matters because the form factor of programming will change a lot and existing environments are too limited in extensibility. He expects both foreground and background work to live in one place: tasks like bug fixes, where correctness is easy to describe, can spin off to an agent from Slack or an issue tracker, then be pulled back in for quick editing.

Lenny jokes that everyone is becoming a manager of many not-so-smart reports. Truell says the customers who succeed remain fairly conservative. They lean on next-edit prediction and scope down what they hand off, either specifying a lot upfront and reviewing once, or chopping the work into small specify-write-review loops. The latter is what the most successful users tend to do.

The first version of Cursor was hand-rolled rather than built on VS Code, which meant implementing language support, navigation, error tracking, an integrated terminal and remote connections. After about five weeks the team was living in it full time. They launched roughly three months after the first line of code, expecting a few hundred users for a long time.

Instead there was an immediate rush of interest and feedback, which led them to rebase on VS Code. Truell is candid that the three-month version wasn't very good. He credits the growth to sustained paranoia about how far the product was from the end goal of a new form of programming, rather than over-rotating on the launch.

He describes growth as a consistent month-over-month exponential, sped up by launches, which feels slow when the numbers are small. The team let sales and marketing "fires burn" for a long time and focused on the product. He adds that the work is interdisciplinary, sitting between a normal software company and a foundation-model company, which makes prioritization hard.

The most counterintuitive lesson, Truell says, is that Cursor ended up doing heavy model development. The team had done the math on training from scratch and ruled it out, but found existing models weren't doing things they could do because the right tools hadn't been built. Now it's a major hiring focus, and he says every magic moment in the product involves a custom model.

The clearest case is autocomplete. In code, the next 5 to 30 minutes of work can be predictable from looking over your shoulder, unlike writing. Cursor trains models to predict the next set of diffs across files and within files, which must respond within about 300 milliseconds and be cheap enough to run on every keystroke.

A second group of custom models sits around big models such as Sonnet, Gemini or GPT. On the input side they act like a mini search engine to find relevant parts of a codebase; on the output side, fast specialty models turn the large model's high-level sketch into full diffs. Truell says these are mostly built on open-source base models, sometimes with closed providers, because the team only cares about being able to post-train them.

On moats, Truell says there are ways to build inertia, but mostly it is incumbent on everyone to keep building the best product because the ceiling is so high that any entrenchment can be leapfrogged. He compares the market to search engines around 1999 and to early personal computers, where more R&D kept paying off and distribution improved the product through data. The result feels more consumer-like than a contract-locked enterprise market.

He rejects the argument that editors are a poor business because past IDE markets were fragmented. In the 2010s there was only so much to build for programmers, whereas the opportunity now spans automating knowledge work. He expects niches to survive, but predicts one company will build the general tool that produces almost all the world's software, a generationally large business.

Asked why Microsoft, with Copilot and its distribution, seems to be falling behind, Truell offers a structural and a historical reason. The market isn't friendly to incumbents, since switching is easy and tools can be compared directly, and the original Copilot team has largely moved on. He also notes Copilot was a big inspiration for Cursor.

Asked for tips for new users, Truell says success depends on having a taste for what models can do: how complex a task they can handle and how much specification they need. The product doesn't yet educate people well on this. His first tip is to avoid writing one giant prompt and accepting or rejecting the whole result; spend the same specification effort in smaller pieces instead.

His second tip is the opposite: on a side project, try to fall on your face and be ambitious with AI, because many developers haven't given it a fair shake and underestimate it. Lenny sums this up as building a gut feeling, which must be rebuilt with new models. Truell says each model has different quirks and personalities, and Cursor hopes to take that burden off users.

On who benefits more, juniors or seniors, he says both do, with different anti-patterns. Junior engineers lean on AI too wholesale, which doesn't yet work end-to-end in a long-lived codebase with many contributors. Senior engineers, on average, underrate what AI can do and stick to existing workflows, though developer-experience teams often push the boundaries.

Looking back, Truell says Cursor was patient on hiring to the point of hiring too slow. The team wanted intellectual curiosity, experimentation, honesty and a level head, and eventually settled on recruiting world-class people over long stretches, sometimes years. Early on they biased too much toward young candidates with well-known credentials and later found excellent people with different profiles.

A surprisingly durable piece of the process is a two-day onsite work test in which candidates do a real mini project in the codebase, largely on their own. It spreads out interviewer time and doubles as a check on whether you want to be around the person. Early on, when the product was weak, it also let candidates meet a team they might want to join. The company was nearing 60 people, with a very high share of engineering, research and design.

On staying focused amid AI news, Truell credits hiring level-headed people who care less about external validation, plus talking about it often and leading by example. Having worked in AI since 2021 to 2022, the team has built an immune system: of the many models and modalities that appeared, only a few really affect the business. He sees the same pattern in deep learning research, where a few simple, elegant ideas had staying power.

Truell says people misunderstand AI by sitting at either extreme, dismissing it as hype or expecting everything instantly. He believes it is more consequential than the internet and any tech shift since computers, but that it will take decades. Progress is needed both on the science side (understanding data, being faster, cheaper, smarter, taking actions) and on the human side, deciding what experience people should see and control.

He singles out companies that automate and augment one area of knowledge work, building both the underlying technology and the product experience, as especially consequential. As they scale, he says, they will also push the technology forward. Cursor is trying to do this for software, and its priorities this year are to build the best product in the space and grow it in what he calls land-grab mode.

On whether engineering jobs will slow down, Truell argues humans will still need to decide what software looks like, and that demand for software is lasting. Simple tools remain absurdly expensive to build, and he recalls early work on internal tools for a biotech company where demand far exceeded what he could build. He expects engineers to do much more and long-term demand for them to grow.