Cursor, one of the most prominent AI-powered coding assistants, has revealed an unconventional approach to product development that challenges traditional software engineering practices. According to Jason Ginsberg, the company’s engineering head, many of Cursor’s most critical AI features emerged from bottom-up experimentation rather than formal planning processes.
In a recent episode of the “LangChain” podcast published Thursday, Ginsberg disclosed that he personally built Cursor’s debugging feature over the Thanksgiving holiday simply because he wanted it and to “help people on the team.” This informal side project eventually became Cursor’s official “Debug Mode,” now a core capability of the platform. The company’s metric for determining feature readiness is remarkably simple: “If there’s internal adoption, that’s kind of our metric for this is ready to ship,” Ginsberg explained.
This pattern extends to Cursor’s agent feature, now considered one of its defining capabilities. Ginsberg revealed that the agent was originally prototyped by a single engineer while others on the team remained skeptical. “He prototyped it super quickly, and everyone’s like, ‘Oh wow, this works,’” Ginsberg said, describing how rapid experimentation won over doubters through demonstration rather than debate.
While Cursor maintains short-term roadmaps, Ginsberg emphasized that many of its biggest features emerge organically. The company operates with minimal formal process, with engineers resolving disagreements through code rather than lengthy documents or alignment meetings.
Cursor’s talent-dense structure enables this agile approach. Ginsberg revealed that the company had approximately 20 employees at the start of 2025, attributing the small headcount to an “extremely, extremely high” hiring bar and slow recruitment process. This lean team structure allows Cursor to move quickly without organizational overhead.
The preference for small, elite teams has become increasingly influential across the AI industry. Meta’s superintelligence AI unit operates with a tiny fraction of the company’s 70,000+ employees. CEO Mark Zuckerberg stated in July that he’s “gotten a little bit more convinced around the ability for small, talent-dense teams to be the optimal configuration for driving frontier research.”
OpenAI CEO Sam Altman predicted last year that “we’re going to see 10-person companies with billion-dollar valuations pretty soon,” a vision that Cursor appears to be realizing in the AI coding space.
Key Quotes
If there’s internal adoption, that’s kind of our metric for this is ready to ship
Jason Ginsberg, Cursor’s engineering head, explained the company’s simple but effective approach to determining when features are ready for production. This metric emphasizes real-world usage over theoretical planning, reflecting Cursor’s bottom-up development philosophy.
He prototyped it super quickly, and everyone’s like, ‘Oh wow, this works’
Ginsberg described how Cursor’s agent feature—now one of its defining capabilities—was built by a single skeptical engineer who proved its value through rapid prototyping. This demonstrates how demonstration can overcome organizational resistance more effectively than debate.
I’ve just gotten a little bit more convinced around the ability for small, talent-dense teams to be the optimal configuration for driving frontier research
Meta CEO Mark Zuckerberg articulated this philosophy during the company’s July earnings call, reflecting a broader industry trend toward lean, high-caliber teams rather than large organizations for AI development.
We’re going to see 10-person companies with billion-dollar valuations pretty soon
OpenAI CEO Sam Altman made this prediction last year, and companies like Cursor with approximately 20 employees are proving this vision increasingly realistic in the AI coding and tools space.
Our Take
Cursor’s development approach represents a masterclass in lean AI product development. The fact that critical features like Debug Mode and the agent emerged from individual initiative rather than executive mandate reveals how innovation often happens at the edges, not the center, of organizations. This bottom-up model works particularly well in AI, where the technology evolves so rapidly that traditional planning cycles become obsolete before implementation.
The emphasis on internal adoption as the primary success metric is brilliant—if your own engineers won’t use a feature, why would customers? This approach naturally filters out theoretical features that sound good in presentations but fail in practice. The industry-wide shift toward small, elite teams also suggests we’re entering an era where AI tooling and automation enable tiny groups to accomplish what previously required large organizations, fundamentally reshaping how software companies scale.
Why This Matters
This story reveals a fundamental shift in how successful AI companies are being built and operated. Cursor’s approach challenges conventional wisdom about product development, demonstrating that informal experimentation and rapid prototyping can outperform traditional roadmap-driven processes in the fast-moving AI sector.
The emphasis on talent-dense, small teams represents a broader trend across the AI industry, from startups to Big Tech giants like Meta. This model suggests that in AI development, quality dramatically outweighs quantity when it comes to team composition. The ability to move from prototype to production in days or weeks—rather than months—provides significant competitive advantages in an industry where capabilities evolve rapidly.
For businesses adopting AI tools, this story highlights the importance of choosing vendors that can iterate quickly and respond to user needs organically. For AI professionals, it underscores the value of hands-on experimentation and building what you need rather than waiting for formal approval. The success of features like Cursor’s agent, which emerged despite initial skepticism, demonstrates how breakthrough innovations often come from individual initiative rather than committee consensus.