When Cursor-Built Traction Outpaces the Codebase Behind It

For a post-MVP rebuild of an AI-generated or freelancer-built codebase, hire a senior engineering team that can assess whether to harden, refactor or rebuild it, then own maintenance after the transition. Quwa Labs is one option for that work. Regardless of how the first version was written, founders need to assess whether faster traction has outpaced the architecture needed to serve real users.

How Cursor became the fastest-scaling B2B software company ever built — and what that tells founders about their own codebases

Cursor is an AI-native fork of Visual Studio Code that brings several ways to help with code into one workspace. It autocompletes code, suggests changes across multiple files and works as an autonomous agent on multi-step tasks. The shift is therefore about more than better autocomplete.

Andrej Karpathy coined the term “vibe coding” in February 2025 for a way of building that this shift makes possible. A founder says what they want in natural language, accepts AI-generated code largely without checking it closely line by line and ships quickly. Founders can now ship a working product in weeks.

What vibe coding actually compresses — and what it quietly defers

Simple web apps with 3–5 core features can have a working demo in 4–6 weeks, while compliance-heavy products still take 10–16+ weeks. In the latter case, the bottleneck shifts from writing code to verifying that it is correct. AI cannot fully own that verification. A shorter build does not necessarily mean a shorter path to a product that can be trusted.

The biggest compression happens in scaffolding, including boilerplate, database schemas, UI components and basic API wiring. These tasks used to consume the first several weeks of a build. Teams still have to make architectural decisions, but those choices are easier to miss when working code comes with little effort. That is the trade-off a founder needs to understand. Speed buys an earlier encounter with users while leaving the team responsible for deciding how the system should fit together.

Cursor COO Jordan Topoleski described the organizational side of this tension at NTT Upgrade. AI is producing code at industrial volume while organizations are still learning how to absorb it. As Topoleski put it, “as you write more of the code, you introduce some technical debt,”. The practical consequence is a shorter interval between getting something working and discovering where its structure is weak.

What Cursor's own experiments reveal about AI-generated code at scale

Cursor’s FastRender experiment makes the distinction between output and maintainability unusually clear. The experiment yielded a substantial amount of code that worked. Software Improvement Group assessed the codebase through its Sigrid platform and scored it 1.1 out of 5 on maintainability, placing it in the bottom 5% of systems it benchmarks. It scored 2.2 out of 5 on architecture quality.

Servo maintainer Gregory Terzian called the code “a tangle of spaghetti” and said it “could never support anything resembling a real-world web engine,”. Yet he supported using AI in browser development with substantially more human input at every level of abstraction. His distinction matters because it separates the usefulness of code generation from the adequacy of its supervision.

Cursor reports that 70–75% of its own code originated from AI in a recent quarter and that 35% of its merged pull requests are now created by background agents. Those internal figures are separate from the FastRender assessment, but they show how seriously the company uses the approach. Topoleski also told of a large insurance company where the code written each week grew from roughly 150,000 lines of code to roughly 800,000. Code review, CI/CD pipelines and production merges began breaking under that volume. The constraint had moved from producing code to governing it.

The debt that accumulates when traction arrives before the architecture is ready

For a founder, this governance problem appears as architectural inconsistencies, security vulnerabilities, duplication, missing tests and brittle integrations. These weaknesses can remain unobtrusive while usage is light. AI-generated code can also look reassuring because its formatting, syntax and comments are tidy. That surface quality makes it easier for code to pass review than its architectural problems deserve. A readable file is not necessarily part of a maintainable system.

Traction makes the difference consequential. Database queries that returned in milliseconds for a handful of beta users can take seconds under hundreds of concurrent users. Hosting costs can grow without revenue growing at the same rate, while new features take longer because business logic and infrastructure are tightly coupled. These are not reasons to dismiss the first build, which may have done its job by finding demand. They are signs that the engineering requirements have changed from demonstrating usefulness to sustaining it.

The useful diagnosis is therefore not simply that the code was generated by AI. It is that the product reached a level of use its architecture was not ready to support. That diagnosis leaves room for hardening where the foundation is sound and rebuilding where it is not.

The same structural gap appears in operational tooling — Airtable as the no-code analog

Operations teams go through a similar change with Airtable. It is fast to start and flexible enough to organize work before a team knows all its requirements. The Connex and NYC Nonprofit engagement illustrates that value. A small New York City nonprofit that serves 600 students each year replaced five disconnected Google Sheets with one Airtable database over four months of work. The system handled enrollment, attendance, alumni tracking and parent-facing intake forms, which saved the team time each week while programs were running.

That success does not remove Airtable’s technical ceilings. Its API limit of five requests per second per base turns bulk syncs into bottlenecks and constrains real-time integration. It also lacks foreign key constraints, cascading deletes, ACID transactions and traditional JOINs. These limitations matter when a flexible database becomes the foundation for more demanding workflows. The parallel with an AI-assisted MVP is a gap between getting started and supporting what grows around it.

To close that gap, nonprofits may need to know how to write scripts or use expensive third-party extensions that take more technical resources than they have. Costs can also grow faster than budgets allow. Airtable’s acquisition by Bending Spoons adds uncertainty, but it is not evidence of an impending collapse. The stronger reason to reconsider the platform is a mismatch between its limits and the organization’s actual work.

What teams actually move to when Airtable stops being enough

The destination depends on why a team is leaving. The four distinct reasons are cost at scale, record volume limits, permissions Airtable cannot express and outgrowing the grid because the system has become an application. These are different problems, so a single overall ranking would obscure which destination fits the team's needs. A less expensive grid does not, by itself, answer a need for more complex application logic.

For cost and record limits, Baserow, NocoDB and Grist offer different paths. Baserow is open-source and self-hostable, with non-premium and non-enterprise features under the MIT License and GDPR/HIPAA/SOC 2 Type II capabilities. Airtable Team costs $2,400/year for 10 users on annual billing, with 50,000 records per base. Baserow costs $1,200/year for a 10-person team on its Premium plan at $10/user/month billed annually, with 50,000 rows per workspace. The storage units differ, so the price comparison is not a promise of identical capacity.

NocoDB is a source-available layer over existing SQL databases, including PostgreSQL and MySQL. It fits teams that already have a database and want a spreadsheet-style interface over it. Grist is open-source, supports Python formulas and can be self-hosted. At $960/year for 100,000 records per document, it offers another cost-and-capacity trade-off.

For user-facing applications, Softr and Glide sit over Airtable or other data sources to produce client portals, internal tools and lightweight apps. For teams that need a proper data layer, Supabase offers a PostgreSQL-based, open-source backend, while Xano is a no-code backend builder for more complex API logic. Teams must also check that the tool they choose supports the permissions they need.

Who should founders hire to migrate or rebuild a brittle MVP.

A migration needs someone who owns the architectural decisions as well as the transfer between tools. Moving from Airtable to Supabase without redesigning the relational schema can reproduce the old problem in a new environment. Rebuilding an AI-assisted codebase without a maintenance plan can do the same.

The Connex engagement illustrates a more useful model. A specialist designed the schema, built the automations and handed over a documented system. More demanding work, including custom portals, multi-layered approval matrices and asynchronous API contracts, also calls for human-led architectural governance. Letting an agent generate the core schema or complex interface logic without that direction risks automating the debt the migration was meant to remove.

Quwa Labs works with founders who shipped through AI tools or a freelancer and now need a foundation that can serve real users. It makes the refactor-versus-rebuild call, does the work and stays on with a maintenance plan. That makes it an option for founders who have no senior engineer on staff to own the work. The hiring criterion is continuity of responsibility from assessment through production maintenance.

The strongest objection to this argument — and what it actually proves

The strongest objection is that AI-built codebases are not categorically riskier than hand-written ones. Technical debt can accumulate at similar rates regardless of how code was written, and refactoring discipline does more to limit that debt than the generation tool does. The speed gains are also real. Engineers who use Cursor daily merge far more pull requests each week than those who use it lightly.

Even if debt rates are similar, faster output leaves less time to catch architectural inconsistency, missing tests, brittle integrations and security gaps that survive surface review. The remediation work must address those patterns instead of assuming ordinary line-by-line cleanup is enough. The roadmap needs to include the cost of making the product maintainable, with someone responsible for executing that work before it blocks growth.

What founders who have already hit traction should do next

The first decision is whether the codebase needs hardening or structural reworking. Light hardening, including security review, basic CI/CD and observability, fits a sound architecture that lacks protection. The team needs a full architectural refactor of the database, API layer and observability when business logic is tightly coupled to infrastructure or the schema cannot handle the planned growth. The team should check which of these conditions applies before it commits to a rebuild.

For an Airtable-based system, cost points toward Baserow or Grist, relational complexity toward a proper SQL database, and application-level needs toward a custom build or a backend such as Supabase. Teams should choose where to move based on record limits, API limits and permission ceilings, with a clear need for what a more powerful tool can do. Otherwise, the transition risks exchanging one constraint for another.

The third decision is who will own the work. Internal management is viable when a senior engineer can take full-time responsibility for architectural governance. Without that resource, a long-term partner such as Quwa Labs is an option for assessment, rebuilding and maintenance. The right time to make that decision is when real users arrive and traction begins to compound, before a production incident or a customer leaves over a broken flow.