The Great Productivity Promise Tested Against Ugly Legacy Code

Every software vendor marketing an artificial intelligence programming assistant promises the same transformation: write software twice as fast, eliminate boilerplate fatigue, and let developers focus on high-level architecture. Corporate marketing decks claim engineers save up to forty percent of their working week by letting autonomous copilots autocomplete functions and suggest test suites.

To test those assertions against reality, our engineering team spent forty billable hours across two weeks working in a legacy 180,000-line TypeScript and Rust monorepo. We did not test synthetic algorithmic puzzles from LeetCode. We assigned five leading commercial assistants to real developer tickets: refactoring brittle SQL queries, updating outdated dependencies with breaking API changes, tracking down asynchronous race conditions, and generating end-to-end integration tests.

The five tools evaluated were Cursor with Claude 3.5 Sonnet, GitHub Copilot running GPT-4o, Supermaven, Sourcegraph Cody, and JetBrains AI Assistant. We tracked five objective metrics: completion latency per suggestion, acceptance rate of multi-line completions, frequency of hallucinated third-party library methods, context-window memory retention across multi-file refactors, and net developer time saved versus time spent debugging faulty code suggestions.

The conclusion was immediate and stark: while simple line-by-line tab completions are now a commodity feature that works reliably across every platform, whole-project architectural awareness and multi-file reasoning remains an extraordinarily narrow capability where only one tool consistently reduced cognitive burden without introducing dangerous bugs.

When we challenged assistants with complex database migration scripts involving breaking schema alterations, several models repeatedly attempted to use removed ORM methods from earlier releases, highlighting that without strict repo-level indexing, even advanced foundation models fall back on outdated internet training data.

Cursor and Claude 3.5 Sonnet: The Clear Architectural Winner

Cursor, operating on a specialized fork of VS Code paired with Anthropic's Claude 3.5 Sonnet model, achieved the highest composite benchmark score in our sprint, recording a 9.2 out of 10. What sets Cursor apart is its Composer feature and index-aware codebase embedding pipeline.

When tasked with updating an authentication service across nine distinct files, Cursor correctly identified every dependent route handler, database migration script, and client-side session provider without requiring explicit file prompts. Rather than hallucinating deprecated methods, it cross-referenced our local package.json dependencies and wrote syntactically correct code on the first attempt.

Latency was acceptable, averaging 1.4 seconds for multi-file generation diffs. More importantly, its terminal integration allowed the assistant to read terminal compiler errors directly, self-correct missing type imports, and re-run compilation checks before prompting the developer to accept the changeset.

The primary weakness of Cursor is resource consumption. Indexing massive codebases consumes considerable local RAM, and heavy usage rapidly exhausts baseline subscription credits, forcing teams onto usage-based API billing that can reach fifty to eighty dollars per developer monthly during intensive development sprints.

Yet for teams managing complex full-stack web applications or microservice refactors, the ability to instruct an agent to modify both server-side handlers and client-side state hooks simultaneously with zero syntactic drift is worth every penny of the subscription premium.

GitHub Copilot and Supermaven: The Battle for Pure Speed

GitHub Copilot remains the enterprise default due to native Microsoft ecosystem integration, organizational single sign-on, and legal indemnification policies. In our testing, Copilot scored 8.4 out of 10. Its inline autocompletion is fast and reliable for standard boilerplate functions, test fixtures, and repetitive CSS styling.

However, Copilot struggled significantly when working with complex multi-file architectural dependencies. Its chat panel frequently suggested outdated syntax from earlier major library versions, and when asked to refactor an entire data layer, it lost track of type signatures across imported modules, requiring developers to manually verify every generated interface.

Supermaven took the crown for raw typing velocity. Built by former OpenAI researchers with a custom sub-centisecond inference architecture, Supermaven delivers autocompletion suggestions in under 250 milliseconds. For developers who type fast and want near-instantaneous tab completion that feels like predictive text on steroids, Supermaven is unmatched. Yet it lacks the multi-file autonomous agent capabilities of Cursor, leaving developers to do the heavy architectural lifting themselves.

In high-speed typing flows where a developer already knows the exact logic they want to express and simply wants to avoid typing repetitive variable mappings, Supermaven felt like an extension of thought, predicting four-line blocks before the second keystroke landed.

Sourcegraph Cody and JetBrains: Specialized Enterprise Contenders

Sourcegraph Cody, which scored 8.1 out of 10, shined brightest in enterprise search and large-scale repository navigation. Because Cody leverages Sourcegraph's code graph indexing infrastructure, it was able to answer precise questions about how legacy services interacted across twenty distinct internal microservices.

However, Cody's generated code completions were visibly slower than Copilot and Cursor, occasionally pausing for three to four seconds while retrieving remote context, which disrupted typing flow during rapid prototyping.

JetBrains AI Assistant, scoring 7.8 out of 10, integrates tightly into IntelliJ, PyCharm, and WebStorm. It excels at generating documentation comments, explaining convoluted legacy regex statements, and writing localized unit tests. Unfortunately, its code generation accuracy lagged behind Claude 3.5 Sonnet, frequently producing overly verbose implementations that required manual pruning.

The ultimate takeaway from forty hours of intensive testing is clear: if your engineering team is evaluating tools to accelerate complex feature development rather than just typing boilerplate faster, Cursor with full-repo indexing is the only assistant currently delivering a measurable return on investment.

Whichever assistant you deploy, treating AI output as unverified truth remains the fastest path to production outages. Rigorous automated continuous integration pipelines and peer human code review remain completely non-negotiable.