Why Cursor, Lovable, and Bolt Don't Optimize Your App's Speed (And What Does)

AI coding tools make development fast - but they don't make your app fast. Here's the gap between 'fast to build' and 'fast to load,' and why it matters for your users.

By Paula C · Kraftwire Software

2026-03-08 · 9 min read

# Why Cursor, Lovable, and Bolt Don't Optimize Your App's Speed (And What Does)

AI coding tools have made building apps incredibly fast. Cursor generates entire features in seconds. Lovable turns prompts into full-stack apps. Bolt.new spins up deployable projects from a sentence. Replit's Agent builds and deploys autonomously. Windsurf's Cascade writes multi-file changes in one flow.

But here's what none of them do: **make sure the app they built actually loads fast for your users.**

The "Fast to Build" vs "Fast to Load" Gap

Every AI coding platform is optimized for **development speed** - how quickly *you* can ship. None of them are optimized for **runtime speed** - how quickly your *users* experience the result.

This distinction matters more than most founders realize:

**53% of mobile users abandon pages** that take over 3 seconds to load (Google, 2024)

**Every 1-second delay costs 7% of conversions** (Akamai)

Sites loading in 1 second have **2.5x higher conversion rates** than those loading in 5 seconds (Portent)

Your AI tool got you to launch in a day. But if the app takes 4 seconds to load, you're losing half your visitors before they see a single feature.

What Each AI Tool Actually Optimizes

Let's be specific about what these platforms focus on - and what they skip.

Cursor

Cursor is a code editor with AI superpowers. It excels at:

**Code generation speed**: Composer builds features across multiple files simultaneously

**Editor performance**: They've built a custom AI model (Composer) that's 4x faster than frontier models for code tasks

**Context understanding**: Tab, autocomplete, and inline edits are fast and accurate

What Cursor does *not* do: Analyze the code it generates for runtime performance. It won't flag that the component it just wrote makes 15 redundant API calls, loads a 200KB animation library for a simple fade, or skips code splitting entirely. Cursor's job is to write correct code fast - not to write fast code.

Lovable

Lovable turns natural language into full-stack React + Supabase apps. It's remarkable at:

**Rapid prototyping**: Prompt-to-deployed-app in minutes

**Design quality**: Clean, responsive UIs out of the box

**Database integration**: Automatic Supabase setup with auth and RLS

What Lovable skips: Lovable generates complete apps, but it doesn't audit the output for loading speed. The generated code often includes eager-loading of all routes (no code splitting), duplicate data fetches across components, unoptimized images, and heavy dependencies imported synchronously. The app *works* - it just loads slower than it should.

Bolt.new

Bolt.new runs a full dev environment in your browser via WebContainers:

**Zero setup**: No local environment needed

**Instant preview**: See changes immediately in the browser

**Full-stack support**: Frontend, backend, and database in one place

What Bolt misses: Bolt.new apps are notorious for performance issues at scale. WebContainers optimize for the *development* experience, not the deployed output. Users report browser crashes with larger projects, and the generated code inherits common AI patterns - no lazy loading, no query deduplication, theme flash on every page load.

Replit

Replit's Agent builds and deploys autonomously:

**End-to-end automation**: From idea to deployed URL

**Built-in hosting**: Deploy with zero configuration

**Collaborative editing**: Real-time multiplayer

Replit doesn't audit speed: The Agent builds functional apps but doesn't optimize the output for load time. Generated apps often ship with full bundle sizes, no image optimization, and redundant network requests. Replit even acknowledged slow build times as an area they're actively improving - but build speed is different from runtime speed.

Windsurf

Windsurf (Codeium) focuses on intelligent multi-file editing:

**Cascade**: AI flows that edit across files while understanding your full codebase

**Memory**: Remembers your codebase patterns and preferences

**Enterprise security**: SOC 2, air-gapped deployments

What's missing: Windsurf optimizes *how you code*, not *what the code does at runtime*. Its Cascade feature is excellent at maintaining consistency across files, but it won't flag that your app loads 150KB of unused JavaScript, makes blocking requests before rendering content, or lacks efficient caching strategies.

The Pattern: Every AI Tool Creates the Same Speed Problems

Across all five platforms, AI-generated code consistently produces the same performance anti-patterns:

| Speed Problem | Why AI Tools Create It | Impact |

|---|---|---|

| **Redundant network requests** | AI doesn't track which components already fetched the same data | 500–2000ms wasted per page |

| **No code splitting** | AI writes all routes into one bundle because it's simpler | 100–300KB extra JavaScript loaded |

| **Blocking loading states** | AI defaults to full-screen spinners instead of skeleton screens | Users wait before seeing any content |

| **Heavy dependencies** | AI reaches for familiar libraries regardless of bundle cost | 50–200KB of unused code |

| **Missing image preloading** | AI doesn't add preload hints for hero/above-fold images | 500–1500ms slower LCP |

| **Theme flash** | AI doesn't implement pre-hydration scripts | Visible flicker on every page load |

| **No query caching** | AI doesn't configure staleTime, causing refetches on every navigation | Repeated 200–500ms requests |

These aren't edge cases. They're the *default output* of AI code generation.

What SimplyScan's Speed Optimization Actually Does

SimplyScan fills the gap between "I built it with AI" and "it actually loads fast."

Here's how it's different from what any AI coding tool offers:

1. Post-Build Audit (Not Pre-Build Generation)

AI tools work *before* deployment - they generate code. SimplyScan works *after* - it analyzes the deployed result. This means it catches issues that only appear in production: real network waterfalls, actual bundle sizes, runtime loading patterns.

2. AI-Specific Pattern Detection

SimplyScan doesn't run generic Lighthouse checks (you already have tools for that). It detects patterns **specific to AI-generated code** - like duplicate auth checks across components, or React Query hooks missing `staleTime` because the AI defaulted to zero.

3. Concrete Before/After

Every finding includes:

**The exact issue**: "15 redundant `/api/user` requests on page load"

**The estimated impact**: "~800ms wasted, 7 unnecessary database calls"

**The fix with code**: Exact code to implement, ready to paste

**AI fix prompt**: A prompt you can give back to your AI tool to auto-fix it

4. Business-Impact Framing

SimplyScan doesn't say "your bundle is 250KB." It says "your bundle is 250KB - that's an estimated 1.2 seconds of extra load time, costing you approximately 8% of conversions based on industry benchmarks."

The Bottom Line

AI coding tools are brilliant at getting you from zero to deployed. But deployed ≠ fast. And fast matters - it's the difference between an app that converts and one that bounces.

**SimplyScan is the missing step between "I shipped it" and "it actually performs."**

Your AI tool writes the code. SimplyScan makes sure the result doesn't cost you users.

Run a free scan at [simplyscan.io](https://simplyscan.io) - it takes 30 seconds. You'll see exactly which speed issues your AI tool left behind, and how to fix them.

Scan Your Platform

[Lovable Security Scanner](/security-scanner/lovable)

[Bolt.new Security Scanner](/security-scanner/bolt)

[Cursor Security Scanner](/security-scanner/cursor)

[Replit Security Scanner](/security-scanner/replit)

[v0 Security Scanner](/security-scanner/v0)