GitHub Copilot is incredibly good at writing code. It's also solving the wrong problem entirely.
After shipping Boostr — a platform that does deep codebase analysis for product teams — I've watched hundreds of engineers struggle with the same issue. They don't need help writing for loops faster. They need help understanding what the hell this 500,000-line codebase actually does.
But let me back up.
The Real Bottleneck Isn't Typing
Here's what actually slows down experienced developers:
- "How does authentication work in this system?" (3 hours of code archaeology)
- "If I change this API, what breaks?" (grep through 50 files, still miss edge cases)
- "Why did we build feature X this way?" (the PM who knew left 8 months ago)
- "What's the blast radius of this refactor?" (ship it and pray)
Copilot addresses none of this. It makes you faster at writing code once you know what to write. But figuring out what to write? You're still on your own.
Actually, it's worse than that. Copilot actively makes the understanding problem harder.
Copilot Creates Technical Debt Faster
Watch what happens when a junior dev uses Copilot to "add authentication" to an existing app:
// What Copilot suggests (and they accept)
import bcrypt from 'bcrypt';
import jwt from 'jsonwebtoken';
export async function authenticateUser(email: string, password: string) {
const user = await db.user.findUnique({ where: { email } });
if (!user) return null;
const isValid = await bcrypt.compare(password, user.hashedPassword);
if (!isValid) return null;
const token = jwt.sign({ userId: user.id }, process.env.JWT_SECRET);
return { user, token };
}
Perfectly functional code. Ships fast. Everyone's happy.
Except the existing codebase already has an authentication system using OAuth, with session management, role-based access control, and audit logging. The junior dev just added a second, incompatible auth system because they didn't know the first one existed.
This happens constantly. Copilot optimizes for local correctness but has zero understanding of global architecture. It doesn't know that you already solved this problem. It can't tell you that changing this function will break the mobile app.
The False Productivity Trap
The productivity metrics everyone quotes are misleading:
"Developers complete tasks 55% faster with Copilot!"
Sure, but what tasks? If you're building a greenfield Todo app, Copilot is fantastic. If you're working on a 3-year-old e-commerce platform with 15 microservices and technical debt from three different architectural decisions, Copilot becomes a very expensive autocomplete.
I've seen teams measure "lines of code written per day" and celebrate the 2x improvement. Then spend 3x longer in code review because half the suggestions don't fit the existing patterns.
The real question isn't "how fast can I write code?" It's "how fast can I ship the right code without breaking anything?"
What We Actually Need
Here's the workflow that would actually help:
- "Show me how authentication works in this codebase"
- "What are all the places that call this API?"
- "If I change this database schema, what breaks?"
- "What features does our main competitor have that we don't?"
- Now write the code
Copilot jumps straight to step 5. Everything else is still manual archaeology.
This is why we built the intelligence system in Boostr differently. Instead of generating code, we built 60+ specialized tools that understand code:
// Real tools from our MCP implementation
const tools = [
'search_symbols', // Find classes/methods by name
'get_symbol_call_graph', // Who calls what?
'find_callers', // Reverse dependency lookup
'get_method_body', // Get implementation details
'search_features', // What features exist?
'get_type_relationships', // Class hierarchies
'find_api_endpoints' // All REST routes
];
When a developer asks "How does authentication work?", our AI doesn't guess. It uses search_symbols to find auth-related classes, get_symbol_call_graph to trace execution paths, and find_api_endpoints to show the actual auth routes.
The response includes real code snippets, file locations, and dependency relationships. No hallucination. No outdated Stack Overflow answers.
Copilot vs. Codebase Intelligence
Let me show you the difference with a real example from our system:
Copilot approach:
User: "Add rate limiting to our API"
Copilot: [generates generic rate limiting middleware]
Intelligence-first approach:
// First, understand the existing architecture
const routes = await findApiEndpoints(workspaceId);
const authFlow = await getSymbolCallGraph('AuthenticationService', workspaceId);
const existingMiddleware = await searchSymbols('middleware', workspaceId);
// Then propose solution that fits
// "I found 47 API endpoints using Express middleware stack.
// You already have authentication middleware in src/middleware/auth.ts
// I recommend adding rate limiting middleware here: [specific location]
// This will protect these high-traffic endpoints: [list]"
One approach generates code. The other understands the problem first.
The Feature Discovery Problem
But there's an even bigger issue Copilot doesn't touch: what should we build?
Most product teams have no systematic way to understand their own codebase. They make decisions based on gut feel:
- "I think we need better search" (but don't know current search covers 60% of use cases)
- "Let's rebuild the mobile app" (but don't know which features are actually used)
- "We need microservices" (but can't identify service boundaries)
Our feature discovery system automatically finds 15-25 features in any codebase by analyzing symbol call graphs and API relationships:
// Real code from our discovery algorithm
const features = await discoverFeatures(workspaceId);
// Returns:
// - "User Authentication" (12 files, 45 symbols, 8 routes)
// - "Payment Processing" (23 files, 89 symbols, 12 routes)
// - "Search & Filtering" (8 files, 34 symbols, 5 routes)
This changes everything. Now PMs can make data-driven decisions about what to build next, based on what actually exists.
The Missing Link: Strategic Code Understanding
The future isn't faster code generation. It's strategic code understanding.
Imagine asking your codebase:
- "Which competitor features would take least effort to implement?"
- "What's the technical debt blocking our mobile roadmap?"
- "If we acquire this company, where are the integration points?"
These are the questions that actually matter for product velocity. Copilot can't answer them because it doesn't understand codebases as living systems — it just sees isolated functions.
Don't Get Me Wrong
Copilot isn't useless. For boilerplate, repetitive code, and greenfield projects, it's genuinely helpful. But we're solving the wrong bottleneck.
The constraint isn't typing speed. It's understanding speed.
The teams shipping fastest aren't the ones writing code faster. They're the ones who understand their codebase deeply enough to make confident changes without breaking things.
That's the problem worth solving.