Vibe Coding Won't Save You: Why Skill Still Matters in the Age of LLMs

Engineering Culture•December 15, 2025

LLMs are powerful tools, but they amplify skill—they don't replace it. If you can't evaluate the code an AI generates, you're not programming. You're gambling.

The Vibe Coding Fantasy

"Vibe coding" is the idea that you can build software by describing what you want to an LLM and accepting whatever it produces. No need to understand algorithms. No need to know how databases work. No need to grasp security fundamentals. Just prompt, accept, ship.

It sounds magical. It's also a recipe for disaster.

Don't get me wrong—LLMs are genuinely transformative tools. I use them daily. They accelerate my work significantly. But they accelerate it because I can evaluate their output, catch their mistakes, and know when they're confidently wrong.

An LLM is like a very fast junior developer who has read everything but understood nothing deeply. Powerful in the right hands. Dangerous otherwise.

What LLMs Actually Do Well

Let's be fair. LLMs are genuinely excellent at:

  • Boilerplate generation—CRUD operations, API scaffolding, test setup
  • Syntax translation—converting between languages and frameworks
  • Documentation and explanation—making code more readable
  • Pattern application—implementing well-known solutions
  • Exploration—quickly prototyping ideas to see if they're viable

For an experienced developer, these capabilities are a massive productivity boost. You spend less time on mechanical tasks and more time on actual problem-solving.

Where Vibe Coding Falls Apart

Here's a simple authentication function an LLM might generate:

// LLM-generated auth check - looks reasonable!
async function authenticateUser(username: string, password: string) {
  const user = await db.users.findOne({ username });

  if (!user) {
    return { success: false, error: 'Invalid credentials' };
  }

  if (user.password === password) {
    const token = jwt.sign({ userId: user.id }, process.env.JWT_SECRET);
    return { success: true, token };
  }

  return { success: false, error: 'Invalid credentials' };
}

// Vibe coder: "Looks good, ship it!"
// Experienced developer: "This has at least 5 critical security issues"

If you can't immediately spot the problems, that's exactly my point. Here's what's wrong:

  • Plain text password comparison—passwords should be hashed
  • Timing attack vulnerability—string comparison leaks information
  • No rate limiting—brute force attacks are trivial
  • JWT with no expiration—tokens valid forever
  • Secret from env without validation—undefined becomes the secret

A vibe coder sees working code. An experienced developer sees a security incident waiting to happen.

// What it should actually look like
async function authenticateUser(username: string, password: string) {
  // Rate limiting check first
  const rateLimitKey = `auth_attempt:${username}`;
  const attempts = await redis.incr(rateLimitKey);
  if (attempts === 1) await redis.expire(rateLimitKey, 900); // 15 min window
  if (attempts > 5) {
    return { success: false, error: 'Too many attempts. Try again later.' };
  }

  const user = await db.users.findOne({ username });

  // Constant-time comparison even if user doesn't exist
  const storedHash = user?.passwordHash ?? '$2b$10$invalidhashplaceholder';
  const isValid = await bcrypt.compare(password, storedHash);

  if (!user || !isValid) {
    return { success: false, error: 'Invalid credentials' };
  }

  // Reset rate limit on success
  await redis.del(rateLimitKey);

  const token = jwt.sign(
    { userId: user.id, version: user.tokenVersion },
    getJwtSecret(), // Validated at startup
    { expiresIn: '1h' }
  );

  return { success: true, token };
}

The Debugging Problem

When vibe-coded software breaks—and it will—what happens?

You can't debug code you don't understand. You can ask the LLM to fix it, but:

  • How do you know the fix is correct?
  • How do you know it doesn't introduce new problems?
  • How do you explain the issue to the LLM if you don't understand it yourself?

I've seen this loop: prompt LLM, get code, code breaks, prompt LLM to fix, new code breaks differently, prompt again, and eventually the codebase is a patchwork of conflicting fixes that nobody understands.

You can't outsource understanding. When production breaks at 3am, the LLM doesn't have access to your logs, your infrastructure, or your specific deployment. You do.

The Performance Blindspot

LLMs optimize for "looks correct" not "performs well." Here's a real example:

// LLM-generated: "Get all users with their orders"
async function getUsersWithOrders() {
  const users = await db.users.findAll();

  const usersWithOrders = await Promise.all(
    users.map(async (user) => ({
      ...user,
      orders: await db.orders.findAll({ where: { userId: user.id } })
    }))
  );

  return usersWithOrders;
}

// With 10,000 users: 10,001 database queries
// Response time: 30+ seconds
// Database: on fire

This is the classic N+1 problem. Every experienced developer knows it. But if you've only ever vibe coded, you've never learned why this pattern is catastrophic.

// What someone who understands databases would write
async function getUsersWithOrders() {
  const users = await db.users.findAll({
    include: [{ model: db.orders }]
  });
  return users;
}

// With 10,000 users: 2 database queries (or 1 with a JOIN)
// Response time: 200ms
// Database: fine

The LLM will generate the first version every time unless you specifically know to ask for eager loading. And you only know to ask if you understand the underlying problem.

LLMs Amplify, They Don't Replace

Here's the mental model that actually works:

Your skill level determines the ceiling of what LLMs can help you build.

  • Junior developer + LLM = faster junior developer
  • Senior developer + LLM = significantly more productive senior developer
  • Non-developer + LLM = someone who can build demos but not production systems

The gap isn't in code generation. It's in code evaluation. Knowing what questions to ask. Recognizing when the output is subtly wrong. Understanding the implications of architectural decisions.

An LLM can generate a microservices architecture in seconds. But should your startup use microservices? The LLM doesn't know your team size, your deployment constraints, your actual scaling needs. You need to know enough to make that call.

The Skills That Matter More Now

Ironically, LLMs make certain skills more valuable, not less:

1. Systems Thinking

Understanding how components interact, where bottlenecks form, how failures cascade. LLMs can generate individual functions but they don't understand your system as a whole.

2. Security Fundamentals

LLMs are trained on public code, including millions of examples with security vulnerabilities. They reproduce these patterns readily. You need to catch them.

3. Performance Intuition

Knowing that a nested loop over two large arrays is O(n*m). Understanding when to cache. Recognizing database query patterns that will destroy performance at scale.

4. Domain Knowledge

LLMs know general patterns but not your specific business rules, compliance requirements, or edge cases. That knowledge is yours to apply.

The Actual Hot Take

The real danger isn't that LLMs will replace developers. It's that companies will hire "vibe coders" who can produce impressive demos but can't maintain, debug, or secure what they've built.

The short-term economics look great: ship faster, hire cheaper. The long-term economics are brutal: security breaches, performance problems, unmaintainable codebases, and eventually hiring experienced developers at premium rates to fix the mess.

Technical debt accrued through vibe coding compounds faster than any other kind. Because nobody understands the code well enough to even recognize the debt.

How to Actually Use LLMs Well

If you want to leverage LLMs effectively:

  • Learn the fundamentals anyway. Data structures, algorithms, security basics, database design. These let you evaluate LLM output.
  • Treat LLM output as a first draft. Review it like you'd review a junior developer's PR. Question everything.
  • Use LLMs for acceleration, not replacement. Let them handle boilerplate while you focus on architecture and edge cases.
  • Understand before you ship. If you can't explain why the code works, you can't debug it when it doesn't.
  • Test thoroughly. LLM-generated code needs more testing, not less, because the failure modes are less predictable.

The Bottom Line

LLMs are the most powerful tools we've ever had for writing code. But tools amplify capability—they don't create it.

A chainsaw makes an experienced woodworker dramatically more productive. Give a chainsaw to someone who's never worked with wood, and you get a mess—or worse.

Vibe coding is the chainsaw-with-no-experience approach to software. It produces output that looks like the real thing until it doesn't. And when it fails, it fails in ways that are expensive, embarrassing, and sometimes dangerous.

Learn the craft. Use the tools. But never confuse generating code with understanding it.