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8 min read
January 14, 2026

AI Pair Programming: When It Helps and When It Hurts

The Promise vs. The Reality

Segev Sinay

Segev Sinay

Frontend Architect

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The Promise vs. The Reality

AI pair programming is the most hyped use case for AI in software development. The pitch is seductive: a tireless partner who knows every language, every framework, every best practice, and never gets annoyed when you ask the same question twice.

The reality is more nuanced. I've been using AI as a pair programming partner for over a year across multiple projects — from greenfield startup builds to maintaining legacy codebases. Sometimes it's transformative. Sometimes it actively makes things worse.

This article is about understanding the difference so you can use AI pair programming effectively instead of just hoping it works.

When AI Pair Programming Helps

Scenario 1: Greenfield Boilerplate

You're starting a new project or feature. You need to scaffold a Next.js app with authentication, set up a component library, create API routes, configure a database schema. This is work where you know exactly what you want, and the AI just needs to execute.

This is where AI pair programming shines brightest. You describe the architecture, the AI generates the scaffolding, you review and adjust. What used to take a day takes an hour.

Why it works: The problem space is well-defined. There's a clear right answer. The AI has seen thousands of similar setups and can generate reliable boilerplate.

Example prompt that works: "Create a Next.js API route for user registration. It should validate email and password with Zod, hash the password with bcrypt, store in MongoDB via Mongoose, and return a JWT. Follow the controller pattern with error handling middleware."

Scenario 2: Writing Tests

If there's one area where AI has universally improved my workflow, it's testing. Writing tests is often tedious, and AI handles it remarkably well.

Give AI a function and ask it to generate unit tests. It'll cover the happy path, common edge cases, and often think of boundary conditions you'd forget. It won't write perfect tests, but it gives you a 70-80% starting point that you can refine.

Why it works: Tests are derivable from the code they test. AI can analyze a function's inputs, outputs, and branches to generate meaningful test cases. The pattern is predictable and well-represented in training data.

Pro tip: After AI generates tests, always ask: "What edge cases did you miss?" Then add those manually. The combination of AI's systematic coverage and your domain knowledge creates solid test suites.

Scenario 3: Code Translation and Migration

Moving a codebase from JavaScript to TypeScript. Converting class components to functional components with hooks. Migrating from one state management library to another. These are mechanical transformations with clear rules.

AI handles these beautifully. It understands the source pattern, knows the target pattern, and applies the transformation consistently. I migrated an entire component library from styled-components to Tailwind CSS in a fraction of the time it would have taken manually.

Why it works: Translation is pattern matching at its core, which is exactly what AI excels at.

Scenario 4: Learning New APIs and Libraries

When you're working with an unfamiliar library, AI is an incredible learning partner. Instead of reading documentation for an hour, you can describe what you want to accomplish and get a working example immediately.

I used this approach when learning the Stripe API for a billing feature. Instead of starting from the docs, I described the billing flow I wanted and got a working implementation that I could study, understand, and modify.

Why it works: AI has ingested documentation and examples for most popular libraries. It can synthesize this into exactly the example you need, tailored to your use case.

Scenario 5: Debugging with Context

You have an error message and a stack trace. You paste your code and the error. AI identifies the issue instantly because it has seen the same error pattern thousands of times.

This works especially well for cryptic error messages, configuration issues, and environment-specific bugs. AI turns a 30-minute Stack Overflow hunt into a 2-minute conversation.

Why it works: Error patterns are highly repetitive. Most bugs have been seen and solved before. AI is essentially a compressed version of Stack Overflow with better search.

When AI Pair Programming Hurts

Anti-Pattern 1: Complex Business Logic

You're implementing a pricing engine with tiered discounts, volume-based pricing, promotional codes that stack in specific ways, and enterprise-specific overrides. This is deeply domain-specific logic where the rules are complex, sometimes contradictory, and documented in a mix of Notion pages and Slack conversations.

AI will generate something plausible. It will NOT be correct. And because it looks correct, you might not catch the errors until a customer gets charged the wrong amount.

Why it fails: AI doesn't understand your business rules. It generates code that follows common patterns but misses the specific nuances that make your pricing engine yours.

What to do instead: Design the logic yourself. Write the rules as comments or pseudocode. Then use AI to help with the implementation of individual rules — but you drive the overall design.

Anti-Pattern 2: Performance-Critical Paths

You're optimizing a rendering pipeline, a data processing pipeline, or any code where performance is the primary concern. AI generates code that works but uses naive approaches.

I've seen AI suggest React component structures that cause unnecessary re-renders, database queries that trigger N+1 problems, and data transformations that create intermediate arrays when a single reduce would suffice.

Why it fails: Performance optimization requires understanding the specific context — data sizes, access patterns, bottleneck locations. AI optimizes for correctness and readability, not for your specific performance profile.

What to do instead: Profile first. Identify the bottleneck. Then solve it yourself, using AI only for research ("what's the fastest way to...") rather than implementation.

Anti-Pattern 3: Security-Sensitive Code

Authentication flows. Authorization middleware. Payment processing. Encryption. Anything where a subtle bug becomes a vulnerability.

AI-generated security code often looks correct but has gaps. It might implement JWT verification without checking the algorithm. It might validate user input with a regex that has ReDoS vulnerabilities. It might create an API endpoint that's missing an authorization check.

Why it fails: Security requires adversarial thinking — imagining how an attacker would exploit the code. AI thinks in terms of "how to make this work," not "how could this be broken."

What to do instead: Write security-critical code manually or use well-established libraries. If AI helps, have a security review that specifically focuses on attack vectors, not just correctness.

Anti-Pattern 4: Unfamiliar Problem Domains

If you're implementing a feature in a domain you don't understand — say, implementing a scheduling algorithm when you've never worked with scheduling constraints — AI pair programming can be actively harmful.

AI will generate confident-looking code that you can't evaluate because you don't understand the domain. You'll ship code that seems to work on your test data but fails on edge cases you didn't think to test because you don't know the domain.

Why it fails: AI pair programming works when you can evaluate the output. If you don't understand the domain, you can't evaluate.

What to do instead: Learn the domain first. Read the theory. Understand the constraints. Then use AI for implementation.

Anti-Pattern 5: Long, Evolving Sessions

AI pair programming works well for focused, bounded tasks. It works poorly for long, evolving sessions where context shifts gradually.

In a 3-hour coding session, you might refactor an approach twice, discover a new requirement, change the data model, and pivot the implementation. AI loses the thread. It starts contradicting earlier suggestions. It forgets constraints you established an hour ago.

Why it fails: Current AI has context window limitations. More importantly, it doesn't have the concept of "evolving understanding" that a human pair partner does.

What to do instead: Break long sessions into focused segments. Re-establish context at the start of each segment. Use AI for specific, bounded questions rather than as a continuous pair partner.

The Decision Framework

Before starting an AI pair programming session, ask yourself three questions:

  1. Can I evaluate the output? If you can't tell whether the AI's code is correct, don't use AI for this task.

  2. Is the problem well-defined? If you can clearly specify what you want, AI will likely help. If you're still figuring out what you want, AI will generate confident noise.

  3. What's the cost of a subtle bug? If a subtle bug causes a UI glitch, that's tolerable. If a subtle bug causes a security vulnerability or billing error, use AI with extreme caution.

If the answer to #1 is yes, #2 is yes, and #3 is low — go for it. Otherwise, use AI as a research tool, not a co-author.

Making It Work in Practice

AI pair programming isn't binary — all or nothing. The best approach is a sliding scale of AI involvement:

  • AI drives (you review): Boilerplate, tests, translations, simple features
  • You drive (AI assists): Business logic, architectural decisions, complex features
  • You drive (AI researches): Performance optimization, security, unfamiliar domains
  • No AI: Final security review, production deployment decisions, crisis debugging

The developers who get the most from AI pair programming are the ones who know which mode to be in at any given moment.

AI
Engineering Teams
React
Next.js
TypeScript
Testing
Performance
Refactoring

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