I’ve been using AI code assistants daily for over a year now. GitHub Copilot, Cursor, Claude in the terminal, ChatGPT for design discussions. The question everyone asks is whether these tools are worth it. The honest answer is: it depends on what you’re doing and how you use them.

Here’s what I’ve found after enough time to get past the novelty and form a genuine opinion.

What They’re Genuinely Good At

Boilerplate generation. Writing CRUD endpoints, test scaffolding, configuration files, and repetitive code patterns is where AI assistants shine brightest. The code they produce for standard patterns is correct more often than not, and even when it needs tweaking, starting from a 90%-there draft beats writing from scratch.

Language translation. Converting between languages — Python to JavaScript, Ruby to Go, SQL dialects — works surprisingly well for straightforward code. If you’re porting a utility function or adapting an algorithm from one language to another, the AI handles the syntax differences competently.

Documentation and comments. Asking an AI to explain what a block of code does and generate documentation produces useful results. It’s not replacing carefully written architecture docs, but for inline comments and docstrings, it’s faster than writing them manually and usually accurate.

Regex and one-liners. Nobody likes writing regex from scratch. AI assistants are remarkably good at generating regex patterns from natural language descriptions. Same goes for complex awk/sed commands, SQL queries, and other one-liner tools where syntax is fiddly and easy to mess up.

Learning new frameworks. When you’re exploring a framework you haven’t used before, AI assistants function as a more conversational version of documentation. Ask “how do I set up authentication in FastAPI” and you get a working example tailored to what you’ve described, not a generic tutorial.

Where They Fall Short

Architecture decisions. AI assistants give you answers based on patterns in their training data, which means they suggest the most common approach rather than the best approach for your specific constraints. They’ll default to popular patterns even when your situation calls for something less conventional.

I’ve seen them suggest microservices architectures for projects that would be better served by a monolith, recommend ORMs when raw SQL would be simpler, and propose complex state management setups for apps that don’t need them. They optimise for “what most developers do” not “what this project needs.”

Debugging complex issues. For obvious bugs — typos, off-by-one errors, missing null checks — they’re helpful. For complex bugs involving timing, concurrency, or interactions between multiple systems, they struggle. They can’t run the code, observe the actual behaviour, or replicate the conditions that trigger the bug.

I’ve wasted time feeding context to an AI assistant about a race condition when I would have found the issue faster by adding logging and reading the output myself.

Security-sensitive code. AI assistants generate code that works but may have security implications they don’t flag. Input validation, SQL injection prevention, authentication token handling — these are areas where “it works” and “it’s secure” are very different things. You need to review AI-generated code in security contexts with the same rigour you’d apply to code from a junior developer.

Large codebase context. Despite improvements in context window size, AI assistants still struggle with understanding large codebases holistically. They can work with the files you show them but don’t naturally understand how your authentication module interacts with your payment processing module unless you explain the relationships.

Some tools like Cursor have made progress here by indexing your entire project, but the quality of suggestions still degrades as the relevant context grows.

The Productivity Question

Microsoft’s research on GitHub Copilot claims a 55% improvement in task completion speed. I’d put my own experience at something more modest — maybe 20-30% faster on average, with significant variation by task type.

The biggest time savings come from tasks I’d normally procrastinate on because they’re tedious. Writing tests, creating utility functions, building out API response types, setting up project configurations. The AI handles these cheerfully, and I’m more likely to actually do them when the effort is lower.

The biggest time losses come from trusting AI output without adequate review. I’ve accepted suggestions that introduced subtle bugs, used deprecated APIs, or implemented patterns that conflicted with existing code conventions. The time spent fixing these issues partially offsets the time saved generating code.

Cost Assessment

GitHub Copilot runs $10-19/month. Cursor is $20/month for the Pro tier. ChatGPT Plus is $20/month. If you’re using multiple tools (and many developers do), the total adds up to $40-60/month.

For a professional developer, that’s easily justified by productivity gains. Even a modest 20% improvement in a $100k+ salary developer’s output makes the cost trivial. For hobbyists and students, the free tiers and open-source alternatives (like Cody, Continue) are usually sufficient.

Team400.ai recently published analysis suggesting that teams adopting AI code assistants see the highest ROI when they also invest in code review processes — the AI speeds up writing, but human review catches the quality issues. The combination outperforms either alone.

Practical Advice

Always review AI-generated code before committing. Treat it like a pull request from a productive but sometimes careless colleague.

Use AI for first drafts, not final implementations. Generate the skeleton, then refine it yourself. This gives you the speed benefit while maintaining code quality.

Be explicit about constraints. Don’t just say “write me an API endpoint.” Say “write me a FastAPI endpoint that handles pagination, returns proper HTTP status codes, uses our existing database session pattern, and includes input validation with Pydantic.” The more context you provide, the better the output.

Keep learning fundamentals. AI assistants make it tempting to skip understanding what the code actually does. Resist that. You need to understand the code to review it effectively, debug it when things go wrong, and extend it when requirements change.

Track where AI helps and where it doesn’t. After a month, you’ll have a clear picture of which tasks benefit from AI assistance and which ones are faster done manually. Double down on the former and stop forcing the tool on the latter.

AI code assistants aren’t replacing developers. They’re changing what developers spend their time on. Less time on boilerplate and syntax, more time on design, architecture, and the genuinely hard problems. That’s a good trade if you use the tools thoughtfully. It’s a trap if you use them as a substitute for understanding your own codebase.