You’ve been in a meeting where a developer mentions “Copilot” or “ChatGPT” and you nod along. You’re not entirely sure what these tools are, how they fit into your project, or whether they’re helping or introducing risk. You’re not alone. According to Gartner, 63% of organizations are already piloting or deploying AI coding assistants in their business operations. These aren’t fringe experiments. They’re reshaping how software gets built — and that includes your next web project.

In our previous post, we looked at what AI means for your web project at a high level. Now let’s open the toolbox. We use these tools daily. Here’s what they actually do, how they differ, and why the team behind them still matters more than any single tool.

TL;DR

• AI coding assistants come in several flavors — from autocomplete to conversation to code review — and knowing the difference helps you ask smarter questions.
• Over 1.3 million developers already pay for GitHub Copilot alone, and Gartner predicts 75% of enterprise engineers will use these tools by 2028.
• The specific tool matters less than how your development team uses it — skilled developers with proper quality checks get the best results.
• AI makes experienced developers faster, but it can actually slow down less experienced ones — the team behind the tool is what counts.

Four types of AI coding tools (and what they actually do)

AI coding assistants aren’t one thing. They’re a family of tools, each doing something different. Understanding the categories helps you make sense of what your development team is doing — and lets you ask better questions about how your project is being built.

Code autocomplete — predictive text for programming

Think of predictive text on your phone, but for code. As a developer types, the tool suggests the next lines — sometimes entire blocks — before they finish writing. It’s like spell-check that doesn’t just catch mistakes but suggests whole sentences.

The biggest name here is GitHub Copilot. With over 1.3 million paid subscribers and more than 50,000 organizations using it as of early 2024, it’s the most widely adopted AI coding tool on the market. For your project, this means developers spend less time on repetitive code and more time on your unique business logic.

AI chat assistants — a coding partner on call

This is where AI went from finishing a developer’s sentences to answering their questions. Tools like GitHub Copilot Chat, which became broadly available in December 2023, let developers have a conversation with AI about their code. They can ask it to explain a confusing function, help debug an error, or brainstorm an approach to a tricky problem.

Think of it as a knowledgeable colleague available around the clock to brainstorm with. ChatGPT plays a similar role — many developers use it as a coding sounding board. For your project, this means faster problem-solving. A developer stuck on a bug can get unstuck in minutes instead of hours.

AI-powered code review — a quality inspector that never clocks out

Before code reaches your users, it should be reviewed for bugs, security issues, and consistency. AI-powered code review tools automate part of that process, scanning every change for common problems before a human reviewer even looks at it.

Think of it as a quality inspector on the assembly line who never takes a break. These tools are a growing category, with platforms adding AI features to catch issues earlier in the development process. Combined with automated regression testing, they give your project a second pair of digital eyes on every code change — catching problems before they become your problems.

AI-first code editors — the next wave

Most AI coding tools today are add-ons to existing editors. A newer approach flips that around: editors built from the ground up with AI at the center. Cursor, for example, is an emerging editor where developers can describe what they want in plain language and the tool generates code to match.

The difference is like adding GPS to an old car versus buying a car designed around navigation from the start. This is newer territory, and it’s where the industry is heading — deeper integration between what a developer intends and what the AI produces.

Why AI coding assistants matter less than the team using them

Here’s the most important takeaway for your business: the specific tool your development team picks — Copilot, ChatGPT, Amazon CodeWhisperer (recently rebranded to Amazon Q Developer) — matters less than how they use it.

A McKinsey study broke down where AI helps developers most. Code documentation got 45-50% faster. Code generation saw a 35-45% time reduction. Refactoring — restructuring existing code — improved by 20-30%. Those are real gains.

But here’s the critical part. Experienced developers saw productivity improvements of 50-80%. Junior developers actually saw a 7-10% decline in speed. Why? Because reviewing and verifying AI-generated code takes more expertise than writing it from scratch. A skilled developer knows when the AI’s suggestion is right and when it’s subtly wrong. A less experienced developer might accept a suggestion that looks correct but introduces a bug.

This is why the team matters more than the tool. AI makes great developers even better. It doesn’t replace the need for experienced, skilled people building your project. The landscape is also moving fast — Amazon’s recent rebrand of its AI coding tool is just one sign that major tech companies are investing heavily and evolving these tools rapidly. Your agency’s ability to adapt and maintain quality through that change is what counts.

What to look for when your agency uses AI

So what does this mean for you? When you’re working with a development agency that uses AI tools, here are four things to look for:

• They verify AI output. Every AI-generated line of code gets reviewed by a human developer. No one blindly accepts suggestions.
• Quality gates are in place. Automated testing and code review exist on top of AI, not instead of it. AI speeds up the work; quality checks make sure the work is good.
• They know when AI doesn’t help. Your business logic, your users, your strategy — these still require human expertise. A good team knows where to draw that line.
• They’re transparent. They can explain how AI fits into their process without hand-waving. If they can’t, that’s a red flag.

We’ll go deeper into the right questions to ask your agency in an upcoming post: “5 Questions to Ask Your Web Agency About AI.” In the meantime, this is the kind of transparency we believe in. It’s how we work with every client.

AI coding assistants are real, established tools that make good developers more productive. Knowing the basics — autocomplete, chat, code review, AI-first editors — helps you have smarter conversations with your development team. But you don’t need to become a Copilot expert. You just need to know enough to ask the right questions and recognize a team that uses AI responsibly.

Planning a web project and want a team that uses AI the right way? We’d love to talk about yours.

Next in this series: “How AI Speeds Up Web Development Without Cutting Corners” — a closer look at what AI acceleration means for your project’s timeline and quality.

Frequently asked questions about AI coding assistants

What is an AI coding assistant?

An AI coding assistant is software that helps developers write, review, and debug code faster. Think of it as an intelligent helper that suggests code, answers questions, and catches mistakes — but still needs a skilled developer to guide it. The most well-known example is GitHub Copilot, which over a million developers use daily.

Do AI coding assistants write code by themselves?

Not in any meaningful sense. They suggest code based on patterns, but a developer still decides what to build, how to build it, and whether the suggestion is correct. It’s closer to a fast research assistant than an autonomous builder. Your business requirements and the developer’s judgment still drive the project.

Should I ask my development agency if they use AI tools?

Yes — and go further than “do you use AI?” Ask how they use it, how they verify AI-generated code quality, and what their review process looks like. A good agency will be transparent and specific. We dig deeper into this in our upcoming post, “5 Questions to Ask Your Web Agency About AI.”

Can AI coding assistants introduce bugs into my project?

They can if the output isn’t properly reviewed. AI suggests code based on patterns, and sometimes those patterns don’t fit your specific situation. That’s why quality assurance matters more with AI, not less — proper testing and code review catch issues before they reach your users. We explore this further in a later post in this series.