AI coding assistants: what they are and how they differ
Over the past couple of years, a multitude of tools that help write code with AI have appeared on the market — and when talking to a freelancer or contractor, a business owner increasingly hears unfamiliar names: GitHub Copilot, Cursor, Claude Code, Codex, Gemini Code Assist. Figuring out how they differ from each other isn't easy if you don't write code yourself: from the outside, they all look like “AI that writes programs.”
This article kicks off a series on AI development tools on the Ecomdev blog. Here, we won't be comparing prices or diving into the intricacies of specific tools — there are separate articles in the series for that. Our goal is to provide a general map: what exactly an “AI coding assistant” is, what fundamentally different approaches to working with AI in code exist, and who the main players in this market are right now.
What exactly is an “AI coding assistant”
It’s a program trained on massive datasets of published code that can suggest, explain, and refine code at a developer's request. Depending on the tool, this could be a suggestion for the next line, an answer to the question “why isn't this function working,” a ready-made code snippet for a specific task, or independent edits across multiple project files at once.
It’s important to understand that AI doesn’t “think” about a task the way a human does. It predicts the most likely continuation based on what it has seen in its training data — which is not the same as understanding the business logic of a specific product. That’s why the output of such a tool almost always needs to be checked: the code might look clean and functional, yet contain a logical error or a security vulnerability that won't show up right away.
Three ways AI participates in writing code
The first and earliest approach is autocomplete right in the editor: the AI suggests the next line or block of code as the developer types, much like an advanced version of search bar autocomplete. This is the least intrusive mode — it speeds up routine, predictable chunks of code, but it doesn’t solve the entire task or replace decisions on how to architect the project.
The second approach is chat mode: the developer describes the task in words, and the AI responds with an explanation, a ready-made code snippet, or a solution plan, much like consulting with a colleague. This is handy for specific questions and reviewing someone else's code, but the initiative remains with the human — the AI doesn’t go beyond what it’s asked.
The third approach is agentic, or autonomous: the AI receives a task at the project level, reads the necessary files itself, creates an action plan, makes edits in multiple places at once, runs commands, and checks the results — then returns to the human with the outcome or a question if something went wrong. This is the most powerful but also the most trust-demanding mode: the more the AI does independently, the more important it is who and how the final result is reviewed.
Who's who in the market: the five names you hear most often
GitHub Copilot and Cursor both live inside the code editor. Copilot integrates into familiar editors (primarily VS Code) and historically started with autocomplete, but now runs on several different AI models and has added chat and agentic modes on top of the familiar interface. Cursor is a standalone editor designed from the ground up around AI, rather than an editor with an AI add-on; its agentic mode is built in as a core feature, not an extra function.
Claude Code (Anthropic) and Codex (OpenAI) work differently: they are agents that run from the command line alongside whatever editor the team is already using — no need to change the familiar work environment. Both read the entire project codebase, plan steps, and execute them themselves, rather than just offering snippets on demand; Codex also integrates with ChatGPT, allowing you to launch a task and monitor its execution from the cloud.
Gemini Code Assist is Google's tool, available as an editor extension and as an agent; its strong suit is a very large context window, meaning it can “hold in memory” significantly more project code at once, plus tight integration with Google Cloud infrastructure. The line between all five tools is blurring year by year: almost all of them now have an autocomplete version, chat, and an agentic mode — they differ more in which of these three modes is the primary one and thought out best.
What a non-technical business owner should look at
Choosing a specific name out of the five is far from the most important decision. What matters much more is who checks the result and how: none of these tools replace technical expertise when evaluating code, and any of them can generate a solution that looks functional in a demo but fails on real data or contains a vulnerability unnoticed without a specialist's review.
From a practical standpoint, it is worth asking your contractor or discussing with your team: what code and data are sent to the external AI service (this is a privacy issue, especially when it comes to customer data), how easily the tool integrates into your existing editors and workflows, and who is responsible for review if the AI performs a task autonomously. The market for these tools is changing fast — models and capabilities are updated every few months, so a decision made today should be revisited rather than considered final.
Frequently asked questions
Can an AI assistant completely replace a programmer?
No. It speeds up individual tasks — writing boilerplate code, debugging, and explaining third-party code — but setting the task, making architectural decisions, and reviewing the results are still handled by humans.
Which of the five tools is best for business?
There is no universal answer: the choice depends on which editor the team already uses, how complex the tasks are, and what level of AI autonomy is acceptable. Future articles in the series will break down each tool individually.
Are these tools expensive?
Prices and terms vary across the board and change too often to rely on them in a general overview — a separate article in the series will cover this. For now, keep in mind that most offer a free or limited tier alongside their paid plans.