AI coding assistants like Continue, Codex, GitHub Copilot, Windsurf, and Devin have quickly shifted from experimental to everyday developer tools. While some still see them as nothing more than magic text/code generators or even fear they’ll replace human developers, the reality looks different. Developers aren’t just adopting and leveraging AI for quick snippets or vibe coding sessions. They’re using them for building and designing production-viable systems, coding, testing, and even documentation.
Recent industry surveys show that between 76% and 89% of developers already use these tools or plan to soon, and 94% of companies surveyed have some teams actively using AI coding assistants. Despite debates about accuracy, many find them invaluable for boosting productivity, reducing repetitive work, and shortening time-to-market.
In this article, we’ll explore how these tools are shaping the day-to-day lives of developers at every level, from startup founders to senior engineers and junior developers contributing to open source.
What Are AI Code Assistants?
AI coding assistants, otherwise known as coding companions, are tools powered by large language models (LLMs) trained on massive amounts of code and natural language (Java and Python). The difference from traditional autocomplete is the intelligence. Instead of just filling in the next word or bracket, AI assistants can understand context, spot patterns, and help solve bigger problems.
Software developers utilize AI coding to integrate AI into their workflows, achieving higher productivity and quality while also learning new concepts and insights. Most of these AI assistants are integrated into familiar environments where developers already work, such as Visual Studio Code, JetBrains IntelliJ IDEA, or even the command line.
They offer a growing set of capabilities that span the entire development workflow, such as:
- Generating code snippets to match a developer’s request.
- Building test cases that cover multiple scenarios.
- Translating code across programming languages.
- Upgrading legacy code to newer versions.
- Explaining code in plain language to support learning.
- Creating documentation to speed up DevOps processes.
Who Is Actually Using These Tools?
The Start-Up Founder
A lot of people have had brilliant project ideas, only for them to remain stuck in their notebooks. This is often the case for most non-technical founders. Without a clear path to execution, many great concepts simply fade away.
However, in the past years, with the prevalent use of AI models and coding assistants, it has become easier for non-technical founders to move from ideation to releasing a product MVP.
A study by Cornell University found that tools like ChatGPT, Continue, and GitHub Copilot can generate code that runs without errors about 90% of the time. This surpasses 30% to 65% of unit tests and are secure around 60% of the time. With the right prompts, these tools can be leveraged by anyone, even those with no technical background, to create code samples right in their IDE or CLI.
For instance, Mary is a non-technical founder with a brilliant idea for a new productivity app. She knows her market inside out but struggles with initially developing the source code. With a lack of funds available to her, hiring a developer is out of budget, and online tutorials are overwhelming and will take longer before the idea goes live.
Instead, Mary can turn to AI website builders like Lovable. By simply describing the app’s features, core functions, and desired outcomes, she can generate a ready-made user interface in HTML, CSS, and JavaScript. She can then take that code into her local IDE. With an AI coding assistant like Continue, which integrates directly into her development environment, Mary can refine the application further, add new features, and even deploy it seamlessly using platforms like Netlify or GitHub.
Just like that, Mary’s idea can become a real, existing project she can share with the world. Using a coding assistant like Continue allows you to build an MVP in record time. Since Continue runs on local models (on her own computer, instead of sending data to the cloud), it also keeps Mary’s original ideas safe.
Junior Developer
Real-world, legacy open-source code written in R, Python, JavaScript, or Go applications isn't like the clean, simple, controlled projects from boot camps. Maintainers aren't always available to answer every question, and getting a timely response on Stack Overflow can be time-consuming. AI coding assistants like Continue can bridge the gap by helping juniors quickly grasp a new language’s syntax or a framework’s API without drowning in endless documentation.
Take David, for example. He ran into a puzzling TypeError in his addToCart function while contributing to an open-source e-commerce project. Instead of just copy-pasting the error into Google, he asked the AI coding assistant directly in VSCode, “Why am I getting this TypeError in my addToCart function?” The AI assistant will then break down the concept of data types in JavaScript, explain why his input was invalid, and suggest a quick fix.
This will turn a frustrating roadblock into an opportunity for David to actually understand the problem, making it a good learning experience.
However, there’s a caveat. These tools don’t replace the need for a solid foundation in programming. Research by Computers in Human Behavior indicates that a student's frequent use of AI chatbots for programming tasks is associated with a negative correlation in their academic performance. These tools exist to help implement ideas and learn more quickly, but the core knowledge still needs to come from you.
The Senior Engineer
Senior engineers often face a different challenge. For them, it’s not just about learning syntax or getting started. It’s about managing complexity at scale and fast. Many engineering leaders are skeptical of AI coding tools, as they worry that these tools could weaken their team’s skills or compromise security. But here’s the thing: AI assistants are incredibly fast, and while speed is their forte, the senior engineer is well-versed in the fundamental skills. And in practice, editing and refactoring code is often quicker than writing it from scratch, which is where Continue comes in handy.
Take Sarah, a senior staff engineer at a legacy cloud platform. She can use the Continue CLI version to scan and refactor the legacy module that no one wants to touch. Based on her prompts, Continue will then identify the inefficiencies and suggest modern patterns in the terminal. Sarah can review these changes and determine if they align with her expected outcome. By deploying the AI with her team's specific models on their private cloud, she can ensure the team's sensitive intellectual property remains secure.
For Sarah, the AI didn’t replace her expertise, but rather amplified it, helping her tackle bigger problems and boosting her team’s overall output.
In Summary,
AI coding assistants are becoming everyday companions for developers at every level. Their potential impact is evidenced as Gartner projects that “90% of enterprise software engineers will utilize AI code assistants by 2028, a significant increase from less than 14% in early 2024.” Developers can now utilize AI coding assistants to automate repetitive tasks, such as creating standard blocks of code and generating routine documentation.
Ultimately, the goal of these tools is not to replace human creativity, judgment, or skill, but to free up development time, accelerate delivery, and open new paths for learning and creativity.
Top comments (1)
Hey girl👋🏻 Yeah, I use AI coding assistants mostly as a shortcut when I’m stuck. If I can’t remember some syntax, need a quick example or just want a faster way to debug, I’ll throw it at the assistant instead of spending ages on Google. It’s not perfect, but it speeds up the boring parts so I can focus on actually building stuff😂 hope that helps😊