Don’t guess your way into AI adoption.
This guide lays out the trade-offs, wins, and blind spots behind today’s top AI tools.
Intro
Generative AI coding has come a long way in a remarkably short time. When it first arrived, these tools were mostly glorified autocomplete, suggesting small snippets or finishing your current line of code. They were helpful but limited to speeding up the “typing” part of development.
Today, the game has completely changed. With newer models like GPT-4.1, GPT-5, and the recently released Claude Sonnet 4.5, the intelligence behind AI coding has taken a noticeable leap forward. If you haven’t tried coding with these newer models or tools, you really haven’t experienced what AI development feels like today, and I highly recommend giving it another go. These models are significantly better at generating not just code, but also surfacing ideas and implementation details you might not have thought about.
Modern AI coding assistants can also bring entire files into context, follow rules that encode your team’s coding standards, and reference example implementations. With just a small prompt, they can create and edit complete parts of a larger feature, not just fragments. You can even ask them to scan your codebase and provide thoughtful suggestions for improvements or refactors. Some of those can be applied immediately, while others can be captured as stories for future technical debt work.
While I don’t believe jobs are at stake just yet, AI has firmly established itself as an assistant. What once made its name as a handy way to generate unit tests has now evolved into something far more versatile. For junior developers, it’s a patient “rubber duck” that helps troubleshoot logic and provides learning examples. For senior developers, it’s an apprentice that can handle boilerplate, scaffold out complex code structures, or suggest refactors, and even take your rough ideas or thoughts and turn them into code on screen that you can then iterate over until it meets your objectives and standards, freeing up mental space for higher-level architecture decisions.
The ecosystem has also diversified. Some tools are aimed squarely at supporting large-scale application development, with deep integration into established workflows. Others prioritize rapid prototyping, allowing developers to spin up a proof of concept in minutes for a business stakeholder, quickly demonstrating the need and potential value of a more robust, scalable implementation.
Code Quality, Workflow, and Model Selection
One important thing to call out early is that code quality across these tools is fairly similar when they’re powered by the same AI model. Where they really differ is in workflow integration, what context they automatically pull in, how they apply coding standards, and how much autonomy they’re designed to have. Ultimately, it all comes down to which tool provides the best user experience for the developer and how comfortable they are with AI in their workflow, whether they’re just starting to experiment or already leveraging it as a trusted coding partner. This is what will ultimately determine which AI coding tool you consider the “best” for your own work.
Most of these tools provide two interaction modes:
- Ask/Chat Mode works like a helpful Q&A partner. It answers questions, makes suggestions, and offers snippets of code without ever touching your source files. You can copy or insert suggestions as you go, keeping full control. Almost anyone who has used AI before will be comfortable here, since the experience mirrors what you get in tools like ChatGPT, Microsoft Copilot in the browser, or Claude Chat.
- Agent/Code Mode is where the real power of AI starts to come into play. In this mode, the AI can directly edit your source code, iterating over your prompts and updates. It acts like a scribe or apprentice, turning your thoughts into code, which lets you focus on the design patterns and architecture you want to build for a feature or story, instead of worrying about putting your fingers to the keyboard to write every line. You can iterate over the code to your heart’s content, and when it meets your expectations, you can tell it to keep the changes. If it makes a mistake, you can instruct it to fix things or simply click to undo its edits. The workflow feels a lot like reviewing diffs in Git, and with integrations like Github Issue tracking and automatic pull requests, it becomes a surprisingly effective lightweight coding collaborator. In many tools, Agent Mode can also take over and run commands within its context. If a build isn't working or a test is failing, it can read the output, understand the problem, and attempt to fix it, acting not just as a code generator, but as a debugging assistant too.
It’s also important to consider model selection when using these tools. Some of the more advanced models, like GPT-5 or Claude Sonnet 4.5, can provide deeper reasoning and stronger code suggestions, but they also come with trade-offs. They may cost more per call, and most subscription plans place limits on how often you can use them before you start incurring extra charges. Using lighter-weight models can be perfectly fine for everyday tasks, while saving the more expensive models for complex problems or larger features is often the smarter, more cost-efficient approach.
Assessment 1: GitHub Copilot
One of the main advantages of GitHub Copilot is availability. As a plugin, you can drop it right into the IDE you’re already using, whether that’s JetBrains, Visual Studio, VS Code, Neovim, or others, without needing to change your setup. The plugin provides both Ask/Chat Mode and Agent/Code Mode (with some IDE’s), giving developers the flexibility to either get suggestions and answers without touching source files, or have the AI directly edit and iterate on their code.
However, Copilot does have some limitations compared to newer tools like Cursor or Windsurf. It doesn’t automatically take your entire project into context. If you want the AI to “know about” a file, you’ll have to manually attach it to the conversation rather than just referencing it by name.
Copilot relies on its own convention for defining coding standards and project structure. You can place a .github/copilot-instructions.md file at the root of your project to specify global rules, or create more granular .instructions.md files under the .github/instructions/ directory to define rules for different parts of your codebase. These files are automatically pulled into context, allowing Copilot’s agents to follow your conventions and coding style without extra prompting.
Multi-file editing is an area that feels limited, at least for me. Copilot tends to be overly cautious, asking for explicit permission before making changes file by file. That means something as simple as “remove unused imports across the project” can turn into a slog of clicking “yes, go ahead” repeatedly. Cursor, by contrast, takes the “ask forgiveness, not permission” approach. It will go ahead and make project-wide changes, and if it does something you don’t like, you can quickly undo it. This small shift in philosophy makes Cursor feel faster and more fluid in practice, especially when working on larger codebases. That said, some developers may appreciate Copilot’s caution and prefer that level of granular control, especially in sensitive or shared code environments.
Assessment 2: Windsurf (previously Codeium)
Windsurf offers two ways to interact with it. Like Copilot, it can be used as a plugin for many popular IDEs such as JetBrains, Visual Studio, VS Code, Neovim, and others. The plugin, however, only offers Ask/Chat Mode, there is no Agent/Code Mode in the plugin version. Similar to Copilot, any files you want the AI to consider must be manually added to the context.
The real power of Windsurf shines in its standalone IDE, which is a fork of VS Code. This means you can use essentially any extension or plugin from your existing VS Code setup, and as part of the Windsurf installation, it will import and install them from your existing VS Code installation if you already have one. The IDE version offers both Ask/Chat Mode and Agent/Code Mode.
Its Agent/Code Mode is a step up from Copilot’s. Windsurf automatically brings the entire project into context, allowing you to reference any file or class by name without manually attaching it. It also supports its own standard for defining coding rules and architecture decisions through the .windsurf/rules/ directory. Within this directory, you can have multiple markdown files to capture different standards, with each file containing rules outlining specific objectives. For example, you might define global.md for overarching conventions, alongside frontend.md or backend.md for more targeted rules. Even more powerfully, .windsurf/rules/ can be placed not just at the project root but also within subdirectories like /backend, /frontend, or even deeper paths such as /backend/controllers, allowing you to define highly specialized rules that apply only to certain areas of the codebase. These files are automatically pulled into context, ensuring the AI follows your conventions with precision.
In practice, having all files automatically in context and giving the AI control over the IDE made Windsurf feel more responsive and faster at making code updates in Agent/Code Mode.
Assessment 3: Cursor
Cursor takes a slightly different approach. It does not offer a plugin version and exists solely as a full IDE solution, also built as a fork of VS Code. When you install it, you can import all the extensions from your existing VS Code setup and add any additional ones you need. From the start, Cursor feels like it’s built for serious project work.
The IDE provides Ask/Chat Mode, Agent/Code Mode, and a unique Background Mode. Background Mode runs tasks behind the scenes for a story or feature, executing in a Cursor-managed cloud environment where the agent operates in isolation and creates pull requests upon completion. This is similar to GitHub Copilot’s coding agents, which can automatically launch based on GitHub Issues and generate their own pull requests. However, both approaches raise potential security concerns, since you don’t have full control over the environments in which the code is being executed. This approach is also more costly.
Just as Windsurf was a step up from Copilot, so is Cursor. From the moment you open a project, the entire codebase is automatically in context, but the real power comes from how you can define rules. You can place all your conventions into a single .cursorrules file, or use it just for global project-wide standards. For more granular control, Cursor supports a .cursor/rules directory within subdirectories, such as /backend or /frontend, similar to how Windsurf handles rules. This allows you to define standards specific to each area of the codebase, and Cursor will automatically pull them into context and apply them hierarchically, so the deeper you go, the more specialized the rules become. To make things even more flexible, Cursor also supports the newer AGENT.md standard, an agent-agnostic convention. Anywhere you would use a .cursor/rules directory, you can instead drop an AGENT.md file, and Cursor will respect it, ensuring consistency while also aligning with a broader industry standard.
Cursor also provides flexibility with AI models. You can select a model manually, as with the other tools, or let Cursor automatically determine the best model for your prompt.
Another subtle but impactful difference is its philosophy of “asking forgiveness, not permission.” Unlike Copilot, which pauses for confirmation before making changes to multiple files, Cursor quietly executes edits across the project. This approach shines on multi-file stories, where iterative refinement is inevitable, making the workflow feel faster and smoother. Because mistakes are expected with AI assisted code, this design allows you to focus on improving and iterating rather than repeatedly granting permission for the AI to act.
Assessment 4: Claude (Claude Code)
Claude approaches AI-assisted development less like an autocomplete engine and more like a collaborative teammate. Rather than centering on inline suggestions alone, Claude Code is designed around agentic workflows, shared context, and long-running reasoning. The result feels less transactional and more deliberate, especially for teams working on complex systems where consistency and intent matter as much as speed.
One of Claude’s biggest strengths is how it handles context. Through files like CLAUDE.md or AGENT.md, teams can codify engineering standards, architectural principles, and conventions once, then rely on Claude to apply them consistently across sessions. This shifts the burden away from repeatedly re-prompting the model and toward upfront clarity, which pays dividends as projects grow. When paired with structured documentation, Claude behaves less like a generic assistant and more like a domain-aware contributor.
Claude also leans heavily into specialization. Its sub-agent model allows teams to define focused agents for backend work, frontend implementation, testing, infrastructure, or even architectural review. Each agent operates in its own isolated context, enabling deeper focus without polluting the main session. This mirrors how strong teams already work and makes Claude particularly effective for multi-step stories that span layers of the stack.
Where Claude really differentiates itself is in workflow design. By defining reusable, multi-step workflows, teams can move from one-off prompts to repeatable development patterns. Claude can reason through requirements, pause for confirmation, delegate to specialized agents, and validate outcomes in a predictable sequence. This makes it well suited for spec-driven development, where humans define acceptance criteria and success metrics, and Claude handles execution within those bounds.
Claude is also more explicit about tradeoffs. Model selection is a first-class concern, with lighter models for fast, inexpensive tasks and more powerful models reserved for architectural or reasoning-heavy work. This encourages teams to think intentionally about cost, performance, and fit rather than defaulting to maximum capability for every request.
That said, Claude’s approach assumes a certain level of maturity. It rewards teams willing to invest in standards, specs, and workflows, and it is less optimized for quick, low-friction experimentation than tools that prioritize inline assistance. For organizations looking to scale AI-assisted development responsibly, however, that tradeoff often feels intentional rather than limiting.
Overall, Claude shines when used as a system, not a shortcut. It’s best suited for teams that want AI to reinforce good engineering habits, preserve institutional knowledge, and support thoughtful decision-making rather than simply accelerating code output.
Wrapping it up
AI is fundamentally changing how developers work on a day-to-day basis. In my current role, I use AI every single day, and I’ve found that the more I rely on it, the more I’ve relinquished the act of typing out code myself, letting the AI act as my scribe. There are still moments where I write a few lines manually, but the bulk of development now involves iterating on what the AI generates.
At times, the code it produces falls below junior-level standards, which makes it essential to develop the skill of reading, understanding, and clearly articulating fixes and improvements through prompts. As one of my colleagues, Kevin Sanders, aptly put it: “I feel like I no longer write C# or Java code, instead, I am writing at a more abstract level using AI prompts.” I couldn’t articulate it better myself.
This shift in development style has made tooling and workflow integration more important than ever. The right tools can dramatically amplify your productivity and effectiveness when working with AI models. Personally, I prefer the tools in the order of their pricing, Cursor, Windsurf, then Copilot, but the most important factor in choosing the right AI coding tool is understanding where you are in your own AI journey and selecting something that fits your current workflow, comfort level, and goals.
That said, diving into AI isn’t optional anymore, it’s becoming an essential skill for modern software developers. Those who ignore it will likely find themselves left behind. Learning to work with AI effectively means not just prompting well, but also understanding how to guide it, fix its mistakes, and integrate its suggestions into your broader system.
A strong foundation in reading code and understanding how systems work holistically is more important than ever. AI can generate code, fix bugs, and scaffold features, but it still struggles with architectural awareness and long-term planning. If you aren’t writing stories, keeping track of what’s changing, and staying on top of the work being done, especially as AI makes updates across multiple files, it’s easy to lose the thread of your own project. Treat AI like a junior developer that works quickly and makes a lot of assumptions, and your job becomes ensuring the direction is clear, the guardrails are in place, and the work stays aligned to your objectives.
You don’t have to slow down to start building with AI.
Let’s figure it out together.
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