The question has been floating around since ChatGPT launched a few years ago: will AI replace developers, or at least meaningfully help them get more done? For a while, nobody had a good answer.
The early tools generated plausible-looking code that fell apart under scrutiny, hallucinated APIs that didn't exist, and required so much correction that you weren't always sure they were saving time at all.
That's changed.
The tools that actually moved the needle
Over the past few months, a new tier of tools has emerged: Claude Code, OpenAI Codex, and significantly improved versions of GitHub Copilot. They behave differently than what came before. They're more reliable, better at holding context, and genuinely faster to work with. Even Google's free AI Mode has become a useful resource for general development questions. The hype is finally catching up to the reality.
At Springthrough, we've settled on Claude Code as our primary AI development tool, and it's earned that position.
What we've actually used it for
The range of tasks where Claude Code has proven useful is broader than we expected. Refactoring legacy codebases. Migrating features from one framework to another. Tracking down elusive bugs. Generating development plans and talking through architecture decisions before writing a line of code.
It can produce quality code, dig through logs and surface relevant information, connect to a live browser to verify its own output, and make well-reasoned suggestions for the kind of nuanced, hard-to-diagnose bugs that require real depth in a specific technology area. That last one is where it has surprised us the most.
Where it still falls short
It's not a silver bullet, and it's worth being honest about that.
Claude Code doesn't always get it right on the first try. The output isn't always production-ready, and treating it as if it is will create problems. Getting genuine value out of it requires giving it proper context, writing a thoughtful prompt, and testing what it produces. That's not a small investment, especially for developers who are used to moving fast.
The best way we've found to think about it: it's a second developer. One you need to guide and review, but one you can count on to do work that is mostly right, most of the time. That's a real asset on a team.
Where this is all going
The tools we have today already make every level of developer more capable. Junior developers get a knowledgeable resource they can think alongside. Senior developers get an executor for the tedious parts of the job: the boilerplate, the repetitive refactors, the first-pass documentation.
The tools of the next few years will likely go further. We're heading toward AI that can handle well-scoped tasks from start to finish, with enough reliability that developers can trust the output without reviewing every line. We're not there yet. The direction is clear, though, and the pace of improvement is real.
If you're a developer who hasn't seriously incorporated AI tools into your workflow, start now. The productivity gains are real, and the tools keep improving.
The question now is how much AI will help developers, and how soon.
How much could AI help your developers?
We've put these tools to work on real client projects. If you're trying to figure out where AI fits in your team's workflow, we can help you skip the trial-and-error and get to the productive part faster.
Talk to us about AI in development arrow_forwardAnton Aleynikov is a Developer at Springthrough.