AI Just Leveled Up for Developers—Here’s What Changed (and Why It Matters)
TL;DR
AI for software development isn’t just autocomplete anymore. Tools like Claude Code, now available via Anthropic’s Pro subscription, enable large-scale refactors, schema definitions, and test scaffolding—all through precise, high-context prompts. I recently finished a two-day Go project in just four hours using Claude Code. More importantly, we’re entering a new era where AI tools don’t just save time—they fundamentally reshape what kind of engineering teams are even needed. This post explains how and why every developer should take another look, especially if they haven’t checked in since Copilot’s early days.
If you tried AI coding tools six months ago—heck, even three months ago—and thought, “cool, but not game-changing,” ME TOO. At the time, most of these tools felt like clever autocomplete: useful, but limited.
Today? They’re a whole new class of developer tooling.
Anthropic recently launched Claude Code, a terminal-first, prompt-driven coding assistant for anyone on the Claude Pro plan. It works directly in your local environment, understands your codebase, and makes it easy to request big, high-context changes like “refactor this logic into its own package” or “convert this test suite to table-driven tests.” It doesn’t just guess what you want—it follows explicit instructions and lets you approve or tweak the result.
It’s the most “pair programming with AI” experience I’ve had yet—and it’s made me completely rethink what the next year of developer workflows might look like.
From Copilot to Claude Code: The Shift in AI Productivity
When GitHub Copilot launched a couple of years ago, I (like many devs) noticed an immediate quality-of-life boost: tab-complete for boilerplate, error handling, and test templates. It shaved minutes off here and there. It sped up the parts I already knew how to write. That was the first wave.
Now we’re entering the second wave—and it’s a leap, not a step.
Tools like Claude Code, ChatGPT’s Codex CLI, and even Cursor give us something Copilot can’t: intentionality. Instead of passively suggesting lines while you type, these tools let you step back, describe your goal in plain language, and apply sweeping changes across your codebase—knowing that you, the developer, are still in control.
This kind of prompt-driven AI changes how we code, not just how fast we type.
My Use Case: Cutting a Two-Day Project to Four Hours
Here’s the job I had to do: build a Go-based script that pulls a .jsonl file from Google Cloud Storage and loads it into BigQuery.
What should have been a simple task quickly got messy:
The .jsonl format (JSON Lines) doesn’t work with BigQuery’s schema inference.
The .jsonl schemas were irregular—different fields in each row, optional values, weird nesting.
Go doesn’t support .jsonl natively, and I didn’t want to pull in sketchy third-party packages.
In the past, this kind of work takes a couple of focused days. You write the file pull, then write the parser. You build your schema manually. You test against multiple payloads. You define structs and update them as you go. You rerun tests, tweak types, and debug failed loads.
But this time, I used Claude Code. I gave it a high-level prompt like:
“Write a Go program that pulls a .jsonl file from GCS, defines a struct for BigQuery with the right tags, and loads the file into the warehouse.”
Then I said:
“Split this into three packages: GCS pull, BigQuery write, and a transform step in between.”
And it just did it. I still reviewed the code. I still edited for edge cases. But Claude handled the boilerplate, test generation, and 90% of the scaffolding. I spent my time tweaking and validating—not typing.
Total time: four hours.
Prompt-Driven Means You’re in Charge
One of the things I love about Claude Code is that it doesn’t try to drive. It’s not Copilot, silently injecting guesses mid-keystroke. It’s a partner waiting for instruction. You prompt it with the exact change you want to make, and then it shows you the results.
You can:
Refactor packages
Define test suites
Move code into new files or modules
Bulk edit across functions
Validate output before it lands
And because you’re in charge, the quality of what you get out depends on the clarity of your prompts—and your own understanding of the codebase.
This is critical: AI doesn’t replace knowing how to write good code. It amplifies it.
If you’re sloppy or vague, you’ll get messy output. But if you’re experienced, methodical, and precise, AI can accelerate your entire workflow. Think of it as moving from “pairing with an intern” to “pairing with a senior who works fast and waits for direction.”
Why This Matters for the Future of Teams
This isn’t just a story about tools—it’s about teams and how we build software.
Developer advocate Mehdio, who works with DuckDB, recently wrote a phenomenal piece titled “The Slow Death of Medium-Sized Software Teams.” His thesis is if AI gives every engineer a 20%–30% productivity boost (and I think we’re already seeing that) it radically shifts what’s possible with small teams.
You don’t need 30 engineers to scale anymore. With AI-driven productivity, three good engineers can outpace a whole mid-sized team from five years ago.
I couldn’t agree more.
We’re at the beginning of that shift. Right now, it feels like a solid 20% speed boost. But with every new iteration—from Sonnet 3.5 to Opus to Claude Code and beyond—that number creeps higher.
How to Try Claude Code
If you’re ready to try it yourself, Claude Code is available via the Claude Pro plan. Once enabled, it works in your terminal (macOS and Linux), and can:
Read and edit local files
Run CLI commands (like go test, npm run build, etc.)
Suggest and apply changes via prompts
Refactor entire packages
Help scaffold new features, CLI tools, pipelines, and more
Prompt it like you would talk to a junior engineer or a teammate:
“This function should move into its own package. Add tests while you’re at it.”
“Update this struct to reflect the new schema and regenerate the test data.”
It’s not always perfect. But it’s fast, collaborative, and lets you stay in control.
Final Thoughts: It’s Time to Revisit AI
I’m still skeptical of AI in the same way I’m skeptical of magic: it’s powerful, but you need to know the rules.
You shouldn’t blindly trust code generation tools. You shouldn’t abdicate responsibility for your codebase. But you should absolutely learn how to use them well—because the devs who do will build faster, cleaner, and with far more leverage than those who don’t.
If you tried AI tools six months ago and found them underwhelming, try again. Claude Code is different. This isn’t vibe coding. This is fast, intentional development—with a partner who doesn’t get tired.
And we’re only getting started.
🔗 Watch my original video:
▶️ Claude Code: Real Dev Tool or Just Vibe Coding?
🔗 Read Mehdio’s insight on small teams and AI productivity:
📖 The Slow Death of Medium-Sized Software Teams
🛠️ Try Claude Code:
https://claude.ai