Lately the price of AI coding assistants has been climbing. Claude Code’s max plan runs $200 per month. Cursor’s Ultra tier is another $200. Even GitHub Copilot has crept up to $39/month. It’s easy to dismiss these as too expensive and move on.
But let’s do the math.
The Simple Economics
A mid-level developer in the US typically costs their employer around $100 per hour when you factor in salary, benefits, and overhead. At that rate, an AI coding assistant needs to save just 2 hours per month to pay for itself.
Two hours. That’s one debugging session cut short. One feature implemented faster. One refactoring completed smoothly instead of stretching into the evening.
I’ve been using these tools for the past year, and I can confidently say they save me 2 hours in a typical day, not month.
The Chicken and Egg Problem
Here’s the catch: how do you prove the value before you have the subscription? Your boss wants evidence, but you need the tool to generate that evidence.
This is a classic bootstrapping problem, but there are ways around it:
1. Start with Free Trials
Most AI coding assistants offer free trials or limited lower cost tiers. Use them strategically:
- Time yourself on similar tasks with and without the assistant
- Document specific examples where AI saved time
- Track error rates and bug fixes
2. Run a Pilot Program
Propose a 3-month pilot with clear success metrics:
- Reduced time to complete user stories
- Fewer bugs making it to production
- Increased test coverage
- Developer satisfaction scores
3. Use Personal Accounts for Proof of Concept
Yes, it’s an investment, but spending $200 of your own money for one month to demonstrate concrete value can be worth it. Track everything meticulously and present hard data.
Overcoming Common Objections
“We already pay for developers, why should we pay for their tools too?”
You wouldn’t expect a carpenter to bring their own power tools. Modern development requires modern tooling. This is infrastructure, not a perk.
“It’s just autocomplete, how much time can it really save?”
This dramatically undersells what modern AI assistants do:
- Generate entire test suites
- Refactor legacy code
- Debug complex issues
- Write documentation
- Translate between programming languages
- Explain unfamiliar codebases
“What if the AI writes bad code?”
AI assistants don’t replace code review or testing. They accelerate the development cycle. Bad code gets caught the same way it always has – but you’ll have more time to write good code because you’re not bogged down in boilerplate.
The Cost of NOT Adopting
Here’s what many managers miss: your competitors are already using these tools. While you’re debating the cost, they’re shipping features faster, fixing bugs quicker, and keeping their developers happier.
Developer productivity isn’t just about speed – it’s about:
- Reduced cognitive load
- Less context switching
- Fewer repetitive tasks
- More time for creative problem-solving
Burned out developers cost far more than $200/month in turnover, recruitment, and lost productivity.
This Is the New Normal
In 2025, asking whether to pay for AI coding assistants is like asking whether to pay for high-speed internet in 2010. It’s not a luxury – it’s table stakes for competitive software development.
The question isn’t whether to adopt these tools, but how quickly you can integrate them effectively. Every month you wait is a month your competitors gain ground.
Making the Investment Case
When presenting to management, focus on:
- Concrete ROI: 2 hours saved = break even, everything else is profit
- Risk mitigation: Faster bug fixes, better code quality, happier developers
- Competitive advantage: Ship faster, iterate quicker, outpace competitors
- Talent retention: Developers want modern tools; denying them risks turnover
The $200 monthly cost? That’s not an expense. It’s the cheapest productivity multiplier you can buy. In a world where developer time is the scarcest resource, tools that amplify that time aren’t optional – they’re essential.