The Honest Question
GitHub Copilot costs $19/user/month for business plans. For a 20-developer team, that's $4,560/year in subscription fees.
But is that the total cost? Probably not. AI-generated code may require additional debugging, review, and maintenance. The question is: how much additional cost?
Here's the honest answer: we don't really know. And neither does anyone making confident claims about specific dollar amounts.
What the Research Actually Shows
| Claim | Evidence Level | Key Caveats |
|---|---|---|
| AI tools speed up initial code writing | Well-supported | GitHub's 55% figure is for completion time, not delivery time |
| Developers feel more productive with AI tools | Well-supported | Subjective perception may not match objective metrics |
| AI-generated code may have quality issues | Moderately supported | Studies vary; depends heavily on use case and review process |
| METR study: 19% slower on familiar codebases | Single study | Small sample (16 developers); may not generalize |
| Security vulnerabilities in AI code | Moderately supported | Human-written code also has vulnerabilities; comparative rates unclear |
| "$100K+ hidden costs per year" | Speculation | No rigorous methodology; varies enormously by context |
Potential Cost Categories Worth Considering
While we can't give you a reliable dollar figure, here are cost categories that might matter. Your actual costs depend on your team, codebase, and processes.
Debugging and Verification Time
The concern: AI-generated code may look correct but contain subtle bugs—logic errors, edge cases, race conditions. Developers may spend time debugging code they didn't write and don't fully understand.
What we know: Some studies suggest AI code requires more review time. The METR study found developers were slower overall despite faster initial coding. But results vary significantly.
What we don't know: How much additional time, on average? It depends on code complexity, developer experience, review processes, and many other factors. Anyone claiming specific hours-per-week figures is probably guessing.
Code Review Overhead
The concern: AI-generated code may require more careful review because reviewers can't assume the author understood what they wrote. Or reviewers may be less careful because "the AI probably got it right."
What we know: Anecdotally, some teams report longer review cycles for AI-heavy PRs. But we don't have rigorous studies measuring this.
What we don't know: Whether careful review catches the issues, or whether problems slip through. The cost depends entirely on your team's review practices.
Technical Debt
The concern: AI tools optimize for "code that works now" not "code that's maintainable later." This might lead to duplicated code, inconsistent patterns, or hard-to-refactor structures.
What we know: GitClear's analysis suggested AI-assisted codebases have more "churn" (code rewritten shortly after creation). But causation is hard to establish.
What we don't know: Whether this represents actual technical debt, or just different development patterns. Long-term maintenance cost data doesn't exist yet.
Security Considerations
The concern: AI tools might suggest code with security vulnerabilities—SQL injection, XSS, insecure authentication patterns.
What we know: Studies have found AI can suggest vulnerable code patterns. But human developers also write insecure code. The comparative rates are unclear.
What we don't know: Whether AI makes security better or worse overall. It might catch some issues while introducing others. Depends heavily on the security review process.
License and IP Questions
The concern: AI trained on public code might sometimes generate output that matches copyrighted or GPL-licensed code.
What we know: This has happened—there are documented cases of AI generating code similar to training data. GitHub offers some filtering ("code referencing").
What we don't know: How often this happens in practice, or what the legal exposure actually is. The law is still being developed.
A Framework for Thinking About Costs
Rather than claiming specific dollar figures, here's a framework for evaluating your own situation:
Questions to Ask About Your Team
If you can't measure these things, you can't know your true costs—and neither can anyone else making claims about your situation.
What We Don't Know
Honest Uncertainties
- Net productivity impact: Does AI make developers faster or slower overall? Studies contradict each other. It probably depends on the task, the developer, and the codebase.
- Quality trade-offs: Is AI code better or worse than human code? Probably depends on who's writing and reviewing it.
- Long-term effects: What happens to codebases after 2-3 years of heavy AI use? We don't have data yet.
- Skill development: Does AI help or hurt developer learning? Genuine concern, no clear answer.
- Optimal use patterns: When should you use AI tools? When should you skip them? We're all still figuring this out.
What You Might Actually Do
Rather than making dramatic claims, here's practical advice:
If You're Using AI Coding Tools
- Track your metrics: Before and after comparisons of delivery time, bug rates, review cycles. Without data, you're guessing.
- Maintain code review standards: Don't let "AI wrote it" lower the bar for review quality.
- Watch for patterns: If you're spending significant time debugging AI suggestions, that's a signal.
- Stay skeptical of ROI claims: Including GitHub's marketing and including this article.
If You're Evaluating AI Tools
- Run pilots with measurement: Don't just ask if developers like it—measure actual outcomes.
- Consider your context: Results vary by team skill, codebase complexity, and problem domain.
- Factor in learning curve: Initial productivity may not match steady-state productivity.
- Plan for governance: How will you handle code review, security scanning, and quality assurance?
The Bottom Line
AI coding tools have costs beyond subscription fees. That's almost certainly true. The question is how much—and the honest answer is that it varies enormously and is hard to measure.
Claims of specific dollar figures ("$100K hidden costs!") are almost certainly fabricated or cherry-picked. The reality is messier: some teams benefit significantly, some break even, some probably lose productivity.
Your job is to figure out which category you're in, not to trust dramatic claims from vendors (including us) who have obvious incentives to tell a particular story.
About This Article's Original Version
The original version of this article claimed "$100K-400K hidden costs" based on fabricated survey data ("50+ companies"), fake testimonials from anonymous executives, and made-up time estimates. We presented speculation as fact to make a competitor look bad. That was wrong. We've rewritten it to be honest about what we know and don't know. Our business interest in criticizing Copilot remains—you should account for that bias.