The Hidden Costs of AI Development Tools: What CTOs Need to Know
Matt Watson
Product Driven Engineer, Founder/CTO for 20 years, Bootstrapped a SaaS company to a 9 figure exit, CEO of Full Scale
As a CTO who has spent decades in the trenches, I've seen countless "revolutionary" tools promise to transform how we build software. Today, AI coding tools like GitHub Copilot and Cursor are making similar promises.
Recently, I had an conversation with Joe Giglio on my Product Driven podcast about his extensive testing of these tools. What we discovered might surprise you.
The Allure vs. Reality
The marketing hype around AI coding tools is intense.
If you believe the headlines, we're all about to be replaced by AI. Microsoft, GitHub, and others are publishing carefully selected case studies showing dramatic productivity gains.
But here's the reality I've observed: Most developers spend only about 20% of their time actually writing code.
The average developer writes just six lines of code per day.
Why? Because the real work is in reading code, debugging, attending meetings, and understanding requirements.
The Integration Challenge
Joe's experience testing various AI coding tools revealed a critical insight: while these tools excel at basic tasks, they struggle with complex, real-world applications.
During our discussion, Joe shared his attempt to build a help desk system using AI tools.
"The basics work, but as the project becomes more complicated, it starts to go off the rails," Joe explained. He encountered issues with circular imports, server startup failures, and what he calls "hallucinated code" – where the AI generates code that looks good but doesn't actually work.
The Debugging Dilemma
One of the most concerning findings from our discussion was the maintenance burden these tools create.
Joe attempted to use AI to generate automated tests using Playwright and Selenium. The results were sobering.
The AI would create test scripts that looked perfect on the surface but were fundamentally flawed:
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The kicker? This was happening with code the AI itself had generated.
Imagine the complexity when dealing with legacy codebases.
The Real Cost of "AI-First" Development
A story that resonated with me was about a non-technical friend who tried using Cursor to build an application. Initially impressed, he quickly discovered that each AI-suggested fix broke three more things.
After three days, he gave up entirely.
This highlights a crucial point: AI coding tools might actually slow down development in complex projects. When you factor in:
The productivity gains can quickly turn into losses.
Looking Forward
Does this mean AI coding tools are worthless?
Absolutely not. I see them as productivity enhancers, not replacements.
In our conversation, Joe and I agreed that if these tools can make developers even 50% more productive – moving from six lines of code per day to nine – that's a significant win.
But CTOs need to be realistic about implementation. These tools work best when:
The Bottom Line
The future of AI in software development is promising, but we're not at the point where it replaces human developers.
As I told Joe during our discussion, programming is essentially English – we're writing human-readable instructions. At what point is it easier to write the code than to write the prompt?
For CTOs and technical leaders, the message is clear: AI coding tools can be valuable additions to your development stack, but they're not magic bullets. The key is understanding their limitations and implementing them strategically.
That's exactly why we need to use AI not only for coding, but primarily to improve collaboration, reduce misunderstandings and meetings to get the real productivity gains
AI Innovator | Founder of Recursive AI | Creating next-gen AI technology
3 周While raw coding speed can improve with AI, the real challenge is maintaining code quality and architectural consistency. I've found the key is using AI as a pair programmer rather than a code generator - letting it handle implementation details while developers focus on architecture, edge cases, and overall system design. This approach actually reduces debugging and maintenance time because the code is written with full context and alignment with existing patterns. When AI is treated as a team member rather than a magic solution, it enhances rather than complicates the development process. The productivity gains come not from writing code faster, but from better task distribution - letting developers focus their expertise where it matters most.
Co-Founder/COO Estenda Solutions, Digital Health/AI Expert, Podcast Guest, Public Speaker, Aspiring Author (2025), Triathlete, Life Goal: 100+ with a good quality of life
3 周what's the source for "6 lines of code/day"? It seems click-bait low.
Founder, CEO: MSSP Token | AI, Token-Based ITSM | Ontario: Licensed Private Investigator
1 个月We find that most of the development roadblocks are easily removed if you use programmers that are from Canada and or the US. Programmers that you can meet and know personally, and not over some zoom call 12 hours away. Why, because you don't have to spend any time translating requirement to code were English is a fourth language. We also do away with development companies that charge 450 to 600 percent over the actual time it takes to develop the code.
Building Software Products for Legal
1 个月6 lines of code a day is outstandingly mediocre.