AI Coding Assistants: Superpower or Technical Debt Generator?

I’ve spent nearly two decades working in every kind of tech company—lean startups, unicorns, and household-name behemoths. I started out in the trenches writing code, sometimes almost being lost in code and over time, I transitioned into roles where I scaled large engineering teams. I’ve built, shipped, scaled, and optimized—seen it all, done it all.

However, AI-assisted automation/AI-assisted workflows is unlike any technological shift I've witnessed before. I've seen major transitions while working, mainstream adoption of agile practices, containerization, and microservices. We've evolved from monolithic systems to microservices, simplified our processes from intricate ANT scripts to smooth one-click CI/CD deployments (remember when moving servers meant physically hauling machines onto trucks?), yet AI changes the game in ways I’ve never seen. I am both excited and scared of what the future will look like. Suddenly, developers can generate boilerplate code, tests, and entire features in minutes with just a few lines of prompting. It’s simultaneously a stunning productivity leap and the biggest wildcard in years, forcing critical questions: how does AI really reshape our workflows? Are we truly gaining efficiency, or just hiding deeper issues?

I didn’t want to rely on guesswork or anecdotal observations, so my team and I built a system—eventually evolving into our platform, Hivel—to measure and understand the role of AI in coding. Our goal was to see how AI usage truly affects developer productivity and code quality.

The Motivations Behind Tracking AI Usage

1. AI’s Invisibility

AI-assisted coding happens behind the scenes. The adoption rate of AI-assisted coding tools like Copilot, Codex, Cursor has been staggering, developers install a browser extension or tweak their IDE, and suddenly an LLM is auto-completing vast chunks of their code. Because these changes often blend seamlessly into commits, it’s almost impossible to tell which lines were human-generated and which were generated by AI—at least by looking at a typical commit history.

🛠️New AI tools emerge daily. By the time you finish reading this, there might be two more on the market.

2. Efficiency Gains vs. Hidden Costs

AI tools promise efficiency, but do they deliver genuine long-term benefits—or do they invite more technical debt? We wanted data to test industry assumptions like:

 • Do AI suggestions reduce busy work, or do they lead to over-reliance?

 • How frequently are AI suggestions accepted?

 • Does AI usage affect how quickly commits get merged or how often code is pushed?

3. Shifting Team Dynamics

Collaboration has always been core to engineering, whether it’s pair programming or group code reviews. But AI is shifting these dynamics. Prompt-writing is now a skill—some developers might generate complete solutions in minutes, while others spend extra time debugging and reviewing AI-generated code. AI can’t review itself, meaning we risk losing the checks and balances that come from peer collaboration.

4. Code Quality and Risk

While AI is great for automating repetitive tasks, it can still produce flawed or buggy code—especially in domain-specific areas. The real issue isn’t the mistake itself; it’s not knowing where the AI-generated code lives in the repo. Blind reliance on AI can turn bug-hunting and refactoring into a real headache, driving up maintenance costs.

How We Built a Tool to Track AI-Assisted Coding With Copilot (Without Creeping on Developers)

As a former developer and CTO, I know how sensitive engineers can be about monitoring. We don’t track clicks or keyboard strokes, we just want to ensure that the needle keeps moving. From day one, we designed our AI-assisted coding analytics to be:

 • Opt-in and Developer-Friendly - We track usage at a team or project level, not at an         individual engineer level.

 • Outcome-Oriented - Just counting AI suggestions doesn’t prove value; we measure real productivity and quality outcomes. Are AI-assisted PR’s taking longer to review and merge?

 •  Integrated with Git, Jira, and IDEs - Zero extra steps or forced workflows. Developers don’t have to do anything extra, everything blends in seamlessly.

Accelerating the Build with AI

Ironically, we used AI to build our own AI usage detection features. Tasks that might’ve taken weeks—writing PRDs, TRDs, and multiple rounds of prototyping—were compressed into a few days. We validated our approach rapidly, built out a proof of concept, and integrated it into Hivel. Now we are making a full scale version ready for launch.

The Challenges while Building 

 •  Constant Change - AI platforms and features update almost daily, which makes it tough to keep everything aligned.‍

 • Avoiding “Hallucinations” - AI can still spit out half-baked suggestions. We had to implement rigorous checks to maintain accuracy.

How We Measured Productivity (Beyond Just Speed)

Tracking AI usage alone doesn’t tell us much. We correlate AI-assisted coding with real indicators:

1. Commit Frequency & Volume: Are AI-assisted devs pushing more code?

2. Time-to-Merge (PRs): Does AI-generated code speed up or slow down releases?

3. Bug Rates & Rollbacks: Is AI introducing more production (security and scalability) issues or rework needing more hotfixes?

4. Review Difficulty: Do peers (sometimes same dev) find AI-generated PRs difficult to understand and maintain?

What We Learned: The Brutal Truth About AI-Assisted Development

Through our customers who tested out our PoC — and together analysed millions of lines of code and a few thousand commits where AI tools were used—we uncovered a few consistent trends:

1. More Code, Sometimes Lower Quality

  • AI-assisted developers produced 30–50% more code.

  • However, AI-generated code often required 20–35% more revisions and 2X the review time.

  • Senior engineers rejected AI-driven code about 3x more than junior engineers.

2. AI Introduces More Bugs—But Also Fixes More

   • AI-created code tends to have higher bug rates, especially in domain-specific logic.

   • But developers also used AI to debug, fixing issues roughly 40% faster.

3. PR Reviews Get Tougher

   • AI-generated changes took 25% longer to review.

   • Reviewers left 2–3 times more inline comments for explanation or clarification.

The Future of AI in Development: Better Understanding with Hivel

AI-assisted coding isn’t going away. But blindly accepting every AI suggestion can create more problems than solutions. That’s precisely why we built this at Hivel. We are a developer + data-first productivity tool that gives real visibility into how these changes affect your team’s workflow—without snooping on individuals. We help you see:

   •  The true impact of AI on productivity (not just how many lines get generated)

   •  Whether AI code is helping or hurting your engineering output

   •  Which practices actually pan out in production

Final Question: Is AI Making Developers Better—or Just Noisier?

We know AI can supercharge speed. But the data shows it also brings added complexity and risk. So the question isn’t whether to use AI—it’s how you should use it.

What has your experience been with AI-assisted coding? Have you seen similar trends in your team?