AI Coding Tools: Why Developers Disagree

Source: Dev.to

You hear two very different stories:

• Friend (startup founder) – “Our team now ships features twice as fast using AI. I’m excited!”
• Developers – “We tried it. We spent more time cleaning up after the AI than writing the code ourselves.”

Both are right. They’re describing different moments in a space that moves faster than most people realize. As the person making hiring, timeline, and tooling decisions, you need to understand why the experiences don’t line up.

Why the Conversation Feels Like a Debate

People are talking about different eras and different workflows as if they were the same thing. When you talk to engineering teams and founders, you keep running into the same archetypes, each with a completely different experience with AI coding.

1. The Skeptic

• Who: Seasoned software developer.
• When: Tried AI coding tools a year ago or more.
• Result: Output was brittle, buggy, and required so much cleanup that writing the code themselves was faster.
• Conclusion: Tools were not ready – a reasonable judgment for that era.
• Current stance: Has not revisited because they don’t believe the tools could change that fast.

2. The Second‑Chancer

• Who: Same kind of developer as the Skeptic, same standards.
• When: Tried AI tools recently (after the latest breakthroughs).
• Result: Surprised by the quality. Now trusts AI and integrates it into daily workflows:
  • Automated bug detection
  • Incident response
  • Code reviews
  • Test generation
  • Documentation
• Conclusion: Not hype – a technology that actually works now.

3. The Copy‑Paster

• Uses AI through a chat window, copying code in and out.
• Helpful for unfamiliar technologies, but treated like a search engine.
• Not using AI to write dozens of files a day.

4. The Budget User

• On the cheapest subscription; constantly hits token limits.
• Spends more time crafting prompts than building features.
• Results are decent, but friction is high.
• Often wonders, “What’s the fuss about?”

5. The Founder‑Builder

• Builds products almost entirely with AI – no engineering team, no code reviews, just a big idea and a premium subscription.
• Ships fast; results can be genuinely impressive.
• No one is checking what’s underneath. Works today, but future durability is uncertain.

The Reality Behind the Numbers

If you’re the one making technology decisions, these five people will give you five completely different answers to “Should we invest in AI for development?” None are lying; they’re using different tools, from different time periods, with different workflows. You can’t compare their experiences as if they tried the same thing.

The November‑December 2025 Breakthrough

In November 2025, three major AI companies shipped coding models that changed how developers write software.

These releases didn’t happen because of a single breakthrough. The models got smarter and the tools built around those models matured at the same time, creating a step change in developer productivity.

What This Means for Your Decisions

1. Hiring

• The mix of skills you need is shifting.
• You may need fewer people writing code from scratch but more experienced engineers who can review and direct AI.
• Architecture and code‑review become more valuable than ever.
• One senior engineer who can evaluate AI‑generated code can be more valuable than three juniors writing boilerplate.
• If your hiring plan was set six months ago, it may need revisiting.

2. Timelines

• Projects that took three months might now take six weeks—but only if the team uses current tools with the right workflows.
• Teams stuck in the copy‑and‑paste camp won’t see the speedup.
• The gap between well‑equipped teams and everyone else is widening fast, affecting your competitive position.

3. Technical Debt

• AI‑generated code ships faster, but can accumulate hidden quality issues:
  • Verbose logic
  • Duplicated patterns
  • Shallow edge‑case handling
• Without experienced oversight, you’re trading speed today for expensive problems six months from now.
• Moving fast is only an advantage if you’re not building on a shaky foundation.

4. Tool Investment

• The difference between $20/month and $100+/month isn’t vanity; it changes what’s possible.
• Spending more without the right workflow is waste.
• You need a deliberate strategy: the right tools, the right training, and someone who knows how to evaluate the results.

5. Team Opinion Has an Expiration Date

• Whatever your senior developer told you about AI coding in mid‑2025 may already be out‑of‑date.
• This space moves in quarters, not years. Decisions made on stale information carry real cost.

A Safe Place to Start

Consider your QA team. AI is remarkably good at writing automated tests. Starting there lets you reap immediate benefits while you build up the necessary review expertise.

Questions to Ask Your Team

You don’t need to become a technical expert to make better decisions, but you do need to ask the right questions. Here are some to start with:

1. When did you last seriously evaluate AI coding tools?
2. Which model or product did you test, and on what date?
3. What workflow did you use (copy‑paste, integrated IDE plugin, full‑project refactor, etc.)?
4. How much time did you spend on prompt engineering versus actual development?
5. What was the ratio of AI‑generated code to manual cleanup?
6. Which tooling tier (free, basic, premium) did you use?
7. What metrics did you track (cycle time, bug count, post‑release defects, developer satisfaction)?
8. How did you handle code‑review and quality‑gate processes for AI‑generated output?
9. What technical debt concerns, if any, emerged from the AI‑generated code?
10. What training or onboarding would help the team use the latest tools more effectively?

Answering these will give you a current, comparable snapshot of where your organization stands and where it can go.

Assessment Timing

The answer is before October 2025, the assessment is outdated. The tools available today are noticeably better than what existed six months earlier.

Tool Usage

• Do we use tools like Cursor, Codex, or Claude Code?
  • If not, what are the real reasons?
  • If yes, what does the process look like and how do we check the results?

Impact on the Next Quarter’s Roadmap

What would change about our next quarter’s roadmap if AI tools cut implementation time by forty percent?

This question forces your team to think about strategic impact, not just tool preferences.

Ownership & Continuous Evaluation

• Do we have someone who stays current on this?
  • The landscape is changing fast enough that someone needs to own it.
  • Responsibilities include:
    1. Evaluating new tools.
    2. Updating workflows.
    3. Translating what’s possible into what your business should actually do.

The Bottom Line

The AI coding debate isn’t really a debate. It’s people describing different eras and different workflows as if they shared the same experience—they don’t.

The pace of change means your team’s perspective has a shelf life measured in months, not years. Having someone who can cut through the noise, evaluate what’s real, and translate it into your roadmap and hiring plan is the difference between riding this wave and watching it from shore.

This article was originally published on kamenski.me.