AI Coding Tools Are Powerful, But They Cannot Replace Senior Engineering Review

I have been experimenting with both Claude and ChatGPT for development tasks.

While both platforms are impressive, neither is 100% accurate nor reliable for production-ready solutions.

AI tools can generate functional code quickly, but that does not mean the solution is optimal, scalable, or safe for production environments. This is why any AI-generated solution should still be reviewed by an experienced engineer before deployment.

I have over 20 years of experience as a software engineer, and when I review code, I look beyond whether something simply “works”.

I look at how the solution behaves under real-world load and scale.

For example, I evaluate how a system behaves at:

• 1 request
• 10 requests
• 100 requests
• 1,000 requests
• 10,000 requests
• 100,000 requests
• 1,000,000+ requests

At each level, I consider how the code impacts:

• Server load
• CPU utilisation
• Memory usage
• Concurrency handling

These are the areas where AI-generated solutions often fall short.

Many developers currently using AI tools are junior to mid-level engineers who mainly verify that “the code works”. They often do not evaluate deeper system characteristics such as scalability, performance, or architectural efficiency.

This is how production systems start failing under heavy traffic.

A common reaction when systems slow down is to upgrade the server infrastructure, increasing costs. But in many cases, the real issue is inefficient code or architecture, not insufficient hardware.

Throwing more hardware at the problem rarely solves the underlying issue.

The Hidden Risk: Losing the Ability to Debug

Another challenge appears later in the project lifecycle.

As systems grow and become more complex, debugging becomes significantly harder — especially if developers rely heavily on AI-generated code they do not fully understand.

Over-reliance on AI can lead to:

• Weak familiarity with the codebase
• Poor architectural understanding
• Reduced problem-solving skills
• “Lazy thinking” when approaching complex engineering problems

I recently encountered a scenario where a developer could not debug their own code, because it had been largely generated through AI tools. I had to step in and walk through the logic to identify the issue.

If you do not deeply understand the code you ship, you are not truly in control of the system.

Where AI Tools Actually Excel

Despite these concerns, AI tools are still extremely valuable when used correctly.

They are excellent for:

• Brainstorming ideas
• Reviewing existing code
• Identifying potential risks
• Suggesting architectural approaches
• Refactoring or improving code
• Speeding up repetitive tasks
• Exploring alternative implementations

In fact, using tools like Claude and ChatGPT side-by-side can accelerate development and expose different perspectives on a problem.

But they should be treated as engineering assistants, not as final decision makers.

Key Engineering Checks After Using AI

If AI is used to generate or assist with code, engineers should always validate the solution against critical production factors.

These include:

Performance & Scalability

• Server load
• CPU utilisation
• Memory usage
• Request throughput
• Latency and response time
• Thread or async efficiency
• Concurrency limits

Infrastructure Impact

• Disk I/O
• Network bandwidth
• Database query efficiency
• Connection pooling
• Cache utilisation

Reliability

• Error handling
• Retry logic
• Timeouts
• Circuit breakers
• Fault tolerance

Resource Optimisation

• Garbage collection behaviour
• Memory allocation patterns
• Object lifecycle management
• Background job processing

Architecture

• Horizontal scalability
• Stateless design
• Microservice vs monolith trade-offs
• Queue systems
• Event-driven architecture

Observability

• Logging
• Metrics
• Monitoring
• Distributed tracing
• Alerting

Security

• Input validation
• Authentication and authorisation
• Rate limiting
• Injection vulnerabilities

Operational Testing

• Load testing
• Stress testing
• Soak testing
• A/B performance testing

Final Thoughts

1. AI-generated code must always be reviewed by experienced engineers.
2. Developers may struggle to debug AI-generated code if they do not fully understand it.
3. Over-reliance on AI reduces familiarity with your own codebase.
4. Always test performance factors such as server load, CPU usage, and memory behaviour before deploying AI-generated solutions.

AI tools are powerful assistants, but they do not replace engineering decision-making.

There is a lot of discussion online about Claude, and it is currently receiving strong promotion through influencers.

From my own technical experimentation with both tools, I find ChatGPT to be stronger in technical reasoning and logical structuring, although both tools can be useful when used correctly.

Ultimately, the responsibility for building reliable, scalable systems still lies with the engineer, not the AI.