AI Is Like Artificial Flavoring—Convincing, But Lacking Depth
Why AI Can Mimic Human Work, But Can’t Replace Human Thinking
If you scroll through LinkedIn or tech forums, you might think AI is on the verge of taking over entire industries—software engineering, marketing, program management, copywriting, and more. Every day, a new article or video claims that AI will replace jobs, automate complex tasks, and even surpass human intelligence.
I’ll admit that I’m getting a little tired of the endless posts and videos predicting AI’s domination. But one discussion I actually enjoyed was the NYT Opinion piece where Hank Azaria—famous for voicing characters on The Simpsons—tested an AI voice model trained to mimic him. The AI did well, but not well enough.
Why? Because it lacked the human characteristics that make voice acting compelling—character motivation, emotional depth, subtlety in physicality, and facial expressions. This is a perfect analogy for what’s happening in software engineering.
AI is Fast, But Is It Smart?
AI can generate code at lightning speed, but does that make it as effective as an experienced engineer or architect? Not quite.
Many people equate AI’s ability to process vast amounts of data with actual intelligence, but there’s an important distinction. AI doesn’t reason independently—it follows predefined objectives and optimizes for efficiency based on algorithms. It lacks the creative problem-solving, long-term thinking, and architectural foresight that make software engineering more than just code output.
Where AI Falls Short in Software Engineering
Lack of Context Awareness: AI tools like GitHub Copilot can generate functional code snippets, but they don’t understand the broader system architecture. They can’t weigh trade-offs like maintainability, scalability, or security in the way a seasoned engineer does.
No Deep Problem-Solving: Debugging is often about intuition and experience—patterns that engineers recognize after years of working with complex systems. AI might point out syntax errors, but can it predict a race condition caused by an unusual user interaction? Highly unlikely.
More Than Just Test Coverage: AI can churn out thousands of test cases for, but does more coverage equal better software quality? Not at all. Many of the most critical issues—usability flaws, accessibility problems, performance bottlenecks—require human judgment. AI can’t replicate the nuanced thinking needed to spot real-world user experience gaps.
AI: A Powerful Tool, Not a Replacement
If AI were an engineer, it would be a junior developer—fast, eager, and good at following patterns but lacking the strategic thinking required for genuinely innovative solutions.
This brings me to a simple analogy:
Artificial Intelligence is like artificial flavors—it can mimic the real thing, but only at a surface level.
It’s a tool to assist, not a substitute for human expertise. The best outcomes will come from AI augmenting human creativity and decision-making, not replacing it.
I’d love to hear your thoughts! Drop a comment below and let’s discuss. And if this post made you think, share it with a colleague or friend who has strong opinions on AI!