“Any sufficiently advanced technology is indistinguishable from magic.” – Arthur C. Clarke

Artificial Intelligence (AI) has grown incredibly powerful in recent years, seamlessly handling tasks from generating articles to helping programmers write code. However, building products that people genuinely love requires more than powerful tools—it demands empathy, creativity, and deep understanding, areas where AI still has much to learn.

I am personally using AI every day to enhance my productivity. However, it is not yet replacing humans. Especially not where we need to empathize or navigate uncertainty.

AI certainly feels like magic. Let’s dig into where it excels and where we need to exercise caution when using it.

What AI Has Learned Recently (and Why It Matters)

Today’s AI has made significant leaps forward, especially in understanding its limitations, breaking down complex tasks, and becoming consistent in its output. For example, modern chatbots can recognize when they cannot solve a problem directly and then automatically use external tools, like looking up recent facts online or doing calculations.

Another practical example is AI-powered writing assistants, such as Grammarly, which consistently adhere to grammatical rules, enhancing readability and coherence for millions of users every day.

“The greatest enemy of knowledge is not ignorance, it is the illusion of knowledge.” – Stephen Hawking

The Human Touch: Understanding Customers

Despite these improvements, AI is notably lacking when it comes to customer-centricity. Products people love typically solve specific, real-life problems—but AI doesn’t inherently understand problems the way humans do. It doesn’t talk to customers directly or empathize with their daily frustrations.

Imagine an AI attempting to design an ergonomic chair. Without listening to real users’ feedback about comfort, usability, and daily use, its design might be technically correct but still uncomfortable or unpopular.

Similarly, while AI-generated customer service replies may seem efficient, they often miss subtle human cues, causing customer frustration. For instance, automated responses from chatbots frequently feel impersonal and fail to solve nuanced user issues effectively.

“People don’t buy products, they buy solutions to their problems.” – Seth Godin

Navigating Complexity: More Than Just Patterns

Real-world product building involves balancing constraints like budgets, timelines, team dynamics, and technical limitations. AI is great at identifying patterns from previous data, but struggles to extrapolate those patterns into innovative solutions that fit seamlessly into complex systems.

Consider scheduling software for hospitals. AI might schedule shifts efficiently based on historical data, but without fully understanding human aspects—like team morale, fatigue, or personal preferences—it could unintentionally cause dissatisfaction and lower productivity.

Moreover, AI currently doesn’t manage interpersonal dynamics well. Imagine an AI managing a creative team: without genuine understanding of emotions or team culture, it could struggle to mediate conflicts or encourage meaningful collaboration.

“Complexity is your enemy. Any fool can make something complicated. It is hard to make something simple.” – Richard Branson

Quality Matters: Where AI Still Stumbles

When it comes to quality—whether engineering, security, or overall usability—AI has significant gaps. It can replicate coding patterns but doesn’t inherently prioritize long-term quality or security best practices unless specifically instructed.

Take security: AI-generated code might function perfectly but may inadvertently include vulnerabilities due to a lack of deep understanding of cybersecurity practices. Practical tests have revealed instances where AI-generated apps failed basic security reviews because the AI didn’t automatically consider encryption or proper user authentication practices.

Likewise, automated AI-generated code often neglects rigorous quality checks like edge-case testing unless explicitly guided, potentially leading to unstable software in real-world use.

“Quality is never an accident; it is always the result of intelligent effort.” – John Ruskin

Creativity & Simplicity: Solving Problems, Not Creating Them

Creativity involves navigating constraints elegantly and making tough trade-offs—something uniquely human. AI can suggest countless ideas but struggles to creatively simplify complex problems without adding unnecessary complexity.

For example, when Steve Jobs envisioned the iPod, the goal wasn’t just “store music digitally”—it was to create a simple, beautiful way for anyone to carry thousands of songs. AI, lacking intuition and insight into human desires, may miss these opportunities for elegant simplicity.

Real creativity in product development means knowing what features to exclude, prioritizing user experience, and taking responsibility for trade-offs—things AI can’t yet fully grasp.

“Simplicity is the ultimate sophistication.” – Leonardo da Vinci

Bridging AI’s Gap to Product Love

AI has undeniably advanced, becoming a powerful assistant. But genuine, beloved products require more than efficiency—they demand empathy, simplicity, quality, and deep insight into human lives.

Until AI can truly understand human emotions, navigate complex social interactions, and creatively solve real problems with simplicity, it remains a helpful tool rather than a visionary builder of products.