"Show Me Something You Can Do That I Can't": Why Most Professionals Are Trapped in the AI Knowledge Bubble

Over eight years ago, I founded Netek, an affective computing startup exploring how to measure human emotion through technology. This was before “AI” dominated every conference keynote and LinkedIn bio.

When a local university launched an innovation degree, two students from that first cohort joined us for a summer internship. Word spread. More followed. Last week, one of them reached out. He’s now in data analysis and business intelligence. He wanted to pitch me his services.

The pitch was competent but uninspired, the kind you’ve heard a dozen times. He mentioned using AI. When I pressed him on how, it became clear he really wasn’t.

So I showed him something. In real time, I built a custom GPT that could handle most of what he’d just proposed; faster, more scalable, and arguably smarter.

His reaction said everything.

I looked at him and asked, “Show me something you can do that I can’t.”

That question hung in the air.

The Real Bubble

Everyone says we’re in an AI bubble, that the hype outpaces the value. But the real bubble isn’t financial (although this is another topic). It’s cognitive.

The world has split into two groups: people who talk about AI and people who think with AI.

Most professionals, even in technical fields like data and analytics, still operate with pre-AI mental models: They see AI as a tool, not a thinking partner. They describe tasks, not transformations. They talk about automation when they should be exploring augmentation.

Their environments don’t challenge this. They’re surrounded by others who also believe they “get” AI because they’ve used ChatGPT a few times.

The Reframing

When I asked, “Show me something you can do that I can’t,” it wasn’t arrogance. It was an invitation to rethink what value means now.

If AI can already handle baseline work, your edge isn’t your toolkit; it’s your ability to see what others can’t imagine yet.

That’s the new literacy. Not prompt engineering. Not model selection. But creative orchestration, combining intelligence, context, and design to create outcomes that weren’t possible before.

The Diffusion Lag

Everyone’s heard of AI. Few can build with it. Almost no one knows how to think through it. We’re in 1997 again. The infrastructure is being built. The imagination hasn’t caught up. Most “AI solutions” remain trapped in old paradigms: automation, dashboards, and reporting.

The real opportunity lies in new models of intelligence, systems that think, learn, and act with us, not just for us.

The Real Advantage

Access to AI tools is almost universal now. The advantage is fluency.

Fluency means: Translating business problems into intelligent systems. Designing workflows around human-AI collaboration. Seeing possibilities invisible to others, because you understand how intelligence compounds.

This is where the next generation of innovators will emerge.