Beyond AI Hype: Why 'Normal Technology' Might Be the Boring Truth

Marc Andreessen declares AI will liberate the human soul. Respected researchers warn of "extinction risk" and compare AI development to nuclear proliferation. But there's a third voice getting drowned out—the researchers actually studying how these systems work in the real world. Their message lacks dramatic appeal, but it might be the most important one of all.

The Quiet Middle Ground

Scroll through any tech news feed and you'll find two dominant narratives. Venture capitalists publish manifestos declaring "intelligence is the ultimate engine of progress." On the flip side, open letters warn of civilization-ending risks, signed by some of the field's most prominent names.

Both camps share something beyond their certainty—they're drowning out everyone else. The researchers who show up to conferences, publish papers, and do the slow work of understanding how AI actually behaves rarely break through. Their perspectives lack the quotable drama of salvation or extinction.

They're arguing for something far less clickable: AI might be genuinely important but ultimately ordinary technology.

What 'Normal' Actually Means

A paper from Princeton researchers Arvind Narayanan and Sayash Kapoor offers a useful framework. What if AI is just normal technology? Normal doesn't mean insignificant. The printing press, electricity, and the internet all fundamentally changed the world—but they did it piecemeal, over decades, through messy adoption processes that gave societies time to respond.

Factory owners didn't immediately understand how to harness electric power. It took years of experimentation with layouts, worker training, and production processes before productivity gains materialized. The technology was revolutionary, but the revolution was gradual.

This framing cuts against both utopian and dystopian visions. We don't need to prepare for superintelligent AI taking over or plan for college graduates working on spaceships by 2035, as OpenAI'sSam Altman recently suggested. Instead, we need to think about discrimination in hiring algorithms, erosion of press freedom, and labor displacement in specific industries.

After the Gold Rush

The problem? This framing doesn't serve the AI industry's interests. Tech that takes decades to pan out won't help you raise billions in venture capital this quarter. It doesn't offer a get-out-of-jail-free card for companies wanting to skip safety testing because "we're in an arms race with China."

Extreme narratives are useful precisely because they justify extreme responses—showering AI labs with cash or exempting them from oversight. The boring middle justifies nothing except careful, deliberate work.

But that work can still account for extremes. We stress-test banks for financial crises that may never come. We build earthquake codes into cities that might not shake for decades. Planning for normal doesn't mean ignoring tail risks—it means not only planning for tail risks.

The American Reality Check

For US policymakers and business leaders, this perspective offers a different playbook. Instead of racing to either embrace or ban AI wholesale, we could focus on targeted regulations where they're needed most. Think hiring discrimination, deepfakes in elections, or AI-generated medical advice.

For investors, it suggests looking beyond the $100 billion valuations and asking harder questions: Which AI applications actually solve real problems? Which companies are building sustainable businesses versus riding the hype wave?

The Harder Path Forward

The middle path requires something more difficult than prophecy—patience, empiricism, and the willingness to admit we don't know how this plays out. That's not a satisfying story for venture pitch decks or congressional hearings. But it might be the right one.