Tech advances solve old issues but breed new challenges.

Today, strong advocates of technology dominate the ranks of influential Silicon Valley figures. They envision a promising future for humanity driven by rapid advancements in innovation.

Undeniably, technology and science are among humanity’s greatest strengths and sources of hope. However, this optimism often goes too far, as technological progress invariably introduces new challenges even while solving existing ones—a lesson well-documented by scientific inquiry. Blind faith in technology risks delivering fleeting benefits while imposing long-term consequences. To truly harness technological potential, a more measured and thoughtful approach is essential.

Why does technology frequently create unexpected issues alongside its successes? Anthropologist Sander van der Leeuw proposed an explanation years ago, now resembling a natural principle. When confronting a problem, we develop a simplified model of how the world works, then design a solution based on that understanding. Often, this fixes the immediate issue—but inevitably, the model proves incomplete. What was omitted leads to unintended repercussions once the technology interacts with reality.

This pattern recurs because simplified models are useful yet misleading. Their omissions blind us to full consequences. More efficient fishing methods boost food supply, only to deplete ocean life. Non-stick cookware revolutionizes kitchens, until its hazardous chemicals spread globally. Plastics, prized for convenience, now saturate ecosystems, infiltrating even our bodies. Such outcomes accompany technology’s triumphs.

Recognizing this demands proactive problem-solving within innovation itself. Acknowledging our limitations doesn’t mean rejecting progress but prioritizing foresight and restraint. It means implementing safeguards, allowing corrective measures, and minimizing severe fallout.

Current trends in artificial intelligence (AI) research exemplify a dangerous lack of caution. A few dominant corporations race to dominate the AI landscape, releasing models rapidly with minimal scrutiny. The consequences remain uncertain, yet oversight lags behind deployment. Responsible development requires balancing ambition with accountability—a lesson history underscores but often goes unheeded.