Make AI boring, like the electric grid, say Princeton researchers

Princeton AI researchers argue that our current view of artificial intelligence as an exceptional technology is misguided, suggesting instead we should consider it a “normal” general-purpose technology similar to electricity or the internet. This perspective offers a grounding counterbalance to both utopian and dystopian AI narratives, emphasizing practical considerations of how AI will integrate into society rather than speculative fears about superintelligence.

The big picture: Princeton researchers Arvind Narayanan and Sayash Kapoor have published a 40-page essay challenging the widespread tendency to view AI as an extraordinary, potentially autonomous entity requiring exceptional governance.

• They argue AI should be treated as a general-purpose technology more comparable to electricity or the internet than to nuclear weapons, though they acknowledge limitations in this analogy.
• This view counters popular narratives from tech leaders like OpenAI‘s Sam Altman, who has compared AI’s impact to the Renaissance, and former Google CEO Eric Schmidt, who suggested AI models should be controlled like nuclear materials.

Why this matters: The researchers’ perspective shifts focus from speculative long-term fears to immediate concerns about how AI will affect existing social problems and institutions.

• Over half of Americans report being more concerned than excited about AI’s future, indicating widespread anxiety about the technology’s trajectory.
• By reframing AI as “normal,” the researchers aim to guide attention toward practical governance approaches rather than science fiction scenarios.

Key arguments: Narayanan and Kapoor present several provocative positions that challenge current AI discourse.

• They argue terms like “superintelligence” are too incoherent and speculative to be useful in serious policy discussions.
• Rather than complete automation, they predict AI will create a new category of human labor focused on monitoring, verifying, and supervising AI systems.
• They emphasize AI’s potential to exacerbate existing societal problems rather than create entirely new ones.

Reading between the lines: The essay distinguishes between AI’s laboratory capabilities and its real-world applications, suggesting a significant gap between the two.

• Kapoor specifically notes that AI methods develop rapidly in research settings, but their practical applications typically lag behind by decades, similar to other general-purpose technologies.
• This perspective suggests many current fears about imminent AI transformation may be premature.

What they’re saying: The researchers emphasize AI’s relationship to existing economic and social systems rather than its standalone potential.

• “AI supercharges capitalism,” Narayanan explains, highlighting how the technology could either help or harm inequality, labor markets, free press, and democratic institutions depending on implementation.
• Instead of planning around speculative fears, Kapoor advocates for “strengthening democratic institutions, increasing technical expertise in government, improving AI literacy, and incentivizing defenders to adopt AI.”