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Lambert: AI is still 'a rounding error' in everyday life

TL;DR

  • Nathan Lambert argues AI's benefits so far accrue mostly to elite knowledge workers and tech company founders, not to ordinary households.
  • He invokes Engels' pause, the 1790 to 1840 stretch when British working-class wages stagnated during industrial upheaval, as a warning template.
  • He still expects AI to reach roughly 90% adoption within his lifetime, but says wide distribution will require deliberate work.

Nathan Lambert's argument in his Interconnects essay is that for all the boom's velocity, most people still have "no super tangible new goods thanks to it." He contrasts the current wave with the First and Second Industrial Revolutions, which delivered cheaper clothing, indoor plumbing and electrification straight into everyday households.

"AI is still a rounding error in everyday life," he writes. The applications that reach ordinary users remain fringe or confusing, while the biggest wins accrue to people already best positioned to use them. "AI is primarily a tool to serve the elite," Lambert argues, calling it "the greatest tool ever for scaling technology companies and starting new online-native small businesses."

His warning template is Engels' pause, "the period from 1790 to 1840, when British working-class wages stagnated" even as per-capita GDP expanded rapidly during a technological upheaval. Knowledge work, he notes, is "roughly half of the U.S. economy," and he worries about a repeat in which a productivity revolution routes around the other half first. Lambert also flags a political overhang: AI is arriving on top of unresolved backlash against platforms like Google and Meta, not decades after it.

He is not bearish on the long arc. "All of us younger folk following the story today will get to see powerful AI go from effectively 0% to 90%+ full adoption in our lifetime," he writes, and "if given 100 years to diffuse into society, its impacts will certainly become much more obvious." But the diffusion is not automatic. "We have a lot of very hard work to do in making sure they're distributed widely."

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