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Many AI optimists tend to compare what is happening in this AI boom to the industrial revolution, or to other periods of rapid technological advancement and diffusion into society. These comparisons fit on the scale of technological change, but miss a crucial factor in how most people are exposed to that change. The problem facing AI is that most people have no super tangible new goods thanks to it and society has more inertia resisting change than in previous eras.

I’m writing this coming back online from a few weeks off for my wedding in New England. In this time it would have been very easy to not think about AI at all. The touch-points that average people have to AI products today are fringe, marginally beneficial, or even just very confusing to them (e.g. many people have heard about and brought up the OpenAI-HuggingFace incident, but don’t know what to make of it). People on the positive side think of AI as a way to make fun images, enhanced Google Search, etc. These are very small benefits. On the negative side is an association with addictive social media algorithms, friends of friends addicted to AI chatbots, and a plethora of takes on data centers.

AI is still a rounding error in everyday life

Core aspects of everyday life — family, food, transportation, and entertainment — have few direct impacts yet. It’s a remarkable breath of fresh air to pop out of the bubble and realize how little what is happening really matters today. Being obsessed with AI is a choice that a very few people have yet opted into. For example, the only thing I used AI for in this time was search and creative work (making the pretty seating chart for my wedding guests to find their table).

In industrial revolutions past, average people got absolutely life changing outcomes. The First Industrial Revolution in the late 18th century gave access to cheaper clothing, cooking ware, reading material, and a shift to new livelihoods. The Second Industrial Revolution in the late 19th century introduced household machines (e.g. sewing machines), preserved food, indoor plumbing, photography, better light sources, bicycles, and further benefits of manufactured goods and electrification. The list is remarkable — most of these we still use regularly today — and very physical.

While even the most optimistic versions of AI will usher in new scientific discoveries, advanced therapeutics for rare diseases, and potentially even sustained economic abundance, these benefits have the risk of being too indirect.

How will a common citizen come to credit OpenAI or Anthropic for saving their life, if they went to their family doctor who told them about a new miracle cure?

What percentage of Americans will care about OpenAI solving the Navier-Stokes Millennium Prize Problem?

It feels very likely in 50 years that the average American’s day to day life looks very similar. Their home, appliances, relationships, and vehicles will be similar (of course, self-driving will continue to diffuse, but that has been developing on a very independent trajectory from the innovations of LLMs). In this time, AI will get a lot of credit. 50 years is a remarkable length of time with how fast everything is changing today in this, AI-focused narrow slice of the world.

The most important part of what is happening early in the AI revolution, is building foundational infrastructure, and a general process, which will compound over decades. A major mathematical breakthrough today will look astonishingly minor in scope relative to the advancements that come later in the compounding journey. It is hard to predict what it looks like for every technology you use daily to get faster compounding improvements due to AI.

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Breaking social stasis

Much of the narrative around AI is trying to push people to care, due to this long-term reality of progress, at least as a subconscious motive. This will take a long, long time to get right, and AI’s buildout faces immediate political problems due to this imbalance.

Today’s AI is primarily a tool to serve the elite. For knowledge work, which is roughly half of the U.S. economy, AI is as fundamental as electricity (or quickly will be so, with rapid improvements to agents in the next 18 months). It’s highly destabilizing to have such a transformative, productive tool only bring half of society along. It’s not hard for many people to pick up on this — the technology economy booms while life stays otherwise stagnant.

In writing this, I learned of Engels’ pause, which is “the period from 1790 to 1840, when British working-class wages stagnated and per-capita gross domestic product expanded rapidly during a technological upheaval.” If we — the leaders of the AI industry — think this is the closest analogue to what comes next for AI, those not benefiting are right to push back.

AI is the greatest tool ever for scaling technology companies and starting new online-native small businesses. I don’t even expect the tech industry to grow in headcount and nurture its workers through an era of massive success — I agree with Doug OLaughlin that headcount would likely shrink while knowledge work output explodes. These sectors were already the most successful in the American economic system, so the brand of AI will be tarnished as not being a collective good. I worry that this instinctive reaction will kneecap AI’s development, sending it down a path that looks closer to the cautionary tale of American nuclear power.

Part of the challenge is the speed and relentlessness of expectations in society. The AI industry has millions of eyes on it, and won’t get much patience to wait and bring innovations later. If given 100 years to diffuse into society, its impacts will certainly become much more obvious, like the industrial revolutions of centuries past.

Together, the AI industry is facing a few simple issues, in what I would call the first half decade of 50-year diffusion process.

* AI’s positive impacts early in its evolution are too indirect.

* AI is facing a political backlash deeply intertwined with the history of Big Tech in Western society. This is only an AI story due to timing, and if AI’s exponential growth came decades after the growing pains of today’s technology platforms like Google and Meta, it seems likely that the datacenter issue would’ve never risen to such a central political position.

Solving either of these would alleviate a substantial amount of pressure, and give the AI industry a lot more time in showing the positive case for why people should be okay with changes to the status quo (primarily economic). These are both made more challenging by AI self-labeling itself as negative and/or unsafe technology, through proclamations of doom and mass unemployment. The leading figures have begun addressing this issue, but the public needs more work to fully buy into the overarching trajectory.

When zooming out long-term, I could see robotics and self-driving becoming closely linked in storytelling to the current AI revolution. If the intelligence explosion from mass-producing large language models does spill over into enabling the acceleration of robots in everyday life, humans will quickly latch onto the tangible benefits of AI. This is ironic, as many people have spent time trying to convince people that what is happening specifically with LLMs is very different than the previous decade or two of general AI progress. If the same dynamic later saved (or massively overshadowed) LLMs, it would be funny.

Reflecting on what I expect the history of this era to look like, it feels a lot like growing pains of AI. Society needed to break out of old habits and work through problems that predate ChatGPT — which releases a lot of energy and frustration — in order to tap into the longer term growth. The diffusion story will take a lot longer than the fight against it. 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. This sort of AI that is deeply integrated in businesses, acting as personal assistants, etc. is just starting to become viable. It’ll take far longer to gain adoption than easier to understand applications like ChatGPT, and is the true marker of AI’s evolution.

Taking this perspective makes it clear that it is crucial to keep progressing the technology — the benefits will be astounding, but they are not a given — and we have a lot of very hard work to do in making sure they’re distributed widely.

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When will average people feel AI’s impact?

Interconnects

When will average people feel AI’s impact?