
I’ve used this analogy before in other contexts, but the way we respond to or think about artificial intelligence reminds me of the old parable – first recorded in a Buddhist text from about 500 BCE – about the blind men who encountered an elephant for the first time. The man near the trunk thought it was a kind of snake, and the one near its legs thought it was a kind of tree; the man near the tusks thought it was a kind of spear, and so on. Casual users of OpenAI’s ChatGPT or Anthropic’s Claude or Google’s Gemini probably have one vision of what AI is or can do – it can answer simple questions. University students probably see it as a way to write term papers more quickly. Others who use tools like OpenAI’s Sora video-generation engine have a different idea: they might see it as a tool much like Adobe’s Photoshop or Apple’s iMovie, but one that can create things that don’t exist. And those using AI tools in the lab might see it as a kind of supercomputer that can detect cancers or fold complex proteins.
All of these things are applications of current AI engines – so-called large-language models, or generative pre-trained transformers (which is what the GPT in ChatGPT stands for). In a sense, they are just tools, like a slide rule or a personal computer, or a steam-powered locomotive. But in the aggregate, all of these tools – and newer ones that are still being developed – look a lot like a tidal wave of disruption that could sweep through virtually every industry. In other words, AI looks a lot more like the Industrial Revolution, where mechanical processes took over a host of different industries, leading to the extinction of some jobs and the creation of others – new tasks that no one had even thought of before the machines came. In economic terms, it’s a classic example of what Joseph Schumpeter called “creative destruction,” in which new innovations replace and make obsolete older innovations. So electricity replaces fire, cars replace horse-drawn carriages, and refrigeration replaces ice harvesting.
In their book AI Snake Oil, Princeton computer scientist Arvind Narayanan and PhD student Sayash Kapoor argue (among other things) that the blanket term AI covers a wide range of different technologies and methods, some reliable and others not. It’s as if we didn’t have terms for different forms of transportation, they write – all we have is the word “vehicle.” In such a world, they say, there would be “furious debates about whether or not vehicles are environmentally friendly, even though no one realizes that one side of the debate is talking about bikes and the other side is talking about trucks.” Someone who only uses ChatGPT to generate grocery lists or recipes probably thinks all the talk of an AI-powered apocalypse is nonsensical, because they may be unaware that modern LLMs have been shown to fabricate lies about both their behavior and their motivation, especially if they have been given an incentive, which we know because Anthropic continues to do research in an attempt to understand why Claude does what it does.
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