A program like OpenAI’s GPT-4, which can write sentences to order, is something like a version of Wikipedia that includes much more data, mashed together using statistics. Programs that create images to order are something like a version of online image search, but with a system for combining the pictures. In both cases, it’s people who have written the text and furnished the images. The new programs mash up work done by human minds. What’s innovative is that the mashup process has become guided and constrained, so that the results are usable and often striking. This is a significant achievement and worth celebrating—but it can be thought of as illuminating previously hidden concordances between human creations, rather than as the invention of a new mind.
As far as I can tell, my view flatters the technology. After all, what is civilization but social collaboration? Seeing A.I. as a way of working together, rather than as a technology for creating independent, intelligent beings, may make it less mysterious—less like hal 9000 or Commander Data. But that’s good, because mystery only makes mismanagement more likely.
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This concept, which I’ve contributed to developing, is usually called “data dignity.” It appeared, long before the rise of big-model “A.I.,” as an alternative to the familiar arrangement in which people give their data for free in exchange for free services, such as internet searches or social networking. Data dignity is sometimes known as “data as labor” or “plurality research.” The familiar arrangement has turned out to have a dark side: because of “network effects,” a few platforms take over, eliminating smaller players, like local newspapers. Worse, since the immediate online experience is supposed to be free, the only remaining business is the hawking of influence. Users experience what seems to be a communitarian paradise, but they are targeted by stealthy and addictive algorithms that make people vain, irritable, and paranoid.
In a world with data dignity, digital stuff would typically be connected with the humans who want to be known for having made it. In some versions of the idea, people could get paid for what they create, even when it is filtered and recombined through big models, and tech hubs would earn fees for facilitating things that people want to do. Some people are horrified by the idea of capitalism online, but this would be a more honest capitalism. The familiar “free” arrangement has been a disaster.
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Consider what might happen if A.I.-driven tree-trimming robots are introduced. Human tree trimmers might find themselves devalued or even out of work. But the robots could eventually allow for a new type of indirect landscaping artistry. Some former workers, or others, might create inventive approaches—holographic topiary, say, that looks different from different angles—that find their way into the tree-trimming models. With data dignity, the models might create new sources of income, distributed through collective organizations. Tree trimming would become more multifunctional and interesting over time; there would be a community motivated to remain valuable. Each new successful introduction of an A.I. or robotic application could involve the inauguration of a new kind of creative work. In ways large and small, this could help ease the transition to an economy into which models are integrated.
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There are also non-altruistic reasons for A.I. companies to embrace data dignity. The models are only as good as their inputs. It’s only through a system like data dignity that we can expand the models into new frontiers. Right now, it’s much easier to get an L.L.M. to write an essay than it is to ask the program to generate an interactive virtual-reality world, because there are very few virtual worlds in existence. Why not solve that problem by giving people who add more virtual worlds a chance for prestige and income?
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