We find ourselves at a fragile moment in history—where the power and flexibility of these models have made them irresistibly useful for a large number of commercial and public applications, and yet our standards and norms around how to use them appropriately are still nascent. It is exactly in this period that we should be most cautious and conservative—all the more so because many of these models are unlikely to be substantially changed once deployed into real-world use. As Princeton’s Arvind Narayanan puts it: “Contrary to the ‘tech moves too fast for society to keep up’ cliché, commercial deployments of tech often move glacially—just look at the banking and airline mainframes still running. ML [machine-learning] models being trained today might still be in production in 50 years, and that’s terrifying.

Brian Christian, The Alignment Problem, loc. 92711