When I tell people I’m writing a book about AI (forgive the early plug) they usually reply with something along the lines of, “ooh, good timing!” The inference being that AI is currently the hot topic and thus the audience for said book should be bigger than it was, say, a year or even six months ago, i.e. Before ChatGPT. Being of polite disposition I mostly smile and nod at this, while inside I’m thinking, “Really? You think AI is hot now?”

Because, try becoming Global Head of Policy for DeepMind (now Google DeepMind, which I left a couple of years ago) in April 2016, a month after their AlphaGo program triumphed over the Go world champion, Lee Sedol, across five tense matches in Seoul that were streamed to a bigger audience than the Super Bowl. Or in 2017, when Xi Jinping announced his intention that China should be the leading AI superpower by 2030 and Putin glowered that “whoever becomes leader in [AI] will be ruler of the world.” The multitude of think pieces, reports, government roundtables, and books about the dangers of an optimising paperclip machine during the first big AI hype cycle of 2015-7 was dizzying.

No, AI has been exhaustingly ‘hot’ for quite some time. Admittedly the hype had somewhat quietened, as popular fashions turned instead to, variously, cryptocurrency, NFTs, and the metaverse. But since the end of the so-called ‘AI winter(s)’ and the start of the latest AI explosion from the 2010s, driven in part by growth in access to data and availability of computer power, there has been a steady stream of funding and an insatiable appetite for anything called ‘AI’.