The basic principle of venture capital is to put a dollar in each of 10 companies, accept that three will go to zero, one or two to $10 or more, and the rest kind of meh. The winner more than makes up for the losers, but you spread your money around in the hope of securing ten-baggers.
Investing in artificial intelligence increasingly has the same mindset—but without the diversification. This leaves investors exposed to all the many risks as they chase one big bet: artificial general intelligence, an AI that can match or surpass humans. This isn’t a chatbot, but a truly capable alternative to the human brain: Think Terminator, Hal, Blade Runner.
If what’s known as AGI ever worked, it would deliver massive societal change, as well as potentially huge productivity gains and, barring state seizure, profits on a literally science-fiction scale. This is the better-than-ten-bagger bet that AI luminaries talk up as they set out plans for trillions of dollars to be sunk into data centers.
Along the way, there’s “agentic AI” and other propositions that could still produce decent productivity gains and make lots of money if widely adopted. The problem: The entry cost is increasingly high because of the race to AGI, and even the prospects for take-up of the less exciting propositions remain uncertain.
Excluded from the VC world, ordinary investors are mostly invested in AI via funds that have a slice of their money in private companies such as OpenAI, or through Big Tech companies that are dedicating more of their cash piles to AI research and holdings in private AI firms.
Here are the risks: