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we now know that as the price of computing equipment fell, new use cases emerged to fill the gap – which is why today my lightbulbs have semiconductors inside them, and I occasionally have to install firmware updates my doorbell.

surely the compute freed up by more efficient models will be used to train models even harder, and apply even more “brain power” to coming up with responses? Even if DeepSeek is dramatically more efficient, the logical thing to do will be to use the excess capacity to ensure the answers are even smarter.

ure, if DeepSeek heralds a new era of much leaner LLMs, it’s not great news in the short term if you’re a shareholder in Nvidia, Microsoft, Meta or Google.6 But if DeepSeek is the enormous breakthrough it appears, it just became even cheaper to train and use the most sophisticated models humans have so far built, by one or more orders of magnitude. Which is amazing news for big tech, because it means that AI usage is going to be even more ubiquitous.

Over the past six months, I spoke with more than two dozen annotators from around the world, and while many of them were training cutting-edge chatbots, just as many were doing the mundane manual labor required to keep AI running. There are people classifying the emotional content of TikTok videos, new variants of email spam, and the precise sexual provocativeness of online ads. Others are looking at credit-card transactions and figuring out what sort of purchase they relate to or checking e-commerce recommendations and deciding whether that shirt is really something you might like after buying that other shirt. Humans are correcting customer-service chatbots, listening to Alexa requests, and categorizing the emotions of people on video calls. They are labeling food so that smart refrigerators don’t get confused by new packaging, checking automated security cameras before sounding alarms, and identifying corn for baffled autonomous tractors.