For years, AI companies have been selling investors on the idea that scaling up operations is the key to success — the bigger the AI model, the more powerful it becomes.
But in practice, that premise is leading to enormous business problems. As The Atlantic points out, LLMs are suffering from severely diminishing returns: the cost of inference, or the act of using a trained AI model to process new data, is rising exponentially, making the tech far less lucrative than it was even half a year ago.
Put simply, it’s effectively the opposite of what investors would conventionally want to see, as The Atlantic argues, which would be a continuous drop in cost per user instead of the reverse.
That’s bad news for an industry pouring billions of dollars into the construction of enormous data centers across the country, despite having no clear path to profitability in the foreseeable future. While it’s impossible to predict when exactly fears of an AI bubble will hit a breaking point, analysts warn it’s a matter of when, not if. The consequences could be disastrous if the industry were to collapse in on itself, bringing down entire economies — which have vastly over-indexed on AI tech — with it.
