待翻譯：How I learned to stop worrying and love hyperscaler capex

AI 服務暫時不可用，以下為來源正文，待恢復後補全翻譯。

The AI boom is an oddly miserable bubble. Despite interesting tech, huge new companies, and products with global reach, AI has attracted legions of detractors. Some have valid complaints, like seeing their roles automated, or the value of human art being pressured by machine generation. Other complaints have had less staying power. It was once in vogue to argue that AI companies would run out of data, and thus their models would stop improving. False. Some of the same voices argued that AI lacked a use case and was thus little more than a fancy toy high on its own hype. Incorrect. Later, the argument shifted to AI being too expensive to use, an incredible flip from the AI has no real use argument. This is being proved false, as low-cost models from China now face both low-cost, closed-source AI models from OpenAI and new, open models from Meta. Agents were too brittle to start; now they are hacking the world. You get the idea. Lately, I’ve read criticism about the AI boom from a financial perspective. Namely, that the major cloud players (AWS, Google Cloud, Azure) are spending too much money on AI infra. Surely we can’t use all that compute, the argument goes, and thus hyperscalers are torching their nest egg and investor goodwill at the same time. I wanted to put the contention to the test, so I pulled together data from Amazon, Alphabet, and Microsoft’s cloud groups (here) to peel back the onion a little. Here’s what I found: Growth is accelerating, hyperscaler profitability scales with scale, and hyperscaler capex efficiency is improving. Continue reading on Cautious Optimism This is an excerpt from Cautious Optimism, a modestly upbeat publication focused on technology, business, and power. Read more about the concern of hyperscaler cost on Cautious Optimism. The post How I learned to stop worrying and love hyperscaler capex appeared first on The New Stack.