This does not mean businesses will stop experimenting with and using AI, but it may mean that they are becoming more cautious about how they do so. The problem with that is that AI companies and their investors have been projecting revenues and profits based on maximized expectations. Forecasts of spending accuracy will increase with experience, but that 'in the meantime' period could take more than impatient investors and lenders are willing to wait. JL
Jillian Vordick and Stephanie Stamm report in the Wall Street Journal:
Jillian Vordick and Stephanie Stamm report in the Wall Street Journal:
A recent study found that only 11% of nearly 400 businesses surveyed were able to accurately forecast AI spending. Unlike traditional software, AI behaves more like a human worker: It takes action, makes decisions, sometimes even makes mistakes—all on the clock. While more-advanced models tend to cost more per token, they can sometimes perform tasks more efficiently, leading to lower overall costs. Likewise, asking a “cheap” model to do something it isn’t suited to handle could cause a token run-up.“Total costs depend on many factors for a given task, which can make it hard to forecast new and evolving technology with precision.” As more companies add AI to their daily workflows, they will also need strategies to manage their use























