Why AI spending isn't slowing down
Subscribe to enjoy similar stories. Despite a brief period of investor doubt, money is pouring into artificial intelligence from big tech companies, national governments and venture capitalists at unprecedented levels. To understand why, it helps to appreciate the way that AI itself is changing.
The technology is shifting away from conventional large language models and toward reasoning models and AI agents. Training conventional large language models—the kind you’ve encountered in free versions of most AI chatbots—requires vast amounts of power and computing time. But we’re rapidly figuring out ways to reduce the amount of resources they need to run when a human calls on them.
Reasoning models, which are based on large language models, are different in that their actual operation consumes many times more resources, in terms of both microchips and electricity. Since OpenAI previewed its first reasoning model, called o1, in September, AI companies have been rushing to release systems that can compete. This includes DeepSeek’s R1, which rocked the AI world and the valuations of many tech and power companies at the beginning of this year, and Elon Musk’s xAI, which just debuted its Grok 3 reasoning model.
DeepSeek caused a panic of sorts because it showed that an AI model could be trained for a fraction of the cost of other models, something that could cut demand for data centers and expensive advanced chips. But what DeepSeek really did was push the AI industry even harder toward resource-intensive reasoning models, meaning that computing infrastructure is still very much needed. Owing to their enhanced capabilities, these reasoning systems will likely soon become the default way that people use AI for many tasks.
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