The Money-Hungry Future of Reasoning Models
Tech giants and investors are doubling down on AI spending, with Google, Microsoft, and Meta planning to pour $215 billion into AI infrastructure in 2025 alone—a 45% increase from last year. This surge is driven by reasoning models, a new class of AI that vastly outperforms traditional large language models but requires up to 100 times more computing resources to operate. Unlike chatbots that rely on pre-trained responses, these models use complex “chain-of-thought” reasoning, consuming massive amounts of electricity, microchips, and data center space. While breakthroughs like DeepSeek R1 have slashed AI training costs, the sheer demand for these more advanced models is fueling an exponential rise in power consumption. As AI becomes more embedded in everyday life, from deep research to enterprise applications, the shift from model training to real-time reasoning is accelerating—ensuring that AI infrastructure spending will only keep climbing.
My Take
AI spending isn’t just rising—it’s shifting. The real cost explosion isn’t in training models anymore; it’s in running them. Unlike traditional AI, reasoning models don’t just retrieve answers—they think through problems step by step, consuming exponentially more power per query. As AI transitions from simple chatbots to autonomous research tools and real-time decision-makers, companies are pouring billions into data centers, AI-specific microchips, and power infrastructure to keep up. This isn’t a short-term trend—it’s a long-term commitment to AI dominance, where only those who can scale efficiently will survive. With businesses relying more on AI-powered automation, deep research, and real-time insights, spending will keep climbing as reasoning models reshape the global economy.
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Credit: WSJ
This post reflects my own thoughts and analysis, whether informed by media reports, personal insights, or professional experience. While enhanced with AI assistance, it has been thoroughly reviewed and edited to ensure clarity and relevance.