"IBM CEO Arvind Krishna does not see the current AI wave as a bubble. In The Verge's Decoder podcast, he argues that generative AI and large language models represent a structural technological shift, especially in the business world. According to Krishna, AI is not growing on speculation, but on actual value creation. While he believes the technology is sustainable, he also sees a notable financial threat: the enormous investments in AI data centers are developing at a pace that is difficult to sustain economically."
"The core of Krishna's reasoning is that AI now delivers direct productivity gains. Companies are deploying systems for automation, software development, analysis, and internal efficiency improvements. This, he argues, makes this phase fundamentally different from previous waves of hype. He sees no signs of a bubble, because the underlying demand is driven by concrete business applications rather than speculative expectations."
"At the same time, Krishna points to risks outside the technology itself. In an analysis highlighted by Tom's Hardware, among others, he points to the capital expenditures planned by major AI players. According to his calculations, filling a one-gigawatt AI data center costs about $80 billion in hardware. Large companies are now working on plans for tens to even hundreds of gigawatts of future capacity. In total, this could theoretically amount to around $8 trillion in investments."
"According to Krishna, that amount is arduous to recoup. To cover the capital costs, companies would have to generate about $800 billion in profits per year collectively. In addition, current AI accelerators are typically depreciated over five years. Due to rapid technological progress, Krishna expects that replacing virtually the entire hardware fleet will remain necessary. He argues that the equipment must be fully utilized during that period, as it will become obsolete afterward and have to be repurchased."
Generative AI and large language models constitute a structural technological shift that delivers direct productivity gains in automation, software development, analysis, and internal efficiency. Adoption is driven by concrete business applications and measurable value rather than speculation. Simultaneously, planned capital expenditures for AI data centers pose a major financial risk: filling one gigawatt of AI capacity requires about $80 billion in hardware, and aggregate plans could reach roughly $8 trillion. Recovering such capital would demand roughly $800 billion in annual profits. Rapid hardware obsolescence and five-year depreciation profiles require full utilization and frequent replacement, stressing economics.
Read at Techzine Global
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