In AI Networks, Inefficiency Has A High Cost
Techstrong.ai, Friday, February 7th, 2025
Here's a pop quiz for you: For companies building out new AI data centers, what's the biggest barrier to profitability? The options are a) overhyped demand for AI, b) high costs of graphics processing units (GPUs) or c) inefficient network infrastructures?
If you chose the first, think again. Market demand for AI applications shows no sign of slowing down. One 2024 survey found that 43% large enterprises already use generative AI (GenAI) and 46% use large language models (LLMs). Gartner estimates that spending on AI software will reach nearly $300 billion by 2027, with an exceptional compound annual growth rate (CAGR) of 19.1%.
If you picked option b), operators are indeed investing billions into high-end servers, GPUs, and other accelerators (xPUs) to support ever-larger AI models. But as customer demand explodes, they are getting more concerned with making sure they can meet it. As Alphabet CEO Sundar Pichai told investors last July, 'In technology, when you're going through transitions 'like AI', the risk of underinvesting is dramatically greater than overinvesting.'