Jim is a principal analyst and partner at TIRIAS Research, a high-tech research and advisory firm consisting of experienced analysts. Jim has over 30 years of technical and business experience with leading high-tech companies including Intel, Motorola, ON Semiconductor, STMicroelectronics, and General Dynamics Space Systems.
The pace of AI continues to be relentlessly fueled by new software, hardware, and learning paradigms, and it remains a challenge to meet the required exponential increases in performance. Gone are the days when a 10x to 20x generational increase is enough to meet increasing performance needs. AI requires 100x to 1000x performance increases from generation to generation, especially as we move from traditional AI to generative AI.
Edge startups faced an even more formidable challenge as platform size, power, and size constraints often left no room for additional silicon solutions, and developing basic integrated neural processing units proved relatively easy for the existing chip providers. The remaining edge AI startups with a fighting chance are those that are providing a complete System on a Chip .
This has resulted in a massive demand for the construction of new data centers at a time when real estate prices, power distribution, water resources, and local politics are challenging many new data center projects. It has also resulted in a need to make AI processing more efficient. While using smaller, more optimized models will help alleviate some of the pressure on resources, it is not a panacea because of the continued growth in demand and performance requirements.
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