AI chip manufacturing
The chips that power AI are manufactured by a tiny number of companies using some of the most complex processes on earth. This concentrated supply chain creates bottlenecks — when demand for AI computing spikes, chip shortages follow. For business planning, this means AI costs won't drop as fast as you might expect, and building your AI strategy around efficiency (doing more with less compute) is smarter than assuming unlimited cheap capacity.
Go deeper
Your 90-location behavioral health network just got approved to deploy AI-powered clinical documentation across every site. You budget for software licenses, but six months in, your vendor says compute costs are going up 30% because of chip allocation delays. This is the supply chain reality — the companies making these chips can't scale production the way software scales. Your AI roadmap needs a hardware awareness layer: know which of your AI tools depend on cutting-edge chips versus commodity hardware, because the pricing trajectories are completely different.
The trap most companies fall into is assuming AI costs follow the same curve as cloud storage — always getting cheaper, always available. Chip manufacturing has multi-year lead times and geopolitical exposure that cloud storage never had. A single export restriction or factory disruption can ripple through your vendor's pricing within a quarter.
Questions to ask
- Which chip architectures do our AI vendors depend on, and do they have supply agreements or are they buying spot?
- If our primary AI vendor faces a compute shortage, what's our fallback?
- Are we building any AI workflows that only run on premium hardware when a lighter approach would work?