TSMC warns AI chip shortages extending to memory, packaging, and cooling

The broadening of supply constraints means that even record-breaking foundry output cannot resolve the AI hardware deficit. For the streaming sector, this translates to sustained high costs and longer lead times for the specialized compute required for AI-assisted AV1 encoding, real-time super-resolution, and multimodal content search. As the bottleneck shifts toward packaging and cooling, pricing power is concentrating among a smaller pool of secondary component suppliers. This structural scarcity forces streaming platforms to prioritize inference efficiency over raw model scale to manage escalating OpEx. Watch for shifts in GPU rental rates and lead times for Blackwell-class hardware as a signal for compute-heavy service expansions.

The supply crisis is intensifying as major hyperscalers, including Amazon, Meta, and Microsoft, have committed over $600 billion in AI-related capital expenditure for 2026, according to recent industry forecasts. Per techtimes.com and Digitimes (June 2026), TSMC is attempting to triple its CoWoS (Chip-on-Wafer-on-Substrate) advanced packaging capacity to reach 120,000 to 130,000 wafers per month by the end of the year. Despite this aggressive expansion, the capacity is reportedly already fully allocated through 2026, primarily to Nvidia’s Blackwell and Hopper architectures. This leaves mid-market enterprise buyers and specialized streaming labs competing for minimal remaining slots or relying on older, less efficient silicon nodes.

Further straining the ecosystem is a critical shortage of High-Bandwidth Memory (HBM3E). Per Reuters and EE Times (early 2026), HBM production consumes approximately three times the wafer area of standard DDR5 memory, creating a zero-sum game that is driving up costs across the board. Leading memory suppliers like SK Hynix and Micron have indicated that their 2026 output is pre-sold, with HBM prices surging more than 250% over the past year. In the streaming video space, these hardware constraints are directly impacting the unit economics of generative video and automated metadata tagging. While production costs for generative video have dropped significantly, Fora Soft noted in August 2025 that real-time processing remains the 2026-2027 frontier, a goal now complicated by the persistent lack of the advanced cooling and memory components cited by TSMC.