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AI Chips: why they cost as much as a car, and why companies can't get enough
Brief
AI chips sit at the center of modern AI progress: specialized accelerators (Nvidia Blackwell/Hopper, Google TPU, Amazon Trainium, Huawei Ascend) are designed by a few firms but mostly fabricated by TSMC, with critical upstream suppliers such as ASML (EUV) and HBM vendors Samsung, SK Hynix, and Micron. By Q4 2025 roughly 20 million AI chips had been shipped by the five largest designers, with Nvidia supplying ~50% of units and >66% of deployed compute capacity. Metrics matter more than sticker price: although flagship chips rose from $5,700 (2016) to $34,000 (2022), computation per dollar improved dramatically (H100 ≈17× P100), and compute-per-dollar has doubled ~every 2.5 years. Chips consume ~1,000 W each and total datacenter power reached tens of gigawatts by late 2025; energy efficiency has improved ~40% annually, but aggregate electricity use still grows because deployment outpaces per-chip gains.
Why it matters
TSMC dominates fabrication of leading-edge AI chips (most fabs in Taiwan; new fabs in Arizona, Japan, Germany) while ASML (Netherlands) is the sole supplier of EUV lithography tools and HBM memory is supplied mainly by Samsung, SK Hynix, and Micron — HBM was a binding constraint on production in late 2025.
Key details
- By Q4 2025 the five largest chip designers had shipped roughly 20 million AI chips; Nvidia accounted for ~50% of units and >66% of total AI computing capacity, Google was second, and Huawei (using an alternative chain after US export controls) held about 6% in 2025.
- Nvidia flagship prices rose from $5,700 (2016) to $34,000 (2022) but cost-effectiveness improved: the H100 (2022) delivered ~17× more computation per dollar than the P100 (2016); compute-per-dollar has roughly doubled every 2.5 years and chips/computing account for ~54–62% of AI firms' spending.
- Power and efficiency tradeoffs: a single high‑end chip draws ~1,000 W; total AI datacenter capacity reached 'tens of gigawatts' by late 2025 (comparable to New York peak); energy efficiency has improved ~40%/year (doubling ≈2.7 years) — e.g., Nvidia B100 (2024) ≈3× computation/watt vs A100 (2020) — yet aggregate consumption rises as installed chips scale faster than efficiency gains.