The AI capacity conversation has spent two years fixated on chip allocation — who can get NVIDIA’s next generation first, who’s secured supply from TSMC. That conversation is starting to …
Every AI budget conversation in 2024 and 2025 obsessed over training cost — the eye-watering figures behind GPT-class and Claude-class runs, the billions hyperscalers were pouring into pretraining clusters. That …
The AI chip conversation is dominated by what happens on the silicon: TFLOPS, memory bandwidth, interconnect speed. The infrastructure conversation that determines whether any of that silicon actually works at …
Every discussion of AI chip performance focuses on TFLOPS — the raw compute throughput number that vendors put on their datasheets. It is the wrong metric for most AI workloads …
Compute has become a geopolitical asset. That sentence would have sounded abstract in 2020. In 2026 it is operationally concrete: which chips you can buy, in which quantities, to deploy …
Every CFO who approved an AI budget based on 2024 pricing models is about to have an uncomfortable conversation. The problem is not that token prices have risen — they …
Something significant has shifted in the AI chip market that most enterprise technology analysis has not fully absorbed: the biggest customers of NVIDIA — Google, AWS, Meta, Microsoft — are …
OpenAI, Anthropic, and the $121 Billion Question: Can AI’s Biggest Labs Outgrow Their Compute Bills?
In my recent trilogy analyzing AI market concentration — “Is There An AI Concentration Crisis: When 42 Stocks Become the Entire Market,” “Why Enterprise AI Strategy Must Diverge From Hyperscaler …