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What cost drivers and infrastructure choices characterize frontier AI training?
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Questions in this line of inquiry 20
Specific inquiring lines the field asks around this — ordered from the most general framing down to the most specific angle.
- How much of frontier AI cost growth comes from chips versus skilled staff?
- How much of frontier AI training costs come from staff versus hardware?
- What drives consolidation of frontier training onto specific hardware vendors?
- Why did frontier training consolidate around specific chip designs after V100?
- Which spending categories drive the rising cost of frontier AI training?
- Did non-frontier AI training spread across more chip designs than frontier training?
- How long do leading AI chip designs stay in use for frontier training?
- Why do AI labs lose more money than they earn from revenue?
- What makes compute allocation a verifiable lever for pacing frontier AI development?
- Why do AI labs face shorter repayment windows than traditional software companies?
- Will custom ASICs and new architectures reverse the chip design consolidation trend?
- What timeline do AI labs need to recoup model development costs before obsolescence?
- Why did frontier training concentrate onto fewer chip designs after the V100?
- What data-center infrastructure costs are excluded from frontier model training estimates?
- Why are Anthropic and other labs spending far more than their revenue?
- Could deploying GPT-4 for everyone require 100 million specialized chips?
- Did OpenAI's GPT-5 actually recoup its R&D costs before being replaced?
- Do custom ASICs and new TPU generations extend chip lifespan beyond four years?
- What testing requirements would a frontier model legislation proposal actually mandate?
- Why did Hugging Face turn to Chinese models instead of US frontier labs?