Respectfully, this thinking is very common among AI bears, but gets the unit economics of AI almost perfectly backwards! With this thinking you'll get the Math wrong by orders of magnitude. Let me break down why: 1. "input costs are skyrocketing" input cost per gpu is definitely the wrong unit. what matters is cost per token, and that's collapsing because each new generation makes far more tokens from the same power. for example: $NVDA gb300 makes up to ~50x the tokens per MW of hopper. cost per 1M tokens went from $4.20 to $0.12. that's ~35x cheaper, even though each gpu "costs more". 2. "token prices are hyper-deflating" the falling prices are for old capability. new models make smarter tokens, and smarter tokens sell for more openai's cheapest model is $0.50 per 1M output tokens, its top model is $50. 100x apart. Quite important to understand that not all tokens are equal. 3. simple example: hopper: cost $4.20 per 1M tokens. sell at $10 gb300: cost $0.12 per 1M tokens, ~50x the tokens. now let's just for the sake of argument assume we sell at half the price, $5 profit per 1M tokens: hopper: $10 - $4.20 = $5.80 gb300: $5 - $0.12 = $4.88 about the same per token. but gb300 makes ~50x as many from the same power total profit: ~40x higher, with token prices down 50% cheaper tokens also unlock way more usage. google went from 9.7T tokens a month to 3.2 quadrillion in two years what you call the bear case is the most bullish setup in the industry.