Cheaper AI Inference Means the Industry Spends More: The Mismatch Between Efficiency Dividends and Capital Returns
The Sequence's editorial connects this week's multiple AI developments—DeepSeek's open-source inference framework, NVIDIA's modular AI factory design, Meta's negative free cash flow, SpaceX's AI division revenue surpassing Starlink, and legal AI company Legora's $8.8 billion valuation—into a single economic argument: falling inference costs expand total demand, thereby driving up infrastructure spending, but who captures the growth and who owns the profit margins remain unresolved questions.
AI is becoming more efficient, which apparently means everyone needs to spend more money on it. This is the core paradox outlined in The Sequence's editorial this week. The author brings together several seemingly unrelated events: China-based AI lab DeepSeek open-sourced its production inference framework DeepInfer; NVIDIA unveiled the modular AI factory design DSX POD at GTC Berlin; Meta reported quarterly revenue growth of 26% to $64.6 billion but with negative free cash flow of $2.1 billion; SpaceX's AI business generated quarterly revenue of $7.56 billion, exceeding Starlink's $5.25 billion; and legal AI company Legora completed a $550 million financing round at an $8.8 billion valuation.
The economic logic behind these events runs along the same line: as inference costs fall, developers can afford more requests, more requests mean higher total demand, and higher total demand means more infrastructure. DeepSeek's DeepInfer framework does not create new demand itself; it provides the inference core—caching and load balancing still need to be built by operators—but its economic intent is direct: squeeze more output from expensive hardware. The author summarizes the endpoint of this logic with one sentence:
"Efficiency can sell GPUs. Intelligence is impressive. Intelligence with a better gross margin is what gets you to open the spreadsheet."