LLM推理成本低于传统工具,商业模式存疑
问题概述
当前大模型推理成本持续下降,可能低于传统计算工具;但缺乏可持续的盈利数据,投资回报需求高,导致对未来盈利的不确定性。
痛点标签
- 推理成本可能低于传统工具如grep
- 商业模式盈利需求高,缺乏硬数据支撑
- 对AI基础设施长期盈利模型的不确定性
待验证问题
- 是否有公开的财务数据支持AI基础设施的10%回报要求?
- LLM推理成本下降的速度是否真的能够持续到低于传统工具?
- 哪些因素会影响AI模型的长期盈利能力?
证据
原话 · @Anonymous
Tokens become cheaper than tool callsThe author observes that a call to GPT-5.6 Luna is only 4-5 orders of magnitude more expensive than grep, and then predicts that at current rates of progress, calling an LLM will soon be cheaper than a grep. I think this is a good time to invoke Stein's Law: "If something cannot go on forever, it will stop." These efficiency improvements won't continue forever. It's more likely that the per-call cost of high-quality, compiled software like grep will be a lower-bound that LLMs asymptotically approach, rather than a line that they blow past with perpetual exponential progress. (Barring a true breakthrough in something like quantum computing or room-temperature superconductors.)
原话 · @Anonymous
I found the OP insightful and worth a read. Thank you for sharing it on HN.The only aspect that is poorly analyzed by the OP is business model viability. All players are investing insane amounts of money in infrastructure with the expectation that their future profits will justify all that investment. The winner or winners in the AGI race, they believe, will find the proverbial "pot of gold at the end of the rainbow."The OP glosses over questions of business model viability with a brief qualitative discussion and very little hard data. For example, to earn an annual return > 10% on every trillion dollars of capital sunk into infrastructure, the owners of that infrastructure must earn free cash flow (operating profit less investment) in excess of $100 billion per year in perpetuity. Is that
原话 · @Anonymous
A much deeper analysis on the falling price per task was published yesterday by Epoch AI [1]. It's a real statistical analysis and comes to more defensible and grounded conclusions. The headline takeaway is:The cost of a given level of performance often falls fastest right after that level is first achieved, that is, when it is state of the art (SOTA). We see this pattern on three of our five main benchmarks of AI capability. Averaging across all five, cost falls 66% per quarter (75× per year) for performance that has just debuted as SOTA. Two years later, prices fall half as fast, at 32% per quarter (4.7× per year).
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