编者按：当成本随使用量同步增长时，该如何为产品定价？文章剖析了一场迫使整个行业重新思考商业模式的范式转移：AI 彻底摧毁了让传统 SaaS 如此暴 利的经济逻辑。那么，当最优质的客户同时也成了最昂贵的负担时，你该怎么办？本文涵盖了专为这一新现状设计的五种定价模式、每位创始人必须理解的 三大核心原则，以及选择正确方案的决策框架。文章来自编译。 MoviePass 当年那场轰轰烈烈的溃败，虽不像 Theranos 那样有精心编织的骗局，也没有 WeWork 创始人那样反复无常的狂人气质，更不似那款毫无用处的 Juicero 榨汁机那样荒诞不经。在 2010 年代所有的传奇崩盘案例中，它纯粹是显得有些滑稽。 这家公司的坑其实很简单：让客户每月支付 9.95 美元，就能看多达 30 部电影，而公司却要向影院支付每张 10 美元的票价。事实证明，这种高达 96.68% 的 折扣是无法长久维系的。 如今开发 AI 产品的公司也面临同样的陷阱。在传统软件即服务（SaaS）模式下，一旦你付清了开发人员的工资和亚马逊云服务（AWS）的存储费用，即便 使用人数再多，你的边际成本也几乎为零。但 AI 公司是按次向大模型（LLM）供 ...

Core Insights - AI represents a technological revolution larger than the internet, comparable to electricity and microprocessors, and is still in its early stages [2][3][11] - The cost of AI is decreasing at a rate faster than Moore's Law, leading to explosive demand growth [4][41] - Historical patterns suggest that shortages in GPU and data center capacity will eventually lead to oversupply, further driving down AI costs [5][12][41] Group 1: AI Market Dynamics - The future AI market structure will resemble the computer industry, with a few "god-level models" at the top and numerous low-cost "small models" proliferating at the edges [6][19] - The competition between the US and China is intensifying, with Chinese companies like DeepSeek and Kimi making significant strides in open-source strategies and chip development [6][15][59] - AI applications are shifting from "pay-per-token" models to "value-based pricing," allowing startups to integrate and build their own models rather than merely acting as wrappers [7][17] Group 2: Public Perception and Regulatory Landscape - Public sentiment towards AI is mixed, with fears of job displacement coexisting with rapid adoption of AI technologies [8] - The EU's regulatory approach, focusing on leading in regulation rather than innovation, is hindering local AI development [8][60] - The US regulatory environment is shifting towards supporting innovation, with less interest in imposing strict regulations that could hinder competitiveness against China [14][64] Group 3: Economic Implications - The rapid decline in AI input costs is expected to create significant demand elasticity, leading to unprecedented growth in AI applications [41][42] - The economic landscape for AI companies is promising, with many experiencing unprecedented revenue growth as they effectively monetize their offerings [32][39] - The ongoing construction of data centers and GPU production is projected to lead to a significant reduction in AI operational costs over the next decade [41][50]