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Tomer Tunguz 博客(VC 分析)· Tomasz Tunguz·· 2026-06-07精选AI 评分56

AI 替代浪潮:三大力量重塑成本结构

The Substitution Wave in AI

AI 导读

三大力量重塑 AI 成本:前沿闭源模型持续涨价,开源模型在多数场景已足够好,买家开始替代。Coinbase 将提示词路由至更便宜模型,成本持平但 token 用量指数增长。Lindy 全切至 DeepSeek v4,节省数百万美元且多项核心性能提升。Harvey 在 Legal Agent Benchmark 上通过 SFT 使 Kimi 2.6 all-pass 率达 15%,超越 Opus 的 14%,同一 100 任务成本 $84 vs $954(约 11 倍价差)。Cursor 后训练 Kimi K2.5 得到 Composer 2.5,称其“性能优异且效率高达同类模型 10 倍”。闭源越来越贵,开源平价且性能接近,选择决定企业单位经济学的斜率。

推荐理由

Tunguz 用 Coinbase、Lindy 等真实案例,把「用开源/便宜模型替代昂贵前沿模型」的趋势讲透了,做 AI 应用的人该重新算一下单位经济账。

正文 · 原文

In short : AI buyers across enterprise, app, and seller layers are substituting cheaper open-source models for frontier closed models. The savings don't shrink the AI bill — they get reinvested in exponentially more tokens per task.

Three forces are reshaping the AI cost structure :

  1. Foundation labs are moving up the stack into applications,1 2
  2. Frontier model prices keep rising for the smartest models,3
  3. Open-source models have crossed the good enough threshold for most use cases.4 5

The natural response from AI buyers is substitution.

Coinbase6 :

At Coinbase we’re working hot on routing prompts to cheaper models where appropriate, & in some cases have been able to keep costs roughly flat, while token usage continues to grow exponentially.

Lindy7 :

Pulled the trigger today & switched 100% of Lindy traffic to DeepSeek v4, churning from Anthropic models. Saves us millions of $ & we’re actually seeing an increase in performance on many core use cases. Transformative for the business.

Harvey8 :

On a 100-task slice of our Legal Agent Benchmark (LAB), SFT moved Kimi 2.6’s all-pass rate from 11% to 15%, beating Opus’ 14%. But the cost gap was even more striking : $84 vs $954 across the same 100 tasks, or ~11x cheaper.

Cursor went further. They post-trained Kimi K2.5 into their own production model, Composer.9

Composer 2.5 is exceptionally intelligent & up to 10x more efficient than similarly capable models.

Coinbase’s quote shows where the savings go : costs flat, tokens exponential. Buyers don’t pocket the discount — they spend it on more intelligence.

Closed models are getting more expensive at the frontier; open models are getting cheaper at parity. The choice is which slope you want under your unit economics.

Ramp cost curve framing for AI buyers and app purveyors


  1. https://x.com/Law360/status/2062263047578673481 ↩︎

  2. https://theoryvc.com/blog-posts/are-foundation-models-and-application-companies-friends-or-foes ↩︎

  3. https://tomtunguz.com/ai-model-inflation/ ↩︎

  4. https://tomtunguz.com/using-local-ai-to-work-faster/ ↩︎

  5. https://tomtunguz.com/the-thriving-ecosystem-of-open-models/ ↩︎

  6. https://x.com/brian_armstrong ↩︎

  7. https://x.com/Altimor/status/2062389885437366342 ↩︎

  8. https://x.com/harvey/status/2062218656420167785 ↩︎

  9. https://x.com/cursor_ai/status/2056415414977187904 ↩︎

来源:Tomer Tunguz 博客(VC 分析) · tomtunguz.com