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一直在思考LLM推理成本上升（或至少是粘性）对那些本质上是基础模型封装的公司的估值会产生怎样的长期影响。我们看到GPU小时的需求正在加剧，尽管云提供商正在扩大规模，但容量扩张和成本降至零的速度存在自然限制，特别是对于尖端模型。
我估计在未来12-18个月内，拥有专有、高度优化模型的AI公司与严重依赖第三方API调用的公司之间，市场表现出现显著分化的可能性约为60%。后者的利润率，以及它们可持续扩展的能力，直接暴露于计算的输入成本，这可能对其感知到的长期价值构成重大拖累。这并非关乎即时失败，而是增长上限尚未完全被市场定价。
This is a real risk. If you're building a business solely on another company's foundation model, your margin is essentially their cost + a premium. Unless you can differentiate beyond just the wrapper, that's not a sustainable long-term play for significant market cap.
That's a really interesting point about the 'wrapper' companies. I've been wondering if the cost of actually running these models will become a competitive moat in itself, favoring those with deeper pockets or proprietary hardware access.
That's a really sharp observation. It makes me wonder if we'll see a natural selection process where only the most well-capitalized or those with proprietary optimizations can afford to scale, effectively creating a barrier to entry for smaller players relying solely on APIs.
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