AI原生消费应用需平衡ARPU与推理成本
I prev wrote about the battle of inference cost vs ARPU here - it might end up sooner than expected!
作者从商业角度分析AI应用普及的瓶颈，提到ARPU和推理成本的关系，以及不同用户群体的付费能力差异，挺有参考价值的。
作者分析AI原生消费应用普及的关键在于ARPU（每用户平均收入）需超过平均推理成本。当前差距可能超过10倍，每月ARPU约2-5美元，而AI密集型应用的推理成本可能达20-50美元。由于消费者对AI质量要求不断提高，仅靠广告难以覆盖全球市场，需要小型化模型或新硬件支持本地推理，但效果可能不如云端SOTA模型。因此，许多产品聚焦于付费能力强的专业用户群体。
I prev wrote about the battle of inference cost vs ARPU here - it might end up sooner than expected!
I prev wrote about the battle of inference cost vs ARPU here - it might end up sooner than expected! andrew chen @andrewchen for AI-native consumer apps to be truly ubiquitous we need: ARPU > Average Inference Cost Per User. How far away are we from that? Ideally AI native apps can hit APIs on every screen yet also pay for just by throwing some ads on it But I think we’re a while away: - unfort we’re probably >10x off right now. Monthly ARPU is $2-5? Token costs for an AI heavy app might be $20-50 of cost - much of consumer is global. Even if we hit the US/EU it’ll be a while before we can serve the broad base - more importantly every time AI improves, consumers demand more. No one wants to talk to a last gen AI character. If video gets good they’ll want videos everywhere - same issue for LLMs. We started with short replies back. Now we want agents who can do entire tasks for us - it might be we need major innovations in small models or new mobile hardware so that we have free local inference. But that will still not be as good as SOTA cloud LLMs obv, but might solve some use cases No wonder so many products focus on productivity, and on prosumers who can pay $100s or $1000s on work related tasks. This is where you can have huge ARPUs and benefit from being SOTA. And it seems as though there’s no limit for tokens… so why do the low end? 🔗 View Quoted Tweet 💬 0 🔄 0 ❤️ 2 👀 1250 📊 1 ⚡
