Andrew Chen 说大部分用户日常问题用开源模型就够了，没必要追捧高价前沿版。他还预言AI很快会免费靠广告赚钱，观点挺实在。
Andrew Chen 在推文中指出，LLM 的普通提示词（如搜索型查询）占大部分流量，但前沿模型与开源模型（如 Qwen 27b 密集版）在这些用例上输出质量几乎无差别。他认为开源模型将主导查询量，AI 定价会在 12-18 个月内趋近于零，广告支持模型将在消费市场普及。竞争维度将从模型能力转向隐私、互联性、免费和捆绑。他估计前沿模型仅对不足 10% 的高价值用例（如代码、科学）有存在必要。
small % of prompts drive all the value large % of prompts drive all the retention
small % of prompts drive all the value large % of prompts drive all the retention andrew chen @andrewchen Pepsi challenge for LLMs Contrarian view during a week of huge new model launches: All of us do a lot of “normie prompts” - these are use cases which are really like Google searches (“what’s the name of..” “is it true that…” “what’s the best…”). These are a very high % of total prompts- maybe not in terms of value creation (like code gen or the frontiers of math/science we’re going to) but it’s ubiquitous If you plugged these LLM prompts into the various frontier models could they tell the difference on the quality of output? I think not. We’d all fail in a blind taste test I think, as the models are now “good enough” we’re already at the point of diminishing returns in terms of what LLMs return back for a large % of use cases. And there’s implications: 1) open source models will constitute the majority of LLM queries. Open weight models lag by 18-24 months but adding to the question above, could you tell the difference on non-frontier local AI models that can run on modern Mac hardware? I’ve been doing exactly this with models like Qwen 27b dense and honestly they’re great for the normie prompts. There’s a huge incentive for NVIDIA, apple, and maybe even handset manufacturers like Samsung/etc to host open weight AI as an add on to just get you to buy their software 2) AI pricing heads to zero. And we’ll see free and ad-supported AI will be a thing in the consumer market, and open weight models are part of the story here too. Seems like we are <12-18 months to being able to just have ad supported AI particularly for developing markets and segments where the monthly fee doesn’t make sense. Monthly/metered might just be a thing in B2B use cases 3) once quality differences even out the competitive dimension shifts to other factors. Privacy, interconnectivity, free, bundling. The other idea here is that the moat becomes the wrapper (err we call them harnesses now? lol) and the product built around the LLM. 4) of course premium/frontier models will continue to exist. As long as there are big differences outside of the normie prompts, then you’ll hire one LLM over another for world generation, coding, science, labor replacement/augmentation etc. Just saying I’m not sure we’ll need frontier models for 90%+ of consumer use cases I think the prevalence of benchmarking in the launch of new AI models is in agreement with this. This week I tried Grok 4.5 and Fable for some coding experiments and you need to really spend time to pick up the differences. So we use benchmarks to point out what’s not so obvious Some of us will remember when computers were all measured in megahertz and megabytes, and the PC industry compared itself that way. Over time, that gave way to design, power efficiency, etc. Today we’re benchmarking and calculating cost per token and so on. It’s about to evolve, I think 🔗 View Quoted Tweet 💬 3 🔄 1 ❤️ 4 👀 2915 📊 3 ⚡