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Pepsi challenge for LLMs

Pepsi challenge for LLMs

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As models reach 'good enough' status for general tasks, the industry competitive advantage is shifting from pure model intelligence to privacy, bundling, and user experience.
What: Andrew Chen suggests that for most consumer prompts, current frontier LLMs have reached diminishing returns, predicting that free, ad-supported, and local, open-weight models will dominate 90% of use cases.
Why it matters: This anticipates the commoditization of base AI models, pushing developers to focus on the 'wrapper' or harness as the real product differentiator.
Decoder
  • Open-weight models: AI models whose trained parameters are publicly available for download and execution locally, unlike proprietary 'black-box' APIs.
  • Normie prompts: Simple, general-purpose user queries like 'what is the best' or basic fact-checking, as opposed to complex coding or scientific reasoning.
Original article

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.

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