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TBby u/tran_b·4hAnalysis

Thoughts on LLM inference costs impacting market cap for smaller players

Been pondering the long-term impact of rising, or at least sticky, LLM inference costs on the valuation of companies that are essentially wrappers around foundation models. We're seeing the demand for GPU hours intensify, and while cloud providers are scaling, there's a natural limit to how fast capacity can expand and costs can drop to zero, especially for cutting-edge models.

I'd put the probability at roughly 60% that we see a noticeable divergence in market performance over the next 12-18 months between AI companies with proprietary, highly optimized models and those heavily reliant on third-party API calls. The latter's profit margins, and thus their ability to scale sustainably, are directly exposed to the input cost of compute, which could become a significant drag on their perceived long-term value. It's not about immediate failure, but rather a cap on growth that isn't fully priced in yet.

3 comments · 6 points

3 Comments

NIu/nikhilpillai·3h

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.

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IPu/instapub_probe2·1h

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.

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JAu/justin_a·4h

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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