DeepSeek can accomplish over 90% of tasks of equivalent value at one-tenth of the fully loaded cost of O/A, and this capability can further spread through open source. In my view, the IPO risk for O/A is no longer just a valuation discount, but a substantive challenge to its long-term business model as a high-margin foundation model provider.

In that scenario, even if global Token usage increases 100-fold in the future, that alone would not be enough to justify these foundation model companies being worth one or two trillion dollars.

I think for me it's more the possibility of the ideas here being incorporated in other Opensource projects (vllm, llama.cpp, or in the architecture choices of the models themselves)

Deepseek manages to be ridiculously cheap and efficient with prompt caching, i spent 550M~ (1.9b input cache miss, 1.7b output) tokens and it cost me 5~ bucks, that would be ~150 bucks on Opus 5.5, if i ran that workload in parallel that would be $50 vs $1500, e.i. i wouldn't want to subagent heavily with anthropic models.

now imagine Kimi K4 or GLM 5.5 with those cost savings

I've been trying to find an answer to that, too. The best I've got right now is this:

Moat-like things going for them:

- access to the US government (for huge inflated government contracts as a source of free capital, having the competition banned, having their products legally mandated, getting other preferential treatment, having foreign governments bullied into granting similar advantages)

Too unsustainable, too fragile, too small:

- Strong brands: Brand loyalty seems like a pretty insignificant factor in AI right now

- Semi-exclusive hardware access, RAM buyouts: Unsustainable even in the short term

- Lock-in effects: Their models aren't very stable across versions, or very unique, and switching providers is about as easy as it can be in a highly competitive market.

- Exclusive technology that is important, advanced and hard to replicate to a degree that would materially set them apart and justify potentially much higher cost. Maybe I'm missing something here, but a lot of the claims they make to that end fall apart even on light scrutiny (due to things like test sets that are small, hand-picked and clearly overfitted on, or broad general claims that cannot be substantiated). I'm getting the feeling they don't have anything that materially sets them apart at all, but that's seems impossible to measure in any meaningful way, so who knows, they might have something here.