> They are when they say that inference is incredibly profitable. It is incredibly profits exist only when you pretend that models have 0 cost to be created (since they are a digital asset that can't depreciate), and R&D is a imaginary thing that exists only as a fixed cost of doing business that doesn't need to be recovered.
Expensing the cost of training a model isn't treating it as free. The whole cost is charged to the income statement the year it's incurred, which is actually faster than depreciation would ever recognize it. Calling something R&D does not make the cost "imaginary". It means the cost is counted. It's just not in gross margin.
We are actually asking the same basic question (can the investment made ever be recouped?). What you're struggling with is the accounting concepts and terminology. The costs of creating the models are a different part of the income statement than the costs of serving inference.
Again, "inference has high gross margins" and "training is extremely expensive" can both be true at the same time.
In absolutely none of my comments did I state that R&D never needs to be recovered. In fact, I stated repeatedly that the entire crux of whether these businesses are viable long-term comes down to whether the costs of creating the models is eventually recouped.
> Anthropic released Opus 4.6 in February 2026 and deprecated it in June 2026.
First off, Opus 4.6 is still available, even though Anthropic recommends migrating to 5.5. Google Cloud says it will be on offer until February 2027. So revenue for this model didn't drop to $0 overnight.
https://platform.claude.com/docs/en/models/opus-4-6/overview
More importantly, your comment implies that models cease to have value when the next version is released. This completely ignores the reality of model training, which is that the subsequent models almost certainly built on the research, data and techniques of 4.6. And 4.6 was built on the research, data and techniques of the models that came before it.
So the whole notion that point versions of these models are standalone investments that cost billions to train and then become worthless the minute a new point version is released is incredibly naive. It's just not how model training works.
> A lot of people like to vaguely gesture at the possibility of compute costs coming down, but it is in fact coming up with the rising cost of hardware (and possibly with sweetheart compute deals being replaced by contacts where providers actually have to make money).
You have absolutely no evidence of "sweetheart compute deals" so this is pure speculation.
Furthermore, there's a flaw in your logic. It's entirely possible for chips and memory to get more expensive while the cost per token served falls due to more efficient hardware and better inference software. What matters here is the cost per unit of output, not the price of a GPU.
Bottom line: inference has high gross margins and these businesses have had to invest massive sums. If inference profits grow sufficiently faster than costs, or costs decrease sufficiently over time, these businesses could be viable and perhaps incredibly successful. If that doesn't happen, their viability is questionable.
Lots of people have an opinion about this but as this discussion proves, lots of people also don't understand the basic accounting required to make a cogent argument one way or the other.