I don’t see any reason to think the same thing that happens with all tech won’t happen here.

I understand this, but I just can't bring myself to care. I've been doing professional software work for almost two decades. These sorts of improvements/time savers are great without AI. With AI? Whatever. It's fine.

When the underlying lib has an issue, it'll be quicker to debug with the whole thing in context.

This is at a well-known tech company operating at massive scale (and resulting complexity).

L1/L2 tech support is completely dead within a couple years. The delay is only around how long it takes people to realize.

Not a reasonable assumption for a variety of reasons.

> 2. Deepseek is served at a profit and not a loss

Not a reasonable assumption either.

> Why do you need to know the architecture? Just compare Deepseek V4's performance with GPT 4 and treat internals as a blackbox.

Because the internals are what actually matter and what drives inference cost.

It would be entirely reasonable to expect that GPT-5.5 has some sort of optimizations or changes to the architecture to make it easier to train, or to make runtime ablation easier, or to better handle large batches, or whatever.

Those changes, particularly if they are non-public, can easily result in worse inference performance than a comparably sized model without those changes.

> It is borderline conspiratorial to believe it this way.

It's not any sort of conspiracy. It's how land-grab tech companies have always worked. To presume otherwise is silly.

It would be more surprising if the surrounding architecture hasn't significantly diverged. If it _hasn't_ significantly diverged, then given the performance difference it would imply that the frontier models have significantly greater param counts, which would result in a higher cost.

That blog post is not very compelling either. Without knowing details of the architecture, comparing the various frontier models to open models doesn’t make sense.

I guess (heh) it depends on your definition of 'educated guessing'? Looking at the problem, considering a solution, discarding it, trying another and testing, iteratively, is how most people would approach any tricky problem.

Brute force is substantially different. It would be saying that, other than maybe setting some basic bounds and heuristics, I'm going to try literally everything and test each. That's not at all what the LLM did here.