Comment by bloppe
2 days ago
The AWS moat is a web of bespoke product lock-in and exorbitant egress fees. Switching cloud providers can be a huge hassle if you didn't architect your whole system to be as vendor-agnostic as possible.
If OpenAI eliminated their free tier today, how many customers would actually stick around instead is going to Google's free AI? It's way easier to swap out a model. I use multiple models every day until the free frontier tokens run out, then I switch.
That said, idk why Claude seems to be the only one that does decent agents, but that's not exactly a moat; it's just product superiority. Google and OAI offer the same exact product (albeit at a slightly lower level of quality) and switching is effortless.
There are quite large 'switching costs' from moving a solution that's dependent on on model and ecosystem, to another.
Models have to significantly outperform on some metric in order to even justify looking at it.
Even for smaller 'entrenchements' like individual developers - Gemeni 3 had our attention for all of 7 days, now that Opus 4.5 is out, well, none of my colleagues are talking abut G3 anymore. I mean, it's a great model, but not 'good enough' yet.
I use that as an example to illustrate broader dynamics.
Open AI, Anthropic and Google are the primary participants here, with Grok possibly playing a role, and of course all of the Chinese models being an unknown quantity because they're exceptional in different ways.
Switching a complex cloud deployment from AWS to GCP might take a dedicated team of engineers several months. Switching between models can be done by a single person in an afternoon (often just 5 minutes). That's what we're talking about.
That means that none of these products can ever have a high profit margin. They have to keep margins razor thin at best (deeply negative at present) to stay relevant. In order to achieve the kinds of margins that real moats provide, these labs need major research breakthroughs. And we haven't had any of those since Attention is All You Need.
" Switching between models can be done by a single person in an afternoon (often just 5 minutes). That's what we're talking about."
Good gosh, no, for comprehensive systems it's considerably more complicated than that. There's a lot of bespoke tuning, caching works completely differently etc..
"That means that none of these products can ever have a high profit margin."
No, it doesn't. Most cloud providers operate on a 'basis' of commodity (linux, storage, networking) with proprietary elements, similar to LLMs.
There doesn't need to be any 'breakthroughs' to find broad use cases.
The issue right now is the enormous underlying cost of training and inference - that's the qualifying characteristic that makes this landscape different.
Aren't you contradicting yourself? To even be considering all the various models, the switching cost can't be that large.
I think the issue here isn't really that it's "hard to switch" it's that it's easier yet to wait 1 more week to see what your current provider is cooking up.
But if any of them start lagging for a few months I'm sure a lot of folks will jump ship.