alt Hacker NewsUnlike all your examples, switching out an LLM is both cheap an easy. So easy that every 3 months or so new models are released and people grab them and start using them.
The UX is the same regardless the provider. You send in a prompt, it spits back an answer.
In all your other cases, the cost to switch is losing support and a difficult transition period. But in the case of LLMs, there was no support to begin with. The transition is basically updating your current harnesses to know about the other models.
I think the comparison most apt is the rise of AMD. Sure, it never(?) achieved market dominance, but it did ultimately make a huge dent. And a big part of that was because AMD x86 was pretty close and pretty compatible with Intel x86 at a fraction of the cost.
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>switching out an LLM is both cheap an easy.
Honestly, these days probably less friction switching out Redis or Elasticsearch (backend) than changing LLM provider (human facing).
Fable is seriously good enough now to, in a 20k line project, take "replace Mongoengine with raw PyMongo" and not screw anything up.
Unlike all your examples, switching out an LLM is both cheap an easy.
Rolling out AI access in a large business is still hard, especially if you're trying to do it safely e.g stopping people throwing all your company data including user PII into a chat for productivity reasons.
It's more a staff training and guardrails issue than a choosing which LLM to use issue, but I imagine picking an open model like GLM would make it harder because the 'enterprise stuff' will be missing.
Hard disagree.
Two LLMs with the same numbers on important benchmarks could have vastly different behavior in actual deployment. Not sure if as hard to switch as Excel <> Libre but still not "cheap and easy".
Switching out an LLM? What do you mean by this? Sure some models can run locally but in a company with lots do people they might not be willing to spend to self host a larger model that requires beefier hardware to host, plus all the complexity to scale that out to a bit internal user-base
> So easy that every 3 months or so new models are released and people grab them and start using them
Individuals perhaps, but not organizations.
Switching an agent harness is more difficult, especially on the enterprise/teams level.
Once your team gets settled with Claude teams, cowork, and the various plugins, it’s going to be a pain in the butt to switch.
I don’t exactly see orgs lining up to switch (and train) their employees between claude desktop and codex and whatever copilot is doing. There’s probably some inertia to those harnesses/integrations on top of the llms themselves.
> Unlike all your examples, switching out an LLM is both cheap an easy.
For now
If you're developing on top of LLM APIs directly, this is definitely not true. There are differences in how context caching works, in what's available through native harnesses, the types of tools you're fine-tuned on (GPT uses apply_patch while Claude uses edit, with different formats), the API surface (Agents SDK, Responses API, Managed Agents), cost structures, and best-practice guidance all around.
Not to mention the meta of account limits, billing, ZDR contracts, etc.