I think the article gives a very good overview of a problem that most users of AI rarely evaluate / have to grapple with.

There really is a surprising amount of coupling that happens with many of the "frontier inference providers", where a lot of the powerful non-LLM extensions (web search, code execution) are packaged as simple "tools" on the surface, that build up a lot of moat. Those are parts that are in theory nicely separable from the inference API, and could be externalized via MCP servers, but are usually not offered as such by the inference providers themselves, and are often only available in a slightly less powerful variant from other providers.

We've faced that issue repeatedly while building a on-premise provider-agnostic Chat UI & platform[0], where even adding something as simple as an in-chat image generation tool for the end-users (which is just a build-in tool in the OpenAI Responses API), becomes a bit of an ordeal (though part of that is due to the MCP spec missing a native file transfer protocol as of today[1]).

I am quite hopeful though, as with recent shifts of interest towards open weight models, there will be more opportunities for companies offering alternative implementations in a easier plug-and-play manner.

[0]: https://github.com/EratoLab/erato

[1]: https://github.com/modelcontextprotocol/modelcontextprotocol...