isn't that a problem for large enterprises?

A lot of places already subscribe to both. Indeed, in large enterprises it isn’t uncommon to simultaneously subscribe to Copilot, Claude, OpenAI, Cursor, AWS Bedrock, Gemini, etc — you might subscribe to different ones for different teams/projects/employees/etc, but often the approval by legal/IT/etc is generic not scoped to whoever is using it right now

If you are charged based on usage, you can “soft switch” between them really quickly.

Comes to something then OpenAI’s best hope is to essentially become the next Oracle.

Not sure where you work but at ours we have api access to all of them and can freely switch.

Lol at my $job we have one token quota which can be used with all the frontier models.

We built our harness around swapping endpoints on demand. We can send to one model, many models, cloud or on prem infra. Certainly, not everyone has, but this is the future imho.

Amen. My employer recently switched Cursor -> Claude, and the whole thing stalled out for around 3 weeks while a BAA (Business Associate Agreement, required for HIPAA compliance) was negotiated and signed.

That's pretty much all enterprise software though, it's pretty much how Microsoft stays in business, and even then if the question of switching is even humoured (the idea of a big company switching away from Office would be inconceivable to many), that'd imply its not sticky enough

copilot is not the best example as it's a bit like openrouter for enterprises.

I know several people working at UHC that have access to the big 3 providers and switch between them freely, both agentic and in-editor AI.

They even negotiate with all three aggressively.

So not really.

Agree. Switching models with a keypress is for developer coding.

Jev and this decisions api are mostly useful for inference at scale in a workload where cost and latency matter… and that’s where evals become crucial. Could coding tools use it? Sure, but that’s probably a special case.

My gut is that the market for "ai as a tool" aka Claude Code/Codex/computer use/etc is a significantly bigger one than "models behind the scenes of some service". I've seen people use evals a lot in the latter case and very little in the former case (outside of people's whose job is basically to review the new releases). I haven't personally met anyone with something like "here are a bunch of tickets + a snapshot repo checkout, please try to solve them all" eval approach.

Though honestly I'm also surprised by the hype around Jev from a POV of "wait, are so many people just building on these by using them for classification tasks vs something more multi-step or generative?"

For every production use case I build a eval suite which I use for prompt tuning and model evaluation and config. How else do you establish your model and prompt combination works? How do you upgrade to a newer model or decide on a fallback?

This same suite can simply be run very handoff to switch to a new prod model.

Jev can run your evals too :D

The only way they can get lock in is with the data. Right now it’s like a return to the early Web 2.0 days where everyone had an API. You hook the harness up to Slack, Gmail, GitHub, etc. so it can do work.

And like Web 2.0 I’m sure the day will come where the walled gardens return. All the old SaaS companies are starting to charge for agentic access. Proportionally more of the AI budget will go to them instead of the model providers, who are still stuck in this price war

"interfaces are not copyrightable"

That shorthand rule of thumb is often applicable, but can lead to risky / incorrect assumptions.

The central distinction isn't simply interface versus implementation. It's functional systems and constraints versus protectable expression - a distinction that can cut through both an interface and its implementation.