Why AI independence matters

The biggest AI risk in your company right now isn't a weak model. It's that you can't leave the one you're using.

The numbers say everyone feels it and almost nobody is ready. In one set of 2026 surveys, 81% of enterprise leaders said they worry about depending on a single AI vendor. Only 6% said they could switch without breaking something. And leaving is rarely cheap: changing a platform tends to run 19-34% of the original spend, often more than the build itself.

One case makes it concrete. Builder.ai, a London company valued near $1B and backed by a major cloud provider and a sovereign fund, filed for insolvency in May 2025. Its customers didn't just lose a supplier. Their apps went dark, their data on its servers became unreachable, and the source code they had paid for was gone. They weren't punished for picking a bad model. They were punished for keeping nothing outside someone else's walls.

Our take: independence doesn't mean building everything yourself. It means staying able to walk away. Three things keep that door open.

1. Keep the model swappable. Build against an abstraction layer, not one vendor's proprietary surface, so changing the engine is a configuration decision and not a rewrite.
2. Keep your data on infrastructure you control. This is where the European angle stops being abstract: with the next phase of the EU AI Act landing in 2026, 61% of Western European CIOs now say they prioritise local providers.
3. Keep the knowledge in-house. If only the vendor understands how your AI works, the contract doesn't matter. You're dependent.

This is the bet we made at AIXEL. We built AIXEL Cloud so independence is the default, not an afterthought: model-agnostic at the LLM layer, our own security layer in between, and a read-only connection to your systems so your data stays with you. The point isn't to depend on us instead. It's that you keep the keys.

Independence is a design choice you make early for almost nothing, or a migration bill you pay later.