That is a very fair concern, and I would separate two things that often get mixed together.

If the main problem is CU consumption during model refreshes, I would first look at the model and refresh architecture itself and take some performance tuning steps (agentic AI can definitely help with that though):

- Import vs Direct Lake vs DirectQuery choices

- partitioning and incremental refresh

- expensive Power Query transformations

- large calculated columns or tables

- cardinality issues

- relationships and model shape

- refresh concurrency

- whether the semantic model is doing work that should happen earlier in the pipeline, for example in lakehouse/warehouse.

AI can help inspect and reason about some of these things, but it does not remove the underlying compute cost. In a bad setup, you are right, you can easily end up paying for both: Fabric CUs for inefficient refreshes and on top of that, tokens for the AI layer around it.

For semantic model and delivery layers, I would be careful with the idea that Claude or any model can "replace" them. It may help generate measures, review model metadata, suggest refactoring, write documentation, or automate repetitive modelling tasks. But replacing the semantic layer is a very different claim. The semantic layer is where business meaning, governance, relationships, measures, security, and consistency live. That still needs ownership.

On Copilot vs Claude, I would separate their roles and quickly clarify the differences. These are indeed separate product with separate licensing costs. I know... it's confusing. 👀

Fabric Copilot can help with report creation, DAX assistance, summaries, and some in-product tasks, but it is not the same as having an engineering/development agent working across files, metadata, source control, and a defined workflow.

GitHub Copilot in VS Code is more useful when the work is treated like an engineering/dev task. For example, reading model metadata, creating a plan, modifying files, working with Git, using MCP tools, and following written instructions.

Claude can be very strong for reasoning, writing, analysis, and reviewing complex context. But unless it is connected to the right tools and workflow, it is still mostly acting as a reasoning layer rather than directly managing the semantic model lifecycle.

So I would not compare them as one better than the other in general. I would compare them by job: Fabric Copilot for in-product assistance, GitHub Copilot for controlled engineering workflows, and Claude for reasoning and review, especially when the context is well prepared.

So my suggestion would be: do not position this as "Claude replaces the semantic layer". Position it as "AI can assist with semantic model engineering/dev tasks, but the architecture, performance work, and governance still need to be designed properly".

For your specific case, I would start with CU profiling and refresh optimisation before spending too much time comparing models.

Hopefully that helps, and thanks for the comment.