Yeah, that’s a good point. In real projects it’s rarely about “one best language,” it’s more about what fits each layer of the system. Most teams I’ve worked with default to Python for AI because of the ecosystem and libraries, and then mix in other languages for performance or deployment when needed. That idea is also reflected in how modern AI stacks are usually built, where Python dominates the modeling side but other tools handle infrastructure and frontend integration. A lot of guides explain this balance pretty well, including site where they break down why different languages exist for different stages of AI systems rather than there being a single “winner.” In practice, the decision usually comes down to team skill and system constraints more than anything else.
