Good, realistic take. There's a lot of context that can be drawn on to make better decisions and look for weird.
And then agents come along and some people want their agent to be able to do anything they can do with zero friction. That usually lasts until the first time something goes wrong ;)
Actually zero trust can be seen as an optimization problem, you almost certainly want to globally deny every single request, even for authenticated users under all circumstances to all resources, unless when "necessary", "necessary" here is the key because it needs to be as dynamic and as fine-grained as possible, context-based and scalable. For small businesses, RBAC and ACL rules can be fine, but for enterprises with thousands or tens of thousands of employees, contractors and now AI agents which need varying permissions to different resources at different times, you would have to define thousands or tens of thousands of rules that might conflict with one another and are hard to maintain. ABAC and policy-as-code can mitigate to a certain extent providing dynamic context-based fine-grained access control. But if you could gather as much information as possible about every user and every session, combine it with some boundary permissions and static rules, and feed it periodically to some reasoning engine that is tasked with dynamically increasing/decreasing permissions for each session based on all such info, you can keep your manual/static access control policies to a minimum and maybe even eliminate JIT in some cases. The real question is whether modern LLMs and transformers are the correct and optimal architecture for this problem in the first place.