The best plan for this simple query depends on the selectivity of the predicates. For example, if 20k rows have a.y='foo', but only 10 rows have b.z='bar', then it's best to scan a b.z index, then lookup matching rows in a. But if the #rows is reversed, then it's better to scan an a.y index and lookup in b. This is a simplified example, but we do see queries along these lines in real OLTP workloads.

You're correct that for many OLTP workload queries, simple heuristics are sufficient. However, even if that's true for 90% of queries, it's the last 10% that gets you. We've seen customers with 10 queries in their workload, where 9 work well but the last 1 gets a bad plan that is 10x slower than it could/should be. Maybe they can rewrite it, or maybe they don't have sufficient knowledge to do so. Or perhaps they're using an ORM and don't have control over the queries it's sending to the DB. In addition, many mostly-OLTP workloads contain a few OLAP reporting queries in the mix. Developers don't expect their OLTP database to perform like a dedicated OLAP data warehouse DB, but they also expect it not to fall over when it gets a more complex query.