They are, but that didn't stop people from using them. They're convenient, they're bounded, and as such, ended up having decades of hardware optimization behind

They are, but that didn't stop people from using them. They're convenient, they're bounded, and as such, ended up having decades of hardware optimization behind then. When precision turns out to be really important, it's often cheaper to get some smart people to restructure computation as to mitigate the errors, than it is to switch to arbitrary-precision math and deal with consequences. There's a whole field of CS that's spent the last half century or more developing ways to deal with floating point math problems - extending the scope of their use, and making alternatives even less economical. There's an interesting case of it today: LLMs. In theory, they should be fully deterministic with "temperature" parameter set to 0, but they aren't - all that matrix multiplication is massively parallelized, so with floating point math being order-dependent, partial results quickly accumulate non-deterministic errors.