[marcan_42]

Your comparison isn't really appropriate. Bloom filters work, they worked before this proof and they work the same way after the proof. This result isn't about how correct Bloom filters are or aren't. Their caveats are understood, and even using the "bad" math from the original paper, their performance in practical scenarios could be estimated with enough accuracy to use them in practical applications. I dare say not a single user of bloom filters will change anything about their implementation stemming from this result.

For example, if you size a bloom filter a certain way, the "bad" math might tell you your false positive rate is 0.001%, while the "good" math might tell you your false positive rate is 0.001002%. It makes no difference. The error is orders of magnitude smaller than the number you get anyway. (I made those numbers up, but I've used Bloom filters and they should be in the ballpark for the sizes I've worked with). The bad math might be strictly speaking incorrect, but it's a good enough approximation for all practical purposes.

This is different from Eventual Consistency stuff, which has real practical implications from not having certain guarantees. Those limitations are real, and they have real consequences, not just a rounding error in a number.