AI Row Count Estimates Come From Nothing

AI row count estimates arrive with tremendous confidence. Cardinal said four hundred. The query returned nine million.

Cardinal Rules. A Saturday cartoon about AI and databases.

Two things in your day estimate row counts, and only one of them has ever looked at your data.

The optimizer builds its guess from statistics. It keeps a small summary of what lives in each column, how many rows there are and how the values spread out. That guess still goes wrong, usually because the summary is old and the table has doubled since. Known problem, known fix.

Cardinal has none of that. Cardinal has read a great many sentences about databases. When you ask how many rows come back, the answer gets assembled out of how such answers usually sound. Four hundred sounds like a reasonable number of rows. It sounds reasonable for every table on earth, and that is the tell.

Try this the next time a model hands you a confident number. Ask how it knows. A real estimate can point at something, a statistics object, a sample, a count it actually ran. A guess starts explaining its reasoning in a lovely paragraph and never points at anything.

If you want the true number, the database is sitting right there. It will count for you. Counting is the one thing it has always been extremely good at.

Cardinal Rule

The machine never says maybe. That part is still your job.

Ask how it knows, then watch whether anything gets pointed at.

Cardinal says the estimate was directionally correct. On the desk this season: AI: Nobody’s in There. But we’re still in here. (on Amazon) and 100 AI Interview Questions and Answers for Data Professionals (Kindle, also on Amazon.in, paperback and audiobook).

Next Saturday, Cardinal reads the cover of one of my books and takes it personally.

A confident number is not a measurement, it is a suggestion in a nice font.

Published by Pinal Dave on SQLAuthority. More of my work at pinaldave.com.