Not clear to me from when the chat statement is, but given that the presentation is from 2020, it would actually be funny if he went from 'this is going to get us 100% certainty' to 'well, we might never be fully sure, but at least it's a cool library'.
Jokes aside, I'm pretty sure he was aware of the limitations at all times, and I'm reading this statement in the context of his examples in the presentation. There's a nice story in a Quanta interview about how he was approached by Peter Scholze to help formalize part of a proof. Scholze had asked whether they'd gone through his recent work, and when he said Yes, Scholze asked whether they'd carefully checked theorem 9.1. Buzzard said no, they didn't have enough time left when they got to chapter 9. And Scholze said, see, that's the problem, is anyone in the world really going to check 9.1? And then there are the stories in the presentation, where entire published proofs are put in question, not because of any direct mistake by the authors, but because of a problem in a lemma they used from previous work. This kind of transitive dependency chain could go down multiple papers, and there's really no guarantee that the later results are salvageable. So it seems to me that formalization would be a great practical tool to mitigate those problems.
And then yes, it's also super cool in its own right (imho), just the fact of collecting the knowledge, and then the related work might also prove useful for formal verification (of hard/software systems), which has practical relevance, and mathlib might also just happen to become a great enabler for developing math-focused AI. So lots of good reasons to do it, but correctness might still be the most direct payoff: Terry Tao has a different blog post about how he found a (fixable) error in a recent proof of his, thanks to Lean. If that happens to him on one of the first occasions where he uses the tool seriously, then we can only guess what's waiting to be fixed (or thrown out) in the whole body of published mathematics.