The hardest problem in AI + finance isn't accuracy — it's trust. Where do you draw the line?

I've been building in the AI-for-finance space, and I've noticed the real barrier isn't whether the models are good enough. They're getting scary good at reading documents, reconciling transactions, spotting patterns a human would miss. The barrier is trust — and specifically, the gap between AI that reads your finances and AI that acts on them.

Here's the line I keep bumping into: people are surprisingly comfortable letting an agent categorize expenses or summarize their spending. But the moment it wants to do something — file the taxes, move the money, recommend a five-figure decision — the comfort evaporates. And I don't think that's irrational. The cost of a wrong email is an awkward apology; the cost of a wrong financial action is real money or an audit.

What's interesting is that the same person will draw that line in totally different places depending on stakes and reversibility. A $40 miscategorization? Whatever, fix it later. A mistimed refi or a missed deduction? Now trust matters enormously.

So I'm genuinely curious how this community thinks about it, especially folks building AI products:

• Where's your line between "let it read" and "let it act"?

• Does showing its work / flagging uncertainty actually earn your trust, or do you need a human in the loop no matter what?

• For the builders here — how are you designing for trust, not just accuracy?

I'll be in the replies — I think how we solve this is going to decide which AI-finance products people actually adopt.