In 1950, Alan Turing set aside the question of whether machines could think. He considered it too vague to debate and proposed something testable instead: could a machine hold a five-minute text conversation well enough that a human judge couldn’t reliably tell it apart from a person?
He expected machines to get there around 2000. It took about two decades longer, but in recent studies, language models now pass versions of his test.
Which is exactly why the test no longer tells us much. Sounding human was the hard part in 1950. Today it’s the easy part. The harder test is whether a model gets the numbers right when the numbers carry real money.
In wealth management, that test shows up every day in capital call notices, K-1s, and custodian statements. An answer that reads fluently but transposes a commitment amount is worse than no answer, because someone might act on it.
That’s the standard LEA is built around. Every figure LEA extracts is reconciled against the source document and the firm’s existing records, and anything that doesn’t tie out goes to a person instead of moving downstream. Ops teams don’t need a system that sounds confident. They need one that knows when to stop.
Turing asked whether the difference was observable. For an operations team, the only difference worth observing is whether the numbers are right.
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