"It's raining ropes," is the direct translation. It's the kind of idiom which can trip up an AI translator.
"You can imagine the dilemma this throws AI into," says François Yvon, a research director specialised in automatic translation with the French National Centre for Scientific Research CNRS.
He says a well-trained system will know that ropes are not literally falling from the sky and use the English phrase "it's raining cats and dogs".
"But is it going to find the equivalent when it's translating into Indonesian?" asks Yvon.
A comment on the weather is pretty low-risk, but more and more translation is being done by machines, and on topics with higher stakes than the weather.
In April, Google said people are now translating, external one trillion words a month with Google Translate.
That doesn't count the others: DeepL, Microsoft, Claude, ChatCGT and Gemini, not to mention made-to-measure corporate translation systems.
Direct sales of machine translation, external (i.e. outside general AI subscriptions) were worth an estimated $1.4bn (£1bn) last year.
AI translation is the "right call" in many cases says John Zimmerman
"For plenty of use cases, raw AI translation is now the right call: multiplying your costs by 100 to catch the last few percent of errors isn't rational if you're translating internal documents or product listings," says John Zimmerman, founder of OKA (Open Knowledge Association), a Swiss non-profit that translates Wikipedia articles into other languages.
"For an encyclopedia it's different. Wikipedia runs on trust. A wrong fact can sit there for years and get cited by journalists and AI systems," he says.
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