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AI · Measurement · Synthetic Media

The AI Seismograph: Measuring the Floods of Synthetic Data

We have no shared instrument for how much of what we see is synthetic. We need a scale: a way to read the waves of machine-made information.

SDShailin DharFutureproof TMT
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Published
February 19, 2025
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Earthquakes were chaos until we built the seismograph and agreed on a scale. Suddenly a shared, comparable number existed for something previously experienced only as vibes. Synthetic information needs the same thing: an instrument for how much of a given feed, market, or conversation is machine-made.

Right now we argue about it anecdotally. "The bots took over around 2022." "No, earlier." Without a scale, these are just impressions. An AI seismograph would turn synthetic saturation into a reading you could compare across platforms and over time.

“You cannot manage a flood you have no gauge for. First build the gauge.”

Content provenance standards are the closest thing to a first sensor: cryptographic signals about where a piece of media came from. They are not the whole instrument, but they are the raw data any future scale would have to read.

Filed underAIMeasurementSynthetic Media

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Further reading

AI-assisted recommendations of related work elsewhere on the internet.

  1. 01C2PA Content ProvenanceAn open standard for signing the origin of media: a candidate sensor for the seismograph.C2PA