The AI's impact on the workforce has been intensely debated, without any real understanding. In a recent report, Goldman Sachs estimates that over a 10-year adoption window, 6-7% of the US workforce faces direct displacement risk. Zoom out globally and the number is 300 million jobs exposed to automation. In the US specifically, Goldman's economists find that AI can potentially automate tasks accounting for 25% of all work hours.

The pace of adoption is important. A front loaded curve produces unemployment effects several times larger than a gradual one. None of this will surprise most people who've been watching enterprise AI adoption. What might surprise them is that the federal government, which is deploying AI at a blistering pace, has no reliable way to measure whether any of this is actually happening.

The federal workforce is the most visible place in America where AI deployment and workforce reduction are happening simultaneously, in public, and with a paper trail. The problem is that no reliable methodology exists to capture what that actually looks like. Congress is trying to fix it. This edition examines the economy-wide data gap blurring our understanding of AI's workforce impact — and what, if anything, the government intends to do about it.

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