Anxiety over AI has created a new topic that Republicans and Democrats can agree on.

We should tax it.

And with most bipartisan agreement, this consensus is a bad one.

Specific proposals vary.

Andrew Yang wants to tax computing power.

Bill Gates wants the robots to pay taxes. Elizabeth Warren thinks her wealth tax is the solution. Bernie Sanders wants taxpayers to own about half of AI companies. Donald Trump's view doesn't seem that much different. The proposals all vary in their mechanisms, but they are motivated by the same three-part story. First, that labor's share

of national income has declined in recent decades. Second, that AI will accelerate this loss of

economic power for workers. And third, that the tax code makes all of this worse because we are

already undertaxing capital. The problem with this is that all three parts of

the story are wrong. I'm Adam Michelle, director of tax policy studies at the Cato Institute, and my

guest today is Daniel Bunn, president of the Tax Foundation. We've each written on this topic recently

and came to similar conclusions that this AI panic is generating

some genuinely terrible tax ideas. So today, we're going to take them apart one at a time.

Daniel, welcome to the Cato podcast. Thank you for having me. Before we get into the specific proposals,

I want to start with a big picture question. What do you think is driving

this AI anxiety? And what are the things that made you decide that the reactions that we're seeing

needed a response? Yeah, the biggest thing that I think is driving this issue is just the

uncertainty that surrounds what this technology

will mean for the economy.

And that could be good things that it can mean for the economy or some negative things

that some people have been talking about.

And that uncertainty is not something that, you know, individuals, families, they don't like living with uncertainty.

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