Should we tax AI?

Summary

Podcast Summary: The Indicator from Planet Money – "Should we tax AI?" (June 10, 2026, NPR)

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Episode Overview

This episode tackles the growing economic impact of artificial intelligence, with a focus on whether and how AI should be taxed. Hosts Darian Woods and Waylon Wong explore proposed policies for funding an "AI dividend" to address rising inequality and potential job displacement. The conversation features Alex Borres, a New York State Representative and Democratic congressional candidate, advocating for new AI taxes, and Martha Gimbel, a policy expert who voices skepticism about targeted AI taxation.

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Key Discussion Points & Insights

1. The Context: AI’s Unequal Wealth Effect

• Economic Concerns: AI could eliminate jobs, widen wealth inequality, and accumulate gains among tech elites. (00:12)
  • Notable quote: “There’s a lot of fear out there that AI could eliminate a ton of jobs, form a permanent underclass, and enrich just a few people.” – Darian Woods (00:12)
• Current Impact: While mass layoffs haven’t materialized, tech hiring has slowed and top tech titans’ wealth has soared (Elon Musk, Mark Zuckerberg, Larry Ellison, etc.). Wealth of the richest 26 Americans grew 127% since ChatGPT launched in Nov 2022. (00:31)

2. The AI Dividend Proposal (Alex Borres)

• Direct Response: Congress is considering whether to tax AI and provide the public with an “AI dividend”—cash payments, retraining, and education for affected workers. (01:15, 03:05)
• Personal Background: Borres is a computer scientist and former machine learning startup engineer, giving him credibility on the issue. (02:47)
  • Notable quote: “I’m saying what all Americans are saying, which is, this is happening way too quickly, and government right now doesn’t seem up to the task to protect us.” – Alex Borres (02:57)

Two Main Tax Proposals

1. Reducing AI Tax Deductions: (03:14)
   • Businesses can currently deduct AI expenses, incentivizing AI adoption and layoffs.
     • Notable quote: “Right now, our tax structure actually incentivizes laying people off to invest in AI…we put huge taxes on hiring a human or keeping a human hired… and huge discounts on using AI.” – Alex Borres (03:27)
2. Token Tax: (04:01)
   • Taxing the computational units (tokens) AI models use for language processing, primarily for commercial AI usage (such as AI replacing jobs or commercial subscriptions).
     • Notable quote: “A token is the basic unit of these large language models... the computational unit that a model uses in processing language and understanding.” – Alex Borres (04:07)
   • Example: OpenAI charges $30/million tokens, so taxing tokens targets heavy commercial users. (04:35)
   • Notable quote: “The basics of it are taxing the commercial use of AI. The times when you’re using AI to replace a [worker].” – Alex Borres (05:00)

3. Policy Critique: Martha Gimbel’s Perspective

• Questioning Purpose: “What are you trying to do?” – The goal behind a tax determines how and what you tax. (05:16)
  • Different aims (curbing energy use vs. discouraging AI adoption) require different strategies. (05:35)
• Problems With Token Tax:
  • Taxing inputs (like AI tokens) can distort business decisions and reduce efficiency, similar to tariffs on materials like steel. (06:07)
    • Notable quote: “Economists tend to like taxing the final output rather than inputs because it means that you don’t produce as efficiently. This is the same reason that economists don’t like, like steel and aluminum tariffs.” – Martha Gimbel (06:07)
  • Unintended Effects: May not capture extra profits if companies don’t use many tokens to replace workers, or disproportionately impact scientists or researchers (like biochemists) who need heavy AI computation for critical work like cancer research. (07:09)
    • Notable quote: “If you’re taxing per token and you have a scientist in a lab trying to cure cancer and they’re using a ton of tokens, they then bear the cost of the token tax, whereas the call center that doesn’t require as many tokens … doesn’t.” – Martha Gimbel (07:09)
• Alternative Remedies:
  • Focus on fixing existing loopholes and strengthening social safety nets (corporate taxes, capital gains, unemployment insurance). (07:30)
    • Notable quote: “A standard corporate tax or reforming the capital gains system... will do a better job of capturing the revenue that’s generated from that.” – Martha Gimbel (07:37)

4. Rebuttal (Alex Borres Responds)

• Why Target AI: AI is unique in explicitly seeking to replace human labor, unlike other tech advances that mostly complemented workers. A specific tax is justified by this unique threat. (08:23)
  • Notable quote: “AI is the first technology ever developed where the makers... are explicitly saying that they are trying to replace all human labor… Having a tax ... that this very well could be about replacing human beings is narrowly tailored and targeted at exactly the thing we’re trying to get done.” – Alex Borres (08:23)
• On Global Competition Risks: Borres argues the US shouldn't incentivize replacing humans with AI, and racing to the bottom could devastate the job market—even with international rivalry concerns. (09:07)
  • Notable quote: “We shouldn’t be incentivizing replacing humans with AI. That’s the current policy... if we’re constantly thinking about the race to roll it out everywhere and to everyone, we’re going to devastate the economy, we’re going to devastate the job market, we’re going to devastate our kids.” – Alex Borres (09:07)

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Notable Data & Memorable Moments

• Stark Wealth Growth: The richest 26 Americans have seen their wealth rise 127% since ChatGPT’s debut (00:31).
• AI Hasn't (Yet) Massively Impacted Hiring: A business survey: 96% of firms haven’t changed hiring due to AI; 2% increased hiring thanks to AI (09:36).

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Important Timestamps

• 00:12 – Framing the economic threat and new wealth division brought by AI.
• 02:47 – Interview with Alex Borres and explanation of his AI dividend/tax proposals.
• 05:16 – Martha Gimbel challenges AI-specific taxes and discusses taxation theory.
• 07:09 – Example of token tax’s unintended consequences for scientific research.
• 08:23 – Alex Borres’ rebuttal: why AI should be singled out for taxation.
• 09:36 – Data showing minimal AI-linked layoffs so far.

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Tone and Style

Conversational yet analytical, the dialogue blends economic analysis with accessible policy explanation. The hosts and guests speak plainly, often with humor and relatable analogies, keeping the dense subject matter approachable within the episode’s brief runtime.

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Conclusion

The episode presents a snapshot of the ongoing debate over taxing AI: should new technology be specifically taxed to fund social supports and curb inequality, or are traditional tax reforms better suited to distribute gains and offset harms? Policymakers are keeping watch, as the AI-driven economic wave shows no sign of slowing—or, for now, of mass layoffs. The discussion remains vital as policy catches up to technological innovation.

Transcript

[Alex Borres]

Npr.

[Darian Woods]

There's a lot of fear out there that AI could eliminate a ton of jobs, form a permanent underclass, and enrich just a few people. Right now. We haven't seen large layoffs, but tech hiring is slow and we've seen huge growth in wealth at the top since the launch of ChatGPT.

[Waylon Wong]

Yeah, just scan the rich list. Elon Musk, Oracle's Larry Ellison, Mark Zuckerberg, Jeff Bezos, Google's Larry Page. These are all people whose wealth is ballooning with the race to build AI. The richest 26Americans wealth has grown an estimated 127% since November 2022.

[Darian Woods]

And so if this is just a taste of the future, some lawmakers are demanding we tax AI and give the rest of us an AI dividend. This is the indicator from Planet Money. I'm Darian Woods.

[Waylon Wong]

And I'm Waylon Wong. Today on the show, should we tax AI? The AI race has made a lot of people richer, but most of those gains seem to have gone to those already with metaphorical bathtubs full of money.

[Darian Woods]

Yes.

[Waylon Wong]

Meanwhile, everyday workers aren't seeing big pay raises. We speak to a congressional candidate who wants to tax AI, and we talk to a tax expert who has her reservations.

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[Darian Woods]

New York State Representative and current Democratic Congressional candidate Alex Borres has a range of measures he's proposing to fund an AI dividend.

[Waylon Wong]

Alex Boris is an interesting politician on AI because he really knows the technology. He's a computer scientist who's actually worked at machine learning startups.

[Alex Borres]

I'm saying what All Americans are saying, which is, this is happening way too quickly, and government right now doesn't seem up to the task to protect us.

[Darian Woods]

Alex's AI dividend would be a direct cash payment to Americans. It would also pay for jobs retraining and education to help workers laid off

[Waylon Wong]

by AI, Alex has two main ways he's proposing to tax AI, which will help fund this dividend. The first is by reducing the ability for businesses to deduct AI costs as a normal business expense.

[Alex Borres]

Right now, our tax structure actually incentivizes laying people off to invest in AI. We put huge taxes on hiring a human or keeping a human hired. We have employment taxes, we have payroll taxes. We have requirements for benefits, and we put huge discounts on using AI.

[Darian Woods]

At the moment, AI is generally not taxed. In some states, like here in New York, I'll pay a sales tax if I use AI. But for businesses using AI, let's say they use ChatGPT, they'll actually deduct that expense from their revenue so they pay less tax.

[Waylon Wong]

Alex's second proposal to raise government revenue from AI is what's called a token tax.

[Alex Borres]

So a token is the basic unit of these large language models, and it can be either a small word or a piece of a word, but it is the computational unit that a model uses in processing language and understanding.

[Waylon Wong]

So asking a chatbot for, I don't know, a lunch recommendation might only use a few tokens, but asking it to generate an essay on the major themes of war and peace would use a lot more tokens.

[Darian Woods]

Large AI users are charged by the token. As of this recording, OpenAI charges a standard rate of up to $30 for 1 million tokens of output. So that war and peace essay, that'll cost you a few cents.

[Waylon Wong]

OpenAI, what is it good for? Alex says we can think of this token tax as just a tax for commercial AI use, subscriptions, data usage, whatever

[Alex Borres]

the basics of it, are taxing the commercial use of AI. The times when you're using AI to replace a.

[Darian Woods]

But taxing AI might have unintended consequences. Martha Gimbel is the executive director of the Budget Lab, a policy think tank at Yale University.

[Martha Gimbel]

The thing that I think is a little bit frustrating for the tax nerds, so, you know, for the 15 of us, is why, right? Like, what are you trying to do?

[Waylon Wong]

Martha says the reason that the why is important is because addressing different problems means using different policies.

[Martha Gimbel]

So if you are interested in decreasing energy usage, you would design the tax one way. If you are trying to discourage the use of AI overall. Then you might design something like a token tax.

[Darian Woods]

Martha says she's skeptical of a token tax because it's a tax on businesses inputs. So for a call centre, your inputs might be office supplies, rent workers and an AI subscription to help answer customers questions.

[Martha Gimbel]

In general, economists tend to like taxing the final output rather than inputs because it means that you don't produce as efficiently. This is the same reason that economists don't like, like steel and aluminum tariffs.

[Waylon Wong]

Yeah, it's like the government steering how businesses should be run, not leaving that to business managers. Martha says what would happen if it turned out that not that much AI computing power was required to replace all the call center workers?

[Darian Woods]

Yeah, I mean like we all like to think that our problems are individual and special are, but most of us calling this call center, we have the same issues. And maybe the AI bot doesn't have to work very hard.

[Martha Gimbel]

They don't need that many tokens. And the token tax isn't aimed at getting, you know, at the increasing profit that that company is seeing from that because there isn't as much AI usage as there was relative to like the number of workers that they had.

[Darian Woods]

Martha says contrast this with a hypothetical biochemist who might want to use a lot of AI tokens.

[Martha Gimbel]

If you're taxing per token and you have a scientist in a lab trying to cure cancer and they're using a ton of tokens, they then bear the cost of the token tax, whereas the call center that doesn't require as many tokens to replace its workers doesn't.

[Waylon Wong]

Martha would prefer that lawmakers look at existing policy gaps. That would be an issue with or without a big wave of job losses due to AI.

[Martha Gimbel]

A standard corporate tax or reforming the capital gains system, things like that will do a better job of capturing the revenue that's generated from that.

[Waylon Wong]

In other words, Martha says focus on the loopholes in the current system. That means companies in general don't pay their fair share of taxes from profits. She also wants the unemployment insurance system to be more generous to help laid off workers. Whatever the cause, we brought these critiques to lawmaker Alex Boris.

[Darian Woods]

Are there existing tools that are more about general inequality or general ways that we tax corporations that don't kind of presuppose a particular pathway for the technology to evolve. Why does AI need to be taxed?

[Alex Borres]

Specifically, AI is the first technology ever developed where the makers of the technology are explicitly saying that they are trying to replace all human labor. Every other technology before was by default a complement to human beings, not a substitute. Now, it doesn't mean that the labs will succeed, but having attacks to deal with that difference, that this very well could be about replacing human beings is narrowly tailored and targeted at exactly the thing we're trying to get done.

[Darian Woods]

Are you trying to slow down the adoption of AI? And if so, do you think there's a risk that American rivals like China might advance AI faster? If the US Is taxing AI and

[Alex Borres]

they're not, I don't primarily think so, but I also think that we shouldn't be incentivizing replacing humans with AI. That's the current policy. I think we should continue research into AI, especially AI safety. But if we're constantly thinking about the race to roll it out everywhere and to everyone, we're going to devastate the economy, we're going to devastate the job market, we're going to devastate our kids. That's not the way to win this race with all of those costs.

[Waylon Wong]

Right now, AI hasn't devastated the labor market. Martha points to a census survey of businesses. It shows that 96% of them haven't changed their hiring because of AI, and 2% have actually increased their hiring because of AI.

[Darian Woods]

But if that starts to change, you can bet that policymakers will be looking at proposals like Alex's. This episode was produced by Angel Carreras and Julia Ritchie with engineering by Cole Takasugi Chernevan. It was fact checked by Tyler Jones. Cake and Cannon edits the show, and the indicator is a production of npr.

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