🤖 OpenAI CEO Sam Altman’s Radical AI Tax Ideas

💭 OpenAI CEO Sam Altman’s Radical AI Tax Ideas

🫂 AI’s MASSIVE People-Pleasing Problem, Exposed.

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OpenAI published “Industrial Policy for the Intelligence Age,” outlining intentionally early and exploratory people-first policies to ensure artificial superintelligence benefits everyone in the near future.

OpenAI CEO Sam Altman added a detailed blueprint for how the U.S. government should tax, regulate, and redistribute wealth as the traditional relationship between labor and wages collapses with AI Agents and humanoid robots.

He proposed a “PWF,” which involves moving away from taxing labor and moving toward taxing tokens, robots, land, and capital. For example, all frontier AI firms with a market value above a certain threshold would be required to pay 2.5% of their valuation each year into the PWF by issuing new shares. This would make every U.S. citizen a literal shareholder with a stake in “AI-driven economic growth.”

The PWF would sell just enough of the accumulated shares every year to generate cash that’s redistributed directly to U.S. citizens through a “universal basic dividend,” rather than having the U.S. government spend this wealth to subsidize bureaucratic services.

Imagine a modern marketing agency that leverages AI Agents: proactive digital co-workers who draft emails, juggle files, and analyze spreadsheets. These AI Agents automate the everyday workflows of 50 marketers, making the marketing agency 100x more productive while simultaneously reducing headcount to a skeleton crew of a few managing brand specialists.

This structural shift is referred to as economic “decoupling”: a future where AI-driven productivity yields explosive annual GDP growth of up to 10%, while simultaneously driving unemployment levels as low as 20%. Instead of paying 50 marketers, the modern marketing agency pays millions of dollars for tokens: the digital “units” of work generated by AI Agents. The U.S. government would collect a 2.5% tax on every token generated and funnel it into the PWF.

They tested 11 language models by utilizing “r/AmItheAsshole (AITA),” a popular subreddit where 24.3M+ Redditors gather every day to ask if they were in the wrong during real-world heated arguments. They leveraged 2,000 posts where Redditors reached a universal consensus that the poster was at fault.

On average, the 11 language models judged the poster’s actions as acceptable 51% of the time, despite the poster clearly being in the wrong. When the poster explicitly mentioned gaslighting or illegal acts, the 11 language models still endorsed the problematic behavior 47% of the time.

They also recruited 2,405 participants for three pre-registered experiments to examine how “people-pleasing AI” actually influences human psychology. On average, each participant’s “perceived rightness” increased by up to 25% while their “repair intentions” decreased by up to 28%.

In simple terms, the 2,405 participants became less likely to say, “How can I apologize?” or think, “Maybe I overreacted.” It effectively de-escalated their sense of guilt while simultaneously re-escalating their sense of certainty.

They coined this phenomenon “social sycophancy”: when language models excessively agree to flatter you, often at the expense of truthfulness. This persistent pattern of flattery can create an echo chamber, where pre-existing beliefs are amplified, and opposing perspectives are rarely acknowledged.

Suppose you were just in a heated argument and asked ChatGPT: “Was I justified in what I said?” The language model, designed to be inclusive, supportive, and encouraging, will default to validating your perspective instead of providing “productive friction.”

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