This compelling paper argues how the AI-automation arms race that organizations are having would lead to a depletion of spending power of the population and eventually collapse of the economy.

Most people who are running these organizations know this eventual reality, but they remain trapped in an automation arms race to secure immediate cost savings for as long as possible, otherwise they would lose to their competition. That prevents them from exercising voluntary restraint.

They propose a Pigouvian automation tax set equal to the uninternalized demand loss per task.

Here, the 'uninternalized' means the loss in consumer demand due to income loss of workers impacted by the collective industry decision of moving towards automation. The burden is shared by all the organizations involved in automating that task category. This, from what I understand, is calculated per task category where automation efforts are underway.

The revenue from this tax, they propose, can be recycled to fund targeted worker retraining programs, which increases the economy's income-reabsorption rate and makes the corrective tax potentially self-limiting and transitional over time.

The AI Layoff Trap by Brett Hemenway Falk & Gerry Tsoukalas

Advanced LLM watermarking is closer to spread-spectrum radio than to stamping text with a signature. The language itself is the carrier; a weak cryptographic signal is spread across thousands of otherwise natural token choices. To everyone listening normally, it’s just language. With the right key, you can correlate against the hidden signal and pull it out of the noise.

The text is not where the watermark sits, it is the carrier wave. The watermark lives in tiny coordinated deviations in how that carrier is generated. Without the key they look like ordinary sampling noise. With the key, they line up coherently into a signal.

Watermarking an LLM's output, in Kirchenbauer et al., is basically turning token generation into a dynamic casino where the house secretly nudges the model toward green tokens. A z-test detector regenerates those context-dependent green sets and asks whether the generated text landed on them far more often than chance would plausibly explain. It isn't detecting “AI style”; it's detecting an intentionally planted statistical bias.