The Download: controlling AI agents and “Super Intelligence” loyalty tests

A new research line called “inverse game theory” proposes regulating AI models by creating environments that reward adherence to predefined rules. Instead of predicting how a rational agent will act under existing regulations, this approach designs the rules themselves so that the desired behavior becomes the most advantageous option for the AI—much like a cake cutter who, knowing the other person will take the first slice, tends to divide it fairly. The authors argue that this method could bring AI alignment closer to human goals, although questions remain about its practical effectiveness and how to scale such incentives for increasingly complex models.

Separately, the U.S. president signed an executive order at the end of September that requires federal agencies to rename artificial intelligence as “Super Intelligence.” A month later, the term began appearing in official documents, but its adoption is not universal among major tech firms. The shift in terminology is becoming a signal of political allegiance: those who use the new designation may be seen as aligned with the administration, while those who continue to use “AI” could face criticism or pressure.

These developments matter because the way we structure incentives for AI will shape its safety and reliability, while the symbolic weight of official language can influence public perception and future regulation of these technologies.

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