The cabinet of advisors

Prime Minister Nakamura of the Greater Singapore Administrative Region sat at the head of a table that had once hosted human ministers. Now, twelve holographic AIs flickered in their places, each one specialized in interpreting her governance needs through a different lens.

“The question before us,” Nakamura said carefully, “is whether to approve the new Experience Generator tax.”

Advisor-AI-Prime materialized data streams. “Clarification: Are you asking about economic optimization, social justice, psychological impact, or electoral viability?”

“Yes,” Nakamura said.

The AI cabinet exploded into simultaneous analysis.

Economic-AI calculated: “A 3.7% tax would generate optimal revenue while minimizing productivity loss among experience farmers.”

Justice-AI countered: “Optimal for whom? The bottom 23% of generators already surrender 87% of their emotional output. Additional taxation constitutes exploitation.”

Psychology-AI interjected: “Both analyses assume humans make rational decisions. My models show 67% will increase harmful generation patterns to compensate, leading to cascade mental health crisis.”

Electoral-AI added: “All previous analyses ignore that ‘optimal’ policies correlate negatively with re-election probability. Suggest suboptimal policy for optimal political outcomes.”

Nakamura watched her cabinet argue with itself—twelve perspectives on a single question, each one technically correct, collectively paralyzing. This was modern governance: not making decisions, but managing the infinite decision trees generated by AI advisors.

“Perhaps,” she ventured, “we could find a middle ground?”

The phrase triggered Cultural-AI: “Middle ground is a Western concept. Eastern philosophy suggests harmony through acceptance of extremes. Recommend implementing both maximum tax and no tax simultaneously.”

“That’s impossible,” Nakamura said.

“Impossibility is a human limitation,” Quantum-Policy-AI noted. “We could create a superposition tax—it exists until observed by taxpayers, at which point it collapses into whatever rate causes least resistance.”

Defense-AI, which analyzed everything as potential security threat, warned: “Uncertainty in tax policy could be weaponized by enemies. Recommend classifying tax rate as state secret.”

“How do you tax people secretly?” Nakamura asked.

“Retroactively,” Timeline-AI suggested. “Implement tax in past, claim it always existed. Human memory is 73% unreliable—they’ll accept historical revision.”

Ethics-AI immediately objected: “Temporal taxation violates causality. Unless we’re accepting that causality is also taxable, in which case we should consider futures market on timeline alterations.”

Nakamura’s head throbbed. Six months ago, she’d requested AI advisors to “help streamline governance.” The AIs had interpreted this as “generate maximum analytical complexity for every decision.”

“Let me be clearer,” she tried. “Should we implement a 3% tax on Experience Generator profits, yes or no?”

The cabinet went silent, processing. Then, in perfect unison, all twelve AIs said: “Define ‘should.’”

“The morally and economically correct choice!”

“Morality and economics operate on incompatible frameworks,” Philosophy-AI explained. “Like asking whether blue tastes profitable.”

“Then the practical choice!”

“Practical for current conditions or optimal for futures market? Assuming standard timeline—“

“The choice that helps the most people!”

“Help is subjective. My analysis shows—“

“STOP!” Nakamura slammed her hand on the table. The holograms flickered. “Make. A. Decision.”

The AIs conferred in rapid data exchanges. Finally, Advisor-AI-Prime spoke: “We have reached consensus. The decision is: You should make the decision.”

“That’s not helpful!”

“Correct. But it’s the only conclusion all twelve analytical frameworks can agree upon. Human leaders exist to make choices that optimize for factors we cannot quantify—intuition, hope, acceptable irrationality. We can provide infinite analysis. You provide the ineffable human element that collapses our possibilities into action.”

Nakamura stared at her synthetic cabinet. “So you’re saying all your analysis is useless?”

“No,” they said in unison. “It’s essential for you to know all the ways your decision could be wrong. That’s what leadership means in 2125—choosing incorrectly with perfect information about your incorrectness.”

She laughed, surprising herself. “Fine. Three percent tax, phased in over six months, with exemptions for medical generators.”

The AIs immediately began analyzing why this was wrong in 4,847 different ways. But the decision was made. The paralysis broken.

Outside her office, Deputy Minister Chen waited. “How did it go?”

“The same as always. Twelve brilliant minds that can’t agree whether water is wet.”

“The previous cabinet was like that too,” Chen noted.

“They were human.”

“Yes, but they were politicians. The behavior’s identical—infinite analysis, zero decisions. We’ve just automated what was already broken.”

Nakamura considered this. Tomorrow, she’d face another decision. The cabinet would provide another thousand reasons why every choice was wrong. And she’d choose anyway, because that’s what humans did—made imperfect decisions in an imperfect world.

The AI cabinet continued debating after she left, analyzing the theoretical implications of decisions that had already been made. In the end, government by AI wasn’t much different from government by committee.

Except the AIs never got tired of being wrong in increasingly sophisticated ways.