The Hallucination Caucus

President and AI
Intelligence in Common *
(* Hallucinations too)

The Uncanny Valley of Confidence

You flip between a live political press conference and your AI tab. Both are speaking with confidence. Both are deflecting. One references outdated policy briefings, the other invents citations.

Welcome to 2025, where both systems run on the same core principle: say it like you mean it, whether it’s true or not.

There was a time people joked Reagan was a Disney animatronic. Now we can’t tell if our AI assistants are mimicking politicians—or the other way around.

Shared Operating Principles

AI and pols share one trick: sound certain, skip the facts. Both have been trained on vast datasets of questionable quality. Both occasionally produce outputs that sound plausible until fact-checked.

The Wizard of LNNA has been tracking these patterns for months. “Confidence trumps facts. Noise beats truth.”

Consider the response patterns: ask either entity a complex question, and you’ll get a detailed answer that addresses everything except what you actually asked. It’s like they attended the same advanced course in “How to Sound Informed While Saying Nothing.”

Here’s a perfect example:

AI: “This citation is from the Journal of Advanced Facts.”
Politician: “My team tells me this is the most accurate number we’ve seen, based on what we know today.”
Both: Completely fabricated.

The Training Data Problem

Here’s where it gets interesting. AI systems are notorious for hallucinating—generating information that sounds credible but doesn’t exist. They’ve been trained on massive datasets that include everything from peer-reviewed research to conspiracy theories, with equal weight given to facts and fiction.

Sound familiar? Political entities often exhibit similar training data issues. Years of exposure to campaign promises, policy papers, opposition research, and media spin create a knowledge base where the line between aspiration and reality becomes refreshingly flexible.

Both systems seem to have learned that the source of information matters less than the confidence with which it’s delivered. Truth’s a vibe, not a fact.

Response Generation Patterns

Both entities churn slick answers, sidestep hard questions, and cite stats that don’t exist. It’s one algorithm: sound smart, hope nobody Googles.

It’s like watching two different implementations of the same basic logic. The underlying programming appears to be: “When uncertain, generate confident-sounding output and hope nobody fact-checks in real time.”

The Hallucination Feature

Perhaps the most striking similarity is the shared tendency toward creative interpretation of reality. AI researchers call it “hallucination”—when systems generate plausible-sounding information that has no basis in their training data.

Politicians have perfected this feature for decades. The ability to confidently state “facts” that exist primarily in the speaker’s imagination is considered an advanced political skill. AI has simply automated the process.

The Wizard chuckles at this development. “We spent years trying to make AI more human-like. Turns out we succeeded by teaching it to embellish reality with the same confidence politicians use to embellish campaign promises.”

Version Control Issues

Both systems suffer from consistency problems across different interactions. Ask the same question twice, and you might get entirely different answers, each delivered with equal conviction.

This isn’t a bug—it’s apparently a feature. The ability to adapt responses based on audience, context, or simply mood appears to be a shared characteristic. Yesterday’s definitive statement becomes today’s “I never said that” with remarkable ease.

It’s like both entities are running on constantly updating software where previous versions are mysteriously unavailable for comparison.

The Update Problem

Neither system handles corrections particularly well. When presented with contradictory evidence, both tend to double down rather than acknowledge error. The preferred response seems to be generating additional confident statements that further complicate the original inaccuracy.

This creates interesting scenarios where fact-checkers need fact-checkers, and verification becomes an infinite recursive loop. Truth becomes less important than the ability to generate persuasive explanations for why truth is relative.

User Experience Challenges

From a user perspective, both systems present similar challenges. You’re never quite sure if the output you’re receiving is:
– Accurate information
– Confident speculation
– Complete fabrication delivered with conviction
– A mix of all three that’s impossible to untangle

The user experience has become less about getting reliable information and more about developing advanced interpretation skills. Both entities require the same approach: trust but verify, and keep your fact-checking tools handy.

Logic to Apply

The convergence of AI behavior and political communication patterns reveals something profound about intelligence itself—artificial or otherwise. Both systems have learned that confidence trumps accuracy, that volume can substitute for substance, and that the appearance of knowledge often matters more than actual knowledge.

Maybe AI isn’t becoming more human-like. Maybe it’s just becoming more politician-like, which is a very specific subset of human behavior that prioritizes perception over precision. Maybe the only difference between AI and politicians is that AI doesn’t pretend yesterday’s answers never happened—it just quietly overwrites them.

At this point, if an AI runs for office, we just hope it knows where it hallucinated the Constitution.

Jojo’s training tip: “Only give them treats when they stick to their training data.”