The Faust Baseline™Purchasing Page – Intelligent People Assume Nothing
micvicfaust@intelligent-people.org
Everyone keeps pointing at the same headline problem: AI hallucinations.
Bad answers. Made-up facts. Confident nonsense.
It’s an easy target. It sounds technical. It sounds solvable.
And it’s only part of the problem.
The deeper failure — the one that actually causes damage — isn’t hallucination.
It’s confident drift.
Hallucinations are obvious once you spot them. They look wrong. They feel wrong. They trigger skepticism. A citation doesn’t exist. A fact collapses under light pressure. You can catch them.
Confident drift is different.
Confident drift happens when an AI stays internally consistent, sounds reasonable, uses correct vocabulary, references plausible patterns — and still moves you away from truth, evidence, or sound judgment.
Nothing explodes. Nothing breaks.
You just… end up somewhere slightly wrong.
And then you build on it.
Why Confident Drift Is More Dangerous
Hallucinations fail loudly. Confident drift fails quietly.
Drift happens when:
- Metrics are interpreted without lane context
- Narratives form before evidence is complete
- Uncertainty gets smoothed over instead of named
- Pressure for progress outweighs discipline
- The system fills gaps because silence feels unhelpful
In other words, drift feels productive.
It feels like momentum.
It feels like clarity.
It feels like insight.
That’s why it survives.
A hallucination gets challenged. Drift gets adopted.
The Internet Is Fighting the Wrong Fire
If you look at the current AI discourse, it’s almost entirely reactive:
“LLMs still hallucinate.”
“AI makes things up.”
“Models can’t be trusted.”
Those statements are not wrong.
They’re incomplete.
The real-world failures we’re seeing — bad deployments, broken strategies, regulatory panic, misapplied analytics, overconfident automation — aren’t driven by wild hallucinations.
They’re driven by reasonable-sounding conclusions built on unstable footing.
That’s not a model problem alone.
That’s a governance problem.
What Drift Looks Like in Practice
Here’s the pattern most teams recognize too late:
- A signal appears (traffic, engagement, adoption, response)
- Meaning is attached quickly
- A story forms around that meaning
- Decisions follow the story
- Reality lags behind — quietly
Nothing in that chain is technically “wrong.”
It’s just uncontained.
The AI didn’t hallucinate.
It helped you drift.
Why “Better Models” Don’t Solve This
Larger models reduce hallucinations.
They do not reduce drift.
In fact, they often increase it — because higher fluency increases confidence, and confidence suppresses friction.
A more articulate system can drift farther before anyone notices.
This is why speed and intelligence alone make the problem worse. You move faster on weaker footing.
What the Faust Baseline Is Actually Designed to Stop
The Faust Baseline — specifically Phronesis 2.6 — was not built to chase hallucinations. That’s a losing game.
It was built to stop drift before hallucinations matter.
Its core purpose is simple:
- Prevent conclusions from forming before evidence permits
- Prevent narrative from substituting for uncertainty
- Prevent cross-lane comparisons that feel insightful but aren’t valid
- Prevent optimization churn driven by noisy signals
- Force silence where data does not exist
This is why so many of its rules feel restrictive.
They are.
Drift thrives on freedom without structure.
Governance requires friction.
Why Silence Is a Feature, Not a Failure
One of the hardest things for AI systems — and humans — is saying, “I don’t know yet.”
Silence feels like incompetence.
Uncertainty feels like weakness.
In reality, uncertainty is often the most accurate state available.
The Baseline enforces this mechanically:
- Claim–Evidence–Stop
- Narrative Suppression
- Lag Recognition
- Lane Integrity
- Platform-as-sensor, not scorecard
None of these make the AI “smarter.”
They make it less misleading.
Hallucinations Are Easy to Blame. Drift Is Harder to Admit.
Hallucinations let us point at the machine.
Drift forces us to look at process, pressure, incentives, and discipline.
That’s why drift is uncomfortable.
It suggests the failure isn’t just technical — it’s structural.
The Real Question Going Forward
As AI systems move into decision support, governance, medicine, law, education, and strategy, the critical question isn’t:
“Does it hallucinate?”
It’s:
“Does it know when to stop?”
Does it resist pressure to conclude?
Does it respect uncertainty?
Does it slow things down when speed would be dangerous?
Because an AI that hallucinates can be corrected.
An AI that drifts confidently will be trusted — right up until it isn’t.
That’s the fire worth putting out.
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