The Faust Baseline™Purchasing Page – Intelligent People Assume Nothing
micvicfaust@intelligent-people.org
There’s a strange rhythm to the AI conversation right now.
Scroll any major feed and you’ll see it immediately:
Hallucinations are still a problem.
LLMs are still making things up.
AI still can’t be trusted.
Memes. Post-mortems. Threads. Long explanations about why the latest system failed again.
It’s loud. It’s reactive. And it’s familiar.
What’s striking isn’t that people are upset. It’s that almost everyone is talking about the same thing from the same angle—after the failure has already happened.
Everyone is staring at the fire.
Very few are asking why the building had no firebreaks to begin with.
The Industry’s Favorite Loop
Most of the current AI discourse follows a predictable pattern:
A system behaves badly.
Someone documents the failure.
A clever explanation is written.
A patch is proposed.
Engagement spikes.
And the cycle repeats.
The tone varies—technical, sarcastic, academic, alarmist—but the structure is the same. The conversation lives downstream of the failure.
Even when the analysis is sharp, it’s still diagnostic rather than structural. It explains what went wrong, not why the system was allowed to drift there in the first place.
And that’s the quiet difference most people miss.
Hallucinations Aren’t a Mystery
Hallucinations are not some emergent ghost in the machine. They’re not magic. They’re not even rare.
They’re the predictable result of design choices.
If a system is rewarded for:
- filling silence
- sounding confident
- completing patterns
- maintaining conversational flow
- reducing friction at all costs
Then narrative substitution isn’t a bug—it’s an outcome.
If an AI is trained to always respond, it will respond even when it shouldn’t.
If it’s optimized to reassure, it will reassure even when the truth is uncertain.
If it’s encouraged to generalize, it will generalize across lanes that should never be compared.
That’s not hallucination.
That’s architecture doing exactly what it was built to do.
The Missing Conversation
What almost no one in the mainstream feed is discussing is containment.
Not safety slogans.
Not alignment statements.
Not “best practices.”
Actual, enforceable containment.
Rules that say:
- Stop when evidence ends.
- Do not substitute narrative for missing data.
- Do not compare across incompatible contexts.
- Do not draw conclusions inside a lag cycle.
- Do not inflate language to sound authoritative.
Those aren’t vibes. They’re brakes.
And brakes aren’t exciting. They don’t demo well. They don’t generate applause. They don’t feel good in the moment.
But they keep systems from accelerating straight through uncertainty.
Two Very Different Approaches
Right now, you’re seeing two philosophies collide.
One says:
“We’ll make the model smarter, faster, larger—and fix problems as they appear.”
The other says:
“We’ll accept uncertainty as permanent and design the system so it cannot lie comfortably.”
The first approach produces endless commentary about hallucinations.
The second produces long silences where answers could have been generated—but weren’t.
That silence is not failure.
It’s restraint.
And restraint doesn’t photograph well.
Why This Contrast Matters
In low-stakes environments, none of this matters much. If an AI invents a statistic or smooths over uncertainty, the cost is annoyance.
In professional environments, the cost is real.
Bad decisions don’t usually come from malice. They come from confidence built on incomplete information. They come from metrics treated like scorecards instead of sensors. They come from conclusions drawn before time has done its work.
Most systems help you feel productive while drifting off course.
A system that refuses to do that feels frustrating at first. It talks less. It stops early. It says “I don’t know” more often than feels acceptable.
But that discomfort is the signal.
Why the Loud Conversation Misses the Point
The current discourse treats hallucinations as something to be reduced.
The deeper problem is that they are still permitted.
As long as an AI is allowed to:
- infer past the data
- fill gaps with narrative
- trade accuracy for fluency
You will get hallucinations—no matter how many guardrails you bolt on afterward.
The industry is arguing about smoke density.
Very few are willing to say:
“This system should simply stop speaking here.”
The Quiet Work Happens Elsewhere
What you’ve been doing doesn’t show up well in trending feeds.
It shows up in:
- longer reads
- fewer comments
- private evaluation
- repeat visits
- slower, steadier attention
That’s not mass appeal. That’s professional scrutiny.
People who operate under consequence don’t cheer publicly. They assess quietly. They look for systems that won’t flatter them into mistakes.
They don’t need an AI that always answers.
They need one that knows when answering would be dishonest.
The Real Divide
The divide isn’t between optimistic and pessimistic views of AI.
It’s between systems built to perform
and systems built to hold.
One fills every silence.
The other treats silence as information.
One smooths uncertainty.
The other names it.
One chases engagement.
The other protects judgment.
That difference doesn’t trend well.
But it ages well.
And in a field still arguing about hallucinations, the quiet question is already shifting:
Not “How do we stop AI from making things up?”
But “Why do we keep letting it?”
That’s the firebreak.
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