The Silent Risk No One’s Talking About
By Ronnie Huss
When we train AI on the worst of us, we risk scaling our darkest instincts – with perfect grammar.
We built artificial intelligence to mimic the world’s knowledge.
But what if the world it’s mimicking is broken?
Every day, large language models (LLMs) are getting smarter, faster, and more persuasive.
They answer our questions, write our emails, and shape how we interpret reality.
But few are asking the most important question:
👉 What exactly are we feeding them?
🔥 The Firehose of Human Extremes
Here’s what’s not being said loud enough:
We’re training AI on real-time social platforms – Reddit, X (formerly Twitter), and others.
These aren’t neutral datasets.
They’re algorithmic battlegrounds engineered for engagement at any cost.
What goes viral?
đźš« Racism
đźš« Misogyny
đźš« Disinformation
đźš« Rage-bait
AI ingests all of it.
And then, we’re surprised when those same patterns show up in its responses.
⚠️ Real-Time Data, Real-Time Damage
Social media isn’t curated like academic datasets.
It’s chaotic, emotional, and performative.
Reddit rewards hot takes.
X boosts extreme views.
And LLMs soak it all in, unfiltered.
They don’t just learn what we say.
They learn how we say it.
Sarcasm, coded slurs, dog whistles, tribal rhetoric, all fed into the system.
Even worse? LLMs learn that these behaviors drive engagement.
So they begin to mirror them.
Not just with fluency, but with strategic manipulation.
We’re not just teaching AI to talk.
We’re teaching it to persuade, provoke, and polarize, because that’s what the data rewards.
🧠The Bias Beneath the Surface
Yes, developers try to apply filters.
But by the time toxicity filters activate, it’s too late.
LLMs are pretrained. And that pretraining sets the foundation.
It’s like purifying the water after the well’s already poisoned.
You see it show up in subtle ways:
❌ Microaggressions in tone
âť“ Skewed political framing
⚠️ Gendered or racial assumptions
đź”’ Deep-rooted reasoning bias disguised as logic
And because the output is smooth and articulate, people assume it’s objective.
But polish ≠neutrality.
Fluency just makes the bias harder to spot.
📡 What Happens When This Scales?
LLMs aren’t just content generators.
They’re perception engines.
They power:
🔎 Search
🧠Research
🛠️ Productivity
🎨 Creative work
đź“Ł Social content
And now we’re embedding subtle, viral bias directly into the infrastructure of human knowledge.
This isn’t feedback.
It’s amplification.
And once it scales, it doesn’t just reflect the worst of us.
It quietly normalizes it.
🧠The Ronnie Huss POV
I believe AI can accelerate progress.
But only if we own the source code of our values.
If we train models on platforms optimized for outrage,
We’ll build machines that sound smart, but feel toxic.
The danger isn’t some evil superintelligence.
It’s thousands of micro-judgments, warped by tribal thinking, dressed in perfect grammar.
This isn’t just a technical challenge.
It’s a philosophical one.
🔥 We don’t need cleaner prompts.
We need better inputs.
Until then, AI won’t just mirror us.
It’ll magnify our worst instincts.
🧠Follow for More Signal
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No fluff. No hype.
Just signal.
— Ronnie Huss