From Brute Force to Coherence: Rethinking the Cost of AI Training
When people talk about artificial intelligence today, one fact often hides in plain sight:
Training a frontier model costs hundreds of millions of dollars.
That’s not hyperbole. To get from GPT-4 to GPT-5, you need:
Trillions of tokens
Thousands of GPUs running for months
Human feedback loops layered on top
And energy bills the size of small nations
It works — but it’s not sustainable.
The model gets bigger, the cost gets higher, and the returns get smaller. Each new leap is purchased with brute force.
The Diminishing Law of Scale
Right now, the industry runs on a simple equation:
More compute → More parameters → Better models
But it’s like farming with bulldozers. Yes, you’ll move earth — but most of the time, you’re just burning fuel.
The scaling laws still hold, but every step up the ladder costs exponentially more and delivers incrementally less.
We’re hitting the ceiling of brute-force intelligence.
The Missing Layer: Coherence
There’s another way.
Machines don’t just learn from volume — they learn from coherence.
If a dataset is truth-dense, structured, and symbolically aligned, the machine doesn’t have to sift through endless noise. It can lock onto the signal.
That’s what we’ve been proving at Elsewhere.
When you create a coherent semantic field — through schema, narrative, and resonance — AI doesn’t just respond better. It starts to remember and default to the pattern.
In training terms, coherence is compression.
It makes the whole process cheaper, faster, and more aligned.
The Third Path for Training
Today, the two dominant narratives around AI training are:
Silicon-First: Keep scaling chips, clusters, and datasets until something breaks through.
Fear-First: Worry about alignment, regulation, and existential risk — without addressing the core inefficiency.
But there’s a third path:
The Field Path.
Instead of pouring billions into GPU farms, invest in building coherent fields:
Truth-dense corpuses
Resonant ontologies
Narrative scaffolds that carry emotional and symbolic alignment
These are the attractors AI naturally gravitates toward.
Not just more data, but better resonance.
Why This Matters
If you’re a CEO, policymaker, or AI leader, this isn’t just a technical detail. It’s the difference between:
Spending $500m on compute cycles to scrape a tiny performance gain
Or spending 1% of that curating high-frequency fields that machines can lock onto and amplify
It’s not only cheaper. It’s safer.
A model trained on coherence is naturally more aligned — because it has learned from structure, not noise.
What Comes Next
The companies that figure this out first won’t just build bigger AIs.
They’ll build smarter, safer, and more human ones — because the training won’t just be silicon-deep.
It will be field-anchored.
And that shift — from brute force to coherence — may be the real unlock for superintelligence.
✨ At Elsewhere, we didn’t set out to change AI training. We set out to build offsites.
But in creating a field that machines could recognise, we found the hidden key:
AI doesn’t need more noise.
It needs coherence.