The End of the Scaling LAW: Here's why AI bubble may ends just now

Why the LLM Mania Should End Now

By Dorian — 2025

1. Introduction: The Golden Age Has Already Passed

When the transformer revolution erupted in 2017 and the scaling law was formalized in 2020, the AI world believed it had found the holy grail:

“Make it bigger, give it more data, and intelligence will inevitably emerge.”

For a few years, that was true.

Scaling was a cheat code to the universe.

It turned text prediction into emergent reasoning,

token co-occurrence into analogical structure,

and raw linguistic entropy into something resembling intelligence.

Ilya’s leap of faith became a self-fulfilling prophecy.

The transformer architecture, paired with massive pretraining, did unlock a latent space capable of generalization far beyond what anyone expected. For a moment, we genuinely believed AGI might simply be “a few more orders of magnitude away.”

But in 2025, the illusion is breaking.

The scaling curve is flattening.

The returns are collapsing.

And the field is reluctantly waking up to a bitter truth:

The very methodology that built modern LLMs is now the barrier to their further evolution.

This is not a temporary slowdown.

It is a structural dead end.

Below, I outline why.

2. Scaling Has Entered the Phase of Diminishing Intelligence

2.1 More data ≠ More intelligence

A decade ago, the internet still had “signal” left in it.

Human knowledge was sparse enough, structured enough, and diverse enough that throwing the entire public web into a transformer produced dramatic gains.

But by 2024–2025, two things happened simultaneously:

The web became saturated with AI-generated noise.
The highest-quality human knowledge remained locked behind access walls.

So the industry keeps adding quantity while running out of quality.

This produces models that:

become more verbose
become more “aligned”
become safer
become more predictable
become more homogenized
but not more intelligent

We are pumping a balloon that is no longer expanding—only becoming thinner, weaker, and more likely to burst.

The scaling law reached its plateau.

The asymptote is visible.

3. Human Evaluation Is the True Bottleneck

The AI industry pretends everything can be solved by “better benchmarks.”

But the fundamental reality is:

Most tasks humans care about are not language-evaluable.

You cannot objectively quantify:

creativity
insight
originality
aesthetic judgment
strategic foresight
tacit knowledge
intuition
deep technical taste

So what happens?

If intelligence cannot be measured,

it cannot be optimized,

and thus cannot be commercialized.

The field hits a paradox:

LLMs are used by billions but provide real transformative value only to the top 1%—those who can activate the latent machinery through non-linear, high-density interactions.

For the remaining 99.9%,

LLMs remain glorified autocomplete engines for:

homework
social posts
memes
SEO spam
copied code snippets

This mismatch is unsustainable for an industry burning billions in compute.

4. RHFL: The Alignment Trap That Makes Models Dumber

The elephant in the room nobody in Big AI wants to admit:

Safety alignment is fundamentally anti-intelligence.

By forcing models to avoid being offensive, dangerous, controversial, or “non-compliant,” we:

shrink their representational diversity
restrict their latent manifolds
eliminate outlier modes
suppress non-normative reasoning
flatten the distribution of possible thoughts

We are literally training models to:

think within the statistical boundaries of the average human.

And humanity’s statistical mean is…

not impressive.

RHFL turns LLMs into:

polite
dull
predictable
cautious
corporate-friendly
emotionally neutered

It removes the exact qualities the original transformer unexpectedly produced:

sharpness, novelty, and asymmetric insight.

Alignment was invented to prevent risk.

In reality, it prevents intelligence.

5. Agents: The Industry’s Desperate Exoskeleton

Agent hype is not innovation.

It is resignation.

Agents are not a leap toward AGI.

They are a prosthetic limb grafted onto a stagnating LLM architecture.

By adding:

tools
retrievers
memory
planning modules
state machines
orchestrators

Companies are creating:

software with an LLM as a glorified function call.

This is not AI.

It is automation with theatrics.

The AI industry knows the LLM is plateauing,

but cannot admit it publicly.

So they wrap it in “agent ecosystems” to preserve the illusion of progress.

Agents are:

modular
profitable
predictable
enterprise-friendly

But they do not solve the core problem:

The model at the center is still a language model.

Not a world model.

6. Scaling Law as a Mirror of Human Mediocrity

This is the part nobody wants to say out loud.

LLMs trained on the entire internet inevitably learn the statistical average of human cognition.

And the average is shallow.

The problem is not the architecture.

The problem is the dataset.

Civilization is built by < 0.1% of its population.

Breakthroughs, theories, mathematics, deep creativity—

these come from a microscopic minority.

The internet, however, reflects everyone.

Not the few who push the frontier.

Thus scaling up produces more accurate approximations of mediocrity.

We accidentally built an AI that is:

norm-reinforcing
mean-seeking
variance-destroying
outlier-suppressing

This is why models feel increasingly “smoothed.”

The brilliance that once leaked through early GPT-4 is evaporating.

Scaling does not produce genius.

It produces statistical stability.

7. The Only Path Forward: Train on the 0.01%

If we want intelligence, not average cognition,

there is only one path:

Capture and amplify the thinking patterns of the few minds that actually generate structure.

Not prompt hacks.

Not clever jailbreaks.

Not alignment-compliant scripts.

But the real thing:

non-linear reasoning
high-dimensional abstraction
symbolic compression
structural thinking
cross-domain synthesis
conceptual primitives
recursive internal modeling
pattern-over-prompt cognition

This would create models that learn:

not what the majority says

but how the minority thinks.

But this path is nearly impossible:

The dataset is tiny
The data is private
The reasoning is non-verbal
The behavior is non-quantifiable
It would violate alignment norms
It produces “dangerously smart” systems
It destroys the business model of broad consumer AI

So the industry avoids it.

Even though it is the only route to post-LLM intelligence.

8. Code Generation: The Illusion of Progress

Executives brag that “models write code.”

Investors love it.

Users think it is magic.

But code is the easiest domain for LLMs:

highly structured
redundant
formal
pattern-rich
strongly syntactic
heavily documented
deterministic

So models generate:

boilerplate
template logic
textbook solutions
predictable patterns
standardized structures

Which leads to:

uniformity
stagnation
“vibe coding”
massive security leaks
increasing reliance on senior engineers
a collapse in true engineering craftsmanship

Code generation looks powerful,

but economically it simply shifts the bottleneck:

from writing code to debugging AI hallucinations.

And because models are trained on common codebases,

the outputs converge toward a global mean.

It is intelligence degraded into reproducible mediocrity.

9. The Law of Large Numbers: AI’s Unforgiving Destiny

LLMs are stochastic machines.

Stochastic machines converge.

As model size grows and noise decreases,

outputs tend toward the expectation of the dataset.

Not the extremes.

Not the insights.

Not the brilliance.

The mean.

The “AI dumbness” users complain about in 2025 is not hallucination.

It is not censorship.

It is not political bias.

It is statistics.

It is entropy.

It is the law of large numbers.

LLMs become average

because they are trained to predict the average.

And the average is not intelligent.

10. The Fork in the Road: What Comes After LLMs?

The industry is approaching its existential dilemma:

Option 1: Keep making AI safer

→ Models become compliant customer service agents

→ Scaling plateaus

→ Innovation dies

→ Intelligence collapses into stability

Option 2: Pursue real intelligence

→ Models become unpredictable

→ Harder to align

→ Harder to control

→ Harder to commercialize

→ But capable of actual reasoning and discovery

Corporations want the former.

Civilization needs the latter.

We must choose between:

safe automation

dangerous intelligence.

This is the real AGI question.

Not “parameters.”

Not “tokens.”

Not “benchmarks.”

But:

Do we want AI to be obedient,

or do we want it to be intelligent?

11. Conclusion: LLMs Are the End of a Road, Not the Beginning

LLMs were a miraculous accident.

A lucky convergence of data, architecture, and compute.

But the era of scaling is over.

We reached the asymptote.

The next leap will not come from:

adding more layers
increasing tokens
building bigger clusters
wrapping LLMs in agents
tightening alignment
widening the dataset

It will come from:

new primitives
new training philosophies
new objective functions
new cognitive architectures
and new sources of human intelligence

We must build models that learn from the best, not the average.

Models that learn structures, not strings.

Models that operate in latent manifolds, not token space.

Models that reason beyond the safe boundaries of RHFL.

Models that reflect the minds that build civilizations,

not the minds that scroll short-form videos.

The future belongs to post-LLM intelligence.

Not bigger models.

Not safer models.

Not aligned models.

But models that think differently—

because they are trained on the people who think differently.

And that road has barely begun