A clarification before taking sides

Recent progress in GANs, LLMs, and large-scale machine learning systems is real.
These systems represent a genuine engineering breakthrough.

They have achieved something that was previously impractical at scale: the statistical extraction and completion of abstract conceptual structures.

The problem is not that these systems are useless.
The problem is that we are placing them into a conceptual category they do not belong to.

What these systems actually do

At their core, modern ML-based systems operate by:

  • learning high-dimensional statistical regularities
  • extracting abstract patterns from large corpora
  • completing sequences in ways that align with those patterns

In language models, this appears as fluency, coherence, and contextual sensitivity. In generative models, it appears as realism and stylistic consistency.

This is statistical intelligence.

It is powerful. It is valuable. It is also limited in very specific ways.

What they do not do

Despite common narratives, these systems do not:

  • form their own goals
  • possess intentions
  • maintain a world model grounded in consequence
  • understand the meaning of action
  • bear responsibility for outcomes

Any appearance of reasoning, planning, or decision-making emerges from pattern completion guided by external input.

The system does not decide. It responds.

Why this matters for the concept of an agent

If we use the term agent rigorously, it implies more than competence.

An agent minimally requires:

  • ownership of goals
  • continuity between action and consequence
  • the capacity to evaluate outcomes
  • responsibility over time

Current ML-based systems satisfy none of these conditions.

They are not agents. They are interfaces over statistical structure.

Calling them agents is not metaphorical shorthand. It is a category error.

The role of prompting: delegated intelligence, not emergent agency

Much of what is described as “reasoning” in LLMs is better understood as delegated human abstraction.

Prompting does not create intelligence. It externalizes the user’s cognitive structure and asks the model to complete it.

This explains a consistent observation:

  • the systems are extremely useful to people with strong abstraction skills
  • they are far less useful to those without them

The capability does not generalize downward. It amplifies what already exists.

This alone makes these systems unsuitable as universal agents.

Why this cannot lead to AGI by scaling alone

From this foundation, the leap to AGI or ASI is not incremental—it is discontinuous.

Statistical abstraction does not naturally evolve into:

  • autonomous goal formation
  • value generation
  • ethical judgment
  • social understanding

Scaling computation improves interpolation within a space. It does not create new kinds of space.

Pursuing AGI through ever-larger models and more electricity is a misallocation of intellectual and material resources.

It mistakes capacity for agency.

The real cost of this misclassification

When we label statistical systems as “AI agents,” several things happen:

  • responsibility is implicitly shifted away from humans
  • system failures are treated as intelligence problems rather than design errors
  • governance focuses on model behavior instead of institutional boundaries
  • resources flow toward compute instead of human capability

The result is impressive demonstrations with limited social impact.

What is being neglected instead

The real bottleneck is not model intelligence.

It is:

  • how humans think
  • how humans abstract
  • how humans coordinate
  • how humans use language as an operational tool

We invest heavily in artificial second brains while neglecting the conditions that strengthen the first.

This is why progress appears dramatic in research settings yet shallow in everyday life.

A different framing

The central question is not:

How intelligent can our models become?

It is:

Do these systems meaningfully improve human judgment, coordination, and life?

That question cannot be answered by scaling models alone. It requires rethinking how language, tools, and institutions interact.

Closing

Statistical intelligence is a real breakthrough. It deserves precision, not mythology.

The danger is not artificial intelligence. The danger is artificial agency—
assigning responsibility, authority, and expectation to systems that were never designed to hold them.

Until we correct this classification, we will continue to misplace effort, responsibility, and hope.