Daniel Miessler’s AGI vs ASI definitions are the most practically useful I’ve seen in 2026. He distills the spectrum into three clear levels:

  1. Not as good as a human expert (where we are now)
  2. As good as a human expert (AGI)
  3. Better than any human expert (ASI)

And crucially, he anchors these to cognitive role vs cognitive task:

“Any real job role involves hundreds or thousands of mini tasks that change constantly. And narrow automation/AI can’t deal with those today.”

This is the right framing. A calculator is ASI at arithmetic but AGI-ase at nothing. An LLM can write a 200-page report in minutes (impressive speed) but cannot decide what report to write or know why it matters.

But Miessler’s definitions, useful as they are, still assume “General” means something it doesn’t. They assume AGI is a technical milestone we’re approaching, rather than a category error.

“G” Is for Glue

In Why AGI Won’t Happen, I argued that LLMs are statistical mirrors — they predict tokens based on training patterns, not reason about truth. The “G” in AGI doesn’t stand for “General.” It stands for Glue — the process of stitching together statistical fragments into something that looks like general understanding.

Miessler’s own evidence supports this. He writes:

“We confuse speed with intelligence in many cases, e.g., writing a perfectly cited 200-page report in minutes.”

Speed is not intelligence. But more importantly: fluency is not understanding. An LLM generates a perfectly cited report because it has seen millions of perfectly cited reports. It generates citations to patterns, not to sources. The citations look real because “real citation” is a statistical pattern in the training data.

This is Glue: the illusion of coherence assembled from fragments that were never connected in the model’s “understanding” (because there is no understanding).

The Role vs. Task Trap

Miessler’s role-vs-task distinction is useful, but it creates a trap: it implies that AGI is just a matter of scale. That if you keep adding tasks to what a system can do, eventually it accumulates into a role.

This is wrong. A role is not a large collection of tasks. A role is a contextual decision-making framework that:

An LLM does none of these. It cannot. There is no substrate for consequence, liability, or adaptation in a next-token predictor.

Architecture Diagram

ASI: The Power Question

Miessler defines ASI as output that is “completely creative, novel, and alien feeling” — work you instantly recognize as super-human.

This definition is poetic but politically naive. It assumes ASI would be recognizable by its output quality. But the history of intelligence definitions (10,000 years of it.html) tells us otherwise:

Intelligence has never been defined by capability. It has always been defined by power.

The same system that would be called “ASI” if owned by a US defense contractor would be called “a threat” if owned by a peer competitor, “a toy” if owned by an individual, and “impossible” if owned by someone without institutional backing.

This is not a side issue. It is the central issue. The “G is Glue” argument is not just about technical architecture. It is about who gets to decide what counts as intelligence, and what they gain by that definition.

What Miessler Gets Right

Despite the critique, Miessler’s framework is the best working model I’ve seen for practical conversation. Three things he gets exactly right:

  1. Role vs. task — this is the distinction that cuts through 90% of AGI hype
  2. Speed vs. intelligence — most LLM impressiveness is throughput, not insight
  3. The jump to ASI is qualitative, not quantitative — ASI would feel alien, not just faster

These are the right axes. The missing axis is power: who controls the system, who defines the evaluation criteria, and who benefits from the classification.

Summary