Three reasons why AI is not conscious and perhaps never will be

The Philosophy of Language Models

ABSTRACT The success of large language models (LLMs) across many domains of AI research has generated intense debate. Some attribute their impressive performance on complex tasks to human‐like linguistic and cognitive capacities, whereas others ascribe it to shallow pattern matching. These disputes stem from deep‐seated philosophical disagreements about the nature of language and cognition. We provide an opinionated survey of these disagreements across core topics in the philosophy of mind and language, including syntactic competence, compositionality, linguistic meaning, representation, attitudes, reasoning, agency, and consciousness. We contend that progress on these issues requires not only clarity about background philosophical commitments but also, in many cases, close engagement with emerging empirical evidence.

The Human Skill That Eludes AI

Why can’t language models write well?

The Thoughts The Civilized Keep

The hype around a new AI language generator reveals the sterility of mainstream thinking on AI today — and indeed on how we think about thinking itself.

Why I believe current AI models can really think and understand - Part 1 of 2

How the human mind is different from our conception of it

AI Is Not Conscious, But It Is Our Unconscious

The Convivial Society: Vol. 7, No. 4

AI learns language from skewed sources. That could change how we humans speak – and think | Bruce Schneier

Large language models aren’t trained on real-life conversations. As we encounter their language, it could affect our own

Sense-making reconsidered: large language models and the blind spot of embodied cognition

Large Language Models (LLMs) demonstrate a kind of linguistic competence that theories of embodied and enactive cognition have long deemed impossible for systems lacking the meaningful perspective of a living being, i.e., the capacity for sense-making. Facing up to this unexpected technological development requires confronting what I propose to call the “AI dilemma”: either frontier LLMs are capable of sense-making despite lacking biological embodiment, or the kind of linguistic competence they exhibit does not necessarily require sense-making. In their chapter on cognition, Frank, Thompson, and Gleiser (2024) maintain that no AI system comes close to realizing relevance, a position that derives much of its motivation from past practical failures. However, frontier LLMs have effectively overcome Dreyfus’ commonsense knowledge problem, such that their dismissal as categorically mindless risks undermining Frank et al.’s central claim that human cognition is deeply intertwined with lived experience. I therefore argue in favor of the alternative side of the AI dilemma: human-level linguistic competence of LLMs should be recognized as a novel non‑biological form of sense‑making, based on a technologically‑mediated embodiment whose enabling properties are in need of further theoretical analysis. This reorientation invites enactive theory to clarify which aspects of sense-making may be universal and which aspects are specifically contingent on organic life, thereby advancing its conceptual framework in dialogue with contemporary AI.

Nassim Nicholas Taleb on Twitter / X

The problem isn’t that AI is intelligent. It isn’t. The problem is that too many adults (but not all) are at once mechanistic, imitative, unimaginative, stupid, formulaic, influenceable, & gullible, arrogant beyond their skill set, & too expensive to maintain.— Nassim Nicholas Taleb (@nntaleb) October 24, 2026

The Octotypic Mind

Carcinization, Cognitive Prosthetics, and the Shape of Intelligence After AI

Machine god metaphors eat your brain

There are other ways of thinking about AI

Humans can coexist with AI. Here’s how.

Some advice for people who are anxious, skeptical — or hoping AI just goes away.

Are AI Models on the Autism Spectrum? Exploring the Parallels

Large language models are celebrated for their ability to process information quickly and generate human-like responses. However, they…

We Don't Understand Neural Networks At The Algorithmic Level

The largest ongoing debate about AI is “Are Large Language Models (LLMs) intelligent?” That makes sense, at least: the evidence is ambiguous and the stakes a...

Reciprocal Research — Empirical AI Consciousness Research

Developing the empirical science of AI consciousness.