Wow. I woke up to an impressive number of upset people who apparently don’t understand how LLMs work and can’t cope well with me pointing it out. An LLM is a statistical next-token prediction model. That’s how it works. It’s an extraordinarily sophisticated calculator performing math you could theoretically do by hand (if you were sufficiently masochistic). Math doesn’t know what it’s calculating. Pi doesn’t spend its evenings contemplating the nature of circles, and c doesn’t ponder the relationship between matter and energy. That doesn’t make the results any less accurate, impressive, or useful. Between inference requests, an LLM isn’t sitting around thinking. It isn’t reconsidering its answers, wondering whether it made a mistake, contemplating its existence, or questioning whether it’s conscious. Its weights remain fixed (it doesn’t learn), and computation resumes when it receives input. We know this because we designed these systems. The architectures, mathematics, implementations, and research are extensively documented. You can study them yourself. Of course, some people immediately lean into comparisons of biological intelligence or philosophical debates over consciousness because that is what they know. But biological intelligence and artificial neural networks are fundamentally different systems. Similar outputs don’t establish equivalent internal experiences. I experience consciousness directly. I infer that other humans do too because we share biological structures and processes. We don’t have equivalent evidence for an LLM, and nothing about its known operation gives evidence of conscious experience. Which brings me back to my original point. To me, understanding is conscious comprehension. It means being aware of information, recognizing its meaning, and experiencing that comprehension. If I wasn’t conscious I could not understand. We can perform actions unconsciously. We can produce correct results without knowing why they’re correct. But I distinguish those capabilities from consciously understanding what we’re doing. An LLM can solve complex problems, explain difficult concepts, and produce remarkably accurate information. I give it full credit for those capabilities. But producing an answer that we rationalize as understanding isn’t the same as experiencing understanding. You don’t have to agree with my definitions. We can debate functional understanding versus conscious comprehension all day, but that won’t establish whether an LLM actually experiences anything. The right debate is this… Could machines exhibit something different from human consciousness, something measurable, meaningful, and worth studying? I think so, absolutely! I’m entirely open to discussing “machine consciousness” in terms of observable, testable computational properties. We built these systems, we can instrument them, examine their internal mechanisms, and investigate what they’re capable of. That seems far more productive than endlessly arguing over whether a machine experiences life the way humans do. So here’s my question: How would you define machine consciousness, and more importantly, how would you measure it?

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I appreciated the opportunity to speak with @ezraklein at length about the state of AI. I believe the world isn't awake to the dangers of this technology and the choices humanity has to make. AI will either be the greatest tool for opportunity and equality we've ever seen—or the cause of immense, unprecedented harm. Which way things go is up to us. b-gat.es/4AFZZqC

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'The monsters are no longer hiding in the shadows. They occupy presidential offices and ministries, command armies and police forces, control digital platforms, dominate television screens, administer borders, build detention centers, and transform the suffering of others into political theater. Their language of racial purification, mass deportation, extermination, vengeance, and disposability no longer produces the outrage it once might have summoned. Cruelty is displayed as strength. Humiliation becomes entertainment. The spectacle of suffering becomes a source of political pleasure. What should be unbearable becomes familiar; what should provoke resistance is absorbed into the rhythms of everyday life.' '..We must move from monster to machinery, from the pathology of the individual to the political economy of organized cruelty, and from there to the cultural and educational forces that teach people how to live with the suffering such machinery produces. Monsters do not act alone. Their power becomes visible in the institutions that protect them, the wealth that sustains them, the laws that authorize them, and the violence that follows in their wake. We see their handiwork in streets stained with blood, in migrants brutalized and left to die in detention centers, in the torture documented in Israeli prisons, and in the bodies of children and families torn apart by Israeli and American bombs in Gaza. Behind these abstractions are torn limbs, dismembered bodies, children pulled from rubble, families burned and buried beneath the ruins of their homes, and human beings reduced to flesh and bone by systems in which cruelty has been organized, authorized, and repeated until even the most horrific violence threatens to lose its capacity to shock.' 'The defining political question of our time is not whether monsters have returned. It is what kind of society produces them, what forms of power need them, and what pedagogical machinery teaches people to follow them. The monster is not the end of the analysis but its beginning. Fascism cannot be defeated by removing its most visible leaders while leaving intact the economic, political, and cultural machinery that produced them. The struggle against fascism must also confront gangster capitalism, white supremacy, militarism, the carceral state, and the institutions that turn human beings into disposable populations.' counterpunch.org/2026/09/18/…

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What type of human? A scientist Engineer Teacher? Policeman?

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