Why AI Will Never Achieve Consciousness

Rob Urie

Jul 02, 2026

Recent public comments made about AI suggest that Americans have difficulty with the implications of linear time. This is odd given that its conception is largely Western and is centered on the clock time used to coordinate capitalist employment. The conceptual difficulty regards sequencing, or plans for future actions. But it also involves the distribution of profits. 100% of the capital equipment used in Western economic production was produced by workers. So, why does the resulting product belong to financiers rather than those who produced it?

To use a physical metaphor, if I 1) buy a car, 2) aim it in the direction of a cliff, 3) put a stone on the gas pedal and 4) put the transmission into drive, the car will move forward and plunge off of the cliff. Question: did I, through my actions, cause the car to plunge off of the cliff? Or did the car ‘drive itself’ off of the cliff? The answer depends on where you imagine that my own actions ended. In fact, I conceived and created a series of events that if carried through with competence would lead to the car plunging off of the cliff. The car is inert metal and rubber without human direction.

Likewise, if I create and set in motion a three-hundred step algorithm, is the algorithm producing the output, or did I? The distinction is between intent and process. My intent guides the conception and creation of the three-hundred step algorithm. But the work from that point forward is carried out by the algorithm being run in a computing environment. So, the algorithm didn’t conceive of the project. I did. The algorithm didn’t plan (sequence) the project. I did. The algorithm didn’t code the problem. I did. So, who produced the output, me or the machine?

A similar conceptual problem applies to claims of machines ‘thinking.’ Physically speaking, AI is a bundle of algorithms housed within a large computing environment. AI didn’t conceive itself. It was conceived, if memory serves, at Carnegie Mellon University in the 1970s. AI didn’t build itself. It was built in fits and starts by computer scientists in academia and later business. AI didn’t code itself. It was coded by AI developers. And the massive physical infrastructure on which AI depends was built by workers. The point: AI is wholly produced by humans.

The question then is how it is imagined that AI output represents more than the human effort that was put in to creating it? What process makes AI output more than the product of algorithms? If the answer is that something does, are you aware of sequencing algorithms? This would be code that organizes other code to follow a series of steps to complete a task. I’ve conceived and coded sequenced algorithms that run through multi-step processes from a single set of instructions. The output looks like reasoning. And it is reasoning. I coded it. The models did what I coded them to do.

So again, if a series of steps are conceived, planned and launched by humans on equipment that was created by humans, at what point does their dimension shift from inanimate to animate? Or more simply, at what point does a bundle of algorithms housed on a computer think or reason or possess intelligence or consciousness? In fact, the claim that any of these describe AI is a category error. Is a rock rolling down a hill imagined to be rolling itself down the hill rather than being moved by unseen physical forces (e.g. gravity). So, claims that AI can reason emerge from either ignorance or misunderstanding of basic physical processes.

Back in the world, there has been a debate in the West since the early nineteenth century over whether factory automation produces the product of factory automation, or whether the people who automated the factory produced the output? On the one hand, automation creates the appearance that its product is self-generated. On the other, the automation process was created by humans and would not exist otherwise. With the current ability to ‘sequence’ the production process using algorithms, another level of abstraction has been added to this debate.

Having conceived and coded ‘sequencing’ models, most who haven’t find the concept difficult to understand. These models are instructions for how a model ‘thinks.’ Question: how is a model ‘thinking’ when it is just following instructions? Answer: it isn’t. It is just following instructions. What looks like reasoning to AI users is the reasoning coded into the model by human coders. It appears to be reasoning because the instructions it is following were reasoned. It is written instructions being carried out. Nothing more.

The question is political as well in that the answer determines how income is distributed in the West. If ‘capital’ in the form of an automated factory produces the output, do the proceeds then belong to capital, meaning to the capitalist? Without workers first creating the automated factories, there would be no automation process. The political answer was to end the claims of workers to this product through wages. However, while workers receive one-time payments (wages) for their effort, the capitalist receives the profits from this labor for as long as they last.

With AI, this question is back on the table, conceptually at least. Whichever way one cares to perceive AI, as a thinking machine or as a bundle of related algorithms, it was built by workers. AI didn’t conceive itself. It was conceived by workers. This is an important clue into how it works. AI was built by human workers based on their desire to produce a machine that simulates human thought. However, the digital realm is a closed system. All AI ‘knowledge’ has been mediated by humans. Within AI’s Cartesian framing, AI has no direct access to the world. It is the proverbial Cartesian brain-in-a-vat.

One of the paradoxes of debating the nature of AI is that AI models describe themselves as variations on ‘word organizers and word sequencers.’ Focus on the word ‘sequencers’ for a moment. Again, a sequencer establishes and executes the order of a multi-stage process. With the launch of AI, a multi-stage process is set in motion. Words and phrases are identified and matched against similar words and phrases found in AI training sets. The sequencing then runs models to assign the words and phrases their human-determined meaning.

Important to understand is that neither the sequencer nor the broader AI model understands the words and phrases that are being acted on. The meaning of the words, semantics, is created by humans and is stored in a retrieval cache. Sequencing here is the matching of (human defined) meanings to words to provide semantic context to the words and phrases being matched. To be clear, AI ‘decides’ nothing. It is following algorithmic instructions. AI is neither deciding what to do nor how to do it. That is written out for it by humans.

Google AI Chatbot Analogy of AI to a Skyscraper:

End Google AI Chatbot Output------------------------------------------------------------------

The distinction is between coding mathematical models to set in motion a series of steps versus the idea that the models reason on their own. Missing from casual analysis of AI is understanding of how large and complicated this process is. Developers have been building a ‘thinking machine’ in earnest since the 1970s. The infrastructure needed to run AI approximates that of a modern skyscraper. The question that has yet to be answered is: is AI worth it? Is it a crucial new technology that will justify its costs, widely considered? Or is it an occasionally interesting toy whose environmental footprint will end the planet?

Recent public discussion has puzzled over how AI can solve math problems if it doesn’t think? Consider the concept from physics of ‘work.’ What those considering the matter are imagining is lone mathematicians sitting in rooms and thinking through the solutions to math puzzles. But with unlimited computing power, optimization programs can use brute force computing to work through every conceivable iteration of a problem in seconds. What AI users aren’t seeing is the skyscraper’s worth of infrastructure behind the scenes producing a result.

Doesn’t this vast computing power illustrate the value of AI? No. It gets to the nature of technology. One explanation of technology is that it provides a benefit. Another is that it simply changes that way that humans do things. On the one hand, we can drive long distances quickly in cars versus walking. On the other, many of us now spend three hours per day sitting in traffic in cars. So, are cars a benefit? In some ways yes, in some ways no. What they aren’t is an unequivocal benefit, meaning that the jury is still out.

Image: the guts of the automaton featured in the movie Hugo. The mechanical refinement of fake humans can be seen in the gearing. The thought was that finer gearing made automatons closer to being human. That in retrospect the automaton can be seen as a better robot rather than being closer to human is an important insight for understanding AI. AI is a digital robot. It is no closer to thinking or reasoning than a doorstop. Source: dickgeorgecreatives.

If asked if they would like a machine that transports them from one place to another quickly, most Westerners would likely answer yes. When asked if they want to spend three hours per day sitting in a car in traffic, most Westerners would likely answer no. But the latter is the direct consequence of the prior. This is how capitalism works. We are offered a benefit. In the current case, the ability to travel quickly from one place to another. But almost immediately the social consequences of the ‘benefit’ become a burden that hadn’t been imagined when the benefit was offered.

In the present, a lot of Americans are worried that AI can think. It will take our jobs. But what we should be worried about is that AI can’t think. It is but one more layer of labor de-skilling. Consider: AI ‘art’ is artless. AI ‘thought’ is the aggregated wisdom of the Pentagon cobbled to the AEI (American Enterprise Institute). Every AI query written increases greenhouse gas emissions to levels that are suicidal for the species. And AI ‘solutions’ are regurgitated feints like carbon capture. All of the proposed solutions will more likely make the problems worse.

While AI users imagine that ‘thought’ is producing AI results, what is in fact being applied is work. Work here is similar to the concept of horsepower, the crude conversion of the pulling power of horses to that produced by an internal combustion engine. Recall the lone mathematician sitting and thinking. Now imagine running an AI program that is the equivalent in terms of capacity of 10,000 humans laboring for one million years. One would imagine that a lot of complicated questions could be answered in such a scenario.

Google Gemini AI Output

End Google Gemini AI Output---------------------------------------------------------

Were 10.000 humans to labor for one million years, this would represent the largest undertaking in human history. And given that humans have finite lifespans, this thought experiment is entirely conceptual. Further, AI doesn’t use the methods of mathematicians. Instead of isolating a metaphorical tree in a forest by its qualities (the mathematician), AI chops down every other tree in the forest to declare that the tree left standing is the solution (optimization).

AI’s methodology represents a different way of solving math problems that may be of interest to a few dozen mathematicians, but that comes with a computational cost equivalent to a moon landing. Were 10,000 humans actually put to the task of solving mathematical problems, questions of agency and whether or not this is a good use of social resources would arise. It is only by hiding / sidelining the question of environmental and social costs that AI is claimed to add value beyond profits for a few insid

[truncated for AI cost control]