Is Copilot a Better Author?

As an experiment I asked Microsoft Copilot to analyze my blog post

AI, The Digital El Dorado

I have come to the conclusion that the search for Artificial General Intelligence (AGI) is a modern quest for a mythical solution with no foundation in fact. A Digital El Dorado. The myth is compelling, causing many to waste so much time, energy and resources into finding it.

I asked Copilot, “Can you identify and summarize the logical errors in the attached blog post?” And this is how it responded:

1. False Analogy

The essay repeatedly compares the pursuit of AGI to mythical quests like El Dorado or Frankenstein’s monster. While evocative, these analogies oversimplify the nuanced and empirical nature of AI research. Myths are based on fantasy; AI development is grounded in scientific experimentation and engineering.

2. Straw Man Argument

The author misrepresents the goals and assumptions of AI researchers:

Claiming that AGI is based on the belief that “brains are computers” oversimplifies the field. Many researchers acknowledge the brain-computer metaphor as limited and use it as a heuristic, not a literal equivalence.

Suggesting that AI development is based on a “myth” ignores the diversity of approaches in cognitive science, neuroscience, and machine learning.

3. Appeal to Ignorance

The essay argues that because we don’t fully understand human memory or intelligence, we cannot build AGI. This is a fallacy:

Lack of complete understanding does not preclude progress or partial replication.

Many technologies (e.g., flight, medicine) were developed before full theoretical understanding.

4. Slippery Slope

The author suggests that creating AGI will inevitably lead to catastrophic consequences like moral chaos, slavery, or societal collapse. While these are valid concerns to explore, the argument assumes a worst-case scenario without evidence that such outcomes are inevitable or even likely.

5. Circular Reasoning

The essay claims AGI is impossible because it’s based on a myth, and it’s a myth because AGI is impossible. This circular logic doesn’t provide independent justification for either claim.

6. False Dichotomy

The author presents only two outcomes:

AGI is impossible and a waste of resources.

AGI is possible and leads to ethical catastrophe. This ignores a wide spectrum of possibilities, including beneficial and controlled development of AI technologies.

7. Emotional Reasoning

The essay relies heavily on emotional appeals — fear, cynicism, and moral outrage — rather than empirical evidence or balanced analysis. For example, comparing AI development to colonial exploitation (El Dorado) is emotionally charged but not logically rigorous.

8. Overgeneralization

Statements like “everything built on that perception is therefore, just as wrong” overextend the critique. Even if the brain-computer metaphor is flawed, it doesn’t invalidate all AI research or its practical applications.

9. Misuse of Authority

The essay references Nobel laureate Eric Kandel to support the idea that understanding memory will take 100 years. While Kandel is a respected neuroscientist, this quote is used to imply that AI development is futile, which is a leap beyond what the quote supports.

Would you like a rewritten version of the essay that maintains the core concerns but avoids these logical missteps?

As one would expect, I disagree each of the points raised. To summarize, Copilot missed the nuances that the post is built on and “overgeneralized” the overall argument. But I didn’t feel like arguing with a computer, because that would be pointless. But each point could be used as a tool to revise what I wrote, if I so chose, which would be the true point of the exercise. The point isn’t to argue with an algorithm. As tempting as it was.

To the question, “Would you like a rewritten version of the essay that maintains the core concerns but avoids these logical missteps?” I answered yes. And this is how Copilot presents my argument.

AI and the Mirage of General Intelligence: A Cautious Perspective

Introduction

The pursuit of Artificial General Intelligence (AGI) has captivated researchers, technologists, and futurists alike. It promises machines that can think, reason, and learn like humans. But is this vision grounded in reality, or are we chasing a digital mirage?

The Brain-Computer Metaphor: A Useful but Limited Analogy

Much of the enthusiasm around AGI stems from the analogy between the brain and a computer. This metaphor has been useful in guiding early AI research, but it has limitations. Neuroscience increasingly shows that human cognition does not operate like digital computation. Memory, for instance, is not a matter of storing and retrieving data like files on a hard drive — it is reconstructive, fluid, and deeply tied to emotion and context.

This doesn’t mean AGI is impossible, but it does suggest that replicating human intelligence may require fundamentally different approaches than those currently in use.

Anthropomorphism and Its Pitfalls

Humans have a long history of projecting our traits onto the world around us — from mythological golems to talking animals in folklore. This tendency to anthropomorphize extends to AI. We often assume that if a machine behaves intelligently, it must think like us. But intelligence can take many forms, and a machine’s “thought process” may be entirely alien to our own.

This misunderstanding can lead to misplaced expectations and ethical oversights. Just because an AI can mimic human conversation doesn’t mean it understands or experiences the world as we do.

The Ethical and Practical Challenges of AGI

Even if AGI becomes technically feasible, we must ask: should we build it? The development of AGI raises profound ethical questions. How do we ensure such systems align with human values? Can we imbue them with a sense of morality or social responsibility? These are not trivial concerns. We struggle to teach ethics consistently to humans — how can we expect to do better with machines?

Moreover, the creation of AGI could introduce new forms of inequality and control. If an AGI is truly intelligent, does it deserve rights? Can it be owned? These questions must be addressed before — not after — such systems are developed.

Resource Allocation and Opportunity Costs

The pursuit of AGI also demands enormous resources — computational, electrical, and human. While innovation is important, we must consider whether these resources could be better spent addressing urgent global challenges like climate change, healthcare, and education.

Some argue that AI can help solve these problems, but this creates a paradox: we are using vast energy to build systems that might help us manage energy more efficiently. This circular logic deserves scrutiny.

A Call for Humility and Responsibility

None of this is to say that AI research should stop. Narrow AI systems already provide immense value in fields like medicine, logistics, and language processing. But the leap to AGI should be approached with humility, caution, and a commitment to ethical foresight.

We must resist the allure of technological myths and focus instead on building systems that are transparent, accountable, and aligned with human well-being. The future of AI is not predetermined — it is a choice we make, collectively and deliberately.

It’s an interesting essay. It used what I wrote to come up with something completely original. And I find that it is a constructive alternate argument that helps strengthen my original argument.

So I asked Copilot to analyze its own output, “Can you summarize the logical errors and missteps in the attached essay?” It found 6 logical errors and asked “Would you like me to rewrite the essay with improved logical structure and balance?”

That’s an amusing road to hell that I chose not to follow.