The Four Fallacies of Modern AI

And Why Believing in Them Hinders Progress

Photo by Matt Artz on Unsplash

I've spent the last few years trying to make sense of the noise around Artificial Intelligence, and if there's one feeling that defines the experience, it's whiplash. One week, I'm reading a paper that promises AI will cure disease and unlock unimaginable abundance; the next, I'm seeing headlines about civilizational collapse. This dizzying cycle of AI springs, periods of massive investment and hype, followed by the chilling doubt of AI winters isn't new. It's been the engine of the field for decades.

After years of this, I've had to develop my own framework just to stay grounded. It’s not about being an optimist or a pessimist; it’s about rejecting both extremes. For me, it’s a commitment to a tireless reevaluation of the technology in front of us; to using reason and evidence to find a path forward, because I believe we have both the power and the responsibility to shape this technology’s future. That begins with a clear-eyed diagnosis of the present.

One of the most useful diagnostic tools I've found for this comes from computer scientist Melanie Mitchell. In a seminal paper back in 2021, she identified what she claims are four foundational fallacies, four deeply embedded assumptions that explain to a large extent our collective confusion about AI, and what it can and cannot do.

My goal in this article isn't to convince you that Mitchell is 100% right. I don't think she is, either, and I will provide my own criticism and counter arguments to some points. What I want is to use her ideas as a lens to dissect the hype, explore the counterarguments, and show why this intellectual tug-of-war has real-world consequences for our society, our economy, and our safety.

Deconstructing the Four Fallacies

For me, the most important test of any idea is its empirical validation. No plan, no matter how brilliant, survives its first encounter with reality. I find that Mitchell’s four fallacies are the perfect tool for this. They allow us to take the grand, sweeping claims made about AI and rigorously test them against the messy, complicated reality of what these systems can actually do.

Fallacy 1: The Illusion of a Smooth Continuum

The most common and seductive fallacy is the assumption that every impressive feat of narrow AI is an incremental step on a smooth path toward human-level Artificial General Intelligence (AGI). That is, that intelligence is a single, unidimensional metric on a continuum that goes from narrow to general.

We see this everywhere. When IBM's Deep Blue beat Garry Kasparov at chess, it was hailed as a first step towards AGI. The same narrative emerged when DeepMind's AlphaGo defeated Lee Sedol. This way of thinking creates, according to Mitchell, a flawed map of progress, tricking us into believing we are much closer to AGI than we are. It ignores the colossal, unsolved challenge known as the commonsense knowledge problem—the vast, implicit understanding of the world that humans use to navigate reality.

As philosopher Hubert Dreyfus famously said, this is like claiming that the first monkey that climbed a tree was making progress towards landing on the moon. Well, in a sense, maybe it is, but you get the point. We didn't get to the moon until we invented combustion rockets. Climbing ever taller trees gets us nowhere closer, it's just a distraction. In the same sense, mastering a closed-system game may be a fundamentally different challenge than understanding the open, ambiguous world.

But here's the nuance. While beating Kasparov isn't a direct step to having a conversation, the methods developed can be surprisingly generalizable. The architecture that powered AlphaGo was later adapted into MuZero, a system that mastered Go, chess, and Atari games without being told the rules.

Furthermore, can we really call a Large Language Model narrow in the same way? Its ability to write code and summarize text feels like a qualitative leap in generality that the monkey-and-moon analogy doesn't quite capture.

This leaves us with a forward-looking question: How do recent advances in multimodality and agentic AI test the boundaries of this fallacy? Does a model that can see and act begin to bridge the gap toward common sense, or is it just a more sophisticated version of the same narrow intelligence? Are world models a true step towards AGI or just a higher branch in a tree of narrow linguistic intelligence?

Fallacy 2: The Paradox of Difficulty

We have a terrible habit of projecting our own cognitive landscape onto machines, assuming that what's hard for us is hard for them, and what's easy for us is easy for them. For decades, the opposite has been true.

This is Moravec's Paradox, named after the roboticist Hans Moravec, who noted it's easier to make a computer exhibit adult-level performance on an IQ test than to give it the sensory and motor skills of a one-year-old.

This explains why we have AI that can master the ridiculously complex game of Go, while a fully self-driving car remains stubbornly just over the horizon. The "easy" things are built on what Mitchell calls the "invisible complexity of the mundane." This paradox causes a chronic mis-calibration of our progress and priorities, leading us to be overly impressed by performance in formal domains while underestimating the staggering difficulty of the real world.

Of course, some would argue this isn't a fundamental barrier, but a temporary engineering hurdle. They’d say that with enough data and compute, the "invisible complexity" of the real world can be learned, just like the complexity of Go was.

From this perspective, the problem isn't one of kind, but of scale. This forces us to ask: as sensor technology and robotics improve, are we finally starting to overcome Moravec's Paradox? Or are we just discovering even deeper layers of complexity we never knew existed?

Fallacy 3: The Seduction of Wishful Mnemonics

Language doesn't just describe reality; it creates it. In AI, we constantly use anthropomorphic shorthand, saying a system "learns," "understands," or has "goals." Mitchell argues this practice of using "wishful mnemonics" is deeply misleading, fooling not just the public but the researchers themselves.

When a benchmark is called the "General Language Understanding Evaluation" (GLUE) and a model surpasses the human baseline, headlines declare that AI now understands language better than humans. But does it?

The term "stochastic parrot" was coined as a powerful antidote, reframing what LLMs do as sophisticated mimicry rather than comprehension. This isn't just a semantic game, Mitchell argues; it creates a flawed mental model that leads to misplaced trust, encouraging us to deploy systems in high-stakes situations where a lack of true understanding can have serious consequences.

A fair critique is that these terms are a necessary cognitive shorthand. At a certain level of complexity, a system's emergent behavior becomes functionally indistinguishable from "understanding," and arguing about whether it really understands is an unprovable philosophical distraction.

But that still leaves a crucial question: can we develop a more precise, less anthropomorphic vocabulary to describe AI capabilities? Or is our human-centric language the only tool we have to reason about these new forms of intelligence, with all the baggage that entails?

Fallacy 4: The Myth of the Disembodied Mind

This is the most philosophical, and in my opinion, the most important fallacy. It's the deep-seated assumption that intelligence is, like software, a form of pure information processing that can be separated from its body.

This "brain-as-computer" metaphor leads to the belief that AGI is simply a matter of scaling up compute to match the brain's raw processing power. It's challenged by Mitchell and many others with the thesis of embodied cognition, a view from cognitive science which holds that intelligence is inextricably linked to having a body that interacts with the world. If this is correct, then our current approach may just be creating ever-more-sophisticated systems that are fundamentally brittle because they lack grounded understanding.

This is where we hit the great intellectual battle line in modern AI. The primary counterargument can be framed in terms of Rich Sutton's famous essay, "The Bitter Lesson," which argues that the entire history of AI has taught us that attempts to build in human-like cognitive structures (like embodiment) are always eventually outperformed by general methods that just leverage massive-scale computation.

From this viewpoint, embodiment isn't a magical prerequisite for intelligence; it's just another fiendishly complex problem that will yield to more data and processing power.

This tension poses a critical question for the future: do multimodal models that can process images and text represent a meaningful step toward solving the embodiment problem? Or are they just a more sophisticated version of the same disembodied mind, a brain in a slightly larger digital vat?

What is Intelligence, Really?

As we dig into these fallacies, a deeper pattern emerges. They aren't just four isolated mistakes; they're symptoms of a fundamental schism in how the AI world thinks about intelligence itself. Again, my goal isn't to pick a side but to avoid falling prey to cheap heuristics or ideological banners, and instead evaluate which of these paradigms gives us a more useful map of reality.

On one side, you have what I’ll call the Cognitive Paradigm, championed by thinkers like Mitchell and her mentor, superstar AI researcher and philosopher Douglas Hofstadter. This view sees intelligence as a complex, integrated, and embodied phenomenon. It assumes that the things we associate with human intelligence—common sense, emotions, values, a sense of self—are likely inseparable components of the whole, emerging from rich interaction with a physical and social world.

From this perspective, the path to AGI requires a deep, scientific understanding of these integrated components, not just more processing power.

On the other side is the Computationalist Paradigm, which is the implicit philosophy behind many of today's leading labs, and best captured by The Bitter Lesson. This posits that the biggest breakthroughs have always come from general methods that leverage massive-scale computation—in other words, from scaling things up.

In this paradigm, intelligence is a more abstract, substrate-independent quality of optimization. Problems like embodiment aren't fundamental barriers; they are just incredibly complex computational tasks that will eventually be solved by ever-larger models and ever-faster chips.

Of course, it's not a perfect binary. Most researchers are pragmatists, like me, working somewhere in the messy middle. But these two paradigms represent the poles of the debate, and the tension between them defines the entire field. It shapes which research gets funded, which systems get built, and ultimately, which vision of the future we are collectively racing toward.

Why This Debate Matters

This debate isn't just an academic parlor game. These fallacies have a massive ripple effect across society because they obscure a fundamental rule of technology and economics: there's no free lunch, only trade-offs.

The hype generated by fallacious thinking isn't just an innocent mistake; it's the fuel for a powerful economic engine. The intense competition between tech giants, the flood of venture capital, and the geopolitical AI race all depend on a constant narrative of imminent, world-changing breakthroughs. This political economy of hype forces us into a series of dangerous trade-offs.

First, we trade long-term progress for short-term hype.

The fallacies create an unstable, boom-and-bust funding cycle. During an AI spring, capital flows to projects that can produce impressive-looking demos, often based on narrow benchmarks. This starves the slow, methodical, foundational research needed to solve the hard problems like common sense and reasoning. The result is a field that lurches from one hype bubble to the next, leaving a trail of abandoned projects and unfulfilled promises that trigger the inevitable AI winter.

Second, we trade public trust for market excitement.

The cycle of over-promising and under-delivering is deeply corrosive. When we use wishful mnemonics to describe a system that "understands," and it then fails in spectacular, nonsensical ways in the real world, it breeds public anxiety and skepticism. Recent studies show the public perceives AI scientists more negatively than almost any other field, specifically because of a perceived lack of prudence. This isn't a vague feeling; it's a direct reaction to the unintended consequences of deploying brittle, overhyped systems.

Finally, and most critically, we trade responsible validation for speed to market.

This is where the consequences become most severe. Believing a system is on a continuum with general intelligence, or that it truly "understands" language, leads to its premature deployment in high-stakes domains.

When a mental health chatbot, which is fundamentally, at least today, a sophisticated pattern-matcher, gives harmful advice to a person in crisis, it’s a direct result of these fallacies. When we over-rely on brittle systems in healthcare, finance, or autonomous vehicles, we are making a dangerous bet, trading real-world safety for the illusion of progress.

Conclusion

So where does this leave us? The value of Mitchell's fallacies isn't just in spotting hype, but in exposing the deep, productive tension between these two powerful ways of thinking about intelligence. We can't ignore the fallacies, but we also can't deny the incredible, world-altering power of the scaling paradigm that fuels them.

Mitchell in her paper compares modern AI to alchemy. It produces dazzling, impressive results but it often lacks a deep, foundational theory of intelligence.

It’s a powerful metaphor, but I think a more pragmatic conclusion is slightly different. The challenge isn't to abandon our powerful alchemy in search of a pure science of intelligence. The goal, at least from a pragmatist point of view, should be to infuse our current alchemy with the principles of science, to make scaling smarter, safer, and more grounded by integrating the hard-won insights about how intelligence actually works.

The path forward, I believe, requires more than just intellectual humility. It also requires a willingness to synthesize these seemingly opposed worldviews, and a commitment to a tireless reevaluation of the technology before us. The ultimate question is not if we should choose the path of scaling or the path of cognitive science, but how we can weave them together to guide the raw power of our modern AI alchemy with the deep understanding of a true science of intelligence.