The Four Fallacies of Modern AI

Summary

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.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 agains...