Pat Your Head & Rub Your Belly
Inspector 34, AI, and the Problem with Perfection
In a quietly subversive episode of The Adventures of Pete & Pete, we meet Inspector 34—a character so obsessed with perfection that he inspects every piece of underwear at the factory where Little Pete briefly works. Every stitch must be flawless, every waistband aligned. In the episode’s poignant conclusion, Little Pete challenges Inspector 34 to a BBQ chicken eating contest during which the Inspector finishes his plate without any mess — and Pete has him, as he says “BBQ chicken is supposed to be messy, eating perfectly is imperfect.” Inspector 34 could be a stand-in for certain expectations of AI as technology rapidly evolves today. The episode’s lesson, like the series itself, goes deep: perfection isn’t just exhausting, it’s often irrelevant. Worse, it might even be an obstacle to something more interesting, meaningful, or alive.
This idea has stuck with me for years, especially now as conversations around artificial intelligence and machine learning saturate every corner of the technological and philosophical landscape. Because when you strip it down, perfection—whatever we think that means—is a deeply human concept. And not necessarily a helpful one.
The Mirage of the Ideal
We tend to associate perfection with achievement: a perfect score, a flawless performance, the ideal self (hello, Foucault). But if we scratch the surface of any of those metrics—standardized tests, aesthetic judgments, moral ideals—we find that they’re riddled with subjective assumptions, cultural bias, and arbitrary constraints.
Yet we often carry this same flawed notion of perfection into our aspirations for machine intelligence. We imagine machines as infallible solvers. Either they’re useless (see: John Oliver’s AI slop) or they’re terrifying—unassailable overlords of cognition. The conversation is obsessed with the “I” of AI: intelligence, capability, risk. But we don’t talk enough about the “A”: artificial. As in, made by us.
The Limits of Human-Centric Intelligence
Artificial intelligence is progressing at a pace that feels both breathtaking and unnerving. But let’s be clear: we are still in the early stages. We’re somewhere between Rancho’s first principles framework for the definition of Machine in 3 Idiots and the first chaos-theory butterfly flaps of Jurassic Park. And we should be asking not just how smart our machines are becoming, but what kind of intelligence we’re trying to create—and whether it needs to reflect us at all.
Perfection, at least as humans define it, may not be the most useful design goal. In fact, imperfection—the detours, failures, glitches, and contradictions—has often been the birthplace of innovation and discovery. Creativity, empathy, and depth often emerge because of flaws, not despite them.
This is one reason I’ve always preferred the term “machine learning” to “artificial intelligence.” The former implies process, iteration, evolution. The latter too often implies arrival, replacement, finality.
Looking Beyond Ourselves
In endeavoring to build for the truly innovative, we may do well to look beyond our own species for inspiration. The natural world is teeming with intelligence systems that operate nothing like ours—and are often vastly more efficient and elegant.
Ant colonies and octopuses show us what distributed intelligence can look like: complex systems with no central command, capable of solving problems, adapting, and collaborating.
Cetaceans (whales and dolphins) exhibit emotional intelligence, social bonding, and memory systems that could radically expand what machines understand about feeling and consciousness.
Birds in flock and fish in school make instantaneous group decisions, modeling something like consensus in real time.
These aren’t just poetic metaphors. They’ve given rise to concrete technologies: swarm robotics, neural nets, decentralized systems (hello, blockchain). Even our basic understanding of flight, sonar, and network organization has come directly from the study of non-human intelligence.
We’ve already borrowed so much from nature. Why stop now?
Embracing Pat-Head-Rub-Belly Potential
We’re in an extraordinary time: not just in what AI can do, but in how we conceive of its purpose and potential. Instead of striving for a sterile, idealized “perfection” that mirrors our own assumptions, we might instead embrace messy, multi-threaded, emotionally complex, and even contradictory models of intelligence.
Like the inimitable Dr. Manhattan, we might imagine systems that can process and act on multiple planes at once—intellectual, emotional, spatial, ethical. Not as gods, but as radically different kinds of agents. Not perfect, but fascinating.
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We don’t need to aim for absolute perfection. We may aim for something stranger, deeper, and more alive. The next frontier in AI might not be about beating humans at our own game. It might be about playing a completely different one—one we never imagined, but that already exists all around us.