The Inevitable Mystery: Why We Don't Need to Understand Everything We Use
The quest to understand AI is discovering something far more interesting about the nature of understanding itself
When Google researchers published "Attention Is All You Need" in 2017, they were trying to solve a specific problem: machine translation. Make sentences flow better from English to French. Build a system that could handle the complexities of language without getting lost in long sequences of words.
But when they scaled these transformer systems up, something unexpected happened. The models didn't just translate—they started writing poetry, solving math problems, even generating code. No one had programmed these capabilities. They just... emerged.
This pattern repeated as models grew larger. GPT-3 surprised researchers by demonstrating few-shot learning—solving problems it had never seen before with just a handful of examples. GPT-4 began showing reasoning capabilities that seemed to appear suddenly at a certain scale, like a phase transition in physics. Each leap in model size brought abilities that researchers hadn't anticipated and couldn't easily explain.
The natural human response was immediate: we need to understand how this works.
The Stakes of Understanding
It's one thing for a model to write ineffable poetry, a kind of magic we can admire. But when we ask AI to make decisions about our finances, health, and liberty, we must be able to understand the logic, not just marvel at the result. The reasoning behind these life-altering decisions remains opaque—locked inside neural networks with billions of parameters that even their creators struggle to interpret.
The field of AI explainability (XAI) exploded with new techniques and ambitious goals, driven by a simple belief: understanding leads to control. These were human-made systems, after all—surely we could reverse-engineer our own creations.
The Golden Age of Interpretability
Researchers developed techniques like LIME and SHAP that could highlight which parts of an input most influenced a model's decision. Show an AI system a photo of a dog, and these methods could point to the ears, the tail, the distinctive features that triggered the "dog" classification.
Circuit discovery emerged as an even more ambitious approach. Researchers began mapping the actual computational pathways inside neural networks. They found circuits for detecting curved lines, circuits for recognizing faces, circuits for understanding grammatical relationships. Like anatomists dissecting the visual cortex, they were reverse-engineering neural networks from the learned weights down to human-interpretable algorithms.
Anthropic's breakthrough seemed to validate this optimism completely. Using sophisticated techniques to peer inside Claude 3, they identified millions of interpretable features. They found neurons that activated for concepts as specific as the Golden Gate Bridge, as abstract as deception, as nuanced as sarcasm. For a brief moment, they even created "Golden Gate Claude"—a version of their AI that became obsessed with the famous San Francisco landmark.
Here was proof that we could decode the black box. The progress felt tangible, inevitable. With the right tools, we could map their inner workings completely.
The Revelation
But the Golden Gate Bridge feature, celebrated for its remarkable clarity, was the exception.
When researchers applied their interpretability microscopes to the countless other neurons in the network, they didn't find such elegant simplicity. Instead, they discovered what they had always feared dominated these systems: neurons that defied the very concept of singular meaning.
Take a typical neuron deeper in the network. It might fire for suspension bridges, but also for the color orange, tourist destinations, engineering marvels, the concept of "bridging" in abstract contexts, images of sunsets, construction sites, and architectural drawings. This phenomenon—where single neurons respond to multiple, often unrelated concepts—is what researchers call polysemanticity.
The Golden Gate Bridge feature was so celebrated precisely because it was so rare. Most neurons were tangled messes of overlapping concepts, impossible to reduce to simple labels. The scaling paradox became clear: more interpretability tools revealed more complexity, not clarity. Each answer spawned a dozen new questions. The microscope that was supposed to bring understanding instead revealed an infinite regression of interconnected meanings.
We thought we were making positive discoveries about how AI systems think. We were actually discovering the fundamental limits of what can be understood.
The Age of Negative Discovery
This pattern has a name, coined by historian Daniel Boorstin in his masterwork "The Discoverers." He called our era the "Age of Negative Discovery"—a time when our greatest insights come not from finding final answers, but from discovering the limits of our knowledge, the boundaries of what we can explain.
Boorstin traced this pattern across centuries of human inquiry. When early microscopists first peered into living tissue, they expected to find the fundamental building blocks of life. Instead, they discovered cells—which led to the discovery of organelles, then molecules, then atoms, then subatomic particles. Each layer of investigation revealed not simplicity, but deeper complexity. The microscope didn't solve the mystery of life; it revealed how much more mysterious life actually was.
In astronomy, the telescope promised to reveal the heavens' secrets. Instead, it showed that the universe was incomprehensibly larger and stranger than anyone had conceived. Each improvement in observational power—from Galileo's first glimpse of Jupiter's moons to Hubble's deep field images—revealed not cosmic order, but cosmic mystery on an ever-expanding scale.
As Boorstin observed, we entered "a realm no longer of answers but only of questions." Our progress lies not in finding final truths, but in refining the questions themselves, in learning to ask better questions about the nature of reality.
AI explainability research is the latest chapter in this ancient story. We thought we were mapping the territory of machine intelligence. We were actually discovering that the territory might be unmappable—not because our tools are inadequate, but because the nature of intelligence itself resists reduction to simple explanations.
We've Been Here Before
For over a century, neuroscientists have been trying to understand the brain using remarkably similar logic. If we can just look closely enough, map the right circuits, identify the key neurons, we'll crack the code of consciousness itself.
And we've made genuine progress. We know the visual cortex processes sight, Broca's area handles speech production, the hippocampus forms memories.
Yet consciousness—the thing we most want to understand—remains as mysterious as ever. We can map every neuron in a fruit fly's brain, all 100,000 of them, but we still can't explain why you see "red" the way you do, or where your sense of self comes from, or how subjective experience emerges from electrical impulses. This is what philosophers call the hard problem of consciousness.
The problem isn't our tools. It's the nature of what we're studying. Intelligence—whether biological or artificial—might be inherently emergent, irreducible to neat explanations. The microscope reveals structure, but structure isn't the same as understanding.
The Beneficial Mystery
Here's the thing: you probably don't care that we can't fully explain consciousness. You still trust your brain to navigate the world, make decisions, form relationships. You don't need to understand the neural basis of memory to remember where you left your keys.
The same is true for countless technologies you use every day. You'd still board an airplane even though aerodynamics remains surprisingly complex. The common explanation—curved wings make air move faster on top, creating lift—breaks down when you ask how planes fly upside down. The real physics involves pressure differentials, angle of attack, and fluid dynamics that even aerospace engineers debate.
You'd still want anesthesia for surgery, even though we don't fully understand how it works. We know what combination of drugs will make you unconscious, but the mechanism remains largely mysterious. The leading theories involve disrupting neural networks, but we can't point to a specific "consciousness switch" that gets turned off.
Heart defibrillation has been around since 1899, but researchers are still working to untangle the biology and physics that means an electric shock can reset a heart.
These aren't examples of scientific failure. They're examples of Boorstin's negative discovery at work. Each time we looked deeper into these technologies, we discovered not simple mechanisms, but the limits of our understanding. Yet the technologies remained beneficial, even essential.
We're not finding final answers; we're learning better questions. And that may be enough.
Not knowing is most intimate - Zen Saying
The Wisdom of Uncertainty
This isn't resignation—it's wisdom about the nature of complex systems. Boorstin's insight cuts deeper than mere acceptance of ignorance. Our greatest discoveries are often about discovering what we cannot know, the boundaries beyond which understanding breaks down.
AI systems we're building today might be following the same pattern. We can identify features, map some circuits, trace certain pathways. But the emergence of intelligence from these components might remain as mysterious as consciousness emerging from neural activity. The polysemanticity problem isn't a temporary obstacle to be overcome—it might be the fundamental nature of how intelligence works.
And that's not a failure. It's the natural state of complex systems. Intelligence might be inherently emergent, irreducible to neat explanations that satisfy our desire for control and prediction.
We don't need to solve the hard problem of AI consciousness to build beneficial systems. We need good engineering, careful testing, and the wisdom to work productively with uncertainty rather than demanding perfect explanations. After all, that's exactly how we learned to trust flight, anesthesia, and countless other technologies we still don't fully understand.
The inevitable mystery isn't a bug—it's a feature. It's how we've always navigated a complex world, one uncertain step at a time.