We've probably heard it by now: the brain is a prediction machine.
We've probably heard it by now: the brain is a prediction machine. It's an appealing metaphor for engineers and builders. It suggests the brain works like a model optimized through data. Feed it enough inputs, minimize error, get better predictions. Stable. Quantifiable. Machine-like. The problem is this: predictive coding explains how the brain keeps us trapped in existing frameworks. It does not explain how we can escape them. And we choose not to escape.
1. How Predictive Coding Works (And Why It Fails Us)
The brain doesn't aim for truth. It aims for stability. It builds models of the world, then evaluates everything new against those models. Data that fits gets amplified as "reality." Data that conflicts gets attenuated as noise. When something doesn't match, the brain doesn't update the model—it filters the perception. Meaning arrives instantly. Uncertainty disappears before we even notice it. This is incredibly efficient. By using a stable, consistent framework, we categorize and understand the infinitely complex world into manageable categories.
We don't have to rethink everything every single moment.
It's also incredibly restrictive.
The framework the brain built yesterday becomes the filter through which we perceive today. Our predictions determine what seems possible. Our models determine what seems real. And here's the part nobody talks about: possibilities that don't fit the current model never appear as "surprising." They don't appear as errors to be corrected. They simply don't appear at all. If we are trying to maintain balance, this is fine. However, if we are trying to create something that has never existed before, this will lead to disastrous consequences.
We ignore, we fail to recognize, we feel anxiety and danger... and then we exclude it. It's automatic.
2. The Experience Predictive Coding Can't Explain
There is a kind of experience that predictive coding doesn't account for. It's different with surprise. Surprise has a reference point: we expected one thing and got another. The brain can update and move on. This is different.
It's discomfort—a persistent, hard-to-name sense that something is off. We can't clearly explain what's wrong. Others may not see a problem. The data may look fine. This is not a prediction error. It's an indication that the current model itself is inadequate. We encounter this when an idea doesn't quite work, but we can't articulate why. When a relationship feels wrong despite all the right reasons. When a business looks healthy on paper, but something feels structurally unsound.
Two signals are competing:
- The brain's deep alert → "This framework doesn't work"
- Culture's insistent instruction → "Rationalize it away, keep moving, find a justification"
Most of us suppress the brain's signal because the force of cultural training is far more powerful.
3. How Modern Culture Trains This Capacity Out of Us
Our systems are designed to eliminate discomfort. Education rewards explanation. Professional environments reward decisiveness. Productivity frameworks push us from confusion to conclusion as fast as possible. Everything drives toward closure.
Algorithms favor predictable patterns and suppress ambiguity. Metrics amplify what can be measured and erase what can't. As predictive systems become more refined—in our tools and in our thinking—discomfort is not eliminated but rationalized away more efficiently. We feel it, but we learn to interpret it as personal failure or anxiety rather than as system failure.
For stable operations, this is useful. When we're working in real uncertainty—new markets, unarticulated problems, unexplored domains—we're trapped in systems optimized to suppress the very signal we need. We are being optimized into narrow frames.
Discomfort Detection System
(Model Insufficiency Detection)
- Oriented toward evolution, change, and new possibilities
- Detects when the current model is insufficient
- Enables access to risk, uncertainty, and the unknown
Predictive Coding System
(Prediction Optimization System)
- Oriented toward safety, stability, and the known
- Refines and optimizes the current model
- Prioritizes risk avoidance, certainty, and reinforcement of existing frameworks
4. What Discomfort Actually Does
There is a form of intelligence that operates before modeling. It doesn't ask: How should I interpret this? It asks: Is this the right interpretive frame at all? This is the difference between optimization and innovation. One improves within a system. The other questions the system itself.
Discomfort enables this. It resists premature meaning. It tolerates ambiguity. It suspends the drive toward closure that defines predictive systems—biological and technological alike. From an efficiency standpoint, this looks unproductive. From a creative standpoint, it's essential. This is how new models emerge instead of old ones being endlessly refined.
Therefore, we need discomfort, and it's important to be able to recognize the feeling of discomfort that we ourselves are experiencing.
5. Why Buddhism Matters (and Why Scientists Are Paying Attention)
Buddhism has spent over 2,000 years examining this exact problem: the suffering that arises when we confuse our mental models with reality. This is often misunderstood as spiritual or religious. Structurally, it's something else entirely.
Buddhist contemplative traditions were developed as systematic investigations into how experience is constructed—how perception, cognition, and attachment reinforce limited frameworks. This is why modern cognitive science has begun taking them seriously.
Discomfort as Data, Not Defect
Predictive systems treat discomfort as something to eliminate—an error, an aversive signal, noise. Our culture is built on this assumption.
Buddhist practice reverses this entirely.
Discomfort (dukkha) is treated as a primary object of investigation.
The question isn't "How do I get rid of this?" but "How does this arise? What sustains it? What happens if I observe it without fixing it?" It's epistemology—a way of knowing through observation. This is not therapy.
Interrupting Automatic Model Repair
When uncertainty appears, the brain usually repairs it immediately—explaining, justifying, fitting it into an existing frame. This happens automatically, without conscious awareness. Vipassana meditation deliberately interrupts this process. It trains sustained observation without intervention.
Not confusion, but sensitivity: to sensation before interpretation, affect before narrative, experience before model has already processed it.
What we need is the ability to experience our inner selves from a perspective different from the one we usually inhabit.
Staying With Insufficiency
Conceptual thought works inside a frame. It can refine details, extend the logic, and make it more sophisticated. But it cannot step outside the frame to see if the frame itself is the problem. That would require a different kind of awareness.
Buddhist methods train exactly this: the capacity to notice when a model is insufficient, to sit with that breakdown, without immediately building a replacement explanation. To stay in the not-knowing long enough for genuine understanding to emerge. This is not mystical. It is disciplined cognitive restraint. This is the first step toward developing awareness beyond what we already know.
6. What This Means for Our Work
If we're building in domains where prediction fails—and most meaningful work is—we need access to this capacity. We need to notice misalignment even when metrics look good. To stay with discomfort without rationalizing it away. To allow it to reshape our frame rather than forcing the frame to absorb it. Iteration assumes the model is right. This is recognizing when the model itself no longer works. The discomfort here doesn't feel productive. It feels like being stuck. And that's exactly why it matters.
Conclusion
Predictive coding explains how the brain maintains stability. Discomfort explains how the brain enables change. In a world optimized for prediction, efficiency, and certainty, the rarest human ability may not be intelligence or creativity, but the capacity to remain with what is not yet understood.
Not trying to solve it immediately. Not trying to optimize through it. Simply waiting for different models to emerge naturally. This is human technology. And no predictive machine can replicate this. The question is whether we still know how to use this ability.
Because this is the technology inherent in the soul that humanity has inherited.
Afterword
For years, we ran Project Rlung—live classes and personal sessions with Himalayan monks, the predecessor to DriftLens. Through that work, we encountered a persistent problem. Even when monks explained how these practices function in daily life—not as spiritual aspiration but as practical tools for recognizing when a mental model is breaking down—the response was often the same. People treated it as beautiful philosophy. They admired the wisdom. But they didn't recognize it as *technology*. This gap became the central question: How do we communicate that this is not special spirituality? How do we help people understand that remaining with discomfort, observing mental patterns without immediate resolution, staying with what doesn't yet make sense—these are not mystical practices. They are disciplined cognitive capacities. This article is one attempt at that translation. If you're experiencing discomfort in your work or life—if you feel like something is fundamentally wrong, but you can't quite put your finger on the reason—that discomfort isn't a problem with your personality. It's a response from your spiritual intelligence. The question is whether you still do not have access to that intelligence, or whether you've been trained to simply conform to the existing system. We hope this reaches whoever needs it.