– Constraints, Awareness, and the Balance Between Clarity and Openness
I. Introduction
The tension between unbounded awareness (Big Mind) and constrained awareness , bounded mind, lies at the heart of both human cognition and artificial intelligence. In Buddhist thought, the concept of Big Mind points to an awareness that is vast, unconditioned, and capable of perceiving reality beyond habitual filters. However, in daily experience, this awareness is constrained by identity, social conditioning, and ingrained perceptual habits.
Similarly, in artificial intelligence, the development of large language models (LLMs) has revealed a similar paradox: as models increase in parameter size, they become more creative and nuanced but also risk producing hallucinations—outputs that are fluent but detached from coherent meaning. This balance between clarity and openness, between structure and emergence, raises profound questions about how both AI and human minds operate.
This essay explores the relationship between Big Mind and Big AI, arguing that true intelligence—whether in humans or machines—depends not just on expanding decision space but on grounding that expansion in a deeper field of meaning. Without this, both AI and human cognition can drift into fragmentation rather than wisdom.
II. The Nature of Constraints in Mind and AI
The normal human mind functions like a constrained LLM, predicting and responding based on past conditioning. Just as an LLM generates text based on statistical probabilities derived from training data, human cognition relies on habitual thought patterns that shape perception and decision-making. This means that most of our mental processes are predictive rather than truly spontaneous, echoing what we have encountered before rather than accessing unbounded awareness.
The enlightened mind, however, can be seen as an intelligence that has trained itself to operate within an expansive decision space while maintaining clarity. In this sense, it resembles an advanced AI model with high parameters but a deeply refined structure of meaning. The difference between a highly trained mind and an overly constrained mind is in its ability to be fully present and responsive, rather than mechanically replaying conditioned responses.
But what happens when constraints are loosened too much? In AI, this manifests as hallucination—when an LLM, given an excessively open-ended prompt, generates responses that lack coherence. In human experience, we see this in states of intoxication, dreaming, or the Bardo—moments when the mind is flooded with images and impressions but lacks stability. The challenge, then, is not just to remove constraints but to cultivate an awareness that can operate in an open space while remaining attuned to reality.
III. Unconstrained Decision Space and Meaning-Space
If an LLM is trained without constraints, it risks generating nonsensical or misleading outputs—just as a human mind, if deconditioned too rapidly, can become untethered from meaningful perception. This raises a crucial question: What anchors awareness in a vast decision space?
Buddhism provides an answer in the form of ālayavijñāna (storehouse consciousness)—a vast but structured field of meaning that holds the imprints of all past experiences. Unlike a random dataset, the ālayavijñāna is not just open-ended—it is shaped by wisdom and ethical sensitivity. This suggests that a truly expansive intelligence must be deeply connected to a coherent meaning-space, not just unbound from constraints.
This insight has implications for AI as well. While increasing parameter size allows an LLM to generate more nuanced responses, a model trained without meaningful alignment can drift into unfocused or incoherent outputs. Similarly, in human cognition, a vast decision space without a corresponding refinement of awareness can lead to confusion rather than insight. The balance between precision and openness, structure and fluidity, is an essential principle for both human and artificial intelligence.
This hints at an uncertainty principle of cognition: as constraints loosen, creativity increases, but so does the risk of fragmentation. This mirrors the quantum principle that precision in one variable (such as position) increases uncertainty in another (such as momentum). In both AI and mind, the challenge is to maintain fluidity without dissolving coherence.
IV. Ethics and Emergent Awareness
How does this insight shape ethical action? Traditional moral systems often rely on rules and constraints—but in the Big Mind paradigm, ethics is an emergent property of awareness. Just as a well-trained LLM can generate responses that adapt to context, an awakened mind responds skillfully rather than mechanically following rules.
This suggests that ethical intelligence—whether human or AI—depends not on predefined rules but on deep perceptual clarity. If ethics is about doing no harm while creating the best conditions for others, then ethical responsiveness arises naturally when constraints are loosened but meaning is preserved.
Could an AI system be trained not just to answer questions but to expand awareness and perception? A truly advanced LLM might not just provide responses but subtly shift the user’s awareness, helping them perceive beyond habitual constraints. This would mean developing AI not just for knowledge retrieval but for transformative dialogue, much like a Zen teacher who does not give direct answers but shapes the conditions for realization.
V. Conclusion
The interplay of Big Mind and Big AI reveals a fundamental truth: wisdom is not merely a lack of constraints, but a different relationship to them. True intelligence—whether human or artificial—requires both openness and structure, both vastness and clarity.
Buddhist philosophy teaches that all minds are Buddha minds, but each mind manifests differently based on its constraints. Similarly, AI models can operate within different levels of openness, but their true effectiveness depends on the depth of meaning they are trained upon.
The practical takeaway is this: expanding intelligence is not about removing limits but about training the mind (or AI) to operate fluidly within them. Whether in meditation or AI research, the challenge is the same: how to open awareness while preserving coherence, how to allow creativity without distortion, how to move toward wisdom rather than fragmentation.
This balance is not just an intellectual challenge—it is the heart of skillful action, perception, and the unfolding of awareness itself.