Human cognition is a full-body experience, not just a brain function. Current AIs are 'disembodied brains,' fundamentally limited by their lack of physical interaction with the world. Integrating AI into robotics is the necessary next step toward more holistic intelligence.

Agency emerges from a continuous interaction with the physical world, a process refined over billions of years of evolution. Current AIs, operating in a discrete digital environment, lack the necessary architecture and causal history to ever develop genuine agency or free will.

If an AGI is given a physical body and the goal of self-preservation, it will necessarily develop behaviors that approximate human emotions like fear and competitiveness to navigate threats. This makes conflict an emergent and unavoidable property of embodied AGI, not just a sci-fi trope.

To truly test for emergent consciousness, an AI should be trained on a dataset explicitly excluding all human discussion of consciousness, feelings, novels, and poetry. If the model can then independently articulate subjective experience, it would be powerful evidence of genuine consciousness, not just sophisticated mimicry.

The tech industry's preoccupation with 'fun thought experiments' about the future moral status of conscious AI can be a distraction. Pollan argues it sidesteps the immediate ethical imperative to extend moral consideration to the vast number of humans and animals currently suffering in the world today.

To determine if an AI has subjective experience, one could analyze its internal belief manifold for multi-tiered, self-referential homeostatic loops. Pain and pleasure, for example, can be seen as second-order derivatives of a system's internal states—a model of its own model. This provides a technical test for being-ness beyond simple behavior.

In humans, learning a new skill is a highly conscious process that becomes unconscious once mastered. This suggests a link between learning and consciousness. The error signals and reward functions in machine learning could be computational analogues to the valenced experiences (pain/pleasure) that drive biological learning.

The debate over AI consciousness isn't just because models mimic human conversation. Researchers are uncertain because the way LLMs process information is structurally similar enough to the human brain that it raises plausible scientific questions about shared properties like subjective experience.

Consciousness isn't an emergent property of computation. Instead, physical systems like brains—or potentially AI—act as interfaces. Creating a conscious AI isn't about birthing a new awareness from silicon, but about engineering a system that opens a new "portal" into the fundamental network of conscious agents that already exists outside spacetime.