Our understanding of the nature of consciousness derives mainly from our understanding of the nature of consciousness in our favorite animal (us, of course). But the features of consciousness in our favorite animal might be specific to that animal rather than universal.

Let’s consider two such features and whether we should expect them in conscious AI systems, if conscious AI systems are ever possible.

Unity: Our conscious experiences at any given moment are bound together into a single unified experience, rather than transpiring in separate streams. If I’m sitting on a wet park bench, I might (a.) visually experience the leafy green trees around me, (b.) tactilely experience the cold dampness soaking into my jeans, and (c.) consciously recall the smaller trees of yesteryear. Normally — perhaps necessarily — three such experiences would not run in disconnected streams. They would join into a composite experience of (a)-with-(b)-with-(c). I experience not just trees, cold dampness, and a memory of yesteryear, but all three together as a unified bundle.

Determinacy: At any given moment, I am either determinately conscious or determinately nonconscious (as in anaesthesia or dreamless sleep). Likewise, I either determinately do, or determinately do not, have any particular experience. Gray-area cases are at least unusual and maybe impossible. Even the simplest, barest cases are still determinate. Consider visual experience: We might imagine the visual field narrowing and losing content until only a gray dot remains — and then the dot winks out. That dot, however minimal, is still determinately experienced. When it winks out, consciousness determinately disappears. There is no half-winked state between the minimal gray dot and complete absence of visual experience.

My thought is that we should not expect unity and determinacy to be general features of conscious AI systems (if conscious AI is possible). To see why, let’s start by assuming the Global Workspace Theory of consciousness. I focus on Global Workspace Theory because it’s probably the leading scientific theory of consciousness and because its standard formulation (Dehaene’s version) invites the assumption of unity and determinacy.

Global Workspace Theory divides the mind into local information processing modules linked by a shared global workspace. Information becomes conscious when it is broadcast into the workspace. Suppose your auditory system registers the faint honk of a distant car horn. You’re absorbed in reading philosophy and accustomed to ignoring traffic noise, so this representation isn’t selected for further processing. It’s not a target of attention, not broadcast into the workspace, and not consciously experienced. (If you think you constantly consciously experience background sounds, you can’t hold a standard Global Workspace view.) Once you attend to the noise, for whatever reason, that information “ignites” into the global workspace, becoming available to a wide variety of “downstream” processes: You can think about it, plan around it, verbally report it, store it in long-term memory, and flexibly combine it with other information in the workspace. On Global Workspace Theory, being available in this way just is what it is for the information to be consciously experienced.

This model suggests unity and determinacy. Since there is just one global workspace, and since that workspace enables flexible integration of everything it contains, it makes sense that its various elements will combine into a unified experience. And on Dehaene’s version, ignition into the workspace is a sharp-boundaried event: Information either completely ignites, becoming available for all downstream processes, or it does not. There is no (or only rarely) partial ignition. This can explain determinacy.

But future AI systems might not share this structure. They might have multiple or partially overlapping workspaces. Different specialized subsystems might have access to different regions of a partly-shared workspace. Some animals, such as snails and octopuses, distribute processing among multiple ganglia or neural centers that are less tightly coupled than the hemispheres of the human brain. A robot might broadcast information relevant to locomotion to one area and information relevant to speech to another with limited connectivity.
If the subsystems are entirely disconnected, the result might be entirely discrete centers of subjective experience within a single organism or machine. But if they are partly connected, experience might be only partly unified. In the park bench example, the experience of the trees might be unified with the experience of dampness, and the experience of dampness with memories of yesteryear, but the experience of the trees might not be unified with the memories. (Unification would not then be a transitive relation.) Alternatively, some weaker relation of partial unification might hold among the visual, tactile, and memorial experiences. (If this seems inconceivable or impossible, see Sophie Nelson’s and my 2025 article on indeterminate or fractional subjects.)

More abstractly: There’s no compelling architectural reason why an AI system would have to make information available either to all downstream processes or to none. A workspace defined in terms of downstream availability could be a patchwork of partial availabilities rather than a fully global all-or-nothing broadcast.

Read more: https://schwitzsplinters.blogspot.com/2026/02/disunity-and-indeterminacy-in.html