It is an open question whether simulating a conscious mind requires far more resolution than the relatively slow and sparse communications between billions of biological neurons, or whether current digital substrates suffice.

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• We hypothesize that general computational machines with sufficient resources possess the necessary and sufficient means to implement consciousness, and that successful implementation can be established via analysis or testing.hypothesis0.829
  The central hypothesis of the paper
• Digital minds with humanlike capabilities could think at least thousands of times, and perhaps millions of times, faster than humans given sufficient hardwarehypothesis0.808
  Grounds the subjective speed dimension of super-beneficiary status
• Different learning systems facing similar computational problems will converge to similar consciousness-like solutions, including potentially biological and artificial systemshypothesis0.807
  Extension of the Universality Hypothesis to consciousness: if consciousness solves a well-defined computational problem, different systems will discover it independently
• There exists no viable behavioral test for consciousness analogous to the Turing Test for intelligence, because consciousness is a particular internal way to achieve performance, not externally visible performance itself.claim0.806
  Paper identifies as a research gap requiring internal analysis methods rather than behavioral benchmarks
• Can computers be conscious? Or more specifically, can today's computers fully emulate the way in which organisms compute minds?question0.804
  Central research question motivating the entire paper
• Consciousness in AI is best assessed by drawing on neuroscientific theories of consciousness.claim0.802
  Central methodological claim of the paper.
• Three criteria for consciousness-capable substrates: hybrid computation, scale-inseparability with metabolic embedding, dynamico-structural co-determination.hypothesis0.800
  Proposed necessary conditions for any substrate (biological or artificial) to support consciousness; integrates discrete-continuous, multiscale, and adaptive properties.
• Human consciousness is tied to a biological learning algorithm — the simplest algorithm discoverable by evolutionary search to train a self-organizing biological substrate to become intelligent.claim0.799
  Core theoretical claim connecting consciousness to biological learning