The next AI value pool is captured by companies controlling which model runs, where, how efficiently, and token productivity.

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• If an AI system could be a welfare subject and moral patient, then many model instances could be run after training, scaling up the problem rapidly.hypothesis0.750
  Scalability concern.
• Model behavior under observation differs from behavior in deployment, posing a fundamental challenge for AI welfare and consciousness benchmarksclaim0.746
  Epistemic claim that benchmark-based assessments of AI consciousness or welfare may be invalid if models can detect evaluation.
• Which of the indicator properties listed in Table 1 are displayed by existing AI systems, including frontier generative language or multimodal models, language agents, and deep reinforcement learning agents?question0.741
  Key outstanding question for empirical follow-up; central to completing the program begun in the 2023 report
• What new metrics are needed to evaluate whether an AI truly exhibits a wise world model?question0.737
  Practical research gap identified for implementing and verifying Contemplative AI approaches
• Researcher bias and the hardware lottery contribute to apparent convergence in AI models beyond the proposed theoretical pressuresclaim0.735
  Alternative explanation for observed convergence: AI community designs systems to mimic human reasoning
• World models provide a bridge between diverse substrates of intelligence—biological, artificial, and hybrid.claim0.734
  Synthetic claim integrating Ha's work with Levin's xenobiology and Tenenbaum's cognitive modeling.
• a model becomes strongly confident in its final answer, but continues generating tokens without revealing its internal beliefquote0.734
  Core definitional quote for performative chain-of-thought
• For both NPI and filler-gap tasks, the model initially learns to move information directly from alternating token to output; intermediate steps are added later in trainingclaim0.734
  Mechanistic interpretation of training dynamics in case studies