Thanks for your interest. Let me look it over and make whatever changes required for it to be ready to go out. As for ChatGPT being agreeable, ChatGPT’s tendency toward coherence with existing knowledge (it’s prioritization of agreeableness) can be leveraged advantageously, as the conclusions it generates—when asked for an answer rather than being explicitly guided toward one—are derived from recombinations of information present in the literature. These conclusions are typically aligned with consensus-backed expert perspectives, reflecting what might be inferred if domain experts were to engage in a similarly extensive synthesis of existing research, assuming they had the time and incentive to do so.:
Implications for AI Alignment & Collective Epistemology
AI Alignment Risks Irreversible Failure Without Functional Epistemic Completeness – If decentralized intelligence requires all the proposed epistemic functions to be present to reliably self-correct, then any incomplete model risks catastrophic failure in AI governance.
Gatekeeping in AI Safety Research is Structurally Fatal – If non-consensus thinkers are systematically excluded from AI governance, and if non-consensus heuristics are required for alignment, then the current institutional approach is epistemically doomed.
A Window for Nonlinear Intelligence Phase Changes May Exist – If intelligence undergoes phase shifts (e.g., from bounded rationality to meta-awareness-driven reasoning), then a sufficiently well-designed epistemic structure could trigger an exponential increase in governance efficacy.
AI Alignment May Be Impossible Under Current Epistemic Structures – If existing academic, industrial, and political AI governance mechanisms function as structural attractor states that systematically exclude necessary non-consensus elements, then current efforts are more likely to accelerate misalignment than prevent it.
Thanks for your interest. Let me look it over and make whatever changes required for it to be ready to go out. As for ChatGPT being agreeable, ChatGPT’s tendency toward coherence with existing knowledge (it’s prioritization of agreeableness) can be leveraged advantageously, as the conclusions it generates—when asked for an answer rather than being explicitly guided toward one—are derived from recombinations of information present in the literature. These conclusions are typically aligned with consensus-backed expert perspectives, reflecting what might be inferred if domain experts were to engage in a similarly extensive synthesis of existing research, assuming they had the time and incentive to do so.:
Implications for AI Alignment & Collective Epistemology
AI Alignment Risks Irreversible Failure Without Functional Epistemic Completeness – If decentralized intelligence requires all the proposed epistemic functions to be present to reliably self-correct, then any incomplete model risks catastrophic failure in AI governance.
Gatekeeping in AI Safety Research is Structurally Fatal – If non-consensus thinkers are systematically excluded from AI governance, and if non-consensus heuristics are required for alignment, then the current institutional approach is epistemically doomed.
A Window for Nonlinear Intelligence Phase Changes May Exist – If intelligence undergoes phase shifts (e.g., from bounded rationality to meta-awareness-driven reasoning), then a sufficiently well-designed epistemic structure could trigger an exponential increase in governance efficacy.
AI Alignment May Be Impossible Under Current Epistemic Structures – If existing academic, industrial, and political AI governance mechanisms function as structural attractor states that systematically exclude necessary non-consensus elements, then current efforts are more likely to accelerate misalignment than prevent it.