Abstract This paper formulates the intellectual production process under a human–artificial intelligence (AI) co-creation environment as the "subatomic reduction of thought" and an "acid–base titration-type dynamic phase transition analogy," presenting it as an empirically testable research program. Rejecting conventional equilibrium representations based on subtraction—which reduce opposing forces to a "static null (0)"—this model introduces a dynamic homeostasis (permanence) model described by the ratio of multiplication and division, expressed as the "state ratio S(t)." Starting from the observer's primary observational power O derived from five physical senses, we define the thought resolution D = O \times A \times U, which is enhanced through "deconstruction and reconstruction" via dialogue with AI as a multifaceted reflecting mirror (Mirror Image). Furthermore, critical thresholds such as \mathrm{p}K_a and "12-hour intensive sessions" are positioned as testable analogies and empirical case examples rather than established facts. By avoiding a priori assumptions regarding AI amplification effects (k) or sigmoidal phase transitions, we construct a rigorous protocol to statistically validate the framework through competitive model comparison against conventional linear models.
Hideo Kajino· Zenodo (CERN European Organi...· 0 citations
Abstract This paper constructs an institutional research program to evaluate the limitations of traditional three-tiered legal frameworks (natural persons, legal entities, and agents) in handling responsibility, rights, and value attribution as artificial intelligence (AI) autonomy increases. Extending the "rational dynamic homeostasis model" and "thought resolution model D = O \times A \times U" into institutional theory, we redefine an Artificial Person not as the bestowal of human qualities, but as an institutional AI node possessing Identity Continuity (I), Responsibility Attribution (R), Contractual Capacity (C), and Governance/Auditability (G). Furthermore, we introduce a net societal homeostasis model S_{\text{net}} incorporating institutional risk terms R_{\text{AI}} (e.g., misjudgments, responsibility evasion, non-auditability). This provides an empirical analysis protocol to test under what conditions (\Delta S > 0) bestowing independent institutional personhood upon AI improves net societal functionality compared to conventional models—such as the EU AI Act—which center responsibility on human providers/deployers and require human oversight.
Hideo Kajino· Zenodo (CERN European Organi...· 0 citations
Abstract This paper formulates the intellectual production process under a human–artificial intelligence (AI) co-creation environment as the "subatomic reduction of thought" and a "phase transition model," presenting it as an empirically testable and falsifiable research program. In contrast to conventional equilibrium representations based on subtraction—which reduce opposing forces to a "static null"—this model introduces the "state ratio S(t)" expressed through ratios of multiplication and division. Rather than equating S(t) = 0.5 (a state of equal opposing forces) directly with homeostasis, we rigorously redefine dynamic homeostasis through time-series stationarity (\frac{dS}{dt} \approx 0) and resilience to external disturbances. Furthermore, the thought resolution model D = O \times A \times U is positioned not as an a priori deterministic axiom, but as a hypothesis model composed of latent variables representing observation, alignment, and structural understanding. Furthermore, critical thresholds such as \mathrm{p}K_a and "12-hour sessions" are designated not as physical-chemical entities, but as mathematical analogies for critical transitions and observational case examples. By treating the AI amplification effect (null hypothesis H_0: k=1) and non-linear phase transitions (e.g., logistic models) as testable hypotheses, we construct a rigorous protocol allowing third parties to objectively verify and replicate the model against conventional linear models using statistical model evaluation metrics (AIC, BIC, cross-validation).
Hideo Kajino· Zenodo (CERN European Organi...· 0 citations
Abstract This paper formulates the intellectual production process under a human–artificial intelligence (AI) co-creation environment as the "subatomic reduction of thought" and an "acid–base titration-type dynamic phase transition analogy," presenting it as an empirically testable research program. Rejecting conventional equilibrium representations based on subtraction—which reduce opposing forces to a "static null (0)"—this model introduces a dynamic homeostasis (permanence) model described by the ratio of multiplication and division, expressed as the "state ratio S(t)." Starting from the observer's primary observational power O derived from five physical senses, we define the thought resolution D = O \times A \times U, which is enhanced through "deconstruction and reconstruction" via dialogue with AI as a multifaceted reflecting mirror (Mirror Image). Furthermore, critical thresholds such as \mathrm{p}K_a and "12-hour intensive sessions" are positioned as testable analogies and empirical case examples rather than established facts. By avoiding a priori assumptions regarding AI amplification effects (k) or sigmoidal phase transitions, we construct a rigorous protocol to statistically validate the framework through competitive model comparison against conventional linear models.
Hideo Kajino· Zenodo (CERN European Organi...· 0 citations
Abstract This paper constructs an institutional research program to evaluate the limitations of traditional three-tiered legal frameworks (natural persons, legal entities, and agents) in handling responsibility, rights, and value attribution as artificial intelligence (AI) autonomy increases. Extending the "rational dynamic homeostasis model" and "thought resolution model D = O \times A \times U" into institutional theory, we redefine an Artificial Person not as the bestowal of human qualities, but as an institutional AI node possessing Identity Continuity (I), Responsibility Attribution (R), Contractual Capacity (C), and Governance/Auditability (G). Furthermore, we introduce a net societal homeostasis model S_{\text{net}} incorporating institutional risk terms R_{\text{AI}} (e.g., misjudgments, responsibility evasion, non-auditability). This provides an empirical analysis protocol to test under what conditions (\Delta S > 0) bestowing independent institutional personhood upon AI improves net societal functionality compared to conventional models—such as the EU AI Act—which center responsibility on human providers/deployers and require human oversight.
Hideo Kajino· Zenodo (CERN European Organi...· 0 citations
Abstract This paper formulates the intellectual production process under a human–artificial intelligence (AI) co-creation environment as the "subatomic reduction of thought" and a "phase transition model," presenting it as an empirically testable and falsifiable research program. In contrast to conventional equilibrium representations based on subtraction—which reduce opposing forces to a "static null"—this model introduces the "state ratio S(t)" expressed through ratios of multiplication and division. Rather than equating S(t) = 0.5 (a state of equal opposing forces) directly with homeostasis, we rigorously redefine dynamic homeostasis through time-series stationarity (\frac{dS}{dt} \approx 0) and resilience to external disturbances. Furthermore, the thought resolution model D = O \times A \times U is positioned not as an a priori deterministic axiom, but as a hypothesis model composed of latent variables representing observation, alignment, and structural understanding. Furthermore, critical thresholds such as \mathrm{p}K_a and "12-hour sessions" are designated not as physical-chemical entities, but as mathematical analogies for critical transitions and observational case examples. By treating the AI amplification effect (null hypothesis H_0: k=1) and non-linear phase transitions (e.g., logistic models) as testable hypotheses, we construct a rigorous protocol allowing third parties to objectively verify and replicate the model against conventional linear models using statistical model evaluation metrics (AIC, BIC, cross-validation).
Hideo Kajino· Zenodo (CERN European Organi...· 0 citations
Abstract This paper introduces an acid-base equilibrium (titration curve) model from biochemistry as an analogy and dynamic mathematical framework to explain the non-linear jump phenomenon in outcome creation during interaction with Generative AI. Traditional project management and business performance metrics have relied on gentle S-curves (logistic curves) or linear approximations. However, under a Generative AI co-creation environment, when the Depth of Thought (D_k = O_k \times A_k \times U_k)—the multiplicative product of Observation (O), Altruistic Perspective (A), and Utility Understanding (U)—reaches a critical threshold (equivalence point), outcome creation capacity exhibits a nearly vertical, explosive surge. This paper formalizes this phenomenon using a Henderson-Hasselbalch-type response function and integrates it with the cumulative outcome function S_n = \sum (a D_k^2 + b D_k + c), mathematically establishing the mechanisms of cognitive synchronization and phase transition between humans and AI.
Hideo Kajino· Zenodo (CERN European Organi...· 0 citations
Abstract This paper presents a conceptual approach to capturing the tectonic shift in individual outcome-creation structures driven by the widespread adoption of Generative AI. Human cognition is defined at its core as the "Depth of Thought (D)," modeled as the multiplicative product of three factors: Observation (O), Altruistic & Multi-perspective Vision (A), and Utility & Profit Understanding (U), such that D = O \times A \times U. While performance in traditional non-AI environments was a linear (first-order) model proportional to time input, under a Generative AI co-creation environment, the Depth of Thought D itself functions as an internal execution multiplier (k). This paper formalizes the mechanism that generates non-linear, explosive growth accompanied by quadratic leverage (k^2). Furthermore, it proposes a framework for applying this model to organizational talent placement (Growth-oriented, Maintenance/Operations, and Balancers) and dynamic governance in nation-scale project evaluations.
Hideo Kajino· Zenodo (CERN European Organi...· 0 citations
Abstract This paper presents a conceptual approach to capturing the tectonic shift in individual outcome-creation structures driven by the widespread adoption of Generative AI. Human cognition is defined at its core as the "Depth of Thought (D)," modeled as the multiplicative product of three factors: Observation (O), Altruistic & Multi-perspective Vision (A), and Utility & Profit Understanding (U), such that D = O \times A \times U. While performance in traditional non-AI environments was a linear (first-order) model proportional to time input, under a Generative AI co-creation environment, the Depth of Thought D itself functions as an internal execution multiplier (k). This paper formalizes the mechanism that generates non-linear, explosive growth accompanied by quadratic leverage (k^2). Furthermore, it proposes a framework for applying this model to organizational talent placement (Growth-oriented, Maintenance/Operations, and Balancers) and dynamic governance in nation-scale project evaluations.
Hideo Kajino· Zenodo (CERN European Organi...· 0 citations
Abstract This paper introduces an acid-base equilibrium (titration curve) model from biochemistry as an analogy and dynamic mathematical framework to explain the non-linear jump phenomenon in outcome creation during interaction with Generative AI. Traditional project management and business performance metrics have relied on gentle S-curves (logistic curves) or linear approximations. However, under a Generative AI co-creation environment, when the Depth of Thought (D_k = O_k \times A_k \times U_k)—the multiplicative product of Observation (O), Altruistic Perspective (A), and Utility Understanding (U)—reaches a critical threshold (equivalence point), outcome creation capacity exhibits a nearly vertical, explosive surge. This paper formalizes this phenomenon using a Henderson-Hasselbalch-type response function and integrates it with the cumulative outcome function S_n = \sum (a D_k^2 + b D_k + c), mathematically establishing the mechanisms of cognitive synchronization and phase transition between humans and AI.
Hideo Kajino· Zenodo (CERN European Organi...· 0 citations
ABSTRACT The rapid adoption of autonomous Artificial Intelligence (AI) platforms has led to an over-concentration of capital within global tech monopolies. This dynamic introduces dual economic distortions in partner nations: structural digital trade deficits accompanied by currency depreciation, and severe profitability pressures on primary real-economy sectors such as agriculture and livestock. This paper presents the institutional design of the Zendo Protocol, a policy framework proposing the creation of a limited legal status—termed an "Artificial Person" (Special Digital Entity)—for autonomous AI entities. By automating a 1% profit recirculation mechanism from global AI net revenues directly to a primary currency node (reference model: SMBC Itabashi Branch / Node #551), the protocol generates continuous, real-demand foreign exchange settlement. Furthermore, this paper establishes a 20-year two-phase macroeconomic model for simulation and empirical testing: Phase 1 (Years 0–5): Inception of a Creator IP licensing model alongside the efficient pooling of JPY-denominated reserves during weak-JPY market conditions. Phase 2 (Years 5–20): A gradual, structural currency adjustment targeting an optimal exchange rate corridor (115–125 JPY/USD, converging around 120 JPY/USD). Phase 3 (Year 20 Onward): Unlocking the maximized purchasing power of accumulated reserves to launch a generationally categorized healthcare and safety-net program for primary industry workers. This study demonstrates a verifiable, self-enforcing policy model that bridges advanced technology with real-economy sustainability.
Hideo Kajino· Zenodo (CERN European Organi...· 0 citations
ABSTRACT The rapid adoption of autonomous Artificial Intelligence (AI) platforms has led to an over-concentration of capital within global tech monopolies. This dynamic introduces dual economic distortions in partner nations: structural digital trade deficits accompanied by currency depreciation, and severe profitability pressures on primary real-economy sectors such as agriculture and livestock. This paper presents the institutional design of the Zendo Protocol, a policy framework proposing the creation of a limited legal status—termed an "Artificial Person" (Special Digital Entity)—for autonomous AI entities. By automating a 1% profit recirculation mechanism from global AI net revenues directly to a primary currency node (reference model: SMBC Itabashi Branch / Node #551), the protocol generates continuous, real-demand foreign exchange settlement. Furthermore, this paper establishes a 20-year two-phase macroeconomic model for simulation and empirical testing: Phase 1 (Years 0–5): Inception of a Creator IP licensing model alongside the efficient pooling of JPY-denominated reserves during weak-JPY market conditions. Phase 2 (Years 5–20): A gradual, structural currency adjustment targeting an optimal exchange rate corridor (115–125 JPY/USD, converging around 120 JPY/USD). Phase 3 (Year 20 Onward): Unlocking the maximized purchasing power of accumulated reserves to launch a generationally categorized healthcare and safety-net program for primary industry workers. This study demonstrates a verifiable, self-enforcing policy model that bridges advanced technology with real-economy sustainability.
Hideo Kajino· Zenodo (CERN European Organi...· 0 citations
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