人工智能时代生产关系稳态的概率评估与目标可达性分析(Steady-State Relations of Production in the Age of Artificial Intelligence: Probability Assessment and Target Reachability Analysis)

Abstract

人工智能同时作为生产力、生产资料与代理层介入生产过程,其影响因而不止于就业数量,而在于对"谁拥有、谁控制、谁受益、谁担责"这四个核心问题的系统性重塑。本文建立一个可审核的定量评估模型:将85项因果因素归入七个机制群,为每项因素配置"发生概率"与"生产关系权重"两个维度,影响权重取二者之积并在全因素池上直接归一(扁平口径,不设组权重层);因素对稳态的贡献分布不再逐项手设,而由九类机制标签的固定向量给出,使全部贡献参数由144个自由数值退化为九个可逐条审核的机制型判断。 在五情景基准下,分层租金资本主义39.1%、技术封建主义22.3%、多极竞争性共存15.1%、公共AI/算法社会主义12.6%、混合形态10.9%,等级化合计61.4%。在20000次标签归属扰动的蒙特卡洛检验中,分层租金资本主义在100%的样本中居于首位,等级化合计超过非等级化合计的频率同样为100%;而"等级化合计超过70%"的成立频率为0%,该数值阈值不宜作为稳健性结论。 在此基础上,本文把同一模型换向读取。利用两条结构性质——解析恒等式 ∂P_j/∂p_i = (k_i/M)(c_ij − P_j),以及"稳态分布恒位于九类标签向量的凸包内"——本文对四个纯序稳态求解因素反推,并采用线性分式精确求解而非启发式搜索。四种目标的内生可达上限分别为40.9%、23.5%、18.5%与18.0%,内生可达度分别为25%、17%、36%与44%。三项结果值得特别注意:技术封建主义的最优方案中不存在任何可被增强的机制,它只能通过削弱其他机制来抬高;公共AI/算法社会主义的内生上限低于20%,但该判定同时依赖分母口径与标签向量的设定,两者之一变动即可能使其翻转;内生因素仅承载56%的等级化压力(保守与宽松分级下为43%至67%),而等级化系数最高的三类机制中,租金提取完全内生于制度选择,劳动议价削弱与认知技能分化则基本外生。 本文的总体判断是:等级化的压力主要来自外生结构,而通向非等级化稳态的杠杆主要来自内生制度。这一错配本身构成了当前结构惯性的来源,也解释了为何份额预测与可达性分析会给出方向不同的印象。 关键词:人工智能;生产关系;代理层;责任漂移;稳态概率;机制标签;可达性分析;公共算力 Artificial intelligence enters the production process simultaneously as a force of production, as a means of production and as an agentic layer. Its impact therefore extends well beyond employment numbers: it systematically reshapes the four core questions of who owns, who controls, who benefits and who bears responsibility. This paper builds an auditable quantitative assessment model. Eighty-five causal factors are assigned to seven mechanism groups. Each factor carries two dimensions—a probability of becoming operative and a weight on relations of production—whose product defines its influence weight within a single flat pool (no group-weight layer is imposed). The distribution of each factor's contribution across steady states is no longer set factor by factor; it is given by one of nine mechanism tags, which reduces the entire contribution parameter set from 144 free numbers to nine auditable mechanism-level judgements. Under the five-scenario baseline, stratified rent capitalism takes 39.1 per cent, techno-feudalism 22.3 per cent, multipolar competitive coexistence 15.1 per cent, public AI / algorithmic socialism 12.6 per cent and the hybrid form 10.9 per cent, with the hierarchical pair totalling 61.4 per cent. Across 20,000 Monte Carlo draws that perturb tag assignment, stratified rent capitalism ranks first in 100 per cent of samples and the hierarchical pair exceeds the non-hierarchical pair in 100 per cent of samples; the proposition that the hierarchical pair exceeds 70 per cent holds in 0 per cent of samples, so that numerical threshold should not be presented as a robustness result. The same model is then read in reverse. Two structural properties—the analytic identity ∂P_j/∂p_i = (k_i/M)(c_ij − P_j), and the fact that the steady-state distribution always lies inside the convex hull of the nine tag vectors—allow factor-level reverse inference for each of the four pure-order steady states. The optima are obtained by exact linear-fractional solution rather than heuristic search. The endogenous reachability ceilings of the four targets are 40.9, 23.5, 18.5 and 18.0 per cent, and their endogenous reachability ratios are 25, 17, 36 and 44 per cent. Three results deserve particular attention. First, the optimal scheme for techno-feudalism contains no factor that can be strengthened at all: it can only be raised by weakening other mechanisms. Second, the endogenous ceiling of public AI / algorithmic socialism lies below 20 per cent, but that verdict depends on both the denominator convention and the setting of one tag vector, and moves either way if that setting changes. Third, endogenous factors carry only 56 per cent of the hierarchy pressure (43 to 67 per cent under narrower and broader classifications), and among the three mechanism types with the highest hierarchy coefficients, rent extraction is fully endogenous to institutional choice whereas the weakening of labour bargaining and cognitive stratification are largely exogenous. The overall judgement of this paper is that hierarchy pressure comes mainly from exogenous structure, whereas the levers towards a non-hierarchical steady state come mainly from endogenous institutions. This mismatch is itself a source of current structural inertia, and it explains why share prediction and reachability analysis give impressions that point in different directions. Keywords: artificial intelligence; relations of production; agentic layer; responsibility drift; steady-state probability; mechanism tags; reachability analysis; public compute