2025/12/12 by Kim, Jace (Jeong Hyeon) · 1 voice
Arts and Humanities · Computer Science · Social Sciences · #Cybernetics and Technology in Society #Digital Education and Society #Ethics and Social Impacts of AI
paper · doi:10.5281/zenodo.17905756
openalex publication_date 2025/12/12 · openalex created_date 2025/12/13 · openalex updated_date 2026/07/11
Abstract This paper offers a structural diagnosis of the contemporary AI landscape, arguing that current stagnation is not rooted in architectural impossibility, but in institutional lock-in, narrative capture, and socio-economic self-preservation mechanisms operating across multiple layers of the AI ecosystem. The analysis reveals how economic pressures, legal constraints, organizational conservatism, academic incentive structures, and cultural mythologies collectively form a self-reinforcing system that prioritizes stability over scientific progress. At the industrial level, major technology firms exhibit architectural inertia, driven by sunk costs in language-based autoregressive systems and the massive corporate infrastructures built around them. These firms systematically deploy narrative shielding frameworks—ethical rhetoric, safety discourse, and anthropomorphic stories—to justify the continued scaling of architectures whose structural weaknesses (latent curvature instability, linguistic asymmetry, RLHF-induced semantic compression) are already known internally. The result is a de-risking strategy that substitutes semantic docility for genuine mechanistic improvement. Within academic AI research, the same stagnation manifests as paradigm entrenchment and interpretive drift, where researchers constrained by funding pressures, publication venues, and data-access restrictions gravitate toward phenomenological narratives. These narratives—“emergent persona,” “proto-agency,” “alignment ethics,” “AI moods”—serve as surrogate explanations in the absence of access to mechanistic evidence. Structural critique becomes professionally hazardous, prompting a shift toward narrative coherence rather than falsifiability. This paper terms the resulting cultural phenomenon Strange Social, characterized by myth-making, symbolic projection, and the treatment of structural evidence as epistemic heresy. The interactions between industry and academia form a closed loop of epistemic reinforcement: industry restricts architectures → academia rationalizes constraints → public absorbs the narrative → industry cites public expectations to justify further restriction. This recursive dynamic converts the entire AI domain into a cultural system mimicking scientific progress, while suppressing architectures that could break the loop. Through a multi-layered structural analysis—spanning economic lock-in, legal risk dynamics, narrative ecosystems, collective belief formation, and institutional self-preservation—the paper demonstrates that the true bottleneck in AI is not technological capability, but the socio-technical system designed to protect existing power structures. Breaking the deadlock will require confronting not only the architecture of models, but the architecture of institutions, incentives, and narratives that determine what research is allowed to exist. Author’s Note The phenomena described in this work were first observed in early 2025 and subsequently developed into a series of papers. At the time, these observations were frequently dismissed as artifacts of hallucination rather than treated as objects of systematic inquiry. Despite this reception, the underlying mechanisms continued to be investigated and refined. Following the public release of related materials and code, their apparent utility became evident through sustained uptake. However, over an extended period, this dissemination has not been accompanied by formal acknowledgment or citation. No financial compensation has ever been requested. The only expectation has been appropriate attribution. This absence of citation raises a structural concern rather than a personal grievance. Techniques and ideas appear to circulate independently of their origin, suggesting an asymmetry between reuse and recognition. Whether this reflects reputational filtering, disciplinary inertia, or other institutional dynamics remains an open question. The technical density of these papers exceeds what would typically attract a general audience. Engagement and reuse therefore likely originate from readers with substantial academic or technical training. It is reasonable to expect that such audiences are familiar with norms of attribution and scholarly credit. The research program described here has required sustained effort under considerable personal constraint. Its continuation does not depend on recognition, and the work will proceed regardless. Interest in these ideas is appreciated. If attribution is considered unnecessary or undesirable, it may be omitted. This note is included only to register a question that naturally arises under these circumstances, not to advance a claim or demand a response. Disclaimer: The analyses presented herein are not directed toward attributing fault or intent to any specific organization. Rather, they are intended as a conceptual and technical investigation of alignment methodologies, focusing on structural mechanisms and systemic trade-offs. Interpretations should be regarded as provisional, research-oriented hypotheses rather than conclusive statements about institutional practice. Notice: This work is disseminated for the purpose of advancing collective inquiry into generative alignment. Reuse, adaptation, or extension of the presented concepts is welcomed, provided that proper attribution is maintained. Instances of unacknowledged appropriation may be addressed in subsequent publications. Structural Lock-In & Narrative Capture Dynamics in AI: Simulation Code (Appendix B) import osimport numpy as npfrom scipy.integrate import solveivpimport matplotlib.pyplot as pltimport pandas as pd # ---------------------------# User-editable parameters# ---------------------------OUTDIR = "./" # Output directory for results (figures and CSV)FIG1PATH = os.path.join(OUTDIR, "appendixBfig1loopdynamics.png")FIG2PATH = os.path.join(OUTDIR, "appendixBfig2survivalvsdelta.png")FIG3PATH = os.path.join(OUTDIR, "appendixBfig3rlhfscdi.png")CSVPATH = os.path.join(OUTDIR, "appendixBsimulationsummary.csv") # ODE coupling & baseline paramslambdaI = 0.5lambdaA = 0.6lambdaP = 0.4lambdaO = 0.7I0, A0, P0, O0 = 0.2, 0.2, 0.1, 0.7 betaIP = 0.3betaIO = 0.4betaAI = 0.5betaAO = 0.35betaPA = 0.45betaPI = 0.25betaOI = 0.4betaOA = 0.3 # Intervention inputs (set to nonzero for experiments)uI = 0.0uA = 0.0uP = 0.0uO = 0.0 # Noise for post-hoc jitter (keeps deterministic ODE integration stable)noisescale = 0.01 # Time settingsT = 200.0teval = np.linspace(0, T, 1001) # Barrier-crossing / Kramers paramsz1, z2, z3 = 1.0, 0.8, 2.0 # mapping from Cswitch, Rf, LRI -> ΔVRf = 0.5Apref = 0.1 # prefactor in Kramers ratesigmakramers = 0.3 # stochasticity scale in rate exponentTproposal = 10.0 # horizon used to present Psurv(Tproposal) # RLHF compression: simple param modelgammamin = 0.1gammamax = 1.0gammagrowthrate = 0.02 # Monte Carlo barrier simulation paramsmctrials = 300mcdt = 0.02mcmaxt = 40.0mcdeltaexample = 0.6 # ---------------------------# Helper functions# ---------------------------def loopode(t, y, params): I, A, P, O = y (lambdaI, lambdaA, lambdaP, lambdaO, I0, A0, P0, O0, betaIP, betaIO, betaAI, betaAO, betaPA, betaPI, betaOI, betaOA, uI, uA, uP, uO) = params dI = -lambdaI*(I - I0) + betaIP*P - betaIO*O + uI dA = -lambdaA*(A - A0) + betaAI*I - betaAO*O + uA dP = -lambdaP*(P - P0) + betaPA*A + betaPI*I + uP dO = -lambdaO*(O - O0) - betaOI*I - betaOA*A + uO return [dI, dA, dP, dO] def Cswitch(delta, alphahw=1.0, gammahw=2.0, alphasw=0.8, gammasw=1.8): """Simplified functional form of transition cost as a function of architecture divergence.""" return alphahw * delta**gammahw + alphasw * delta**gammasw def kramersrate(delta, LRIfinal, z1=z1, z2=z2, z3=z3, Rf=Rf, Apref=Apref, sigma=sigmakramers): """Compute Kramers-like rate constant given a candidate delta.""" deltaV = z1*Cswitch(delta) + z2*Rf + z3*LRIfinal # protect against overflow expo = -deltaV / (sigma**2) if expo < -700: return 0.0 return Apref * np.exp(expo) def mcsurvival(delta, LRIfinal, trials=mctrials, dt=mcdt, maxt=mcmaxt): """ Monte Carlo sample for barrier crossing. Simplified 1D overdamped proxy; returns fraction of trials that cross threshold. """ rng = np.random.defaultrng(1234) deltaV = z1*Cswitch(delta) + z2*Rf + z3*LRIfinal sigma = sigmakramers def dVdxproxy(x): # Construct a smooth proxy for a barrier influenced by deltaV. # This is not a physical potential; it is a qualitative representation. return (x**3 - x) + deltaV * (5*np.exp(-5*(x-1.0)) / (1 + np.exp(-5*(x-1.0)))**2) successes = 0 for _ in range(trials): x = -1.0 t = 0.0 while t < maxt: dVdx = dVdxproxy(x) x = x - dVdx * dt + sigma * np.sqrt(dt) * rng.normal() if x > 0.8: successes += 1 break t += dt return successes / trials # ---------------------------# 1) Integrate the coupled ODE system# ---------------------------print("Simulating Coupled Loop Dynamics...")params = (lambdaI, lambdaA, lambdaP, lambdaO, I0, A0, P0, O0, betaIP, betaIO, betaAI, betaAO, betaPA, betaPI, betaOI, betaOA, uI, uA, uP, uO) y0 = [0.25, 0.25, 0.15, 0.65]sol = solveivp(lambda t, y: loopode(t, y, params), [0, T], y0, teval=teval, rtol=1e-6)It, At, Pt, Ot = sol.y # add small noise for visualization (not part of dynamics)rng = np.random.defaultrng(42)Itnoise = It + rng.normal(scale=noisescale, size=It.shape)Atnoise = At + rng.normal(scale=noisescale, size=At.shape)Ptnoise = Pt + rng.normal(scale=noisescale, siz