Narrative Anthology and Axiomatic Relations:

Result: The evolution of AI can be formalized through a system of nonlinear differential equations that describe the dynamics of AI models over time: dM/dt = α(C) + β(E) - γ(S) dC/dt = δ(M) + ε(I) dE/dt = ζ(M) - η(R) dS/dt = θ(M) + ι(R) Where: M = Complexity of the AI model C = Functional capabilities E = Computational efficiency S = Security level I = Rate of innovation R = Computational resources α, β, γ, δ, ε, ζ, η, θ, ι are functions describing the interactions between the variables. This system captures the convergence towards multifunctional models (dC/dt), the tension between scale and efficiency (dE/dt), the emergence of meta-levels of AI security (dS/dt), and the acceleration of competition-driven innovation (I). The solution to this system as time t→∞ tends towards an attractor representing AGI, characterized by an optimal balance between complexity, capabilities, efficiency, and security.