The future of representative democracy is uncertain. The gradual consolidation of its institutions, procedures, cultural and ideological references, was directly congruent with the evolution of the industrial age economic and social compound. But this historical social pattern is melting down. Fordism’s “mass production for mass consumption” – staged by mass media – was the main paradigm of last century’s political systems. Vertically structured power nodes linked into chains of “intermediate bodies”: trade unions, business groups, political parties and parliaments, hierarchical judicial systems, governmental departments and agencies, military and police forces, one-way media outlets… Big Labor, Big Business, Big Government and Big Media. Now, in its race for survival, the Fordist model has hit an environmental, economic and social wall, creating the possibility of a new paradigm. “Global Fordism,” transnational constraints and threats, ubiquitous information and disinformation, new disruptive technologies, permanent innovation, and the emergence of cyberspace as a new “territorial” reference, is accelerating an epochal social and political transformation. Customized networked production for customized network consumption – under a cloud of interactive personalized communications – is at the core of the expansion of a new “digital” social pattern. This paper tries to contribute to the effort of building new foundations for the “digital” 21st century political and social sciences by tapping into cognitive sciences and AI insights – particularly “neural networks” research – as a metaphor for understanding contemporary mutations in social history. Looking at human society’s dynamics as a self-organizing ”recurrent neural network,” in order to apprehend the path and challenges of future of political power, government and governance. This paper tries to explore Artificial Intelligence (AI) insights as a metaphor for understanding mutations in social history – particularly, through the earliest intuitions developed during the last three decades by Henri Atlan in his works on “neuron-like automata networks,” and the emergence of “intentional procedures” in “self-organizing neural networks.”