Prototyping The Network State
We're currently in the process of building a viable network state (our second attempt of many to come). Let's dig in.
Let’s build a developmental framework to help us understand what is happening in Washington DC.
A Complex Environment
Let’s structure the problem.
The US government is a system that has evolved to manage the nation-state. It was built to solve complicated problems—linear problems solvable through procedures, processes, analysis, etc. Its design optimizes stability, with limited control by politicians and bureaucrats.
However, the environment has changed. Globalization and networking have created a dynamic world that routinely generates complex problems — nonlinear problems that actively resist processes, procedures, and analysis (the polycrisis).
Worse, globalization and networking have also hollowed out the nation-state, leaving it with far less power and control than before the turn of the century. This weakness, in combination with the complexity of the environment, has led to repeated failures (the permacrisis).
Build a Network State
The solution to this problem is to upgrade the US into a system capable of handling complex challenges. The best candidate for this is a network state. A network state combines four different social decision making systems (David Rondfeldt’s TIMN);
Tribal (nationalism, etc.).
Institutions (gov’t and corporate bureaucracies).
Markets (financial, economic, and electoral).
Networks (social networking, AI, etc.).
The first three layers took centuries to evolve into productive societal decision making systems. The network layer is new, so we don’t fully understand how this decision-making layer works or how to combine it with the existing layers to generate the best results. Despite that ignorance, we’re zooming ahead with building it anyway.
Increasing Performance
A good way to conceptualize what is happening is to think of the network state, and the nation-state it’s replacing, as control systems — systems that optimize the performance of the state while preventing it from becoming unstable. We’re currently building a new control system because the old one isn’t providing us with high performance and/or cannot dampen instability. Here’s an example of how it works in aircraft design (among other things, I’m an astronautical engineer with a specialization in control theory).
We have traditionally optimized aircraft design (fuselage, wings, tail, etc.) for stability — planes that like to fly straight and level. This stability makes it possible for a pilot to make adjustments by hand fast enough to control the aircraft in most flight conditions (bad weather, adverse winds, etc.).
However, stability isn’t the goal in a competitive combat environment, it’s maneuverability and performance. To increase the performance of an aircraft, we need to add instability to the design of the airframe. Adding instability increases its performance at a cost of stability.
The problem is that when we add instability into an aircraft’s design, it quickly becomes uncontrollable by a human pilot. To fix that shortfall, a computer system (fly by wire) is used to make the millisecond inputs necessary to stabilize and control the plane (translating what the pilot wants to do into action).
To summarize, a network state must be a system that can a) make decisions quickly and b) make the correct decisions (this is difficult to do in a complex environment).
NOTE; the more instability we add (+%) to heighten performance, the quicker and more exact the computer’s inputs must be to maintain control of the system. For example, with the F-16, the time to instability is 0.5 seconds without a computer control correction; for Grumman’s X-29 it’s 40 milliseconds.
The Stability Model
The first model of the network state to emerge didn’t attempt to optimize its performance. Instead, it focused on enhancing its stability in a complex environment.
China pioneered the stability model of the network state. It uses networks to control speech, behavior (social credit scores) and exposure to outside contagion (the Great Firewall). During the last US administration, there was an attempt to utilize this stability model to moderate political outcomes (censorship, bans, probes, lawfare, etc.).
The stability model's goal is network alignment—a network of people, corporations, and organizations that share the same way of thinking (the same norms, standards, goals, etc.). Alignment reduces environmental complexity, making it easier for legacy institutions to solve problems and function normally. However, network state alignment also reduces innovation and creativity due to its inherent resistance to novelty production and unauthorized thinking.
The attempt to use this model in the US failed (mainly due to Musk’s acquisition of X). The jury is still out on China. However, the long-term prospects are bleak. Narrowing thought down to a narrow orthodoxy will likely eliminate the innovation China needs to keep pace with the rest of the world. Worse, it may fall into a tyranny (a long night of AI-fueled network control) so complete that it would make Big Brother blush.
The Performance Model
With the arrival of the new US administration (the Red Tribe), an alternative model for the network state is being developed in real-time. Instead of using networks to improve stability, it’s using networks (from social networking to AI) to optimize the performance of the US government. Here’s what it is doing;