Six Dynamics That Will Shape Our Future
Last week I listed 12 conditions that will shape our future, much to the surprise of those projecting a largely unchanged continuation of the present.
1. The system is optimized for infinite growth / expansion. If expansion falters, the system crashes.
2. The system is optimized for infinite substitution of whatever becomes scarce as the means to continue expanding essentially forever.
3. These optimizations only function in a narrow envelope. Should the system stray outside this envelope, it crashes.
4. The fundamental principle of the system is “no limits”: there are no limits on human ingenuity, and so there are no limits on technology and growth.
5. There are intrinsically contradictory dynamics in the system.
6. Scale and asymmetry are the core contradictory dynamics. Two ways to summarize this are 1) math and 2) “too big to care.”
7. The system’s optimizations mis-diagnose problems, so it selects “solutions” that accelerate its own dysfunction / demise.
8. The system lacks the values, subsystems, feedback and institutional structures to adapt to changing conditions.
9. As a result, the preferred “solutions” are all forms of play-acting, i.e. the notion that controlling the narrative / framing the “problem” as solvable with existing policy adjustments is actually solving the problem.
10. The system’s core mythology is Technological Progress is unlimited and unstoppable and so it will solve all problems by its very nature. We can remain comfortably seated and watch as Technology solves whatever problems arise.
11. This belief blinds us to the fact that technology also generates Anti-Progress. Since accepting Anti-Progress undermines our core faith in Technological Progress, we deny the existence of Anti-Progress.
12. As a result, the system is involuted: no matter what option we choose, nothing changes systemically. Real change is only possible at the micro-level of our own lives.
This week I’ll add six more dynamics that will shape our future.
1. Over-optimization. If optimizing a process is good, then more optimization is even better, right? The flaw in this “more is better” assumption is invisible until it’s too late: what’s been stripped out by optimization is precisely what’s needed to save the system from collapse when conditions veer outside the expected envelope of normal operation.
The core dynamic in optimization is to eliminate unnecessary / inefficient steps and costs. For example, “just in time” manufacturing eliminates the costs of warehousing parts by optimizing delivery of parts to align with assembly processes.
Consolidating facilities near major transport hubs lowers costs by reducing the number of facilities. For example, 6 million of Costco’s rotisserie chickens are raised and processed in one facility in Nebraska. Many industrial chemicals are stored in large centralized facilities.
The vulnerabilities and fragilities created by centralization and other forms of optimization are not apparent until something goes awry: a poultry virus spreads through the mega-farm, a fire consumes the vast chemical storage complex, the sole source of critical components is shut down, the one route is disrupted, etc.
The erosion of resilience and adaptability is the penumbra of optimization that few even see until the system breaks down, much to the surprise of everyone who assumed normal over-optimized operations were rock-solid in all conditions.
Consider the systems that supply cities with food, fuel and other essentials. Cities have been a feature of civilization for thousands of years, and pre-industrial supply chains managed to provide wood for cooking, food, etc., to great metropolises. For example, Rome’s population in the Imperial zenith (circa 100 A.D.) is estimated to be in the range of 1 million inhabitants.
Given the reliance on sailing ships and animal-pulled carts, most supplies delivered to cities were locally sourced. Cities themselves contained distributed food production. In the 19th century, it’s been estimated that major cities still supplied as much as 50% of the food supply within or adjacent to city limits.
For example, the Sherlock Holmes story “The Adventure of the Blue Carbuncle” features a man going to a house to buy one of the resident’s geese for Christmas supper. Small urban orchards and gardens were core features of many cities.
Supply chains were diverse and decentralized: food and fuel arrived by various means and from a wide variety of courses. This diversified, decentralized supply system was adaptable by its very nature.
Compare this system to the over-optimized supply chains delivering essentials to today’s megalopolis urban centers: the majority of essentials come through a small number of facilities and are sourced from highly centralized and often distant suppliers.
The surrounding areas produce little to no essentials; suburbs and lawns predominate. Urban planning has favored reducing multi-use zoning, as factories, farms, etc. have been eliminated as nuisances or developed as “under-utilized” land for multi-story housing.
Just-in-time optimization limits the amount of essentials warehoused as backup supplies, so any disruption quickly strips the city of supplies. Human nature’s predisposition to respond to perceived shortages by hoarding accelerates this dynamic.
In summary, the supply chains feeding and fueling the enormous populations of urban centers have been over-optimized, generating risks few see due to the complacency generated by recency bias and an ungrounded confidence that over-optimized systems are rock-solid because nothing untoward has happened to date.
Over-optimization also works regionally.
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