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Monthly Reading, July 2022
Res Extensa #22 :: cycles of centralization, product vs. IT mindsets, motivation & productivity, and The Tacit Dimension
The internet's predecessor, ARPANET, was founded on a principle of resiliency. If the system was too centralized, a first-strike on a central node could knock out the system. The internet today isn't completely evenly distributed; it's a scale-free network — one whose degree of decentralization is power law distributed. Many of the protocols developed in the early days of computing leaned on decentralization and composability. Create lego blocks designed to be layered.
With all networks, consolidation tends to emerge over time. But centralization vs. decentralization isn't a binary — it's a continuum of degree. Along the spectrum, decentralization opts for resiliency over efficiency; centralization prefers speed, deliverability, efficiency. Gordon Brander describes this as an evolutionary proclivity to centralize:
Here is a map of the internet. The first thing you might notice is that the internet is not evenly distributed. Instead, we see the emergence of densely connected hubs—centralized islands in the net.
This kind of thing is called a scale-free network. It seems that something like scale-free structure emerges repeatedly within evolving networks, including on the internet, the web, social networks, airline routes, co-authorship in scientific papers, power grids, inter-bank payment networks, Bitcoin mining, train routes, gene regulatory networks, protein interactions, ecological food webs, oligarchies, neural networks…
In fact, it turns out that almost all real-world networks have degree distributions with a tail of high-degree hubs like this. (Newman, 2018. Networks.)
If you see a pattern emerge over and over, it’s a solid bet there are evolutionary attractors pulling the system in that direction. And yeah, it turns out scale-free networks have strange and important structural properties.
The internet was decentralized at the start: TCP, HTTP, web standards. The social web then emerges and centralizes into walled gardens like Facebook and Snapchat. Web3 comes onto the scene and is attempting to decompose these back into decentralized protocols. And before that’s even gotten much foothold, we're already seeing centralized services like FTX and Coinbase introducing the advantages of centralization.
A network's position on the scale isn't fixed. They move through an evolutionary lifecycle:
Networks also have a time dimension, and the shape of the network changes as it ages. Evolving networks exist in punctuated equilibrium, repeatedly evolving through distinct phases of randomness, growth, consolidation, and collapse.
(Phase 1) Random: The system is unstructured. Random events occur without particularly changing the structure.
(Phase 2) Growth: An innovation causes a major phase transition within the structure of the system. The innovation catalyzes other innovations in a positive feedback loop.**
(Phase 3) Consolidation: Growth rates saturate. The ecosystem consolidates into a highly organized network, optimized for efficiency, as each [agent](https://gordonbrander.com/pattern/agent/) seeks to eke out as much as it can from its position in the value chain. Hubs (keystone species) appear at critical points.**
(Phase 4) Collapse: A random shock, or new innovation demolishes one of the keystone species, causing cascade failure within the highly structured network. The ecosystem collapses into a random structure.**
(Repeat): The system begins a slow crawl back up the evolutionary ladder of complexity.
There's something about product companies that's distinct in the tech sector. Just because you make software, or something else "high tech", doesn't imply that you have a product orientation. I think a lot about how to specifically pin down what differentiates a product team versus a team that produces a good or service.
A product team creates new things — addresses new jobs to be done. Marty Cagan differentiates the product mindset from the "IT" mindset. His main point is that continuous innovation is a necessity for a product mindset. Alternatively, too much emphasis on internally-facing concerns like scalability, minor enhancements to things that already exist, or optimizing company processes define the IT mindset. A product mindset biases to effectiveness over efficiency. An IT one is the reverse.
I think Marty nails an excellent dividing line here: a product mindset orients on customers (demand thinking), an IT mindset focuses on the business.
In an IT mindset organization, the staff exists to service the perceived technology needs of “the business.” In a technology-enabled product organization, the staff exists to service the needs of your customers, within the constraints of the business. This is a profound and far-reaching difference. Most of what is below stems from this difference.
"Stable" companies begin to concern themselves too much with how things get done rather than what is getting done or why. Process starts to creep in.
Customers don't care what's happening internal to your business. If there's any concern at all it's only insofar as it changes the output you deliver to them.
I always say my most productive times are those where I have the most going on. When I was in college, I did better in classes when I worked 30 hours a week. I was more consistently writing when I was much busier at work. I've gotten more house projects done after having kids than before. Nat Eliason dives into this phenomenon of seemingly-backward causality on things like rest vs. energy, meaningful work, and waiting to have kids until you have certain things in order. On all of these themes, motivation is key. We need some form of motivation to make progress. But where does the motivation come from? Nat has an insight I love about the relationship between order and chaos:
The intuition is that we need to create some degree of order before we add chaos. But perhaps adding chaos is what generates the motivation we need to create order. Each of us has a baseline level of chaos we're willing to tolerate, and we're only driven to make a change once that level of chaos is exceeded. Efforts to reduce the chaos below that level are often procrastinated because we aren't truly motivated by them.
We need chaos to induce order. Waiting for some degree of order to exist before we enter a situation of "chaos" (and having kids is a great example of an entry into chaos) won't generate the required motivation to reach an ordered state.
In this piece, Nabeel Qureshi hits on the same idea. Energy to get things done compounds on itself:
Energy compounds on itself. If you start the morning by getting something done (a workout, an important task, writing) then you’re going to have a higher baseline energy day overall. It’s as though the initial thing gives you a persistent ‘boost’ throughout the day. Doing additional things becomes easier. Without this boost, there’s a good chance I get nothing important done that day.
It's exactly what I notice with my own productivity, but I've never been intentional about harnessing this effect. Any day where I start off taking action early — going for a run, writing, even reading — drives a higher level of output the rest of the day.
The post has some other great bits about writing, weekly reviews, and more.
In this interview on the Complexity podcast, Sara Walker dives into her work on the origins of life, life as an information transfer mechanism, and Assembly theory. Her work is fascinating, and she packs in so many interesting ideas into an hour and a half that I'll have to listen again to find all the side paths to go down. One of the big topics was the idea of planet-scale intelligence, which is the subject of her recent paper which you can read in its entirety here. I'm looking forward to digging into it.
📚 A Book for July: The Tacit Dimension
Economist and philosopher Michael Polanyi wrote extensively about what he called "tacit knowledge": the things we know that we can't articulate how we know, or are hard to put into words or explain to others. Think of things like how we can instantly recognize a particular person's face, even in a fuzzy photograph, or how we know a language, or physical skills like riding a bike or shooting a basketball. The type of knowledge derived from experience rather than theory. So far it's been an interesting read. Polanyi's ideas oppose central planning and the technocratic belief in our ability to know anything if we just let the experts have at it. It's an early work that covers much of the same ground as Seeing Like a State or Make to Know.
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