<aside> ✴️ Directory for Planetary-Scale Computation: An industry primer on the hyperscale CSP oligopoly (AWS/Azure/GCP):
Let’s Get Physical, (Cyber)Physical!: Flows of Atoms, Flows of Electrons
A Cloudy History: Four Histories of Cloud Computing
Primer on the Economics of Cloud Computing
Three-Body: Competitive Dynamics in the Hyperscale Oligopoly
[WIP] The Telos of Planetary-Scale Computation: Ongoing and Future Developments
Appendix:
[WIP] Clouds with Chinese Characteristics
[WIP] Deployment Models: Private/Hybrid/Multi-Cloud and Edge
[TBD] Green Clouds
[TBD] Netflix Case Study
[TBD] Snowflake Case Study
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<aside> ✴️ Table of Contents for Primer on the Economics of Cloud Computing:
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<aside> ✴️ Resource List:
Zonal Harmonic (2017) by Tomás Saraceno
Techne vs Metis.
The disciplines of economics and finance are typically taught by oscillating between:
This process is typical for the teaching of any discipline but [I believe that] economics and finance are a special case in that the delta between the disciplines’ Platonic ideals and reality of the disciplines’ practices (aka techne vs metis, respectively) are the largest of any discipline and remain large even as one learns more about them. Whereas students of fields like theoretical physics, mathematics, philosophy, etc. tend to gravitate towards techne as they further the development of their theories, and students of agriculture, engineering, medicine, marketing, accounting, etc. tend to gravitate towards metis as they begin practicing their disciplines, neither techne nor metis seem to be an exclusive, stable attractor for students of economics and finance.
In finance, metis without techne results in the “I use technical analysis exclusively. What do you mean automation? Python?” Davey daytrader archetype who underfits reality, while techne without metis can contribute to global financial collapse through model overfit. The ability to properly synthesize practice and theory can lead to profitable opportunities, as was the case for oil traders in 2020 who were quick to switch from Black/Black-Scholes-based options pricing models to the Bachelier model when oil futures went negative for the first time in history.
That this gap between the ideal and the real exists makes sense given that the practice business and commerce exists to solve real problems that the disciplines of business/economics/accounting/finance only attempt to systematize the analysis and practice of after the fact. The job to be done (JBTD) for the healthcare industry is to improve peoples’ health; the JBTD for the airline industry to get people where they want to go; the JBTD for a bakery is bake bread. While principal-agent problems and regulatory capture can eventually lead to market distortions that pervert the industry’s JBTD, these distortions are usually born after the fact — the metis of the business of breadmaking eventually leads to the techne of managing the business and finances of the bakery (metis → techne). Not so with the cloud computing industry (techne → metis) .
The modern cloud computing industry (as well as its computer timesharing predecessor) was initially born of an attempt to capitalize on what was an internal financial problem rather than an already existing, external demand.
<aside> ✴️ From Amazon Enters the Cloud Computing Business (2008):
Amazon's computing demands experience large seasonal variations, such as the surge in traffic before the winter holidays. Ensuring the necessary capacity to handle peak usage results in up to 90% idle time for Amazon's servers. Amazon identified this excess processing power as a revenue stream by offering cloud computing services.
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In a sentence, the basic idea behind cloud computing is to aggregate demand for computing resources at scale in order to diversify the timing of resource use, thereby maximizing asset (i.e., computer/server) utilization and exploiting various economies of scale. Cloud computing was initially a financial innovation that only subsequently enabled and benefited from (and still enables and benefits from, present tense) technological innovation. That Jeff Bezos was a financial analyst (no less at a quant firm that heavily utilized computational resources) prior to founding Amazon should come as no surprise to anyone who learns this fact.