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Machine Economy

Machine Economics: When AI Eats Software.

Human economics has always been a behavioral system that is irrational, inconsistent, and shaped by negotiation and emotion. When machines become the dominant economic participants, that premise collapses. Economics stops being the study of people and becomes the study of coordination among intelligent systems.

In the 2000s, software ate the world. Deterministic systems digitized everything: banking, commerce, communication, logistics, entertainment. Every industry became a software industry. This digitization created unprecedented efficiency, but it also created something else: layers upon layers of patched-together systems. Legacy code wrapped in APIs, databases built on older databases, banking systems that still run on COBOL from the 1970s, just with modern interfaces bolted on top. Upgrading meant adding enhancement layers, not rebuilding. The path of least resistance was compounding complexity rather than simplify.

Now intelligence is eating software. Non-deterministic systems AI can see through the accumulated layers and find paths of least resistance that deterministic systems couldn't. Where deterministic systems required explicit instructions for every edge case, non-deterministic systems can navigate ambiguity, make judgment calls, and then build new deterministic systems to execute those decisions. The AI decides what to build and why; deterministic systems handle the how.

AI in Finance and The Machine Economy

AI in Financial Transactions and Economic Modeling

AI-Driven Financial Forecasting and Market Predictions

Financial forecasting is a core area where AI excels. Microsoft Research has developed numerous deep learning models and platforms for market prediction. For example, Microsoft’s open‐source Qlib platform provides high‐performance infrastructure for AI‐driven quantitative investment research 1. It enables end‐to‐end workflows (from stock trend prediction to portfolio optimization) and accommodates the data‐driven nature of AI in finance 2 1. Google Research introduced advanced neural architectures like the Temporal Fusion Transformer (TFT), an attention‐based model that achieved state‐of‐the‐art multi‐horizon forecasting with interpretable insights into market dynamics 3. TFT combines recurrent layers for short‐term patterns with self‐attention for long‐term dependencies, helping analysts understand which factors drive predictions 3. On the industry side, Amazon’s AI teams have applied deep learning to large‐scale time‐series forecasting. Amazon scientists note that “some of the world’s most challenging forecasting problems can be found inside Amazon or posed by AWS customers,” spanning demand prediction, capacity planning, and workforce scheduling 5. By using “deep learning and probabilistic methods”, Amazon improved forecast accuracy and efficiency across these business and financial scenarios 5. Such advancements in AI‐driven forecasting are directly translatable to financial markets – hedge funds and banks are beginning to leverage these models to predict asset prices, volatility, and market trends with increasing precision.

The Machine Economy

The traditional economy, as we know it, is predicated on the exchange of goods and services using fiat currency, which is underpinned by governmental and institutional trust. This system has enabled trade, commerce, and economic growth for centuries. However, as technology progresses, the rise of a machine-driven economy is reshaping our understanding of value and exchange. In a world where machines independently negotiate, transact, and refine operations, traditional economic principles may become obsolete.

As we transition from a human-driven economy to one led by machines, the definition of value will evolve from a fiat currency-based system to a multifaceted, resource-oriented framework that reflects the operational priorities of autonomous systems. This shift will require a reimagining of economic principles, where value is dynamically determined by the context-specific needs of machines rather than by human-centric metrics. This essay explores the implications of this transformation, arguing that the machine economy will necessitate new forms of value measurement and exchange, fundamentally altering the economic landscape.

Reflections on Aggregation Theory & Software Eats the World

I am writing this quick synopsis of these very important theories for some research I am conducting on the future of the economy—specifically, the emerging concept of the machine economy. My goal is to explore how the principles of Aggregation Theory and "Software Eats the World" have shaped our understanding of the current economic landscape. This is not intended to be a comprehensive overview of the work, but rather a marker to signify where we are today and the key principles that have brought us here.

My WTF. Edition Three. (Aug 12, 2024 - Aug 16, 2024)

Welcome to another thrilling edition of "My Weekly Timeline Feed" (or as I like to call it, My WTF). The world is a fascinating, ever-changing circus, and here I am, trying to keep up while juggling my very limited understanding of it all. Just when I think I’ve finally got a handle on something, the universe throws in a plot twist, reminding me how much I don’t know. So, in this third edition of My WTF, I’m diving headfirst into the wild worlds of the Machine Economy, Security, Life Sciences, and Governance; trying to make sense of recent developments, or at least pretending I do.

Let’s get into it: