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AI

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.

My WTF. Edition Two. (Aug 5, 2024 - Aug 9, 2024)

My Weekly Timeline Feed (My WTF), where I dive into the stories and events that grabbed my attention. Think of it like a quirky twist on "you are what you eat" – but instead, it's "you are what you read," and those juicy tidbits of the present shape our future.

In each edition, I'll focus on how the future is unfolding and what today’s events mean for tomorrow. Sure, my day job is all about allocating resources to seize opportunities, but my true passion lies in deciphering current events to find solutions for future challenges. So buckle up and join me on this journey – who knows what WTF moments we’ll uncover together!