Finance on AIBussin — AI applications, systems and books

The State Optimized the Dashboard and Lost the Citizen

Wed, 24 Jun 2026 20:19:56 +0100

An AI-assisted model of how GDP optics, housing pressure, debt rollover, and fiscal constraint can hide citizen insolvency.

1. A Country Is Its Citizens

A country is not its GDP, its bond market, its housing index, or its stock-market capitalization. A country is its citizens.

That sounds obvious enough to be useless, but most modern economic dashboards quietly forget it. They measure the state, the asset market, the tax base, the debt stock, the growth rate, the investment flow, the headline employment number, the budget balance, the bond spread, and the index level. They do not directly measure whether citizens can stand on their own.

The Asset‑Price State: How the U.S. Fiscal Machine Now Depends on Rising Markets

Wed, 17 Jun 2026 17:18:52 +0100

What this post argues

The U.S. does not merely like a high stock market. It increasingly needs one.

The stock market has become an amplifier inside the largest federal revenue pipe: individual income tax. When asset prices rise, capital gains, stock compensation, options, bonuses, business equity, and other asset-sensitive income strengthen federal receipts. When the market falls hard, that same amplifier runs in reverse.

This matters because the real debt problem is not debt alone. It is interest cost relative to federal revenue. If interest grows faster than normal revenue, the system becomes more dependent on asset inflation, AI valuations, national champions, and foreign capital to keep the fiscal machine stable.

AI and the End of Easy Growth

Tue, 19 May 2026 09:52:38 +0100

Constraint, Continuity, and Human Agency

1. Introduction: The Strange Feeling Around AI

Artificial Intelligence is clearly real. The infrastructure buildout is enormous, adoption is accelerating, and AI systems are already reshaping software, research, administration, and knowledge work. Yet for many people, daily life feels strangely unchanged. Housing remains expensive, wages remain under pressure, debt continues to rise, and institutions across the West increasingly appear constrained rather than confident.

This post explores the possibility that AI is currently being deployed less as a technology of broad civilizational expansion and more as a technology of continuity management, a way for highly complex systems under demographic, economic, and energetic pressure to optimize themselves, compress costs, and maintain stability in an era where traditional growth models are becoming harder to sustain.

The Silent Reset: Currency Devaluation and the Extension of the Debt Cycle

Fri, 17 Apr 2026 18:44:49 +0100

Abstract

Recent analysis of U.S. fiscal dynamics suggests a structural constraint emerging around the end of this decade, driven by rising interest burdens relative to government revenue.

This paper explores the possibility that a system-level reset may already be underway, not as a discrete event, but as a gradual process of currency adjustment, inflation, and asset repricing.

A modeled 20–40% devaluation of the U.S. dollar (~30% midpoint) materially alters debt sustainability trajectories, extending the fiscal runway by an estimated 10–20 years.

Canada: When Interest Meets Reliable Revenue

Wed, 15 Apr 2026 12:55:18 +0100

Executive Summary

Canada’s fiscal position looks stable on paper. Headline interest costs consume only ~10.6% of federal revenue. But this ratio masks a structural reality: the engine that drove revenue growth has stalled, and the cost of past debt is rising faster than the system can generate new fiscal space.

For decades, population expansion concealed weak per-capita productivity. In 2025, that demographic engine stopped. At the same time, Canada does not fully capture or retain the economic value it produces, due to commodity pricing discounts, single-customer trade concentration, and high-skill outflows. When these factors are applied to the revenue base, the effective denominator shrinks.

From Fuel Protests to Fiscal Risk: What’s Really Happening in Ireland

Tue, 14 Apr 2026 17:53:37 +0100

Executive Summary

This post applies a simple, testable framework to Ireland’s fiscal system:

Fiscal constraint emerges when the cost of debt rises relative to the revenue supporting it.

In large, stable systems like the United States, this dynamic unfolds gradually. Ireland presents a different case.

While headline metrics suggest strength, three structural factors create a distinct risk profile:

Revenue composition: A significant portion derives from multinational activity and is not fully under domestic control.
Measurement distortion: The effective economic base (GNI*) is ~43% smaller than GDP implies.
Debt repricing: Existing debt is being refinanced at materially higher interest rates.

These factors introduce a critical refinement to the model:

Real Problems. AI Solutions.

Mon, 13 Apr 2026 08:53:47 +0100

How We Used AI to Analyze When U.S. Debt Becomes a Constraint

Executive Summary

We demonstrate a human + AI research process.

AI was used to:

refine the question
identify the correct metric
expose assumptions
and test the model through adversarial critique

The goal was to transform a vague macro concern into a quantifiable, testable system model.

The Question

We began with a simple but vague concern:

“Is U.S. debt becoming a problem?”

Fin-R1: a Financial Reasoning LLM with Reinforcement Learning and CoT

Fri, 21 Mar 2025 03:27:30 +0000

Introduction

Fin-R1 is a new model specifically fine-tuned for financial reasoning, with performance that beats much larger models like DeepSeek-R1.

This post will use this model and compare it with phi3 across various tasks.

phi3 for comparison

Phi-3: a lightweight, general-purpose model known for its efficiency and strong reasoning performance at smaller parameter scales. It serves as a great baseline for assessing how domain-specific tuning in Fin-R1 improves financial understanding and response structure.

MR.Q: Model-Based Representations for Model-Free Trading

Tue, 18 Mar 2025 13:05:34 +0000

Introduction

Model-free reinforcement learning learns a policy directly from experience, but it can struggle to discover useful representations from sparse or noisy rewards. Model-based reinforcement learning receives a denser training signal by learning how states, actions, rewards, and termination relate to one another, but it often pays for that knowledge through planning complexity and model error.

MR.Q: MR.Q

Can a model-free agent keep the representation-learning benefits of a learned model without using that model to plan?

Self-Learning LLMs for Stock Forecasting: A Python Implementation with Direct Preference Optimization

Thu, 13 Feb 2025 14:22:06 +0000

Summary

Forecasting future events is a critical task in fields like finance, politics, and technology. However, improving the forecasting abilities of large language models (LLMs) often requires extensive human supervision. In this post, we explore a novel approach from the paper LLMs Can Teach Themselves to Better Predict the Future that enables LLMs to teach themselves better forecasting skills using self-play and Direct Preference Optimization (DPO). We’ll walk through a Python implementation of this method, step by step.

Mastering LLM Fine-Tuning: A Practical Guide with LLaMA-Factory and LoRA

Wed, 12 Feb 2025 07:07:50 +0000

Summary

Large Language Models (LLMs) offer immense potential, but realizing that potential often requires fine-tuning them on task-specific data. This guide provides a comprehensive overview of LLM fine-tuning, focusing on practical implementation with LLaMA-Factory and the powerful LoRA technique.

What is Fine-Tuning?

Fine-tuning adapts a pre-trained model to a new, specific task or dataset. It leverages the general knowledge already learned by the model from a massive dataset (source domain) and refines it with a smaller, more specialized dataset (target domain). This approach saves time, resources, and data while often achieving superior performance.