---
title: The macroeconomic effects of AI technology shocks
url: https://www.ml-quant.com/papers/repec/bdi-wptemi-td-1542-26/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-10-02
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:bdi:wptemi:td_1542_26
source_url: https://econpapers.repec.org/RePEc:bdi:wptemi:td_1542_26
featured: 2026-10-02
citations: unknown
topic: Econometrics & Forecasting
---


# The macroeconomic effects of AI technology shocks

AI-intensive patents generate delayed productivity and employment gains alongside falling consumer prices, with substantially larger aggregate effects than broader ICT shocks but reducing labor share and increasing wealth inequality.

- Source: https://econpapers.repec.org/RePEc:bdi:wptemi:td_1542_26
- Identifier: RePEc:bdi:wptemi:td_1542_26
- Released: 2026-09-28
- First featured: Quant Letter No. 133 (2026-10-02): https://www.ml-quant.com/issues/2026-10-02/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Econometrics & Forecasting
- Authors: Andrea Gazzani, Filippo Natoli

## Related

- [TKAN: Temporal Kolmogorov-Arnold Networks](https://www.ml-quant.com/papers/ssrn/4825654/): The article presents Temporal Kolomogorov-Arnold Networks (TKANs), a new neural network design that merges the benefits of Recurrent Neural Networks and Long Short-Term Memory for improved multistep time series forecasting.
- [Robust agents learn causal world models](https://www.ml-quant.com/papers/arxiv/2402.10877/): The research suggests that intelligent agents must learn an approximate causal model to generalize to new domains, impacting fields like transfer learning and causal inference.
- [Directional-shift Dirichlet ARMA models for compositional time series with structural break intervention](https://www.ml-quant.com/papers/arxiv/2601.16821/): The article introduces a new Bayesian model that analyzes compositional time series data, effectively handling structural breaks and enhancing forecasting accuracy during these changes.
- [FinCARE: Financial Causal Analysis with Reasoning and Evidence](https://www.ml-quant.com/papers/arxiv/2510.20221/): KG+LLM for Financial Causal Discovery: Combines SEC knowledge graphs, LLM reasoning, and causal discovery to build more accurate finance‑grounded causal models.
- [FinZero: Launching Multi-modal Financial Time Series Forecast with Large Reasoning Model](https://www.ml-quant.com/papers/arxiv/2509.08742/): Multi-modal Forecasting: FinZero, a pre-trained model fine-tuned by the Uncertainty-adjusted Group Relative Policy Optimization method, is introduced in the article, enhancing the accuracy, adaptability, and scalability of financial time series forecasting.
- [FinCast: A Foundation Model for Financial Time-Series Forecasting](https://www.ml-quant.com/papers/arxiv/2508.19609/): Time-Series Forecasting Model: FinCast, a new model for financial time-series forecasting, outperforms existing methods by effectively capturing diverse patterns without needing domain-specific adjustments.