Browsing by Author "Oseback, Jack"

This thesis investigates the impact of the 2022 release of ChatGPT and other LLMs on corporate budget allocations, specifically analyzing the shift from general and administrative (G&A) expenses towards innovation-driven research and development (R&D) expenses. To go about this, this study uses a panel dataset of 68 US firms evenly split amongst the S&P 500 IT and S&P 500 Consumer Staples indices for the years 2019-2025 while conducting a Difference-in-Differences framework with Two-Way Fixed Effects regressions on AI engagement metrics. The results reveal that strategic commitment to AI within firm 10ks is positively and significantly associated with higher R&D to G&A ratios, and more specifically that each additional mention of AI or related keywords in firm 10ks corresponds to a 0.2% increase in the R&D to G&A ratio following the 2022 shock. While the sector model (IT vs. Consumer Staples) showed insignificant results, the regressions suggest that firm size is a strong proxy for AI engagement, with larger firms leading the way in this movement towards innovation spending. Ultimately, the findings support the classification of AI as a modern General Purpose Technology that will only continue to create innovation in the years to come.