Quantitative Monographs "New Frontiers in Quant Finance Conference, NY 2..."

Research evidence excerpt

Quantitative Monographs "New Frontiers in Quant Finance Conference, NY 2..."

servable in financial statements, the research proxies it using

the share of AI-skilled labour, constructed from large-scale job postings and

employment profile data. This approach captures both earlier machine-learning

adoption and the newer wave of generative and agentic capabilities, with AI roles now

representing a non-trivial share of the workforce and diffusing beyond traditional

technology firms into sectors such as healthcare and financial services. The results show

significant heterogeneity across firms: AI investment is associated with a reallocation

toward intangible spending (SG&A and R&D) and meaningful changes in workforce

composition, including increased hiring of senior, highly educated workers alongside

weaker demand for junior roles.

Professor Babina interpreted these findings through a “J-curve” framework for

productivity, where initial AI investments require substantial organisational change and

intangible capital accumulation before measurable gains emerge. Consistent with this,

earlier evidence showed growth driven primarily by product innovation rather than

efficiency improvements, while the updated data now indicate that productivity benefits

are beginning to materialise with a lag. For investors, the combination of flat

employment, rising productivity, and increased reliance on intangible assets points to

emerging operating leverage, albeit with substantial dispersion across firms. The key

open question is whether these early productivity gains, particularly from newer AI

technologies, will broaden across the economy or remain concentrated among leading

adopters.

Thematic Investing at Scale

Makhtar Ba, Senior Quantitative Researcher, BlackRock, Inc.