Follow the money: a startup-based measure of AI exposure across occupations, industries and regions

TL;DR

The AISE index, a novel metric based on occupational descriptions from O*NET and AI applications developed by startups funded by the Y Combinator accelerator, provides a dynamic, forward-looking tool for policymakers and stakeholders to monitor AI's evolving impact and navigate the changing labour landscape.

Abstract

The integration of artificial intelligence (AI) into the workplace is advancing rapidly, necessitating robust metrics to evaluate its tangible impact on the labour market. Existing measures of AI occupational exposure largely focus on AI's theoretical potential to substitute or complement human labour on the basis of technical feasibility, providing limited insight into actual adoption and offering inadequate guidance for policymakers. To address this gap, we introduce the AI Startup Exposure (AISE) index-a novel metric based on occupational descriptions from O*NET and AI applications developed by startups funded by the Y Combinator accelerator. Our findings indicate that while high-skilled professions are theoretically highly exposed according to conventional metrics, they are heterogeneously targeted by startups. Roles involving routine organizational tasks-such as data analysis and office management-display significant exposure, while occupations involving tasks that are less amenable to AI automation due to ethical or high-stakes, more than feasibility, considerations -- such as judges or surgeons -- present lower AISE scores. By focusing on venture-backed AI applications, our approach offers a nuanced perspective on how AI is reshaping the labour market. It challenges the conventional assumption that high-skilled jobs uniformly face high AI risks, highlighting instead the role of today's AI players' societal desirability-driven and market-oriented choices as critical determinants of AI exposure. Contrary to fears of widespread job displacement, our findings suggest that AI adoption will be gradual and shaped by social factors as much as by the technical feasibility of AI applications. This framework provides a dynamic, forward-looking tool for policymakers and stakeholders to monitor AI's evolving impact and navigate the changing labour landscape.

Figures (16)

• Figure 1: Scatter plot of AISE and AIOE values for different jobs of O*NET. Each point represents a job classified by O*NET and its color represents the corresponding education and training level by the job zone feature available in O*NET, as explained in the main text. The left column of points not available collects the job with a AISE value but no AIOE.
• Figure 2: AISE vs Education and Training level for different fixed ranges of AIOE values. Panels a-b-c: box-plots of the AISE values for the different education and training levels from the O*NET job zone feature compositions of jobs for increasing AIOE. The boxes ranges from the first quartile to the third quartile of the AISE distribution of values, with a line at the median. Each whisker extends within $1.5\times$ the interquartile range. Violin plots show the distribution of the AISE values extending to the possible outliers. The bottom left panel shows the regions of AIOE separation used in the previous panels divided into AIOE tertiles.
• Figure 3: Frequency of crucial skills for two regimes of the AISE -AIOE diagram. The bars indicate the frequency of presence of crucial skills (skills with importance larger than 4, see the text for a detailed discussion) in the jobs in two parts of the c-region of the AISE -AIOE diagram. The top red bars indicate the frequency of crucial skills in the top-c region while the blue bars indicate the frequency of crucial skills in the bottom-c region. The bottom inset shows the AISE -AIOE diagram with the top-c and bottom-c regions highlighted, being the top and bottom AISE quartiles of the occupations in the c-region (top AIOE tertile). The top inset shows the barplot of the AISE values for different ranges of crucial skill presence in the job definitions for the jobs in the whole c-region.
• Figure 4: Map of average exposure to AI of the workforce of different Metropolitan Statistical Areas in the USA. The color-code of the figure indicates the geographical AISE of the Metropolitan Areas, that are not covering the whole nation.
• Figure 5: Sectoral AISE Sectors are based on two-digit NAICS classification, and occupations within sectors are weighted with the national employment data (US).