---
title: AI Startup Exposure Index (AISE)
url: https://www.emergentmind.com/topics/ai-startup-exposure-index-aise
type: topic
---

# AI Startup Exposure Index (AISE)

The Occupational AI Exposure Score (OAIES) is a quantitative, occupation-level index measuring the share of an occupation’s tasks that current or near-future artificial intelligence (AI) systems could in principle perform. OAIES methodologies and formulations have proliferated in the literature, underpinned by advances in large language models (LLMs), abundant real-world task data, and the need for timely, interpretable indicators of evolving AI capabilities and their labor-market implications [2507.08244]. This article overviews the concept, construction, empirical uses, and critical interpretations of OAIES, synthesizing technical details from leading research.

## 1. Formal Definition and Theoretical Foundation

The OAIES operationalizes potential AI exposure as a weighted average across all tasks performed by workers in a given occupation, where each task is scored for its AI-performability based on contemporaneous or staged AI capabilities. For a Census-SOC occupation $o$ and AI capability stage $S$, the canonical formula is:
\[
\text{OAIES}_{o,S} = \sum_{t\in T(o)} w_{o,t} \cdot e_{o,t,S}
\]
where:
- $T(o)$ is the set of occupation $o$'s tasks (e.g., from O*NET),
- $w_{o,t}$ is the normalized relevance or importance weight for task $t$,
- $e_{o,t,S}$ is the percentage (0–100) of task $t$ that an AI model at stage $S$ can perform [2507.08244].

OAIES aims to measure the “technical exposure” of human labor to AI at a fine-grained, task-by-task level, in contrast to coarse occupation-level or industry proxies. Various studies trace its intellectual lineage to both empirical and theory-driven approaches, including the application of Moravec’s paradox and evolutionary models of skill automatability [2510.13369].

## 2. Multistage Capability Frameworks and Dynamic Deployment

To reflect the rapidly shifting landscape of AI competence, recent OAIES implementations adopt a multistage framework. For example, [2507.08244] calibrates five distinct AI capability stages:
- Stage 1: "Pre-LLM" (pre-ChatGPT, traditional ML/NLP tools)
- Stage 2: "Early LLM" (first public LLMs, e.g., ChatGPT v1)
- Stage 3: "Multi-modal LLM" (text+image/video, e.g., DALL·E 3)
- Stage 4: "Reasoning Models" (chain-of-thought, advanced LLMs)
- Stage 5: "Agentic AI" (future autonomous agents)

For each $S$, OAIES is recalculated. This dynamic approach enables quantification of step-function increases in task exposure and analysis of temporal differences. Typical empirical analyses focus on changes in exposure, e.g., $\Delta \text{OAIES}_o^{S3-S1} = \text{OAIES}_{o, S3} - \text{OAIES}_{o, S1}$, assessed in parallel with changes in employment outcomes during corresponding periods [2507.08244].

## 3. Task Scoring Methodologies

The assignment of $e_{o,t,S}$—the estimated AI-performable share of task $t$—is central. Approaches include:

- **LLM self-assessment via prompt engineering**: Prompts are crafted to elicit stepwise reasoning and quantitative exposure scores for each task at fixed AI capability stages, with additional fields for model confidence [2507.08244]. For example, a prompt will instruct a model (e.g., GPT-4o or Claude 3.5 Sonnet) to estimate, for a specified stage $S$, what percentage of a given O*NET task it could perform, with reasoning steps and a confidence level.

- **Expert- and LLM-derived probabilistic ratings**: E.g., [2507.22748] uses LLMs to output, for each task, a probability vector over exposure thresholds (no exposure, direct LLM exposure, latent LLM+integration, multimodal), then aggregates via importance-weighted task shares.

- **Theory-inspired scoring**: [2510.13369] calculates exposure for each task as an average of four subcomponents reflecting performance variance, data abundance, tacit knowledge (reverse-coded), and algorithmic gap (reverse-coded). These are theory-motivated proxies for automatability, rated 0–2 per task.

## 4. Aggregation, Weighting, and Final Score Construction

Aggregation is universally performed as a normalized, relevance-weighted sum or average over task-level scores:
\[
\text{OAIES}_o = \sum_{t\in T(o)} w_{o,t} e_{o,t,S}
\]
Weights $w_{o,t}$ derive from O*NET’s “importance,” “frequency,” or “relevance” scores, normalized to sum to one. In some frameworks, task-level ratings are probabilistic or multi-valued, and binary or continuous indicators are defined via thresholds (e.g., probability of $\geq$25% time saving as per [2507.22748]).

Occupational OAIES scores thus capture the share of an occupation’s activity—adjusted for importance or prevalence—potentially performable by a specified frontier of AI systems.

## 5. Empirical Applications and Labor Market Linkages

OAIES is employed as a dynamic regressor in labor market studies linking exposure increments to real-time employment, unemployment, and work intensity dynamics. In [2507.08244], OAIES is matched at the occupation-period level to Current Population Survey (CPS) microdata spanning 2021–2025, with the empirical specification:
\[
\Delta Y_{o, P4-P2} = \alpha + \beta \Delta \text{Exp}^{m}_{o, S3–S1} + X_{o,P2}\Pi + \text{TaskIndices}_o \Gamma + \epsilon_o
\]
where $\Delta Y_{o,P4-P2}$ is the labor outcome change, and $\Delta\text{Exp}^m$ is the OAIES increment for model $m$ (e.g., ChatGPT, Claude). Key findings include significant negative associations of OAIES with employment (10-point higher exposure implies a 5–8 point decline, $\beta<0$) and positive associations with unemployment ($\beta>0$) [2507.08244].

Additional extensions use OAIES in wage regressions and analysis of job vacancy rates [2507.22748], and as predictors in panel models of unemployment risk, demonstrating that OAIES-based ensemble scores explain substantially more variance than prior individual metrics [2308.02624].

## 6. Comparative Analysis and Correlation with Alternative Indices

Simultaneous consideration of multiple OAIES formulations reveals that:
- Theory-based indices (e.g., [2510.13369]) exhibit high correlation with LLM annotation methods (e.g., $\rho=0.72$ with GPT-4-based exposure).
- OAIES correlates positively with wage at the occupation level (e.g., $\hat\beta\simeq 0.32$, $p<0.001$) [2510.13369], though this is interpreted as evidence against pure substitutability narratives and may indicate complementarity or skill bias.
- Pre-LLM indices (e.g., Frey & Osborne, Webb Robot exposure) often show weak or negative correlation with current/post-LLM OAIES, substantiating a paradigm shift in the locus of automatability [2510.13369].
- Start-up–driven OAIES variants (AISE) track real-world venture, regulatory, and market targeting, revealing that some high-skilled occupations with high theoretical exposure are not actively targeted due to ethical, social, or regulatory factors [2412.04924].

Table: OAIES Top and Bottom Occupational Groups (selected studies)

| Rank         | [2507.08244]: Higher OAIES | [2507.22748]: Higher OAIES | [2412.04924]: Higher OAIES (AISE)   |
|--------------|---------------------------|----------------------------|-------------------------------------|
| Top          | Complex reasoning, problem-solving | IT/Telecom, R&D, Conservation | Office Clerks, Data Scientists      |
| Bottom       | Manual physical labor      | Food Processing, Construction | Athletes, Judges, Surgeons         |

## 7. Limitations, Interpretive Challenges, and Future Directions

OAIES quantifies technical task exposure, not realized employment losses or actual human-AI substitution. Limitations include:
- Ambiguity between task “coverage” and true automation vs. augmentation (OAIES does not distinguish displacement from productivity effects) [2507.08244], [2312.04714].
- Dependency on O*NET’s task taxonomy, which may lag occupational change or omit informal/emergent tasks.
- Bias due to LLM self-estimate optimism, prompt formulation, and incomplete representation of tacit knowledge and physical skill [2510.13369].
- Real-world adoption factors (organizational, regulatory, social desirability) that lie outside technical feasibility but moderate exposure (as evidenced by market index divergence in [2412.04924]).
- OAIES is most predictive when used in ensembles integrating multiple measurement paradigms; no single method fully captures AI’s labor impact heterogeneity across space, time, and sectors [2308.02624].

A plausible implication is that OAIES will continue to evolve as a modular, updatable construct, integrating new measurement protocols and aligning with high-frequency labor market signals to monitor and analyze technological change and workforce vulnerability in the AI economy.

Source: https://www.emergentmind.com/topics/ai-startup-exposure-index-aise