Decoding AI Diffusion
Mapping the path of transformative AI across industries
By
Matt Hodak, Mishaal Lakhani, Deric Cheng
July 01, 2025
This report develops a framework to assess transformative AI (TAI) diffusion patterns across different economic sectors. The framework organizes critical factors which may accelerate or impede TAI adoption into five major categories, enabling policymakers and business leaders to better anticipate sectoral susceptibility to TAI and potential labor market disruptions.
Authors
Key Advisor
Originally Published
July 01, 2025
Research program
Abstract
In this report, we develop an organizing framework to assess the diffusion potential of transformative AI (TAI) across different sectors of the economy. We argue that many existing methodologies for assessing TAI’s potential to augment and automate cognitive labor oversimplify the structural, cultural, and technological differences between industries which will impact their susceptibility to disruption from AI. Drawing upon TAI literature and historical diffusion patterns of past digital general purpose technologies, we identify the sectoral factors most likely to impact the breadth and speed of TAI diffusion. We propose a five-category framework to organize these factors: 1) technological readiness, 2) workforce and human capital, 3) investment, 4) markets and competition, 5) regulation and oversight. This structure can be applied sectorally, leveraging qualitative and quantitative indicators for a given sector in order to determine the likely path and speed of TAI within that sector, or to compare TAI’s potential impact across different sectors. We aim to provide policymakers, business leaders, and researchers with a tool to assist more nuanced foresight, enabling stakeholders to better anticipate labor market shifts from AI.
Executive Summary
1. The Challenge: Understanding Transformative AI's Uneven Economic Integration
Transformative AI (TAI)—artificial intelligence systems with the potential to cause societal shifts comparable to past industrial revolutions—is rapidly advancing. While discourse often focuses on when TAI might arrive or which tasks it could automate, there's a critical underestimation of the complexities surrounding its economic integration. Prevailing assumptions of rapid, uniform adoption overlook significant historical precedents and inherent sectoral differences. This paper argues that TAI diffusion will be an uneven, relatively gradual process, with adoption patterns varying significantly across economic sectors, organizations, and job roles, particularly within knowledge work.
The critical challenge lies in moving beyond generalized predictions to a more nuanced understanding of these diffusion pathways. Such understanding is paramount for policymakers, business leaders, and researchers to anticipate labor market shifts, design targeted interventions, and develop effective AI governance. To address this, our research introduces a structured, five-category framework designed to assess the varying susceptibility of different economic sectors to TAI diffusion, based on their unique structural, technological, economic, and cultural characteristics.
2. The Core Contribution: A Five-Category Framework for Sectoral TAI Diffusion Analysis
Our research proposes a qualitative framework that organizes the key determinants of industry-level AI diffusion into five overarching categories. This framework moves beyond task-level automation predictions to offer a more holistic, sectoral analysis, enabling a systematic evaluation of why certain industries may lead in TAI adoption while others lag.
I. Technological Readiness: Assesses an industry's existing digital foundation and capabilities to implement AI.
Digital Maturity (+): Higher existing technological sophistication (data, IT systems, digital processes) lowers adoption barriers.
AI Ecosystem Density (+): Greater availability of relevant AI tools, models, and frameworks accelerates diffusion.
Complementary Technologies (-): High dependence on other technologies (e.g., IoT, robotics, advanced networks) can create bottlenecks if those technologies are immature or not widely adopted within the sector.
II. Workforce and Human Capital: Examines the social, cultural, and human skill elements influencing AI adoption.
Cultural Resistance to Change (-): Entrenched traditional norms, risk aversion, and hierarchical structures slow diffusion.
AI Skills & Expertise (+): A strong talent pool (data scientists, ML engineers, AI-literate managers) increases diffusion rates; shortages are a bottleneck.
Labor Market Dynamics (-): Strong labor representation can slow or alter diffusion through negotiations over job security, retraining, and implementation terms.
III. Investment: Considers the financial dynamics and capital flows driving AI adoption.
Private AI Investment (+): Significant venture capital, corporate R&D, and private equity funding for AI accelerate innovation and diffusion.
Public AI Investment (+): Government funding, subsidies, and incentives can de-risk research and promote adoption, especially in strategic sectors.
AI Capital Expenditures (+): Direct industry investment in foundational AI capabilities (e.g., specialized compute infrastructure, AI talent acquisition) directly fuels diffusion.
IV. Markets and Competition: Analyzes the competitive landscape, labor market conditions, and demand dynamics.
Market Competitiveness (+): Intense rivalry compels firms to adopt AI for efficiency gains or competitive advantage.
Sector Economic Health (+): Worker shortages and rising wage costs make AI automation or augmentation more economically attractive.
Demand Patterns (+): Strong and growing customer demand for an industry's products/services, especially for AI-enabled features, is a powerful accelerator for AI investment and diffusion.
V. Regulation and Oversight: Evaluates the impact of the governance structures and legal frameworks.
Regulatory Burden (-): Complex, stringent, or costly existing non-AI-specific regulations can slow AI diffusion by increasing compliance overhead.
Enforcement and Oversight Strength (-): While predictable enforcement fosters trust, increasing scrutiny, severe penalties, or unclear oversight can heighten perceived risks and slow adoption.
AI-Specific Regulations (-): New laws explicitly targeting AI (e.g., data governance, algorithmic accountability) typically slow initial diffusion as firms adapt and invest in compliance, though they can also provide clarity in the long run.
This framework provides a memorable and structured approach for stakeholders to analyze the multifaceted forces governing TAI diffusion, facilitating a more nuanced understanding of industry-specific adoption trajectories.
3. Framework in Action: Contrasting High-Frequency Trading and Retail Banking
The utility of the framework is demonstrated by comparing TAI diffusion prospects in High-Frequency Trading (HFT) and Retail Banking:
High-Frequency Trading (HFT): Exceptional Susceptibility and Rapid Transformation HFT is poised for rapid and comprehensive TAI diffusion.
Key Enablers (reflecting framework categories): Exceptional Digital Maturity and established AI Capital Expenditures (Technological Readiness, Investment Factors); extreme Market Competitiveness driving innovation (Market and Competitive Factors); and high availability of specialized AI Skills (Workforce and Human Capital).
Primary Bottleneck: Potential Regulatory Burden related to the explainability and oversight of AI-driven trading algorithms (Regulation and Oversight). TAI is expected to automate most labor, focusing human roles on high-level strategy and risk oversight.
Retail Banking: Moderate Susceptibility and Uneven, Protracted Transformation Retail banking faces a more moderate, uneven, and longer TAI diffusion timeline.
Key Enablers: Strong Demand for personalized digital experiences and ongoing AI Capital Expenditures (Market and Competitive Factors, Investment Factors).
Primary Bottlenecks: Significant Legacy IT Systems hampering Digital Maturity (Technological Readiness); high Regulatory Burden (Regulation and Oversight); Cultural Resistance to Change in traditional institutions; and relatively lower AI Skills density compared to other financial sectors (Workforce and Human Capital). TAI is likely to augment many customer-facing and advisory roles while automating back-office tasks, with a slower transformation of core banking systems.
Comparative Insights: The stark contrast highlights that TAI diffusion speed and impact are not solely determined by the technology's potential but are fundamentally shaped by an industry's unique configuration across the five framework categories. HFT’s digital-native structure and intense competitive dynamics create an ideal environment for rapid adoption, whereas retail banking’s legacy systems, regulatory complexity, and cultural inertia lead to a more measured and phased approach.
4. Implications and Strategic Imperatives for a TAI-Driven Future
Understanding the uneven and sector-specific nature of TAI diffusion is critical for effective preparation. This framework offers several key implications:
For Policymakers: Enables the design of targeted interventions, such as sector-specific skill development programs, infrastructure investments, or adaptive regulatory sandboxes, rather than one-size-fits-all approaches. It also informs where regulatory attention might be most urgently needed to manage risks or foster innovation.
For Business Leaders: Provides a tool for strategic foresight, helping to identify sector-specific opportunities and threats, guide investment decisions in AI and complementary capabilities, and manage workforce transitions proactively.
For Researchers: Highlights the need for more granular, sector-level data collection on AI adoption and its impacts. It also opens avenues for future quantitative analysis, such as developing weighted indices for the framework factors to enhance predictive power.
The diffusion of TAI will not be an overnight revolution but an evolving process of integration, disruption, and adaptation that varies significantly by industry.
We will likely only recognize the full transformative impact of TAI in retrospect. Therefore, a proactive and nuanced understanding of its likely diffusion pathways, as facilitated by this sectoral framework, is essential. By better anticipating how TAI will reshape the economic order, stakeholders can make more informed decisions, mitigate negative consequences, and harness the benefits of this powerful technology for broader economic well-being. Advancing data collection efforts and fostering a grounded, evidence-based dialogue on AI diffusion are paramount as we navigate this period of profound technological change.
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