The Future of Productivity: AI’s Economic Impact Through 2075

Introduction

In recent years, generative artificial intelligence (AI) technology, particularly in the form of large language models (LLMs), has seen rapid advancements, integrating into workplaces and reshaping economic landscapes. Recent estimates suggest that AI could boost productivity and GDP by 1.5% by 2035, nearly 3% by 2055, and 3.7% by 2075. This article deep dives into the multifaceted implications of AI on productivity growth, its effects on various occupations, and a closer look at the driving mechanisms behind its economic impact.

The Mechanisms Behind AI’s Productivity Growth

Generative AI technologies can lead to increased productivity in several ways. According to the task-based framework developed by economist Daron Acemoglu, AI can enhance worker efficiency by automating certain tasks, thus reallocating resources to newly created functions. This framework hinges on two crucial factors: the share of economic activity that AI tools impact and the cost savings realized through their adoption.

The Scope of Automation

Our estimates suggest that approximately 42% of current jobs are exposed to automation by generative AI, meaning that at least 50% of the tasks within these roles could be automated. Occupations such as Office and Administrative Support, Business and Financial Operations, and Computer and Mathematical Occupations show high exposure rates, as indicated in Table 1. Workers in these roles may experience a transformation in their tasks, leading potentially to job displacement in specific areas but also to new opportunities in others.

Earnings and Exposure Correlation

Interestingly, the impact of AI isn’t uniform across all income levels. Our research reveals a clear trend:

• Occupations around the 80th percentile of earnings are most exposed, with around 50% of their work susceptible to automation.
• Conversely, low-earning occupations show the least exposure, largely because they involve predominantly manual labor or personal services.

As seen in Figure 2, this relationship indicates that while many professional roles could see significant transformations, higher-earning positions, such as those in business or specialized fields, are likely to remain somewhat insulated from complete automation effects.

Projections of AI’s Impact on Economic Activity

Contribution to Total Factor Productivity (TFP)

The strongest boost to annual productivity growth from AI is expected in the early 2030s, peaking with an increase of 0.2 percentage points in 2032. After this phase, as the speed of AI adoption saturates, the growth rate is projected to revert to trend levels. However, due to sectoral shifts, the lasting effect on overall growth may add an additional 0.04 percentage points.

Long-term Economic Activity Levels

The projected cumulative effects of AI on TFP and GDP levels suggest a significant shift in economic activity. By 2035, TFP levels are estimated to be 1.5% higher, advancing to a 3% increase by 2055, and 3.7% by 2075 compared to scenarios devoid of AI integration. These increases indicate that while growth rates may stabilize, the overall size of the economy will be permanently enlarged owing to AI advancements.

The Timeline for AI Adoption

The pace of AI adoption closely resembles historical patterns observed with previous transformative technologies, such as personal computers or the internet. By the latter half of 2024, around 26.4% of workers were reported to be using generative AI tools at work, with adoption rates anticipated to reach 40-50% within a decade following the technology’s introduction.

Labor Market Trends

AI’s arrival is already influencing the labor market. Analysis indicates that job growth has stagnated in sectors with a high potential for AI automation. For roles that could be entirely performed by AI, employment numbers began to decline by 0.75% in 2024 compared to levels in 2021, signaling that jobs deemed highly vulnerable to automation are already experiencing disruptions.

Task-Level Cost Savings

Generative AI applications have demonstrated substantial labor cost savings, averaging around 25% based on various studies. These findings, ranging from 10% to 55% cost reductions, signify that businesses adopting generative AI tools often realize meaningful efficiency gains—and this trend is expected to accelerate, enhancing savings to as much as 40% over the next few decades.

Examples of AI Adoption Case Studies

Several real-world applications serve as exemplary models of generative AI in action:

• Customer service applications report a 14% increase in task completion rates.
• Job interviews managed by AI exhibit a 17% rise in job starts.

These instances underscore the significant impact that AI can have across diverse sectors, supporting the notion of AI as a crucial driver of productivity growth.

Navigating Caution

While these projections present an insightful outlook into the economic landscape transformed by AI, they should be interpreted with caution. The data surrounding AI’s initial effects is inherently limited, requiring ongoing evaluation as technology continues to evolve. Key aspects that remain unaccounted for include potential changes in product quality, the emergence of novel tasks as AI evolves, and the broader impact on innovation cycles that can feedback into TFP growth.

Final Thoughts

Generative AI’s trajectory suggests a lasting impact on productivity and economic structure over the coming decades. It is characterized by an initial surge in productivity growth, followed by a normalization once adoption reaches saturation. As AI continues to reshape industries and labor markets, understanding the implications of this transformation will be crucial for businesses, policymakers, and workers alike.