The global economy is at the threshold of a new era—one where AI-driven "digital brains" are poised to transform not only industries but also the very structure of economic growth. In traditional economic theories, growth is driven by factors such as human capital, technological innovation, and knowledge spillovers. However, the integration of artificial intelligence into these models introduces a paradigm shift that changes the way we understand the mechanisms of long-term economic expansion.

1. Understanding the Endogenous Growth Model

The endogenous growth model, pioneered by economists like Paul Romer, posits that long-term economic growth is driven by internal factors—namely, human capital, innovation, and knowledge spillovers. Unlike earlier models, such as the Solow-Swan model, which treated technological progress as an external factor (exogenous), endogenous models explain how investments in R&D, education, and knowledge creation lead to sustained growth.

In Romer’s framework, the productivity of an economy is largely a function of how well it can innovate. New ideas, technologies, and knowledge are created through deliberate economic activities such as research and development. The key equation in this model looks like this:

Where:

Y = Output (GDP)

A = Total factor productivity, driven by innovation and knowledge accumulation

K = Capital

L = Labor

α = Output elasticity of capital.

2. AI as a Knowledge Amplifier

In traditional endogenous growth models, the accumulation of knowledge is limited by human cognitive capacity, time, and access to information. AI disrupts this dynamic by serving as a knowledge amplifier, capable of processing vast amounts of data, identifying patterns, and generating insights far beyond human capability. Digital brains—advanced AI systems—have the potential to exponentially increase the creation and application of knowledge.

By integrating AI into the growth equation, we can redefine A as not only driven by traditional R&D but also by AI's continuous learning and innovation capacity:

Where:

AI = Artificial intelligence input

γ = The productivity-enhancing effect of AI.

3. AI-Driven Innovation and Knowledge Spillovers

One of the defining characteristics of AI in the knowledge economy is its ability to rapidly accelerate innovation and facilitate knowledge spillovers across industries. In the classical endogenous model, knowledge spillovers occur when R&D in one firm leads to innovations that benefit other firms. However, AI transforms this process in several key ways:

• AI-enhanced R&D: AI can accelerate the pace of scientific discovery and technological development by autonomously conducting experiments, running simulations, and analyzing large datasets.

• Instantaneous knowledge diffusion: Through AI-powered platforms, knowledge can be shared and applied almost instantaneously across sectors.

We can represent the impact of AI on knowledge spillovers in the following equation:

∂A = δR + λ(AI ⋅ K)

Where:

∂A = Rate of change in productivity

R = Research and development

δ = Effectiveness of R&D

λ = AI’s effect on knowledge spillovers

K = Capital invested in AI.

4. AI and Non-Diminishing Returns to Knowledge

In traditional endogenous growth models, diminishing returns to capital and labor eventually set in. While human-driven R&D can sustain growth for a time, each additional unit of knowledge or capital yields progressively smaller returns. However, AI may change this dynamic by creating a system of non-diminishing returns.

5. AI-Augmented Human Capital

While AI enhances productivity, it also raises concerns about labor displacement. However, in an AI-empowered knowledge economy, human capital remains crucial—though in a different role. Instead of replacing human workers, AI augments their capabilities, enabling them to focus on creative, strategic, and high-value tasks. This creates a new form of cognitive capital, where AI and human intelligence complement each other.

Where:

H = Human capital

AI ⋅ H = The augmented productivity of labor due to AI.

6. The Ethics and Challenges of AI-Driven Growth

While AI offers unprecedented opportunities, it also poses significant challenges. Wealth inequality may widen as AI benefits capital owners disproportionately, while lower-skilled workers risk displacement. Ensuring that the benefits of AI are broadly distributed requires thoughtful policies on reskilling, education, and equitable access to technology.

Conclusion: The Future of the AI-Empowered Knowledge Economy

The integration of AI into the endogenous growth model heralds a new era of economic progress. By transforming how knowledge is created, disseminated, and applied, AI promises non-diminishing returns, enhanced productivity, and continuous innovation. However, realizing the full potential of this growth will depend on how societies address the ethical, economic, and social challenges that arise along the way.

References

Romer, P. M. (1990). "Endogenous Technological Change." Journal of Political Economy, 98(5), S71-S102.

Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company.

Brynjolfsson, E., Rock, D., & Syverson, C. (2021). "The Productivity J-Curve: How Intangibles Complement General Purpose Technologies." American Economic Journal: Macroeconomics, 13(1), 333-372.

Nordhaus, W. D. (2015). "Are We Approaching an Economic Singularity? Information Technology and the Future of Economic Growth." American Economic Journal: Macroeconomics, 7(1), 160-185.

OECD. (2019). "Artificial Intelligence in Society." OECD Publishing.