Long-Term Returns To AI

Risks and Opportunities

Kurt Winkelmann, Raghu Suryanarayanan, Ferenc Szalai

Recent advances in artificial intelligence such as generative language processing (Chat GPT 4) have generated hype but also raised concerns about its economic and financial benefits. To grapple with these questions, our macro-consistent models and framework suggest focusing on the long-term and tracing the impact of AI on total factor productivity growth (TFP). Changes in TFP growth, in turn, effect long-term real GDP growth and portfolio returns. By contrast to recent misplaced commentaries overjoyed by the potential benefits of AI, our models reveal a more nuanced picture. A continuation of the current trend in AI, increasingly (excessively?) focused on labor displacing automation, is likely to erode TFP growth, real economic growth and portfolio returns in the long run. Conversely, investments in AI that target improving efficiency of current occupations, reallocation of resources, and developing new technologies could contribute to sustainable TFP growth, and hence higher long-term expected real growth and returns.

Let's recall that TFP growth measures both the direct impact of technological innovation on real economic growth through improved productivity, net of the effect of capital and labor, and the impact of resource reallocation efficiency across the economy. Investments in AI effect both channels. AI technology adopting firms could enhance their productivity, by improving the efficiency of current tasks or creating new, more productive tasks. The issue is whether these (potential) gains also ripple through the rest of the economy, as they did for the most part since the start of the industrial revolution and through the 20th century. It turns out that the recent trend of AI innovations that are excessively focused toward automation and increasingly displacing the existing workforce have led capital and labor reallocation efficiency losses across the economy at large, with losses that actually outweighed the realized mediocre improvements in productivity as displaced workers were for the most part reallocated to tasks that use labor less productively. Contrary to common (mis)perception, the net result of AI innovations could then be a loss in aggregate TFP growth.

Exhibit 1 - Aggregate TFP Growth Sensitivity To Capital-Biased (AI) Technological Shocks

Aggregate TFP Growth Sensitivity To Capital-Biased (AI) Technological Shocks
Source: Navega Strategies LLC Research

Exhibit 1 illustrates this point. The Exhibit shows how the sensitivity of TFP growth to technological shocks biased towards capital (i.e. AI) declines with the ease of replacing the labor force by capital (algorithms), without giving up on output . Economists measure this easiness by the elasticity of substitution between capital and labor. The greater this elasticity, the greater the incentive for firms that invest in AI technologies solely focused on automation to replace their labor workforce by algorithms, and the greater the negative ripple effect on aggregate TFP growth, as the displaced labor is being reallocated to less efficient jobs. Recent empirical studies estimate this elasticity of substitution between capital and labor to be about 1.5 . At such levels, Exhibit 1 indicates that investments in automation-driven AI could lead to a relative loss in TFP growth of about 30%! In turn, long-term real GDP growth could also be 30% lower (on a relative basis), assuming no change in the labor force growth.

Exhibit 2 - Impact of Automation-Driven AI On Long-Term Equity Returns

Impact of Automation-Driven AI On Long-Term Equity Returns
Source: Navega Strategies LLC Research

Thus pervasive, negative effects of automation-driven AI innovations on long-term real economic growth prospects could be quantitatively significant. The next question then for long-term investors is the likely impact on future equity returns. To address this issue, we envision a worst case yet plausible scenario under which investments in new technology (about 0.5% of GDP per year) pursue their current trend over the next 10 years, primarily focusing on automation-driven AI. At the same time and over the same period, the income share of automation intensive firm increases from its current level of 20% to 40%, and the ease of labor replacement by algorithms increases, with a shift in the elasticity of substitution between capital and labor (from 1.5 to 2).

Exhibit 2 traces the scenario impact implied by our models on long-term TFP growth, long-term real GDP growth and long-term real equity returns. According to the Exhibit, long-term TFP growth would hover around an annualised average rate of zero over the next 10 years, a 40bps decline from our current baseline scenario. About half the decline can be attributed to the increased income share of automation intensive firms, while the remaining half can be attributed to the accelerated ease of labor replacement by algorithms. Framing this result in historical perspective, US TFP growth experienced a 100bps decline on average in the last 20 years. Our scenario analysis suggests that automation-driven AI could well have been a significant contributor to this experienced decline in TFP growth trend .

Assuming the labor force growth remains unchanged, long-term real GDP growth would also decline 40bps, to an annualised average of about 1.5%. As for the equity market, annualised average long-term real returns would materially decrease from 6% to about 4.2%. According to our models, this 180bps decline can be attributed to the impact of expected declines in real Treasury bond yields (40bps) and the impact of expected declines in real growth in dividend per share (140bps).

These results raise important issues for long-term investors. Somewhat paradoxically, provided we account for resource reallocation effects, not all types of AI technology innovations contribute positively to aggregate long-term TFP, real economic growth, and (balanced) portfolio returns, even if they (potentially) benefit the few firms that adopt the innovation.

Exhibit 3 - Differentiated AI Use Case Examples

Differentiated AI Use Case Examples
Source: Navega Strategies LLC Research

The takeaway then for investors is to dive deeper into firm (and consumer) level interactions to identify AI innovations likely to contribute positively and sustainably to long-term TFP growth. As suggested by Exhibit 3, a first step is to list the likely use cases. As a second step, our macro-consistent analytic framework can help trace the wider social, economic and portfolio return impact, accounting for reallocation effects. In addition, the likely environmental risk impact should also be evaluated. For instance, blockchain applications to digital (crypto) currencies are (to this date) highly energy intensive, with little or no prospective economic nor societal gains. Machine learning technologies for medical diagnostics and investment management seem promising, as long as the risk of data overfitting and misjudgment is effectively controlled. Finally, ChatGPT applications also seem fruitful, from enhancing productivity of academic researchers to helping advisors offer differentiated financial solutions to investors, provided they are not geared toward excessive automation, and the risk of disinformation and hallucinations is well managed.

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