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On Productivity Gains, Cost Savings, and Return on Investment in (Generative) AI

Posted: May 17th, 2026 | Author: | Filed under: Artificial Intelligence | Tags: , , , | Comments Off on On Productivity Gains, Cost Savings, and Return on Investment in (Generative) AI

For quite some time now, we have been living through a moment of almost unrestrained enthusiasm surrounding artificial intelligence. Big Tech companies that own the major large language models, together with governments and large corporations making multi-billion-dollar investments in generative AI, promise — and expect — spectacular productivity gains, extraordinary returns on investment, significant cost reductions, and a radical transformation of economic growth. The dominant narrative seems clear: AI will become the great engine of prosperity for the next decade.

However, if we want a more rational perspective on what is actually happening, it is worth revisiting Daron Acemoglu’s -winner of the 2024 Nobel Prize in Economics and professor of economics at MIT- paper The Simple Macroeconomics of AI. Dense and published a couple of years ago, its arguments and analytical framework remain perfectly applicable to today’s AI landscape.

Acemoglu invites us to view these expectations with far greater caution. His central thesis is both simple and uncomfortable: the macroeconomic effects of AI depend fundamentally on two very concrete variables — what real percentage of tasks AI will actually be able to transform, and how much cost reduction or productivity improvement it will generate in those tasks. And once the available data are analyzed within his framework, the numbers turn out to be far less spectacular than current discourse often suggests.

Using current estimates of occupational exposure to AI and observed productivity improvements in specific tasks, Acemoglu concludes that aggregate total factor productivity growth could remain below 1% over ten years. That is a long way from the almost revolutionary narratives dominating much of today’s technological and financial debate.

One of the paper’s most interesting contributions is its distinction between “easy-to-learn” and “hard-to-learn” tasks. AI performs particularly well in activities where objectives are clearly defined and there are objective metrics of success: basic programming, information classification, text generation, or structured customer support. But much of valuable human work — diagnosis, creativity, contextual decision-making, expert judgment — remains far more difficult to replicate.

Acemoglu also reminds us of something fundamental that is often forgotten amid technological euphoria: every major technology generates enormous organizational adjustment costs. Companies do not transform automatically simply because they adopt a new tool. Processes, structures, incentives, and human capabilities must evolve as well — and that process is usually slow and expensive. Drawing on classic research on digitalization, the author reminds us that productivity gains often follow a J-curve: long initial periods of adaptation before meaningful benefits materialize. Greenwood, Yorukoglu, and Brynjolfsson, among others, already estimated that, in the case of digital technologies, the lower part of that curve could last at least 20 years. If the same pattern holds for AI, even today’s cost-saving estimates may be significantly overstated for the next decade.

Be careful with the siren songs and the inflated numbers. Spreadsheets can justify almost anything.

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