In 2013, Thomas Herndon, a graduate student at the University of Massachusetts Amherst, was assigned a seemingly straightforward task: Select an economic study and attempt to replicate its results.

The flawed study influenced European austerity policies that worsened economic recovery.

This example shows the importance of data and the critical need for AI enablers.

AI enablers are expected to see rising demand as they help businesses modernize and prepare data for AI implementation.

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In 2013, Thomas Herndon, a graduate student at the University of Massachusetts Amherst, was assigned a seemingly straightforward task: Select an economic study and attempt to replicate its results.

At the time, the U.S. economy was beginning to recover, but much of the eurozone was still grappling with the aftermath of the financial crisis. 

European policymakers were debating whether to bail out struggling economies with more debt or impose austerity measures to reduce spending and raise taxes to curb debt.

Herndon chose to focus on a 2010 paper titled “Growth in a Time of Debt,” authored by renowned Harvard economists Carmen Reinhart and Kenneth Rogoff. 

This study, which analyzed the relationship between debt levels and economic growth, concluded that when a country’s debt exceeds 90% of its gross domestic product (GDP), its economic growth slows significantly. 

This finding became influential on Europe’s economic policies, leading countries like Greece, Spain, Portugal, and Ireland to adopt strict measures to control their debt levels.

However, as Herndon worked through the paper, he encountered an issue: He could not replicate the study’s results.

Despite extensive efforts, including assistance from his professors, the 90% debt threshold that Reinhart and Rogoff identified remained elusive. 

Determined to resolve the discrepancy, Herndon continued working on the project even after his course ended. Eventually, he contacted the authors, who shared their data with him.

Herndon discovered that the study contained significant errors. The original analysis included only 15 countries, not the 20 the authors claimed. 

Furthermore, it gave undue weight to outliers, such as a single year of economic data from New Zealand in 1951, which skewed the overall results. 

After correcting these issues, Herndon found that high debt levels were not nearly as detrimental to economic growth as the paper suggested.

European nations had based critical economic decisions on flawed data, implementing austerity measures that may have worsened their economic struggles. 

Many of these economies took years to recover, and Greece, in particular, is still dealing with the aftermath.

The bad data has been a problem for years. It’s not easy to know whether you can trust a particular source or study… and it’s not practical to recreate every study you want to use.

That’s where “AI enablers” come in.

Enablers aren’t AI solutions themselves. They’re companies that enable AI technology to work the way it’s supposed to.

This in itself involves a pretty wide range of companies. Cloud-computing businesses provide a lot of the storage and data transfer needed to make AI work. Consulting firms help companies figure out how to build their businesses to use AI effectively.

Some enablers focus on modernization and data resiliency. These companies don’t generate AI models themselves—they create software that helps organize customer data so it’s ready for use in AI.

Most companies are not ready to implement AI today… though they’re starting to build the infrastructure for it.

That’s why demand will explode for these enablers in the next few years… and their stocks should follow suit.

Best regards,

Joel Litman & Rob Spivey
Chief Investment Strategist &
Director of Research
at Valens Research