Artificial Intelligence & Economic Growth: Boom or Bust?
Atlas Analytics Founder and CEO, Jake Schneider, was joined by economists, Cliff Waldman and Daniel Bachman, for a conversation on the hype, data, and trajectory of AI.
AI has rapidly become the centerpiece of economic debate, with claims ranging from an imminent productivity boom to warnings of mass displacement, extreme resource extraction to power data centers to the potential of a bubble event.
In this roundtable conversation, Atlas Analytics Founder and CEO, Jake Schneider sat down with Cliff Waldman and Daniel Bachman to discuss where AI is really taking the economy.
Cliff and Daniel bring decades of experience in economic forecasting, productivity, and industrial analysis, and offered thoughtful, data-driven perspectives that complement how Atlas thinks about macro trends and forecasting.
The economists spoke about the gap between AI hype and measurable impact, what past technology cycles can teach us, and what the data suggests about long-term growth. This is a grounded, evidence-based discussion about where AI may matter most over time.
Growth Outlook
The discussion opened with a reminder that most economics forecasters—including the Congressional Budget Office—have not meaningfully revised long-term productivity assumptions in response to generative AI. Trend labor productivity growth remains around 1.4%, roughly where it stood before AI became a mainstream topic.
The reason is historical: major technologies rarely show up cleanly or quickly in aggregate statistics. Electricity, railroads, computers, and the internet all took years before their productivity effects were visible in macro data. Short-term bursts of growth may reflect many forces at once, making attribution difficult.
From this perspective, AI may matter enormously in the long run, while changing very little about near-term growth forecasts today.
AI and the Victorian Railroad Mania
To better understand the future, the discussion weighed the significance of previous market episodes like the dot com bubble, internet boom, and even Britain’s 19th century railroad boom.
“When the British started building railroads, there was a huge financial boom. But in the end, British railroads actually had a lot of trouble in the 19th century being profitable.”
Railroads undeniably transformed commerce, logistics, and economic integration, yet many projects struggled for years to generate profits, despite massive investment and public enthusiasm. The episode illustrated a recurring pattern in economic history: technologies can deliver long-term societal value while disappointing investors in the short and medium term.
Applied to AI, the analogy cautioned that even transformative infrastructure can experience periods of overinvestment, volatility, and financial stress before finding a sustainable role in the economy without invalidating its underlying economic importance.
An Uneven Future, Revealed Only in Hindsight
Still, uncertainty dominates the outlook. AI is not a single technology but a constellation of tools whose effects will vary widely across firms, sectors, and regions. Its success depends not only on algorithms, but on management execution, labor integration, consumer strength, and demographic trends.
If AI accelerates entrepreneurship and enables new industries, it could reinforce long-term growth. If gains accrue narrowly or consumer demand weakens, today’s optimism may fade. As with every major technological shift, the decisive verdict will arrive slowly and only in hindsight.
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Cliff Waldman, CEO, New World Economics, LLC., has been an active and in-demand public speaker on topics ranging from the U.S. and global economic outlooks to new markets, productivity, and automation. From 2003 to 2018, Cliff served as Senior Economist and Chief Economist of the Manufacturers Alliance for Productivity and Innovation (MAPI). He has spent nearly two decades writing and speaking on the global economic picture as well as a range of issues of central importance to the manufacturing sector, including productivity, demographics and emerging markets. His career has also included tenures as an economic researcher with a state government forecasting and policy research unit as well as with a small business research team in Washington, D.C. Cliff directed a large contract for the Small Business Administration on the entrepreneurship potential of the veteran and service-disabled veteran population. He has won three national research awards. He received his M.A. in economics from Rutgers University. Cliff can be reached at cliff@newworldecon.com.
Daniel Bachman, Ph.D., is an experienced U.S. and international macroeconomic forecaster and modeler. He is now retired, but from 2013-2024 he was the lead US Economic Forecaster for Deloitte. He also worked as a senior economic analyst and forecaster for the U.S. Commerce Department, Forecaster and model expert for Wharton Econometric Forecasting, and taught economics at Temple University. Mr. Bachman holds an M.A. and Ph.D. in Economics from Brown University and a B.A. in Political Economy from the Johns Hopkins University. Dr. Bachman maintains a blog that uses traditional methods to predict near term GDP, as well as providing opinions and books reviews on other economic topics. Daniel can be reached at danieldbachman@gmail.com and his blog, The Nowcast, can be found on Substack.
This article was written with valuable research assistance from Morgan Reppert, Executive Operations Associate for Atlas Analytics.
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