Corporate America is talking about artificial intelligence more than ever, but a new analysis by Goldman Sachs reveals a stark divide between boardroom hype and macroeconomic reality.
In a research note analyzing fourth-quarter earnings, senior U.S. economist Ronnie Walker noted that discussions surrounding AI completely overshadowed what was fundamentally a strong quarter, with core corporate revenues (excluding the energy sector) growing by a robust 4.6% year over year. Amid this market fervor, Walker wrote, “We still do not find a meaningful relationship between productivity and AI adoption at the economy-wide level.” However, the data reveals a substantial hint of something bigger to come: a median reported productivity gain of around 30% for two specific, localized use cases.
Walker’s analysis adds some real meat to a debate that has rocked Wall Street—and many retail traders’ portfolios—as several viral doomsday essays about AI eating the economy trickled into actual stock market volatility. AI executive Matt Shumer and the top finance Substack, Citrini Research, both warned that AI will be much more capable of doing white-collar work, and much sooner, than many people think. Top executives including Microsoft’s Mustafa Suleyman (“Human-level performance on most, if not all professional tasks” will be automated); Amazon’s Andy Jassy (“You won’t need as many human beings”); and JPMorgan’s Jamie Dimon (“Now’s the time to start thinking about it”) added their voices to the chorus.
Torsten Slok, the influential chief economist at Apollo Global Management, wrote in his Daily Spark on Saturday: “The dramatic change in recent weeks in the narrative in markets from ‘The economy is strong’ to ‘We are all becoming unemployed’ is truly remarkable.” He argued that markets are beginning to believe the view of “techno-optimists” about AI’s productive capabilities over the consensus of the Federal Reserve and economists.
To a master data-cruncher like Slok, it doesn’t make much sense that AI expectations have “sparked a macro conversation about a coming rise in the unemployment rate,” given that he sees no change in the “underlying incoming economic story of a strong U.S. economy driven by AI spending, the industrial renaissance, and the One Big Beautiful Bill.” Slok added that he thinks this narrative is wrong, that AI adoption will take much longer than the next 12 to 18 months mentioned in these viral essays, and the risk of an overheating economy is larger than, say, unemployment going to 10%.
Goldman agreed with Slok at least that the vibes are pretty freaked out, titling its report “AI-nxiety,” and highlighting how corporate chatter has far outpaced tangible implementation. A record 70% of S&P 500 management teams discussed AI on their quarterly calls, with 54% specifically framing the technology around productivity and efficiency. Yet when it came to providing hard numbers, the narrative faltered, lending support to the research of Wharton management professor Peter Cappelli, who has embedded with several firms attempting AI adoption and previously told Fortune that the productivity gains are real, but getting there is really hard work and quite expensive to implement.
Only 10% of S&P 500 management teams actually quantified AI’s impact on specific use cases, Walker wrote, and a mere 1% quantified its impact on earnings. Furthermore, broader economic adoption remains sluggish. While half of companies in the broader Russell 3000 discussed AI, U.S. Census survey data indicates that fewer than 20% of establishments are currently utilizing AI for any business functions.
Here comes the “but.”
But AI is having a considerable impact in 2 areas
Despite the lack of an economy-wide macro impact, the firms that have successfully integrated and measured AI are reporting dramatic improvements. Goldman Sachs found that management teams quantifying AI-driven productivity impacts on specific tasks experienced a median gain of around 30%.
Two primary areas are driving these substantial gains:
- Customer support
- Software development tasks
In these targeted functions, the technology is already delivering on its transformative promises, significantly streamlining core business operations.
Perhaps it’s no mistake, then, that the doomsday predictions are coming from tech types who see firsthand how 30% of software development work is vanishing into the oncoming advance of the robots. Venture capital billionaire Marc Andreessen famously predicted over a decade ago that software would “eat the world,” but software has found itself being consumed. Goldman offered some clues as to how much greater AI’s appetite will be from here.
Earnings data suggests to Goldman that localized productivity gains are already beginning to influence corporate hiring strategies, leading to a “nascent reluctance to hire in anticipation of potential productivity gains.”
Walker observed a modest but rising share of management teams explicitly mentioning AI when discussing hiring freezes or layoffs. The companies that discussed AI in the context of their workforce reduced their job openings by 12% over the past year, a steeper drop than the 8% reduction seen across all companies. While the current correlation between AI adoption and broad labor market outcomes remains small and statistically insignificant, Goldman’s baseline forecast is that 6% to 7% of workers—roughly 11 million jobs—will eventually be displaced by AI automation over the long term.
Even without widespread productivity gains, AI is drastically reshaping capital expenditure. The “hyperscalers”—the massive tech companies providing cloud and AI infrastructure—are driving an unprecedented spending boom. Analysts have revised their 2026 capex expectations for these tech giants to an astonishing $667 billion, a 24% increase from just the start of the earnings season and representing a 62% jump compared with 2025. Goldman Sachs anticipates that this AI spending will contribute roughly 1.5 percentage points to measured capex growth this year, though its net impact on overall GDP growth will be a minimal 0.1 to 0.2 percentage points owing to a heavy reliance on imported capital goods.
Ultimately, Goldman’s findings paint a picture of an economy in transition. While Wall Street is consumed by “AI-nxiety” and tech giants pour hundreds of billions into infrastructure, the promised productivity revolution remains highly localized to software coders and customer service representatives. For the broader U.S. economy, the true macroeconomic benefits of the AI revolution have yet to arrive.
