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.<\/p>\n
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, \u201cWe still do not find a meaningful relationship between productivity and AI adoption at the economy-wide level.\u201d 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.<\/p>\n
Walker\u2019s analysis adds some real meat to a debate that has rocked Wall Street\u2014and many retail traders\u2019 portfolios\u2014as several viral doomsday essays about AI eating the economy trickled into actual stock market volatility. AI executive Matt Shumer<\/a> and the top finance Substack, Citrini Research<\/a>, 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\u2019s Mustafa Suleyman<\/a> (\u201cHuman-level performance on most, if not all professional tasks\u201d will be automated); Amazon\u2019s Andy Jassy<\/a> (\u201cYou won\u2019t need as many human beings\u201d); and JPMorgan\u2019s Jamie Dimon<\/a> (\u201cNow\u2019s the time to start thinking about it\u201d) added their voices to the chorus.<\/p>\n Torsten Slok, the influential chief economist at Apollo Global Management<\/a>, wrote in his Daily Spark<\/a> on Saturday: \u201cThe dramatic change in recent weeks in the narrative in markets from \u2018The economy is strong\u2019 to \u2018We are all becoming unemployed\u2019 is truly remarkable.\u201d He argued that markets are beginning to believe the view of \u201ctechno-optimists\u201d about AI\u2019s productive capabilities over the consensus of the Federal Reserve and economists.<\/p>\n To a master data-cruncher like Slok, it doesn\u2019t make much sense that AI expectations have \u201csparked a macro conversation about a coming rise in the unemployment rate,\u201d given that he sees no change in the \u201cunderlying incoming economic story of a strong U.S. economy driven by AI spending, the industrial renaissance, and the One Big Beautiful Bill.\u201d 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%.<\/p>\n Goldman agreed with Slok at least that the vibes are pretty freaked out, titling its report \u201cAI-nxiety,\u201d 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<\/a> that the productivity gains are real, but getting there is really hard work and quite expensive to implement.<\/p>\n Only 10% of S&P 500 management teams actually quantified AI\u2019s 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.<\/p>\n Here comes the \u201cbut.\u201d<\/p>\n But AI is having a considerable impact in 2 areas<\/p>\n 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%. <\/p>\n Two primary areas are driving these substantial gains: <\/p>\n Customer support<\/p>\n Software development tasks<\/p>\n In these targeted functions, the technology is already delivering on its transformative promises, significantly streamlining core business operations.<\/p>\n