Big Tech companies are successfully cashing in on artificial intelligence while the rest of corporate America registers flat productivity growth and stagnant profit margins, according to an analysis published by Apollo Global Management chief economist Torsten Slok on October 24.
While infrastructure providers and model developers scale their revenues, mainstream enterprises struggle to extract measurable financial value from heavy technological expenditures.
The S&P 500 Divide Between Tech Giants and Mainstream Corporations
Torsten Slok’s analysis reveals that revenue per worker and profit margins are rising specifically for the Magnificent Seven technology stocks. Meanwhile, those same financial metrics remain entirely flat across the other 493 companies in the S&P 500 index.
Despite massive capital allocations and widespread enterprise hype, the technology has delivered zero financial payoff to corporate America outside of the technology sector, as Slok noted in his report.
Running advanced models at scale quickly drives up operating costs. Many software and cloud providers charge by token usage, meaning corporate bills climb rapidly as employees issue more prompts or deploy autonomous systems to process enterprise data.
Apollo Economist Says AI Has Yet to Deliver Measurable Financial Gains Outside Big TechCompanies are investing in artificial intelligence, but where are the financial returns?Apollo Global Management chief economist Torsten Slok says there are still no clear signs of an… pic.twitter.com/1X9mIUhTYW
Mainstream corporations are beginning to view token spending as an overhead cost center rather than an engine for top-line growth. Venture capital-backed businesses continue to accelerate their spending on high-end tokens, but established enterprises have fewer incentives to pay premium rates for flagship models when cheaper alternatives can handle standard operational tasks.
The Financial Reality of Fintech and Enterprise Deployment
This monetization bottleneck is not unique to traditional US industries. A PwC India survey reported by Business Standard on September 11 found that 84 percent of surveyed financial technology respondents had yet to see measurable financial results from their AI investments.
Despite the lack of an immediate return, 48 percent of those surveyed fintech companies identified proprietary intelligence as a leading strategic priority. At the same time, 30 percent expected customer experience to drive AI agent adoption over the next three to five years, while cost reduction and productivity gains lagged behind at 22 percent.
Trust remains a formidable barrier for businesses looking to deploy multi-step autonomous workflows in financial services. The survey indicated that 41 percent of respondents identified customer trust and adoption as the biggest hurdle to scaling agentic payments, while 33 percent pointed to fraud, security, and risk management as major concerns.
Autonomous agents expand the technology’s utility by handling complex, multi-step tasks instead of simply answering basic prompts. However, their ability to access tools, process information, and execute commands across proprietary company systems introduces severe usage costs and heightened security exposures.
Infrastructure Costs and the Search for Enterprise Accountability
Rising token consumption has prompted major platform providers including Microsoft, Meta, and Amazon to rein in internal AI usage as employee experimentation and agentic workloads drive up infrastructure bills.
OpenAI CEO Sam Altman has also acknowledged the problem, describing AI token costs as a “huge issue” for customers seeking better economic value from their software spending. This dynamic creates a difficult dilemma for model providers hoping to turn initial enterprise adoption into a lasting, high-margin revenue stream.
Selling computing power and model access boosts top-line sales for hardware vendors and hyperscalers, but corporate customers must see corresponding gains in productivity, revenue growth, or operating cost reductions to justify maintaining their current pace of spending.
If enterprise expenses continue to rise without a measurable improvement in underlying business performance, the additional spending could weigh on margins.
Artificial intelligence cannot handle human nuance, accountability, trust, culture, unwritten rules, ethical trade-offs, and fairness. These factors shape corporate reputational risk and ultimately drive long-term revenue growth and profit margins. The ultimate trajectory of the artificial intelligence boom will be decided within the S&P 493 rather than the Magnificent Seven, and the current financial data shows no evidence that margins are improving outside of the tech sector.
