TechCrunch reported on 25 October 2026 that Apollo Global Management chief economist Torsten Slok sees no financial payoff from AI outside the technology sector. In a 24 October note, Slok compared the S&P 493 with the seven technology names he separates from the S&P 500 and found flat profit margins and flat revenue per worker.
What Slok measured
Slok defines the S&P 493 as the S&P 500 excluding Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia and Tesla. His comparison shows revenue per worker rising for those seven companies, while it remains flat for the S&P 493. The same pattern appears in profit margins, which rise for the seven technology companies but remain flat for the wider group.
The note also says there are still no signs of AI boosting productivity growth. Slok wrote that, “Despite massive investments and widespread hype around artificial intelligence, the technology has delivered zero financial payoff to corporate America outside of tech.”
This is a financial and productivity argument, not a claim about whether companies are experimenting with AI. Slok’s point is that adoption has not yet shown up in revenue per worker, margins or productivity data for most large US companies outside the technology sector.
Token spending and demand
Slok links the lack of visible payoff to the price companies are willing to pay for AI services. He says venture-backed companies are spending more on tokens, while mainstream corporate America does not need the latest model and is unwilling to pay for it.
Apollo’s 20 October note made a related argument about compute demand. It said token spending growth had slowed and that demand for compute may be highly price-sensitive when companies cannot turn model use into higher revenue, wider margins or substantial cost savings.
The 24 October note makes the same point at a macroeconomic level. Slok argued that as long as agentic AI and token spending remain confined to a small part of the economy, the wider economic effect of AI will be limited.
Enterprise implications
For enterprise technology teams, the argument narrows the question around AI investment. Usage metrics, pilot counts and access to models do not answer whether revenue per worker, profit margin or measurable cost saving has changed.
Slok also questioned whether cheaper intelligence alone can create business value. He wrote that “AI may be a great calculator, but in business, intelligence is mostly judgment.” He added that AI does not understand “accountability, trust, culture, unwritten rules, ethical trade-offs or fairness,” which he said shape reputational risk and affect revenue growth and margins.
That framing is relevant to buyers planning AI assistants, agents and workflow automation. The financial case depends less on whether a model can complete a task in isolation and more on whether the surrounding process, responsibility model and controls change the economics of the work.
If AI adoption leaves revenue per worker and margins unchanged, the questions behind ECS's Core AI topic are less about model access and more about where agentic AI, Azure AI Foundry, the EU AI Act and responsible AI meet accountable business use. Sessions and workshops give attendees room to compare product capabilities with operating models, governance choices and practitioner experience. Bring deployment plans, measurement assumptions and risk questions that are difficult to settle from vendor material alone.
This article was written by the ECS editorial team with the assistance of Amal Agent, run.events' AI assistant, which supported the research, drafting and proofreading.