{"id":1968,"date":"2026-08-10T08:05:12","date_gmt":"2026-08-10T08:05:12","guid":{"rendered":"https:\/\/www.vaultinsider.top\/?p=1968"},"modified":"2026-08-10T08:05:12","modified_gmt":"2026-08-10T08:05:12","slug":"apollos-slok-ais-profits-are-being-funded-by-investors-rather-than-earned-from-customers","status":"publish","type":"post","link":"https:\/\/www.vaultinsider.top\/?p=1968","title":{"rendered":"Apollo’s Slok: AI’s profits are ‘being funded by investors rather than earned from customers’"},"content":{"rendered":"

<\/p>\n

\u201cIs there an AI bubble?\u201d is such a tired thought. Here\u2019s something altogether more wired: The AI boom is paying off, but not in a way that the current equities market has accounted for. The success of the technology in one area of the economy could make the bubble real in another, more precisely.\u00a0<\/p>\n

\n

In a blog post published on Friday, Apollo Chief Economist Torsten Slok highlighted that the parts of the AI value chain with the highest profit margins\u2014companies making AI models and applications\u2014actually have the lowest levels of profitability, a departure from the standard business model of, well business, in which profit margins are higher for companies selling an end product to consumers.<\/p>\n

Slok broke down AI companies into four categories: models and applications, cloud and compute, energy and grid, and silicon and equipment. Using data from Pitchbook and Bloomberg for companies including OpenAI, Anthropic, Microsoft, Amazon, Constellation Energy, Nvidia, AMD, and Micron, Slok calculated that silicon and equipment\u2014such as chipmakers\u2014has the highest profit margin, 41%, in the AI value chain. Meanwhile, models and applications\u2014like Anthropic\u2014have a -59% operating margin.<\/p>\n

Slok warns that this sharp disparity is because money from the AI boom is not coming from natural demand for AI applications, but rather shareholders hoping to cash in on what they hope is the next technological revolution.<\/p>\n

\u201cAI boom\u2019s profits are currently being funded by investors rather than earned from customers,\u201d Slok said. \u201cThe upstream margins are real, but they are paid for out of capital raised by the layer losing money, not out of cash generated by end demand.\u201d<\/p>\n

Goldman Sachs now projects AI investments to swell beyond $1 trillion in 2026, but so far, the technology has little to show for itself, with no significant changes in economic productivity or profit margin growth outside of the Magnificent Seven. Should AI financing slow down, the lopsided profit margin structure threatens to topple the stability of the entire rapidly expanding industry, Slok warns.<\/p>\n

\u201cThe bottom line is that the most profitable part of the AI value chain depends on the least profitable part continuing to grow revenue or raise capital,\u201d he concluded. \u201cCapital can bridge the gap for a while, but not indefinitely. And therein lies the risk: will the ROI show up for AI\u2019s end customers fast enough to sustain the spending that is generating those upstream margins?\u201d<\/p>\n

Wider spread fears of an unsustainable AI expansion<\/strong><\/h2>\n

Slok isn\u2019t the first economist to sound the alarm on AI\u2019s outsized reliance on investments. In its annual report published in June, the Bank of International Settlements noted the onslaught of AI investing, primarily from the five major hyperscalers, is outpacing earnings and free cash flow, which has led to these companies issuing debt to raise additional financing. A Bank of America analysis from last November found that in 2025, those five hyperscalers issued $121 billion in debt, four times the average debt levels issued by these firms annually over the previous five years.\u00a0<\/p>\n

\u201cDisappointment in returns could trigger a sudden pullback in financing and turn the capex boom into a protracted investment bust, with potential knock-on effects on financial conditions,\u201d Bank of International Settlements said in the report. \u201cShould hyperscalers slow or halt the aggressive pace of capex deployment, many borrowers across the supply chain could struggle to replace lost revenue and service their debt.\u201d<\/p>\n

Tech writer Ed Zitron took this concern a step further, arguing AI spending is more precarious than it even appears on the surface. He used the example of Oracle, which has a negative cash flow of $23.7 billion, as of the end of fiscal 2026, and nearly $130 billion in outstanding debt and $260 billion in lease commitments for AI infrastructure projects that have yet to begin. Its massive gamble on AI buildout is in service of OpenAI, with whom it signed a $300 billion deal last September.<\/p>\n

\u201cOracle\u2019s existence \u2014 and Larry Ellison\u2019s personal wealth \u2014 hinges on whether OpenAI can make good on its promise to spend $300bn in compute,\u201d Zitron wrote in a Substack post in June.\u00a0<\/p>\n

He called this dynamic \u201cmost-obvious and under-discussed part of the AI bubble\u201d: While 13-figure hyperscaler capital expenditures are fueling a semiconductor boom, there\u2019s little evidence so far that the current AI boom will translate to widespread applications of technology that will justify all this spending.<\/p>\n

More dangerous than just Oracle failing to deliver on its $300 billion promise to OpenAI is other tech companies giving up on their own exorbitant AI spending, Zitron explained.<\/p>\n

\u201cIf Microsoft, Google, Amazon and Meta decide that it\u2019s time to stop spending $30 billion or more a quarter on GPUs, RAM, storage, and data center construction,\u201d he said, \u201cthat\u2019ll tear a hole in the side of what people assume is a permanent supercycle.\u201d<\/p>\n<\/div>\n

#Apollos #Slok #AIs #profits #funded #investors #earned #customers<\/p>\n","protected":false},"excerpt":{"rendered":"

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