JUICED EARNINGS: WHAT'S INSIDE BIG TECH'S RECORD PROFITS

Price-to-earnings ratios, revenue growth rates, operating margins, free cash flow, return on invested capital…these are among the key biometrics that let investors understand the relative health of a business.

Earnings can be viewed as the pulse, another generally reliable way to check up on a company's vital signs: how its products and services, such as cloud computing contracts, digital advertising, and semiconductor sales, are translating into profits. Investors use this information to decide whether to buy, hold, or sell a stock, and to judge what price is fair to pay for a claim on future results. High earnings can signal that the underlying business is performing well. Low earnings can mean the opposite, or simply that management has been investing heavily in the hopes of future growth.

Simple, right? But beyond this single, monolithic metric, however, the process of determining the figure can get considerably more complicated. In the age of artificial intelligence and increasingly creative financial engineering, it is becoming harder to know how useful these numbers actually are.

Big Tech's second quarter of 2026 was, by the headline numbers, extraordinary: Alphabet reported net income of $112.1 billion; Amazon reported net income of $62.6 billion, up 245% year over year. However, read past the top line and a large share of that profit did not come from selling search ads, cloud capacity, or anything else to a paying customer; it came from marking up the value of stakes these companies hold in private artificial intelligence companies, chiefly Anthropic. That is one of three mechanisms currently doing quiet, substantial work inside record tech earnings, alongside circular financing arrangements among AI infrastructure players and increasingly aggressive depreciation assumptions on the chips underlying the buildout. Nvidia, the chipmaker at the center of that buildout, posted results just as outsized, reporting $96.2 billion in total revenue last quarter, up 106% year over year per its own August 2026 earnings release.

None of these mechanisms is improper on its own. All three are worth understanding before treating this quarter's per-share results as a clean read on the trajectory of future earnings.

Alphabet's own Q2 2026 earnings release discloses a $99.0 billion pre-tax gain on equity securities, a figure that, net of tax, lifted net income by $77.1 billion and added $6.26 to diluted earnings per share. Put another way, the house made more money on its side bet than on the casino. Alphabet does not break the gain out by individual holding, but the quarter coincides with a period in which Anthropic (in which Alphabet has invested) raised capital at a sharply higher valuation. In that round, a $65 billion Series H raise announced in May 2026 at a $965 billion post-money valuation, Alphabet was the largest individual investor, alongside Amazon and a series of asset managers and venture investors; as an existing shareholder, its participation in the company's next round helps set the price of that round, and because the shareholder's own earlier stake is marked to the new valuation, a higher price becomes a gain on its own books. Alphabet's stake is now estimated at more than $135 billion. Against Alphabet’s own $28.2 billion in net income for the same quarter last year, that markup accounts for most of the company’s reported growth; net income rose roughly 298% year over year on a headline basis, but only about 24% with the equity gain excluded.

Amazon's disclosure is more direct. Its own earnings release states that “second quarter 2026 net income includes non-operating pre-tax other income of $53.4 billion, primarily from our investments in Anthropic,” against operating income of just $27.5 billion for the quarter. Amazon's own position in Anthropic follows a similar pattern with roughly $13 billion invested to date and up to $20 billion more committed against future milestones. In both cases, the accounting is standard and permitted for minority equity stakes in private companies. No cash changed hands and no rule was bent, but it means two very different kinds of “profit” now sit inside the same net income line: profit from selling something alongside profit from someone else's private funding round. The two behave very differently when markets turn and coverage of “blowout” tech earnings rarely separates them for the reader. Both companies, in other words, are not simply marking gains on someone else's fundraise, but are among the investors whose capital helped support the valuation now showing up on their own income statements. Measured the same way, Amazon’s reported net income grew roughly 245% year over year; excluding the Anthropic-related other income, using the quarter’s own implied effective tax rate as a rough guide since Amazon does not disclose the after-tax, investment-specific figure as cleanly as Alphabet does, the increase is closer to 17%, compared with $18.2 billion in net income in the same quarter last year. That reframing also matters for what these stocks cost today. Alphabet’s trailing P/E stood at roughly 17x as of September 14, 2026, but backing out just this quarter’s $6.26 per-share markup from trailing earnings pushes that multiple to roughly 25x; Amazon’s trailing multiple of roughly 21x moves into the high-20s on the same basis, using the same approximation.

The same distortion is not confined to these two companies’ income statements; it shows up in the S&P 500’s own aggregate growth rate. First-quarter 2026 index earnings grew a reported ~28% year over year; stripping out an estimated $69 billion in combined unrealized gains at Alphabet, Amazon, and Nvidia brings that down to roughly 16%, as cited by Acadian Asset Management. The pattern intensified in the second quarter, when the index’s blended earnings growth as of August 7, 2026, reached 50.4% year over year but fell to 32.0% excluding Alphabet and Amazon alone; per FactSet Earnings Insight, the two companies, combined, account for “about 71% of the increase in dollar-level earnings for the index since June 30.”

A second, related mechanism runs through the AI infrastructure buildout itself. Nvidia agreed in 2025 to invest up to $100 billion in OpenAI as part of a joint plan to deploy 10 gigawatts of AI compute, infrastructure for which OpenAI is, in significant part, buying from Nvidia. That $100 billion figure did not hold. Nvidia’s own November 2025 disclosures said there was no assurance a definitive agreement would follow, and by February 2026 Jensen Huang said the commitment had never been binding to begin with. What closed instead, on March 31, 2026, was a $30 billion Nvidia stake in a $122 billion OpenAI funding round, a stake the companies describe as unconnected to the 10-gigawatt deployment plan. By mid-2026, reporting indicated Nvidia was weighing additional financing arrangements, including a possible guarantee on roughly $250 billion of debt tied to an OpenAI data center project, on top of financing OpenAI's chip purchases directly. Nvidia's own credit default swaps widened sharply on the news, a sign that some credit investors are seeing manufactured revenue within a structure where the seller is also underwriting the buyer's ability to pay.

Manufactured revenue is not fake revenue; Nvidia's chip sales are real and GAAP-compliant. However, it does mean that a portion of the demand is of a different quality than that of an independent customer buying because it needs and can afford the product.

The same dependency shows up on the hyperscalers’ own revenue lines. An independent third-party analysis estimates that roughly 70-75% of AI-related revenue at Amazon, Microsoft, and Google, and more than 60% of the AI backlog hyperscalers point to in justifying their capital spending, traces back to just two customers: OpenAI and Anthropic.

Nvidia’s own numbers show how concentrated that exposure has become on the supplier side. Hyperscalers, who buy chips in part to serve OpenAI, a company Nvidia has directly invested in, and Anthropic (in which Nvidia is reportedly in talks to invest up to $10 billion), reported $49.0 billion of Nvidia’s $89.0 billion in data center revenue last quarter, or just over half, per Nvidia’s own August 2026 earnings call. Nvidia has also backed several neocloud, or GPU cloud, providers directly, agreeing in at least one case to buy back unused capacity if the provider cannot find customers for it. A third-party independent analysis puts total neocloud exposure at 30% or more of Nvidia’s revenue, which would put over 70% of Nvidia’s revenue as coming from customers it has itself invested in. The risk that follows is straightforward; if one of those customers cannot pay, Nvidia loses the sale and the investment at the same time.

Circular financing like this is not without precedent. A similar structure ran through the telecom buildout of the late 1990s. Lucent, Nortel, and Cisco extended vendor financing directly to the competitive local exchange carriers (CLECs) buying their switches and fiber gear, in effect financing their own equipment sales. Lucent alone committed roughly $8.1 billion; one analysis puts combined exposure across nine major suppliers at about $25.6 billion by the end of 2000. As capital markets tightened, roughly four dozen CLECs, largely cash-burning, pre-profitability startups, filed for bankruptcy between 2000 and 2003; Lucent alone absorbed about $3.5 billion in customer-loan losses, including roughly $700 million tied to a single carrier, Winstar Communications. In the end, Lucent shareholders lost over 99% of their peak valuation, destroying over $250 billion in shareholder value and leaving the equity of what was once the seventh largest stock in the S&P 500 virtually worthless.

However, while the parallel to today's AI buildout is real, it’s narrower than it might appear at first glance (as the above analysis notes). The CLECs generated close to no revenue of their own; OpenAI and neocloud providers like CoreWeave already have substantial, if not yet profitable, revenue, while most of today's vendor-financed exposure runs through equity stakes and purchase commitments rather than the trade credit that turned into Lucent's bad debt. That concentration of risk in a single counterparty is not new, but what has changed is where the cash is actually coming from today and which instrument is left holding the risk.

For a fuller picture of how these arrangements connect, The Research Note has mapped 111 disclosed financial relationships among 23 companies across the AI buildout, distinguishing what can be traced to a filing or public statement from what cannot. Even that effort, by its own account, leaves much of the underlying value undisclosed; many of the supply commitments, warrants, and guarantees it tracks carry no stated price. No source, including this one, can currently size the full web of circular financing across the AI economy, which is precisely why its true impact remains difficult to determine.

The third mechanism is more technical, but no smaller in scale: how long a hyperscaler assumes a GPU or server will remain useful for purposes of spreading its cost across the income statement. Meta has extended its assumed server life from four years in 2022 to five and a half years currently, a change reported to be worth roughly $2.9 billion in reduced depreciation expense in 2025 alone. In November 2025, investor Michael Burry publicly argued, in a post that CNBC reported on but said it could not independently verify, that Oracle and Meta were overstating cumulative 2026-2028 earnings by roughly 27% and 21%, respectively, by extending useful-life assumptions on hardware he believes should be depreciated over two to three years given Nvidia's roughly annual chip-release cycle. He put the industry-wide understatement of depreciation at about $176 billion over that period. The hyperscalers' counterargument, echoed by research firms including Yardeni Research, is that old chips don't go to waste; once they're too slow for training new AI models, they get shifted to easier jobs and keep earning for years, the same pattern that made earlier rounds of server-life extension look reasonable in hindsight.

Multiples are a bet on repeatability. The premium currently being paid for AI-linked earnings assumes this quarter's numbers are a clean, repeatable signal of demand, an assumption the companies themselves are doubling down on; https://www.cnbc.com/2026/07/22/google-earnings-q2-goog-live-updates.htmlhttps://www.investing.com/news/transcripts/earnings-call-transcript-meta-misses-eps-in-q2-2026-as-stock-sinks-after-hours-93CH-4821910combined 2026 capital spending guidance across Alphabet, Amazon, Meta, and Microsoft has climbed to roughly $720-745 billion, and the group has collectively raised that guidance at least six separate times so far this year. That is a lot of capital being deployed with the hope that an immense flow of profitable revenue will materialize over the next few years. At the same time, the demand signal itself is growing more dependent on private-market valuation marks, vendor-financed demand, and depreciation assumptions that credible analysts dispute by tens of billions of dollars. The evidence that such levels of capital expenditure will pay off soon is both thin and uneven.

All of this leads to a simple truth, which is that measures such as price-to-earnings, which many investors rely upon to set market prices, suggest all is well. The S&P 500’s forward twelve-month P/E stood at 20.0 as of August 7, 2026, only modestly above its five- and ten-year averages of 19.9 and 19.0. On that measure alone, the index looks only mildly rich relative to its own recent history.

Price-to-free-cash-flow, a multiple that is harder to move with a one-time unrealized mark, tells a more pointed story. On Crewcial’s internal analysis of data provided by CapIQ of S&P 500 (ex-Financials) constituent free cash flow and price, that multiple stood at 38.0x in the second quarter of 2026, a reading that predates results reported since this note was drafted. That multiple implies a free-cash-flow yield of roughly 2.6% compared with a 10-year Treasury yield that closed at 4.96% on September 11, 2026, a gap that raises the question of how much compensation equity investors are actually receiving for the added risk, even where the P/E has stayed close to its own historical range.

Ultimately, we would treat this quarter's headline earnings-per-share figures as a starting point for further diligence. For an institutional portfolio, the practical takeaway is to ask AI-exposed managers a simpler question: how much of their thesis depends on GAAP earnings continuing to grow at this pace, versus operating cash flow and contracted backlog that would hold up on their own. It is worth asking what price that thesis carries too. Measured in P/E, the index looks fine; measured in free cash flow, it does not. The earnings are real, or at least technically factual; whether they reflect sustainably healthy underlying businesses is a separate question, one the market has not yet had to answer.