Anthropic signaled last week that it planned to release its S-1 prospectus publicly, clearing the path for a November listing at a valuation that could reach two trillion dollars. The offering would surpass SpaceX’s June debut as the largest stock market listing in history. The filing enters a market that has turned against AI, after token prices for inference halved since May and a Gartner survey of two thousand CIOs found that only thirteen percent report significant value from the technology. Every other AI company that attempted public markets in 2026 has either crashed on arrival or retreated.
A hostile runway
Anthropic told investors that third-quarter revenue reached $16.8 billion, up from $11.5 billion in the prior quarter, and that the company is profitable on an adjusted basis. The Wall Street Journal reported first-half revenue of $16.3 billion against a net loss exceeding eighty billion dollars. A seventy-two-billion-dollar accounting charge on convertible bonds accounts for most of that loss. Investors will have to reconcile adjusted profitability with a GAAP loss of that magnitude once the full prospectus becomes public.
The market around Anthropic has gone cold. South Korean data-center firm Elice fell thirty-three percent on its first trading day, the worst opening for a sizable Korean IPO on record according to Bloomberg. Nscale, the British neocloud that filed for a US listing in September, faces investor pushback that has compressed its target valuation from thirty billion dollars to as low as sixteen billion, after Microsoft pulled out of a major chip-supply deal. CoreWeave is paying as much as thirteen percent on new debt. OpenAI, having ruled out a 2026 IPO, is racing to close a thirty-billion-dollar private raise at $1.4 trillion — timed, according to Reuters, to land before Anthropic prices its shares.
Cheaper than ever
The unit economics underneath these valuations have deteriorated throughout the year. The Silicon Data LLM Token Expenditure Index, a usage-weighted measure of the effective price per million tokens, fell from $2.07 at its May peak to $0.97 by the end of August, a decline exceeding fifty percent. Goldman Sachs noted that seventy-eight of the ninety-one hyperscaler bonds issued in 2026 were already trading below their issue price by late summer, and that hyperscaler return on invested capital had reached an all-time low. Competition drove the decline: open-weight models and intelligent routing platforms have shifted inference workloads toward cheaper alternatives, compressing the effective price without any single frontier provider announcing a cut.
Hyperscaler AI spending in 2026 is approaching six hundred billion dollars. Revenue from that infrastructure depends on token price multiplied by token volume. The first term has fallen by half in five months. The entire investment thesis rests on whether consumption can grow fast enough to compensate — whether, as with semiconductors and bandwidth before them, collapsing unit prices generate enough new demand to more than offset the decline. A company pricing an IPO in November cannot afford to wait for that question to resolve itself.
Blind spending
From the demand side, the evidence offers little reassurance. Gartner’s 2027 CIO and Technology Executive Agenda survey, conducted from May through July with 2,002 respondents, found that the likelihood of an AI use case succeeding remains roughly one in five. Alicia Mullery, a vice president and analyst at Gartner, delivered a three-word summary at the firm’s Orlando symposium last week: “Volume is not value.”