Anthropic 的 S-1 揭示 AI 業務的真實資產負債表

Anthropic officially filed its Form S-1 with the SEC on Monday, setting up a Nasdaq listing under the ticker ANT. For two years, the private artificial intelligence market operated on private marks, secondary desk whispers, and venture rounds structured around liquidation preferences. Now we have an audited registration statement. The numbers quantify what the foundation model business actually costs to operate.

For the first half of 2026, Anthropic reported $16.3 billion in revenue, up from $1.2 billion in the first half of 2025. That top line came alongside an operating loss of $693 million, narrowed from a $2.1 billion operating deficit a year earlier. The bottom-line net loss touched $80.7 billion, driven by non-cash mark-to-market adjustments on convertible debt instruments that convert upon the offering. At the customer level, Anthropic counts over 11,900 enterprise accounts generating more than $100,000 in annualized spend, 1,900 spending above $1 million, and roughly 250 paying over $10 million annually.

Enterprise software growth at this velocity usually commands extreme valuation multiples. The constraint sits lower on the statement: the off-balance-sheet commitments.

According to the filing, Anthropic has entered into at least $518 billion in multi-year infrastructure commitments with hyperscale cloud and hardware partners over the next decade. Crucially, roughly 80 percent of those contracts are non-cancelable or require fixed payments regardless of actual server utilization. That includes at least $111.1 billion committed to Alphabet, $110 billion to Amazon, $31.4 billion to Microsoft, and $161.2 billion in equipment lease structures tied to Broadcom silicon.

In classical corporate finance, software companies trade at high multiples because their cost structure scales smoothly: gross margins exceed 75 percent, marginal distribution costs approach zero, and capital expenditures stay minimal. Anthropic's cost profile resembles a capital-intensive utility or an industrial commodity refinery. By signing hundreds of billions in take-or-pay cloud obligations, the lab traded variable margin flexibility for dedicated physical capacity.

That commitment creates severe operating leverage. When enterprise inference demand expands, fixed clusters clear high incremental cash flow. But if demand plateaus, or if competing open-source model architectures depress token pricing faster than hardware amortizes, those fixed obligations do not adjust downward. Every empty megawatt of reserved compute drains gross profit straight through to operating cash flow.

The public markets will not price ANT as an asset-light SaaS business. They will price it as a high-beta spread between token real estate yields and non-cancelable debt-like power contracts. When public trading begins, the entire frontier laboratory cohort will finally get marked to market every single morning.