Anthropic's IPO Gains Momentum with $65B Revenue Run Rate and $559M Profit in Q2 2026

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Anthropic's IPO plans gained traction as the fear and greed index for AI stocks hit extreme optimism. The company’s annualized revenue run rate jumped from $9B in late 2025 to $65B by July 2026, with Q2 2026 revenue at $11.5B and a $559M profit. Anthropic closed a $65B Series H round at a $965B valuation, with IPO funding rates expected to be aggressive. Goldman Sachs, Morgan Stanley, and JPMorgan are underwriters, with an S-1 filing likely in late September 2026.

Anthropic is sprinting toward what could become the largest AI IPO in history, and the investors lining up to participate have one persistent request: show us the receipts.

The Claude maker’s annualized revenue run rate ballooned from roughly $9B at the end of 2025 to over $65B by the end of July 2026.

The numbers behind the frenzy

Anthropic’s preliminary Q2 2026 revenue clocked in at $11.5B, up from $4.73B in Q1 2026. For context, Q2 2025 revenue was $787M. So the company roughly 15x’d its quarterly top line in a single year.

That growth has also pushed the company into unfamiliar territory: profitability. Anthropic posted its first quarterly operating profit of approximately $559M in Q2 2026.

The company’s most recent private round, a record $65B Series H completed in late May 2026, valued Anthropic at $965B post-money. Some prospective IPO investors are now projecting a public market valuation north of $2 trillion.

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Internal revenue projections for 2028 sit in the $190B to $200B range, according to figures being shared with potential backers.

The accounting question investors keep asking

Revenue growth this steep invites a specific kind of scrutiny, and prospective IPO investors are zeroing in on one issue in particular: how Anthropic accounts for revenue generated through cloud reseller partnerships.

The distinction matters more than it might sound. When Anthropic sells its models through a partner like Amazon Web Services or Google Cloud, the question is whether the company books the full amount customers pay (gross revenue) or only its share after the cloud provider takes a cut (net revenue).

Enterprise customers already account for roughly 80% of Anthropic’s revenue. Over 1,000 businesses were spending at least $1M annually on Anthropic’s products as of April 2026. Many of those customers access Claude through AWS Bedrock or Google Cloud’s Vertex AI, which means the gross-versus-net question touches a significant portion of total revenue.

The path to public markets

Anthropic confidentially submitted its draft S-1 registration statement to the SEC in June 2026. The company is working with Goldman Sachs, Morgan Stanley, and JPMorgan as underwriters.

The public filing of the S-1 is anticipated in late September 2026, with a roadshow potentially kicking off in mid-October.

The company has raised between $118B and $130B in private capital across its funding history. Amazon holds approximately 21% of the company, while Alphabet owns around 15%.

For Amazon in particular, the math is striking. A 21% stake in a company valued at $2 trillion would be worth roughly $420B.

What this means for the AI sector

Anthropic’s IPO will function as a pricing signal for the entire AI industry. If the company achieves a $2 trillion valuation, it effectively sets a new ceiling for what public markets are willing to pay for frontier AI capabilities.

There’s a risk dimension worth watching, too. Revenue that grows from $787M to $11.5B in a year is extraordinary, but it also means the company has very little historical baseline for predicting churn, seasonality, or customer concentration risk. Investors projecting $190B to $200B in 2028 revenue are essentially betting that a trend line drawn from fewer than four quarters of meaningful data will extend smoothly for another six quarters.

The first operational profit is encouraging, but $559M on $11.5B in quarterly revenue implies thin margins relative to pure software businesses. Capital expenditure on compute infrastructure, talent costs for top-tier AI researchers, and ongoing model training expenses all weigh on the bottom line.

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