TradingKey - Anthropic plans to host a Pre-IPO Investor Day at its San Francisco headquarters on October 14 local time and disclose financial data for the third quarter of 2026 to invited institutional investors. This will mark the first time the company provides relatively comprehensive quarterly operational information during its IPO preparation stage, helping management and underwriting banks assess market demand to provide a basis for subsequent valuation and offering size.

The meeting will be held behind closed doors. As Anthropic has not yet made its S-1 registration statement public, information obtained by the outside world in the short term may still come from attendees or media reports. Therefore, investors should not only pay attention to the revenue scale, but also confirm the statistical period and accounting standards of the relevant figures.

According to previous media reports, Anthropic's target valuation could reach $2 trillion, with outside expectations that the company may go public in November this year. However, the specific offering size, valuation, and listing date remain uncertain as financing needs for AI companies expand and market scrutiny of industry profitability tightens.

Beyond Revenue Growth, Statistical Scope Demands a Clearer View

The market's initial focus is on Anthropic's third-quarter revenue and its annualized revenue growth rate.

Some reports indicate that Anthropic's annualized revenue as of July may have reached approximately $65 billion, with expectations to top $100 billion by year-end. However, the $100 billion figure here more likely refers to an annualized run rate extrapolated from the most recent month or quarter, which does not mean the company actually achieved $100 billion in full-year revenue for 2026.

This distinction is crucial. Annualized revenue typically multiplies revenue from a shorter period by 12 to reflect a company's current growth rate; however, if customer demand fluctuates, contract terms are short, or prices decline, this metric may not fully represent actual revenue over the coming year.

The revenue recognition method for cloud partners also warrants close attention. Anthropic distributes its Claude models through platforms such as Amazon AWS, Google Cloud, and Microsoft, recording some channel transactions on a gross basis as revenue while classifying fees charged by cloud platforms under sales and marketing costs. OpenAI does not use the exact same methodology, so directly comparing the annualized revenues of the two companies can easily overestimate or underestimate the actual business gap.

The $50 billion annualized revenue figure OpenAI provided to investors last week was lower than the $70 billion previously circulating in the market, primarily due to differences in channel revenue accounting rather than a sudden decline in AI demand. This incident demonstrates that if Anthropic discloses only a single revenue figure without explaining the breakdown of direct sales, cloud marketplace revenue, and subscription revenue, the market could still misinterpret the data. Axios also cautioned that private AI companies frequently use projected metrics such as annualized revenue, but these figures cannot replace complete financial statements.

Can High Growth Cover Computing Power and Infrastructure Costs?

Data previously disclosed by Anthropic reflects a business model typical of frontier AI: rapid revenue expansion, coupled with similarly massive compute investments and operating losses.

According to leaked IPO materials, Anthropic's revenue for 2025 was approximately $4.59 billion, growing more than tenfold compared with around $400 million in 2024; excluding accounting expenses mainly related to changes in the valuation of financing instruments, the company's operating loss still exceeded $8 billion.

Among this, computing and infrastructure expenses accounted for approximately $7.33 billion, representing more than half of the $12.65 billion in total operating expenses for the year. The company also faces massive future commitments related to cloud computing, compute resources, and data centers.

Therefore, the most critical metrics for the third quarter are not just revenue, but gross margin, operating loss, and cash burn rate. If revenue growth is primarily driven by lower-priced, high-compute-cost API calls, scaling up may not immediately improve profitability. Conversely, if the share of revenue from enterprise subscriptions, Claude Code, and high-value AI agent services increases while unit inference costs fall, the company could gradually build operating leverage.

Customer concentration is also worth noting. Anthropic previously disclosed that nearly a quarter of its 2025 revenue came from just two customers, and many large clients have not signed long-term contracts. If this meeting provides metrics such as customer count, single-customer concentration, renewal rates, and remaining contract value, it will help assess whether the growth is sustainable.

$2 Trillion Valuation Faces Public Market Test

As Anthropic's investor day convenes, the financing environment for the AI sector is changing. Rising interest rates have increased the cost of capital and led the market to focus more on when AI companies can generate free cash flow. Broadcom (AVGO) CEO Hock Tan recently stated that if interest rates continue to rise, financing capabilities could become the primary constraint on the expansion of AI enterprises.

Based on a rough calculation from reported figures, if Anthropic's annualized revenue reaches $100 billion by the end of the year, a $2 trillion valuation would equate to roughly 20 times annualized revenue; if revenue remains close to the July level of around $65 billion, the corresponding multiple would exceed 30 times. This valuation requires the company to maintain extremely high growth while continuously reducing inference costs and loss rates.

The market will also focus on whether Anthropic's IPO proceeds according to its November timeline. Recently, some AI infrastructure companies have postponed or withdrawn their listing plans due to market volatility, indicating that investors are not unconditionally accepting high valuations for all AI assets.

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