Is Anthropic's $1.2 Trillion Valuation Too Expensive? Breaking Down the Numbers and Buy Strategy

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Is Anthropic's $1.2 Trillion Valuation Too Expensive? Breaking Down the Numbers and Buy Strategy

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Anthropic plans to go public, with its valuation falling from $2 trillion to the $1.2 trillion range. This article breaks down the value of its Cloud Code product, the slowing growth signal behind its quarterly revenue of $16.96 billion, estimates a reasonable valuation range using three scenarios, and offers a tiered buying approach with a margin of safety.

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The U.S. AI sector recently experienced a sharp sell-off, with the Nasdaq plunging 5.5% in a single day and the S&P 500 dropping 4.4%. At the center of this storm are precisely the AI giants that drove U.S. stocks higher over the past two years. And Anthropic, which plans to list on the Nasdaq, has seen its valuation expectations fall from a rumored $2 trillion all the way down to the $1.2–1.3 trillion range. In his latest video, Bilibili creator "加密星球" (Crypto Planet) conducted a complete investment analysis of Anthropic, from product experience and financial data to valuation models.

Anthropic's Product Value: Starting with Cloud Code

The creator uses Anthropic's Cloud Code almost every day, and his experience is that "AI delivers exponential improvements in productivity." To use his analogy, he clearly has only two hands, but because he uses Cloud Code, he can accomplish the workload of eight hands—or even sixty-four.

This experience is not unique. The creator has observed that many senior engineers have barely written code by hand over the past year or two; their daily work has become conversing with Cloud Code or GPT's Codex. Cloud Code can debug, process data, and handle everything from user requirements analysis all the way to product launch. Companies are willing to pay for this—some employees consume up to several hundred thousand dollars' worth of Cloud Code tokens per month.

From a product standpoint, Anthropic has already proven with Cloud Code that it can create real productivity value, and the market is willing to pay for it. This is the first layer of logic for judging whether a company is worth investing in: Does anyone use the product, and are users willing to keep paying?

Financial Data: High Growth, but Growth Is Slowing

According to Anthropic's latest disclosed financial data, its third-quarter revenue reached $16.96 billion, up 47% quarter-over-quarter. If you simply annualize by multiplying quarterly revenue by 4, the annualized revenue is already close to $68 billion. This figure means Anthropic is no longer a startup with only technology and a story—it is a commercial entity with real revenue at considerable scale.

But the creator points out a key detail: the previous quarter's quarter-over-quarter growth was 144%, which dropped to 47% in the third quarter. 47% quarterly growth is still astonishing, but the signal of slowing growth cannot be ignored. You cannot simply extrapolate today's growth rate linearly five years into the future.

More critically, revenue does not equal profit. To train models and run Cloud Code, Anthropic needs large amounts of GPUs, data centers, and expensive compute. According to its disclosures, future compute commitments related to partners are close to $900 billion. This does not mean it owes $900 billion today, nor that this money must be paid immediately, but it clearly illustrates the other side of the AI industry: money is made extremely fast, and money is spent just as fast.

The creator's first conclusion: Anthropic is a company that has proven its product value and has high-speed revenue growth, but how much money it will ultimately make is far from certain. Competition from OpenAI, Google, and other AI companies means that today's technological lead does not equal sustained leadership five years from now.

Annualized Recurring Revenue (ARR) is a metric commonly used by SaaS and subscription-based companies, typically estimated by multiplying the most recent quarter's revenue by 4 to approximate the full-year revenue level. Its advantage is that it reflects the latest business trends in a timely manner; its drawback is that it ignores seasonality and one-time factors in revenue and may overestimate or underestimate actual full-year revenue. For high-growth companies, ARR helps investors quickly grasp scale, but it should be judged together with quarter-over-quarter growth, customer retention, and other indicators.

The Divergence Between Price and Value: Lessons from Moutai

The creator uses the example of Kweichow Moutai to illustrate an investment commonplace. The company has always been profitable, and in recent years it has even earned more each year than in 2021. But if you bought at over 2,600 yuan per share in early 2021, the price later fell below 1,400 yuan at one point—a loss of nearly half. The company did not stop making money; it even made more. Yet investors could still lose a large sum. The reason is simple: the purchase price was too expensive.

Stock prices are determined by two factors: how much money the company itself can earn, and how high a premium the market is willing to pay for that revenue and profit. On October 5, Nvidia fell over 9%, AMD dropped about 17%, and the U.S. semiconductor index fell about 12% in a single day. These declines were not because the companies' earning power suddenly weakened; what truly changed was the premium the market is willing to pay for future growth.

The same logic maps onto Anthropic: AI technology has not failed, and Cloud Code has not become less useful because the stock fell. The valuation falling from $2 trillion to $1.2 trillion is because the market is beginning to worry about when the money invested in AI over the past few years will pay off, where the funding and electricity for data center construction will come from, and whether tightening regulation will restrict expansion. And Anthropic happens to sit at the center of the AI value chain—spending heavily on compute on one side, while on the other turning compute into products that customers keep paying for.

The revenue multiple valuation method is often used for high-growth, not-yet-profitable tech companies. It divides enterprise value (or market cap) by annual revenue to arrive at a multiple, which is then compared with peers or historical levels. The level of the multiple reflects the market's expectations for the company's future growth and profitability. For example, valuing at 8x revenue means the market is willing to pay 8 yuan for every 1 yuan of revenue, which implies assumptions about future margin improvement and sustained growth. The method is simple and intuitive, but its drawbacks are that it does not consider profit and cash flow, and the choice of multiple is highly subjective.

Valuation Projection: Reasonable Ranges Under Three Scenarios

The creator makes clear that no one can precisely calculate the true value of a high-growth, high-uncertainty company. Giving a number precise to the decimal looks professional but is actually meaningless, because a slight change in assumptions about future growth will produce very different results.

His method has three steps: first estimate how much money the company can earn in the future, then judge what multiple the market is willing to pay, and finally discount the future value back to today.

Revenue assumptions use 2028 as the endpoint, with three scenarios. Pessimistic scenario: AI competition intensifies, growth gradually slows, and annual revenue reaches $220 billion in 2028. Neutral scenario: Cloud Code and enterprise AI usage continue to grow, with annual revenue reaching $190 billion. Optimistic scenario: strong competitiveness persists, enterprise customers continue to increase, and annual revenue reaches $250 billion. The creator emphasizes that these numbers are research assumptions, not certain predictions.

For valuation multiples, the revenue multiple method is used. The pessimistic scenario assigns a 6x revenue valuation, corresponding to $7.2 trillion; the neutral scenario assigns 8x, corresponding to $15.2 trillion; the optimistic scenario assigns 10x, corresponding to $25 trillion.

But this is not yet today's value. Assuming investors require a 15% annual return, the value two years from now needs to be discounted back to today. After discounting: the pessimistic scenario is about $5.4 trillion, the neutral scenario about $11.5 trillion, and the optimistic scenario about $18.9 trillion.

The creator specifically notes that this is only a preliminary model; formal stock valuation would also need to consider cash, debt, and future share dilution from financing. But the conclusion is already clear: the same company, with different growth expectations and valuation multiples, yields completely different results. The widely discussed $1.2–1.3 trillion happens to be close to the model valuation under the neutral scenario—but that does not mean you can simply buy.

Discounting is a basic concept in finance, referring to converting a future sum of money into today's value. Because money has time value—1 yuan today can be invested to earn a return—future money is worth less than today's money. The rate used in discounting is called the discount rate, which reflects the investor's required return and risk. For example, assuming a required return of 15% per year, $11.5 trillion two years from now discounted to today is approximately $11.5/(1.15)^2 ≈ $8.7 trillion. The higher the discount rate, the lower the present value of future value.

Margin of Safety and Buying Plan

Since the reference valuation under the neutral scenario is about $11.5 trillion, the creator introduces the concept of "margin of safety" from value investing. If you believe a house is worth 1 million, you do not necessarily have to pay 1 million to buy it, because your judgment could be wrong; if you can buy it for 800,000, the safety cushion is thicker. Applied to Anthropic, leaving a 20% margin of safety on top of the neutral valuation of $11.5 trillion gives a reference buying valuation of about $9.2 trillion.

Based on this framework, the creator offers a tiered response approach. If the IPO valuation is $15 trillion, under current assumptions he might not buy, because at that price the company would need to perform significantly better than the neutral forecast to deliver ideal returns. If the IPO valuation is $12 trillion, careful study is needed of the market environment, whether financial data has changed, and whether the original forecasts still hold. If it falls to around $9 trillion, he would more seriously consider building a position in tranches.

But there is a crucial premise: is the price decline due to panic across the entire market, or because the company itself has problems? If long-term value has not changed and the market is merely temporarily unwilling to pay a high premium, it could be an opportunity for long-term investors; if growth falls short of expectations, costs keep rising, and competitive advantages are disappearing, then the decline may simply be the market recognizing true value.

The creator leans toward buying in tranches: first establishing a position with a small portion of capital, then considering adding after new financial data comes out or the valuation becomes more reasonable. He plans to hold for three years, five years, or even longer—not to guess whether it will rise 20% or 30% on the first day of listing.

Margin of safety is one of the core principles of value investing, proposed by Benjamin Graham. It refers to the gap between an asset's market price and its intrinsic value, providing a buffer against investor misjudgment, market volatility, or unexpected events. The larger the margin of safety, the lower the investment risk and the higher the potential return. In practice, investors often set different discount rates based on the degree of uncertainty of a company—for example, requiring a larger margin of safety for high-growth, high-uncertainty companies.

A Sustainable Evaluation Framework Matters More Than a One-Time Judgment

At the end of the video, the creator raises a more fundamental question: today he spends a lot of time researching Anthropic and reaches a judgment he considers reasonable, but three months later the market has changed, revenue growth has changed, and U.S. stocks have crashed again—can he still rationally re-analyze as he does today?

It is easy to fall into greed and FOMO when the market surges, and easy to panic when it crashes. A company's value should not be determined entirely by emotion. The creator is trying to build an evaluation framework on OpenClue that continuously assesses company value from three dimensions: fundamentals, valuation, and risk. When the market environment or company fundamentals change, previous judgments can be re-examined rather than decided by feeling. Currently it is only a prototype; the scoring is undergoing blind testing and calibration, and it is still far from truly assisting investment decisions. But he hopes that in the future this framework can be used not only to research Anthropic but also other companies.

Back to the original question: Is Anthropic a good company? The creator's judgment is yes. But a good company does not mean it is worth buying at any price. $15 trillion is on the expensive side, $11–12 trillion would prompt serious consideration, and lower prices with unchanged fundamentals would be more attractive. This judgment will adjust as revenue, the competitive environment, and the U.S. stock market change. In the creator's words: you can miss a good company, but you do not want to turn a good investment into a bad one because you bought too expensively.

One-line Summary

Anthropic's product value is proven, but valuation needs a margin of safety; a good company does not mean it is worth buying at any price.

Paragraph Summary

Using Anthropic as an example, the article explores the investment logic for AI companies. It first affirms the productivity value of its Cloud Code product; financially, revenue is growing rapidly but growth is slowing, and compute costs are enormous. It then uses Moutai as an example to illustrate the divergence between price and value, noting that valuation must account for changes in market premium. Through three scenario revenue assumptions and the revenue multiple method, it estimates a neutral valuation of about $11.5 trillion, close to the widely discussed $1.2–1.3 trillion. After introducing a margin of safety, the reference buying valuation is about $9.2 trillion, suggesting building positions in tranches and emphasizing that the reason for a decline matters more than the size of the decline. Finally, it advocates building a continuous evaluation framework to avoid emotional decision-making.