While we don’t know yet exactly how many shares Anthropic will sell at its IPO, or at what price, using Morningstar’s valuation methodology, we can arrive at an estimate of how much the giant artificial intelligence lab is worth.

In this article, we are presenting our base case, in which the company is worth $1.7 trillion. In addition, we consider a bull case, where our most optimistic assumptions lead us to an equity valuation of $3 trillion, and a bear case with a valuation of just $500 billion. Other key elements in our process include assigning Anthropic a Morningstar Economic Moat Rating of narrow and a Morningstar Uncertainty Rating of Very High.

Our full company report lays out more of our assumptions around Anthropic and more details on how we see the company’s business prospects evolving.

How much is Anthropic worth?

Our fair value estimate values Anthropic’s equity at $1.7 trillion, implying a 2027 price/sales multiple of around 13 times.

We believe Anthropic is well-positioned to increase its revenue rapidly, with our base-case forecast calling for a top-line compound annual growth rate of 47% over the next five years. We see three key drivers of this revenue growth.

One, we expect the firm to drive deeper into existing accounts. The firm has almost 12,000 customers spending more than $100,000 a year, more than 1,900 customers spending $1 million annually, and more than 250 accounts spending more than $10 million each year. We see Anthropic expanding deeper into these accounts by moving enterprises from simple, low-volume applications like Claude chat to integrating Claude into the agentic infrastructure large enterprises are trying to build. As Claude takes over more automated, long-running tasks, we see a natural inflection upward in total spending on Claude by enterprise customers.

To give some perspective on penetration, we’d note that out of Fortune 500 customers spending more than $100,000 on Anthropic’s models, their spending on Claude as a percent of their total expenditures was less than 0.1%. In other words, for every $1,000 a Fortune 500 client spends, only $1 currently goes to Anthropic. We see Anthropic being able to materially expand this number over time.

Second, while the firm has impressive enterprise penetration, new logo additions are far from over. The firm has more than 50% of the Fortune 500 as active customers. Put another way, almost 50% of the Fortune 500 still aren’t spending much, if at all, on Anthropic’s products. We’d imagine penetration for the Fortune 1000 and beyond is likely even lower. We view these customers, many of whom are still in early innings of broader AI adoption, as greenfield opportunities for Anthropic to target.

Third, instead of trying to build an enterprise sales team directly, the firm’s strategic distribution agreements with AWS and Google Cloud allow it to scale its revenue without linearly adding more sales reps. By incorporating its models within managed AI platforms such as AWS Bedrock and Google Vertex AI, Anthropic can attract net new dollars toward its products.

While we are fairly confident in Anthropic’s ability to execute across these three verticals and drive sales growth, we think it is also appropriate to consider both bull and bear cases for the firm.

Bull case for Anthropic

In our bull case, we estimate Anthropic’s five-year revenue growth rate to be north of 60%, with a 2027 price/sales multiple of 26 times. In our bull case, we estimate Anthropic’s equity value at slightly north of $3 trillion, or almost twice our $1.7 trillion base-case estimate.

Our bull-case forecast is driven primarily by rapid AI adoption in large enterprises and Anthropic gaining significant market share against other frontier labs, including OpenAI and Google, as well as open-weight model providers.

Our bull case captures rapid adoption, with Anthropic’s sales capturing 14% of total software and IT services budgets in five years. Layered on top of this rapid adoption is also an increase in Anthropic’s monetization of available compute capacity, with compute efficiency also increasing rapidly.

Bear case for Anthropic

On the other hand, our bear case calls for a much more gradual adoption curve, with Anthropic’s five-year revenue growth rate coming in at 22%. Our bear-case equity value for Anthropic is $500 billion, with the implied price/sales multiple of 6 times.

Our bear case’s gradual adoption curve has two implied assumptions. First, we see revenue scaling more gradually due to slow enterprise adoption, as existing switching costs and the risks associated with replacing traditional workflows with AI outweigh the potential AI-related benefits in a typical enterprise’s strategic calculus.

Second, in our bear case, alongside slow enterprise adoption, model capabilities also scale more gradually, opening fewer target markets for Anthropic and slowing the pace of adoption of Anthropic’s models.

Has Anthropic built an Economic Moat?

We assign Anthropic a Morningstar Economic Moat Rating of narrow, primarily owing to the firm’s intangible assets and cost advantage. We also see emerging customer switching costs and an indirect network effect further bolstering the firm’s competitive advantage.

Although building LLMs is compute and resource-intensive, the firm’s research velocity, enterprise pricing power as well as penetration, and signed multiyear compute deals give us confidence that Anthropic will be able to generate excess returns on invested capital over the next 10 years.

What are the key risks and uncertainties facing Anthropic?

We assign Anthropic a Morningstar Uncertainty Rating of Very High. This rating reflects our view on the wide range of financial outcomes for a business investing heavily in large language models, a nascent technology.

We see two clear drivers of financial uncertainty for Anthropic.

One, we assume that training larger, more complex models continues to expand their respective capabilities, especially in domains that are monetizable. While this assumption makes sense to many, including us, there is uncertainty worth unpacking here. Model improvements, for the better part of the last half-decade, have operated under a framework known as “Scaling Laws.” Put simply, the framework posits that an AI model’s intelligence scales predictably as you increase the volume of its training data, its post-training alignment, and the time it spends pondering over a specific problem.

Two, there are a myriad ways in which adoption curves of enterprises adopting AI could inflect in the coming years.

If AI adoption is fast across enterprises, Anthropic’s revenues, along with its margin profile, will likely inflect upward well faster than our base case forecast. AI bulls will point to the adoption of cloud computing, social media, and mobile as evidence that the AI adoption curve will be rapid, enabling AI labs that are building the LLMs to rapidly expand their top lines and profit pools.

On the flip side, AI adoption could be slow. Proponents of the slow adoption thesis point to the rearchitecting any enterprise needs to commit to in order to deploy AI at scale, as well as the friction created by incumbent vendors that won’t be displaced by AI labs without putting up a fight. All these factors, bears argue, will lead to a much more gradual adoption of AI, still enabling AI labs to grow revenues and even achieve profitability, albeit on a much longer timeline.

The gulf between the two states of this future world, one where AI adoption is fast and one where it is slow, is substantial. According to our own estimates, the 2031 revenue differential between the two future states is loosely $500 billion for the frontier model labs.