From Eyeballs to Annual Run Rates (ARR): Buzzy Pricing Metrics and the Anthropic Trade!

    I was in San Francisco last week to talk to NYU Stern Alumni on October 15, and it was the day that Anthropic had a pre-IPO meeting with prospective investors. While I was not at the meeting, the focus  of teh gathering was laying a foundation for its upcoming IPO, with the company reporting third-quarter revenue of $16.8 billion, which news reports described as surging 45% over the previous quarter's revenues, and positive profits, albeit on an adjusted basis. During the meeting, Anthropic also reported an annual revenue run rate (ARR) of $67 billion, a metric that those in the AI space have adopted with gusto, with an estimate that the ARR would exceed $100 billion by the end of the year. The nature of ARR, where choices on how revenue is calculated (gross versus net) and on annualization periods (daily, weekly or monthly) can make a significant difference, has led investors to be skeptical about comparing these numbers across time and companies.  While I share some of that skepticism, it is worth remembering that young businesses are often priced on inventive metrics, some with more basis than others. In the late nineties, dot.com companies were priced not just on the number of visitors to their websites, but on eyeballs, where Mary Meeker argued that ad spending at these companies was correlated with time that these visitors spent on websites (eyeballs)  rather than total visitors. In the last decade, you saw social media companies priced based upon the number of users and subscription-based companies on subscriber numbers. In this post, I will try to lay out the rationale for why pricing supplants valuation in young companies and why that pricing almost always has to be built around messy metrics like eyeballs, users and ARR, rather than more conventional metrics like earnings, book value or cash flows.

Young Companies in a Corporate Life Cycle

    All businesses, no matter how large and established they might be today, started as young businesses and matured. With that perspective in mind, I will start this discussion by placing young businesses in a corporate life cycle in the figure below:

One of the first steps in analyzing a company is placing it in the right stage of the life cycle, and there are three choices that you can use in making this placement:

1. Chronological age: The first and simplest choice is to look at the age of a business, putting companies that have been in operation for short periods into the young company grouping and classifying those that have been in operation for longer as mature. While this approach is easy to use, and for the most part, effective, it can be misleading in businesses like infrastructure, where it can take decades to get established, and in technology, where the lack of capital intensity can allow a company can go from start-up to mature in a few years.
2. Industry grouping: Another choice is categorizing industry groups into life cycle stages and assuming that any company that belongs to a specific industry group shares its life cycle characteristics. For decades, investors have viewed technology as a shorthand for growth and put technology companies into the earlier stages of the life cycle and utilities as mature or even declining and put all utility companies into the mature to declining company grouping. The limitation of this approach is that there will be divergence within each group, where some companies separate themselves from their industry group; there are mature and even declining companies that are in the technology grouping, and there are power companies that have taken advantage of the data center boom to become growth companies. With technology in particular, where companies age in dog years, I talked about how companies can get mis-categorized and sometimes mismanaged as growth companies, even though their best days are behind them. 
3. Operating metrics: Ultimately, life cycle effects show up in the operating metrics of companies, and there are common characteristics that young companies share, and they include the following:

• High revenue growth: Young companies usually have high revenue growth, and while this is usually off a small revenue base, there are exceptions, where companies with scaled up revenues are still able to show high growth.
• Operating losses: As young companies scale up, they usually struggle with unformed business models, and at low scale, their fixed costs overwhelm their revenues, resulting in negative or very low operating profit margins.
• Negative free cash flows: Free cash flows are after reinvestment, and as young companies invest to deliver on their potential growth, that reinvestment will generate negative free cash flows.
• Failure risk: The confluence of small revenues, losses and negative free cashflows, which must be funded with external capital (equity or debt) exposes young firms to significant risk of failure.

The figure below captures the shifts in operating metrics, as companies move through the life cycle:

Put simply, if a company has revenue growth in single digits, stable margins and positive free cash flows to the firm, it is a mature company, no matter how it views itself. Conversely, a firm with high revenue growth, negative or very low profit margins and large negative free cashflows to the firms is a young growth firm, no matter what its scale. Recognizing those characteristics is critical in understanding why so many give up on valuing young companies, and why pricing will often be based on unfamiliar metrics.

    How do Anthropic and OpenAI measure up in the life cycle? Using a patchwork of public data and leaked information, here are the numbers on key operating metrics that I have as of October 2026 for both companies:

The sky-high revenue growth and operating losses are a clear indication that these are young, growth companies, but note that the revenues (albeit on an ARR basis) at both companies are already large, suggesting that they are growing at scale. That should come as no surprise, since they are both rumored to be looking at IPOs in excess of a trillion dollars, in terms of total market capitalization. The business models are still in flux in both companies, as they wrestle with how best to structure their business models (usage versus subscription) and struggle with market targeting questions (premium versus mass market). I know that Anthropic claimed to be profitable in the second quarter of 2026, but I don't think that even Anthropic believes this to be true, since these are adjusted profits and the adjustments represent a patchwork of add-backs of legitimate expenses. As for cash flows, both Anthropic and OpenAI are deep in the hole, with negative free cash flows, at the moment, and whether they are able to generate positive cash flows in the future is entirely a function of how the AI business evolves over time.

Pricing versus Valuation: Across the life cycle

   I will start with a distinction that I believe needs to be made between two approaches to attaching numbers to assets and businesses. The first is to value that asset or business, based on its capacity to generate and grow cash flows, and the risk in those cashflows, i.e., intrinsic valuation. The second is to price that asset or business by looking at what others are paying for "similar' assets, thus reducing pricing to demand and supply. The picture below captures that difference:

It is true that if you believed in efficient markets, price would converge on value, but if you did, you would save yourself the time and aggravation associated with trying to price or value assets by investing in index funds, and moving on to more productive pursuits. If you don't believe in efficient markets, you can take one of two paths. You can take the investor route,  trying to value assets and businesses, with the intent of buying undervalued and/or selling overvalued companies, and hoping that price converges on value. Alternatively, you can take the trading path, where you throw up your hands on intrinsic value, arguing that it is too subjective and difficult to estimate, and make your money by buying investments at low prices and selling them at higher prices,  using whatever tools (including charts and technical indicators) you want to use, to make your judgments. 

    While you can value and price any business, there are reasons why the usage of each can vary depending upon where a company is in the life cycle. To value a business, you have to estimate expected future cash flows, and to make these estimates of cash flows, growth and risk, and you generally draw on three sources of information:

1. Financial statements: The current financial statements of a company provide information on unit economics, profitability and risk, and historical financial statements are useful in assessing growth in the business and changes in business profitability. For mature businesses that have been in operating for long period, much or even all of what you need to value the company can be in its financial statements, but for younger companies, the current financials may not yield much (as growth and unformed business models create noise) and there is little history,
2. Market size and growth: Knowing the size of the total market that a company is aiming for, and how quickly it is growing can help put reasonability constraints on the growth rate and market you estimate for a company. For companies in established markets, this should be relatively easy to do, but for companies in new and evolving markets, this can become difficult.
3. Industry statistics: As you forecast profit margins and the risk profile for a company, having that information for the crosssection of companies in the industry can establish bounds and help in making estimates.

If you frame an intrinsic valuation in terms of the big estimation questions that need to be addressed to arrive at value, starting with the cash flows from existing investments, moving on to estimating the value that will be added by future growth and assessing risk, you can see that valuing young companies will always be more complicated than valuing more mature businesses. In fact, looking at Anthropic and OpenAI, you can see why valuation will be daunting:

While I am a glutton for punishment, and I will try, albeit with error, to make my best judgments on Anthropic's future cash flows, risk and value (as I did with SpaceX), when the prospectus is made public, I understand why most will turn away in the face of uncertainty and choose not to value the company. Some of these investors will also look at how those that they believe know more about that companies are assessing its value, and peruse prior venture capital rounds at both Anthropic and OpenAI for clues on value.  Both Anthropic and OpenAI have been active in raising equity as private companies, and the most recent rounds yield estimates of what these private investors thought they were worth, at least at the time the capital was raised. The table below summarizes the number of VC rounds, with capital raised, at both companies, as well as the pricing in their most recent rounds:

Anthropic's VC round in May 2026, from a cross section of big-name venture capitalists, raised $65 billion for the company, at a post-money pricing of $965 billion.  OpenAI's most recent capital raise was in March 2026, when it raised $122 billion, at a post-money pricing for the company at $852 billion. OpenAI also has a pending round of capital raise ahead of an IPO, where they expect to raise $30 billion with a pricing for the company of roughly $1.4 trillion. 

    While you may be tempted to assume that the VC pricing in these rounds are rooted in value, venture capitalists are in the pricing game, as I have argued in prior posts, In fact, they, stretch pricing metrics and definitions of peer groups, and VC valuation is almost always forward pricing:

In the case of Anthropic and OpenAI, this forward pricing process is both more compressed and even less informative than at most young companies, with investors viewing the companies as imminent IPOs, and pricing them based upon their expectations of the pricing of the public offering, and discounting that expected price for negative surprises about the company's prospects that may lower the pricing or delay/cancel the IPO:

The expected pricing will be a function of pricing in prior VC rounds, management wishlists and even rumors, and the discount applied will increase as the time to the IPO lengthens and with the uncertainties about the company and its potential market. For Anthropic, for instance, at the time of the VC round, the expected pricing for the IPO was $1.15 trillion, well below what the expectations are today, and reflective of how much can change over short periods with young companies, and the $965 billion pricing in the VC round would have represented a $150 billion discount on the estimate.

Pricing Process

    While the reluctance to value young companies, and fall back to pricing is understandable, I would push back on the notion that doing so dispenses with the need to make the assumptions about cashflows, growth and risk that are required in intrinsic valuation. In this section, I will break down the pricing process into steps, and look at the issues that you faced when valuing these companies also create impediments in pricing, and in the next section, why using a multiple of annualized revenue run rates (ARR) or even eyeballs makes implicit assumptions about future profitability, growth and reinvestment. 

The Steps in the Process

    If, in pricing, you attach a number to an asset or business, based upon what others are paying for similar assets or business, how exactly does this process play out when valuing a company or business? No matter who does the pricing, and how sophisticated they claim to be, there are three steps in pricing companies:

1. Standardize the price: The nature of pricing shares in public markets is that the share price is a function of share count, which is arbitrary; a stock split for instance halves the price while doubling the count and total market capitalization or value is a function of company size. To allow for comparisons across companies, you standardize the market price by dividing by a common variable that you can obtain for each of the companies in your comparison, with the choices ranging from revenues (or revenue drivers) to earnings to cashflow variants to book value. Since the market price can be for just equity (market capitalization) or for the entire business (enterprise value, which includes net debt), the denominator has to be defined in the same terms, with equity (operating) income and book value of equity (invested capital) going with market capitalization (enterprise value).
2. Find peer group companies: For pricing to be functional, you have to find other priced companies just like the one that you are pricing. The simplistic solution is to start with other companies in the same industry or business grouping, with added constraints on geography and company size thrown in. Needless to say, even if done with care, the choice of companies in the peer group is subjective and can vary across analysts and investors, even when looking at the same company.
3. Control for differences: No mater what pricing metric (PE, EV to Sales etc.) you pick, there will be fundamental reasons why companies will trade at different values, and for a pricing to have substance, you have to control for differences. Thus, if PE ratio is your metric, a company that expects higher growth in earnings or lower risk than its peer group, should trade at a higher PE ratio than the peer group does, and that difference has to enter your discussion.

The picture below captures these steps, with the variants on pricing metrics and the considerations in peer group construction included:

While this process remains the same for every pricing, there are reasons why pricing metrics will vary across companies and why peer group construction may be easier in some cases than other, and I will take Anthropic and OpenAI through the process.

1. Pricing metrics

    With some companies, your choice of pricing metrics will span the spectrum, with revenues, earnings, cash flows and book value all competing as scalars. However, your choices will narrow at others, and one reason that is relevant to the Anthropic/OpenAI discussion is where a company falls in the life cycle:

As you can see, early in the life cycle, when companies are losing money, have negative free cashflows and little tangible capital invested in assets in place, you will see revenue often as the only viable metric, leading to revenue multiples dominating the discourse. Put simply, desperation drives you up the income statement until you find a positive number. It should be no surprise, therefore, that the metric that companies and investors have focused on with Anthropic and OpenAI is revenues. Those revenues are growing so quickly at these companies, that their argument is that trailing numbers are dated by the time hey are released, and that has led to the shift to annualized revenue run rates (where using revenues from the last week or month allows for more updated vales). As we noted earlier, the perils with annualized revenue rates (ARR) is that they are not standardized and can be easily gamed. Just in the last few weeks, there have been news stories on the divergence between gross and net revenues with OpenAI shifting towards using the latter, lowering ARR from $70 billion to $50 billion, while Anthropic continues to report gross revenues and the cloud provider payments as expenses. In addition, you can compute ARR using the last day's, the last week's and the last month's revenues, and while Anthropic and OpenAI have both converged on monthly annualization, there is nothing that binds them to continue to do so.

    There is a second choice that will come into play as the debate about Anthropic's pricing becomes more heated, and that is whether to use revenues in a future period, i.e.. forward revenue. By forecasting the annualized revenue run rate of $100 billion plus by the end of 2026, Anthropic has already opened the door, but do not be surprised if investors and analysts shift to looking at revenues in 2030 or 2036, since scaling prices today to forward revenues five or ten years out will make the pricing multiple look lower, and thus more defensible. Once you decide to go forward in time, it is plausible that you can even shift to expected earnings in 2031 or 2036, built around expected operating or net profit margins; arguing that Anthropic is cheap because it trades (today) at 25 times 2036 earnings (based on your estimated revenues and net margins) brings the multiple back to earth, but at what cost? Investors use frames of what comprises normal or high, based  upon their acquaintance with a multiple, and many will (mistakenly) compare these forwards earnings multiples to current earnings multiples in the market to make their investment judgment on cheap or expensive.

    There is a third choice, and that is to relate the pricing of these companies to a variable that you believe will be correlated with future revenues and earnings. With that choice, you can see the rationale for estimating enterprise value per user or subscriber for user and subscriber based companies, with the connector being the logic that more users and subscribers today is a leading indicator of higher revenues and earnings in the future. It was that logic that led Mary Meeker and other internet analysts to use website visitors and eyeballs in pricing dot com companies, and while you may disagree, the logic holds. With OpenAI and Anthropic, where the power, reach and revenues of AI agents is dependent on having access to compute power, you could scale the market cap and enterprise value at these companies to the compute power at each company, based upon either measures of compute power (in gigawatts) or money spent to acquire the power (compute commitments). The downside of using metrics that are specific to a business (users, eyeballs or compute power) is that you can compute that multiple only for the companies in this business, restricting comparisons, and you have no perspective on what comprises high, low or typical for that multiple. 

    The table below captures the pricing metrics that emerge with each of these choices for Anthropic and OpenAI, at $1.5 trillion and $2 trillion dollar market capitalizations:

As you can see, you can make the case that Anthropic is cheaper than OpenAI, based upon the most recent ARR or the ARR in 2026, but OpenAI is cheaper relative to expected revenues in 2030, compute costs/capacity and based on user count. Each of these pricing multiples comes with caveats, with obvious explanations for differences, some in how the metric is computed (gross revenues for Anthropic versus net revenues for OpenAI) and some in fundamentals (Anthropic is priced more highly more user, but given that its usage base is more enterprises than individuals, and that they pay based on usage, each Anthropic customer should be priced more highly than an OpenAI customer).  No matter which of these pricing metrics you use, though,  the truth is that it is entirely possible that both Anthropic and OpenAI may be overpriced, and by comparing the two,  you are just finding the less overpriced entity.

2. The Peer Group challenge

    Pricing loses much of its power, without access to a peer group of similar companies that are priced in the market place. With a software, chemical or retail company, pricing becomes easier because you have dozens, or even hundreds, of companies that you can define as being in your peer group. It is true that the company you may be pricing may be an outlier in terms of growth and risk in your peer group, but that is a problem for the next section, where you have to control for differences. Pricing becomes much more difficult, when you have unique companies or companies in cutting edge businesses, where there are a handful or even no other companies that are comparable, with market pricing. Using both of these components (number of companies in the same space and whether they are publicly traded), there are four scenarios that play out, ranging from least to most difficult.

• If you are pricing a company in a business, where there are lots of publicly traded companies, you will not only have a choice of pricing multiples, but also more robust pricing, since each of the companies has an updated market price and the financial statements are standardized (GAAP or IFRS). 
• If you are pricing a company in a business, where there are only a few publicly traded players, you may have a choice of multiples, but small sample size will reduce the power of your pricing, though having a long historical pricing record for all of the companies may sometimes compensate. For much of the last century, in the United States, GM and Ford were priced against each other, with traders developing ranges of what each company should trade at relative to the other.
• If you are pricing a company in a business, where there lots of companies that are privately held, as is often the case when you have a nascent business with lots of potential, you are confronting a challenge that many venture capitalists face, and you can try to get the most recent pricing for each company, though that pricing will often be dated (depending on when the last capital raise was) and sometimes untrustworthy.
• Your most difficult challenge will be when you are pricing a company in a business, where you have both very few companies in the space, and almost none are publicly traded. 

If you define peer group for Anthropic and OpenAI as other companies in the LLM space, you are in effect in the last category, since neither of the companies is traded yet, and their competitors are either parts of bigger businesses - Gemini is a small part of Alphabet, xAI is a bigger part of SpaceX which has other functioning and profitable businesses and Muse is obscured by Meta's advertising business. When Anthropic's IPO show hits the road, the lack of a peer group will be an impediment, but investors and traders are creative and come up with solutions:

• In the narrowest version of the pricing game, Anthropic and OpenAI will be twinned with each other, since they are not just competitors but also playing the same business game. Initially, or at least until OpenAI files its prospectus and goes public, the comparison will be between a public pricing for Anthropic and private pricing for OpenAI, with SpaceX thrown into the mix as a noisy peer.
• in a broader version of the pricing game, and especially if Anthropic is even close to its $1.5 trillion market cap, the comparisons will be to the Mag Seven companies, all of which command market capitalizations close to or well in excess of a trillion. The argument will be that investors who seek out large market cap companies will compare them to each other, even though Tesla, Apple and Anthropic are very different companies in terms of business model and where they are in the corporate life cycle. You could also bring in SpaceX, not part of the Mag Seven, but also a trillion dollar company perhaps at about the same stage of the life cycle: The table below compares Anthropic's pricing (using a target pricing of $1.5 and $ 2 trillion) to the pricing of the Mag Seven companies, as well as SpaceX:

As with the pricing comparison with OpenAI, there is good news and bad news for Anthropic bulls and bears. The bulls can argue that Anthropic is cheaper than SpaceX, and that if it looks expensive relative to the Mag Seven, it is because it has higher expected growth. The bears will push back, noting that the AI buzz that is carrying Anthropic is also pushing SpaceX to highs, and that the yawning gap between the Mag Seven companies and Anthropic on revenues and earnings (which are not reported in this table, but make the case for the Mag Seven stronger) cannot be explained away with hope and potential. 

3. The Connection to Value

    You can price a company based upon any metric that you choose to use, but if pressed on which metric is the best one, I will deflect (and perhaps deliver a non-response), because I don't believe that there is a "best" metric for pricing. The key, though, is that no matter what metric you pick, you should think through how that metric will play out in the company's value and what you need to control for in your pricing. Let's start simple with PE ratios and look at the chain that connects current earnings to overall value:

The first link is earnings growth, to generate future earnings, with higher growth leading to higher value. The second is the efficiency with which you deliver that growth, which results in reinvestment, and converts earnings to growth; a company that can earn a higher return on equity on its reinvestment should be able to generate more in cash flows, for the same earnings, than a company that earns a lower return on equity. Finally, risk comes into play when you estimate the present value of future cash flows; less risky cash flows will allow you to use a lower discount rate and attach a higher value for equity. Put simply, when comparing PE ratios across companies, you have to control for differences in earnings growth, return on equity and equity risk.

    Since PE ratios are a non-starter with Anthropic and OpenAI, I will turn to enterprise value to revenues (using either the ARR or the trailing 12 month revenues):

As you can see, for Anthropic to be highly priced relative to revenue, its revenues will have to not only grow a lot, but do so quickly, making the revenue growth rate and the speed at which it ramps up the first determinant of pricing. The second component is profitability, since the company is reporting sizable losses now, and can expect to do so in the near term, the "adjusted" profit kabuki dance notwithstanding. Here, there are two key determinants that will drive value, with the first being unit economics, since having higher gross margins will ease the pathway from losses to profits, and the second being economies of scale, with compute costs in particular growing slower than revenues, pushing up operating margins. The third is risk, with operating risk (from evolving business models), financial risk (from the compute commitments) and regulatory risk (from restrictions on AI) all coming into play; companies that are less exposed to these risks should be worth more. To make a case for Anthropic at a $1.5 or $2 trillion pricing, you will have to make defensible arguments for sky-high revenue growth in the early year, a surge in operating margins to 30% or higher, with favorable unit economics and economies of scale providing tailwinds and a risk profile that quickly converges towards the average, as regulatory risks recede and the business model finds it feet. While you may find this story to be a reach, it is a plausible one, and I plan to give those who tell the story a chance to make an argument, rather than dismissing it outright.

Conclusion

    As Anthropic heads towards its initial public offering, my suggestion is that you step back from the frenzy, since like the parable of the six blind men who visit an elephant, and having touched different parts of it, come away with wildly different descriptions of what an elephant looks like, there will be Anthropic bulls and bears who will latch on to a part of the company's lore and spin tales about its future. Thus, an Anthropic bull whose entire argument for why you should buy the company is that it has had high revenue growth in the last year or that gross margins right now are 80% is telling part of a story, and not a very persuasive one, whereas a bear whose entire thesis is built around the company losing money and having negative cash flows is missing the narrative train just as badly.

    A full (bullish) story for Anthropic will be anchored in a belief that AI's reach in terms of market will give it a massive addressable market, that Anthropic will be one of the big winners in that market and that it will find a way to convert its dominance into market share and high profits; the high pricing will be justified with high revenue growth and great unit economics (with high gross margins) arguments. For bears, a fully thought through story will be premised on an AI market that struggles to get off the ground, partly because of flaws in AI agents, partly because of competition from cheaper open-source models and partly because of regulatory constraints, and Anthropic, as a company, is constrained by weak corporate governance and an untrustworthy management. The overpricing argument will point to the revenue growth that will be lower than anticipated, which combined with their compute commitments will lead to profit margin compression, will leave you at a much lower justified pricing. I will not be taking sides here, but not because I am copping out. It is because I am investor at heart and feel more comfortable making my decision based upon an intrinsic valuation of Anthropic, which will have to wait until the full prospectus gets filed (hopefully next week). 

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