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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Stock Prices, Earnings and Cashflows: The AI Effect plays through!

In a post at the end of August 2026, I talked about interest rates in 2026 and marveled at the capacity of equities to keep rising in the face of rising rates. I argued that the resilience of stocks during the year could be traced to higher-than-expected earnings being reported by companies in 2026, and a concurrent increase in expected earnings in 2027 and 2028. Now that September 2026 is one for the record books, it is time to take stock and dig a little deeper, especially since the month brought about one of the largest increase in treasury yield rates in recent memory, and stock prices still held their own. In particular, I want to examine the earnings at US companies, in the aggregate and by sector, trying to trace out where the earnings increase is coming from, and how that increased earnings is playing out on corporate balance sheets and cash flows.

Stock Prices and Rates

In my earlier post on interest rates, I had looked at treasury rates by day through the end of August, and I will begin this section by updating that chart to include a tumultuous September:

I had described the rise in rates between January and August as gradual, but the rise in rates in September was anything but, as the ten-year rate rose from 4.75% at the start of the month to 5.29% at the end. In fact, to put the 54 basis point rise in the ten-year rate in context, take a look at the distribution of monthly rate changes in the ten-year treasury in the chart below:

The September rise in rates would put in the top ten percent of 770 monthly rate changes that we have seen between 1962 and 2026. In the face of the mark up in rates, stocks held their own in September, at least in the aggregate, and you can see that in the chart below, where I look at aggregate market values, by month, and by sector:

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The market added $2.5 trillion in market capitalization across all stocks in September 2026, but almost $1.5 trillion of that came from technology As you look across the entire year, broken down by quarters, here is what you see in the aggregate market caps:

Only companies listed at the start of 2026 included

I excluded firms that were not listed at the start of 2026 from the list since including them will give a misleading sense of returns to investors; adding SpaceX, for instance, which was listed in June 2026, will increase the value of communication service companies by more than $2 trillion, but it was listed at roughly that value. There are many who have pointed to the fact that US equities, while up for the year, have seen divergence in performance, and you can see that phenomenon play out in two statistics. The first is that three sectors - technology, energy and materials have carried the market, with technology being the biggest contributor to market gains. The second is that the percent of companies within each sector that are up for the year is about 50% across the market, and in the third quarter, about 65% of all listed stocks saw dropping stock prices.

    It is often difficult to make sense of what is moving stock prices because there are so many factors from growth to interest rates to cash payout that are pulling in different directions. It is to counter this confusion that I have resorted to estimating an implied equity risk premium, where I estimate the internal rate of return you can earn by buying equities, given how they are priced, and their expected earnings growth and cash flows, as well as  interest rates. That calculation, which I have done at the start of each month since September 2008, yields an expected return of 8.99% for equities and an equity risk premium of 3.70% (3.92%) over the ten-year treasury rate (dollar riskfree rate) of 5.29% (5.07%) at the start of October 2026: 

Note that this estimation is model-agnostic and is an internal rate of return for investing in stocks, given expectations of the cash flows from investing in equities. If you are a market timer, and I am not one, you could use this implied equity risk premium as a barometer of market priciness, with a lower number indicating overpricing and a higher number indicating underpricing. 

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The jump in US treasury rates in September 2026 has had an effect, with the equity risk premium dropping below 4% for the first time this year.  In fact, even as the ten-year treasury rate has climbed this year from 4.18% to 5.29%, the expected return on stocks has also gone up from 8.41% at the start of 2026 to 8.99% on September 30, 2026.

The AI Cap Ex Boom: Accounting and Corporate Finance Consequences

    The focus on stock prices and interest rates can sometimes distract us from paying attention to corporate investing, financing and cash return policies that drive value. It should not surprise you, given the times we live in, that AI is at the heart of the business story that is driving corporate behavior, and in the process, providing the fodder for market resistance to higher rates. I will begin with an assessment of how the trillions of dollars in AI cap ex will show up in financial statements:

As you can see, the AI cap ex story is a complicated one, if you are looking at market aggregates, because the market includes both the companies that are spending the money building the AI architecture, which includes data centers and other infrastructure, as well as the companies that are supplying the ingredients for that infrastructure. 

• The builders of the architecture are the hyperscalers (Meta, Alphabet, Amazon, Microsoft et al.), and the money they spend on cap ex will cause lower earnings, at least until the cap ex starts paying off, in the form of amortization of the AI cap ex, as well as a hit to their free cash flows, which are after cap ex. 
• The money spent on AI cap ex though becomes revenues to the chip makers (Nvidia and TSMC leading the way), network equipment manufacturers (Broadcom, Micron and Marvel, to name just three), power plant builders (Constellation Energy et al.) and even real estate developers focused on data centers (Equinix, Digital Realty), and ultimately net profits (with net margins driving the bottom line). 

It is true that there are gray zones here, with some AI builders also  benefiting from being suppliers (Amazon is spending money on AI cap ex but is also benefiting from the usage of its cloud space for data storage, and Nvidia, while selling the chips that go into the architecture, is also investing directly or indirectly into data centers). 

    As we trace through the aggregated effects of the AI cap ex boom on accounting statements, there are two caveats that need to be stated up front. The first is that the data that is accessible to the public, and which I will be using, will be data from publicly traded companies. To the extent that some of AI's big players (builders and suppliers) are private, I will be missing the revenues, earnings, cash flows and invested capital at these large private players (which include at least two companies in Anthropic and OpenAI that are expected to command trillion dollar plus market caps. The second is that some of the AI cap ex is taking the form of joint ventures and off-balance sheet entities, and the accounting for these (especially on the debt side) may not fully reflect the consequences for firms. As a result, the numbers you see in the public company financials will understate the full effect across all businesses.

    With those caveats in place, the business story for US equities starts with massive capital expenditures in AI, with trillions being invested into data centers and AI architecture. It is true that this cap ex is top heavy, with the top ten hyper-scalers accounting for more than $2 trillion of the AI cap ex, but in the table below, I look at the aggregate cap ex reported in corporate financial statements at all publicly traded companies in the United States:

Even with the caveats about understatement, but you can still see that cap ex in the second quarter of 2026, which is our last completed quarter of reported financials, was up $133.4 billion from the cap ex in the second quarter of 2025, an increase of almost 36%. Again, the surge in cap ex is concentrated, with technology, communication services and consumer discretionary all registering growth of more than 50% in the quarter-to-quarter comparison. 

    Accounting incorporates capital expenditures into the balance sheet as assets, and reflects how this cap ex is funded (debt or equity) by increasing the book values of the funding used in the investment. A surge in cap ex, such as the one that we have seen in 2026, will show up as higher book values for equity, debt and invested capital, and we capture this effect, by sector, in the table below:

Across all US stocks, the book equity has increased almost 13%, between the second quarter of 2025 and the second quarter of 2026, and total debt is up almost 8%. In dollar terms, the book equity at US companies increased by $1.8 trillion between the second quarter of 2025 and the second quarter of 2026, and book debt by $1.9 trillion, over the same  period.  Technology, being the most active player in AI cap ex, has seen much bigger increases in both numbers, with book equity rising almost 30% and total debt up about 18.8%. 

    From an earnings perspective, the focus on cap ex and book values may seem misplaced, since the former can only decrease cash flows and the latter impacts accounting returns. In 2026, though, the increased capital expenditures on AI are affecting earnings for a simple reason. The money spent on cap ex by a company building AI architecture will become revenues (and earnings) for other companies that supply the building blocks for the architecture. There is a reason why Nvidia has been the biggest beneficiary from the AI cap ex boom so far, since its chips, marked up massively, power the data centers, and there others, from electrical equipment makers to power companies to real estate developers who have also reaped the benefits. It is true that there should be increased amortization expenses at the AI builders, but the longer amortization schedules being used by many of them is reducing the current hit to earnings at these companies. The earnings effect of the AI story can be seen in the table below, where I look at aggregate net income at US companies, broken down by sector:

Note that in both the first and second quarters of 2026, US companies have seen earnings surge over the corresponding quarters in 2025, with aggregate earnings increasing from $511 billion to $692 billion (translating into an earnings growth rate of 35%, quarter-to-quarter) in the first quarter of 2026 and from $576 billion to $904 billion (translating into an earnings growth rate of 57%, quarter-to-quarter) in the second quarter of 2026. As with stock prices, the earnings benefits are not broad-based, with more than half of all companies in the market reporting declines in net income, and there are wide differences in earnings growth across sectors. Technology, financials and communication services have seen the biggest increases in earnings, and health care, utilities and real estate have lagged.

    A cap-ex driven surge in aggregate earnings comes with an asterisk, since the higher earnings across firms will be partially or even fully offset by capital expenditures across firms, leading to free cashflows to firms often growing at much lower rates than earnings. Since these free cash flows are what fund dividend payments and stock buybacks, I looked at cash returned to shareholders in both forms in 2026:

In the aggregate, dividends in the last twelve months are up about $43.3 billion (about 5%) from dividends in the 2025 calendar year, and stock buybacks are up about $106.7 billion (about 9%). In fact, if you net out stock issuances, which spiked in the second quarter of 2026, from buybacks, net buybacks have grown bout 7% between the last calendar year and now. Those numbers represent reasonable step ups from the last year's numbers, but they clearly have not kept up with the earnings growth in 2026. One way to see the disconnect that is occurring between earnings and cash flows is to look at the cash returned as a percent of earnings for the S&P 500 companies over time:

For much of the last two decades, US companies have returned 80% or more, and sometimes more than 100% of their earnings, to shareholders in dividends and buybacks. Starting in about 2024, you can see a divergence with earnings rising much faster than cash returns, and in the last twelve months leading into 2026, the companies in the S&P 500 returned 63% of their earnings to shareholders, a low not seen since 2004. Many of those who were criticizing US companies for buying back too much stock and not investing enough back into businesses are now finding fault with those same companies scaling back buybacks and investing more into AI cap ex, leading to the conclusion that these critics will find fault no matter what companies do. 

The AI Business Resolution: Accounting and Market Consequences

    It is true that the massive investments in AI cap ex are being driven by expectations that AI as a business will enjoy not only a large market, but one where the winners can sustain huge profits for the long term. As I noted in my post on AI as a business, this is a plausible path, but there are vast disagreements on whether this is the expected one, given uncertainties about all three layers of the business story - the size of the total addressable market, the unit economics/operating margins of companies in the business and the moats and competitive advantages that will allow for sustainability in profits. In effect, the only point on which you have a consensus on is that companies have collectively invested a huge amount in AI cap ex, and the point at which AI optimists and pessimists is diverge is on what the payoffs from that investment will be, with optimists arguing that AI will deliver on expectations and that the profits that will be generated by AI cap ex investors in the future will be sufficient to earn returns on that investment that exceed the cost of capital, and pessimists positing that this is wishful thinking, and that the profits from AI, even if considerable, will not be large enough to justify the up front investments. It is also worth remembering that determining which side is closer to the truth will not be made by looking at arguments about annualized revenue run rates at Anthropic and OpenAI today, but will require waiting for the AI product and service market to gain substance. Waiting does not come easily to any of us, and there will be premature claims of victory from both sides, often based upon paltry evidence and incomplete information.

    So, what will the accounting and market consequences be, if the AI pathway diverges from expectations? In the table below, I trace out the accounting and market consequences of the AI business working better than expected at delivering growth and profits, as well as if i does much worse than expected:

• In the best case scenarios for AI, the companies that have invested in AI, at least collectively, will be able to deliver not just earnings growth from the cap ex, but enough incremental earnings to generate returns on the AI cap ex that exceed their cost of capital for those investments.  Their lenders will be made whole, with interest and principal payments, and the AI builders will see their cashflows  become more positive,  but the companies, while successful, will emerge as very different businesses than when they entered the space, more capital intensive than they used to be. 
• In the worst case scenarios for AI, there will be both accounting and market carnage, as accountants write off large portions of the AI cap ex, because of its failure to deliver promised profits, and while cash flows may recover, markets will correct the pricing of these companies to reflect a lack of trust in management. For companies that were excessively dependent on debt for their AI cap ex, there will be defaults and increased distress, with lenders feeling the pain as well. 

There are also intermediate scenarios, ranging from AI being a moderate success, where the companies investing AI may be able to extract some earnings from their investment, but not enough to cover the cost of capital, to a moderate failure, where some companies may be able to justify their investments and most will not.

    Given that AI business surprises will have both market and accounting consequences, I will hazard a guess that the market will lead in this process and accounting will follow. In short, if AI is working better (worse) than expected, you should see stock prices at AI-centered businesses rise (fall) before you see accountants respond. Put simply, in the event that AI does not deliver on its promise, waiting to act until accountants write off AI cap ex to sell your AI company implies that you waited too long.

An Investor Perspective: Taking Stock and Taking Action

    In my post on AI as a business, I zeroed in on the debate between AI optimists, pointing to huge (albeit unspecified) markets for AI products and services, and AI skeptics, drawing attention to the outsized capital expenditures. While that debate plays out, financial markets and businesses cannot afford to wait for resolution, and are acting now, with companies making investments in cap ex and markets building in their expectations of what that will mean for future earnings into stock prices. That has made not just the market but also the economy a giant bet on AI, with success vindicating the companies and investors who have bet on it, and failure manifesting in massive write offs at companies (feeding into losses) and stock price markdowns. As investors, there are four choices that you can make, and unfortunately, none of these choices give you the luxury of sitting out the AI debate:

1. Go all in on the AI story winning (and doing it soon): The first betis that AI is an unstoppable force, destined to change the way we live and work, with AI businesses reaping the benefits of the disruption. It is a plausible story, albeit one that raises significant questions about the economic and social costs of disruption, with disagreements about the speed and extent of the disruption. It was the story that Leo Aschenbrenner built Situational Awareness around, and while excessive leverage, driven by hubris and over-conviction, brought him down, it is possible that you could mimic his strategy, of buying the AI disruptors and/or selling the AI disrupted, albeit with far less leverage, and win in the long term.
2. Go with the market consensus: In an age where we worship at the altar of crowd wisdom in almost everything we do, you could examine what the market is pricing in, as its AI story, and go along. At the moment, at least, the market seems to be building in the expectation that AI will be a major disruption that will give rise to large and valuable businesses and it is picking its winners among the AI architecture companies (with Nvidia the biggest so far) and among the LLMs (SpaceX in the public markets and OpenAI and Anthropic in the private markets). For better or worse, you may have already chosen this path implicitly, if your pension funds and savings are invested passively, getting partial exposure to this story with an S&P 500 fund, and more complete exposure if you buy a total market fund for US equities. 
3. Be an AI skeptic: There are many reasons to be skeptical about the AI story, and for some, that skepticism may lead to the belief that AI will not make it as a viable business, or at least one large enough to sustain the pricing and investment you are seeing for it. While that belief may not be strong enough to lead you to act on it, you can steer your new investing away from the AI space, investing in businesses that are least likely to be altered by AI (food processing and leisure) and in geographies where AI is less likely to be a threat, such as the EU (perhaps because of regulation) and parts of Asia (because AI is too expensive to replace human labor in many businesses).
4. Crash out on the AI story: If your belief that AI will fail hardens into a conviction that failure is imminent, you can try to actively cash in on your story. You should, at the minimum, reduce your exposure to equities, especially in the US, by selling your holdings and putting that money into cash,. If you are more risk taking, you can sell short on the companies that have seen their pricing surge on the AI story and perhaps buy the companies that AI was meant to disrupt, flipping Leo Aschenbrenner's story. This has not been a winning strategy for many of the traders and investors who have tried it out for the last two years, and it is worth remembering the adage hat markets can stay irrational longer than you can stay solvent

I am personally going with the "market consensus' choice for the bulk of my portfolio, since I do hold five of the Mag Seven (all except Tesla and Nvidia) and four of my holdings in this group (Amazon, Alphabet, Meta and Microsoft) are heavy investors in AI cap ex, but the new money added to my portfolio in the last year or two has gone mostly into cash (short term treasuries, yielding 4%) for much of the last year, leaving my portfolios more cash-laden than usual. I have left money on the table undoubtedly by doing so, but it has helped me sleep better at night, and my advice to you is that you find a pathway in the AI jungle that helps you pass the sleep test as well. 

   There is one final piece of this puzzle that bears watching, and that is a portion of your portfolio that does not show up (yet) in your holdings. The income you will earn in your occupation, over the rest of your working life, is human capital, and to the extent that you believe that AI disruption is coming for your profession, it behooves you to direct your financial capital away from the businesses most exposed to AI disruption to balance your portfolio. I am old enough not to care much about this component, since I have fewer working years left, but if you are much younger than me, this could change your investment game.

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Blog Posts on AI

1. AI's Bar Mitzvah Moment: From Hope and Hype to Business Questions!

Spreadsheets

1. Implied Equity Risk Premium on September 30, 2026

Interest Rates and Stock Prices: An Old Debate Flares up!

    The war in Iran, oil prices and worries about a recession have all taken turns driving stock prices  in 2026 but talk about interest rates and where they are going has been a constant concern all year. In the last few weeks, interest rate worries have come to the surface again, for three reasons, all related. The first is the rise in long-term US treasury rates to levels not seen in twenty years. The second is US debt exceeding $40 trillion for the first time, bringing attention to a long-standing worry that this debt burden may be hitting a tipping point for bond buyers. The third is a that the Federal Reserve has a new chair in Kevin Warsh, and for the many Fed Watchers, who are uncertain about where he plans to lead the Fed, the Federal Open Market Committee (FOMC) meeting coming up in mid-September looms larger than ever. 

    In this post, I hope to step back from the day-to-day coverage of US treasury yields and look at their performance in 2026, not only with a longer term perspective, but also in the context of movements in long terms yields in government bonds in other currencies. I also intend to revisit a discussion that I initiated in 2022, when interest rates were the central act in markets, about the relationship between interest rates and stock prices, and why higher rates do not always translate into lower stock prices, and why that effect will vary across sectors and companies.

Government Borrowing Rates

    Governments borrow money and often do so by issuing bonds in financial markets. The rates on these bonds reflect the concerns that lenders have about the purchasing power of the currencies that they are issued in, and government bond rates, for better or worse, become indicators that drive day-to-day market movements in almost all asset classes. In this section, I will begin with an examination of US treasury rates before expanding the discussion to government bond rates in other currencies.

The 2026 Interest Rate Experience

    Coming into 2026, US treasury rates had mostly moved sideways for a couple of years, but during the course of 2026, rates have risen steadily across the maturity spectrum with long term treasury rates showing more movement than rates at the short end. Both the 20-year and 30-year treasuries rose above 5% during the course of the year, and the 10-year rate, which started the year at 4.18% reached 4.75% at the end of August. The graph below looks at US treasury rates across maturities in 2026:

I know that there are some who attribute almost everything related to interest rates to the Fed, and while I think that is simplistic and wrong-headed to do so, I show the four FOMC meetings that have occurred in 2026, as well as the date of Kevin Warsh’s ascension to the chairmanship of the Fed. While there is little in this graph to indicate that the meetings or the changing of the guard at the top had a material impact on treasury rates, the divergence in rate movement across maturities has removed the kink (at the 2-year maturity) at the start of the year and made the yield curve more upward sloping:

US Treasury Department

A Longer Term Perspective on Interest Rates

    To put the increase in rates, especially long term, during 2026 in context, I looked at movements in 3-month, 10-year and 30-year rates between 1962 and the start of September 2026, with the caveat that the 30-year rate series is available only since the mid-1970s.

Federal Reserve Data (FRED)

As you look at the entire time period, you can see the trauma of the 1970s, a decade where interest rates rose to levels not seen in the US in a century, and peaking in 1981, as the Fed struggled to put the inflation bogeyman back into the closet. While rates did come down from those highs, they stayed in the 8-9% range in the second half of the 1980s, and in the 6-8% range in the 1990s  with the dot-com boom operating as a ballast. In the first decade of this century, ten-year rates declined below 5% in 2002, but stayed in the 4-5% range until 2008, when the financial crises drove rates below 4%. In the last decade (2011-2020), rates trended down, dropping below 3% in 2011 and staying in the 2-3% range for most of the period, with the Fed lending a helping hand. The economic shutdown in 2020, after COVID, pushed long term rates below 1% for the first time in history, and rates stayed low in 2021. The extended stretch of low interest rates from 2009 to 2021 was broken in 2022, when the ten year rate more than doubled, from 1.52% to 3.88%, and rates since have largely stayed in the 4-4.5% range, with the 4.75% rate in September 2026 representing a breakout. The graph also includes the 3-month treasury bill rate, and it moves largely with the 10-year rate, albeit with bigger swings, and rates close to zero for much of the last decade, and the 30-year treasury rate, which has generally traded at slightly above the 10-year rate, with the difference widening in September 2026.

The Drivers of US Interest Rates

    In my prior posts on interest rates, I have argued that the focus among many investors and market-watchers on the Federal Reserve as the all-powerful force moving interest rates has diverted attention from the fundamentals that move rates over time. The first of these fundamentals is inflation, with higher expected inflation manifesting as higher rates, and the second is a real interest rate, which at least in the long term, you can proxy with real growth in the economy. One of the indicators that I track is what term an "intrinsic riskfree rate", which I obtain by summing up the inflation rate and real GDP growth in the US economy each year. Financial markets have minds of their own, and the observed rates are a function of demand and supply:

If your pushback is that actual inflation is a noisy estimate, and that expected inflation is anyone's guess, you are right, but for much of this century, we have had market estimates of expected inflation, that can be obtained from the US treasury market, by comparing the 10-year US treasury yield to the yield on a ten-year US TIPs (inflation-protected rate):

Federal Reserve Data (FRED)

The expected inflation numbers embedded in the US treasury market show a market that is less swayed by year-to-year changes in inflation than more by long term expectations, with a dip in expected inflation between 2008 and 2021, and an increase in expected inflation estimates, starting in 2022.  It is interesting that notwithstanding the surge in oil prices this year, and the increased talk of inflation, there has been only a very mild increase in the long-term expected inflation rate, as calculated using yields on treasuries at the start of September 2026.

    In the graph below, I use the actual inflation rates and real GDP growth rates for the US, going back to 1962, and compute the intrinsic 10-year treasury rate (the cumulative column) and actual 10-year treasury rate each year:

Federal Reserve Data (FRED)

The graph tells the interest rate story well, as the surge in inflation in the 1970s played out as higher rates (intrinsic and real) for much of that decade and the next, and the decline in inflation and anemic real growth translated into the lower rates that we observed from 2009 and 2021. When inflation surged in 2022, interest rates went up, but since the rates that I am tracking are long term rates, the intrinsic risk free rate vastly exceeded the actual rate that year, but the difference has narrowed over time, and almost dissipated by September 2026 (when the US 10-year treasury bond rate was 4.75% and and the intrinsic ten-year rates yielded 5.41%).

Government Bond Rates in Other Currencies (Countries)

   As investors focus on movements in US treasuries, interest rates have been on the move across the globe since 2021. In the graph below, I start with a look at government bond rates in five other currencies: the Euro (with the German 10-year bond rate), the Japanese Yen, the Australian and Canadian dollar and the British pound:

You will notice that the rise in rates from COVID lows (which pushed the Euro ten-year rate into negative territory) has been across the board, with rates surging in 2022. Focusing just on 2026, you see the same pattern, with rates rising across all of the currencies tracked in this graph. What about the currencies of other economies? I track ten-year government rates in four  currencies - the Chinese Yuan, the Indian rupee, the Brazilian Real and the South African Rand - in the graph below:

Here, the results are more nuanced, with no or a muted 2022 effect, and ups and downs since; rates are lower in September 2026 than they were in 2021 in three of the four currencies. 

    The convergence of government bond rates across currencies in the last few years has laid waste to the carry trade, where you borrow money in a low-rate currency and lend it out at a higher-rate currency, and the punishment meted out to its practitioners is, in my view, well deserved. The carry trade is the laziest of investment strategies, with its successes due entirely to lags in how exchange rates respond to fundamentals, and calling it an investment strategy does a disservice to investing, in general.

Interest Rate Ripple Effects

    Changes in government bond rates clearly play out in the pricing of government bonds, and returns you will earn on them, but the ripple effects play out across the rest of the market (financial and real). In this section, I will start with the corporate bond market, where the interest rate effect is dominant, before looking at equities, where interest rate effects are more nuanced. 

Corporate Bonds

    Just as governments need to borrow money to fund their expenditures, businesses also borrow money, either through bank loans or if they are positioned to do so, by issuing bonds. The rates at which businesses can borrow start with a riskfree rate in the currency as a base, with a credit spread reflecting the business borrower's default risk added on. If governments are perceived to be riskfree, the government bond rate stands in as the riskfree rate, but if they are not, the riskfree rate can be extracted from the government bond rate, by netting out the default spread for the government. That makes working with US dollars tricky, since the US lost its Aaa rating (Moody's) in May 2025, and I wrote about the consequences for computing dollar riskfree rates at the time.

   In the graph below, I look at the day-to-day movements in default spreads over the ten-year US treasury rate, across seven bond ratings classes - AAA, AA, A, BBB, BB, B and CCC & lower - in 2026: 

Federal Reserve Data (FRED)

Since default spreads are added to the US treasury rate, and the ten-year rate has risen in 2026 from 4.18% at the start to 4.75% on August 31, 2026, corporate bond rates are all higher than they were at the beginning of the year. For all of the ratings classes, other than high yield (CCC & below), spreads are largely unchanged or lower. The only ratings class where you see a surge in spreads is in the lowest rated bonds, where the default spread has increased by 1.57% during the course of the year.

    The implications for corporate borrowing and costs of capital are direct. Debt is now costlier than it was at the start of the year, for business borrowers across the world, with almost all of the increase coming from rising riskfree rates, in different currencies, with an added cost for the borrowers with the highest default risk. For bond investors, with money in long-term corporate bonds, the year has played out in lower bond prices, in both the treasury and corporate bond markets.

Note that while the returns have been low or even negative, across bond categories, the effect is nowhere near the carnage that we saw in 2022, partly because the rate change has been more muted and partly because the price effect of a rate change is much greater when rates are very low, as they were at the start of 2022.  

Stock Prices

    The essence of intrinsic value is that the value of an asset is the present value of the expected cashflows from that asset. As you take your first steps through discounted cashflow valuation, the question of what should happen to value, as interest rates increase, seems obvious. After all, as interest rates rise, discount rates should go up, and as they go up, the present value should decrease. That is, in fact, the process that I used to estimate the changes in bond value during 2026, in both US treasuries and corporate bonds. The reason that the effect of higher rates on value is direct, with bonds, is because the cash flow on a bond is the coupon and the coupon is set at the time the bond is issued, and does not change as interest rates change. With stocks, the effect of higher interest rates is not as direct for a simple reason. The expected cash flows on stocks are the residual cashflows from operations at businesses, and these residual cash flows reflect the revenues, earnings and reinvestment at these businesses. 

If, as interest rates rise, both cash flows and discount rates change, the effect of interest rates changes on equity prices requires grappling with how these interest rates changes play out in operating metrics:

• With revenues, the key determinants of how higher interest rates play out in value will depend first on why interest rates rose in the first place (higher inflation or higher real rates), and if it is higher inflation, how much pricing power a business has to pass through that inflation to its customers.
• With earnings, the question is how higher interest rates play out in profit margins, through their effects on costs of goods sold (gross), other operating expenses (operating) and interest expenses (net).
• When interest rates rise, and that rise is due more to real rates rising rather than inflation going up, businesses can scale back reinvestment, since fewer investments will generate the returns needed to pass muster. This reduction in reinvestment can increase near-term cashflows, at the expense of future growth.

The effects of higher interest rates will therefore vary across companies, with some companies seeing decreases in value (as the discount rate effect dominates any cash flow effects), some seeing no impact (as the discount rate and cash flow effects cancel out), and some benefiting with higher value, because their cash flows rise more than enough to compensate for higher discount rates:

The effect on equities, in the aggregate, will depend on the composition of the market, and which of the three groups (companies hurt by. not affected by or helped by) dominates. There is the added complication of risk premiums (equity risk premium and bond default spread) being affected by higher rates, adding to the discount rate effect.

As you can see, the question of how higher interest rates will play out in stock prices, will vary across different equity markets, and with any given market, it will vary across time. As US treasuries have risen in 2026, US equity indices have, for the most part, taken that increase in stride, with the S&P 500 and NASDAQ both rising strongly over the first eight months of the year:

To zero in on the interest rate effect, I looked at the yield on the 10-year US treasury bond, by day, during 2026. Of the 169 trading days of the year, from January 1 through August 31, 2026, there have been 84 days when yields increased, 73 days that they decreased and 12 days where they remained unchanged, and I looked at S&P 500 average daily returns for each group:

Were stock prices affected by changes in treasury rates during the trading day in 2026? The answer is yes, but only for larger movements in the yield (>3 basis points), with the S&P 500 down almost half a percent on days when the 10-year rate increased by more than 3 basis points and up about half a percent on days when the rate decreased by more than 3 basis points. Thus, at the risk of sounding contradictory, while stocks have held their own during 2026, in the face of rising rates, they have done much worse on days when the 10-year treasury rate went up than they did on days that rate decreased. The secret to equity resilience in the face of higher oil prices, interest rates and political turmoil has been in equity earnings, which have surged over the course of 2026. In the graph below, you can see the analyst consensus estimates of earnings for the S&P 500 for 2026 and 2027 over the course of 2026: 

Ed Yardeni

Over the first eight months of 2026, analysts who track the S&P 500 companies have raised their estimates for corporate earnings by more than 11% for both 2026 and 2027, indicating that companies are finding ways to get more to the bottom line, in the face of macro concerns and higher rates. I know that you have questions about these earnings, and I do as well, especially in the context of how companies are reporting the effects of AI on their earnings, but at least on the surface, the numbers are impressive. I plan to revisit these earnings numbers in a future post, and take a deeper look at what's driving these numbers, but for the moment, they are the reason that stocks have held their own in 2026.

Equities: Cross Company Comparisons

    While equities, at least in the aggregate, have held their value, how have higher interest rates played out across sectors? In the table below, I break down all US companies, broken down into sectors, and look at the change in aggregate market capitalization for the sector, as well as statistics on individual companies within each sector (lower quartile, median, upper quartile and percent up and down):

Source Data: S&P Capital IQ

During 2026, energy was the best performing sector, not surprising given the spike in oil prices, followed by technology, at least based upon aggregated market cap returns. The divergence between the at measure of return and the returns on the median company in the sector is a measure of how top-heavy a sector's returns are, and with technology, it is clearly the largest tech companies that are driving the returns; the median tech company had returns of only 7.75%, well below the aggregate tech sector returns of 25.22%. The worst performing sectors in 2026, at least through August, are the consumer sectors (discretionary and staple), utilities and communications, with lower pricing power and higher input costs to blame.

   Since the rise in interest rates is not restricted to the United States, I looked at the performance of equities across the globe, based upon aggregated market capitalization (in dollar terms) and looking at individual company metrics on returns:

Source Data: S&P Capital IQ

While global equity value has increased about $17 trillion (11.28%) in 2026 (through August 31, 2026), there are wide differences across regions, with Indian and Chinese equities struggling with low single digit returns, and far more stocks down than up. Some of the performance that you see in this table comes from movements in exchange rates, since regions with currencies that have appreciated (depreciated) against the US dollar will see increases (decreases) in US dollar returns.

Conclusion

    As we get closer to the FOMC meeting date, it is likely that there will more talk about interest rates, and what the Fed can or cannot do to change their course. Much of that debate, in my view, is pointless, since the pathway of rates is and will continue to be set by fundamentals. In fact, the ten-year US treasury rate has been stuck in a fairly tight range, between 4% and 5%, since 2022, and that is largely because expected inflation has settled in at about 2.5%, even as actual inflation has remained volatile. For rates to change significantly, up or down, there has to be a break in inflation expectations, to the up or downside, and there is little that Kevin Warsh or Scott Bessent can do to alter that trajectory. As to how equity markets and businesses are dealing with higher rates, the pain from moving from a low-rate to a high-rate world was most acutely felt in 2022, and both have adapted quickly to the new environment, with businesses finding ways to deliver higher earnings in the face of higher rates, and markets pricing in these earnings to deliver solid returns. 

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Data Links

1. US Treasury Rates, Corporate Bond Spreads and Equities in 2026 (1/1/26-8/31/26)
2. US Treasury Rates: Intrinsic vs Actual 10-year Rates (1954 to 2026)