I knew that my post on Anthropic would draw strong reactions, when I made it public last week. I did mention in the course of the post that I have no interest in selling my estimate of value to the rest of the world, both because I don't own the high ground here and because it is meant to guide my decisions, not yours. That is why I made my model open to changes that you can make to my story and inputs, and while you have to trust my mechanics, I tried to carry your inputs into value. Much of the pushback I have received has been thoughtful and productive, some of it has been emotional (either from people who love Anthropic or from those who would not touch it with a ten-foot pole) and some of it, especially on the cost of capital, reflects a fundamental disconnect with market history, current market pricing and just basic business sense. I plan to play devil's advocate with my own valuation, looking at my story inputs, and ranking them in order of fragility, partly to stress-test my value estimate and partly to reinforce some corporate finance first principles, and then move on to your feedback and what I take away from it. Playing Devil's Advocate: A self-critique of my Anthropic valuation     Since I posted my valuation only a week ago, rather than rehash how I approached the valuation and my end results, you can read the gory details in the post that accompanied it. I will start with a summary of the valuation, though, and then list  the parts of my story that I believe are most directly driving my value estimate on both magnitude (how much it is affecting value) and fragility (how uncertain I feel about the part).  The IPO Valuation     I did summarize my valuation of Anthropic in a figure in my original post, but since there was that much to compress into that picture, I will try a different format for the same here: Download updated spreadsheet If you check against my estimates from my earlier post, you will notice that the equity value at $1.45 trillion is slightly lower than my prior estimate of $1.475 trillion, as I adjusted the trailing 12-month revenue and operating income numbers to include the just-leaked third quarter numbers. Note that replacing the third quarter revenues from 2025 with the third quarter revenues of 2026 raises the base revenues, and since I still believe that the revenues for the coming twelve months will stay at my original estimate of about $100 billion, the growth rate in revenues in year 1 has been adjusted down to deliver that result. This reframing allows me to add a few details that I might have glossed over in my last post, as well as correct errors (none of which thankfully changed by value estimate:  Value per share: Though I have a share count, created by adding the shares reported as outstanding in the balance sheet to the restricted shares granted to employees, that share count will change before the public offering, as bankers adjust it to deliver on a per share value that they believe will make the company more "sellable". I will revisit this number, and update it when the company does (and it has to, before setting an offering price), but it should not change the aggregate value of equity. Cost of capital choices: I used a 9.74% cost of capital which incorporates risky technology cost of equity and a cost of debt based on a BB+ rating for the company, putting the company at close to the 90th percentile of US companies in 2026. I know that some of you felt this was too low, and I will come back to address what a cost of capital can and cannot do for you in valuation, later in this post, and why I don't think that this is the input that most moves the valuation. Compute commitments: I wish that Anthropic had created a table in their prospectus, where they specifically listed total commitments, both contractual and total, by year, similar to the table that they have for purchase commitments. Instead, they have sprayed content commitment numbers in the text, with little detail on timing and contractual standing, which results in differences of opinion in what those commitments are., with estimates ranging from $400 billion to $800 billion, with no clarity on timing or contractual terms. In my valuation, my estimate of contractual commitments results in an aggregated value of just over $450 billion and a present value of about $373 billion. It is possible that these numbers will need revisiting as details emerge of these commitments, but this will remain a long term issue for anyone valuing or pricing the company. To offer an imperfect analogy, I struggle with dealing with content commitments at Netflix, whenever I value the company, and especially so in the early years, when the entire Netflix library was leased or rented from content providers. Dilution Risk: Anthropic, like many young companies, has used its equity as currency, paying employees with restricted stock and options, and used it as an add-on to borrow money in the form of convertible bonds. In addition, if the company needs to raise capital in future years, it will have to issue additional shares for that purpose, and the larger its ambitions, the greater these issuances will be. When valuing Anthropic, there is no escaping this dilution overhang, and here is how it is incorporated into the value per share. With stock-based compensation, I valued the equity options (at their option value, rather than exercise value) and netted that number ($472 million) from my estimated equity value to get to value of equity in common stock; I also included the restricted shares as part of my share count to compute value per share. With the convertible debt, I have assumed that the marked-to-market number (it was the marking to market that created the $72 billion hit to earnings in 2026) is close to fair value, and subtracting it out will remove the conversion option from the equity. The future share issuances are to fund negative free cashflows, and since those negative cashflows are already weighing down my present value, there is no need to add the shares issued to the share count; adding it will be double counting. If you are surprised that adding the third quarter numbers did not move my value by much, it is because it was an open secret that Anthropic's third quarter revenue growth had slowed and I had already incorporated that into my valuation story. Value Drivers     The equity value that I obtain for Anthropic is close to the lower end of the offering price estimates, but as I noted at the time that I posted it, and will reinforce again now, it is a function of not just my upbeat story about the company, but also one about how the AI products and services market will evolve over time. To the question that some posed about whether I was concerned that I could be wrong, of course I am, but as I will argue in the next section, I cannot let that concern paralyze me. I would, of course, rather be right than wrong, but I am trying to forecast the future, and it is a guarantee that I will be wrong. That said, I am more uncertain and exposed on some parts of my story line than others, and here is how I would rank them for Anthropic, from most critical to less so: Target Revenues: If I were forced to pick one variable around which my valuation revolves the most, it is my estimate of the target revenues for Anthropic, which I estimate to reach a trillion dollars in ten years. Not only would this put them at or close to the top of all companies, in terms of total annual revenues, but they will also be sharing that grouping with companies like Amazon and Walmart that deliver on scale, but at the expense of margins. The revenue assumption is also inextricably linked to not only what you think AI agents and products can do, but also how demand for these will be monetized. In addition, there is a company-specific component to this story, where Anthropic emerges as a winner in the premium portion of the AI market. capturing a dominant market share, with pricing power. The fragility of the target revenue assumption comes from it being susceptible to any of the parts failing, and if a part fails, revenues will end up below a trillion, and perhaps significantly so. That, in turn, will have ripple effects on the other parts of your story and inputs, with lower revenue growth pushing down margins, creating idle compute power and increasing the risk of failure.  Governance: I did use harsh words to describe the governance structure at Anthropic in my last post, describing the combination of its structure as a public benefit corporation and a voting share concoction that leaves founders in control, no matter how much capital the company raises in equity markets, as a red flag. I did get some pushback from some arguing that this is a good feature, not a bad one, since these are smart people who understand AI better than anyone else in the world. Even if you accept these views  I would still argue, perhaps even more forcefully, that concentrating power in the hands of founders is a bad idea, and here is why. Anthropic has a plausible or even probable pathways to business riches, but those pathways come with setbacks (technological, regulatory, competitive), and you need management that can respond quickly and decisively, sometimes abandoning existing policies. For the latter to happen, you often need outsiders, not as immersed or even as informed on the technology front, but with different  backgrounds and experiences, to call attention to management missteps and push for change. I don't know any of the founders of Anthropic, and this may be unfair, but they seem to share a common worldview and operate in an echo chamber. My concern, as an investor, would be that they will not see changes occurring in business economics, and even if they do, be unable to or unwilling to adapt to a changing environment, and for Anthropic, that may be fatal. Profitability: My story for Anthropic is that it will not only be able to deliver operating profits soon, but do so on a sustained basis, with a target operating margin of 32.5% (before taking the regulatory risk haircut). Implicit in my profit story are assumptions about pricing power, the unit economics of the AI business and economies of scale and each is breakable: One of the most frequent critiques I received in responses to my Anthropic valuation is that this is a business without moats, and while I agree that moats matter, I am not sure that I go along with the notion that this business will get commoditized. The truth in technology is that unlike consumer products or old economy companies, where the moats take the form of brand names, patents or cost advantages, the companies that win find ways to make their products stickier to their customers, where stickiness implies that customers will not switch to alternatives, even if these alternatives are technically superior and cheaper. I recognize that Anthropic has work to do in creating stickiness, but I can visualize ways in which Claude agents, twinned with client data on customers and employees, can become sticky, allowing the company to preserve its pricing power and market share. Continuing investment needs: While much of the focus on Anthropic has been on their compute commitments, disclosed (loosely) in their prospectus, my valuation of Anthropic is driven by expectations of what those commitments will look like over time, and especially if my growth story for the company comes into fruition. Delivering a trillion dollars of revenues will require Anthropic to not just maintain and replenish the compute power that is has already locked down, but add to that power over time. Implicit in my story line for the company, where the company is able to add this compute power at lower costs (reflecting declining chip prices) and in time, is the assumption that the compute power story moves along smoothly, working through resource constraints and pushback:  If you have been tracking news stories about data centers, it seems clear that building new data centers is getting more difficult, as local communities push back, and politicians realize that opposing them is a net vote winner.  Regulatory risk: You may find this surprising, but risk, in my story, is not as critical as my first four inputs, partly because it is determined by shifts in operating variables. Put simply, Anthropic will become riskier, if revenues do not measure up to expectations and the pathway to profitability becomes rockier, making it more an output than an input variable. I know that for some of you, the cost of capital of 9.74% is the main stumbling block, but I will provide a more cohesive (and hopefully) more persuasive argument of why it is not as critical to value as you may believe it is. I also know that for others, the notion of a money-losing company borrowing hundreds of billions, either directly as debt or through contractual commitments, creates a failure risk probability that is so high that the rest of the valuation is moot. I agree with you on the general argument that some players in the AI ecosystem, such as Coreweave, have abandoned corporate finance first principles and risk bankruptcy, but Anthropic is no Coreweave, especially if you consider that most of its debt is in contractual commitments spread out over time and that it will have a cash balance large enough to cover its first few years of those commitments. It is on the regulatory risk front that I worry the most about being wrong, because regulatory risk is as much driven by politics as it is driven by economics. You could be one major election away from onerous regulations on AI, where unlike my base case, where those regulations cost you only a small slice of your operating profits, they take a major bite out of revenues and profits.  In sum, not only will I be wrong on any of my key inputs, but being wrong on one (especially target revenues) will cause me to be wrong (in the same direction) on my other inputs, causing value to shift more dramatically in response.  Investment Followthrough     At this point, you are probably why, if I am this uncertain about a company's future, I would even consider investing in it and it is a question that I have pondered as long as I have been an investor. Uncertainty is a feature of investing, no matter what you invest in, with the differences across investments lying in how much uncertainty you face, rather than whether you face. There are four responses that an investor can take to uncertainty: Ignore it: There are investors who are capable of ignoring uncertainty and going in fully even on the most uncertain investments. They are able to do so because they are far less risk averse than the rest of us and are willing to live with the consequences of investments (and their portfolios) blowing up. They are more gamblers than investors, but with good instincts and timing, gamblers can get wealthy too. Avoid it: If you completely want to avoid uncertainty, you cannot invest in equities, corporate bonds, which have risk of default, and even treasury bonds, which have price risk from shifting interest rates. Unless you want to hold just hold cash, you have to therefore decide what your threshold for risk is, and stay within that threshold. In conventional value investing, that would lead you to restrict your investments to companies with assets in place (captured in book value) and earning power from those assets (high current earnings and cash flows) and stay away from growth companies, since growth is "speculative" and therefore risky. While I understand the impulse, this choice comes with a cost, which is that your portfolio will be almost entirely invested in mature and declining companies, with no growth companies in emergent technology.  Margin of Safety: The third approach to dealing with uncertainty also borrows from conventional value investing and imposing a margin of safety, where you will buy a stock only if its price is less than your value estimate by a margin (20%, 30%, 40%) that you specify. While your inclination, if your risk averse, is to keep increasing your margin of safety, it is worth remembering that like almost everything else in investing, it creates a trade off between type 1 errors (where having a higher margin of safety protects you from the losses of investing in stocks that you should not be investing in) and type 2 errors (where that higher margin of safety keeps you from investing in good stocks that you should be holding): The question of whether you should have a margin of safety, and if so, how large it should be, will be a function not only of how many opportunities you see in the market, relative to your portfolio size, and how much cash you have in your portfolio (with larger cash balances suggesting that you should lower your margin of safety). Law of large numbers: If I had a million dollars in investable funds, would I invest that entire amount in Anthropic, even if it trades at well below my assessed value? The answer would be no, given the uncertainties associated with the future of the company, but that answer would change if my portfolio became less concentrated, since its performance would be less a function of any individual company's performance (good or bad). In effect, I am using the law of large numbers as an ally to not only reduce my risk exposure to individual companies, but also as a basis for making investments in far riskier stocks than I would be otherwise inclined to invest in. As you assess your story and valuation of Anthropic, it is crucial that you frame the question that you are trying to answer with your analysis. Is it whether you should buy or sell short Anthropic, as a stand-alone investment or are you looking at Anthropic as an add-on to an existing portfolio? If it is the latter, how concentrated (across stocks and sectors) is your existing portfolio? The answers will vary, as a consequence. Keeping the feedback loop open: Your pushback on my valuation   In the days since I posted my Anthropic valuation on multiple platforms (X, LinkedIn, Google, Substack and YouTube), I have heard from many of you, and in the interests of keeping the feedback loop alive, I did a breakdown of two statistics - the percent of people who thought that I was over valuing and undervaluing Anthropic, and among the majority who thought I was overvaluing, their reasons for disagreement. I used the Google shared spreadsheet that I created last week, where some of you were kind enough to enter your story/inputs and value for the company, and here is what you found: Clearly, a lot of people disagree with my assessment, with more feeling that I have over valued Anthropic, rather than under value the company, with the crowd assessment of its value closer to $1 trillion, rather than $1.5 trillion. I also used your inputs from the shared sheet, as well as a reading of the comments on my valuation, across platforms, to see the source of your disagreement, especially if you believed that I have overvalued the company. Among those who disagreed with me, the reasoning varied, but I was disappointed, albeit not surprised, that the biggest single reason outside of "my gut tells me that Anthropic is worth nothing" was a judgment that my cost of capital estimate for the company (9.74%) was much too low. I was disappointed because I don't think that the cost of capital is at the center of the DCF universe (and I used words to that effect in my original valuation) but the lack of surprise at its prominence comes from observing appraisers and analysts obsess over this number, often without understanding what it is designed to measure and without a frame of reference (as to what a reasonable value for it is), and using it as a receptacle for their hopes, fears and fantasies. The Cost of Capital - The Swiss Army Knife of Finance     I teach and practice across three different areas of finance - corporate finance, which looks at the first financial principles that govern how to run businesses, valuation, which looks at those businesses and tries to attach a value to them and investing, which is about finding market mistakes and taking advantage of them. I describe the cost of capital as the Swiss Army Knife of finance, because it is in every aspect of finance, performing different roles for different players (businesses, investors, activists). I won't bore you with an extended discourse of all of these roles, which I have expanded on in a practitioner paper on cost of capital, but you can get a glimpse of its uses in the figure below: For many analysts and bankers, the cost of capital is the discount rate that they use to value businesses, but its usage in corporate finance is deeper and more impactful. It is the hurdle rate that businesses use to decide whether to invest in a project or make an investment, tailored to the risk in the business, the optimizing tool that businesses can use, if they so choose, to determine the right mix of debt and equity to fund themselves and the divining rod to determine whether businesses should be returning more or less cash to their owners.  It is true you can break the cost of capital into its components, by categorizing the funding into debt and equity and attaching a cost to each: At the risk of stating the obvious, all debates about cost of capital become discussions of what comprises a reasonable cost of equity, what the cost of debt should measure and how they should be weighted. Estimating the Cost of Equity - Checks and Constraints     While there is much to be learned from modern portfolio theory, one of its more deleterious consequences has been a dumbing down of the cost of equity, where intuition and common sense are supplanted by heated arguments about asset pricing models and parameters.  To bring the discussion back to steady ground, start with a recognition that the cost of equity for an investor should reflect what investors can earn elsewhere in the market on an investment of equivalent risk, i.e. it is an opportunity cost,  and understand that is what you are trying to measure, often imperfectly, in every model. That number has subjective elements, and can vary across investors, based on risk aversion, but the requirement that you can earn that number elsewhere creates reality checks.  a. History     What do you think the cost of equity for the average US company is today? In an unbounded world, you can give me any answer you want, from 3% to 30% or higher, but one check you can run is against history. On my webpage, I maintain a dataset of historical returns on US stocks from 1928 to 2025, and in the graph below, I compare the average annual returns you would have earned on US equities (all as well as just small cap) over 25 years (2001-2025), 50 years (1976-2025) and close to a hundred years (1928-2025) to annual returns on treasuries (3 month and 10-year): There are two ways this historical data can be used to frame cost of equity estimation. You can use the average annual return on stocks over the period, which ranges 8.74%, if you look at just this century (2001-2025), 11.92%, if you go back fifty years (197-2025) or 10.13%, if you go back all the way to 1928 (1929 -2025), and use that as your cost of equity. Since investors had the alternative of investing in treasuries, and rates over treasuries have varied over time, a second option is to look at the premiums you would have earned investing in stocks instead of treasuries. That number has a tighter range, between 5.5% and 5.75% over the 10-year bond and 7-7.5% over treasuries; using the 10-year treasury rate of 4.9% on October 26, 2026, that would yield a cost of equity of about 10.4-10.65% for the average risk firm. There is value in knowing history, but it is worth recognizing that these returns capture stock market performance over a century in the United States,  the most successful equity market in the world over that period. There is a selection bias that may be leading to overestimate future returns, if you use historical averages. That would make using a cost of equity higher than what this history delivers even more indefensible. b. Market-Implied     If the essence of cost of equity is estimating an opportunity cost, i.e., a return you can make investing elsewhere, I would argue that a better way of estimating the cost of equity for a company is to back it out of market prices. In effect, you can start with what the market is pricing equities in the aggregate, estimate expected cashflows from buying these equities and solving for the expected rate of return. If that sounds familiar, it is the process I use at the start of each month to compute the implied expected return and resulting equity risk premium for the S&P 500: In a post earlier this month, I reported on what those numbers are at the start of October 2026: Note that the expected return from investing in US equities, in the aggregate, at the start of October 2026 was 8.99%, and this number is model-agnostic. Thus, you can choose to believe or disbelieve the CAPM and think that betas are a waste of time, but the expected return from investing in US equities will still be 8.99%. I know that there are estimates of growth and cash flows that help determine the number, but almost every pushback I get (the cashflows cannot be sustained, the growth rate is too high etc.) will lead to a lower expected return, not a higher one.      So what? If this is the market you are in, contending that the cost of equity for a median US company should be 12% or 15% borders on the delusional, since you cannot make that return on an investment of equivalent risk in this market. To see why costs of equity that are 15% or higher are indefensible, you can check out the graph below where I report the expected return on US equities (using the implied premium approach) going back to 1960: The expected return on US equities, and by extension, the cost of equity for the average US company has not exceeded 10% since 1994. If you are reaching for 15-20% costs of equity, you have to go back to the early 1980s, when the 10-year treasury rate was 10% or higher.  c. A Pricing Perspective     There is a third way to assess the reasonableness of a cost of capital, and this approach is especially effective if you think in terms of pricing multiples (PE and price to book ratios) and have a frame of reference on those (in the sense that you have a measure of what a reasonable multiple of earnings stocks should trade at). There is a simple (and perhaps simplistic) way to think about the cost of equity and capital in terms of pricing multiples.  You can convert a cost of equity assessment into a price earnings ratio, with relatively few assumptions. To see how, start with a simplistic valuation model where the value of a stock is the present value of its expected dividends over time, and the company is in stable growth: If you believe that the cost of equity is 15% for the average US firm, given that nominal growth in the US economy is about 5% and that the average return on equity across US firms is about 20% (and that is a generous estimate), the PE ratio for US firms today should be roughly 7.5. Given that the actual forward PE ratio is closer to 20, you have two choices. One is to sell all your equities, though I am not sure where you would put that money, and the other is to revisit your cost of equity assessment. The cost of capital maps on to enterprise value multiples, using a similar approach, with a 12% cost of capital translating into an enterprise value multiples of 9 times operating income and  6-7 times EBITDA for the entire market, numbers that are vastly below today's values. Put simply, if you content that the cost of equity for US firms is 15% and in the same breath argue that a PE ratio of 15 for the market is reasonable, you are being internally inconsistent, and you have to decide which of these assertions you plan not just stick with, but act upon, as an investor. The Cost of Debt: Intuition and Estimates     One reason that the Anthropic cost of capital discussion may have been hijacked is the almost mindless use of debt in funding data centers, a practice that I have attacked as senseless for both the borrowers and for the lenders. With Coreweave as poster child, this has led to an explosion of concerns about default risk and in  market-implied default rates and spreads for some companies in this space. By extension, Anthropic has become part of this discussion because of the compute commitments that it has locked in, for good and bad reasons. In fact, the argument for a higher cost of capital goes as follows. If it costs Anthropic 10% or more to borrow money today, it should have a much higher cost of equity to reflect the residual risk.      It is true that the cost of debt will tend to rise as worries about default risk rise, and that ratings operate as a shortcut to measuring that default risk. Using the default spreads that you can observe in markets right now, with a 10-year treasury bond rate of 5% as the base, here are updated default spreads and costs of debt, by bond rating: As you can see, the default and cost of debt for companies rises as the rating drops and kicks into hyper drive at a B rating. You may have a hard time reconciling the very high costs of debt at the lowest ratings with the earlier table that constrained cost of capital, but that is because at the lowest ratings, the risk of default is imminent and large. It would be inconsistent to incorporate this cost of debt, in its entirety into a cost of capital, a going-concern concept, and that is why I separate out failure risk as a way of capturing that risk.      My estimate for the cost of debt for Anthropic reflects my estimate of a bond rating of BB+, and a recent news story in the Financial Times suggests that they will remain below investment grade. Anthropic may owe a lot in commitments, but they are spread out over time, with about $70 billion coming due next year and $76 billion the year after. Those are large commitments, but Anthropic has a cash balance of $114 billion, which will increase to over $200 billion after the IPO. There are lots of reasons to take issue with Anthropic, but worries that it may default in the next year or two or even the next five don't make it to my top ten list. That does not mean that I am ignoring the risk of default, because I am incorporating a failure risk of 10%, where Anthropic does run into headwinds, is unable to meet its contractual obligations and has to have fire sales. If your concerns about default are higher, teh failure risk is the number you should be moving, not the cost of capital. In fact, if you were ignore failure risk, as many analysts do, and try to jam default risk it into cost of capital, you would need a cost of capital of about 11% to arrive at an estimate of value close to mine, in my valuation. Cost of Capital - The Cross Section     I know that you may still argue that if you have a very risky company, and you throw Anthropic into that grouping, you could have a much higher cost of equity than a median company. That is true but you may be vastly over estimating the divergence in costs of equity across companies, when you do so. At the start of every year, I estimate the cost of equity for every public company in the world, and graph the distribution to get a sense of what high, low or typical numbers look like, and the start of 2026 numbers are shown below: In almost every sector, take heed of the tight range between the first and ninth deciles. In technology, a sector where investors perceive wide differences in risk, 80% of all firms have costs of equity between 9.88% and 14.49%, and costs of capital that range from 9.02% to 12.27%.  When you have this little room to diverge, you can see why spending your time finessing the cost of capital is an exercise that will bear little fruit, especially if you also factor in that your starting numbers for these costs in valuation will change over time, as your company becomes larger and more profitable.  This table is in US dollars and for US companies, and if you are working with a different currency and with a company in a different country, you should add the differential inflation and country risk premium to the cost of equity. In the table below, I have the adjustment computation for a subset of currencies/countries: Thus, if the inflation rate is 1.77% higher in Indian rupees, and the Indian equity risk premium is 2.49% more than the US, you should add 4.26% across the table to get the ranges in rupees for Indian companies. A median Indian company would therefore have a cost of capital of around 12.05%, in Indian rupees, and a median Swiss company would have a cost of capital of about 5.85% (adjusting for an inflation rate about 1.71% lower than the US and an equity risk premium about 0.23% lower).     It is true, based on this table, that you could push the cost of capital for Anthropic higher, perhaps towards the 90th percentile of technology firms, and give it a cost of capital of 12%, but there are two caveats. The first is that you would then have to set the failure rate to 0%, since the costs of capital in this table do not adjust for failure (and especially so at the higher risk percentiles). The second is that your cost of capital, in your valuation, has to change over time to be consistent with your story line for Anthropic as a company. Thus, if you see Anthropic scaling up revenues over time and turning the corner on profitability, its cost of capital will move towards a more typical cost of capital for tech companies over time. If you don't see that happening, you don't need a high cost of capital to conclude that Anthropic is worth little or even nothing.     You will notice that nowhere in this discussion have I used a greek alphabet (beta) or a finance acronym (CAPM) because not only do I not need either to make my point about costs of capital but also because they have become easy excuses for laziness on the cost of capital front. I don't estimate costs of capital as an academic exercise, but for purely pragmatic reasons, since I need them in my corporate financial analysis and valuation. I need a relative risk measure for a firm to estimate how much higher or lower than the average company its cost of equity should be, and all that beta provides is an estimate of relative risk. If you like it, use it. If you don't it, come up with your alternative, and if you cannot think of any, try any of the following: Put simply, if you don't like betas (or the CAPM), you should consider why (and it cannot just be because Buffett or Munger told you so) and follow through to come up with your proxy for risk. I am okay with any of your choices, since I can use them to estimate costs of equity and capital, and to be honest, none of them will make or break my valuation. Cost of Capital Delusions - Investors and Businesses     I know that there are analysts and investors out there who have picked a required rate of return as their hurdle rate, sometimes arbitrarily,  for investing and never revisit that number, and almost as many CFOs who do the same for the hurdle rates at companies. That number can be 10%, 12% or 20%, and when you push back, the response is that it their choice to make, and that they are just setting high standards. With individual investors and privately owned businesses, the consequences of setting an artificially high cost of capital are personal, and if, after being made aware of them, they stick with a 25% cost of capital, they are entitled to do so. With institutional investors and publicly traded companies, where you are investing other people's money, you have to consider the costs that they will bear for your overreach. Professional Investors - Grasp exceeding Reach!     With subgroups of professional investors, especially venture capitalists and private equity, the numbers that they offer as target or expected returns are so staggeringly out of sync with reality that the cynic in you should wonder whether these numbers are marketing props, rather than  financial metrics. Let's start with venture capital, where VCs routinely set target rates of 20%, 30% or even 50% for their investments, depending on how early in the process they invest, with angel financing commanding the highest rates. Private equity fund sponsors also present potential investors with investments that least on paper deliver internal rates of return of 20% or higher. We know that these target returns are fiction, because the returns that venture capitalists and private equity investors actually deliver are not just much lower but surprisingly close to what investors can earn on public equities, say on index funds.  Cambridge Associates Note that not only do VC and PE funds lag public equity indices over long periods, there is not a single number in these tables that comes remotely close to the target returns that PE and VC claim up front.      You may be tempted to cut venture capitalists and PE investors slack for using fictional target returns,  viewing them as sales and negotiating tools, but those fictional values feed back into how much founders have to give up in exchange for capital, and may also explain the failure of VCs and PE investors to deliver value for their investors. As a generalization, I believe that active investors who promise their clients too much (as an expected return) will overreach in terms of the risks they take, and if the target returns they promise are set too high (relative to what they should be), they will spend their time looking for ten or hundred baggers, rather than solid long term investments. Publicly Traded Companies - Inertia and Herding     While it is true that company hurdle rates are for internal use and not often visible from the outside, surveys of CFOs, asking them about what the costs of capital are for their company, often come back with the conclusion that hurdle are set about 2-3% higher than justified by market numbers. Often, these hurdle rates are either arbitrary or reflect the history of the company, where high returns earned on projects taken in the past are employed as hurdle rates for the future. You may be tempted to let this practice go, as just being conservative, and protecting companies from taking bad investments, but here again, there is a trade off. For companies that estimate returns on projects rigorously, having a higher hurdle rate than justified will mean the good investments will be left untaken. Early in a company's life, when project choices are plentiful, this may not be a deal breaker, but as companies mature, that underinvestment will cost them (and their investors).      There is another, and potentially an even more dangerous consequence that undermines the entire process of investment analysis. When hurdle rates are set unrealistically high, relative to what you can earn in the market, managers, recognizing that they will never find projects that earn more than those hurdle rates will start to skew cashflows and growth upwards to compensate for discount rates being too high. Top managers will then push up hurdle rates to compensate for the bias, and when managers respond with even more positive skews in cash flows, you have set in motion a process where both the cash flows and the discount rate become fictional and made-up numbers, rendering the entire process moot.     Ultimately, though, as with PE and VC, the proof is in the pudding, and with publicly traded companies, the returns on equity and invested capital reflect what they actually earn on projects, rather than what they claimed they would in the original analysis. With my sample of publicly traded companies, in 2025, here is what the numbers look like: As you can see from the 2025 statistics, and this is with my cost of equity and capital calculations from the earlier table (which you may have found too low), less than 30% of companies earn returns on equity (capital) that exceed their costs of equity (capital) and less than 20% beat their hurdle rates by more than 5%. If you raise the costs of equity and capital to 12% or 15%, that underperformance will only get worse.  Individual Investors and Privately owned businesses     As an individual investor, investing your own money, you do have more freedom on setting your required returns, but there will be consequences to setting unrealistically high hurdle rates.  If you stick with these "high" hurdle rates. you will find it difficult, if not impossible, to find equities that can deliver those rates. I can assure you that with a 20% cost of equity, for instance, there is not one firm in the S&P 500 that will be undervalued for you to invest in, and if you do find firms that look undervalued in the broader market, it is because they are basket-case firms on the verge of tipping over. Thus, a reasonable and logical follow-up would be to reduce or even eliminate your equity exposure in your portfolio, though if your unreasonableness on hurdle rates carries over into other asset classes, you will find no risky asset class that meets your rate of return criteria. More dangerously, it will leave you open to scams, where your search for high return investments will lead you invest with a "friend of a friend" or a "YouTube investment celebrity", claiming to have found a way to deliver more than your hurdle rate with investing alchemy. I have long heard critics of intrinsic valuation argue that it does not work for them because it seems to find everything that they try to value to be over valued. That is less a problem with the approach and more a reflection of using unrealistically high costs of equity and capital in your valuation.     For businesses with private owners, setting the hurdle rate too high will lead them to be more conservative in taking investments, opening the door to more aggressive competition, often from publicly traded companies or private business competitors with more reasonable hurdle rates. The bigger danger here again is when you get ready to sell, you will sell your business  for too low a price. You will facilitated in this process by private company appraisers who come up with costs of capital that are outlandishly high, usually getting there with what they call the "build up" approach, a  piling on of premiums that are either thinly sourced (a small cap premium that ceases to exist in 1981), double counted (an illiquidity premium, with an additional illiquidity discount of value) or just made up to (a non a company-specific risk premium,  where a number is added on to get to whatever discount rate you want).  Conclusion     This post started with a sell assessment of my Anthropic valuation, and I hope that you can see that while I believe in the plausibility of my valuation, there are multiple dimensions on which I can (and will) be wrong. In my view, the key assumption that drives almost of the others is my trillion-dollar revenue projection in a decade for that company, insofar that if that does not come to fruition, it will ripple through my other assumptions, with lower profit margins and higher failure risk following. That should make it the central question that we should be debating, and my extended discourse on why making cost of capital your central discussion point is misguided is designed as much to bring attention back to things that matter as it is to restoring balance to discussions of what a reasonable or high cost of capital for a company can be.      You are, of course, welcome to quote any number you want as the cost of equity and capital (and they are not interchangeable), but while you are entitled to your opinions, you are not entitled to make up facts. When you claim that the cost of equity is 12%, 15% or 20% for a typical firm, you are either valuing a company in Turkish lira or Zambian kwacha (where inflation is 15%+) or a company in Afghanistan or South Sudan (where country risk is daunting) or you are just making up stuff! Would I like to make 20% returns on the stocks I invest in? Of course, but I would also like to have a milk shake with every meal, without affecting my glucose and cholesterol scores, and get older without any of the symptoms of aging.  YouTube Video Data Costs of equity and capital (in US $), by Industry (US, Global) Historical returns on US stocks. bonds and bills: 1928 - 2025 Implied Expected Returns for US equities: 1960 -2025 Blog Posts on AI in 2026 To a Trillion Dollars and Beyond: A SpaceX Odyssey! Revisiting SpaceX: A post-Prospectus valuation OpenAI, Anthropic and SpaceX: The S&P 500 Inclusion Question and Investment Consequences AI Scenarios: From Doomsday to Do-nothing! AI's Bar Mitzvah: From hope & hype to business questions! Stock Prices, Earnings and Cashflows: The AI Effect! From Eyeballs to ARR: Buzzy Pricing Metrics and the Anthropic Pricing The Anthropic Prospectus Arrives: My (but not the) valuation of Anthropic

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: = 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: Download spreadsheet 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.  Download spreadsheet 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 that 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. 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: 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. 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.  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 buainwaawa). 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. YouTube Video Blog Posts on AI AI's Bar Mitzvah Moment: From Hope and Hype to Business Questions! Spreadsheets Implied Equity Risk Premium on September 30, 2026

Earlier this year, I was asked what I thought about Leopold Aschenbrenner and I admitted that I knew little about him other than what I had read about him, more in social media, than in the press - that he was a 25-year-old wunderkind who had started at OpenAI but had left to start a hedge fund. That hedge fund, built entirely around a bet that AI would pay off big time and near term, buying companies in the AI orbit and selling short on the businesses (especially software) that AI would disrupt, had been able to raise billions of dollars from well-heeled and presumably sophisticated investors, and had  posted eye-popping returns, up almost 450% through late June. Success of that magnitude needs no nitpicking, but it is worth remembering that there is nothing that markets enjoy more than cutting inflated egos and reputations down to size. In this case, the fall from grace was precipitous, and over the course of four weeks, the fund's public equity holdings lost more than two thirds of its value, but was also forced to liquidate, with Citadel buying almost all of its public equity holdings.     The reads on the swift rise and fall of Leo have been fascinating, a Rorschach test of investing priors. For older investors, the lesson was that you can be blessed with intelligence, but that wisdom required experience, which, at least in their saying, conveniently comes with age. For value investors, many of whom chafed at Leo being hailed as the next Buffett, there was vindication that there will never be another Buffett. For AI skeptics, who have long been on the lookout for catalysts that will break AI fever, there was at least a brief moment of hope that this was the catalyst. There is some truth and some overreach in each of these responses, and I don't plan to rehash them. Instead, I would like to use this story to talk about investing conviction, words used mostly in a favorable way, when people talk about success in markets. I am not as convinced that investing conviction is a net plus, but to get to that conclusion, I think we need to look at what it is, where it comes from and what it leads investors to do.  The Story of Situational Awareness     The Situational Awareness story is inextricably tied to the story of Leopold (Leo) Aschenbrenner. The short version of his life story is that he was born in Germany in 2001, and enrolled at Columbia University when he was 15. After graduating with a degree in economics in 2021, doing research briefly at Oxford University and working at Sam Bankman-Fried's FTX fund, Leo joined OpenAI as part of the team working on AI safety. He was fired in 2024 for leaking data on the firm, though his motivations for doing so are murky and he contests the allegation, and he published a long (167 page) paper titled "Situational Awareness: The Decade Ahead", which was not only widely circulated, but also became the blueprint for the fund that he created.     The Situational Awareness fund, founded in July 2024, and initially funded by tech luminaries, quickly took off as its bets on AI chips and infrastructure and against AI-damaged sectors paid off. Its early success allowed it to attract more money, with Jane Street being one of the more prominent names involved., and with the additional capital in play, it expanded its presence. While most of the companies that made the fund's list were publicly traded, it also had a stake that Leo had acquired in Anthropic, still a private business. The numbers posted by the fund made investors notice, as can be seen by its rise From August 7, 2025, to June 23, 2026, its highest mark day: Source: Portfolios Lab Measuring returns from August 7, 2025, Situational Awareness was up about 367% through June 19, 2026 and its returns since founding are even more stratospheric. Even at its peak, there were three caveats that any investor with experience in the market would (or should) have brought up. First, in market time, where decades of over performance are needed to separate luck than skill, a fund that has been successful for a little more than two years qualifies more as a shooting star than as a beacon of light. Second, to deliver returns of this magnitude, you not only have to be right in your guesses, but those guesses must be super-charged by adding substantial leverage to your strategy, either explicitly (by borrowing) or implicitly (by using options). Third, the fund followed the classic 2% (of funds under management) and 20 (% of specified upside) fee structure, an abomination that not only creates an almost insurmountable handicap, in the long term, for investors in the fund, but also encourages reckless risk taking on the part of management.     If the rise of the fund was breathtaking, its fall was even more so, and you can see the meltdown in the four weeks of July in the graph below, which looks at the fund performance from June 19, 2026 to July 29, 2026, the last day of trading for the day, before the fund was liquidated: Source: Portfolios Lab Note that the loss of principal (of more than 43%), with the Anthropic holding value retaining mostly intact, as a private holding, but with the public investment portion of the portfolio down by almost 67%. I am sure that there will be case studies and forensic analysis of what happened in these weeks, but for me, the lesson is one of market symmetry. What the market gives easily, it also takes away just as easily, and if you put into place strategies that are designed to deliver outsized returns, you have to live with the reality that you can have outsized losses. The surprise, though, for many is not that the fund lost money in July, but that it did not survive the month, and was forced to sell much of its public investment portfolio to Citadel, at prices, that at least in hindsight, look like bargain basement levels.  Conviction: What is it and where does it come from?     How do I get from the Situational Awareness story to a discussion of investment convictions? Simple! Leo's core belief that AI would win the battle with the status quo in most businesses, and that the win would be decisive and quick, was not unique, and not only are there other investors who shared that view, but there are also companies that are investing in AI cap ex, driven by that view. That said, to get from that view to a hedge fund built entirely around buying AI winners and selling AI losers requires that the belief to be deeply set and using debt to magnify those returns suggests strong conviction. What is investment conviction?     Before we embark on a discussion of investment conviction, it is worthwhile to start with an understanding of what it means. While there a myriad of definitions out there, the general consensus is that investment conviction measures the belief that an investment opportunity will generate significant returns, relative its risks, and with that definition, you can see that conviction is a continuum, rather than an absolute.  At one end of the spectrum, you have absolute conviction, where you know (or think you know) with certainty that an investment will pay off. At the other end of the spectrum is investment mush, where your feelings about an investment paying off are so weak that you are unwilling to even voice that opinion, let alone put money behind it. With most investments, you fall in the middle, with the variations being in the degree of confidence that you have in being right.     As you can see, the elevation of conviction as something to be sought after in investing is because in the complete absence of conviction, you will not act and that paralysis can result in portfolios entirely or almost entirely held as cash. I don't think, though, that even the strongest proponents of conviction as a good quality in investing  would make the argument that you should feel certain about the outcome of investments, when uncertainty is part and parcel of investing.  Where does conviction come from?     So, what is it that determines investment conviction or the lack of it? To generate a basis for that discussion, let's go back to basics, and start with an assessment of what has to happen for an investment to be viewed as a money maker. No matter what your investment philosophy, the process starts with a market price for an investment, and an assessment of what you believe is a "fair price" for that same investment. I am being agnostic about how you arrive at the fair price, leaving the door open for chartists, who may find it by looking at past price patterns, fundamentalists, who believe that you can assess fair price, only by looking at the fundamentals of the investment and traders, who may be in possession of information that leads them to believe that the current price is wrong. For the process to deliver winnings, though, there is a second part to the process that often gets less attention, which is that the market has to correct, with the market price moving towards or even to your fair price.  To have conviction in an investment, you therefore need to believe strongly in three aspects of this process: That your assessment of "fair" price is right (or at least more right than the market consensus)  That the market will correct, i.e., that the market price will move to, or towards, your fair price That this correction will happen during the time you plan to hold the investment (your time horizon), either because the investment has an expiration date (maturity) or because you feel that there will be a catalyst that causes the correction. With this description in place, you can see that investment conviction will depend on the investment in question, the market that it is priced in and on the investor making the judgment. a. Investment Mispricing     If the investment process starts with an assessment of a fair price that is different from the market price, there are at least four reasons why you may be more confident in your assessment of the price of an investment, relative to the market consensus: Private information: At the risk of treading on or crossing the line between the legal and the illegal, you may be in possession of information about an investment that is not available (or at least widely enough available to the public to be priced in) that you believe will change its price. Information processing: To the extent that private information is rarely available to investors, and even if available, difficult to act on legally, much of active investing is built around collecting and processing information that is available to the market. That private information can range from past prices and trading volume (used by technical analysts) to public filings (the financial data that the company provides, often the basis for fundamental investors) to quasi-public information (analyst forecasts and revisions, hedge fund and mutual fund holdings). If you are using this information to assess a fair price, it is because you believe that you have found patterns in the data that others have not. Business understanding: In some cases, your fair price will derive from your belief that you understand the business economics for a company better than other public investors do, with your superior understanding coming either from working in the business or from technical training. This is especially true in complex businesses (like bio pharma) and complicated assets, and likely to be  more the case with young companies, where financial history can stand in for business understanding. Pricing mistake: There are some investment theses that start with a market mistake, whether it be in pricing an individual asset or a pair of assets. With a pair of assets, often with similar fundamentals, the mispricing manifests with one of the assets being mispriced against the other, and the correction take place when the mispricing disappears. It is at the heart of derivatives trading, where options or futures on a traded asset can be mispriced enough that you can lock in the profits from the pricing mistake, with the guarantee of correction, when the option or futures expire. In sum, you can see that the degree of investment conviction that an investor has can vary across different asset markets (real estate, equities, fixed income, derivatives), geographies (developed versus emerging markets) and within equities, across industry groups and sectors (less for technology and more for utilities, for example). b. Market correction     Investment conviction may start with the spotting of a market pricing mistake, but for conviction to build, you need to get a measure of when, why and how the market will correct its mistaket. Here are some of the forces that can cause variations on the market correction dimension:     Finite maturity versus indeterminate end game: An investment with a finite maturity date comes with a greater likelihood that prices will correct than one without. A bond that is mispriced, by itself, or against other bonds of equal maturity, comes with a greater chance of correction than a stock that is mispriced against its own fundamentals or a paired stock. For the same reasons, if you can lock in a mispriced option against its underlying asset (or stock) or against other options of equal maturity, you are moving the odds in favor of correction. It should come as no surprise that true arbitrage, i.e., positions that can lock in guaranteed profits that exceed the riskfree rate are almost always in the fixed income or derivatives markets and that much of what passes for arbitrage in equities is quasi or pseudo arbitrage, where risk remains in the position. Market frictions: Market mispricing can sometimes reflect market inattention or irrational trading, but they more often are the consequence of market frictions, including restrictions on selling short and exerting control over an investment (such as buying and liquidating a company). If that is the case, it is possible that market mistakes, while visible and seeming obvious to everyone involved, may never get corrected, or at least not get corrected until the friction is removed. Market liquidity and depth: In equity markets, where there is no date by which mispricing has to disappear, corrections often require catalysts, i.e., events that lead market participants to reassess a market price, and to correct it. Those catalysts can come from corporate disclosures (earnings reports, for instance) by the mispriced company, corporate restructuring (divestitures and spin offs) or high profile investors (activists taking a stake in a under priced company or selling short an overpriced one). All of these catalysts are more likely to be present in liquid and deep markets, where information flows are more frequent and varied. Investment time horizon: No matter what the market mistake, it can be argued that  having time as an ally, and being able to wait for longer periods, for market corrections, should not only give you a greater chance of gaining from a market correction, but also give you more conviction in your investment, other things being equal. The bottom line is that you can feel (relatively) certain that an investment is mispriced, but have no conviction in that investment, if you have no sense or faith that the market will correct in your time frame for investing.  c. Investor characteristics     Is it possible for two investors to find the same market mistake, with the same underlying rationale for the mispricing, and have different degrees of conviction in following through? Absolutely, and while part of the reason is differing time horizons, there are other investor-specific forces that also come into play: Intelligence and educational background: This may be a generalization, and if you disagree, you should take exception, but the smarter an investor is, and the more exceptional his or her educational background (the right schools, credentials and certification), the greater the conviction that investor is likely to bring to investments. One reason is that it becomes easier to attribute perceived market mistakes to the lack of intelligence of other market participants than it is to take a closer look at real reasons why they may not be mistakes in the first place. Personality: Like conviction, self confidence falls on a spectrum with wide differences across human beings. Some investors measure low on the self confidence scale, and look to others for big decisions. Others measure higher  on the self-confidence scale and are willing to make decisions with incomplete information and in the face of uncertainty and disagreement. Still others have so much self confidence and so little self doubt that they risk having convictions that are out of sync with reality.  Over five decades of research in behavioral finance has highlighted overconfidence as one of the key drivers of irrational investor behavior, and underscored the reality that not only does this trait vary widely among individuals, but also that the most overconfident players often rise to the top of the investment and corporate world. In the conviction discussion, overconfidence enters the game early and is perhaps the best explainer of why some investors, looking at what they think is a market mistake, feel so much more convinced that they are right than other investors looking at exactly the same mistake. Track record: When you invest, you receive almost instantaneous and continuous feedback on whether your investments are paying off, and while investment success is always preferable to failure, it is undeniable that some of the worst investment lessons are learned from that success. Much as Wall Street likes its adages of "not mistaking dumb luck for skill", investors are quick to forget them when an investment bet that they make pays off, especially when their success leads to iconic and admiring profiles ("the next Buffett") and investor capital pouring in. You will notice that I don't list age as an additional characteristic, but that is because I don't equate aging with wisdom or temperance. It is true that aging usually muddies your investment track record, and that there is no better bound on over confidence than losing lots of money on what you thought was a sure bet.  The Consequences of Conviction     At this point in the post, I don't blame you for wondering why conviction is such a big deal, since buying Palantir or SpaceX with low conviction counts just as much buying shares in these companies with high conviction. Conviction matters in investing because it affects two choices that investors make - the sizing of positions (with more conviction leading to larger positions in the same investment) and the use of financial leverage (with more conviction often translating into a willingness to borrow more money to fund the investment).  Concentration     One of the fundamental questions in investing, and one that evokes strong disagreement, is how much, if at all, investors should spread their bets. The debate is an old one and there are many views that fall between two extremes. At one end is the advice that you get from a believer in efficient markets: be maximally diversified, across asset classes, and within each asset class, across as many assets you can hold. The proverbial “market portfolio” includes every traded asset in the market, held in proportion to its market value. At the other is the “go all in” investor, who believes that if you find a significantly undervalued company, you should put all or most of your money in that company, rather than dilute your upside potential by spreading your bets. The discussion of investment conviction ties into the answer to this question.  I am not an absolutist on this front, because what you do as an investor should reflect your circumstances.  At one limit, if you are certain about your assessment of value for an asset and that the market price will adjust to that value within your time horizon, you should put all of your money in that investment.  This may seem like an impossible dream, but it it is what you hope to pull off if you find mispricing in the bond or derivatives markets (true arbitrage), where you can lock in the mispricing, with a guaranteed price correction at maturity (of the bond or options).  At the other limit, if you have doubts aplenty and no conviction in your investment choices, you should be as diversified as you can get, given transactions costs. If you have no transactions costs, you should own a little piece of everything, a very real choice in a world of index funds and ETFs. After all, you gain nothing by holding back on diversification and your portfolio will be deliver less return per unit of risk taken. If you are active investor who is constantly in this position (of having no conviction), it is best to retire the "active" part of your investment profile and go all in on index funds.      For most active investors then, the question of how much to diversify will depend in large part on how strong or weak their investment conviction is, and that, in turn, depends on what investments these investors are focused upon. In this context, I would argue that the right amount of conviction and by extension concentration (or diversification) will depend upon the types of companies you invest in (with more diversification needed when you invest in younger companies) and your time horizon (with concentration increasing with time horizon)  Leverage     Financial leverage is an instrument that investors can use to enhance returns, but its use in investing has always been controversial. While borrowing money to fund an investment will increase upside, if you are right, it will also magnify downside, and in some cases, precipitate collapse, if you are wrong. In the early year of investing, financial leverage generally took the form of borrowing money to buy riskier investments (stocks), but the wave of default and distress triggered for investors by the great depression led to restrictions on the use of margin in stock markets. However, those restrictions varied across investor groups (with individuals facing more restrictions than institutions) and across asset classes (with more leverage in real estate than in stocks). The growth of derivatives markets opened the door to bypassing these borrowing restrictions, since buying a (naked) call option is equivalent to borrowing the underlying asset with debt, with leverage increasing with how out of the money the call option is.     Since leverage magnifies both upside and downside, it stands to reason that investors with more conviction in their investment, i.e., that it is under priced and that correction is imminent or very likely to happen, will borrow more money than investors with less conviction: When you are certain that an investment will pay off, you can use maximal leverage, and in true arbitrage, this leverage can be used to convert small mispricing into pure profits. In fact, when options and futures are mispriced relative to their underlying assets, you can borrow 100% of your investment needs, and since the pricing error will disappear at maturity, make a pure profit.     In sum, financial leverage magnifies your core investment judgments. If they are good, leverage will make them look better over time, and if they are bad, they will make them worse. That said, there is a component to the use of leverage that needs to be brought into the picture. Even if you are a good investor, with solid conviction in your investment ideas, using debt to turbocharge your returns can sometimes shrink your time horizon by forcing you to liquidate your mispriced investments before the market corrects its mistakes. That truncation risk eliminates the possibility that you can come back from your losses, and perhaps even have your investment thesis vindicated. A Corporate Life Cycle Perspective on Conviction, Concentration and Leverage     I use the corporate life cycle construct in almost every aspect of finance, because it not only helps understand corporate and investor behavior, but also provides perspective on why one size (or proposal) does not fit all. The corporate life cycle maps out a firm's evolution from a start-up to a growth company to maturity and eventual decline, and traces out changes in revenues, earnings and risk over the aging process: There is no deep intellectual insight here, but as companies age, the challenges that investors face in pricing them shifts. With young firms, where there is little history, the business model is still in flux and the value lies almost entirely in the future, the pricing will be less precise than for more mature firms, where there is an established business model and more financial history, and more of the value comes from investments already made. For declining firms, where liquidation looms as a viable option, the pricing exercise becomes one of estimating liquidation value, i.e., what others will pay for the individual assets owned by the firm. The higher pricing uncertainty that investors face when valuing younger companies is accompanied by a second problem, which is that pricing mistakes, if they exist, need catalysts for market correction. Those catalysts are less frequent and decisive with younger companies, where earnings reports have little of substance to report and operating targets are diffuse, creating more open questions about whether a market mistake will be corrected, and if so, when that will happen.     Using this framework, you can see that you can be more tolerant of concentrated portfolios, with debt added on, when you invest in mature companies than you should be when investing in younger companies. By the same token, investors who find pricing mistakes in younger companies, but are daunted by the noisiness of their estimates or uncertainty about markets correcting, and thus are low on the conviction scale, can overcome their reluctance to act by spreading their bets across many such companies. As some of you may be aware, I did value SpaceX at the time of its IPO at about $100 a share, and as the price has drifted down towards that price, I may very well be faced with an underpriced stock (where the market price drops below $100). Given the uncertainty that is associated with my estimate, and you can see it in the simulation that I reported in my post, it is unlikely that I would ever have sufficient conviction to make SpaceX the biggest or only investment in my portfolio, but I stand ready to buy the stock as part of a portfolio, where it is one of many bets that I make on markets. Lessons from Leo I started this post with the story of Situational Awareness and Leo Aschenbrenner, but I spent most of it talking about investment conviction, what it is, its sources and consequences. I want to end the post by returning to Leo’s story and what we can learn from it as investors. Lesson 1: Investment actions that are inconsistent with investment conviction risk ruin     I have no issues with Leo's story of AI winning big in the near-term and buying the winners and selling the losers that will result. It is macro story investing, and it has worked for some in the past and failed for others, but if timed right, it can deliver significant returns.  In fact, I will concede that Leo knows far more about AI than I ever will and is using that knowledge in constructing his AI story. I also have no bone to pick with investors using financial leverage to supercharge their returns,  with low-risk investments, though I remain concerned that the (2 & 20) fee structure may lead them to use too much debt. My concern with Situational Awareness, as a fund, and this would have been true on June 19, even at it peak, is that combining a macro story about AI winning with maximal leverage creates a time bomb. The AI story, no matter how well told, has multiple obstacles to overcome, some related to business economics and some to politics and regulation, and is a risky bet, and it makes sense to fund it with significant amounts of debt. I know that there are defenders who will point to the fact that the fund, even after its markdown, was up substantially from its inception date, but the fact that leverage cut the fund's life short only strengthens the case that if it had been run with less debt, it would have had a bad month in July, but lived to tell the tale and perhaps even deliver on its AI promise. Lesson 2: Momentum is a wild card in every investment strategy, and you ignore it at your own peril. It is a well-established finding in equity markets that momentum is one of the strongest forces moving markets and that it can overwhelm the best planned strategies of most investors. If you look at the composition of Situation Awareness portfolio through much of its rise and fall., the long positions were primarily in companies that benefit from the build-up of Ai architecture, selling their products and services to the hyper scalers and LLM companies, and the short positions were in software and other businesses that would be disrupted by the rise of AI. With both groups, Situational Awareness was taking bets that were in line with what the market was pricing in already, albeit in a more concentrated and leveraged form. While market observers were quick to link both the rise and fall of Situational Awareness to the AI story, you can make just as strong a case that much of that happened at the fund over its brief existence can be explained by momentum, with leverage acting as a super charger; continued momentum generated the outsized return though June 19, and the market reversal in July caused the correction. Lesson 3: Humble money beats smart money     The legend of smart money persists in markets, where investors who are smarter than the rest of us, with access to information and capital that others do not possess, deliver supersize returns for themselves and those that they invite into their inner circle. That legend serves everyone's interests, since the smart money uses its reputation to attract more capital and the not-so-smart money has someone else (hedge funds, insiders, activists) to blame for investment setbacks. While many money managers aspire to be part of the smart money group, most never make it into that rarefied circle, and those that do often have to pay their dues over long periods. Leo Aschenbrenner, in contrast, broke into the group in just a few months, perhaps helped by his pedigree as an AI insider and with an assist from his post on the coming AI revolution. The problem with smart money is that  its self-regard makes its susceptible to attributing more precision to its own convictions, than merited by the circumstances, and that, in turn,  results in over reach (portfolios that are much too concentrated or levered). Situational Awareness clearly overused leverage, and while some will attribute that to the youth and inexperience of its lead manager, it is worth remembering Long Term Capital Management, where John Merriweather, after a long and distinguished trading tenure at Salomon Brothers, aided by two Nobel Prize winners in economics, brought the fund to its knees by borrowing too much on risky trades. In a post from years ago, I drew a contrast between smart money and humble money, with the former including investors who attribute every basis point of excess return earned to their investing brilliance, and the latter open about the fact that their performance, no matter how stellar, has as much to do with being in the right place at the right time (being lucky) as it has to do with skill. Investors looking for someone to manage their money are likely to do much better with the latter than the former.        I hope that you don't view this as a hit piece on Leo or AI, since that was not my intent. In fact, I hope that Leo persists and perhaps even comes back as a fund manager, since he strikes me as an original thinker who is willing to take a stand, both scarce qualities among active fund managers.  I also hope that he has learned some lessons, especially on humility and restraint, for his next go around, and that he adopts a fee structure that gives his investors a chance of beating the market in the long term. YouTube Video Links to the Leo Aschenbrenner story Situational Awareness: The Decade Ahead (Leo's post on AI) The 24-year old AI Wiz who counts Jane Street as an investor (The Wall Street Journal in early June, prior to blow-up) Inside the Meltdown of a Wunderkind's AI Hedge Fund (New York Times) His Wedding Guests were arriving - Just as his $45 billion fund was falling apart (The Wall Street Journal) What does the humbling of Leopold Aschenbrenner mean for the AI bubble? (The NewYorker)

I am a creature of habit in my personal and professional life, and in the context of the content that I post online, there is a ritual that I follow with my data updates. I start the year with my general data update online, and follow up with a series of posts where I examine the implications of this data for investing and corporate finance.  Since 2008, I have also done annual update papers on equity risk premiums in March of each year, with the link to the 2026 update here, and country risk in July of each year, where I look at the topics in more details, trying as best as I can to integrate the data, research and my own thinking. This year's country risk update paper is now available, and as in prior years, I will spend this post looking what causes risk to vary across countries, how to measure those risk variations and the implications for businesses and investors. Country Risk: Relevance   In my years as a business school student, country risk was given short shrift and I don't remember spending much time talking or thinking about it. Part of the reason was that business school education  was dollar-centric and built on the presumption that most graduates would go to work in New York, London or Tokyo, and have little need to confront country risk on a day-to-day basis. For those who raised country risk as an issue, the response was that you could, as a company or investor with global exposure, diversify it away. Both presumptions were wrong even then, and have become even more flawed over time as we have sold both companies and investors on the benefits of globalization.     For businesses, the exposure to country risk comes from both the revenue side, as larger portions of every company's revenues come from foreign markets, and the cost side, as production gets outsourced to locales overseas. That exposure tends to increase as companies scale up, and is higher in some sectors than others; technology companies, for instance, get far more of their revenues from other non-domestic markets than manufacturing or service businesses. Outside of utilities (power, water), it is rare for a company to be entirely domestic-focused on both its revenue and cost sides. For investors, the initial draw of investing in foreign markets might have been diversification but the greater pull has come from greed, i.e., the belief that you can higher returns in the rest of the world. That process was accelerated by the creation of investment vehicles (index and mutual funds) that made investing overseas easier, the lowering of transactions costs across markets and a greater standardization of financial statements and disclosure across the globe. The home bias in portfolios, i.e., the skewing of portfolios towards domestic market investments, has not disappeared but it is lower than it was at the turn of the last century.    The notion that country risk is diversifiable, i.e., that if you are operating or investing across the world, the risks will average out across countries, has been undercut by the increased correlation across global equity markets, and especially so during market crises (which is when you care the most).  At the risk of being hyperbolic, there is no place to hide from country risk, for either businesses or investors, and ignoring or dismissing country risk is not an option. I discovered this truth in the 1990s, when I found myself in need of a mechanism to incorporate country risk into my corporate financial analysis and valuations, and the process that you see described in this post was born from that need. I would hasten to add that the process that I describe has very little intellectual firepower behind it, puts pragmatism ahead of theory and most importantly is a work-in-process. Country Risk: Drivers     I don't think that there would be much disagreement, if I assert that it is riskier to invest in some parts of the world than others, but there is likely to be plenty of disagreement on why there are risk differences and which parts of the world are riskiest. In the broadest sense, I argue that variation in business risk across countries can be traced to four factors - the political structure of the country (democracy vs authoritarian), the prevalence of corruption in the country (operating as a hidden tax and distorting business outcomes), the extent of violence in the country (from internal and external forces) and the strength of the legal system in enforcing property rights and contractual obligations.      On the political risk front, I looked at the EIU's Democracy Index, a composite score measuring both political freedom and protections of civil liberties, with the caveat that any index that tries to measure these will make subjective judgements that not everyone will agree with. In their most recent update, here is what the EIU scores looked like around the world: Source: Economist  Low (High) score: Least (Most) freedom Based on these scores, the tilt towards authoritarianism has increased over the last decade, with only 7.3% of the world's population living in democracies at the end of 2025. Note, though, that there is still an open question of whether businesses and economies do better under democratic than authoritarian regimes, and the answer in the research is at best a "maybe".  From a risk perspective, democratic regimes create more continuous risk for businesses, with elections bringing regulatory and rule changes to economies, than authoritarian regimes, where governments can promise more continuity in policy, but when change does come to the latter, it is more likely to be large and wrenching.          Corruption is a fact of life in much of the world, and businesses often have no choice but to pay the price to survive and grow. Transparency International, a global coalition against corruption, tries to capture the extent of corruption, comes up with corruption scores for countries, with lower scores indicating less corruption, and the most recent edition contains the following: Source: Transparency International Low (High) score: Most (Least) corruption Northern Europe has the lowest corruption scores, followed by Canada, United States and Australia, but large portions of Africa have high exposure to corruption, with Latin America and much of Asia falling in the middle.      Living in the midst of violence takes a toll, and that toll is extracted from businesses that try to operate in its presence. Vision of Humanity computes peace scores for countries, measuring exposure to both violence within the country as well as from wars and terrorism. The most recent peace scores are reported below: Source: Vision of Humanity Low (High) score: Most (Least) peaceful Canada, Australia, Japan and much of Europe score high on the peace dimension, and while Latin America and Africa score lower, there are portions of each continent that are more peaceful. The Russia-Ukraine war has created a huge area of violence across Eastern Europe and Russia, and exposure to gun violence creates a drag on the United States.     Businesses are dependent on the legal system  to enforce property rights as well as contractual obligations. Countries that have legal systems that are either capricious on these fronts, or hopelessly slow in acting, create challenges for businesses that operate in them, creating both costs and risks that they otherwise would not face. Property Rights Alliance is an entity that tracks international property rights across the world, and in their most recent update, their property rights scores by country are captured below: Source: International Property Rights Low (High) score: Least (Most) property rights There are wide differences across regions, when it comes to legal and property rights, with Latin America, Africa and Asia lagging and Europe, Australia and much of North America leading.      There is one final dimension that I have added to country risk in recent years that captures exposure to climate risk. While there are many different entities that measure this exposure, each one with its own skews, the map below which shows the climate risk exposure, by country, from GermanWatch: Source: GermanWatch Low (High) score: Least (Most) affected There are two reasons why climate risk has not become a bigger topic in country risk discussions. The first is that there is no part of the globe that is unaffected, making it less of a differentiator across countries on the risk dimension. The second is that climate risk, by itself, is an abstraction for businesses, until it starts affecting the bottom line, and while there are individual companies that are being impacted, the aggregate effects, at least at the moment, are not big enough to change the discussion.   Country Default Risk     While country risk is determined by multiple factors, the challenge that businesses is  in consolidating all of those risks into one number. The market that does this most directly is the debt market, where, when countries (sovereigns) seek to borrow money, lenders determine the interest rates to charge them, based upon perceived default risk. To understand why lenders worry about default with sovereign debt, you can start by looking at the history of sovereign defaults in the graph below: Source: BoC & BoE Sovereign Default Database Debt defaults, which soared in the 1980s and 1990s, have been lower in this century, with a shift away from loan defaults (where banks are usually the lenders) to defaults in the bond market. It is also worth noting that a non-trivial portion of sovereign defaults in each year are local currency defaults, indicating that for some borrowers, the costs of defaulting are viewed as smaller than the costs of inflation arising from printing more currency to pay off debt. Over time, Latin America has been the epicenter for sovereign default, but at the end of 2023, sovereign debt in default had a wide geographical spread: Source: BoC & BoE Sovereign Default Database The most widely accessible measures of sovereign default risk remain sovereign ratings, with ratings agencies operating as (imperfect) arbiters. At the start of July 2026, the graph below reports the sovereign ratings for all rated countries, from S&P, Moody's and Fitch: Source: Multiple public sources As you can see, the ratings agencies mostly agree on their assessments of default risk, and sovereign ratings are correlated with the risk drivers (politics, corruption, violence, legal system) that we outlined in the last section. I do believe that ratings agencies, notwithstanding the critiques of bias and mis-measurement leveled against them, do a reasonably good job in their ratings assessments, but they are often slow to act, when confronted with change.      The sovereign CDS market offers a market-based alternative for measuring sovereign default risk, with investors making assessments of how much they would demand to insure against sovereign default in the form of (annualized) spreads. In the graph below, I list 10-year sovereign CDS spreads as of July 1, 2026: Source: Bloomberg Note that sovereign CDS spreads are available for only 84 countries, and that there are swaths of the world (Central and North Africa, frontier markets) where they are not available.  Country Composite Risk     When you lend money to governments or buy government bonds, sovereign default risk is your key concern, and both sovereign ratings and CDS spreads try to measure that risk. When running a business in a country, you are exposed to a much wider range of risks, and measuring exposure to those risks may require different measures. One alternative is country risk scores, where services evaluate how  countries measure up on different risk drivers, and come up with composite scores for these countries. In the table below, I report the country risk scores from two services - Political Risk Services (PRS) and the Economist (EIU), at the start of July 2026: Sources: EIU (Economist) and PRS The table illustrates three problems that you face with political risk scores. The first is that the scoring is idiosyncratic, with the Economist going from low scores for the safest countries to high scores for the riskiest, and PRS doing the reverse. The second is that each service picks different factors to consider, and different weightings, leading to scoring divergences that sometimes confound; PRS, for instance, ranks the United States as riskier than Ghana, on a composite risk basis. The third is that the scores, by themselves, are difficult to convert into inputs in financial analysis, either in cash flow or discount rate adjustment.    It is to combat the third problem that I started estimating country equity risk premiums, and while the details of the process and the data that I use have changed over the last three decades, the basic structure has remained unchanged. I start with an estimate of the equity risk premium for a mature market, and build a country risk premium, if needed, for riskier countries. Until 2025, I estimated the mature market premium by computing an implied equity risk premium for the S&P 500, and using that as the base, arguing that the US, as a Aaa rated country (at least according to Moody's), represented a mature market. The Moody's downgrade for the US, from Aaa to Aa1, has thrown a wrench into that approach, requiring adaptation. In response, I now start with an estimate of the implied ERP for the S&P 500, but then adjust that estimate for the default spread (based on the Aa1 rating) for the US, with the resulting values at the start of July 2026 below: Spreadsheet: https://pages.stern.nyu.edu/~adamodar/pc/implprem/ERPJuly26.xlsx As you can see, with the S&P 500 at 7499.36 on July 1, 2026, the implied equity risk premium for the United States is 4.42%, and netting out the default spread of 0.22% for the Aa1 rating yields a mature market premium of 4.20%.     To estimate country risk premiums, I start with the sovereign ratings for rated countries and convert those ratings into default spreads. To adjust for the higher risk associated with equities, relative to government bonds, I estimate a composite measure of that relative risk, by scaling the volatility in an emerging market equity index to the volatility in a emerging market government bond ETF, and scale the default risk up with this relative risk measure (1.55 in July 2026) to get country risk premiums: For the two dozen countries that have no sovereign ratings, I adopt an even more makeshift approach, where I used political risk scores for these countries, and then looked for rated countries with similar scores. The table below has equity and country risk premiums, by country, for all of the countries that I evaluated in July 2026: Download data I did post an earlier version of this table a couple of weeks ago, but the numbers that I reported reflected in incomplete update of sovereign default spreads, and this table (and the data on my webpage) now reflect the corrected (and lower) spreads. (As a solo act, I am deeply grateful for the checking that those who use my data do, and thankful when they point out mistakes that I have made.) Company Exposure to Country Risk     If you buy into my argument that every company has a narrative, and it is the narrative that drives its value, it is worth considering where country risk fits into that narrative. The answer, I believe, comes from looking at where the country in question falls in the life cycle: The message from this life cycle view is a sobering one, especially for those analyzing companies that operate in very risky countries, since the narratives for these companies implicitly or explicitly incorporate a country risk component. You cannot value a Venezuelan company without taking a strong view about Venezuela, or even an Indian and Brazilian company without an India or Brazil country story underpinning value. In contrast, you may be able to value US and European companies, without explicitly considering the evolution of country risk in those parts of the world.     When looking at an individual company, I believe that country risk exposure comes less from where the company is incorporated and more from where it operates. It is undeniable that companies around the world have substantial exposure outside their domestic markets, and that exposure has increased over time. In the graph below, I look at the revenue breakdown of companies in four indices - the S&P 500 (US), the FTSE 100 (UK), the Nikkei 225 (Japan) and the Sensex (India):      In every single index, companies that comprise that index get a significant portion of their revenues from outside the domestic market. Looking across sectors, exposure to foreign markets varies widely with technology companies often generating more than half of their revenues outside their domestic settings. I believe that equity risk premiums for companies should reflect exposure to foreign markets, though it is worth debating how best to weight that exposure - revenues work well for consumer product and service companies, production works better for natural resource companies and a mix of revenues and production may be the right choice for manufacturing companies: With this framework, you can see why almost all analysts will confront country risk, sooner or later, no matter where they operate in the world and which companies they analyze.      For companies, country risk will also come into play when faced with capital budgeting decisions, where they need estimates of hurdle rates for individual projects, to decide where to invest. For a multinational operating in many businesses, the project cost of equity will have to then also reflect the business the project is in, in addition to country risk. Thus, the cost of equity for a Siemens Appliances for a project in India should reflect the beta for the appliance business, in addition to the country risk for India. In contrast, a Siemens power tool project in Hungary should be computed using the beta for an power tools project and the country risk for Hungary. It is also possible that country risk is not easy to isolate, if the production facilities are in one country but revenues are generated in another. If the Siemens appliance factory in India will be producing products that will be sold in Japan, should we be showing the country risk of India or Japan in the cost of equity calculation? The answer, as was the case in the earlier section on valuation, is that it depends on where the company sees risk coming from. If the risk is that production will be delayed or disrupted by political and economic risk in India, it is Indian country risk that should be looked at, but if the primary concern is that revenues in Japan will be volatile because of economic conditions there, it is Japanese country risk that matters more. If both risks are considerations, you should use a weighted average of Indian and Japanese country risk. Currency Questions     For some of you, it may seem odd that I have spent almost an entire post talking about country risk without bringing up currencies. The reason is simple. Currencies are measurement mechanisms, and while they may be affected by the same political and economic factors that drive country risk, they don't determine country risk and in my view, should not command risk premiums, on their own.      It is true that hurdle rates are affected by both the equity risk premiums that you estimate and the riskfree rate that you use, and that riskfree rates vary across currencies. In the figure below, I estimate riskfree rates in about 40 currencies, where a local-currency government bond rate is present, and I adjust that government bond rate for the default risk of the government in question: When estimating the cost of equity for a Turkish project or company in Turkish lira, we start with a riskfree rate in excess of 20% and build on it, by adding equity risk premiums to it, but the cost of equity for the same project or company in Euros will begin with a riskfree rate close to 3% (the German Euro bond rate) and arrive at a much lower number. While this may sound farfetched, the value that you derive for the project or company should be the same using either currency, if you are consistent about estimating your cash flows in the same currency: Since much or almost all of the differences in riskfree rates come from inflation differentials, matching the high Turkish lira discount rate with a high growth in cashflows in Turkish lira, and the low Euro discount rate with the low growth in cashflows estimated in Euros will yield results that are consistent.     If you do want to estimate riskfree rates in currencies where there is either no local currency government bond that is traded or where you mistrust the government bond rate, because of light trading or government intervention, the fact that riskfree rate differences across currencies can be tied to differential inflation can be used for estimation; the riskfree rate in any currency can be computed from a base currency (dollar or Euro) riskfree rate and the difference in expected inflation between the local and base currencies: Put simply, if the expected inflation rate and riskfree rate in US dollars are 2.5% and 4% respectively, and the expected inflation rate in Brazil is 10.5%, the riskfree rate in Brazilian reais should be roughly 12%. The implication of this approach is that currency pegs, when they do exist, will hold only if the inflation in the pegged currency matches or is close to the inflation in the index currency to which it is pegged. It is true that the estimates of riskfree rates will only be as good as the expected inflation rates that are embedded in the estimation, but the good news is that being wrong on expected inflation will be largely offsetting, since both your cashflows and your discount rates will be wrong in the same direction; if you underestimate expected inflation, you will underestimate (overestimate) your riskfree and hurdle rates, but you will also underestimate (overestimate) your expected growth rate in cash flows. Conclusion    One of the side effects of the rise of globalization is that there are fewer and fewer companies that are entirely local-country focused in both their revenues and production, and as a result, almost every business and investor is exposed to risk in other parts of the world. The problem with measuring country risk is that while its consequences are economic, it has its sources in history, politics and governance structures. The measures of country risk, whether they be entity-based like sovereign ratings, or market estimates like sovereign CDS spreads, reflect this interplay.     I confess that I have made simplistic assumptions and cut corners in my attempt to estimate equity risk premiums, by country, and there will be individual countries, perhaps even your own, where you might disagree with my assessments. As I noted earlier, my estimation approach remains a work-in-progress and I am always open to suggestions on how to estimate these premiums better, but keep in mind that whatever those improvements may be, they will have to work across 180 countries.  YouTube Video Papers on country risk and equity risk premiums Country Risk Premiums - The 2026 Edition (July 2026) Equity Risk Premiums - The 2026 Edition (March 2026) Data Equity Risk Premiums, by country - July 2026 Implied Equity Risk Premiums for the S&P 500 (Annual since 1960 & Monthly since Sept 08) Inflation-based riskfree rates, by currency - July 2026 Spreadsheet Implied ERP for S&P 500 (July 2026)