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)

A few weeks ago, I assessed the value of SpaceX ahead of its initial public offering, with the admission that I was making my estimates with drabs of data, some of it coming from unofficial sources. I also promised to revisit my valuation, when the prospectus came out, and now that it has, I will examine how the information it contains has changed my view of the company and its valuation. I will also use this post to talk about the information gained by having access to a company's financials, and why the information you glean from those financials is different at younger companies, with growth potential, relative to mature companies. The Prospectus: Data versus Information     The requirement that companies that plan to go public in the United States have to register with the Securities Exchange Commission (SEC) and file a prospectus has been in place for decades, but the contents have changed over time, with disclosures added on partly by regulation and partly in response to investor demands. In a paper focusing on IPO disclosures from a couple of years ago, I noted that prospectuses have become more bloated over time, often running four to five times longer than those filed by companies that went public three or four decades ago, but not necessarily more informative. The SpaceX prospectus that we made public on May 20, 2026, is 277 pages long, with an addendum that runs another 100 pages, with dozens of pictures (mostly of spaceships going into orbit), a soaring story, but with weak links and multiple distractions. To get a measure of how the prospectus changes my pre-prospectus story and valuation, I will start with the easy part of the update, where I use the numbers from the financial statements in the prospectus to replace my pre-prospectus estimates, on operating metrics like revenues and earnings as well as on share count and IPO proceeds. I will then move on to the weightier part of the analysis, where I assess how the information in the prospectus has changed my story line and value for the company.  The Prospectus: Data update     In my pre-prospectus valuation, where I assessed the value of SpaceX at roughly $1.2 trillion, I relied on scraps of information, including leaked stories of estimated revenues ($15.5 billion) and EBITDA of $8 billion, since I did not have access to the company's full financial statements. With the release of the prospectus, that shortcoming has been remedied, and I started by updating the operating metrics that drive the intrinsic value of the company: SpaceX prospectus As you can see, my estimates for revenues for the launch and connectivity (Starlink) businesses were close to the actual numbers, but my xAI revenue estimates were much lower than reported. Overall, I had estimated an operating loss of $2 billion in 2025, and the prospectus yielded a larger loss of $2.57 billion. With almost $2 billion in interest expenses, unavailable prior to the prospectus, incorporated, the company reported a net loss of about $5 billion. A big factor in the operating losses reported by the company were its ballooning R&D expenses, and in keeping with my argument that these expenses should be capitalized, I estimated an earnings before interest, taxes and R&D of $4 billion.     On the financing front, the prospectus filled in details on cash and debt that were unavailable prior to the prospectus being made public: SpaceX prospectus My pre-prospectus estimate of book value of equity was a shot in the dark, at $20 billion, but the acquisition of xAI caused that number to jump to $41.3 billion, as did the total debt (inclusive of leases) to $22.9 billion. The former (book value of equity) played little role in my valuation, but ignoring debt of this magnitude may seem monumental, there are two offsetting factors that reduce the impact on my value estimate. The first is that I also ignored the presence of cash, and with $24.7 billion in cash, the company's net debt is −$1.9 billion (cash exceeds debt), making the impact on value minimal. The second is that with my estimate enterprise value of $1.21 trillion, the debt, even if considered in full, is small enough to represent rounding error.     The prospectus did contain information on share count and structure, as well as on the company's plans for the proceeds, and both were useful at the margin, with the former affecting my estimated value per share and the latter determining the treatment of the cash that will be raised from the offering: Share count: In my initial valuation, I used the private company pricing per share in conjunction with estimated market cap to back out a share count of 2467 million shares. With the prospectus, we get a clearer sense of shares outstanding, with a basic share count of 12,535 million shares reported in the prospectus (pages 246 & 247) in computing per share numbers. That share count does not include the new shares that will be issued in the offering, but that share count will be determined by the magnitude of the offering as well as the expected issuance price, and while the total share count includes options, warrants and rights that are exercisable before June 30, it does not include restricted stock units held by employees (see prospectus, page 18) and that information is still blanked out in the prospectus.  Use of proceeds: It is estimated that SpaceX plans to raise $75 billion from the offering, and the prospectus specifies that the company plans to hold the proceeds to cover infrastructure investments in these businesses (see prospectus, page 66). That implies that any money raised in the offering will add to the company's cash balance, right after the offering, and will augment firm value (but not enterprise value).  The prospectus also lays bare the governance questions that will overhang the firm, with information that there will be two classes of shares- 6,932 million class A shares with one vote per share and 5,602 million class B shares with ten votes per share. The public offering will be class A shares, and with Elon Musk holding all of the class B shares, he will control more than 85% of the voting rights in the company. In summary, the prospectus is long and filled with distractions, but there is almost nothing in it that surprises me. SpaceX is a growing company that is money-losing and cash-burning, that will be a Elon Musk vehicle (with all the pluses and minuses that entails).  The Prospectus: Story update     In my original post, I noted that SpaceX is a company, where it is the story about how its businesses will evolve over time that drives value, rather than the base year numbers (on revenues, earnings and cash flows). That story, broadly speaking, has three key spokes to it and they are summarized below: The first of these spokes, target revenues, frame how big each business can grow over time, and is a function of the total market and market share. The second, the target operating margin, will capture how profitable each business can become, and is determined by unit economics and economies of scale. The third, reinvestment, measures how much each business has to invest to get to target revenues, and will vary with the capital intensity of the business. To frame how my valuation will change, as a result of what I learned from looking at the prospectus, I will start by presenting by pre-prospectus estimates on these key inputs, and then look at the impact of the prospectus on each input. Pre-prospectus inputs and value     My pre-prospectus valuation of SpaceX contains my storyline for the three businesses that the company is in, with an add-on for the expansion options embedded in each business: With these inputs in place, I estimated a value of $1.2 trillion the SpaceX enterprise, and since I ignored cash and debt, this yielded an equivalent market value. Driving these numbers are upbeat stories about each of the three businesses that SpaceX is in, with large revenues and high margins in stable growth. The Prospectus Effect     To the extent that the prospectus contains information that alters the storylines on any or all of these businesses, it will affect my estimate of value for SpaceX. 1. Revenue Growth (Target Revenues)     I will start with the growth (target revenues) input and use two parts of the prospectus to reexamine my story. The first is the historical growth reported by the company for each of its three business lines - launch, connectivity and AI. As you can see, the company saw its revenues grow by a third in 2025, relative to 2024, with divergence across businesses; the connectivity business led with revenues growing by almost 50%, the AI business saw an increase in revenues of about 22% but the space business reported only modest growth in the year (7.64%). In short, notwithstanding the star role played by AI and the appeal of the rockets in the space launch business, it is Starlink that carried the company in 2025. The prospectus mentions Colossus, xAI's compute center, which has been leased to Anthropic for an eye-popping $1.25 billion a month, which should kickstart revenues next year, with the potential of tension in future years if xAI plans to go head-to-head against Anthropic in the AI products market.     The other relevant section of the prospectus contained estimates of total addressable market (TAM) for the company, broken down by business: If the prospectus is to be believed, SpaceX has the largest TAM of any company in history, with a total TAM of $28 trillion, and AI accounts for $26 trillion of that market estimate. This estimate borders on fantasy, but I will cut the bankers who came up with these numbers some slack for two reasons. First, the estimation of TAM has been gamified by Silicon Valley, with bloated and patently unreachable numbers floated for companies, as I noted when I valued Uber (which was given a TAM of $5.7 trillion in its prospectus) for its IPO in 2019 and Airbnb (with a TAM of $3.4 trillion in its prospectus) in 2020.  Second, it is true that AI agents are usable across almost every business and geography, giving it much wider reach than most products and services, and while the details of how the TAM was estimated are not specified in the prospectus, my guess is that the $26 trillion estimate includes all or most of the operating expenses of all businesses.  Story takeaway: I will stick with my estimates for target markets for the space launch and connectivity businesses, since the TAMs in the prospectus are, in my view, over reaches, and I will slow growth in the near years, to reflect that these businesses will take time to mature. In the AI business, I disagree with the magnitude of the TAM in the prospectus, but the acquisition of Cursor and the indications in the prospectus suggest that xAI very much wants to be part of the enterprise solutions space, notwithstanding its immense capitalization needs, and I will double my target revenues for AI from $80 billion to $160 billion, reflecting my estimate of a TAM of about $3 trillion to $4 trillion for AI products and services from businesses. 2. Profitability      On the profitability front, the first part of the prospectus that I looked at was its breakdown of income statements, by business: With the caveat that we have only two years of detailed information, there are interesting findings that emerge from the historical data on each of the businesses.  The space business has the best unit economics of the three business, with a gross margin of about 67%, reflecting the cost advantages of its reusable rocket technology. While the space business reported an operating loss, that was entirely because of its weighty R&D expenses, and capitalizing those expenses results in a healthy operating margin for the business. The connectivity business does not have gross margins as high as the space business, but those gross margins are improving, with gross margins jumping from 37% in 2024 to 48% in 2025. This business had positive operating income in 2025, even before capitalizing R&D, and improves substantially with capitalization.  The AI business not only has the lowest gross margins of the three businesses, but saw deterioration of those margins in 2025, reflecting intense competition from other LLMs as well as the rising costs of delivering AI products and services. There are other parts of the prospectus that come into play in the profitability discussion, with each of the businesses: On the space launch business, the cost of launching payloads at SpaceX have been trending down, making its already large cost advantages in the business even larger.  On the connectivity businesses, there is bad news and good news on the per user front. The bad news is that the revenues, per month, per subscriber, declined from $99 in monthly revenues in 2024 to $66 in monthly revenues in the first quarter of 2026. The good news is that the number of subscribers has doubled from 5 million in the first quarter of 2025 to 10.3 million in the first quarter of 2026, with the bonus that the company has been able to improve its profitability (see gross margins in the table above) over time.  On the AI business, there is not much to go on, on the profitability front, since the focus in the prospectus is more on the increase in compute capacity (see nameplate compute draw on Page 90 of the prospectus) than it is on revenues, especially on the enterprise front. Here again, though, the Colossus lease with Anthropic should help with profitability in the near term. Story takeaway: The unit economics for the space businesses, in conjunction with the recognition that there are no other substantial operating expenses (outside of the misclassified R&D expense) in either business, lead me to increase my estimate of the target margin for the business to 45%, from 40%. I will leave intact the target margin of 60% for the connectivity business, because once the satellites that service this business are in space, this is the business that will benefit the most from scale. My biggest shift is in my estimated target margin is for the AI business, where the dynamics that are pushing gross margins down, i.e., increased competition and high costs of delivering AI services, will persist; my estimated operating margin drops from 45% to 25%. 3.  Reinvestment     In my post prior to accessing the prospectus, I did describe SpaceX as a capital intensive business, but the actual spending on capital expenditures and R&D in the prospectus is breathtaking in its magnitude: In 2025, the company spent almost $14 billion in capital expenditures and almost $9 billion in R&D, a doubling of its reinvestment from 2024. In particular, it is AI that is driving the bulk of this surge, accounting for more than $14 billion in total reinvestment in 2025, with $9.1 billion in capital expenditures and $5.1 billion in R&D. The positive twist that a SpaceX optimist would put on these numbers is that the spending on AI in particular is a positive, indicating that the company is not planning to settle on a niche market strategy, but instead will will go head-to-head with Anthropic, Google and OpenAI for the enterprise solutions markets. The negative spin is that this ambitious agenda will translate into tens of billions more in capital expenditures in the near years, creating a drag on the cash flows and value destruction if they lose the AI market competition. Story takeaway: Given that SpaceX is continuing to invest substantial amounts in its space launch and connectivity businesses, I will increase reinvestment in the near term (years 1-5) by lowering how much they will generate as additional revenues for every additional dollar of capital invested (lower sales to capital ratios). With AI, where I was already assuming that reinvestment would be large (with a low sales to capital ratio), the tripling of target revenues will result in a surge in reinvestment to generate the higher sales. Updating Story and Value     While the core story of SpaceX being a company with growth potential and strong competitive advantages that I framed prior to reading the prospectus remains intact, there are changes to that story that come from the information in the prospectus. The prospectus reinforces the notions that the company is best positioned in the connectivity business to generate both revenue growth and profits in the near term, that its cost advantages in the space launch business will persist and deliver profits, but that target market will be slower to develop, and that the AI business has both the largest target market and poses the biggest challenges, in terms of profitability and capital intensity, for SpaceX.      Bringing together my changes in target revenues, operating margins and reinvestment inputs allows for an update of the input table that I started this section with: Clearly, some of the changes in inputs (such as the higher margins for the space launch business and a bigger target market for AI) will push value higher, and some of the inputs (including a slowing of near term growth for all business, and the much lower margin for the AI business) will push in the opposite direction. Since US treasury rates have risen from 4.20% at the time of my earlier valuation to 4.56% at the start of June, I have increased the costs of capital that I use in the valuation accordingly (to 8.37% from 8.02% to start the valuation, and the steady state cost of capital to 8.25% from 8.00%; both numbers would put SpaceX at close to the median for all US companies). With these updated inputs, I reestimate the cash flows and the valuation for SpaceX, with the IPO proceeds (estimated at $75 billion) added to the mix: Download spreadsheet The enterprise value for SpaceX edges up from $1.21 trillion, in my pre-prospectus valuation, to $1.22 trillion with the post-prospectus numbers, and the overall equity value increases to $1.3 trillion, with almost all of the increase coming from the influx of $75 billion in cash from the IPO, albeit with a higher share count. The value per share of about $100 will need some revisiting as the IPO numbers firm up and more information is forthcoming on restricted stock units owned by employees, but just as I was finishing this post, a news story hit the wires that the offering price would be set at $135/share.    If I were to summarize the impact of the prospectus on my SpaceX story, it would be that it has made the story bigger, but also more volatile. There are a multitude of risks that SpaceX faces in each of its businesses, but the one that I would be concerned about the most is that it will overreach in the AI business, beginning with an overestimate of the target market for AI products and services and the strength of its own competitive position in that market, and following through with investments that reflect those misplaced assessments. Those concerns are heightened  by a voting share structure that locks in Elon Musk's control of the company, since there is little that shareholders can do to restrain the company, if SpaceX doubles down on capital expenditures and acquisitions in the AI space, even after it becomes clear that the AI market is much smaller than anticipated and/or that xAI's offerings are not as good as the competition. If you add to this mix the antipathy that exists between Musk and Sam Altman, you have the potential for a UFC match between two monstrous egos, funded by tens of billions of dollars shareholder money. Financial Statements and Value: The Life Cycle Effect     Financial analysis and valuation, going back to Ben Graham's Security Analysis, has always been centered on financial statements, and that focus has become more intense over the last few decades as access to data and analysis tools has expanded. In fact, much of what passes for valuation has become financial modeling, where line items in financial statements are forecast based upon the historical time series, with the proverbial bottom lines being earnings and cash flows. Along the way, ratios computed from financial statement numbers are used to screen companies for investment quality. Some of these ratios, such as accounting returns on capital and equity, have become the basis for assessing company quality and competitive moats in the hands of consultants and investors. The SpaceX prospectus is a case study in why this approach to investing is often myopic and misleading, and why the informational value of financial statements will change as companies grow and mature.      In valuing companies, you are always trying to forecast revenues, profits and cash flows in future, but they key questions you want answered and the drivers of value shift as you move through the life cycle: As you can see, for young companies, the key determinants of value include sizing the total market and assessing unit economics, and not the proverbial bottom lines in accounting statements including the magnitude of revenues and profitability. As companies move through the life cycle from start-up to mature to decline, you should expect to see financial statements evolve as well. Young and high growth companies will generally report small revenues (though they expect those revenues to ramp up over time) and losing money and having negative cash flows is a feature, not a bug. As companies mature, revenues will get larger (albeit with lower growth) and profits turn positive, as will free cash flows available to return to shareholders in dividends and buybacks. If you allow for the fact that all three of SpaceX's businesses are young, falling in the young to high growth categories, the big questions driving value are about market size and unit economics, since the former provides the basis for revenue growth and the latter determines profitability. That is why, when looking at the prospectus for SpaceX it was the data on total addressable markets, unit economics and capital intensity that had a bigger impact on value, and this information, for the most part, was in the footnotes to the financials, rather than in the financial statements themselves.     For those who are focused on value metrics/constraints (consistently money making, high profit margins and accounting returns)  and pricing multiples (low EV to EBITDA or low PE), the SpaceX prospectus is full of red flags. SpaceX is a company with small revenues and large losses, and paying a hundred times revenues for it (which is where a $1.8 trillion pricing would put it) seems foolhardy. I have no quarrels with this point of view, which animates old-time value investing, but this perspective comes with a cost in terms of investment choices. Investors who are wedded to never buying money losing companies or never paying more than twenty times earnings for a stock will end up with portfolios of mature (and declining) businesses. If that is their comfort zone, the strategy is perfectly defensible, but they should dispense with complaints about never being able to find high growth stocks to invest in or critiques of others who find these stocks attractive, notwithstanding the weak numbers.      There are many good arguments that can be made about why you should not invest in SpaceX, but basing that conclusion on the fact that they are money-losing or have negative cash flows or trade at a high multiple of revenues is both lazy and unconvincing. In contrast, making a case against investing in SpaceX because you believe that the target markets for its businesses will be far smaller than the company thinks they will be, or that cost and competitive pressures will drive margins down or even that you find its corporate governance structure and dependence on a personality (Elon Musk) off-putting is perfectly reasonable. If you do make that case, though, it is worth remembering that this is your point of view, and that disagreements about market size and profitability across investors, especially in young companies, are natural and healthy. In short, based on my inputs and story, I think that SpaceX is worth about $1.25-$1.3 trillion, but if you contend that it is worth $3 trillion or only half a trillion, it is neither my job nor my place to convince you that I am right and that you are wrong.  The IPO Pricing Game     In the coming weeks, you will undoubtedly be exposed to multiple perspectives on SpaceX, and that is healthy. That said, you will be better equipped to make sense of these perspectives, and perhaps incorporate some of the views into your own, and reject those that do not make sense, if you have an understanding of what an IPO process involves. In particular, understanding the motivations of the different players in the game (the investment bankers setting the offering price and managing the offering,  the issuing company, the investors and traders jockeying for shares at that offering price and the traders positioning themselves for the first day of trading) will help determine whether you should be playing this game or sitting it out, at least for the moment. The Bankers     Let's start with the sequencing that goes into a conventional initial public offering, though alternatives have emerged to it in recent years: As you look at the role played by bankers to the IPO process, allowing them to keep a  slice of the IPO proceeds, the SpaceX IPO is a testimonial to the dwindling value added by bankers on every dimension: Timing: It is urban (or market) legend that investment banks can time markets, and that this market timing can help determine the best time to go public. Just one look at the track record of market strategists at investment banks should dispense with this delusion, since banks (and most institutional investors) are (and have never been) good at gauging market momentum and shifts in mood.  Filing and Offering details: It is true that there are technical details and logistical steps to filing a prospectus and setting offering details, but they are almost all mechanical. With SpaceX, I am not sure whether the prospectus, as filed, was the work of a team of bankers, but if it was, I wonder what an entirely Grok-written prospectus would have looked like, and whether we would have noticed the difference.  Pricing: In an IPO, the bankers' mission is to price companies for their offering, not value them, and while they usually draw on pricing multiples and peer groups to make that pricing judgment, they are guided by the pricing in the most recent private transactions, usually in the form on venture capital rounds. With SpaceX, that task is simplified by the reality that this company, while private, has had active trading in its private shares, and that it was priced at roughly $1.2 trillion prior to the IPO process commencing. Adding the $75 billion in offering proceeds, and incorporating the advantages of increased liquidity from being a public company and becoming part of the S&P 500, it is not surprising that there is a sense that the offering will be priced at between $1.5 trillion to $2 trillion, with or without the investment banking input. My guess is that we will end up somewhere in the middle, with some handwaving about revenue multiples and other AI companies used to justify that pricing. (After I finished this post, a news story popped up that the offer price would be set at $135/share, translating into about a $1.8 trillion pricing for the company.) Selling/Marketing: In an age where investment banks have lost credibility and social media is where marketing happens, SpaceX can generate its own marketing spin, and has an army of influencers behind it. In addition, almost every institutional investor has a point of view on whether to own SpaceX or not, it is unclear what exactly a roadyshow can do to augment the sales pitch. Price guarantee: The pricing guarantee that investment bankers offer in initial public offerings is a mostly empty promise, since they systematically set offering prices at below (by 15-20%) what they believe the market will pay. That is the reason that the offer price for SpaceX will be set below the upper end of the range, and while the discount may seem like a significant loss to funders and current owners, the fact that the offering is for less than a tenth of the shares in the company will soften the blow. Post-market support:  As a follow-up to the price guarantee, investment banks often offer after-market support for companies in the days after they go public, buying shares if the stock comes under selling pressure. With SpaceX, that option is off the table, since no investment bank has the capital to support the pricing of a two-trillion company, if investors turn negative on it. In fact, given that banks are perhaps getting more from the initial public offering, in terms of publicity and allotments for their preferred clientele, than SpaceX is getting from their services, you could argue that the bankers should be paying the company for reflected glory, rather than charging them fees. The only good reason that I can think of for SpaceX not going the direct listing route, where you dispense with the kabuki dance of offerings and let the market set the offering price, is that the company needs the cash from the offering, and that route is much more difficult to take in a direct listing. Issuer (Company, Founder and Investors)     Looking at the IPO from the SpaceX perspective, the public offering will provide benefits. For the investors in the company in its private form, including venture capitalists from early in its life to public investors in more recent years, the IPO will allow them to cash out, albeit after the lock-out period expires in a few months. For the company, the increased access to capital from being a public company will allow it to fund the capital expenditures and investment needs that emanate from the company's ambitions in the AI business. For Elon Musk, the public offering has the potential to make him the first trillionaire in history, in addition to unlocking new pathways to further enrichment for meeting specified targets (including getting a million people on Mars).    Since some of these benefits have been in existence for many years, the fact that company stayed private for that period is an indication that there are costs to going public that have held it back. The first is that, notwithstanding Musk's voting control of the company, become a public company will open SpaceX to market scrutiny, in the form of earnings reports every quarter and insider trading reports. The second is that the market is fickle, and while it is rewarding companies that invest in AI with high market prices today, it can change its mind and punish them for the same reason. The third is that while there is little that investors can do to trade and make money on overpriced private businesses, they can sell short on public companies. Investors and Traders     SpaceX is a company that has been in the public eye for a decade or more, even as a privately owned enterprise, partly because of its social media boosters and partly because its space launches make it a magnet for attention. There are many who are drawn to the company, but unable to invest in it as a private business, will now have a chance to do so, if it goes public. But should they try to partake in the initial offering? The answer to that question  depends on whether you are an investor, where you buy (sell) companies that you believe are trading under (over) their assessment of value and hope the gap closes or a trader, where you buy (sell) companies where you expect prices to go up (down) in the future, for a multitude of reasons, only some of which may relate to company fundamentals.     I am more investor than trader, and I say that without judgment, since the end game in markets is to make money, not score intellectual points. The truth is that I am not a very good trader, and I am better off staying in my preferred domain, which is valuation, albeit with no guarantees of a payoff.  My valuation of SpaceX was driven by my interest in the company and belief that it is in unique, cutting-edge businesses, and my decision on whether to buy into the offering is therefore driven by my assessment of its value. At the rumored pricing of $1.8 trillion for the company, it is too richly priced for my tastes, given my valuation of $1.25-$1.35 trillion for the equity in the company. That does not mean that I will never buy the stock, since the market does change its mind, and if the price does drop by enough, my decision would change accordingly. It is worth remembering that Facebook was selling at half its offering price a few months after its IPO, and that Uber lost more than 50% of its market cap in the year after its public offering, moving both companies from over to under valued.     If you are a trader, though, the game changes. Specifically, the intrinsic value of the company is not central to your decision, perhaps even irrelevant, and your judgment on whether you seek to partake in the SpaceX offering will depend on your reading of market mood and momentum. I would not be surprised in the least to see the offering priced at $1.8 trillion, and see a jump in the price on the day of or in the weeks after the offering, and if that is your most likely scenario, being able to get into the offering at the offer price or even in the first few hours or days of trading will be a winning strategy. The risk, of course, is that momentum can shift quickly, causing a significant price drop, effectively making  timing your trades right key to your trading strategy. The shifting and often unpredictable forces of mood and momentum are also the reason that as an investor, I would not sell short, notwithstanding my value assessment, even if the pricing for the company pushes from $1.8 trillion to $2 trillion or more.  A Loaded Bet on AI!     As the IPO process for SpaceX heats up in the coming weeks, you should prepare yourself for a flood of selling from the company and its bankers, with talk of possibilities and potential dominating the discussion, as well as arguments from the other side, where it will be framed as a vehicle for AI hype, destined to fail. If you are on the receiving end of these sales pitches, you should listen but check the numbers for plausibility and make your own judgments. For the bankers involved and the issuing company, the biggest danger to a successful offering is not that there will be near-term reality checks on their hype, but that the market mood will shift, either in the aggregate or specifically related to AI, in the weeks leading up to the offering.  No matter what your views are about the SpaceX IPO, positive or negative, there is no denying that this company is a loaded bet on the AI  and Elon Musk, and while that may concern some, there are others who will look at Musk's track record with Tesla and feel the odds are in their favor.  YouTube Attachments SpaceX prospectus Valuation of SpaceX, post-prospectus on 6/2/26 Blog posts on SpaceX To a Trillion(s) and Beyond: A SpaceX Odyssey!