Finding your investing lodestar: In Search of an Investment Philosophy

    When uncertainty roils markets, as is the case right now, it is natural for investors to get knocked off balance, a when off-balance, to make investment decisions that they often regret later. It is during those times that it helps to have a core set of beliefs about markets, and an investment philosophy that reflects those beliefs. You may not be able to mend the damage to your portfolio, but it will help you find balance again and make sense of the noise around you. As an investor, my investment philosophy has been a work-in-progress, but I have had an interest in how the investors around me develop their philosophies, and why differences persist. That interest was precipitated by a seminar class that I organized for NYU Stern MBAs in the late 1990s, where successful investors with very different market perspectives and investing styles presented their points of view, and students struggled to reconcile their different and contradictory points of view. In the aftermath of the class, I started working on a book and a class on investment philosophies, where the end game was not to find the "best" philosophy, but to provide a framework for investors to find the philosophy that best fits them. The first edition of the book came out almost two decades ago, followed by a second edition in 2012. In conjunction with the second edition of the book, I created a free online version of the class on my webpage in the same year, and NYU created a certificate class about six years for the class. While my core thinking on investment philosophies has nto changed, markets and the economy have, and both the book and the class have been in need of an update. I spent the last few months working on that update, and the third edition should be available at book stores in the coming week, and in conjunction, I have an updated (free) online version of the class on my webpage and on YouTube

The Origins

    In the late 1990s, I was approached by the Stern School of Business with a request to serve as the organizer for a class on investing, where MBA students would spend a session a week, for a semester, hearing from successful investors of all stripes, and discuss what they learned from that talk in a second session each week. Over the course of the semester, the class had fourteen speakers, and because of our New York location, it drew from a range of investing types. Thus, students heard from a well-known value investor one week, the manager of one of the best-regarded growth mutual funds the next, a high-profile technical analyst in the third, and so on. The speakers approached investing in very different ways and had different perspectives on financial markets and how to exploit market mistakes, but they all had been successful as investors. 

    As I led the discussion of each speaker's market views and investment practices each week, I noticed students in my class developing whiplash, as they instinctively try to incorporate the views and practices of each speaker into their thinking. As the weeks went on, that became a problem, since other than investment success, the speakers shared little in common, and their views about markets were sometimes contradictory. By the end of the class, there was a fairly large subset of the students who ended up more confused by what they had heard during the semester, rather than enlightened. As I reviewed the class, before handing it off to someone else, I took an inventory of what I had seen not just in the class, but in investing in general, and came to the following general judgments about investing:

1. There are very few active investors, who win consistently over time: Active investing is one of the most difficult games to win at, and one reason is that you match the average investor, effortlessly and almost costlessly, by investing in index funds. Active investing has the unenviable task of trying to be better than average, and by enough to cover the costs (research, data, personnel, transactions) associated with being active. Just as illustration of how much of a mountain this is to climb, take a look at the percentage of active institutional investors who beat their respective indices over the last decade:
   
   
   
   While there some active money managers who "beat the market" over a year, two years or even five, very few are able to hold on to these excess returns as you lengthen their active investing stint. Like gamblers in a casino, who strike it lucky early, but stay gambing too long, they often leave with none of their gains, or worse. Before I get a blowback, I am fully aware that that there are investing legends (Warren Buffett, Jim Simon and George Soros, to name just three), but the very fact that we can name them suggests that they are the exceptions, not the rule.
2. Even with those successful few, it is very difficult to separate luck from skill: Much as investment books and classes claim otherwise, investing results are affected by so many forces that are out of your control that disentangling how much of your final returns can be attributed to skill and how much to luck is very difficult to do. 
3. These successful investors have widely different pathways to delivering success: If you were to make a list of the investors who have had the most success in markets in the last century, I would wager that you would be looking at a very diverse group, not just in terms of how they succeeded, but also in terms of personality. The three investors I named as legends - Buffett, Simon and Soros - obviously had very different views on markets, and how to exploit market mistakes, but even with investors who are often viewed as being from the same grouping, differences remain. Buffett may have learned his early lessons from Ben Graham, but the Graham and Buffett approaches to value investing are varied, with the former more focused on screening for cheap stocks and the latter more interested in finding companies with solid moats and great management. 
4. Imitating successful investors does not seem to provide much payoff: The practices of successful investors have been probed and investigated by other investors and journalists, and some of them have dozens of books that claim to tell you the secret of their success. Warren Buffett is perhaps the winner in this race, with not only a multitude of books that track his investing life but also his annual letters to Berkshire shareholders which laid out his investing perspective in detail. That said, the investors who tried to follow in his footsteps, often imitating every aspect of his approach, have, for the most part, not been able to match his success. 

My takeaways from these assessments are two fold. The first is that there can be no one dominant investment philosophy that is the best for all investors, and any claims to the contrary, whether it be for value investing or market timing or trading, are disingenuous. The second is that there is a right investment philosophy for each individual that reflects that individual's views and beliefs about markets and characteristics as a person.

The Core Idea

      The recognition that each investor needs an investment philosophy that is tailor-made to his or her beliefs and personality became the starting point for my creating a class, and writing a book, about the topic. Before I describe what I try to do in the book, I should start with a definition of what I mean by an investment philosophy, and perhaps the best way to do that is by describing what it is not. First, an investment philosophy is much richer and more complete than an investment strategy, with the latter often coming out of the former. Thus, applying a screen to find stocks that trade at low multiples of earnings (low PE ratios or low multiple of EBITDA) is an investment strategy, but the investment philosophy that gives rise to that strategy is one that is built on markets under pricing companies with low growth or boring businesses, perhaps because investors are dazzled by growth and drawn to the excitement of newer businesses. Second, an investment philosophy is not an investment slogan. "Buy low, sell high" is an investment slogan, and a meaningless one at that, since that is the end game of almost every investment philosophy. 

    If you have been investing for a while, and have never stopped and asked yourself what your investment philosophy is, it is understandable. In fact, you may wonder why you should constrain yourself to an investment philosophy instead of looking for bargains wherever you can find them. The problem with not having a core philosophy is that is exposes you, as an investor, to a whole host of consequences, most of which are negative:

1. Chasing winners: If you don't have an investment philosophy, it is almost a given that you will find yourself drawn to whatever strategies worked best in the recent past. Your portfolio will suffer from whiplash as you chase last year's winners, whether that be the Mag Seven or technology stocks or small cap stocks, and while your turnover and transactions costs rise, you will have little to show in terms of returns.
2. Scam target: Greed is universal, and that leads us to look for ways to make lots of money with very little risk. Without an investment philosophy constraining you, you will be an easy mark for investment scams, drawn in with promises of upside with little or no downside.
3. Empty investing cupboards: If you do find an investment strategy that works at delivering returns, it is worth remembering that the clock is ticking, and that imitation and market corrections will cause that strategy to stop working, sooner rather than later. If that is all you brought to the market, your investing cupboard will be empty and you will find yourself running to stay in place. The advantage of having a coherent, well thought through investment philosophy is that you can go back to it and mine it for other strategies that may exploit the same market mistakes. Thus, if your investment philosophy is that markets undervalue boring, low-growth companies, and low PE ratios are no longer doing the trick (of finding cheap stocks), you may look for other screens (low volatility)  that find you boring companies that are mispriced.

Simply put, every investor needs an investment philosophy to guide him or her in the difficult task of trying to delivering success.

     Rather than create a laundry list of philosophies, I will use the investment process as the vehicle to describe how and where the different investment philosophies emerge from, as well as diverge:

Using this process, the choices in investment philosophies emerge:

1. Active investing versus Passive indexing: If, as we noted in the last section, doing nothing can deliver returns approximating the average, and nine out of ten investors who try to beat the average fail, there is no shame in adopting a passive indexing philosophy, where your allocation across asset classes is determined by your risk aversion and need for liquidity, and index funds fill out the rest of the dance card. It is human nature, though, to seek to be better than average, and it is perhaps that desire that drives many into active investing choices, and there are multiple pathways that they can adopt.

2. Investing versus Trading: The second divide in investing philosophies comes from the difference between value, which is driven by cashflows, growth and risk, and price, determined by demand and supply. Investing requires assessing the value of an asset, buying if the price is lower than that value and selling if it is higher, and waiting for the gap to close. Trading, on the other hand, is about gauging market mood and momentum, buying if you expect those forces to drive the price up and selling otherwise. 

Within each of these groupings (investing or trading), there are sub-groupings. Trading can take different tacks, depending on where you think that market mistakes lie. The first, price traders, use the information on prices and trading volume to detect shifts in mood and momentum, with charts and technical indicators as tools, to try and generate profits. The second group, information traders, trades around information releases, such as earnings reports, acquisition announcements or even insider trades, with some trading ahead of the news, some at the time the news is announced and some in the aftermath, all trying to take advantage of what they see as market mistakes in reacting to that information. The third group, arbitrageurs, focused on finding the same or related assets trading on different markets, looking for mispricing across these markets, and locking in that mispricing as excess returns. 

    Investors, for instance, can be drawn to value or growth, and while that difference is often stated in terms of pricing multiples, with value investors buying low priced stocks (low PE, low price to book etc) and growth investors drawn to higher growth and high priced companies, I prefer to think of the differences in terms of where each group thinks it can find bargains. Using my financial balance sheet construct, where I divide the value of a firm into the value of investments already made (assets-in-place) and investments anticipated in the future (growth assets), value investors view their odds of finding market mistakes to be greater with assets-in-place, whereas growth investors feel that their odds are better in finding misvalued growth assets:

Within value and growth investing, there are further sub-divides. Value investing can span the spectrum from passive screening, where you screen for stocks that have specific characteristics (low PE, high growth, high ROE) and label them as cheap, to more activist poses, where investors with deep pockets (individual activist, private equity funds) not only take positions in companies that they believe are under or over valued, but also push for change at these companies. Growth investing has its own version of activist investing, in the form of venture capital, invested in young, growth companies, where in addition to supplying capital for growth, venture capitalists take an active role in how these companies evolve over time and exit the marketplace (IPOs, sale to another company).

3. Market Timing vs Stock/Asset Picking: In market timing, your focus is less on individual stocks or assets and more on deciding whether a market (equities, bonds, real estate etc.) is under or over priced. Returning to the investment process, your focus is on allocating your portfolio across asset classes, based on your market views, underweighting "expensive" asset classes and overweighting "cheap" ones.  In stock/asset picking, you take the market as a given and try to find the best individual investments within each investment class for you - the cheapest stocks, bonds and real estate that you can find. There is an ironic contradiction in making this choice. It is undeniable that a successful market timer will make far more money than a good stock picker, but it is also true that it is much more difficult to be a successful market timer than it is to be a good stock picker. The picture below captures the choices in terms of investment philosophy, framed in terms of where they enter the investment process:

Even if you feel that you have an investment philosophy in place, I think being aware of how others approach markets and keeping an open mind, where you borrow parts of other philosophies and incorporate them into yours will make you a better investor.

Finding an Investment Philosophy

    Looking at the menu of investment philosophies, from passive indexing to arbitrage, my end game in my book and for the class on investment philosophies was not to advance a single philosophy or even compare them, but to provide as unbiased and complete a picture, as I could, of the data backing each philosophy and more importantly, the personal characteristics that you would need to succeed with that philosophy. 

Step 1: Views on Market Mistakes and Corrections

    The first step in finding your investment philosophy is with a view of where (and why) markets make mistakes, and how they correct them. Even the firmest believer in efficient markets will concede  that markets not only make mistakes, but sometimes make big ones, but the divergence between them and active investors lies in the nature of these mistakes. In an efficient market, market mistakes will be random, and since there is no systematic pattern to them, there is no pathway for active investors to find these mistakes, even with access to data and powerful tools. Active investors, in contrast, believe that there are systematic patterns that you can use to find these mistakes, and to exploit them for profits, with traders believing that those patterns are in the pricing and volume data and investors hewing more to fundamentals.  That said, active investors can and will disagree about the types of market mistakes, with some buying into the notion that markets learn slowly, whereas others believe that markets overreact, and it is healthy for investors to have these disagreements. 

Step 2: Pick an investment philosophy that reflects market views

    Your views on market mistakes and corrections should guide you in your choice of investment philosophies. Thus, if you believe that markets overreact to news, good or bad, you may decide to become a contrarian, either trading (by buying after bad news and selling after good) or by investing (by buying companies with solid fundamentals whose stock prices have dropped by far more than they should have). Conversely, if you believe that it is momentum, not fundamentals, that is the biggest drivers of stock price movements, you may choose to ride that wave, based on charts and technical indicators. Superimposing time horizon onto the types of mistakes that markets make, you can create a matrix of investment philosophies:

Do you have to pick a single philosophy? Not necessarily! You can meld two or even more than two philosophies together, as long as you meet two conditions. The first is that the melded philosophies have to share a core belief about market mistakes. Thus, if you believe that market s overreact, you can be a contrarian value investor, buying companies that have been beaten up in markets but have intact fundamentals, and timing your purchases right after bad news releases, when markets overreact. The second is that you have to identify which of the philosophies is your dominant one, and which one is secondary, allowing you break ties where the two push you in different directions. Staying with the melded contrarian philosophy, and assuming that the contrarian value philosophy is your dominant one, you will choose to not to buy a stock that is down 15% after a bad earnings report, if it is still trading closer to its highs than lows.

Step 3: Check for viable strategies

    Investment philosophies are a critical component, but to make money on a philosophy, no matter how well thought through, you need to devise investment strategies that can generate profits for you. In coming up with these strategies, you will confront the two realities that cause many strategies that look good on paper to fail: transactions costs and taxes. 

• On the transactions cost front, the brokerage trading cost is just a small part of the overall cost, with two other costs that can often be much larger. The first is the bid-ask spread, small for large, very liquid stocks, but much larger for smaller and less liquid investments. The second is price impact, again non-existent if you are a small investor buying or selling shares in a large market-cap company, but substantial if you are a large investor trading on an obscure stock.
• On the tax front, some strategies will create more tax costs than others, partly because of how investment income is taxed (dividends create immediate tax consequences but capital gains require trading to incur tax liabilities) and partly because of how much trading your strategy will require of you, with higher turnover generally creating more tax liability.

If you are planning on being an active investor, there is one final skill set that you will need to acquire, and that is the capacity to test whether a strategy can beat the market. The volatility in returns can sometimes create illusions, where a strategy looks like it is delivering excess returns, but those returns are almost entirely due to statistical noise.

Step 4: Check for personal fit

    Investment philosophies, and the strategies that emanate from them, come with different demands in terms of time horizon, with some requiring holding on to investments for many years and others requiring trading in minutes, different risk exposure and divergent tax consequences. Investors who choose to adopt these philosophies have to reflect on whether they are good matches, on the following fronts:

1. Capital to invest: If you are just starting on your investment journey, and have only a small amount of capital to invest, your choices in terms of investment strategies narrow. You will definitely not be able to be an activist investor, since you will have no weight (in terms of money invested or shares held) to throw around, and you may lack the wealth to buy illiquid, small companies, if that is where you think market mistakes are most often found, since you will not be able to spread your bets. The good news is that you continue to build up your capital, your investment choices will widen, and you can modify your investment strategies accordingly. At the other end of the spectrum, and this is perhaps more the case if you are managing other people's money, you can have so much capital to invest that some investment strategies become infeasible. For instance, if you are planning on investing in illiquid, small cap stocks, having billions of dollars to invest will increase your transactions costs (by increasing price impact when you trade).
2. Time horizon: Many investors, when asked the question about time horizon, claim to have long time horizons, often because they believe that it is the answer that "good" investors give. The truth is that for most investors, time horizon is as much determined by external factors, such as age, health and liquidity needs, as it is by internal motivations. If you have to pay tuition for your children or expect to have substantial hospital bills in the near future, your time horizon just became shorter, and that has to be factored into your choice of investment strategies.
3. Risk exposure: As with time horizon, the willingness to take risk is partly a function of your personal makeup and partly determined by your life standing. If you have accumulated wealth and have a job with a stable (or rising) income that more than covers your expenses, you are better positioned to take risks than if you are on the verge of retirement, and are investing money that you will be needing soon to cover your post-retirement cash needs.
4. Personal qualities: Your personality and characteristics also come into play in your choice of investment philosophy and strategies. If you are, by nature, impatient, it is unlikely that you will be able to sustain a strategy of buying undervalued companies and waiting for a long time for mistakes to correct. Similarly, if you are easily swayed by peer pressure and what the rest of the world is thinking and doing, it is difficult to be invested in contrarian causes, short-term or long-term. Finally, if your strategy requires special skills to be put into motion, you will have to either have or acquire those skills; a strategy built around finding undervalued companies will require that you know how to value companies and one built around analyzing large and complex datasets looking for mispricing needs statistical and data analysis knowhow.

When investor characteristics and investment philosophy needs are mismatched, there are two negative consequences. The first is that, lacking staying power, investors will abandon strategies well before they should, simply because they are uncomfortable with how they are playing out. The second is that a mismatch creates an emotional cost, where investors struggle with their portfolios and fail what I call the sleep test, where their portfolio's gyrations keep them awake at night.

Step 5: Keep the feedback loop open

    If you have found an investment philosophy that maps on to your market beliefs, found viable strategies that reflect that philosophy and matched it to your personal makeup, you have reached steady state, but only for the moment. That is because almost every part of this process is subject to change, some because of outside forces, and some because of personal changes.

1. Economic setting: Over time, economic settings and structures change, and investment philosophies have to adapt or even be abandoned. For instance, I have argued that technology and disruption have created winner-take-all businesses in the twenty first century, and if you buy into that argument, an investment philosophy (and strategies) built around small cap companies will no longer deliver the payoff it did in the twentieth century.
2. Market lessons: Your views on market mistakes come from looking at data and your own experiences in the market, and as a consequence, they should be revisited as markets change. Just in this century, markets have been tested by crises (the financial crisis of 2008, the COVID meltdown in 2020 and the tariff announcements last April, just to name three), and it is becoming increasingly obvious that assets across classes (stocks, real estate etc) and geographies are moving far more in sync with each other than they did in the last century. That reality has to be integrated into your market views and the investment philosophy/strategies that you use.
3. Trading microstructure: It is undeniable that access to information and trading on most assets has become easier over the last few decades. That is good, but it does come with a cost. Investment philosophies built around the assumption that most investors, especially retail and individual, would not be able to access data or trade easily, may need tweaking, adapting or even abandonment.
4. Personal changes: It won't come as no secret to you, but you will get older, the amount of capital you have to invest will change, your health and family obligations will shift, and you may even  become more or less patient or more or less susceptible to peer pressure. Those factors will all feed into your investment philosophy.

The investing world does not lend itself to absolutes. One of the red flags in investors (retail or institutional) is certitude about their investment choices and views, and an unwillingness to even consider alternatives, a sign that they will be unable to change as the world changes around them.

Book, Class, both or neither?

    I like writing, not so much for its commercial potential, but because it allows to get my thoughts in order. I wrote the first edition of my investment philosophies book in ___, and it followed a structure that I have stayed true to, in subsequent editions. I start the book, with a description of what an investment philosophy is and how it first into the investment process, moving on a foundational section, where I look at risk measures, how to read accounting statements and do intrinsic valuation, how transactions costs and taxes drain returns, and at how to test investment strategies that claim to beat the market. In chapters 7 through 12, I spend each chapter looking at a broad investment philosophy (and related strategies), examining evidence for and against each one in the data before outlining what you (as an investor) need to bring to the table to succeed with each one. I close the book, by providing the sobering counter evidence to active investing, where I look at how difficult it to win at that game and the promise and peril of alternative investments (gold, cryptos, fine art, real estate). 

If you have one of my earlier editions, is it worth upgrading? If you have the first edition, I do believe it is time, but if you do have the second edition and are budget-constrained, you can hold off. You can find the book online at Amazon and Barnes and Noble, with the latter offering a 25% discount, starting today (March 24). 

    In parallel, I developed a class that had the same content, and while the NYU certificate version of the class will cost you, I have had a free online version on my webpage, which I created in 2012. That class was in need of an update, and as I finished up the third edition of the book, I created a new version of this class, with forty two sessions covering the same material as the book. Again, if you have taken the earlier version of the class, you may find the material repetitive, but I hope that the updated data and the add ons allow for a richer experience. If you have never taken this class, and online learning works for you, it is designed for investors, individual as well as institutional, and requires little in terms of technical knowledge, and I hope that give it a shot.

The Investing End Game

    We all share the same end game in investing, which is to generate the highest returns on the capital we invest, though there are wide variations in how much risk we are willing to take and how long we will wait before cashing out. That is the definition of investment success, but given that there are so many forces that are out of our control, you can do everything right and still fail to meet your objectives, leaving you frustrated and questioning yourself. It is for that reason that a better endgame is to seek out investment serenity, where you end up with an investment path that you are comfortable with, and accept the results that emerge, good or bad.  

    I have spent this entire post talking about investment philosophies, and in case you have not noticed, I have not shown my hand, on my investment philosophy. I have never believed in hiding behind vague and opaque generalities, and my investment philosophy is built around three principles:

1. Intrinsic value matters: I believe that every asset (anything that generates cash flows) has a intrinsic value, and that with imagination and a willingness to make mistakes, you can estimate that intrinsic value for any company, from start-ups to companies on the verge of default. I believe that much of what passes for valuation in practice is pricing, where people using pricing metrics (such as PE ratios or EV to EBITDA multiples) to make pricing judgments, and that a good valuation requires understanding business models, telling stories and converting these stories into valuation inputs and value estimates.
2. Markets are for the most part right, but make mistakes during periods of uncertainty and change: I never cease to marvel at markets, where millions of individuals with disparate views and information reach consensus on a price. In an age where we have turned over our choices on what movies to watch to Rotten Tomatoes, and which restaurant to eat at to Yelp!, it is worth remembering that markets were the original fount for crowd wisdom. That said, it is also true that markets have provided us with illustrations of crowd madness, where the collective wisdom is hopelessly wrong, and I believe that this is often the case when investors face significant uncertainty, as is the case when companies transition from one stage of the life cycle to another, entire industry groups are faced with the threat of disruption and markets are put into upheaval by crises. 
3. Do no harm: While I seek out investments to make that will beat the market, I am cognizant of the reality that I am not entitled to rewards, just because I put in the work, and that luck and chance still can wreak havoc on my best-laid plans. In particular, I have learned, through experience, that my biggest mistakes come from overreach and overactivity, and I have built that learning into my investment philosophy by:
• Spreading my bets: I have written before about the concentration versus diversification argument, and that what you choose to do as an investor will be a reflection of how much confidence you have in your investment choices, or “conviction”, in investing parlance. I must confess that I don’t share the conviction that concentrated investors bring to the game, and not only spread my portfolio over three dozen stocks, but also follow rigid rules on not letting any single investment exceed 15% of my portfolio.
• Acting rarely: I don’t trade often, and when I do, I follow the old adage of measuring twice (or three times) before cutting (trading). It helps that I don’t track the market or my portfolio holdings all day, almost never watch the financial news and am not easily swayed by investment sales pitches. 
• Staying away from my weaknesses: I steer away from active market timing and sector bets for a simple reason. I am not good at either, and what I might gain from an occasional win will be wiped out by what I lose in the long term. 
• Being aware of my blind spots: I try to be self-aware, though I don’t always succeed. I know that I am thrown off my game plan by taxes (I don’t like playing them, and that sometimes gets in the way of doing what I should be doing) and I sometimes fall in love with company narratives, because I want them to be true. 

This is my philosophy, it reflects my strengths and personality, and it works for me. I sleep well at night and I have no regrets, but I am lucky since I have an clientele of one (or perhaps two) to satisfy. My hope, with both my book and class, is that it provides you with the choices and material for you to find an investment philosophy that works for you and that it delivers the returns you hope to earn, and even if it does not, lets you sleep well at night!

YouTube Video

Investment Philosophies Book

1. Webpage for book
2. Bookseller links
• Amazon
• Barnes and Noble (25% off through March 26, 2026)

Investment Philosophies Class

1. My webpage for class
2. YouTube Playlist for class

The Price of Risk: An Equity Risk Premium Monologue!

   I start my valuation classes with a question of whether valuation is an art or a science, and I argue that it is neither; it does not have the precision that characterizes a science and unlike an art, it does come with principles that constrain you on what you can and cannot do. I describe valuation as a craft, where you learn as you value companies, and in the process, there are times where you question how it is practiced, and try to find ways to do it better. I have learned my share of lessons in the four decades that I have practiced valuation, and I have often abandoned standard practices, in the hope of developing better ones. There is no input in valuation where I have found myself questioning existing practices more than in estimating the price of risk in equity markets, i.e., the equity risk premium, and I have wrestled with ways of coming up with alternatives. That endeavor was pushed into high gear by the 2008 market crisis, when I started to pay more attention to how markets price risk, what causes that price of risk to change over time and the limitations in the ways that we estimate that price of risk in financial analysis.

Status Quo and Standard Practice

    Leading into 2008, I had long been skeptical about how we approached the estimation of equity risk premiums,  essential ingredients in hurdle rates in corporate finance and discount rates in valuation. It was (and still remains) standard practice to look at historical data, almost entirely from the US, on what stocks had earned over treasuries, and use that historical equity risk premium as the best estimate of the equity risk premium for the future, That approach would have yielded an equity risk premiums of between 5.5% to 14.5%, at the start of 2026, depending on the time period used, the way we compute averages and what we use as the riskfree rate.

These historical equity risk premiums are not only backward-looking and very noisy (see the standard errors), but they allow bias to easily creep in, through the choice of equity risk premiums, with bullish (bearish) analysts picking lower (higher) numbers.  Disconcertingly, they also move in the wrong direction, falling during crises (as historical returns get updates) and rising during good times.

A Forward-Looking Alternative

    To counter the problems that I saw with historical risk premiums, I started estimating forward-looking equity risk premiums, by essentially backing out from stock prices and expected cash flows, the expected return (internal rate of returns) that markets were pricing into stocks. 

That approach yields forward-looking equity risk premiums, and while there is estimation error in the expected earnings growth and payout numbers, it yields vastly more precise estimates that are also model-agnostic. Using this approach, the equity risk premium at the start of 2026 was 4.23% (over the US treasury bond rate):

Note that this estimation is model-agnostic, and is simply a measure of what markets are pricing in, given expected cash flows at the moment.

ERP Estimation during Crises

    Unlike historical equity risk premiums, these implied premiums are sensitive to market gauges of fear and greed, and change, as those change. In fact, I computed the ERP, by day, during the 2008 market crisis, and you can see the shifts during that 14-week period below:

Note that the crisis started with the equity risk premiums at 4.2% on September 12, 2008m but almost doubled over the next two months, as stocks went into free fall. To me, these implied equity risk premiums made far more intuitive sense, rising as market fears about banks and the economy rose.

    I have continued with the practice of estimating equity risk premiums, by day, during market crises (real or perceived). Here, for instance, is my assessment of the UK market in 2016 in the weeks leading up to the Brexit vote, the market reaction to COVID and the global economic shutdown in 2020, and how the tariffs roiled markets last year. In fact, as we wrestle with an war and oil price induced market shock in March 2026, I started my daily estimates for the ERP on March 1 and will report on how that price has changed over the last two weeks, in the next section.

Equity Risk Premiums - Lessons Learned

        The process of estimating implied equity risk premiums on a continuing basis is driven less by intellectual curiosity and more by my need for these numbers, when I value companies. That process has taught me three lessons about equity risk premiums, and I have responded by altering my practices.

    

1. The equity risk premium is a dynamic and shifting number, and a good estimate of the premium should reflect this volatility. Using an equity risk premium that is different from the implied equity risk premium makes every valuation a joint judgment on what you think about the company and what you think about the market. Put simply, sticking with a 4% equity risk premium during a crisis, when the implied risk premium has surged to 6% will lead you to find most companies to be undervalued, almost entirely because you think that the market is undervalued (not the company). In my view, a company valuation should be market-neutral, and the only way you can get there is by using a current implied equity premium.

My response: Rather than compute the implied equity risk premium at the start of every year, and using that premium over the course of the year, I shifted to computing the equity risk premium for the S&P 500 at the start of every month, in September 2008.  I report those numbers on my entry page to my website (damodaran.com) and use them to value companies during the course of the month. You can find these monthly equity risk premium estimates by going to this link. 

2. The implied equity risk premium is a consolidated metric for market pricing, and every debate or discussion about whether the market is under or over priced can be reframed as a debate about whether the implied equity risk premium is too low (over pricing), just right (fairly priced) or too high (under pricing). Since the implied ERP incorporates the level of interest rates, expected growth and cash payout, it is a more complete assessment of the market than looking at dividend yields and earnings yields (or variants of PE ratios), two widely used proxies for market pricing. In this post, I took an extended look at how these different measures of equity risk premiums measure up, in terms of predicting future equity returns.

My response: I have been open about my discomfort with timing markets, but when I am asked what I think of the overall market (Is it too high? Is it a bubble?), I first measure the current equity risk premium and then assess it against history. I used this technique to assess US equities at the start of this year in a post, with the accompanying graph: 

My conclusion, at the start of 2026, was that while stocks were richly priced using almost every conventional metric (high PE ratios, low dividend yields), the implied equity risk premium was in line with what US stocks have generated over the last 65 years. That said, I did note that 2025 was a tumultuous year, with tariffs making the news and the post-war dollar-centric global economic system starting to fray, and argued that the market seems to be too sanguine about catastrophic risk. Almost on cue, two weeks ago, bombs started falling in the Middle East, and US equities and bonds have been struggling to price in the effects of higher oil prices. In keeping with my practice of estimating equity risk daily, during troubled times, I did compute the implied ERP for the S&P 500 every day, during the last two weeks (Feb 27- March 13):

Oil is up to over a hundred dollars a barrel and the S&P 500 is down, but so far, the market is not behaving as if it is in crisis mode. The equity risk premium, which started March at 4.37% has risen, but only to 4.51%, over the two weeks. In fact, it is the ten-year US treasury bond that has had the bigger surge, up from 3.97% at close of trading, on February 27, to 4.28% at close of trading, on March 13, indicating inflation fears are trumping other market concerns right now. All of this could change next week or the week after, and I will continue to track the equity risk premiums, by day, until the market settles in.

3. The equity risk premium is an essential ingredient into almost every part of financial analysis, incorporated into hurdle rates in corporate finance, discount rates in valuation and in expected returns on equity in financial planning. Given this centrality, I was surprised how little attention it has received from both academics and practitioners, when I looked for references. There is very little usable academic research on equity risk premiums specifically, though there is a great deal on asset pricing and risk. As for practitioners, they have, for the most part, relied on historical risk premiums, and often obtain these premiums from services that summarize the historical data. When I took my first finance class, the historical risk premiums came from data from Ibbotson Associates, that contained annual return data on stocks, bonds and bills. That data was acquired by Duff and Phelps, where it became part of a voluminous book on cost of capital, but much of what that book had to say about equity risk premiums reflected slicing and dicing the historical data, hoping to get further insights, and for the most part failing, because of the noisiness in the data. The US historical data is now in the hands of Kroll, but there is little of value that be extracted by doing deeper and deeper mining expeditions on historical return data. In fact, if you are a fan of historical equity risk premiums (I am not, as you can guess), my suggestion would be to use the Credit Suisse Yearbook, which looks at historical equity risk premiums in 20 markets over more than a hundred years, and does not suffer from the selection bias of focusing on just US data.

My response: I am a practitioner and I decided, for my own understanding, to pull together everything I knew about equity risk premiums into a paper that I wrote in early 2009, and shared online that year. Practitioners seemed to find it useful, and I have updated that paper every year since, at the start of the year. It has grown over time, as I have sought to pull together new findings on equity risk premiums and incorporate changes in markets, and my seventeenth annual update is now ready. I have to confess that at this point, much of the change is data-driven, with tables and graphs updated to include the most recent year's data, but I hope you still find it useful. The paper resides on the social science research network (SSRN), an Elsevier-run platform for working papers in the social sciences. Unlike most of the other papers on that platform, I have no interest is ever publishing this paper, but you are welcome to download not just the paper, but all of the data that goes with the paper. 

Equity Risk Premiums - The 2026 Edition

    If you do get a chance to download the paper, I should warn you ahead of time that it long (153 pages), unexciting and entirely directed at practitioners. It is modular, though, and it is broadly broken down into the following sections:

1. The Determinants of Equity Risk Premiums: Given that equity risk premiums represent the price of risk in the market, it should come as no surprise that almost everything that happens in the market, political or economic, affect its level. The picture below summarizes the determinants, and you can find more details in the paper:

As you can see, all of these variables can and will change over time, explaining why the ERP should be a volatile number.

2. Historical Equity Risk Premiums (and spin offs): I spend a section of the paper discussing historical equity risk premiums, examining the statistical properties that make it a faulty approach, and why a belief in mean reversion has made it the status quo. While most of the historical equity risk premiums that you see reported in practice come from the US and are based upon the Ibbotson data going back to 1926, I also look at historical data that goes back further (to 1871) as well as historical premiums in the rest of the world. The historical data on returns in the US has also been mined by services to extract premiums that have been earned by subsets of stocks, and since these premiums often get used by practitioners, I look at the efficacy of these premiums. I specifically look at the small cap premium, a widely used add on in valuation, and not that not only has it been noisy over the entire time period (1926-2025), but that it has disappeared since 1981:

The fact that the small cap premium endures in practice is a testimonial to how once bad practices become embedded in valuation, they never leave.

3. Equity Risk Premiums, by country: While I do have a companion paper that explores country risk in detail, that I update in the middle of the year, I describe my process for estimating equity risk premiums, by country, starting with a mature market premium, and then adding on additional premiums, based on country default risk spreads (based on ratings and sovereign CDS spreads).

4. Implied Equity Risk Premiums and Alternatives: In this section, I start with a description of an intrinsic value model for the market, and use that model to illustrate what you would need to assume for the dividends yield or earnings yield to become reasonable proxies for the equity risk premiums; for the latter, for instance, you have to assume either that there is no earnings growth or that if there is growth, it is value neutral. I then use the full version of the model, allowing for higher growth and cash payout that includes buybacks, to derive my implied equity risk premium estimates. I also look at how my implied equity risk premium estimates relate to other risk proxies (default spreads on bonds, VIX etc.) and how they change over time, as the riskfree rate changes.

5. Efficacy of ERP Estimates: The test of whether an equity risk premium estimate is a good one is in the data, since equity risk premiums measure expectations of what investors hope to earn on equities in future periods. In the last section of the paper, I examine the predictive efficacy of alternative measures of equity risk premiums, by looking at their correlation with actual stock market returns in the next year, the next five years and the next ten years:

Since a good ERP estimate should have a large positive correlation with actual returns on stocks in future years, the current implied premium does best for the five-year and ten-year return, and the historical risk premium does worst, with actual returns increasing (decreasing) when it decreases (increases). In bad news for market timers, none of the equity risk premium approaches does well at forecasting next year's actual return, and even at the longer time periods, there is significant error in predictions.

Paper

1. Equity Risk Premiums (ERP): Determinants, Implications and Estimates - The 2026 Edition
   

Data

1. Historical returns on US asset classes (1928 -2025)
2. Implied Equity Risk Premiums, at end of year (1960-2025)
3. Implied Equity Risk Premiums, start of each month (Sept 2008 - March 2026)
4. Equity Risk Premiums, by country, at the start of 2026

Spreadsheets

1. Spreadsheet to compute implied ERP - S&P 500 on February 27, 2026

AI Scenarios: From Doomsday Destruction to Do-Nothing Bots!

     When Chat GPT made its debut on November 30, 2022, it unleashed the hype of AI, and in the three years since, AI has taken on an outsized role not just in markets, but also in our lives. For much of the time, the AI story has been told by its advocates and its salespeople, and the companies in the AI ecosystem have benefited. Not surprisingly, given that its narrators benefit from this growth, that story has emphasized the positive, with dazzling AI use cases and optimistic extrapolation of the productivity gains from its adoption. In the last few months, we have seen cracks emerge in the AI story, with investors wondering when, and in what form, the immense investments in AI architecture will pay off, and how if they pay off, the businesses that they disrupt will fare. That disquiet has played out as negative market reactions to new AI investments at Meta and Amazon, a markdown in software company market capitalizations and in a sell off last week, in response, at least partially, to an AI scenario assessment from Citrini Research, a publisher of macro and stock research. Given that I know very little about the technology of AI, and that my macroeconomic knowhow is pedestrian,  my intent in this post is less about promoting my favored AI scenario, and more about providing a framework for you to develop your own.

The Citrini AI Assessment - Report and Responses

    The Citrini AI assessment came out on February 22, 2026, and it starts with a preface stating that it is presenting a scenario, not a prediction. I do have issues with that opening, but I will come to them later, but the report itself laid out a story for AI that unfolds with a dark end game for the economy, where by June 30, 2028, the AI disruption has unsettled businesses and displaced workers, with unemployment rates rising above 10% and the market down almost 40% in response. There have been other AI doomsayers, but many of those doomsday scenarios are built around the storyline that AI will not live up to its promise, and the pain comes from having over invested trillions of dollars in building its architecture. In contrast, the Citrini AI  story is built on the expectation that not only does AI work well at doing tasks currently performed by white collar professionals, across a range of firms, but its adoption happens very quickly. The pain in the Citrini story comes from that disruption creating substantial job losses, and especially so among higher-earning workers, and the resulting loss of income driving these job losers to cut back on consumption. The ripple effects play out across businesses, with default risks and spreads rising, private credit collapsing and the market and economy pricing in the pain.

    I do think that there are major flaws in the steps leading to the economic implosion in the Citrini assessment, but credit should be given where it is due. I have always been troubled by how much we have worshiped at the altar of disruption in this century, putting the founders of disruptors on pedestals and preaching disruption's virtue. In keeping with Joseph Schumpeter's description of capitalism as built around creative destruction, I do believe that a vibrant and dynamic economy needs a shake-up and challenging of the status quo, but disruption comes with costs to the businesses that are disrupted, and to the people who work in them. There is much to celebrate, as consumers, in terms of choice and price from the growth of online retail, but that does not take away from the devastation that has been wreaked on brick-and-mortar retail and its constituent parts. Ride sharing has brought car service from its nineteenth century ways into the twenty first century, but at the expense of yellow cabs and conventional car service businesses. The reason that many AI advocates took issue with the Citrini report was precisely because it bought into their sales pitch of how AI bots can not only do what lawyers, bankers, software engineers and consultants do, but also do them better, and then asked the question of "what then?.

   The Citrini AI scenario must have hit some targets, because in the days since, we have been flooded with scenarios countering Citrini and arriving at different outcomes. While I was not surprised to see Goldman Sachs, Moody's and JP Morgan jump in with their AI scenarios, with more benign outcomes for the economy, where the job loss and income effects from AI are modest and temporary, I was surprised to see Citadel wade into the argument, with a direct rebuttal to Citrini, which sees a much more positive end game from AI disruption, and is built around three pillars. The first is the current data on jobs and layoffs in the businesses most directly targeted by AI, such as software, where they note that while jobs have been shed, the job losses have been modest, and AI adoption trends don’t see breakouts consistent with the speedy disruption predicted by Citrini. The second is history, where they look at disruptions in the past (PCs, the internet) and note that none of them have been speedy or have created the job losses or economic collapses predicted in the doomsday scenario. The third is grounded in macroeconomics, where they point to the inconsistency of assuming  that a large positive productive shock, from AI’s success, will play out out as large negative shock to the economy and market in which it happens. 

Completing the AI story

    The problem with all of these AI scenarios is that they are rooted in the weakest of responses to uncertainty, which is to either pick a scenario and to describe it in detail, without establishing, at least in qualitative terms, how likely that scenario is, in the first place, or to list out a whole host of scenarios, without making judgments on likelihood on eany of them. It is entirely possible that what Citrini was presenting was a "worst-case" scenario (I read through the report and could not get a sense of if this was so, and the subsequent responses from Citrini have only muddied the waters), a "low likelihood" scenario or the "likely scenario" of how AI will unfold. If it is a likely scenario, and you buy into the pitch, the investment and personal consequences will be dramatic, since it is entirely possible that, if you are a white-collar worker, you may have lost your job by June 2028, and your savings, if invested in stocks, would have taken a beating. If it is a "low likelihood" scenario, and you are exposed, because of your job, age and portfolio composition, you should consider buying protection, but if it is a worst-case scenario, it is almost entirely useless, except for shock value.

Point Estimates and Probabilities

    For much of its history, financial analysis has been built around point estimates, where you identify key drivers, estimate the effects on your bottom line (earnings, cash flows) and make your best judgments. Thus, when valuing a company, you estimate the earnings growth on base year earning, how much you will reinvest of those earnings to grow to get to cash flows, and discount those cash flows back at a risk-adjusted rate to get to value. The problem with point estimates, where almost everything is uncertain is that you will be wrong 100% of the time, though you may still make money, if you are wrong in the right direction.

    Financial analysts and economics have been slow in adopting and using probabilistic approaches, where point estimates are replaced by distributions, and a single judgment on outcome by a distribution of outcomes. One reason, at least early on, was that economists and financial analysts often did not have rich enough data or powerful enough tools to use decision trees, simulations or scenario analysis in making their macroeconomic and investment judgments, but that is no longer true. Another reason may be that many in this group are uncomfortable with statistical distributions or probability estimates and stay away from using them, because of that discomfort. The third reason, at least for a subset of analysts, is a concern that being open about estimates and the errors in those estimates, which is visible to all in probabilistic approaches, will be viewed as a sign of weakness or lack of conviction on their part. I have a short paper on using probabilistic approaches, where I look not only at when you may want to use which approach (I look at decision trees, simulations and scenario analysis) but also have a short review of statistical distributions, if you are interested. 

    Since Citrini specifically titled their AI thought piece as a scenario, I will stick with scenario analysis in this post. In its most sloppy form, and one that has been around for decades, scenario analysis has taken the form of best case - base case - worst case scenarios, an almost useless exercise, since there are almost no risky investments that are going to pass muster under the worst case scenario, no matter how good they are, or are going to fail under the best case scenario, no matter how bad they are. A scenario analysis, done right, should look at scenarios that cover all possible outcomes on an investment or decision, and for completion, need probabilities attached to these scenarios, which can then be used by a decision maker to estimate expected values. That will be almost impossible to do if you are trying to work out future pathways to AI, since it is so early in the process and so little is known about outcomes. 

    There is an alternate path for scenario analysis that is less information-intensive and thus more feasible, and it draws on the 3P test that I  use when valuing companies, where my company valuation narrative has to start with the possible test (it can happen) to being plausible (which requires more backing) and then on to the probable (where you can estimate a likelihood). In the context of scenario analysis, this would require that you categorize scenarios into their the three groupings:

The discussion around where AI is going would become much healthier if scenario proponents were required to state where their proposed scenarios fall in this spectrum. Citrini, for instance, could have saved itself from some of the backlash, if the writer of the AI doomsday report had specified that it was a possible, but not quite plausible scenario.

The AI Disruption - Gaming the Outcomes

   In the last week, I have seen at least a dozen scenarios touted by individuals and entities, many of whom I respect, and I must confess that I am whipsawed. If, like me, you are drowning in these scenarios, with very different results and outcomes, the only way to retain your sanity and to take ownership of this process is for you to develop a framework where you can not only put each of these scenarios to the 3P test, but also to develop your own assessment of how AI will play out for businesses, investors and the economy.

1. The Disruption - Form and Speed

    The first set of questions that you need to address in the AI story relate to how the AI disruption will evolve, both in form and timing, and to then trace out the aftereffects. 

1. AI Disruption Magnitude - Worker Displacement versus Productivity-enhancing Tools:  If you listen to some of AI’s lead players, AI will have the capacity to replace workers across multiple businesses, as it develops strengths that go beyond the purely mechanical. One reason that the AI effect on unemployment is so large in the Citrini doomsday scenario is because AI’s reach in the scenario is not just restricted to replacing programmers in software but extends to replacing white collar workers in other technology businesses, financial intermediaries, banking and consulting. In contrast, Citadel’s more benign AI reading comes from AI displacing workers in a smaller subset of businesses, while providing tools in others. At the other end of the spectrum, there are still some who believe that when all is said and done, AI will provide tools to workers that may save them time, but will not be powerful or dependable enough to replace them.
2. AI Disruption Speed: Here again, there is disagreement, with some AI optimists believing that its disruption of regular businesses is imminent, whether displacing workers or in giving them tools. Others believe that AI adoption will take time, partly because the tools need work and partly because businesses and workers are slow to adapt to change. The Federal Reserve in St. Louis has created a tracker of AI adoption rates across users, and while it does not capture the depth of the AI adoption, it does provide a measure of how much familiarity and comfort that users are acquiring, with AI tools. 

With the caveats about survey data in place, there are interesting trends in these surveys. First, the use of Gen AI tools in non-work settings has grown more than its usage at work, an indication perhaps of how personal devices (phones, in particular) have changed technology adoption rates. Second, the time that AI has saved people, at least so far, has been modest, ranging from less than 1% in the accommodation and food businesses to about 4% in information and management of companies. Overall, this graph suggests that AI usage is neither as explosively fast growing nor as much of a time-saver, as its proponents suggest that it is. The pushback, though, is that these are surveys of the general population, and that there are data points indicating that the disruption effects are more substantial including the substantial write down in market capitalizations of software companies and layoffs at tech companies. The announcement by Block, the fintech company founded by Jack Dorsey, that it would it be letting go of almost 40% of its workforce, for instance, and blaming AI's rise for the action, was viewed as an indicator of AI's disruption potential. That is a noisy signal, though, since many tech companies have bloated work forces, and AI gives them easy cover, when correcting past mistakes. 

It is true that there is no crystal ball that you can use to gauge the magnitude and speed of AI disruption, but every AI scenario that you see starts with a judgment on one or both. 

2. The Disruption Aftershocks

    Disruptions create aftershocks, some positive and some negative, and while we often avert our gaze and attention from the latter, a full assessment requires considering both. With AI, the positive effects take the form of higher productivity, as it either allows people to do their jobs more efficiently (with AI tools) or actually replaces people and does their jobs instead, in effect allowing for more output with less labor. Relating back to the different pathways that AI disruption can take, both in form and in form and speed, I would hypothesize that these disruption benefits will be a function of how AI disruption plays out.

Proposition 1: The disruption benefits from AI disruption will be greater from people displacement than from AI productivity tools

Proposition 2: The productivity effects from AI disruption will decrease, at least in economic value terms, the longer it takes for the AI disruption to unfold.

The negative effects of AI, in economic terms, will come from the immediate displacement of people, if AI replaces labor, or from the decrease in employees needed to get tasks done, if AI tools make existing employees more efficient. Here again, I would hypothesize that these disruption costs will be  function of how the disruption plays out.

Proposition 3: The disruption costs from AI disruption will be greater from people displacement than from tools, as those laid off lose income and spending power. 

Proposition 4: The productivity costs from AI disruption will decrease, at least in economic value terms, the longer it takes for the AI disruption to unfold, since time will allow new entrants into labor markets to adjust to a disrupted business world.

Intuitively, the longer it takes AI to find roots in business, the more time it gives workers time to adjust, retrain or move on. As you can see, the scenarios where AI displaces existing employees and happens quickly are the ones with the biggest benefits and the biggest costs, and the scenarios where AI supplies tools to existing employees and happens slowly has the least benefits and costs. Building on this theme, I see the net effect of AI disruption playing out as follows:

If AI disruption displaces existing workforces, across many businesses, and happens quickly, the net effect is likely to be negative, at least in the near term, since the economy will not only have to absorb major layoffs quickly, but also because those laid off will be higher-earning white collar workers. While that maps on to the Citrini doomsday scenario, there is still much to debate about which industries will see the most job displacement and how quickly these workers will find other jobs. There is also a discussion that should follow, even in this negative net-benefit scenario, of how quickly the economy (and workers) will adapt, and if and whether net benefits will turn positive in the long term. If AI job displacement is on a limited scale, and/or takes time to unfold, both the benefits and the costs of the AI disruption become smaller, but the net benefit is more likely to be positive, in the short and long term. Finally, the AI disruption takes the form of tools that make workers more efficient, but not efficient enough to reduce workforces, both the benefits and costs of AI become much smaller. In fact, if these tools take a long time to craft and displace little or no labor you get the AI disruption fizzle, with very small benefits and costs.

3. The 3P Test

    Staying true to my earlier assertion that scenarios without probability estimates are not useful, I will try to put the various AI scenarios that I mapped out in the last section on the  3P continuum.

Let me start with the two possible, but not quite plausible scenarios. The first is the a speedy, massive AI disruption, where AI displaces worker across most businesses, and does so quickly, as visualized by Citrini. It can happen, but given the history of disruption, the limits of AI technology and inertia in the process, it is implausible. At the other extreme, it is possible that AI provides tools to workers that improve productivity marginally, with many ending up being more distractions than tools for productivity, effectively emptying its destructive potential, but that too strikes me as implausible, given what we are seeing in terms of AI capabilities. The most plausible scenarios are ones where AI displaces workers in some industries, such as software and some financial intermediaries, and provides tools that help workers to varying degrees in other businesses. As for probable, I think that disruption will reduce workforces in a subset of businesses, that its tools will include some game changers and that it will take longer to unfold, at least when it comes to monetization, than its advocates think. 

    My justification for why AI disruption will take time is based on a mix of factors. The first is that my (limited) knowledge and experience with AI products is that while they sometimes work magically well and quickly, they do have kinks, coming partly from being unable to separate good data from bad, and partly from their imperfect attempt to be imitate humans. The second is history, where no disruption has ever unfolded without delays and drawbacks; remember that the dot com disruption almost lost its moorings during the market bust in 2001. The third is human nature, where much as employees and managers claim to want to move on to new and better options, they remain attached to old technology and products; typewriters and mimeographs took a while to disappear after PCs stormed the workplace and flip phones persisted well into the smartphone era. 

    There are two reasons why I do think that AI disruption is still going to be significant, in the long term. The first is that some of those making the argument that AI will not displace jobs in the long term are assuming that AI in it more advanced form will look like ChatGPT on steroids or be primarily mechanical in its applications. Even my limited exposure to AI's advanced tools suggests that they have far greater capabilities, and their capacity to mimic human intuition and thought processes is unsettling. The second is the blanket assumption that workers in most white collar jobs will not be easily replaced because they bring training, brainpower and experience into those jobs that will be difficult to replicate. Many white collar workers are bright people with specialized knowledge, but the businesses that hire them put them in straight jackets, pushing mechanics over intuition and rule-driven thinking over principle-driven assessments. In short, it is the nature of the jobs that we have created in many white collar settings  that makes them vulnerable to disruption, not the intelligence or training of the people holding those jobs.

    It is worth noting that in my probable scenario, AI will unfold at different rates in different businesses, and if I were pushed to distinguish between the businesses that will be targeted most (and soonest) from the businesses where it will take more time, and have less impact, I would look at four factors:

There may be some confirmation and hindsight bias in this table, but there is a good reason why software, a relatively young industry, with young companies and employees, has been one of the first targets for AI disruption. it is profitable, its products and services are logical and rule-based and much of it has no regulatory or system protection. Within software, though, i would expect software that requires more user interface to be more resilient to AI disruption than software that operates in the background. This table, though, can help determine which white collar jobs will be most exposed to AI disruption, and which least, and perhaps also explain why blanket statements about job displacement in banking, consulting and law are overwrought. With banking and law, a substantial portion of the work done is to meet legal or regulatory requirements, not fill operating needs. I have written about the inanity and uselessness of fairness opinions in M&A, where bankers opine on whether an acquiring company is paying a "fair" value for a target, but this practice persists because these fairness opinions provide cover against lawsuits that ensue when deals fall apart. My guess is that the Delaware courts are not quite ready for an AI fairness opinion bot to take the stand and defend a deal, even if the quality of its work is better than a human banker. With consulting, where cookbook solutions are more the norm than the exception, it is worth remembering the clients pay consulting fees not for the advice, but so that they have someone else to blame, when things go wrong, and there too, an AI bot will not have the same outsourcing power as an army of bankers with Harvard MBAs from McKinsey.

4. Cui Bono?

    Most of the AI scenarios yield net benefits, and even in the most damaging scenarios, where the AI disruption benefits are overwhelmed by its costs, at least in the short term, you could argue for net positive benefits in the long term. That is good news, but it should taken with a grain of salt, since the distribution of these net benefits across businesses and society will be unequal, and it is possible that the net benefits accrue to a few businesses (and individuals), leaving the rest (businesses and individuals) with net costs.

• The interests of the AI companies and the rest the economy/market will diverge on AI disruption, with the former benefiting if the disruption is across many businesses and happens quickly, and the latter benefiting from a slower disruption restricted to a few businesses. This will be the case even if AI tools add to productivity, since the lower costs that companies acquiring these tools will have as a consequence, may not translate into higher profits, especially if their competitors can pay and acquire the same tools.
• The last few major disruptions, starting with the internet, moving on the China and then the smartphone, have all tilted the playing field in many businesses towards larger companies, making businesses more winner-take-all. It is likely that the AI disruption will play out in similar ways, with the winners winning big, and lots of companies losing out. 
• At the individual level, it is not just plausible, but also likely, that a strong AI disruption will make wealth and income inequality worse, with founders of AI businesses joining the ranks of the  deca-billionaires and centi-billionaires.

There is one final cost that may not be explicit in economic terms, at least immediately, but one that has to enter the discussions, As AI threatens to displace workers in white collar businesses, it is worth remembering that a job is not just an income-generator, but also a source of self esteem and worth. When software engineers, who pride themselves on their coding skills, bankers, who have spent decades becoming excel ninjas, and consultants, who have found inventive ways of packaging cookbook solutions and presenting them as new and inventive, find that AI can do what they have spent a lifetime perfecting almost effortlessly, the psychic damage will be significant. The fact that blue collar workers lost their jobs to the internet and China disruptions faced a similar predicament and were largely ignored also means that there may be more than a hint of schadenfreude in society's response to white collar job losses.

The AI Personal Threat

     If you are looking at these side costs and threat to jobs that will come from the AI disruption, and wondering whether we should opt out, by regulating or restricting its reach, I am afraid that the choice is out of our hands. The genie is out of the bottle, and the only pathway that you have, if you operate in a space where AI is ubiquitous, is prepare for a reality where AI tools can automate and do much of what you do on a daily basis, but where you have to create a niche or moat that still makes you necessary.

    Just about two years ago, I wrote about an AI entity called the Damodaran Bot, that was being developed by Vasant Dhar, my colleague at NYU, and noted that having made all that material that I have developed in my lifetime (classes, books, writing, models, videos) publicly available, I was completely exposed to AI disruption. I have watched that bot develop, with quirks and occasional hiccups, to a point where it can replicate much of what I do almost effortlessly. At the time, though, I did write about what I could do to keep the moat at bay, including the following:

• Generalist vs Specialists: I am a dabbler, an expert in nothing and interested in lots of different things, and I do think that gives me an advantage over a bot that is trained to focus on a topic and drill down. The specialist advantages stem from mastering the vast content in a discipline, but those advantages are diluted with AI entities that can also see that content, but the generalist advantage of using multi-disciplinary thinking with be more difficult for AI to replicate.
• Left and Right Brain: I value companies, and early in my valuation life, I decided that financial modeling was not the right path to value businesses, and that good valuations bridge stories and numbers. If the legend of the right and left brains holds, where the left brain controls logic and numbers and the right brain drives your imagination, a bot will have a tougher time replicating what you do, if you use both sides. That said, I have seen the Damodaran Bot get much better at story telling in the two years that I have watched it, and I need to up my game.
• Reasoning muscle: When faced with questions in the days before the internet, you often had no choice but to reason your way to answers. That may have been time consuming, and your answers might even have been wrong, but each time you did this, you strengthened your reasoning muscles. As we move into a period, where the answer to every question is  online, on Google Search and ChatGPT, we are losing the need to exercise those reasoning muscles, and exposing ourselves to being outsourced by our bots. 
• An idle mind: I am not a voracious reader nor a listener to podcasts, and since I don't have much real work to occupy me, I also have plenty of vacant time, with nothing to do. I use that time to daydream and ponder about questions that capture my imagination, including why someone would pay billions of dollars for a sports franchise (like the Washington Commanders), how to deal with the risk of lava from a volcano hitting a spa and ruining its valuation and how streaming has broken the entertainment business. None of these posts include deep insights, but my guess is that the Damodaran bot would have trouble keeping up with my wandering mind.

With the admission that is may not be enough, and that my bot may soon be able write my books and posts, teach my classes and analyze/present data better than I can, I think that you should all be acting as if a bot with your name is looking over your shoulder and trying to learn what you do, and think about what you can do to keep that bot at bay. 

    There is always the possibility that you are arming yourself for a disruption that fizzles, but I will draw on Pascal's wager to explain why you should prepare for an AI imitator or bot, even if you don't believe that it is imminent:

Pascal, a French mathematician, used the wager to explain why be believed in God, even if he was  doubtful of a heavenly presence, because the expected value from believing in God exceeded the expected cost from not believing. In the context of AI, acting as if an AI presence and competitor is present will make you better at whatever you do, as a teacher, banker, consultant or software engineer, and that will persist, no matter what AI's impact is ultimately. Good luck!

YouTube Video

Data Links

1. Federal Reserve in St. Louis AI Adoption Tracker

AI Scenarios

1. The Citrini AI Doomsday 
2. The Citadel Response to Citrini
3. Dario Amodei on the Upside (and Dangers) of AI
4. Daron Acemoglu on the Economics (and Adoption) of AI
5. It's all going to be good - the Jeremy Siegel view on AI
6. The State of AI: Deloitte, McKinsey