Beat your Bot: Building your moat against AI

     It seems like a lifetime has passed since artificial intelligence (AI) became the market's biggest mover, but Open AI introduced the world to ChatGPT on November 30, 2022. While ChatGPT itself represented a low-tech variation of AI, it opened the door to AI not only as a business driver, but one that had the potential to change the way we work and live. In a post on June 30, 2023, I looked at the AI effect on businesses, arguing that it had the potential to ferment revolutionary change, but that it would also create a few big winners, a whole host of wannabes, and many losers, as its disruption worked its way through the economy. In this post, I would like to explore that disruption effect, but this time at a personal level, as we are warned that we risk being displaced by our AI counterparts. I want to focus on that question, trying to find the middle ground between irrational terror, where AI consigns us all to redundancy, and foolish denial, where we dismiss it as a fad.

The Damodaran Bot

    I was in the eleventh week of teaching my 2024 spring semester classes at Stern, when Vasant Dhar, who teaches a range of classes from machine learning to data science at NYU's Stern School (where I teach as well), and has forgotten more about AI than I will ever know, called me. He mentioned that he had developed a Damodaran Bot, and explained that it was an AI creation, which had read every blog post that I had ever written, watched every webcast that I had ever posted and reviewed every valuation that I had made public. Since almost everything that I have ever written or done is in the public domain, in my blog, YouTube videos and webpage, that effectively meant that my bot was better informed than I was about my own work, since its memory is perfect and mine is definitely not. He also went on to tell me that the Bot was ready for a trial run, ready to to value companies, and see how those valuations measured up against valuations done by the best students in my class.

    The results of the contest are still being tabulated, and I am not sure what results I would like to see, since either of the end outcomes would reflect poorly on me. If the Bot's valuations work really well, i.e., it values companies as well, or better, than the students in my class, that is about as strong a signal that I am facing obsolescence, that I can get. If the Bot's valuations work really badly, that would be a reflection that I have failed as a teacher, since the entire rationale for my postings and public valuations is to teach people how to do valuation.

Gauging the threat

    In the months since I was made aware of the Damodaran Bot, I have thought in general terms about what AI will be able to do as well or better than we can, and the areas where it might have trouble. Ultimately, AI is the coming together of two forces that have become more powerful over the last few decades. The first is increasing (and cheaper) computing power, often coming into smaller and smaller packages; our phones are now computationally more powerful than the very first personal computers. The second is the cumulation of data, both quantitative and qualitative, especially with social media accelerating personal data sharing. As an AI novice, it is entirely possible that I am not gauging the threat correctly, but there are three dimensions on which I see the AI playing out (well or badly).

1. Mechanical/Formulaic vs Intuitive/Adaptable: Well before ChatGPT broke into the public consciousness,  IBM's Deep Blue was making a splash playing chess, and beating some of the world's greatest chess players. Deep Blue's strength at chess came from the fact that it had access to every chess game ever played (data) and the computing power to evaluate 200 million chess positions per second, putting even the most brilliant human chess player at a disadvantage. In contrast, AI has struggled more with automated driving, not because driving is mechanically complicated, but because there are human drivers on the surface roads, behaving in unpredictable ways. While AI is making progress on making intuitive leaps, and being adaptable, it will always struggle more on those tasks than on the purely mechanical ones.
2. Rules-based vs Principle-based: Expanding the mechanical/intuitive divide, AI will be better positioned to work smoothly in rules-based disciplines, and will be at a disadvantage in principle-based disciplines. Using valuation to illustrate my point,  accounting and legal valuations are mostly rule-based, with the rules sometimes coming from theory and practice, and sometimes from rule writers drawing arbitrary lines in the sand. AI can not only replicate those valuations, but can do so at no cost and with a much closer adherence to the rules. In contrast, financial valuations done right, are built around principles, requiring judgment calls and analytical choices on the part of appraisers, on how these principles get applied, and should be more difficult to replace with AI.
3. Biased vs Open minded: There is a third dimension on which we can look at how easy or difficult it will be for AI to replace humans and that is in the human capacity to bring bias into decisions and analyses, while claiming to be objective and unbiased. Using appraisal valuation to illustrate, it is worth remembering that clients often come to appraisers, especially in legal or accounting settings, with specific views about what they would like to see in their valuations, and want affirmation of those views from their appraisers, rather than the objective truth. A business person valuing his or her business, ahead of a divorce, where half the estimated value of that business has to be paid out to a soon-to-be ex-spouse, wants a low value estimate, not a high one, and much as the appraiser of the business will claim objectivity, that bias will find its way into the numbers and value. It is true that you can build AI systems to replicate this bias, but it will be much more difficult to convince those systems that the appraisals that emerge are unbiased.

Bringing this down to the personal, the threat to your job or profession, from AI, will be greater if your job is mostly mechanical, rule-based and objective, and less if it is intuitive, principle-based and open to biases. 

Responding to AI

   While AI, at least in its current form, may be unable to replace you at your job, the truth is that AI will get better and more powerful over time, and it will learn more from watching what you do. So, what can we do to make it more difficult to be outsourced by machines or replaced by AI? It is a question that I have thought about for three decades, as machines have become more powerful, and data more ubiquitous, and while I don't have all of the answers, I have four thoughts.

1. Generalist vs Specialist: In the last century, we have seen a push towards specialization in almost every discipline. In medicine, the general practitioner has become the oddity, as specialists abound to treat individual organs and diseases, and in finance, there are specialists in sub-areas that are so esoteric that no one outside those areas can even comprehend the intricacies of what they do. In the process, there are fewer and fewer people who are comfortable operating outside their domains, and humanity has lost something of value. It is the point I made in 2016, after a visit to Florence, where like hundreds of thousands of tourists before me, I marveled at the beauty of the Duomo, one of the largest free-standing domes in the world, at the time of its construction. 
   
   
   
   The Duomo built by Filippo Brunelleschi, an artist who taught himself enough engineering and construction to be able to build the dome, and he was carrying on a tradition of others during that period whose interests and knowledge spanned multiple disciplines. In a post right after the visit, I argued that the world needed more Renaissance men (and women), individuals who can operate across multiple disciplines, and with AI looming as a threat, I feel even more strongly about this need. A Leonardo Da Vinci Bot may be able to match the master in one of his many dimensions (painter, sculptor, scientist), but can it span all of them? I don't think so!
2. Practice bounded story telling: Starting about a decade ago, I drew attention to a contradiction at the heart of valuation practice, where as access to data and more powerful models has increased, in the last few decades, the quality of valuations has actually become worse. I argued that one reason for that depletion in quality is that valuations have become much too mechanical, exercises in financial modeling, rather than assessments of business quality and value. I went on to make the case that good valuations are bridges between stories and numbers, and wrote a book on the topic.
   
   
   
   At the time of the book's publication, I wrote a post on why I think stories make valuations richer and better, and with the AI threat looming, connecting stories to numbers comes with a bonus. If your valuation is all about extrapolating historical data on a spreadsheet, AI can do it quicker, and with far fewer errors than you can. If, however, your valuation is built around a business story, where you have considered the soft data (management quality, the barriers to entry), AI will have a tougher time replicating what you do. 
3. Reasoning muscle: I have never been good at reading physical maps, and I must confess that I have completely lost even my rudimentary map reading skills, having become dependent on GPS to get to where I need to go. While this inability to read maps may not make or break me, there are other skills that we have has human beings, where letting machines step in and help us, because of convenience and speed, will have much worse long term consequences. In an interview I did on teaching a few years, I called attention to the "Google Search" curse, where when faced with a question, we often are quick to look up the answer online, rather than try to work out the answer. While that is benign, if you are looking up answers to trivia, it can be malignant, when used to answer questions that we should be reasoning out answers to, on our own. That reasoning may take longer, and sometimes even lead you to the wrong answers, but it is a learned skill, and one that I am afraid that we risk losing, if we let it languish. You may think that I am overreacting, but evolution has removed skill sets that we used to use as human beings, when we stopped using or needing them, and reasoning may be next on the list.
4. Wandering mind: An empty mind may the devil's workshop, at least according to puritans, but it is also the birthplace for creativity. I have always marveled at the capacity that we have as human beings to connect unrelated thoughts and occurrences, to come up with marvelous insights. Like Archimedes in his bath and Newton under the apple tree, we too can make discoveries, albeit much weighty ones, from our own ruminations. Again, making this personal, two of my favorite posts had their roots in unrelated activities. The first one, Snowmen and Shovels, emerged while I was shoveling snow after a blizzard about a decade ago, and as I and my adult neighbors struggled dourly with the heavy snow, our kids were out building snowmen, and laughing.  I thought of a market analogy, where the same shock (snowstorm) evokes both misery (from some investors) and joy (on the part of others), and used it to contest value with growth investing. The second post, written more recently, came together while I walked my dog, and pondered how earthquakes in Iceland, a data leak at a genetics company and climate change affected value, and that became a more general discourse on how human beings respond (not well) to the possibility of catastrophes.  

It is disconcerting that on every one of these four fronts, progress has made it more difficult rather than less so, to practice. In fact, if you were a conspiracy theorist, you could spin a story of technology companies conspiring to deliver us products, often free and convenient to use, that make us more specialized, more one dimensional and less reason-based, that consume our free time. This may be delusional on my part, but if want to keep the Damodaran Bot at bay, and I take these lessons to heart, I should continue to be a dabbler in all that interests me, work on my weak side (which is story telling), try reasoning my way to answers before looking them up online and take my dog for more walks (without my phone accompanying me). 

Beat your bot!

    I am in an unusual position, insofar as my life’s work is in the public domain, and I have a bot with my name on it not only tracking all of that work, but also shadowing me on any new work that I do. In short, my AI threat is here, and I don’t have the choice of denying its existence or downplaying what it can do. Your work may not be public, and you may not have a bot with your name on it, but it behooves you to act like there is one that tracks you at your job.  As you consider how best to respond, there are three strategies you can try:

1. Be secretive about what you do: My bot has learned how I think and what I do because everything I do is public - on my blog, on YouTube and in my recorded classes. I know that some of you may argue that I have facilitated my own disruption, and that being more secretive with my work would have kept my bot at bay. As a teacher, I neither want that secrecy, nor do I think it is feasible, but your work may lend itself better to this strategy. There are two reasons to be wary, though. The first is that if others do what you do, an AI entity can still imitate you, making it unlikely that you will escape unscathed. The second is that your actions may give away your methods and work process, and AI can thus reverse engineer what you do, and replicate it. Active investing, where portfolio managers claim to use secret sauces to find good investments, can be replicated at relatively low cost, if we can observe what these managers buy and sell. There is a good reason why ETFs have taken away market share from fund managers.
2. Get system protection: I have bought and sold houses multiple times in my lifetime, and it is not only a process that is filled with intermediaries (lawyers, realtors, title deed checkers), all of whom get a slice from the deal, but one where you wonder what they all do in return for their fees. The answer often is not rooted in logic, but in the process, where the system (legal, real estate) requires these intermediaries to be there for the house ownership to transfer. This system protection for incumbents is not just restricted to real estate, and cuts across almost every aspect of our lives, and it creates barriers to disruption. Thus, even if AI can replicate what appraisers do, at close to no cost, I will wager that courts and accounting rule writers will be persuaded by the appraisal ecosystem that the only acceptable appraisals can come from human appraisers. 
3. Build your moat: In business, companies with large, sustainable competitive advantages are viewed as having moats that are difficult to competitors to breach, and are thus more valuable. That same idea applies at the personal level, especially as you look at the possibility of AI replacing you. It is your job, and mine, to think of the moats that we can erect (or already have) that will make it more difficult for our bots to replace us. As to what those moats might be, I cannot answer for you, but the last section lays out my thinking on what I need to do to stay a step ahead.

Needless to say, I am a work in progress, even at this stage of my life, and rather than complain or worry about my bot replacing me, I will work on staying ahead. It is entirely possible that I am embarking on an impossible mission, but I will keep you posted on my progress (or absence of it). Of course, my bot can get so much better at what I do than I am, in which case, this blog may very well be written and maintained by it, and you will never know!

YouTube Video

Blog Posts (referenced)

1. Investing and Valuation: Lessons from the Renaissance
2. Stories and Numbers: How a number cruncher learned story telling!
3. The Google Search Curse (my interview)
4. Snowmen and Shovels: Lessons on Investing
5. Catastrophic Risk: Investing and Business Implications

The Corporate Life Cycle: Managing, Valuation and Investing Implications!

As I reveal my ignorance about TikTok trends, social media celebrities and Gen Z slang, my children are quick to point out my age, and I accept that reality, for the most part. I understand that I am too old to exercise without stretching first or eat a heaping plate of cheese fries and not suffer heartburn, but that does not stop me from trying occasionally. For the last decade or so, I have argued that businesses, like human beings, age, and struggle with aging, and that much of the dysfunction we observe in their decision making stems from refusing to act their age. In fact, the business life cycle has become an integral part of the corporate finance, valuation and investing classes that I teach, and in many of the posts that I have written on this blog. In 2022, I decided that I had hit critical mass, in terms of corporate life cycle content, and that the material could be organized as a book. While the writing for the book was largely done by November 2022, publishing does have a long lead time, and the book, published by Penguin Random House, will be available on August 20, 2024, at a book shop near you. If you are concerned that you are going to be hit with a sales pitch for that book, far from it!  Rather than try to part you from your money, I thought I would give a compressed version of the book in this post, and for most of you, that will suffice.

Setting the Stage

    The notion of a business life cycle is neither new nor original, since versions of it have floated around in management circles for decades, but its applications in finance have been spotty, with some attempts to tie where a company is in the life cycle to its corporate governance and others to accounting ratios. In fact, and this should come as no surprise to anyone who is familiar with his work, the most incisive piece tying excess returns (return on invested capital minus cost of capital) to the corporate life cycle was penned by Michael Mauboussin (with Dan Callahan) just a few months ago.

    My version of the corporate life cycle is built around six stages with the first stage being an idea business (a start-up) and the last one representing decline and demise. 

As you can see, the key tasks shift as business age, from building business models in the high growth phase to scaling up the business in high growth to defending against competition in the mature phase to managing decline int he last phase. Not surprisingly, the operating metrics change as companies age, with high revenue growth accompanied by big losses (from work-in-progress business models) and large reinvestment needs (to delivery future growth) in early-stage companies to large profits and free cash flows in the mature phase to stresses on growth and margins in decline. Consequently, in terms of cash flows, young companies burn through cash, with the burn increasing with potential, cash buildup is common as companies mature followed by cash return, as the realization kicks in that a company’s high growth days are in the past.

    As companies move through the life cycle, they will hit transition points in operations and in capital raising that have to be navigated, with high failure rates at each transition. Thus, most idea businesses never make it to the product phase, many product companies are unable to scale up, and quite a few scaled up firms are unable to defend their businesses from competitors. In short, the corporate life cycle has far higher mortality rates as businesses age than the human life cycle, making it imperative, if you are a business person, that you find the uncommon pathways to survive and grow.

Measures and Determinants

    If you buy into the notion of a corporate life cycle, it stands to reason that you would like a way to determine where a company stands in the life cycle. There are three choices, each with pluses and minuses. 

• The first is to focus on corporate age, where you estimate how old a company is, relative its founding date; it is easy to obtain, but companies age at different rates (as well will argue in the following section), making it a blunt weapon.
• The second is to look at the industry group or sector that a company is in, and then follow up by classifying that industry group or sector into high or low growth; for the last four decades, in US equity markets, tech has been viewed as growth and utilities as mature. Here again, the problem is that high growth industry groups begin to mature, just as companies do, and this has been true for some segments of the tech sector.
• The third is to focus on the operating metrics of the firm, with firms that deliver high revenue growth, with low/negative profits and negative free cash flows being treated as young firms. It is more data-intensive, since making a judgment on what comprises high (revenue growth or margins) requires estimating these metrics across all firms.

While I delve into the details of all three measures, corporate age works surprisingly well as a proxy for where a company falls in the life cycle, as can be seen in this table of all publicly traded companies listed globally, broken down by corporate age into ten deciles:

As you can see, the youngest companies have much higher revenue growth and more negative operating margins than older companies.

    Ultimately, the life cycles for companies can vary on three dimensions - length (how long a business lasts), height (how much it can scale up before it plateaus) and slope (how quickly it can scale up). Even a cursory glance at the companies that surround you should tell you that there are wide variations across companies, on these dimensions. To see why, consider the factors that determine these life cycle dimensions:

Companies in capital-light businesses, where customers are willing to switch from the status quo, can scale up much faster than companies in capital-intensive businesses, where brand names and customer inertia can make breakthroughs more difficult. It is worth noting, though, that the forces that allow a business to scale up quickly often limit how long it can stay at the top and cause decline to be quicker, a trade off that was ignored during the last decade, where scaling up was given primacy.

    The drivers of the corporate life cycle can also explain why the typical twenty-first century company faces a compressed life cycle, relative to its twentieth century counterpart. In the manufacturing-centered twentieth century, it took decades for companies like GE and Ford to scale up, but they also stayed at the top for long periods, before declining over decades. The tech-centered economy that we live in is dominated by companies that can scale up quickly, but they have brief periods at the top and scale down just as fast. Yahoo! and BlackBerry soared from start ups to being worth tens of billions of dollars in a blink of an eye, had brief reigns at the top and melted down to nothing almost as quickly. 

Tech companies age in dog years, and the consequences for how we manage, value and invest in them are profound. In fact, I would argue that the lessons that we teach in business school and the processes that we use in analysis need adaptation for compressed life cycle companies, and while I don't have all the answers, the discussion about changing practices is a healthy one.

Corporate Finance across the Life Cycle

    Corporate finance, as a discipline, lays out the first principles that govern how to run a business, and with a focus on maximizing value, all decisions that a business makes can be categorized into investing (deciding what assets/projects to invest in), financing (choosing a mix of debt and equity, as well as debt type) and dividend decisions (determining how much, if any, cash to return to owners, and in what form).

While the first principles of corporate finance do not change as a company ages, the focus and estimation processes will shift, as shown in the picture below:

With young companies, where the bulk of the value lies in future growth, and earnings and cash flows are often negative, it is the investment decision that dominates; these companies cannot afford to borrow or pay dividends. With more mature companies, as investment opportunities become scarcer, at least relative to available capital, the focus not surprisingly shifts to financing mix, with a lower hurdle rate being the pay off. With declining businesses, facing shrinking revenues and margins, it is cash return or dividend policy that moves into the front seat. 

Valuation across the Life Cycle

    I am fascinated by valuation, and the link between the value of a business and its fundamentals - cash flows, growth and risk. I am also a realist and recognize that I live in a world, where pricing dominates, with what you pay for a company or asset being determined by what others are paying for similar companies and assets:

All companies can be both valued and priced, but the absence of history and high uncertainty about the future that characterizes young companies makes it more likely that pricing will dominate valuation more decisively than it does with more mature firms. 

    All businesses, no matter where they stand in the life cycle, can be valued, but there are key differences that can be off putting to some. A well done valuation is a bridge between stories and numbers, with the interplay determining how defensible the valuation is, but the balance between stories and numbers will shift, as you move through the life cycle:

With young companies, absent historical data on growth and profitability, it is your story for the company that will drive your numbers and value. As companies age, the numbers will become more important, as the stories you tell will be constrained by what you have been able to deliver in growth and margins. If your strength as an analyst or appraiser is in bounded story telling, you will be better served valuing young companies, whereas if you are a number-cruncher (comfortable with accounting ratios and elaborate spreadsheet models), you will find valuing mature companies to be your natural habitat. 

    The draw of pricing is strong even for those who claim to be believers in value, and pricing in its simplest form requires a standardized price (a multiple like price earnings or enterprise value to EBITDA) and a peer group. While the pricing process is the same for all companies, the pricing metrics you use and the peer groups that you compare them to will shift as companies age:

For pre-revenue and very young companies, the pricing metrics will standardize the price paid (by venture capitalists and other investors) to the number of users or subscribers that a company has or to the total market that its product is aimed at. As business models develop, and revenues come into play, you are likely to see a shift to revenue multiples, albeit often to estimated revenues in a future year (forward numbers). In the mature phase, you will see earnings multiples become more widely used, with equity versions (like PE) in peer groups where leverage is similar across companies, and enterprise value versions (EV to EBITDA) in peer groups, where leverage is different across companies. In decline, multiples of book value will become more common, with book value serving as a (poor) proxy for liquidation or break up value. In short, if you want to be open to investing in companies across the life cycle, it behooves you to become comfortable with different pricing ratios, since no one pricing multiple will work on all firms.

Investing across the Life Cycle

    In my class (and book) on investment philosophies, I start by noting that every investment philosophy is rooted in a belief about markets making (and correcting) mistakes, and that there is no one best philosophy for all investors. I use the investment process, starting with asset allocation, moving to stock/asset selection and ending with execution to show the range of views that investors bring to the game:    

Market timing, whether it be based on charts/technical indicators or fundamentals, is primarily focused  on the asset allocation phase of investing, with cheaper (based upon your market timing measures) asset classes being over weighted and more expensive asset classes being under weighted. Within the stock selection phase, there are a whole host of investment philosophies, often holding contradictory views of market behavior. Among stock traders, for instance, there are those who believe that markets learn slowly (and go with momentum) and those who believe that markets over react (and bet on reversals). On the investing side, you have the classic divide between value and growth investors, both claiming the high ground. I view the differences between these two groups through the prism of a financial balance sheet:

Value investors believe that the best investment bargains are in mature companies, where assets in place (investments already made) are being underpriced by the market, whereas growth investors build their investment theses around the idea that it is growth assets where markets make mistakes. Finally, there are market players who try to make money from market frictions, by locking in market mispricing (with pure or near arbitrage). 

    Drawing on the earlier discussion of value versus price, you can classify market players into investors (who value companies, and try to buy them at a lower price, while hoping that the gap closes) and traders (who make them money on the pricing game, buying at a low price and selling at a higher one).  While investors and traders are part of the market in every company, you are likely to see the balance between the two groups shift as companies move through the life cycle:

Early in the life cycle, it is undeniable that traders dominate, and for investors in these companies, even if they are right in their value assessments, winning will require much longer time horizons and stronger stomachs. As companies mature, you are likely to see more investors become part of the game, with bargain hunters entering when the stock drops too much and short sellers more willing to counter when it goes up too much. In decline, as legal and restructuring challenges mount, and a company can have multiple securities (convertibles, bonds, warrants) trading on it, hedge funds and activists become bigger players.

    In sum, the investment philosophy you choose can lead you to over invest in companies in some phases of the life cycle, and while that by itself is not a problem, denying that this skew exists can become one. Thus, deep value investing, where you buy stocks that trade at low multiples of earnings and book value, will result in larger portions of the portfolio being invested in mature and declining companies. That portfolio will have the benefit of stability, but expecting it to contain ten-baggers and hundred-baggers is a reach. In contrast, a venture capital portfolio, invested almost entirely in very young companies, will have a large number of wipeouts, but it can still outperform, if it has a few large winners. Advice on concentrating your portfolio and having a margin of safety, both value investing nostrums, may work with the former but not with the latter.

Managing across the Life Cycle

    Management experts who teach at business schools and populate the premier consulting firms have much to gain by propagating the myth that there is a prototype for a great CEO. After all, it gives them a reason to charge nose-bleed prices for an MBA (to be imbued with these qualities) or for consulting advice, with the same end game. The truth is that there is no one-size-fits-all for a great CEO, since the qualities that you are looking for in top management will shift as companies age:

Early in the life cycle, you want a visionary at the top, since you have to get investors, employees and potential customers to buy into that vision. To turn the vision into products and services, though, you need a pragmatist, willing to accept compromises. As the focus shifts to business models, it is the business-building skills that make for a great CEO, allowing for scaling up and success. As a scaled-up business, the skill sets change again, with opportunism becoming the key quality, allowing the company to find new markets to grow in. In maturity, where playing defense becomes central, you want a top manager who can guard a company's competitive advantages fiercely. Finally, in decline, you want CEOs, unencumbered by ego or the desire to build empires, who are willing to preside over a shrinking business, with divestitures and cash returns high on the to-do list.

    There are very few people who have all of these skills, and it should come as no surprise that there can be a mismatch between a company and its CEO, either because they (CEO and company) age at different rates or because of hiring mistakes. Those mismatches can be catastrophic, if a headstrong CEO pushes ahead with actions that are unsuited to the company he or she is in charge off, but they can be benign, if the mismatched CEO can find a partner who can fill in for weaknesses:

While the possibilities of mismatches have always been part of business, the compression of corporate life cycles has made them both much more likely, as well as more damaging. After all, time took care of management transitions for long-lived twentieth century firms, but with firms that can scale up to become market cap giants in a decade, before scaling down and disappearing in the next one, you can very well see a founder/CEO go from being a hero in one phase to a zero in the next one. As we have allowed many of the most successful firms that have gone public in this century to skew the corporate finance game, with shares with different voting rights, we may be losing our power to change management at those firms where the need for change is greatest.

Aging gracefully? 

    The healthiest response to aging is acceptance, where a business accepts where it is in the life cycle, and behaves accordingly. Thus, a young firm that derives much of its value from future growth should not put that at risk by borrowing money or by buying back stock, just as a mature firm, where value comes from its existing assets and competitive advantages, should not risk that value by acquiring companies in new and unfamiliar businesses, in an attempt to return to its growth days. Acceptance is most difficult for declining firms, since the management and investors have to make peace with downsizing the firm. For these firms, it is worth emphasizing that acceptance does not imply passivity, a distorted and defeatist view of karma, where you do nothing in the face of decline, but requires actions that allow the firm to navigate the process with the least pain and most value to its stakeholders.

    It should come as no surprise that many firms, especially in decline, choose denial, where managers and investors come up with excuses for poor performance and lay blame on outside factors. On this path, declining firms will continue to act the way they did when they were mature or even growth companies, with large costs to everyone involved. When the promised turnaround does not ensue, desperation becomes the alternative path, with managers gambling large sums of other people’s money on long shots, with predictable results.

    The siren song that draws declining firms to make these attempts to recreate themselves, is the hope of a rebirth, and an ecosystem of bankers and consultants offers them magic potions (taking the form of proprietary acronyms that either restate the obvious or are built on foundations of made-up data) that will make them young again. They are aided and abetted by case studies of companies that found pathways to reincarnation (IBM in 1992, Apple in 2000 and Microsoft in 2013), with the added bonus that their CEOs were elevated to legendary status. While it is undeniable that companies do sometimes reincarnate, it is worth recognizing that they remain the exception rather than the rule, and while their top management deserves plaudits, luck played a key role as well.

    I am a skeptic on sustainability, at least as applied to companies, since its makes corporate survival the end game, sometimes with substantial costs for many stakeholders, as well as for society. Like the Egyptian Pharaohs who sought immortality by wrapping their bodies in bandages and being buried with their favorite possessions, companies that seek to live forever will become mummies (and sometimes zombies), sucking up resources that could be better used elsewhere.

In conclusion

    It is the dream, in every discipline, to come up with a theory or construct that explains everything in that disciple. Unlike the physical sciences, where that search is constrained by the laws of nature, the social sciences reflect more trial and error, with the unpredictability of human nature being the wild card. In finance, a discipline that started as an offshoot of economics in the 1950s, that search began with theory-based models, with portfolio theory and the CAPM, veered into data-based constructs (proxy models, factor analysis), and behavioral finance, with its marriage of finance and psychology. I am grateful for those contributions, but the corporate life cycle has offered me a low-tech, but surprisingly wide reaching, construct to explain much of what I see in business and investment behavior. 

    If you find yourself interested in the topic, you can try the book, and in the interests of making it accessible to a diverse reader base, I have tried to make it both modular and self-standing. Thus, if you are interested in how running a business changes, as it ages, you can focus on the four chapters that look at corporate finance implications, with the lead-in chapter providing you enough of a corporate finance foundation (even if you have never taken a corporate finance class) to be able to understand the investing, financing and dividend effects. If you are an appraiser or analyst, interested in valuing companies across the life cycle, it is the five chapters on valuation that may draw your interest, again with a lead-in chapter containing an introduction to valuation and pricing. As an investor, no matter what your investment philosophy, it is the four chapters on investing across the life cycle that may appeal to you the most. While I am sure that you will have no trouble finding the book, I have a list of book retailers listed below that you can use, if you choose, and the webpage supporting the book can be found here. 

    If you are budget-constrained or just don't like reading (and there is no shame in that), I have also created an online class, with twenty sessions of 25-35 minutes apiece, that delivers the material from the book. It includes exercises that you can use to check your understanding, and the link to the class is here. 

YouTube Video

Book and Class Webpages

1. Book webpage: https://pages.stern.nyu.edu/~adamodar//New_Home_Page/CLC.htm
2. Class webpage: https://pages.stern.nyu.edu/~adamodar//New_Home_Page/webcastCLC.htm
3. YouTube Playlist for class: https://www.youtube.com/playlist?list=PLUkh9m2BorqlpbJBd26UEawPHk0k9y04_

Links to booksellers

1. Amazon: https://www.amazon.com/Corporate-Lifecycle-Investment-Management-Implications/dp/0593545060
2. Barnes & Noble: https://www.barnesandnoble.com/w/the-corporate-life-cycle-aswath-damodaran/1143170651?ean=9780593545065
3. Bookshop.org: https://bookshop.org/p/books/the-corporate-lifecycle-business-investment-and-management-implications-aswath-damodaran/19850366?ean=9780593545065
4. Apple: https://books.apple.com/us/audiobook/the-corporate-life-cycle-business-investment/id1680865376

There is an Indian edition that will be released in September, which should be available in bookstores there. The Indian edition can be found on Amazon India.

1. Amazon India: https://www.amazon.in/Corporate-Life-Cycle-Investment-Implications/dp/0143471392