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

A Business Upended: Streaming disrupts the Entertainment Business!

It has been an unsettling summer for anyone with a stake in the movie, television and broadcasting businesses.  The strike by screen actors and writers which started in July is now into almost into its third month, with no end in sight, putting at risk the pipeline of movies and shows that were expected to hit theaters and streaming platforms in the next few months. On August 31, Disney pulled its television channels from Spectrum (owned by Charter, the second largest cable company in the US, after Comcast) after a dispute about payments for carrying these channels. Tennis fans, getting ready to watch the US Open on ESPN, were apoplectic, as their televisions went blank in the middle of matches, and Disney, in addition to encouraging them to complain to Spectrum, offered them an option of switching to Hulu+ Live TV, a streaming service alternative to cable. While actors and writers have been on strike before, and contractual disputes between content makers and cable providers is par for the course, the news stories of this summer seem more consequential, perhaps because they reflect longer time shifts in the movie and broadcasting businesses.

Speaking of Disney, a company that has found itself in the crosshairs of political and cultural disputes, the stock hit $80 on September 7, close to a ten-year low. To add to the angst, the threat of artificial intelligence (AI) overhangs almost every aspect of the business, and is one of the contested issues in the strike. The recent troubles in entertainment, though, reflect a longer term disruption that has occurred in the business, with the rise of streaming as an alternative to the traditional platforms for movies and television shows. In this post, I will focus on how streaming has not only changed the way we consume content, but has also modified the way that content gets made. In the process, it has altered the financial characteristics of the companies in the business in ways that the market is still trying to come to terms with, which may explain the market turmoil this year.

A Cautionary Tale: The Music Business and Streaming

    If, as you watch the broadcasting business go through its struggles with streamers, you get a sense of deja vu, it is because the music business in the 1990s found itself similarly challenged, and its upending by streaming may offer lessons for the movie business. In the twentieth century, the music business followed a well-honed script. It was composed of companies which scouted for music talent, signed these musicians to music label contracts and then worked with them in their studios to produce record albums that were sold in music stores across the country. The music companies provided marketing support, seeking out radio stations that would carry their music, and distributional backing to get albums to retailers. In many ways, it was impossible for a musician to break through, without studio backing, and that power imbalance allowed the latter to claim the lion’s share of the revenues. 

    The disruptor who upset the music business was Napster,  a platform that delivered pirated streams of music to its customers, effectively undercutting the need to go into music stores and buy expensive albums. While Napster downloads left much to be desired in terms of audio quality, and the company walked to (and often beyond) the very edge of legality, it exposed the weaknesses in the music business, from how new artists were found and marketed, to how their music was packaged and finally, how that music was sold. When the music companies of the day were able to shut Napster down in 2001, citing digital piracy, they were undoubtedly relieved, but their weaknesses had been exposed. Apple created the iTunes Store in 2001, allowing customers to buy individual songs, rather than entire albums, and the unbundling of the music business began. In the years that followed, music albums and music retailers became rarer, and the advent of the internet allowed musicians to bypass the gatekeepers at the music studios and go directly to customers. As smart phones and personal devices became more plentiful, Spotify and Pandora introduced the music streaming model, and the game was forever changed, and the consequences for the music business have been staggering:

1. The music business shrank and the center of gravity shifted: The entry of streaming companies changed the economics of music, since it largely removed the need to buy music, even in the single-song format. Spotifyand Pandora allowed subscribers access to immense music libraries, with high audio quality, and as they grew, revenues to existing music labels dropped:
   
   
   
   As you can see, music revenues shifted (unsurprisingly) from studios to music streaming, but in a more troubling sign,  the aggregate revenues of the music business dropped by almost 40% between 2000 and 2016. On a more optimistic note, the revenues are now back to pre-2000 levels, albeit not on inflation-adjusted basis, and 65% of all revenues in 2021 came from streaming. It is undeniable that streaming, by removing many of the intermediaries in the old music business model, has shrunk the business.
2. The status quo crumbled: As revenues shrunk, and moved from the studios to the streamers, the companies that represented the status quo imploded. The music studio business, which had a dozen or more active players in the last century, has consolidated into a handful of firms, most of which are small parts of much bigger entertainment companies (Sony. Vivendi), and many of the biggest labels in music (Abbey Roads, Motown) are historical artifacts that have sold their music rights to others. The music retail business was decimated, as music retailers like Tower Records shut down, and as artists looking to replace lost revenues from record sales with live performances and merchandising sales, companies like LiveNation stepped in to fill the need. 
3. The divergence in musician take became larger: As revenues shrunk and partially recovered, not all musicians have shared in the new pie equally. The top one percent of musicians account for ninety percent of all music streams and close to sixty percent of revenues from concerts. A business that has always been top heavy in terms of rewarding success, has become even more so.
4. Personalities became bigger than music labels: The advent of social media has allowed the highest profile performers to break free of most of the intermediaries in the music business. When you are Beyonce, and you have 15.3 million followers on Twitter and 317 million followers in Instagram, you have more reach and persuasive powers than any music company on the face of the earth. While it is true that social media has allowed a few musicians to break through and become successes, I think it is undeniable that social media is exacerbating the differences between big name musicians and unknowns more than it is helping close the gap.

You could see these the last two phenomena at play, this year, in the Taylor Swift Eras Tour, where Taylor has effectively cut out most of the middlemen in the concert business and laid direct claim to the hundreds of millions of dollars in revenues from the tour.

    As movie and broadcast business executives look over their shoulders at what streaming has in store for them, a few of them are undoubtedly looking at the implosion of the music business and wondering whether a similar fate awaits them. The more optimistic among them will point to differences between the music and movie businesses that will make the latter more resilient, but the more pessimistic will note the similarities. To put it in more existential terms, if the movie business resembles the music business in how it responds to streaming, there is a boatload of pain that is coming for the status quo, with the key difference being that a meltdown similar to the one seen in music will wipe out hundreds of billions of dollars in value, rather than the tens of billions in the music business.

Movie and Broadcasting - The Twentieth Century Lead In

    The movie business had its beginnings in the early 1900s, when the first movies were made and Hollywood became the destination of choice for movie makers, at least in the United States. In the years after, the great movie studios had their beginnings, with the precursor to Paramount being created by Cecil B. DeMille and others in 1915, followed soon by Metro Goldwyn Mayer (MGM), RKO, 20th Century Fox and Warner Bros (creating the Big Five), as well as by smaller players (Universal, United, Columbia), . In the golden age (at least for the studios), these five studios controlled almost every aspect of the movies, including content, distribution and exhibition, with movie actors effectively owned and controlled by the studios that discovered them. It took the  US Supreme Court and use of the anti-trust law, in 1948, to first force studios out of the movie theater ownership business, and then to release movie stars from their  bondage, and in the process, it ended the Studio Age.

    Forced to divest themselves of movie theaters and of their control of movie stars, the studios were able to offset the negatives with the positives from new technologies (Technicolor, stereo sound) and an almost unchallenged claim on American leisure time, with close to two-thirds of Americans going to the movies at least once a week in the 1950s. In the 1970s, Hollywood discovered the payoff from blockbuster movies, and the movie business became increasingly dependent on the biggest blockbusters delivering enough revenues and profits to cover a whole host of movies that either lost money or broke even. While Jaws and the first three Star Wars movies (A New Hope, The Empire Strikes Back, The Return of the Jedi) were not the first mega-hits in history, they accelerated the trend towards the blockbuster phenomenon that continues through today. In the 1980s, the birth of video players created ways for studios to supplement revenues at movie theaters with revenues from selling videos and DVDs, while opening the door to illegal copying and piracy. 

     Through this period, the big studios still controlled a large share of the content business, but independent studies, often more daring in choice of topics and settings, took a share. That said, the movie business remained concentrated, with the biggest players dominating each segment of the business.

That movie business was built around box office receipts at movie theaters, split between the movie makers and the theater owners. The latter were capital intensive, since they occupied valuable real estate, owned or leased by the theater companies. Though the theater-owners were nominally independent, studios retained significant bargaining power with these exhibitors and the sharing of supplemental revenues.  

        The broadcasting business lagged the movie business, in terms of development, because televisions did not start making their way into households in sufficient numbers until the 1950s, but it too was built around a system of content-production, distribution and exhibition, but with advertising at the heart of its revenue generation. The dominance of the three big networks (ABC, CBS and NBC) in television viewing meant that television shows had to reach the broadest possible audiences to be successful, and television show success was measured with (Nielsen) ratings, measuring how much they were watched, and an entire business was built around these measurements. That business was disrupted in the 1970s and 1980s with the arrival of cable television, and cable's capacity to carry hundreds of channels, some of which catered to niche markets, shaking the major network hold on viewers and changing content again. At the start of 2010, it was estimated that close to 75% of all US households received their television through a cable or satellite provider, setting the stage for the next big disruption in the business.

Movie and Broadcasting: The Streaming Era

    Netflix, which is now synonymous with the streaming threat to movies, started its life as a video rental company, more of a threat to Blockbuster video, the lead player in that business, than to any of the larger players in the content business. It is worth remembering that Netflix entree into the business was initially on the US postal system, with the innovation being that you could have the videos you wanted to watch mailed to you, instead of going into a video rental store. As the capacity of the internet to send large files improved, Netflix shifted to digital distribution, albeit with angst on the part of some existing customers, but it still relied entirely on rented content (from the traditional studios). It was in response to being squeezed by the studios on payments for this content that Netflix decided to try its hand at original content, with House of Cards and Orange is the new Black representing their first major forays, and set in sequence the events that have led us to where we stand today.

The Netflix Disruption

    The rise of Netflix as a streaming giant has been meteoric, and it can be seen both in the growth in subscribers and revenues at the company, especially in the last decade.

Embedded in these numbers are two other trends worth noting. The first is that the percent of content that Netflix produced (original content) increased from almost nothing in 2011 to close to 50% of content in 2022. The second is that growth in recent years, in subscribers and revenues, has come from outside the US, with US declining from 52% of all subscribers in 2018 to 33.6% of subscribers in 2022. 

    As Netflix has grown, it has drawn competition not only from traditional content makers, with the largest studios offering their own streaming services (Disney -> Disney +, Paramount -> Paramount+ & Showtime, Warner -> (HBO) Max, Universal -:> Peacock, MGM -> MGM+), but also from large technology companies (Apple TV+ and Amazon Prime). While Netflix remains the most watched streaming service, many customers subscribe to multiple streaming services, and as  streaming choices proliferate, more and more US households have started weaning themselves away from cable TV. This cord cutting phenomenon's effects can be seen in  the percent of households that have no cable or satellite TV:

Between 2015 and 2021, about 20 percent of all US households dropped their cable or satellite television subscriptions, with the drop off being dramatic in younger households. In August 2022, for the first time in history, Nielsen reported that more people watched streaming than cable or broadcast TV, and there is every reason to believe that this trend will only get stronger over time. As a final note, there are two reasons why cable and satellite television has not suffered an even steeper fall. The first is that aging households continue to stick with their television watching habits, and relatively few older Americans have cut their cable subscriptions. The second is live sports, especially (American) football, where cable continues to retain a foothold, though even that advantage is under threat, as sports franchises create their own streaming platforms (MLB) or find streaming venues (MLS soccer on Apple TV, the NFL on Amazon Prime). It is in this context that Disney's battle with Charter over ESPN takes on a larger relevance, since ESPN and cable TV have had a symbiotic relationship for more than two decades.

    As streaming has breached the broadcasting business, you may wonder how it is affecting the movie business. In the early years, streaming allowed studios to augment the value of their content by renting it out  to streamers (Netflix, in particular) for substantial revenues. As its subscription base grew, Netflix turned to making original movies, mostly for its own platform, and in 2019, it spent close to $15 billion on original content, rivaling  the spending of large movie makers. 

The COVID shut down of 2020, in particular, changed the dynamic further, as traditional studios, faced with the shuttering of movie theaters, released their movies directly into streaming. That phenomenon has outlasted COVID, and as it develops as a viable alternative for content distribution, it not only strikes at the heart of the traditional movie business but may also be changing consumer behavior.

The Streaming Effect

    As streaming disrupts both the broadcasting and movie businesses, let us look at how it is changing these businesses from the inside, starting with content (types of movies, movie budgets, number of movies), moving on to talent (actor and writer demand and compensation) and then to customers (how much and how we watch content). 

Content

    The growth of streaming platforms has altered content (movies and broadcasting) in significant ways., with the first being an increase in the total volume of content, as streaming platforms try to fill their content libraries. With Netflix leading the way on original content, this has translated into a jump in movies being made, as can be seen in the graph below, from an annual average of 367 movies a year, in the United States, between 2000 and 2012 to 1200 movies a year between 2013 and 2023. 

Source

That increase in demand for content has been accompanied by an increase in costs of movie making, with the average cost for making a movie increasing from $39.5 million between 2000 and 2012 to about $54.5 million between 2013 and 2023.

    If you are wondering why you have not seen an explosion of movies at theaters, it is because fewer of these movies are being made for movie theaters, with big studios, reducing theater movie production by almost 30%, from 108 movies a year, on average from 2000 to 2012, to about 75 movies a year, from 2013 to 2023. While independent studies increased their production over the period, the overall number of movies reaching movie theaters has seen a significant drop off. 

Source

While the 2020 drop can be attributed to the shut down, movie production has not bounced back in the years since.

    Finally, the most interesting effects of streaming may be occurring under the surface in terms of the content that is produced, and they can be traced to the very different economics of making movies for theaters (or shows for broadcasting) as opposed to creating content for streaming services. With the former, the question of whether to make content can be answered by forecasting the revenues that will be generated by that content, either as gate receipts and ancillary revenues (for movies) or in advertising revenues (for broadcasting). With streaming, the end game with new content (movies or shows) is to add new subscribers to the service, and/or induce existing subscribers to renew their subscriptions, and it is difficult to link either directly to individual shows. Even within streaming services, there seems to be no consensus on what strategy best delivers these results, perhaps because success is so difficult to measure. 

• Netflix has chosen what can be best described as the shotgun approach to content, producing vast amounts of content, often in the form of entire seasons, for shows, with the hope that some portion of that content would be a binge-watching hit. That approach has delivered results in terms of higher subscriber count, but at a huge content cost, with content costs growing at the same rate, or higher rates, than subscriber count, until very recently.
• HBO has used a more curated approach to content, making fewer shows, albeit with less divergence in quality, and releasing episodes on a weekly basis, hoping for more viral reach from successful shows (Game of Thrones and Succession qualify as big successes). The plus of this approach is lower content costs, but with much lower subscriber numbers than in the shotgun model.
• Disney Plus started with the premise that a massive library of content would allow the platform to draw and keep subscribers, but early on, the company discovered that to compete with Netflix on subscriber numbers, it needed new content, and much of that content has come from high-profile, expensive shows from its Avengers and Star Wars franchises. If success is measured in subscriber count, Disney Plus has succeeded, but the spending on content has exploded, dragging Disney’s profitability down with it. 
• With Apple TV+ and Amazon Prime, the game is even more difficult to gauge. Both companies spend large amounts in content and clearly lose money on their streaming platforms, but their benefits may come from tying users more closely into their platforms. with benefits showing up other products and services they sell to those in their ecosystems.

Given that all of these approaches have had difficult delivering sustained profitability, it is fair to say that while streaming has succeeded in delivering subscriber growth and changing content watching habits, it has not developed a business model that can delivered sustained profitability.

Talent

  The angst that many actors and writers about the sharing of streaming revenues can be best understood by considering how how they have historically received residual payments on content. Built around a pay structure negotiated in 1960, actors and writers are paid residuals each time a show runs on broadcast or cable TV, or when someone buys a DVD or videotape of the show. With streaming, that old structure has buckled, as the benefits from a show or movie are more difficult to measure, since subscription revenue or subscriber count cannot be directly connected to individual shows. (There are exceptions, where added subscriber numbers can be attributed to a hit show, say Game of Thrones at HBO, or even a high-profile individual, with Lionel Messi pushing up MLS subscriptions on Apple TV+.) To the counter that you can measure how many people watch a show or movie on Netflix or Disney+, note that streaming companies do not make money from viewers, but only from added subscription revenues. With the more diffuse link between viewership and revenues in streaming, the question of how to structure residuals to actors and writers has become a key point of contention, and one of the central elements of the current strike. 

    In 2019, the Screen Actors Guild made an agreement with Netflix that applied to any scripted projects produced and distributed by the platform where residuals were calculated based on the amount that a performer was originally paid and how many subscribers the streaming platform has. That agreement though has yielded wildly divergent payments to actors, with some taking to social media to showcase how little they received, even on widely watched shows, while other bigger name stars are being well compensated. One of the demands from strikers is that streaming services be more transparent about viewership on shows and that they tie compensation more closely to viewership, but this dispute will not be easily resolved. Given the stakes, an agreement will eventually be reached where actors and writers will receive more than what they are receiving now, but to the extent that streaming gets its value from adding and holding on to subscribers, I expect the divergence in pay between the stars of streaming shows and the rest of the content makers to get worse over time, just as it did in the music business.

Consumption

    Has streaming changed the way that we watch movies and broadcasting content? I think so, and here are a few generalizations about those viewing changes:

1. More choice, but less quality control: The fact that Netflix has built its content production around the shotgun approach, and is being copied by other streamers, you and I as consumers will be spending far more time starting and abandoning shows, before finding ones to watch than we used to. Not surprisingly, quite a few us are overwhelmed by that search for watchable content, and choose to go with the familiar (explaining the success of old network shows like The Office, Friends and Suits on Netflix)  or with the herd, often watching what everyone else is watching (the ten most watched shows and movies that Netflix highlights every day create feedback loops that lead them to be watched more).
2. Copycat Productions: The content business have never been shy about imitation and sequels, trying to remake successful content with slight variations or add sequels to hits, but that has notched up with streaming. Thus, the success of a show on Netflix gives rise not only to more seasons of that show, but to a whole host of imitations. If you add to this the reality that streaming platforms track what you watch, and have algorithms that feed you more of the same, you may very well have the misfortune of being caught in a version of Groundhog Day, where you watch the same movie, with mild variations, over and over again for the rest of your life.
3. YouTube and TikTok: As the content on streaming platforms dilutes quality and shifts to reality shows, it should come as no surprise that viewers are spending less time on streaming platforms and more on Twitch, YouTube and TikTok, where you get to watch people put out reality shows of their own, sometimes in real time.

Finally, the early promise of streaming was that it would allow us to save money, by cutting the cable cord, but as with most things that technology has promised us, those financial savings have become a mirage. If you add together the cost of multiple streaming services to the higher price that you paid to get higher-spreed broadband, to watch your streaming shows, I am sure that many of you are paying more on your entertainment budget than you did in pre-streaming days.

The Streaming Effect: Business Models and Profitability

    The effects of streaming on movies and broadcasting content and distribution are showing up in the financial statements of these companies and in the market pricing of these companies. In this section, I will start by looking at how the operating metrics of entertainment companies, with the intent of detecting shifts in growth and profitability, and then turn my attention to how investors are pricing in these changes.

Operating Effects

    For those who are concerned about a music business-like implosion in movie business revenues, I will start with the good news. At least so far, the cumulative revenues across all entertainment companies c has held up to the streaming disruption, as can be seen in the graph below, where I look at the cumulative revenues of all movie and broadcasting related companies from 1998 to 2023:

Note that since companies are classified based upon their core business in this graph, the streaming component of revenues are understated, since the revenues that Disney, Paramount and Warner get from their streaming businesses are counted as movie revenues. As you can, aggregate revenues did see a drop in 2020, because of COVID, but have come back since. If you are wondering why cable company revenues have been resilient in the face of cord cutting and the loss of cable TV subscriptions, it is because cable companies remain the prime providers of broadband, without which there is no streaming business.

    On a less upbeat note, looking at profitability at these companies, the cumulative operating profits have been less reselient, especially in the post-COVID years, with cumulative operating profits in 2022 and 2023 well below operating profits in 2019:

If you bring the revenues and operating numbers together to compute operating margins, you start to get a clearer sense of why movie companies, in particular, are facing a crisis:

The profitability of the movie business has collapsed in the years since COVID, with operating margins dropping below 5% in 2022 and 2023, from more than 15% in the years before COVID.  Streaming seems to be settling into a modicum of profitability, but here again, we may be overstating the profitability of streaming by not bringing into the metric the losses that Disney, Warner Bros and Paramount are facing on their streaming segments.

    In sum, entertainment companies are delivering higher revenues overall, with revenues from streaming and new technologies increasing enough to offset lost revenues in legacy businesses that are being disrupted, but the entertainment business overall is becoming less profitable.

Market Effects

    As streaming has changed the movie and broadcasting businesses, financial markets have struggled to get a handle on how these changes affect the values of companies int these businesses. Looking at the cumulative market capitalization of all entertainment companies, there are two shifts that we can observe over time, one in the decade leading into COVID and one in the years after:

Note the surge in aggregate market capitalization between 2019 and 2021, with Netflix leading the way, and with other entertainment companies partaking, and the drop in value in the last two years.  The trends in cumulative market capitalization of all entertainment companies also masks shifts in value across companies within the group, as can be seen in the graph below, where I look at the diverging fortunes across the last decade of the five largest entertainment firms (in terms of market capitalization) in September 2023:

Between 2013 and September 2023, Netflix gained $174 billion in market capitalization, posting an annual return of 24.5% a year. During the same period, Comcast, Disney and Warner saw their market capitalizations stagnate, in a period when the market was up strongly, effectively translating into a lost decade of returns to shareholders. Live Nation, the fifth largest company in the group in September 2023, barely registered in the rankings in 2013, but has risen 17.19% a year to reach its current standing.

    While the shifts in value from the status quo players to Netflix and Live Nation is buffering the impact of streaming on the cumulative market capitalization of this industry group, the market has become decidedly more negative on one segment of this group - movie theater companies. In the last graph, I look at the cumulative market cap of the four largest movie theater companies in North America - AMC, Cineplex, Cinemark and the Marcus Group. 

While the COVID shut down clearly impacted the 2020 numbers, note that the market decline in these companies started in 2017, and has picked up steam since.

Corporate Governance

    Corporate governance at companies rarely draws attention during the good times, where managerial mistakes are overlooked, and rising revenues and earnings can hide corporate flaws. However, in challenging times, and disruption clearly has created challenges for entertainment companies, it is not surprising that we are seeing more investor angst at these companies.

1. CEO Turnover: There has been drama in the top ranks of Disney in the last few years, as Bob Iger  first turned over the reins in the company to Bob Chapek in 2020, and then reclaimed it two years later. Some of that blowback can be traced to an expensive bet made by the latter on streaming, reorganizing the company around Disney+,  and investing billions into streaming content, trying to attract new customers. While there are factors specific to Disney that can shed light on that company's CEO wars, I expect CEO turnover and turmoil to increase at entertainment companies, as investors look to replace management at companies that are struggling, in a sometimes futile effort to change their fortunes.
2. Activist Presence: It is no surprise that activist investors are drawn to industries in turmoil, pushing companies to spend less on reinventing themselves and returning more cash to shareholders. Here again, the Disney experience is instructive, where Nelson Peltz's opposition to Chapek's plans clearly played a role in the CEO change this year. While Iger has been given some breathing room to fix problems after his return, the clock is ticking before activist investors return to the company. In fact, I expect the companies in the entertainment group to be prime targets for activist investors in the next few years.
3. Spin-offs, Divestitures and Break-ups: In response to streaming challenges, entertainment companies have started exploring whether splitting up or spinning of businesses will improve their chances of survival and success in the streaming age. Warner Bros. was spun off by AT&T and merged with Discovery in 2022, precisely for this reason, and the push for Disney to spin off or divest ESPN is similarly motivated.
4. Bankruptcy: For the companies whose financials have imploded as a result of streaming, and all have debt, you should expect to see dire news stories not just about layoffs and shrinkage, but about potential bankruptcy. In the theater business, this has become reality as Cineworld (owner of Regal, the second largest theater chain in North America) issued a bankruptcy warning in early 2023, and AMC (owner or both the largest theater chain and a streaming service) had to do a  reverse stock split to keep itself from careening towards penny stock status.

There are three final notes that I would like to add to this (long) post. First, I know that this post has been US-centric in its examination of the streaming effects on entertainment, but I do believe that much of it applies to the rest of the world, with a caveat. The status quo may be better protected in other parts of the world, either because of explicit limits on or implicit barriers to entry. Thus, streaming may be less of an immediate threat to Bollywood, India's immense homegrown movie-making business, than it is to Hollywood, but change is coming nevertheless. Second, as I noted before, the line between content made by professionals (movie makers, broadcasting studios) and individuals (on platforms like YouTube and TikTok) is getting fuzzier, and they are all competing for limited viewer minutes. Third, for those in this business who are naive enough to think that artificial intelligence will rescue their companies from oblivion, I would offer the same caution that I did to the active money management business, a few months ago. If everyone has it, no one does, and with AI, content makers may very well find themselves competing with computer power and technology companies, and that is not a fight where they have the upper hand.

What the future holds…

    The consequential and unresolved question is what the movie and broadcasting business will look like a decade from now, since the answer will determine how stakeholders in the business will be affected. To frame the answer, I start by looking at the most malignant and benign ways in which this could play out:

• At one extreme, you may see the movie and broadcasting business follow the music business and see a collapse of revenues, a destruction of the status quo and a resetting of the competitive landscape.  If this happens, some of the biggest names in movies and broadcasting will disappear as independent entities, either absorbed as pieces of much larger companies or cease to exist. The disruptors, including Netflix and Live Nation, will face different challenges, as they now become the status quo, and they will have to figure out how to make their business models profitable and sustainable, even as they themselves will become targets of new disruptors.
• At the other exhibit, you will see entertainment continue to grow as a business, but with status quo players (content makers and exhibitors) bringing their strengths into play to outflank the disruptors. In this scenario, the big names in the movie and broadcasting business will modify how they make and exhibit content, and come back, bigger, stronger and more profitable than they were in the pre-streaming era. 
• There is a middle-ground, where success will require that you draw on the strengths of both the status quo and new technologies. The players in the status quo who are adaptable and willing to change will absorb those players who are not, and there will be a similar shake up among disruptors, with those disruptors who combine entertainment business wisdom with technological knowhow will win at the expense of disruptors who do not. 

As investors in this industry group, your task is simple, if you believe in either extreme. If you believe that disruption will be absolute and upend the movie and broadcasting businesses, you should, at the minimum, avoid the status quo entertainment companies, and if you are more of a risk taker, sell short on these companies. If you believe that after all is said and done, disruption will expand entertainment business revenues, but will leave the status quo on top, you should buy Disney, Warner and perhaps even AMC, and sell short on the highest-flying newcomers in the business. 

    If, like me, you go for the middle ground, your success will depend on how good you are at assessing adaptability in entertainment companies, buying status quo companies with speedy learning curves on streaming and new technologies and disruptors that acquire content-making skills to pair with technological prowess. That would make both Disney and Netflix works-in-progress, with the former still wrestling with the challenge of making its streaming platform a money-maker and the latter working on a content model that is more disciplined and less costly. I took a run at valuing both companies, assuming that they each find their way to a healthy balance (between growth and profits), with Disney's margins settling in below where the 18-20% levels the company delivered in pre-COVID days, and Netflix reducing its content spending (with content costs growing much slower than subscriber growth), going forward:

	Disney	Netflix
Revenues (LTM)	$87,807	$32,465
Operating Income	$7,725	$5,624
Revenue Growth (last year)	8.30%	5.44%
Operating Margin (LTM)	8.80%	17.32%
Expected Revenue Growth (Yrs 1-5)	10.00%	15.00%
Expected Operating Margin	16.00%	20.00%
Sales to Capital	1.46	3.00
Value per share	$87.52	$238.08
Price per share	$80.00	$443.10
Spreadsheet	Download	Download

Put simply, the market seems to be pricing in the presumption that Netflix will continue to get content costs under control, while still delivering growth similar to what it has delivered in the past, while it is pricing Disney for low growth and margins that will fall short of their historic norms. I agree that Disney is a mess, right now, but I do believe that at current pricing, the odds favor me more with Disney than Netflix, but that is just me!

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