The Scaling and Profitability Trade off: Venture Capital's Weakest Link!

    It is undeniable technology companies have found their most hospitable setting in the United States and while there are many reasons for the US dominance of technology, easier access to capital for young businesses has been a key ingredient. Venture capital in the US, in its institutional and organized form, can trace its roots back to the 1950s, and over the last few decades, it has generated its share of legendary investors. Vinod Khosla is one of those legends, and it is for that reason that I was surprised to see him tweet the following:

I understand that utterances on social media, often in response to comments by others or made in anger, are often quickly regretted, and I believe (though I am not certain) that Mr. Khosla did not quite mean what he said here, confusing profitability with cash flows, and arguing that every business should put scaling ahead of profitability. That said, his view that scaling should be given priority over profitability is more the norm, than the exception, among many venture capitalists, and while it probably always has been the case, I believe the tilt towards scaling has become pronounced in the last two decades. In this post, I want to zero in on the scaling and profitability trade off, how the emphasis on the former over the latter plays out at start-ups and very young companies, and why we live with the consequences, whether they want to or not.

Scaling versus Business Building

    To put the choices you will face on scaling up versus business building into perspective, let's assume that you are a founder, and that your start-up has a tested product and that you believe there is a market for that product. You can stay with what you have built and build a business to take advantage of the immediate market, focusing on financial health and profitability. The fact that you will stay small, and perhaps unrecognized in markets other than your own, is a minus, but there are pluses. You will have little need for external capital, and you will own much or all of the business, facing little pressure from outside to change the way you do things. Alternatively, you can take a more ambitious route, where you seek out a bigger market, augmenting existing or adding new products, and while that path will deliver larger revenues, you may have to work harder to get it to deliver profits and cash flows, and perhaps have to give up more of your ownership and control of that business.

The Scaling Choice

    Before starting on the determinants of scaling, it make ssense to begin with the metric being scaled. For most businesses, it is revenues that is the chosen metric, with scale capturing how big revenues can become over time. With some earlier-stage businesses, many of which are pre-revenue, the metric can become a variable that these businesses hope to convert to revenues; with tech intermediaries and social media companies, it can be users or subscribers. 

    Focusing on scale, though, there are factors that come into play that allow scaling to have a higher likelihood of success in some businesses than others:

1. Market size: It is easier to scale up a company, if it is small player in a big market, than if if the market is small, and scaling up will quickly give you a dominant market share. That said, the way you describe your business, and then run it, can play a role in how big a market you will have for your products. In my posts on valuing Uber, for instance, I noted that describing it as a logistics company (car service, moving, delivery) rather than just a car service company could triple its potential market. 
2. Market growth: It is also easier to scale up a company if the overall market that it is targeting is also growing, since growth does not require going after competitors' customers. A smartphone company (Apple or Samsung, for instance) in 2010 had a growing market to work with, as customers switched from flip phones and smartphones made inroads into large emerging markets.  In 2026, that advantage had largely dissipated, as the smartphone market has matured.
3. Industry Structure: There is a natural structure to industries, driven by economics and business type, with some industries splintered across many players, and some concentrated in a few big players or even in a winner-take-all. You can scale up more in the latter, but you will have to confront the odds favoring you being one of the winners in the industry.
4. Capital intensity: It is easier to scale up a business that does not require large capital investments to be able to generate more in revenues. Using Uber as an example again, scaling up was made easier in the early years, since it did not own the cars or hire the drivers that comprised its car service, and growth came quickly and with little added investment.
5. Customer inertia: Businesses can grow faster and get bigger if there is less inertia among customers and more willingness to try out new products or services. At the risk of generalizing, this may explain why scaling up can happen more quickly in younger industries (like technology) than in older ones (health care, education).
6. Key person(s): There are some businesses that are built around the specific skill sets of a person (usually a founder or business owner) and these skill sets are not easily transferred or taught to others. A master craftsperson, say a furniture-maker, will have a more difficult time scaling up that business, because without being able to pass his skills on to his or her apprentices (which can take time and require intense oversight), he or she is constrained in how much new business he can take on. If that craftsperson has a recognizable name, it is possible that you could build a scalable franchise model, as has been tried by some master chefs (Wolfgang Puck, Gordon Ramsey etc.)

The graph below captures the scaling choices that companies make as a function of these factors:

As you can see, some businesses can scale up quickly, some take more time to scale up and some never scale up, and the businesses that scale up quickly often scale down just as fast. Thus, the decision of whether to scale and how quickly to do so is as much driven by the nature of the business (capital intensity, industry structure, competition) and the characteristics of the market that it is targeting (size and growth, customer inertia). 

Business Building

    While having access to a big, growing market can allow you to scale up more quickly, your capacity to generate profits and build a business will ultimately come from other forces:

1. Unit economics: Unit economics measures the profitability of the marginal unit sold by a business, and is thus determined by the price charged for that unit and what it costs the business to produce that unit. Businesses like software, where the marginal unit costs very little to produce and can still be priced highly, have superior unit economics and will find it easier to convert growing revenues into profits, since much of the increase in revenue will flow into profits.  Conversely, businesses like electric cars, where each additional car sold costs money to make, will struggle to convert scaled up revenues to profits.
2. Economies of scale: Businesses with large fixed costs, whether they be associated with maintaining platforms and infrastructure, or sales and marketing, face obstacles to profitability. While growing can provide scaling benefits, that works only if the fixed costs don't grow with revenues and if they are not so onerous, that you still have losses after scaling up. 
3. Competition: & Competitive Edges (moats): Large and growing markets provide businesses with opportunities to grow, but for that growth to translate into sustainable profits, these businesses will need pricing power and that power comes from barriers to entry that keeps new entrants out and gives existing players advantages. 

It is true that the operating choices that businesses make play out on both the scaling and profit dimensions, sometimes pitting them against each other. A decision to lower product prices may increase revenues at the expense of unit economic profits, and a decision to spend more on advertising and promotion may expand markets, but the higher marketing costs will impose a drag on profitability.

    One way to illustrate the combination of forces that go into business building is to to go back to basics, and to look at what lies under each one:

As you can see, scaling up is not a mantra that automatically translates in profitability, and the pathway to profits will be determined by variables that are often out of the control of a business. 

Scale & Profitability Mixes

    With the multitude of factors determining both scaling potential and business model viability, it should come as no surprise that the outcomes that we observe can range the spectrum, starting with extraordinary companies that scale up quickly, while delivering huge profits, to companies that never scale up, either by choice or because they could not, and some of which never make money.

1. Lightning in a Bottle: Are scaling and profitability mutually exclusive? Put differently, can a company scale up, while delivering profits and perhaps positive cash flows as it grows? The answer is yes, but it does require a fairly unusual combination of circumstances - a big and growing market, being an early entrant into the market with few competitors, low capital intensity and excellent unit economics.  There are a few companies that meet these conditions, and we will call them "Lightning in a Bottle" firms, partly because they are rare, and partly because success can come from being at the right place at the right time. Google and Facebook, in their early years, were good examples, with revenues growing exponentially and profitability in place.
2. Field of Dreams  (Shoeless Joe Jackson version): As a baseball fan, I have always had a soft spot for the movie, Field of Dreams, where a farmer (Kevin Costner) builds a baseball field in the cornfields, and when asked why, responds with "if you build it, they will come". There are companies that seem to be built around this motto, where scaling up comes first, often accompanied by large losses, but with the promise that "if they build (revenues), they (the profits) will come. During Amazon's first decade and a half of existence, I described their business model as a Field of Dreams model, and gave credit for Jeff Bezos for being steadfast in not only telling this story, but also acting consistently with it, and carrying investors along. (If you are wondering what Shoeless Joe is doing in this story, I am afraid you have to watch the movie all the way to the end.)
3. Field of Nightmares: Amazon was not the first successful Field of Dreams company, but as one of its highest profile winners, it gave rise to a legion of young companies, all labeling themselves the "next Amazon". Needless to say, Amazon's success came from being a disruptor of a huge business (retail), which had atrophied and weakened over time, and many of the Amazon wannabes that tried to imitate it managed to do so on the growth dimension, with immense amounts of capital invested in scaling up, but never turned the corner on profitability, partly because they had neither the unit economics nor the economies of scale to pull it off.
4. Niche Star: Scaling is not always the optimal choice, and there are some companies that recognize this reality early, choosing to stay small and focusing on a portion of the market where they have decided advantages. To that extent that they can convert those advantages into premium pricing and niche market dominance, they can have values that are disproportionately large relative to their operating metrics, i.e., trade at high multiples of revenues and earnings. Ferrari, for instance, sells only a few thousand cars every year, but with an operating profit margin in excess of 20%, it trades at a market capitalization comparable to that of auto companies that sell hundreds of thousands of cars each year.
5. Big and Broken: It is no secret that there are some businesses that start with business models with a fatal flaw, i.e,, a broken business model, and rather than being shut down, they are fed increasing amounts of capital and allowed to scale up. A real-estate based business that leases properties long term, and then sub-leases them short term, has a duration mismatch born in hell, and expanding it geographically and allowing it to lease hundreds of properties, as WeWork did, just makes it a really big, bad business. If you are puzzled as to why investors would supply capital to these businesses, you may want to read on.
6. Small winner & Small losers: If you look at all businesses, private and public, most remain small, some due to business and industry structure and some because of owner constraints on capital and control. These small businesses, though, over time, bifurcate into good small businesses, earning more than their cost of capital and delivering value, and bad ones, earning less than the cost of capital, but still worth more as going concerns, than liquidated.
7. Cut your losses: Finally, there are businesses that start up with dreams aplenty, and over time discover that they can neither scale up, nor make money. In the absence of capital infusions, these businesses fail early, but if capital providers keep funneling resources into these companies, they still fail, but do so later and with a much higher price tag.

In the matrix below, with scaling on one axis and profitability on the other, I plot all eight of my scale/profit combinations:

Any investor or founder who blindly follows the pathway of scaling first and profiting later for every business is using a cookbook approach to business building, and runs the risk of making small failures into big ones. 

The Tradeoff between Scaling and Profitability: Determinants

    As you review the factors that govern the trade off between scaling and profitability, it is clear that the right choice (on how much to scale) will depend on the firm, and that not every small firm is destined to become or be more valuable as a larger firm, and that not all large firms have the same profitability characteristics, once scaled up. That said, is it possible for firms to adopt scaling pathways that look, at least from a business standpoint, to be suboptimal? Of course! There are small firms that have viable pathways to scaling up that choose to stay small, and at the same time, there are small firms that are designed to be small, niche businesses embark on scaling that is value destructive, and the reasons are a mix of human frailties on the part of founders, system constraints (from governments and regulators), access to capital (too little or too much) and exit options (sell, liquidate or go public).

1. Founder Characteristics

    The founder or founders of a business not only play a key role in guiding the business through its early days, when most start-ups fail, but they also make key choices that can determine in its end game. In making these choices, they may be guided by the fundamentals we outlined in the last section, that affect scalability, but they are also a function of their personal make-up, on at least a couple of dimensions:

• Control versus Ambition: There is a natural tension between wanting to control the levers of decision-making in a business and scaling that business, since the latter almost always requires raising capital from providers who will either constrain your choices (if borrowed money is used) or demand a share of ownership rights (if equity). With the latter, founders will find their control diluted over time, and with enough scaling up, it is possible that founders end up with less than controlling stakes. For some founders, that fear of dilution and losing power over their business creations runs deep enough to stop them from embarking on growth plans, even though these plans make economic and financial sense.The flip side of control is ambition, and for some founders, the desire to build big businesses that are not restricted geographically or in product offerings can drive the decision to scale up, even though the fundamentals may not support that expansion. This works only if they can convince investors that their ambitions In fact, this tension between a founder’s need to be in control and that same founder’s desire to build big plays out in what Noam Wasserman called the Founder’s Dilemma, where to make a business bigger, its founder has to step down or at least compromise on control.
• Longevity versus Scale: There is an argument to be made that if your intent as a founder is to build a business that is long-lived, your odds of success improve if you keep your business smaller and more focused on what it does well. While there are many exceptions to this generalized rule, it is worth noting that some of the longest lived firms in the world are family owned small businesses, that serve a niche market, and are passed down generation to generation in the same family. It is also true that firms that see a sudden surge in revenues, usually as the result of an external factors or happenstance, often live to regret their good fortune, as they scale up overnight. In the aftermath of the Covid shutdown, for instance, firms like Moderna and Peloton boomed, but they also overreached, and did long-term damage to their business models.

In summary, the choice between scaling and profitability will play out differently across businesses, depending upon what founders value most, thought it is healthy for an economy to a have a mix of founders, since it creates a mix of businesses.

II. Access to capital

    It is true that businesses need access to capital, to varying degrees, to scale up, and the easier it is to raise that capital, the easier it is to make a business bigger. Capital can come from different sources, ranging from family wealth to venture capital to public equity, with each one carrying its pluses and minuses.

• Family (or friend) wealth:  Every business, through human history, having lived through its early days (when failure risk is high and its products and services are still untested) has faced a choice of whether to stay small, serving a market that it knows and understands, or whether to get bigger, going after a bigger market. For much of that history, though, with businesses funded with family funds and access to capital was limited, most businesses chose the first path and remained small businesses, focusing on building business models that delivered profits, with wide differences in success rates. For a few, owned by wealthier families, access to a much larger pool of capital (from family savings and bankers willing to lend to these families) created family groups that dominated economies, and continue to do so in some parts of the world. 
• Venture capital:  The growth of public equity markets in the late 1800s and much of the last century did little to change the family control dynamic, since investors in those markets were primarily interested in funding larger companies with established business models. Recognizing this gap between capital need and capital access at younger businesses, and the opportunities that the gap presented, allowed for the rise of venture capital in the 1950s, primarily in the United States. These venture capitalists provided seed capital for start-ups, using winners to cover their failures, and got the bulk of their winnings when they exited these investments, either by going public or selling to another entity. Over the last few decades, venture capital has grown, and in the last 12 years, that growth has not let up: 

  

  Source: NCVA 2026 Yearbook

In this century, venture capital has also become more global, growing in Asia and Europe, but it is still true that it is easier for a small business to raise capital to scale up in the United States than it is in much of the rest of the world.

• Public equity: There are some growth businesses that bypass venture capital and go after public equity, a much bigger pool of capital and one that may give founders better terms. In some cases, this access to capital might be enabled by going public, even with unformed business models and little to show in terms of existing operations (revenues or earnings), but in most others, it takes the form of capital invested by larger, more mature public companies in return for a share of ownership. These investments may be labeled as strategic, but the motives for making these investments vary across companies. Some invest to get access to a promising technology or product. some to pre-empt competitors and some for the same reason that venture capitalists do.

The bottom line is that businesses that seek out capital, whether from family, venture capital or public equity, have to accept that the capital providers will demand and usually get a say in business decisions, and the more capital you seek, the more sway they will have.

III. Investor Preferences

    Businesses get their cues on whether to scale up or build business models from the investors who fund them, and much as founders want to map their own path, investor preferences matter, as do their end games. Put simply, a family that invests in a business with no plans for exit will choose a very different path for that business than a VC that invests in the same business with the intent of exiting that investment by selling it to another investor or company, or taking it public.    

    Venture capitalists are often viewed as the sherpas who guided young businesses to success, both operationally and in markets, the mythology about venture capitalists and what they do has also built up. Since that mythology extends to almost every aspect of venture capitalist activity, perhaps the best way to dispel myths and bring in reality checks is to look at what venture capitalists are "assumed" to do in each phase, and contrast it with what they actually do:    

If you are reading this as a critique of venture capitalists, you are misreading it. My intent is not to paint a picture of venture capitalists as lazy and greedy, but to bring home the reality that given how venture capitalists invest, act and are judged, it is unrealistic to expect them to do the heavy lifting of building businesses for the long term and to even make business sense, when they talk about companies.

    There are two parts of the venture capital rulebook that you should focus on, to understand why many VCs prioritize scale over profitability. The first is that they price companies, rather than value them, and in a post from a few years ago, I made the argument in more depth. VC pricing based on what other venture capitalists are paying for similar businesses, often scaled to simplistic metrics, users and subscribers for pre-revenue companies and forward revenues or earnings in what passes for VC valuation:

The second is that VC success is measured based on price at entry and price at exit on an investment, rather than the quality of the business built, and using that metric, the median venture capitalist has not been much better at harvesting alpha than the median mutual fund manager or PE investor:

Source: Cambridge Associates

There are, of course, standouts in each of these categories, fund managers who have delivered well above the market, but in mutual funds and to an increasing extent, hedge funds, that success is fleeting. There are two aspects on delivering returns where venture capital stands out, relative to other active investing classes. 

• The first is that failure, always a concern in investing, is much more a part and parcel of investing in venture capital than in other investing grouping. Put simply, not only are there more VC funds that go out of existence every year, but even the most successful VC funds lose on many or even most of the investments that they make, especially in angel financing deals. 
• The second is that venture capital investing, when it works, can generate outsized returns on winners that (hopefully) cover the cost of failures. 

You can see both of these at play in the graph below, which looks at returns that VCs book when they exit investments:

Source: CF Private Equity, from Pitchbook data

As you can see, across all the time periods, it is the top 10% of VC investments that deliver the bulk of returns to VC investors, and over time, that concentration has increased: in the 2023-2026 period, 80% of all returns to VC investors came from their top 1% of investments. The combination of these two forces (losses on most investments and outsized winners), i.e., the power law in venture capital, has two consequences. The first is that only about a quarter of venture capitalists in each year deliver above-average returns, making the average VC returns in the table above more palatable. The second is that success in venture capital, unlike in other areas of active investing (including mutual funds, hedge funds and even private equity), has been more enduring. The power law characteristic also feeds into VC incentives, leading venture capitalists to direct their capital more into chasing the biggest winners than in building businesses. In fact, the more top-heavy VC returns become, i.e., dependent on big payoffs, the more pressure venture capitalists feel (and pass on to their portfolio companies) to find the next big winner, pushing the ecosystem dangerously close to gambling.

A Changing Game

    With the discussion of the scale versus profitability at the business level leading in, and the assessment of the incentives of capital providers following, I think that we are well positioned to examine how changes in public and private markets have increased business incentives to scale, as opposed to building business models. There are two developments, in particular, that have taken the tilt towards scaling in venture capital and made it even more pronounced - the entry of public equity into the funding of private businesses and the fading of reversal, as an antidote to momentum, in public markets.

The Gray Market Effect

    For much of the last half of the last century, after venture capital established a presence in the United States, it remained the only or primary source of capital for young firms. That has changed especially int the last decade, as public equity investors have increased their investments in young, private businesses, supplementing venture capital in some and even displacing it in others. An early measure of this trend is captured in the charts below:

Kwon, Lowry and Yiming (2020)

While this graph looks at only the number of mutual funds investing in private businesses, and stops in 2016, there was a corresponding surge in capital invested by mutual funds in young, growth companies, with T.Rowe Price and Fidelity investing billions in high profile tech companies like Uber.  They were joined by sovereign funds, who invested heavily in these companies either directly or indirectly, through stakes in entities like Softbank's Vision fund.

    We can debate the reasons for why we saw this surge, with fear over missing out (FOMO) and wanting to partake in tech playing roles, but whatever the reasons, capital access surged for young companies, especially in tech, during the period. In effect, rather than two mostly separated markets - one for young, smaller, private business dominated by VCS and one for larger companies more advanced in the life cycle, where public equity suppled the funds, a gray market was created where VC and public equity fund access allowed private businesses to stay private for longer.

Public Markets: Momentum, Fundamentals and Reversals

    Public equity markets have always been momentum-driven, allowing traders who ride that momentum to prosperity, before bringing them down when the momentum shifts. At the same time, fundamentals act as an anchor, operating as a counter to momentum, leading to reversals and allowing investors to hold their own over time. While the congruence is not always perfect, scaling feeds into momentum and profitability is the most critical fundamental, and in markets with balance, when one gets out of sync, the other restores harmony. 

Over the history of stock markets, value investors have often claimed dominance, and pointed to the returns you could have earned by buying companies that look cheap on a value basis (low price earnings or low price to book) and waiting for price reversals. Traders push back by noting that over the same history, momentum has had a decisive effect on returns, especially over shorter time intervals.  While the momentum effect shows up across the decades, there is evidence that the reversal effect has weakened over time, leaving investors who bet on mean reversion and a return to fundamentals in the lurch:

Source: Ken French's datasets

The reasons given for this shift vary, and are often reflective of the biases of the investors giving the reasons. 

1. The Fed did it: For those who view central banks as all-powerful, and believe that the low interest rates of the last decade were their doing, those low rates have also become the proximate reason for market pricing behavior and reckless risk taking. Their argument is that interest rates that are close to zero induce investors to shift from bonds to stocks, and within stocks, to move from low growth, high earnings stocks to high-growth companies with little or negative earnings.
2. The rise of passive investing: In the battle between active investing and passive investing, with ETFs supplementing index funds, the latter has had a decisive edge in terms of returns over the last two decades, and its share of the market now stands are well above 50%. There are some who argue that the flow of funds to passive investing vehicles has contributed to the increased power of momentum, since more new funds flow to the largest market cap companies than to the smaller ones. In addition, it is argued as the number of active investing declines, there are fewer investors looking at business models and profitability, reducing the pull of fundamentals on price.
3. Public market composition: It is noteworthy that the reversal effect started weakening in the 1990s, a decade when young dot.com companies with unformed business models flooded the market, bypassing the more traditional route of using venture capital to grow. With these companies, where value is almost entirely driven by potential and not by operating metrics today, the catalysts needed for reversal may take longer to manifest.
4. Information sources and access: It is undeniable that investors and traders get information from a wider ranges of sources now than two or three decades ago, with social media and online sources supplying information that used to come from newspapers and financial news channels. In additional to being less curated and controlled, that information is also instantaneously accessible to the public, and price reactions tend to follow. 

While I take issue with parts of each of these arguments, there is some truth to all of them, and they have contributed to making pushing back against momentum a more hazardous exercise for investors.

The Consequences

    With larger amounts of capital being deployed by VCs at young, growth companies, substantial capital infusions from public equity funds into private capital markets, and public equity markets that are more used to and receptive to young company listings, it should not be surprising that it is changing how private companies behave. In the graph below, I look at the characteristics of companies going public in the United States, using the data that is generously made available by Jay Ritter:

Source: Jay Ritter's IPO data

There are three clear changes over time that are visible in this graph:

1. Private businesses are waiting longer before going public: As you can see, the average age of a company going public has risen over time, with the median age rising about 11 years in the last 15 years.

2. Private businesses are scaling up (revenues) more, while waiting: While private businesses wait longer to go public, they are spending that time scaling up more than they used to. The inflation-adjusted revenues at the median IPO have tripled or even quadrupled, relative to IPOs in the 1980s.

3. Private businesses are deferring building business models & profitability: The most striking feature of the data, to me, is that while private businesses are waiting longer and scaling up more before going public, they also seem to be deferring business building for much longer as well. While it was routine for companies going public in the 1980s to be profitable (>80% were), less that a quarter of the companies that have gone public in the last decade have been profitable.

While companies that are going public are bigger (in revenue terms) and less likely to be profitable, markets are attaching large market capitalizations to these newly minted companies, as you can see in this graph which zeros in on tech IPOs:

Source: Jay Ritter's IPO data

You will also notice that companies going public are issuing smaller portions of their shares to the public, at least in the initial offering, suggesting that the need for capital that drove companies to go public has become less pressing over time, perhaps because of more capital access as private businesses. While the median market cap of a company going public in the last six years has exceeded a billion, the largest IPOs command market capitalizations that would have been unimaginable a few decades ago. From Facebook, with a pricing of $104 billion, in 2012 to SpaceX, going public in June 2026 at $1.8 trillion, the trend lines are pointing upwards, especially if Anthropic and OpenAI deliver on their trillion-dollar plus pricing promise. 

Implications

    By itself, the trend towards private companies scaling up more, while public, and going public at eye-popping market capitalizations may be understandable and explainable, but there are implications that we need to consider both from an investing and governance standpoint.

1. Corporate governance: One of the reasons that private companies often delay going public is because governance requirements, from board composition to top management compensation, are more stringent at public than private businesses. While Sarbanes-Oxley, which wrote into law many of the current governance rules for public companies, is often toothless and ineffective, it still forces disclosures about governance (on conflicts of interest and board member relationships) at public companies. In addition, public market investors can pressure public companies to change governance practices or top management, if companies underperform in the market place. One of the perils of letting companies scale up more before these governance questions get raised is that the top management in these companies may have few checks on their actions. It is true that venture capitalists could operate as a disciplinary mechanism, but in an age of founder worship and where VCs can be divided and conquered, you can have companies with market pricing of a billion, hundreds of billions or even trillions run by people who are ill-suited for the task.
2. Delayed business model building: If the first imperative for a private business is to scale up, because scaling pushed up pricing both in private and public markets, the challenge of business building will get deferred to a later stage. The problem with scaling up first, and building a business model later, is that it may be too late, since the choices made to allow for scaling up may impede the pathway to profitability. Again, if your response is that VCs will work on fixing this problem, they have little incentive to do so, since they benefit from scaling up and exiting these businesses, before the business problems become too big to ignore. 
3. Scaling stories: If you believe, as I do, that valuation is a bridge between stories and numbers, and that the balance between the two shifts over the life cycle, with stories dominating early in the life cycle and the numbers taking center stage in the later stages, it is understandable that VCs and founders, when marketing their companies are primarily story tellers. I don't have a problem with that, but as I noted in my last post on AI as a business, the stories that are being told for these companies are often incomplete, and almost entirely focused on the scaling question. Thus, in the Anthropic sales pitch it is the growth in the annualized revenue run rate (ARR) and the size of the AI market (huge, but with no specifics) that comprises the bulk of the story, with little or no mention of business models or profitability.
4. Disruption without replacement: Disruption has been a key component of the stories that underlie many of the largest companies that have gone public in this century. Accepting the premise that a healthy economy needs a shaking up of the status quo, and that disruption can lead to economic growth and better practices, it is still legitimate to look at disruption's debris. One of the perils of supplying capital in almost endless quantities to private businesses that aim to disrupt, without challenging them on business models, is that you may succeed at disrupting the status quo (driving existing players out of business) but your disruptor may not be able to build a business that can be self-sustaining in the long term.

Conclusion

    I am sure that you are already aware of the core message of this post, which is that notwithstanding the current emphasis on scaling up businesses, not all businesses are meant to scale up, and that scaling up comes with challenges that founders may be ill-equipped to meet. That said, ambitious founders will feel the urge to make their businesses bigger, and if they raise capital (from venture capitalists) to make this happen, the incentives to scale up will increase, even if it makes little or no business sense to do so, with all parties hoping to exit by selling to others (public or private) who will price based on scale. While this has always been the case, changes in private and public capital markets have tilted the scale even further in favor of scaling, and it is possible that companies, both public and private, with sky-high pricing have been built on bad business models that are irredeemable.

YouTube Video

Blog posts on Venture Capital and Scaling

1. Blood in the Shark Tank: Pre-money, Post-money and Play-money Valuations (February 2015)
2. Billion-dollar Tech Babies: A Blessing of Unicorns or a Parcel of Hogs (June 2015)
3. Venture Capital: It is a pricing, not a value game! (October 2016)
4. Risk Capital in Markets: A Temporary Retreat or a Long-term Pullback (July 2022)

AI's Bar Mitzvah Moment: From Hype & Hope to Business Questions!

Foreword: I wrote and posted this piece on August 20, 2026, with the promise that I would listen to feedback and try to respond and incorporate ideas and suggestions. To keep this promise, I will edit this piece over time and reflect the changes I make.

1. Original post: August 20, 2026
2. First update: August 21, 2026 - Updated AI TAM table to make explicit implications of AI market size for other businesses, employees and the economy
3. Second update: August 21, 2026 - Updated to incorporate impact of new businesses that may be born out of AI

     In a world where AI enters almost every conversation, it takes effort to remember that its breakout moment was less than four years ago, when, on November 30, 2022, ChatGPT was unveiled to the public. I know! I know! Artificial intelligence has been around a lot longer, with a history tracing back to the birth of the computer age. I am old enough to remember IBM's Deep Blue, a machine powerful enough to evaluate two hundred million chess positions per second, and beat the greatest chess players of its time. On the cultural front, we have seen variants of stories, where machines break free from human control and take over the world, in novels and movies, with Hal (the computer) in 2001: A Space Odyssey retorting "I'm afraid I can't do that" to Dave, his human controller, remaining one of my favorite movie lines of all time.

    Notwithstanding its longer history, the effect on AI has been explosive in the last four years, manifesting in multiple developments. The most successful company during this period, in terms of increasing market capitalization, has been Nvidia, the chip maker for the AI revolution. After spending a decade talking about FANGAM, the big tech companies that had become part of our daily lives while carrying equity markets forward over the last decade, it was the Mag Seven that became the stand-in for market dominance, with Tesla and Nvidia replacing Netflix in the mix. The Mag Seven, almost all of which have a stake in AI, have accounted for 45% of the increase in market cap across all US stocks between 2022 and 2025, and have an aggregate market cap on August 16, 2026, of $23.7 trillion. It is not just markets that are besotted with AI, since the massive investments in AI architecture, from data centers to large language models (LLMs) have carried the US economy; it is estimated that these investments accounted for about 1% of the 2.5% in real GDP growth in 2024 and 2025. Almost every conversation of businesses now has an AI component, which if not restrained, can hijack the discussion.

    AI's effects were not restricted to business and markets, as people were exposed to its reach in their personal and work lives, with reactions ranging from awe, at its power to do tasks that used to require skilled human labor, not just effectively, but in a fraction of the time, to dread, at the possibility of being made obsolete by an AI agent. As the arc of the AI story has unfolded over the last four years, it seems to me that is has also transitioned in the public consciousness from a mostly positive phenomenon early on to acquiring a negative tinge, perhaps because of concerns that the genie is out of the bottle, and is not benign, and partly because some of its leading spokespeople are so unlikeable. Not surprisingly, speakers at graduation ceremonies in US colleges in 2026, were booed by students, when their speeches centered around AI. 

The AI Debate: Off the tracks?

    As the AI story has unfolded, there have been reams written about it, for and against, and almost as much said about it on television and podcasts. In spite of being so much in the news, the debate about AI, in my view, has gone off the track with advocates and skeptics often talking past each other, with advocates focusing on its "huge" potential market, and skeptics zeroing in on massive upfront investments as "too large", with each side claiming the high ground and labeling the other side as cultists (AI advocates) or Luddites (AI skeptics). There is a great deal of cherry picking of the data on both sides, with the optimists focusing on usage statistics (level and growth) and the pessimists on capital spending and current profitability (or lack of it).

    This post is not about proving one side right and the other wrong, but about closing the loop and making it a discussion of AI as a business, recognizing that it is ground breaking, while also acknowledging that it has to be judged like every other business in history, not on potential usage, but on the prosaic details of converting potential to products and revenues, being able to deliver these products at a cost that generates profits and building moats to keep new entrants and competitors out. In short, the AI optimists may be right about AI usage exploding in the future, but big markets don't always become big businesses, and the skeptics have to concede that spending a lot on capital expenditures raises the ante for businesses, but don't necessarily doom them to value destruction.

    I will be writing this as an AI novice, a very light user of ChatGPT (I still have only the free version) and acquainted with Claude only in passing (though my content has found its way into some of its bots). If you are an AI expert and feel that I am missing or wrong about a technical component, forgive my ignorance, and feel free to educate me, and if you work at an AI business and feel that I am in error on a business detail, the same offer stands. To be honest, I am writing this post for an audience of one (me), with the purpose of clarifying for myself how to make sense of this space, and if it does help you make sense of this disruption, it is a side benefit.

The Cycle of Revolutionary Change

    Through human existence, revolutionary change has been a constant, and even when that change has been an advance for humanity, it has always come with pain for those that the change renders obsolete and unanticipated side costs. At the risk of overreach, I will argue that every major disruptive change has gone through four phases: a period of hope and hype, where the change is viewed as big, but it is unclear how and in what form it will be delivered,  a period of build-up, where a subset of people (with more belief in the change and more willingness to take risks) start investing and  building products to make the change happen, a  period of business building, where the change is monetized and businesses form, and a recalibration, where the change works its way through the economy and society, in both good ways (increased productivity and welfare, new businesses) and bad ways (displacement and damage).

1. Hope and Hype

    In this phase, the true believers and visionaries that see change coming start the ball rolling, but to succeed, they need to sell it to the broader public. Since the story of change, at least at this stage, has nothing tangible at its core (no products or services, let along revenues or profits), it is inevitable that there will be false starts mixed in, as well a dose of scams pushed by charlatans and pretenders. It is also par for the course that there will be many who will dismiss change talk as fairy tales, without even listening to the arguments, either out of cynicism or because they do not understand what is being sold. For change to take root, the visionaries selling the story need to be persuasive enough to get people to buy into their vision, both to get foot soldiers who will work to make change happen and investors to supply them with capital.

2. The Investing Build-up

    Once the belief that change is possible gets a foothold, there will be a subset of players, with start-ups or in existing businesses, that will invest and build products and services that they believe will be sought after, if change comes. As pioneers in this space, with trial and error and experimentation characterizing these attempts, but even failures will lead to learning, albeit with costs. 

    If the change is perceived as revolutionary, with a big market emerging, this is the phase where the big market delusion, a term I coined over a decade ago, is likely to emerge. That delusion has its roots in selection bias, where the people building products for the change to come tend not only to be true believers but also over confident, resulting in a collective over reach by companies and investors pricing these companies, and a correction. 

Thus, bubbles are a feature, not a bug, when revolutionary change is a possibility.  Finally, if business and investing is a combination of (business) stories with numbers, at this stage of the cycle, where there is little material that has already been accomplished, it is the story that drives growth and investment. Investors with actuarial or accounting mindsets will undoubtedly find these narratives unpersuasive and quickly consign these companies to the overvalued heap. While that impulse is entirely understandable, it is worth remembering that there will be other investors, who are willing to bet on optionality, where they invest in this space, hoping that the entities that they invest in will be the big winners (though they have not won anything yet) in a big market (which does not exist right now).

3. Business Building

    Not all change is revolutionary, not all revolutionary change translates into big markets, and not all big markets create valuable businesses, and it is in the phase of business building that the truth starts to emerge. Since this is very much a test of businesses growing up, it represents a bar mitzvah moment for these businesses, in the sense that investors are no longer willing to just price on promise, and start demanding tangible evidence of progress. It is during the business building phase that you start to create the structure of converting products into businesses, with production processes, supply chains, marketing and distribution all taking form. In the process of building business, you will confront the realities that will determine whether you are a mass market or niche company, including unit economics and economies of scale.  In the process, they will also discover a harsh truth, which is that many creative and talented product-builders lacking business-building capabilities, and either have to partner with someone who does, sell their products to established companies that already have systems in place (expect acquisitions, partnerships) or get pushed out of their own firms by their capital providers (venture capitalists).

4. Recalibration

    As businesses start to succeed, the laws of economics, immutable and powerful, kick in. You should expect to see turnover and consolidation, as new entrants and existing players jockey for position, and business economics determine industry structure from splintered to consolidated to winner-take-all all possibilities. At the same time, the dark side also plays out as those (businesses and individuals) rendered obsolete by the change come under pressure, with some shrinking, some disappearing and some in denial.

    It is worth recognizing that there is no steady state, because as businesses recalibrate to the innovation, they now represent the status quo and become targets for the next revolutionary change, Schumpeter's creative destruction in motion. The picture below summarizes the cycle, mapping out the pathway from hype and hope to investing to harvest to building businesses to recalibration:

Where does AI fall in this cycle? ChatGPT, as I noted at the start of this post, may have been low-tech AI, but it got the hype cycle rolling, and social media amplified and accelerated that rollout, and its broad reach meant that almost everyone has seen it at work. The hope that AI's popularity and reach would create a big market built up in parallel, with capital flowing into firms in its sphere (as well as wannabes that latched on to it, as a buzzword), pushing the market capitalizations of the companies building AI's architecture (computer chips, LLMs, power and water companies, cloud) into the trillions of dollars, funded with equity and debt. It was not just financial market participants that saw its allure, as hyper scalers and new entrants invested hundreds of billions into AI cap ex, partly because they believed in its promise, but partly out of a fear of missing out. The graph below looks at cap ex in just six of the largest players in the space, four of them in the Mag Seven (Alphabet, Amazon, Met and Microsoft) and two outside (Oracle and Coreweave):

Source: Cap IQ

Cumulatively, the total investment from just these companies amount to $1.7 trillion, over the last few years, and their guidance suggests that they are not done, with trillions of dollars in AI cap ex commitments in the next three to four years. You can see why I use the analogy of a factory, and argue that AI has built the most expensive factory in history, and done so in hyper speed. 

    To what end? It is only in the last year or so that you are seeing the beginnings of business building, where companies are generating revenues from selling products made by the AI factory, with Anthropic and OpenAI as the most prominent examples. Those revenues are small for the moment, relative to capital invested, and the profitability is still a reach, but there is a host of experimentation going on on model type (open versus closed), business models (subscription versus usage) and pricing. The seeds of disruption have been sown, and there are signs that AI's rise will make a significant dent in the profitability of some businesses, with technology companies in the software and intermediary segments being the first casualties. The AI story is clearly further advanced than it was a year ago, but it is still early, and there will be changes and challenges that face both the players in the space and the investors in these players, making this AI's bar mitzvah moment.

A Business Framework for AI

    If you are an onlooker or undecided on the AI question, I don't blame you, if you find yourself whipsawed by what seem like persuasive arguments on both sides and waylaid by distractions aplenty. That is because there are so many strands to this story that taking any strand in isolation can lead you to a conclusion about AI as a business that is hopelessly of course. The best way to bring all these strands together is by going back to basics, and establishing the drivers of the value of any business (not just AI):

In this structure, there are three broad drivers that will determine how AI as a business will unfold. The first is with an assessment of the size of the total market for AI products and services, the second is the industry economics in that market, which, in turn, will determine how many companies will cater to this market and the profitability of these companies, and the third will be an assessment, or at least a preliminary judgment, on what the moats or competitive advantages will be in this business. 

1. Market Size

    It is true that the value of a business is tied to how big a market there is for its products and services, but it is also true that this metric, converted into an acronym (TAM) has become a gaming tool in the hands of founders, venture capitalists and bankers. In my SpaceX valuation, where xAI is the primary AI business, I noted that bankers estimated a total addressable market (TAM) of $22 trillion for xAI, which I felt was more hallucination than estimate. That said, any discussion of AI as a business has to start with the total market question, and it is worth starting that discussion with an examination of where we are right now in terms of revenues from AI products and services. 

    As we head towards the end of the third quarter of 2026, with chatter about Anthropic and OpenAI getting louder, both companies are racing to set up their stories by reporting their updated annualized revenue run rates (ARR), an admittedly self-serving metric (for growth businesses) estimated by taking the most recent period (week, month etc.) and extrapolating to a year. On August 17, Anthropic that its ARR at the end of July 2026 was $65 billion. OpenAI's estimate of its ARR at the end of July was about $40 billion. While both numbers represented quantum leaps from their values just a year ago, adding these estimates to the revenues that SpaceX (from xAI), Microsoft (from its AI offerings) and other players, even with the most generous estimates, generate from selling AI products and services yields a total revenues that is modest:

As you look at these numbers, there are a few truths that are undeniable. The first is that the AI product and service market is not only fast growing, as evidenced by the ARR for the lead LLMs, but unpredictable, with Anthropic's most updated ARR coming in $10-$15 billion below estimates. The second is that even with the most upbeat and optimistic estimates of revenues for AI products and services, the current revenue number caps out at about $250 billion, and that sounds like a big number, until you scale it to the trillions invested in the space. Put simply, the big winners in terms of revenues and operating profits, at least so far in this AI cycle, have been the companies that supply the infrastructure components, with chips (Nvidia) electrical equipment providers and power utilities all sharing in the spoils.

    That, of course, is just the existing market and with immense growth built into it, the question becomes about the end game, and that end market size, at least at the moment, seems to be anyone's guess. While the xAI bankers will undoubtedly use this uncertainty as a shield to not have to justify their estimate, there are ways we can start framing our choices, beginning with aggregate measures of what businesses spend as operating expenses, since AI's big sales pitch is that it will lower that spending. In 2025, the aggregate operating expenses at publicly traded companies was about $65 trillion, broken down by sector and geography below:

Source: S&P Cap IQ

These expenses include the costs of raw material and inputs that are immune from AI's efficiency push, since AI cannot replace the rubber you need to make tires, the wheat you need to process to get cereal and the chemicals that go into fertilizer. Consequently, it is the portion of these expenses that took the form of employee compensation, in all of its forms (wages, salaries, bonuses, stock-based compensation) that AI is targeting. While some companies break this portion of expense out explicitly in their financial statements, others do not, but there is macro data on this metric, albeit splintered geographically. Drawing on Federal Reserve data of compensation for all US employees, I get the following numbers:

Source: Federal Reserve

I would argue that AI's total addressable market, in the US, cannot be greater than $12.96 trillion, the total employee compensation in 2025, or an inflation-adjusted variant, if it is in the future, it is roughly twice that amount, if you target global spending on employees. While that number is large enough to set AI optimists' hearts aflutter, a world where every employee is replaced by an AI agent would not just be dystopian, but also an economic basket case. In fact, AI's target market will be smaller, depending on the answers to four questions:

1. Tool or employee replacement: As AI products have become more powerful, the debate about whether AI's future lies primarily as a tool or as replacement for human labor has also raged. In a post earlier this year, I focused on a Citrini report that played out the effects of the latter, highlighting the costs to the economy of laid-off white collar workers (and their income) and the effects on the market. In that post, I did note that notwithstanding public stories of layoffs in software companies, there has been little evidence (so far) of aggregate displacement of labor in any sector, at least so far. The takeaway, at least from this discussion, is that AI's disruptor role will be far greater, as will its total addressable market, if it replaces employees, rather than is used as a tool.

The logic for why AI tools than AI as employee replacement will have a smaller market is simple one, from a business perspectives. Businesses spend money on tools, but that spending will be in addition to what they already spend on employees, and while they rationalize that spending with (promised) improved productivity, it has to be a fraction of employee compensation. You can also why the current AI players (OpenAI, Anthropic) are opting for speedier disruption over a slower one, because it will then increase their odds of winning, albeit with higher displacement costs for society.

2. Pricing of AI products: In the last year. Anthropic and OpenAI have garnered publicity for their most powerful products (Claude, Codex etc.), and while some of them do offer the capabilities that will allow them to replace workers, they are expensive enough that it will make sense to use them only for high-paid labor.  In 2023, the US government estimated, based upon personal income statistics, that the highest quintile accounted for 51% of all employee compensation.  

Source: Library of Congress

Bringing this factor into play with the total employee compensation of $12.96 trillion in the United States, you can argue that only about half of that market (at the most) is open to disruption from AI replacement products,

3. Breadth of use: There are some industries where AI will make more inroads, and do so sooner, than others, and what separates them will be the nature of work in the business. As we noted just a little bit earlier, software and coding have been the easiest entry points for AI products, since the output tends to be more rule driven and easily verifiable An article in the Harvard Business Review, for instance, measured the risk of displacement across different occupations:

Research from Suraj Srinivasan, Harvard Business Review

I would expect AI to be more successful, both as a tool and employee displacer, in settings where there is less client or personal interaction and more rule-driven than principle-driven jobs. If you look back at operating expenses, broken down by sector, the sectors most exposed to AI disruption (technology and financials) have aggregated operating expenses the amount to less than 20% of the global total, whereas sectors more immune (industrials, materials, real estate utilities) amount to a third of the global total.

4. Geography: If you consider the fact that AI is more likely to displace workers and generate revenues in non-manufacturing companies that have high priced labor, it follows that the disruptive effects of AI will be greatest in the United States and have a smaller footprint elsewhere in the world.

Source: Visual Capitalist

Looking back at the table that breaks down operating expenses geographically, for publicly traded firms, you can see that the US, Europe and China are the three biggest markets for AI, since these are regions of the world where companies spent most on operations in the aggregate

    If you consider $26 trillion as your upper limit for AI's total market, in current dollars, your estimate of the size of the AI market will depend on your assessments of whether you fall on each of the four factors, with the largest assessments of TAM emerging from a view of AI as an employee replacement that cuts across industries and geographies, but with a cost for AI agents low enough to replace workers with lower income. At the other extreme, your assessment of the TAM will be much lower, if you view it as a tool, no matter how powerful, with application in select industries and geographies.  

Updated on Aug 21, with implications added on

Rather than view different assessments of AI TAM as "he said, she said" disagreements, the debate would be much more grounded if these assessors were explicit about where they stand on the dimensions (AI as tool or employee replacement, target high-priced workers or all employees, useful in a subset of industries or all industries and primarily US-based or global) that drive their estimates.

    In my first economics class, I became very fond of the words "ceteris paribus", latin for "all other things being held equal", partly because it does make it easier to focus on what's changing, but mostly because latin makes you sound smarter than you really are. Holding all else constant may be reasonable, when change is small, but when the change is revolutionary, as AI could very well be, it is no longer reasonable to make that assumption. Consequently, I think it behooves anyone making an assessment of AI's total addressable market to think through the implications for the rest of the economy and the world, because there may be constraints there that need to be considered. The most optimistic scenarios for AI investors and businesses, where the market for AI products is largest, are also scenarios where the damage done to other businesses is greatest and the impact on employment and the economy, in the near term, is most negative. Put simply, if the AI market explodes in size and does so quickly, and millions of high-paid workers lose their jobs, the economic damage will be deep, and there may very well be insufficient income to buy the products that the AI revolution creates. It is possible that in the long term, AI's benefits may exceed its costs, but that will occur in a very different economic setting than the one we have right now. 

    Every revolutionary change gives rise to new businesses, some of which are extraordinarily successful. The internet business was the starting point for online retailing, with Amazon as its most successful player, streaming entertainment, with Netflix and Spotify, as players, and search, with Yahoo! and Google cashing in. The smartphone was the vehicle used by a host by intermediary businesses, with Uber, Airbnb and Doordash, all changing the businesses they entered, as well as social media, with Facebook and Tiktok emerging as big winners. If AI's promise plays out, it is almost certain that it too will be the launching pad for new businesses. If you are tempted to count the revenues of these businesses as part of AI's payoff, there are two reasons for caution:

1. Net benefit test: All of the companies that I mentioned in the last paragraph were immensely successful, but some or much of their success came at the expense of existing players. Notwithstanding Amazon's astonishing success in online retailing in the last 25 years, retailing as a business has not seen explosive growth, since brick and mortar retailers have largely been left in the dust. The success of entertainment streaming companies has not only come at the expense of traditional entertainment companies, but in the aggregate, it has made the business less profitable. It is true that ride sharing has caused the car service business to become larger, as ride share use changes user behavior, but it has contributed to wounding the auto business. The most telling statistic on the impact of disruption, especially from technology companies in the last four decades, is that real GDP growth has been mostly stagnant in much of the world for that period, suggesting that the net impact on economic activity is modest, at best.
2. Sharing the pie: Even if you take the gross added value from new businesses (in revenues, earnings or value) for revolutionary change as a given, it is unclear what the companies that were the architects of that change gain as a result. The internet was built by telecom companies (with investments in cables and phone wires and supplemented by equipment companies (like Cisco), but none of them benefited from the rise of internet companies like Amazon. The smartphone was built and popularized by Apple and Samsung, but they don't get a slice of what Uber and Doordash make, when customers use smartphones to access their services. Apple benefits at the margin, as customers are coaxed into a cycle of replacing existing phones with newer versions, but the benefits are tangential.

If you look at the history of revolutionary changes, from the steam engine (factory age) and railroads, all the way to the technological changes of the last four decades, it has generally been the case that while the companies that build the architecture for these changes initially benefit, the big winners often catapult themselves off that architecture. You can make the argument that AI will be different, but for AI architecture companies to gain a slice of the new businesses that emerge from their technologies, they will need different business models.   

  

2. Industry Economics

    Accepting the premise that AI products and services will have a big market, in terms of revenues, is just the first step in structuring its business argument, and in this section, I will focus on converting those revenues into profitability, by first looking at the business models that AI producers can consider adopting, with pluses and minuses, as well as unit economics and economies of scale, measuring what it will cost companies to produce the AI products that they are selling.

a. Business Models

    In keeping with the trial-and-error process that characterizes young industries seeking workable business models, we have seen AI businesses experiment with different versions on at least two dimensions:

• Subscription versus Usage Models: Early in AI's business evolution, subscription models for both individuals and enterprises were the entry point for AI companies, and while those subscription models still bring in revenues, companies have shifted away from them for a simple reason. Unlike businesses like streaming or even software, where the marginal cost of an additional subscriber is zero or close to zero, AI products and services are expensive to generate, in terms of compute costs and data, and both Anthropic and OpenAI have discovered that subscribers, left unchecked, quickly become cost generators rather than profit centers. Thus, it should come as no surprise that Anthropic has shifted to usage-based models, where users (especially at the enterprise level) pay based on how much and how intensively they use AI products. OpenAI is still more dependent on subscription models that Anthropic, but it too is seeing a shift to usage-based models.

Source: Leaks and estimates

While advances may change the economics, it looks like while AI-subscription models will stay available, they will come with strict limits on usage, and that the bulk of revenues in this market will be based on usage. At the same time, there will be differences across AI companies, based on whether they are targeting the premium AI market, where usage-based models will dominate, or the mass market, where subscription models will continue to be offered.

• Open versus Closed Models: There is an open and vigorous debate going on in AI circles as to whether the AI businesses should  offer clients and customers open models (where clients can modify, adapt and build on the models) or closed models (where they are not allowed to do so). This debate is complicated because there are multiple forces that come into play including how power in this space is concentrated (with closed models giving its makers more power), how much privacy they offer (where the argument is that open models require less sharing of private data) and how safe they are (where it is posited that open models can be more easily hacked and turned into disinformation). All that said, there is clearly a business economics twist to this debate. Closed models give the companies that sell them more pricing power (higher margins), and perhaps are stickier (making it difficult to switch away), because they are customized, but they require more resources to build and maintain (higher costs) and may work only with premium products. As with subscription versus usage models, you are likely to see divergence, with companies targeting the premium market more likely to stay with closed models and those building more workhorse applications trying their hand at open models.

As I noted at the start, it is still early in the game, and as technology, regulation and cost structures shift, not only will there be more twists and turns involved, but it is likely that in steady state, we will have different choices for different segments of the AI markets, more subscription-based and open models in mass markets and more usage-based and closed models in premium markets.

    

b. Unit Economics

    Through much of its existence, technology, as a business, has largely benefited from beneficial unit economics. It costs a software company almost nothing to sell its next unit, and for most platform companies (Netflix, Uber, Airbnb), the cost of adding a user or subscriber, once the platform is constructed is negligible. As a consequence, being the largest player or a first mover can put you on a pathway to industry dominance, with large market share and high profit margins thrown in. Early in its life, AI was thrown in by some into the technology pile, and it was assumed that it too would have the same characteristics, i.e., that it would cost little or nothing to produce the additional unit and that high margins and dominant market shares will follow.

    With the caveat that things can change quickly, the evolution of the AI product and service market has turned out to be different, and some of those differences look like they are baked in. Beginning with the fact that AI products require more capital intensity, in terms of data center build and cap ex prior to operation, they are also proving costly to produce, at least in their most powerful forms. One way to see the dual forces driving unit economics in AI at play is to focus in on AI tokens, the currency of AI production. The good news, in terms of unit economics, is that the cost of producing a token has dropped dramatically, as AI infrastructure gets built out, but the bad news is that the tokens used to create AI products has surged almost as dramatically, as these products become more powerful. As a result, the price of accessing frontier AI models has increased over time:

Sources: Token Price Index

These two trend lines point to a divergence that is coming to the AI products and services market. In mass market AI, where AI tools are basic and don't need power boosts, you should expect costs for AI products to go down.  In the premium market, where you are building AI products either as tools on high-end tasks or as replacement for highly priced labor, the costs will be tougher to reduce, if each upgrade in product power puts demands on the inputs - more data to process, more power to run data centers and more powerful chips in the data centers - that causes these input costs to rise. 

c. Moats and Competitive Advantages

    Allowing unit economics and economies of scale to play out in the AI business, you still have a final piece of the business puzzle to consider to make the leap to profitability, where you bring, as you would in any business, the moats and competitive advantages that will separate the winners from the wannabes. Since this is a question that we ask about every business, it makes sense to start this discussion by looking at potential competitive advantages in any business:

Focusing in on AI, the question that we first face is which of these moats is most likely to be the defining one in AI, and I believe that the answer depends on which segment of the AI market you are looking at:

• In mass market AI, where products and services are standardized and basic, you should expect competitive advantages to flow from unit costs being lower at a company than at its competitors, either because of scale (with bigger companies having an advantage) or proprietary access to data.
• In premium AI, where products are customized, powerful and pricey, you should expect the winners to be companies that have the technological know-how to craft these products, while bringing the costs of delivering power under control. In addition, products that are built around client data, especially if the client is protective of that data, will become stickier and more difficult to displace, giving companies that make them more pricing power for a longer period.

Does having the people perceived as the smartest in the AI space working for you give you a competitive advantage? At the moment, the answer seems to be yes, and you saw this phenomenon at play when Jeff Dean, chief scientist at Google DeepMind (its AI entity), left the company in 2026 to create his own AI start-up, and the market reacted by knocking Alphabet's market cap down by 5.4% (more than $100 billion). In the same vein, the AI firms (especially Anthropic and OpenAI) have been raiding universities for their computer science and technology talent, with some AI-focused economists thrown into the mix and even a few philosophers. I think that the attention paid to these people hires and departures are indicative of how young this industry is, and how much its success will depend on building products right and marketing them to the right customers, and as it matures, I expect this factor to fade in prominence.

    Is there a potential brand name advantage? Put simply, would you be willing to pay a premium price for a Claude agent over an AI agent crafted by a different company? The answer is still being worked out, because in addition to all of the business elements of this choice (product power, reliability), trust is a factor, since client companies are giving AI products access to secrets and data. It should come as no surprise then that AI companies are all competing in the virtue space, where each one puts itself out as more trustworthy and caring about public good than the next one. It may be cynical of me, but when I hear Dario Amodei or Sam Altman wax eloquent about how they plan to protect the world from the dark side of AI, I feel the urge to quote Shakespeare, and say "thou doth protest too much". Ultimately, actions speak louder than words, and these companies will be judged based more on how they behave, when confronted with ethical questions, than on what the write about themselves.

3. Constraints and Limits

    While much of this post has been about AI's business prospects and evolution, there are parallel discussions that are occurring about AI's impact on society. There are four reasons why AI's social and cultural effects are being so widely debated:

1. Real estate footprint and resource usage: AI, in terms of the investment footprint it is creating, is closer to the railroads in their early years than it is to any technology company. Like railroads, AI requires data centers that sprawl over huge areas, and unlike railroads, many of these areas have people living in them, whose lives will be altered by the presence of these centers. While the proponents of data centers have sold them on the basis of the economic benefits they bring, and these benefits can be real, it is quite clear that for many people who live in the vicinity, the upending of their lives is not worth the benefits. As a result, the backlash against data centers is real, showing up in politics at not only the local level, but also at the national level; it is quite clear that at least in the United States, it will be a lead topic, perhaps even a wedge issue, in the next presidential election. Another reason that data centers lead to resentment is their disproportionate use of power and water, and even if that cost is pushed back to AI companies, the costs to the planet are still being totaled.
2. Data privacy and power: Earlier in this post, I pointed to privacy as one of the dividing lines in the choice between open and closed AI models, but that is a small part of a broader question, which is about data being accumulated by and mined at AI companies. After two decades of seeing social media companies step across the privacy line in their use of private data, it is understandable that there is wariness about granting access to even vaster amounts of data to the likes of Anthropic and OpenAI. 
3. People displacement: Going back to the discussion of the total market for AI, I noted that the best case scenarios for AI, i.e., the scenarios where the market for AI products and services will be greatest, are also scenarios where there will be not just be significant job loss, but losses in high-paying jobs. While tech advocates will point to new jobs that will be created to replace the ones lost, that transition, even if it happens, takes time and will come with pain. Looking back at the disruption of blue collar jobs in the US and Europe, from China, that disruption created pain, albeit unevenly shared, and has led to political and economic aftershocks that are still playing out across the world. If AI's disruption plays out on a broad front, the resulting displacement will be much larger, in terms of economic impact, and perhaps much more painful.
4. Income equality and fairness: For much of this century, one of the recurring themes in both political and economic circles has been the growth of the divide between the super rich and the rest of society. There is a suspicion that AI will make this divide wider, and that suspicion will only intensify with each AI company that goes public. SpaceX, when it went public at a market capitalization of close to $2 trillion created a host of centi-millionaires (worth more than $100 million) and the same phenomenon will play out when Anthropic and OpenAI hit the market. I am not a fan of setting economic policy based on greed and envy, but you can see the pushback against inequality playing out in the political arena.

If you are tempted to ignore these discussions, because you are an investor or interested only in the business aspects of AI, it is only a matter of time before these spill over into economic consequences, and then into the metrics that drive business value. 

1. Data centers will get more expensive to build, and power and water will be more tightly rationed, leading to companies having to spend both more on their upfront and capital expenditures, and as costs in generating AI products.
2. As concerns about data mount, there will inevitably be scandals around the misuse of data, and those scandals will lead, as they did at social media companies, to tighter restrictions on the use of data and higher costs is acquiring and protecting that data.
3. The worries about being replaced by AI agents will create counter movements, starting with system requirements that preserve jobs for humans (even if AI makes what they do obsolete) and in some countries, requirements that their employers continue to pay displaced employees for extended periods, in the event of layoffs. 
4. If AI creates its own cache of billionaires and centi-millionaires, the push towards wealth taxes, no matter what you may think about their effectiveness or fairness, will intensify, as will the creation of new tiers in the tax table for higher incomes, and perhaps for AI income.

It has always been difficult to start and build businesses in new industries, and ti becomes doubly so when the rest of the world consigns you to the dark side. If you are an investor in this space, especially one excited about the size of the potential market and pathways to profitability, you need to be realistic in incorporating the constraints that are already cropping up, and will become more binding, into your valuation.

    As AI's story line unfolds in ways that invite public pushback, it is worth noting the possibility that this storyline can still change, especially if AI delivers on its promise to change people's lives for the better. In the last year, I have seen stories, some already happening and some far fetched, of AI's capacity to solve mathematical challenges that have deterred our greatest mathematicians, to diagnose diseases that the best-trained doctors seem to miss and perhaps even to come up with cures for diseases that have eluded those seeking them. That power comes from tAI's brain capacity, which unlike those of human beings, is expandable and its ability to not just remember everything that is fed into it, but make connections across data in different disciplines. As I noted earlier, the benefits from these advances will, for the most part, flow not just to other businesses, but will benefit society. If the big AI players want a more welcoming environment to grow, it behooves them to make these social investments, perhaps drawing on AT&T's nurturing of Bell Labs for much of the last century as a place for research that benefits humanity as inspiration. It is true that none of the big AI companies is a regulatory monopoly, as AT&T was, but it should be feasible for them to pool resources to fund a Bell Labs like entity. It will be costly, but they will collectively benefit from the social dividends.

From Macro to Micro: Zeroing in on company valuations and investments

    This post has focused on AI as a business, and if your interest is in valuing an enterprise in the space, whether publicly traded like SpaceX or Alphabet or privately owned (but heading for an IPO like Anthropic or OpenAI), you may wonder how it helps you in that endeavor. There are two ways you can approach these valuations. In the first, you can start with the market capitalization (actual in the case of publicly traded companies and estimated in the case of private businesses) and examine what you would need in terms of end revenues and profit margins to justify the market capitalization. In the second, you can start with the company that you are valuing, and lay out a roadmap for that company in terms of product choices (premium or mass market, open or closed) and estimate what it will be able to deliver in terms of revenues, profits and cashflows over its lifetime. 

1. Reverse Engineering Breakeven Points

    In a post, late last year, I looked at the handful of companies, mostly tech, that have trillion dollar market capitalizations, and rather than take the knee-jerk reactions, i.e., that they must be overvalued with that market cap or that they must be great companies, because the market assigns them a lofty price, I looked at what these companies will have to deliver in terms of operating metrics in the future to justify their valuation. 

We are effectively reversing the intrinsic value process, and trying to answer the question of how much revenues will have to be in a future year (where you specify when the company or business will be mature or steady state), given your company characteristics in terms of risk, profit margins and reinvestment needs. With Anthropic, for instance, where the rumored pricing for the IPO is $2 trillion, allowing the company premium pricing margins (after-tax operating margin of 30%) and above-average risk (cost of capital of 10%), the company will have to generate close to $1.2 trillion in revenues, if the AI market matures in ten years, and close to $2 trillion, if the wait is 15 years. That should give ammunition to both those bullish about the company, because in their story line, Anthropic products will be premium priced and replace workers across industries and geographies, and to those who are bearish, since that scenario looks unlikely.

    In fact, you can consider AI companies in the aggregate, by adding up the market capitalizations of companies that are already public (or at least the portion of the revenues that come from AI) as well as the rumored pricing of companies like OpenAI and Anthropic, waiting to go public, and going through the same exercise. Using an aggregated market pricing of $5 trillion (probably a conservative judgment, given the VC pricing of hundreds of companies in the space)  attached to all AI product and service companies, and assigning a blended operating margin of 20% for the industry, the revenues that you would need for the entire business to breakeven would be $5 trillion, with a 10-year wait, and more than $8 trillion, if the wait is 15 years. Looking back at the discussion of the total addressable market in the earlier section, you can see that this would represent quite a reach, a manifestation of the big market delusion. 

    I will be the first to admit the limitations of this reverse engineering, but when data is still scarce or non-existent, it does provide a framework for reasonableness and a constraint on story telling. In fact, you can use this framework to examine what any investment, whether it be the price you pay as an investor for an AI business or the capital expenditure into AI made by a company, will have to generate to break even as an investment. Thus, if you are questioning whether Microsoft or Meta's AI cap ex is value creating or destroying, you can use this generic breakeven spreadsheet, to make your own judgment. 

2. Build up to value

    If you follow the AI business script laid out in the class, you also have a process for valuing any AI company that hopes to make money in this space, but to put this process into the play, here are some of the issues that you will have to address to estimate value.

1. Product choice and market focus: If the AI market splits into premium and mass-market product market, the first step in valuing any company in this space will be to make a judgment on which of these markets the company will target, and how much of its revenues will come each of the segments. 
2. Unit economics and economies of scale at company: The choice of market segment matters  because the unit economics and economies of scale you assume for the company will have to be consistent. AI companies that offer mass market products will charge lower prices, with a greater percentage of revenues coming from subscriptions, but will benefit more quickly from improving unit economics, as the cost of AI tokens continues to fall. In contrast, AI companies that target premium markets, will earn higher profit margins and have stronger moats, but struggle more with unit economics, as token usage increases with product power.
3. Competitive advantages and moats: The types of competitive advantages (moats) that the company you are valuing will seek out, and lock in, if successful, will also vary depending on the targeted market, with cost advantages and scale working in the company's favor, with mass markets, and technological edges and product stickiness being more sought after, with premium products.
4. Investment needed to deliver growth: While AI companies are more capital intensive than their tech counterparts, the additional reinvestment needed to deliver value can be altered by investments already made by a company. Companies that have built capacity in advance of growth will be worth more than companies that will have to reinvest contemporaneously to deliver growth, and companies that find ways to invest more efficiently will also have higher value. It is interesting that starting with Deepseek, China seems to be trying the latter path to AI dominance, using less expensive (and less powerful) AI chips and not investing as much in mega data centers, and it may very well be the right choice, for much of the AI product and service market.
5. Regulatory constraints (current and in the future): To the extent that regulators and governments have a great deal at stake, the value of a company can be affected by where it operates geographically and the rules and regulations that govern AI products in that geography. If past behavior is an indicator, the EU will be an inhospitable setting, for AI products and companies, and that may make a difference in how you value Mistral, with a base in France and more European-focused clients.

I did try my hand at this process, when I valued xAI as part of SpaceX, and I learned from doing so, but the company's stakes in space launch and internet service did muddy the waters. As Anthropic and OpenAI move towards their public offerings, I am looking forward to applying the framework developed in this post to those firms, when their prospectuses are made public. In keeping with my belief that it is best to be open about biases, I will confess that the rumored pricing for both companies ($1.5 to $ 2 trillion) looks rich, but I am open to being surprised.

Conclusion

    At the start of this post, I noted that I was writing this post for myself, because like many in this space, I was finding myself pulled in a dozen different directions on AI, and desperately in need of a framework for thinking about whether I should be paying attention in the first place, how to reconcile competing viewpoints and what it means to me, as an investor. This is my try at creating a comprehensive framework, and I am sure that there are elements that I have missed and holes in my thinking, but it is a start. I am clear eyed about what this AI framework will not and will do for me; it will not tell me what the TAM for AI products and services will be or whether Anthropic is worth $ 2 trillion, but it will give me bounds for my estimates of TAM, allow me to determine that a $22 trillion TAM for AI is fiction and recognize that having your ARR grow 80% a year last year is not even close to being a rationale for why you should buy Anthropic at a $2 trillion pricing.

 As you work your way through the framework, there will be room for significant disagreement on the reach of AI and its value as a business and anyone who claims to have conviction that they know what's coming is being either ignorant or arrogant. That ties in well with my last post, where I examined the swift rise and the even swifter fall of Leo Aschenbrenner, whose entire investment strategy was built around the conviction that AI would decisively and quickly win the disruption war. The problem that I noted was not that his vision was not plausible (it was), but that it was definitely not certain, or at least assured enough to borrow immense amounts to fund it.

YouTube Video

Data

1. Employee compensation for all US workers - 1929 to 2025
2. Revenues & Operating expenses, broken down by sector and geography

Spreadsheets

1. Generic breakeven calculator (for both company pricing and AI cap ex)
2. Anthropic breakeven
3. AI Business (aggregate) breakeven