Musings on Markets is the personal blog of Aswath Damodaran, professor of finance at NYU Stern and widely known as the "Dean of Valuation." Running since 2008, it publishes hands-on intrinsic-value teardowns of headline companies (SpaceX, Tesla, Nvidia) using his narrative-and-numbers DCF framework, plus periodic market-wide reviews.
This piece argues that venture capital's obsession with growth over profit is creating fragile companies. The author warns investors to look beyond revenue and check unit economics and competitive moats. Key examples: Uber (grew fast by not owning cars or hiring drivers), WeWork (its lease-sublease model got worse with scale), and Ferrari (low volume, high margin, valued like big automakers).
One-sentence summary of the author’s current market view: The market’s excessive reward for "scale first" is fueling a surge of high-valuation companies built on unsustainable business models, and investors should be wary of narrative traps. [Cautious]
The article argues that the dominance of U.S. technology companies stems in part from venture capital's tilted support for startups, and that the venture capital community widely embraces the philosophy of "scaling before profitability"—a trend that has become more pronounced over the past two decades. Author Aswath Damodaran uses a tweet from legendary investor Vinod Khosla as a starting point, noting that Khosla conflated profitability with cash flow in his tweet, advocating that all businesses should prioritize scaling over profitability. The author believes that while this view is not new in venture capital, "the tilt towards scaling has become pronounced in the last two decades." Damodaran emphasizes that in this article, he will focus on the trade-off between scaling and profitability, how this prioritization affects startups, and ultimately how it impacts all market participants.
The author argues that whether a business can successfully scale is not merely a matter of intent but is determined by objective conditions of the market, industry, and the business itself. He lists six key determinants:
The author concludes: "some businesses can scale up quickly, some take more time to scale up and some never scale up," and businesses that scale quickly can also decline just as rapidly.
The author emphasizes that revenue growth from scaling does not automatically translate into profits; a company's profitability depends on deeper economic logic. He lists three key factors:
The author notes that operational choices often create a conflict between scale and profit: "A decision to lower product prices may increase revenues at the expense of unit economic profits," and increasing advertising spend may expand the market but drag down profit margins.
This article provides investors with an analytical framework: when evaluating tech startups, one should not only look at revenue growth (scaling) but also examine unit economics, economies of scale, and competitive moats to determine whether growth can translate into sustainable profits. Institutional perspective bias: Author Damodaran is a valuation scholar, and his analysis leans toward theoretical frameworks rather than specific trading recommendations. His critique of the venture capital community's "scale first" philosophy reflects an academic cautious stance; readers should note that this is not the mainstream market view.
The report argues that whether a company can achieve both scale and profitability depends on a combination of factors such as market size, timing of entry, capital intensity, and unit economics. Only a very small number of companies can become "Lightning in a Bottle" firms. Using Google and Facebook in their early days as examples, the author explains that such companies must simultaneously meet conditions including "a large and growing market, early entry with little competition, low capital intensity, and excellent unit economics." The author states: "There are a few companies that meet these conditions, and we will call them 'Lighting 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." This means: "Only a few companies meet these conditions, and we 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."
The author believes that Amazon's "scale first, profitability later" model (Field of Dreams) during its first 15 years was an exception. A large number of imitators only learned the growth dimension but failed to achieve profitability due to a lack of unit economics and economies of scale. The author points out that Amazon's success stemmed from disrupting the massive and long-ailing retail industry. In contrast, many imitators of "the next Amazon" poured huge amounts of capital into expansion but "never turned the corner on profitability, partly because they had neither the unit economics nor the economies of scale to pull it off." The author refers to such companies as "Field of Nightmares."
The report emphasizes that not all companies are suited to pursuing scale. Some choose to focus on niche markets and achieve high profit margins, while others, due to fundamental flaws in their business models, only get worse as they grow larger. Using Ferrari as an example, the author notes that it sells only a few thousand cars annually but boasts an operating margin of over 20%, and its market capitalization rivals that of automakers selling hundreds of thousands of vehicles. In contrast, "big and broken" companies like WeWork have fatal flaws in their business models (e.g., a duration mismatch between long-term leases and short-term subleases), and expansion only exacerbates the problem. The author states: "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." This means: "A real estate business that leases properties long-term and then subleases them short-term is inherently plagued by a duration mismatch. Expanding it geographically and leasing hundreds of properties, as WeWork did, only turns it into a truly large and terrible business."
The report points out that a founder's trade-off between control and ambition for expansion often determines a company's scaling choices more than fundamentals do. The author cites Noam Wasserman's "Founder's Dilemma" theory: to grow a company large, founders often have to cede control. Some founders forgo reasonable growth plans out of fear of equity dilution, while others, driven by ambition, over-expand even when fundamentals do not support it. The author argues that this tension is central to understanding why companies make "suboptimal" scaling decisions.
This article provides investors with an analytical framework for evaluating startups or growth companies: rather than blindly adhering to the "scale first, profit later" mantra, one should specifically analyze market conditions, unit economics, and founder motivations. It should be noted that the author, Damodaran, as a valuation scholar, leans toward fundamental and rational analysis in his framework, which may underestimate the short-term support that capital cycles, market sentiment, or network effects can provide for the "scale first, profit later" path.
The article argues that if a founder's goal is to build a long-lasting enterprise, maintaining a smaller scale and focusing on core strengths yields a higher probability of success. The author believes that, despite many exceptions, the world's longest-lived companies are often small, family-owned businesses serving niche markets and passed down through generations. Meanwhile, companies that experienced sudden revenue surges due to external factors (such as the COVID-19 pandemic), like Moderna and Peloton, often overextend themselves after rapid expansion, damaging their long-term business models. The author concludes that the choice between scaling and profitability depends on the founder's values, and a healthy economy should feature a diverse group of founders to create a varied business ecosystem.
The author contends that a company's ability to access capital determines its scaling potential, but capital providers demand a say in corporate decisions. Sources of capital include family wealth, venture capital, and public equity, each with its own trade-offs:
The author emphasizes that the more capital a company seeks, the greater the influence of its capital providers.
The author notes that VC incentive structures prioritize scale over profitability, as their success hinges on the price differential at exit. The VC operating model conflicts with long-term value creation:
The article reveals the valuation logic of VC-driven tech companies: prioritizing scale over profitability is a natural outcome of VC incentive structures, not a founder's personal choice. When analyzing such companies, investors must recognize that their business models may sacrifice long-term health for short-term growth. As a third-party analyst, the author reminds readers to be aware of the bias in the VC perspective—its success metric is exit returns, not corporate durability.
The article points out that mutual funds and sovereign wealth funds are heavily investing in unlisted tech companies, creating a "gray market" that allows private firms to stay private for longer. The author cites T. Rowe Price and Fidelity's investments in Uber as examples, illustrating that mutual funds have shifted from being a supplement to VCs to becoming competitors. Sovereign wealth funds participate indirectly through channels like the SoftBank Vision Fund. The author argues that whether the motive is FOMO or chasing tech dividends, the result is "a gray market was created where VC and public equity fund access allowed private businesses to stay private for longer."
The author judges that the "reversal effect" in public markets is weakening, with momentum trading increasingly dominant, posing a challenge to value investors who rely on fundamental mean reversion. Citing Ken French's dataset, the author notes that while the momentum effect has persisted over the long term, "the reversal effect has weakened over time," leaving investors betting on mean reversion in a difficult position. The author lists four explanations (low Fed interest rates, the rise of passive investing, changes in the composition of listed companies, and diversification of information channels) and acknowledges that "there is some truth to all of them," but emphasizes that together they amplify the risks of betting against momentum.
Using Jay Ritter's IPO data, the author summarizes three major trends: companies are older at the time of IPO, have larger revenues, and a lower proportion are profitable. Specific data are as follows:
| Characteristic | 1980s | Most Recent Decade |
|---|---|---|
| Median age at IPO | ~6 years | ~17 years (increased by ~11 years over the past 15 years) |
| Median revenue at IPO (inflation-adjusted) | Baseline | Increased 3-4 times |
| Proportion of profitable companies at IPO | >80% | <25% |
At the same time, these unprofitable newly listed companies have achieved astonishing market capitalizations. The author notes that the median market cap of companies listed over the past six years has exceeded $1 billion. The author cites examples from Facebook (priced at $104 billion in 2012) to SpaceX ($1.8 trillion at its June 2026 listing) and mentions that if Anthropic and OpenAI fulfill their trillion-dollar valuation promises, this trend will be further amplified. Additionally, the proportion of shares issued at the time of listing is smaller, reflecting a reduced need for public market financing.
The article reveals a fundamental shift in the valuation logic of tech startups: scalability takes precedence over profitability, and this trend is solidified by the convergence of public and private markets and the dominance of momentum trading. Investors should note that author Aswath Damodaran is a well-known advocate of value investing, and his analysis carries a preference for "fundamental mean reversion," but the data itself (profitability ratios, market cap sizes) objectively reflects market realities. For investors focused on tech stocks, this means that traditional valuation frameworks centered on P/E or P/B ratios may become ineffective, requiring greater attention to revenue growth, total addressable market, and capital-raising ability.
The report notes that companies delaying their public listings mean a longer regulatory vacuum, leaving founder power without effective constraints. The author argues that while venture capital can theoretically serve as a check, in the era of "founder worship," VCs are easily divided and co-opted. He warns: "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"—meaning: "you may see companies valued at billions, hundreds of billions, or even trillions managed by people who are fundamentally unfit for the role." Although the Sarbanes-Oxley Act imposes mandatory disclosure requirements on public companies (e.g., conflicts of interest, board relationships), it has no effect on private companies.
The author believes that prioritizing expansion for valuation purposes squeezes the room to build a profitable model later. VCs lack the incentive to address this issue because they "benefit from scaling up and exiting these businesses, before the business problems become too big to ignore"—meaning: "they profit from scaling up and exiting these businesses before the business problems become too large to ignore." Early choices made for expansion (e.g., burning cash to acquire customers) may directly block the path to profitability.
The report points out that the current marketing narrative of tech companies heavily favors scale, while ignoring the business model and path to profitability. Taking Anthropic as an example, its sales pitch primarily revolves around "annualized revenue run rate (ARR) growth" and "the size of the AI market (vast but without specific data)," with almost no mention of the business model or profitability. The author argues that valuation should be "a bridge between story and numbers," but in the early stages, the story dominates, and the current story is "often incomplete, and almost entirely focused on the scaling question"—meaning: "often incomplete, almost entirely focused on the issue of scale."
The author warns that unlimited capital supporting disruptors without requiring them to build a sustainable business model could lead to a "post-disruption vacuum." Even if they successfully push existing players out of the market, the disruptors themselves may fail to build a long-term self-sustaining business. He calls this "disruption without replacement"—meaning: "disruption without replacement," where the old order is destroyed but the new order cannot be established.
The core judgment of the report is that the current market's excessive reward for scale may spawn a large number of high-valuation companies built on "incurably bad business models." Investors need to be wary of targets that rely solely on a scale narrative without validation of a path to profitability. The author explicitly states that not all businesses are suitable for scaling, and the incentive mechanisms of the capital market (VCs seeking exits, public markets chasing scale) are exacerbating this risk. Institutional perspective bias note: As an academic valuation expert, the author is naturally skeptical of "story-driven valuations," and his warnings may underestimate the flexibility of platform companies to monetize in later stages.
| Ticker | Direction | Author's One-Sentence View | Key Data |
|---|---|---|---|
| Uber | Hold for Observation | Analyzed as a case study of scaling; its potential market triples when defined as a logistics company, but no clear position recommendation is given | Early stage: does not own vehicles or employ drivers; grows rapidly without additional investment |
| Apple | Hold for Observation | Mentioned as a beneficiary of incremental growth in the 2010 smartphone market, but the advantage disappears once the market matures in 2026 | Incremental market from feature phones to smartphones |
| Samsung | Hold for Observation | Same as above, mentioned as a beneficiary of incremental growth in the smartphone market | Same as above |
| Wolfgang Puck | Hold for Observation | Cited as an example of a replicable franchise model, illustrating a solution to key-person dependency | Builds a replicable franchise model |
| Gordon Ramsay | Hold for Observation | Same as above | Same as above |
| Hold for Observation | Used as a case of a "lightning in a bottle" company, meeting conditions of a huge market, early entry, low capital intensity, and excellent unit economics | Achieved both scale and profitability early on | |
| Hold for Observation | Same as above, and was priced at $104 billion at its 2012 IPO | Priced at $104 billion at IPO | |
| Amazon | Hold for Observation | A special case of the "dreamland" model; its success stems from disrupting retail, a massive and declining industry | Scaled first and then turned profitable over the first 15 years, but the model is not replicable |
| Ferrari | Hold for Observation | A "niche star" case, focusing on a niche market to achieve high profit margins | Sells a few thousand units annually; operating margin exceeds 20%; market cap comparable to automakers selling hundreds of thousands of units annually |
| WeWork | Hold for Observation | A "big and broken" business case; the business model has a fatal maturity mismatch, and expansion only worsens the problem | Maturity mismatch between long-term leases and short-term subleases |
| Moderna | Hold for Observation | A case of overexpansion after a pandemic-driven revenue surge, which damaged the long-term business model | Sudden revenue surge after the pandemic |
| Peloton | Hold for Observation | Same as above | Same as above |
| T.Rowe Price | Hold for Observation | A case of a mutual fund investing in unlisted tech companies, participating in the gray market | Invested in Uber |
| Fidelity | Hold for Observation | Same as above | Same as above |
| SpaceX | Hold for Observation | A case of a staggering IPO valuation; valued at $1.8 trillion when it went public in June 2026 | IPO valuation of $1.8 trillion |
| Anthropic | Hold for Observation | A case of a narrative trap; sales pitch revolves around ARR growth and AI market size, with almost no mention of business model or profitability | Sales pitch mainly focuses on "annualized revenue run rate (ARR) growth" and "AI market size" |
| OpenAI | Hold for Observation | A case that may drive up the trend of high IPO valuations, if it delivers on its trillion-dollar valuation promise | Trillion-dollar valuation promise |