Abstract: EBITDA is one of the most frequently cited operating metrics in finance, designed to evaluate a business’s core performance independent of its capital structure. However, because it deliberately adds back depreciation, amortization, and ignores interest burdens, it creates a dangerous structural blind spot for capital-intensive businesses. This essay provides a foundational tutorial on the operational limitations of EBITDA, using simple corporate archetypes to demonstrate how naive reliance on the metric obscures true shareholder value. Moving beyond theory, the analysis traces how this accounting mismatch historically masked major corporate collapses during the telecom meltdown and the Dot-com crash. Finally, the essay examines today’s artificial intelligence boom—specifically evaluating highly leveraged infrastructure players like Oracle and CoreWeave, alongside frontier model developers like OpenAI and Anthropic. It concludes that in hyper-paced environments where rapid technological obsolescence demands continuous, massive capital reinvestment, relying on EBITDA is not just flawed; it actively misprices the cost of survival.
One of the most frequently cited financial metrics is EBITDA. It appears in earnings releases, stock analyses, private equity transactions, and merger negotiations. Yet it is also one of the most misunderstood numbers in finance.
EBITDA stands for Earnings Before Interest, Taxes, Depreciation, and Amortization. In practice, analysts begin with a company’s net income and then add back interest expense, taxes, depreciation, and amortization.
The purpose of EBITDA is to answer a specific question: How profitable is the underlying business before considering how it is financed?
That distinction matters because two companies can operate essentially identical businesses while reporting very different net income. One company may have little debt, while the other may owe billions of dollars and face substantial interest payments. EBITDA attempts to strip away those financing differences and focus on the operating business itself.
For that reason, EBITDA can be a useful tool. But it also has an important limitation: shareholders do not receive EBITDA. They receive whatever remains after all the company’s obligations—including interest payments—have been satisfied.
Consider two companies with identical operations. Each generates $500 million in revenue and incurs $400 million in operating expenses. Both therefore produce $100 million of EBITDA. At first glance, the companies appear equally profitable.
Now suppose Company A carries very little debt. It pays only $5 million per year in interest expense and ultimately reports $75 million in net income.
Company B, by contrast, has accumulated a large amount of debt. Its annual interest expense is $95 million. After paying lenders and taxes, it reports only $4 million in net income.
The striking fact is that both companies report the same EBITDA: $100 million.
An analyst focused exclusively on EBITDA might conclude that the two businesses are equally attractive. In one sense, that is true. Their underlying operations generate the same amount of operating profit. But shareholders do not own the operations in isolation. They own the residual claim after everyone else has been paid.
In Company A, most of the operating profit ultimately belongs to shareholders. In Company B, most of the operating profit belongs to creditors. The business may be generating $100 million of EBITDA, but nearly all that value is being consumed by interest payments.
Defenders of EBITDA would correctly point out that high interest expense is not entirely a deadweight cost. Interest payments generally reduce taxable income, creating what finance professionals call a tax shield. That benefit is real. In this example, Company B pays only about $1 million in taxes compared with roughly $20 million for Company A. Yet the tax shield offsets only a fraction of the additional interest burden. Company B saves about $19 million in taxes but pays approximately $90 million more in interest expense. Shareholders are still substantially worse off.
This illustrates both the strength and the weakness of EBITDA.
The strength: EBITDA helps analysts compare operating businesses without being distracted by financing choices.
The weakness: Financing choices are not irrelevant. A company that has borrowed too much may leave very little value for shareholders even if its underlying operations appear healthy.
This is why experienced investors rarely stop at EBITDA. After seeing the EBITDA figure, they immediately ask additional questions:
How much debt does the company have?
How much interest must it pay each year?
How much cash remains after those payments?
How much profit is left for shareholders?
A useful analogy is that EBITDA measures the horsepower of the engine. It tells us something important about the vehicle’s capabilities. But it does not tell us how much weight the vehicle is towing. Two trucks may have identical engines. If one is pulling an empty trailer and the other is hauling a massive load, their performance will be very different despite having the same horsepower.
Likewise, two companies may report identical EBITDA. If one is carrying a heavy burden of debt, shareholders may receive far less benefit from that operating performance than the EBITDA figure suggests. EBITDA is therefore best viewed as a starting point rather than a conclusion. It can tell us whether a business has a strong engine. It cannot tell us how much of that power ultimately reaches shareholders.
The danger of using EBITDA as a definitive measure of health is not merely theoretical; financial history is littered with corporate collapses where analysts clung to glowing operating metrics while businesses were suffocating. During the telecom meltdown of 2002, companies like WorldCom masked enormous cash drains by classifying routine operating costs as capital expenditures. Because operating expenses lower EBITDA but capital investments do not, this trick kept EBITDA looking robust while the actual business was bleeding cash. Similarly, the Dot-Com crash of 2000–2001 popularized customized “Pro Forma” adjustments that added back marketing and customer acquisition costs, leading investors to back startups with zero path to net profitability. Even successful looking “roll-up” strategies, such as Valeant Pharmaceuticals in 2015, used heavily adjusted EBITDA figures to hide the massive, unsustainable debt loads required to acquire other firms.
Today, this exact dynamic is repeating itself in the artificial intelligence infrastructure boom. Wall Street analysts are aggressively valuing specialized AI cloud providers and enterprise software firms on massive EBITDA multiples, ignoring skyrocketing stock-based compensation (added back as a “non-cash” expense) and billions in hardware debt collateralized directly by depreciating microchips. Because training and maintaining AI requires continuous, intense computing power, treating these foundational, recurring operational outlays as ignorable capital expenditures misstates the actual profit margins. Ultimately, the heavily adjusted EBITDA metrics seen across modern AI valuations closely resemble the deceptive “Pro Forma” metrics of the Dot-Com era, promising future profitability while obscuring the heavy capital burdens required to stay alive.
This exact dynamic is playing out across the AI infrastructure ecosystem, where massive operating profits are paired with heavy capital burdens.
We can see this structural strain clearly in the current financial profiles of the market’s primary infrastructure players.
· For example, Oracle Corporation leverages its legacy profitability to generate a massive $27.4 billion in EBITDA yet carries over $123 billion in net debt to finance its intensive cloud expansion.
· Similarly, specialized private hyperscalers like CoreWeave boast phenomenal EBITDA margins above 50% but operate with gross leverage ratios near 5.8x to secure advanced hardware facilities.
· In the hardware and physical layers, semiconductor giant Broadcom utilizes its $37.2 billion in EBITDA to service a substantial debt load exceeding $70 billion.
This structural blind spot becomes uniquely dangerous in hyper-paced environments where companies must aggressively scale just to keep pace with rapid technological shifts. Frontline AI developers like OpenAI and Anthropic face an unprecedented capital treadmill: the moment a frontier model is completed, the company must immediately secure tens of billions of dollars in compute infrastructure to train the next generation, or risk total obsolescence within a year. Evaluating these foundational players on an EBITDA basis completely strips out the massive interest burdens, capital leases, and compute liabilities required to sustain their market positions. In a sector where technology degrades at an exponential rate, an analyst relying on EBITDA is evaluating a business as if its current assets will last forever, completely ignoring the reality that the cash required for technological survival is already spoken for.
When reading modern analyst reports on enterprise AI and software companies, look specifically for these red flags:
“Rule of 40” calculations using Adjusted EBITDA: Analysts frequently add a company’s growth rate to its Adjusted EBITDA margin to see if it equals or exceeds 40%. If they used net income or free cash flow instead, many hyped AI firms would fail the test completely.
Massive gaps between EBITDA and Free Cash Flow (FCF): If a company boasts $100 million in Adjusted EBITDA but has deeply negative Free Cash Flow, it means they are rapidly burning cash on hardware or heavy stock dilution just to keep their “profitable” operating engine running.
The Ultimate Takeaway: As billionaire investor Warren Buffett famously remarked regarding the metric: “Does management think the tooth fairy pays for capital expenditures?”
Just like the historical market cycles of the past, today’s AI valuations rely heavily on metrics that promise profitability eventually—if you agree to ignore the very real, recurring expenses required to build and sustain the technology. If a company must constantly spend cash to replace its equipment, update its technology, or service its debt, that cash is gone. EBITDA can tell you if a business has a great engine, but it won’t stop you from driving off a cliff if you ignore the balance sheet.
I found this article on the importance of cash flow and the EBITDA limitation useful.
https://www.ghjadvisors.com/ghj-insights/the-importance-of-cash-flow-and-the-ebitda-limitation