The widespread belief that modern AI technology will become an integral part of our daily lives is becoming increasingly pandemic. This is reflected in the surge of startups and investors dedicated to creating and financing new tech companies, all focused on developing or implementing AI innovations. With major funding rounds regularly making headlines and startups working tirelessly to outpace both the current technology landscape and the larger tech giants with their own AI strategies, the race is on.
However, amidst all the excitement, there is an important detail that demands our attention: AI startups often have less favorable economics compared to most traditional software startups.
The recent news that Anthropic, a leading AI startup that has secured billions in funding, had gross margins of only 50% to 55% in December serves as a reminder of the significant costs involved in constructing and operating modern AI models. This suggests that AI-focused startups may have a different valuation profile due to the immense expense of using such computing power.
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The quality of a startup’s revenue is often determined by its gross margins – the difference between revenue and cost of goods sold. Generally, higher margins equate to better revenue, all other factors held constant. For years, startups have justified their massive losses during the scaling phase by citing the high quality of their revenue – despite consuming significant amounts of cash, their generated profits are deemed to be of great value.
For this reason, software companies are often valued based on a multiple of their revenue, rather than their profits. When gross margins are strong, a solid revenue stream yields substantial gross profit – something investors highly regard. However, this valuation model does not apply to companies that, for example, sell groceries.
The discussion around AI gross margins is hardly groundbreaking. In 2020, venture capital firm a16z pointed out that AI startups would likely have lower gross margins due to the “heavy usage of cloud infrastructure and ongoing human support.”
