May 22, 2024
5 investors on the pros and cons of open source AI business models


The expectation that modern AI tech will find a home in every part of our lives is pandemic. Fittingly, startups and investors are working overtime to build and fund new technology companies to either create or implement new AI tech. Major rounds are often in the headlines, and startups are building at breakneck speeds to stay ahead of both the technology curve and the largest tech companies that have their own AI strategies.

But despite all the enthusiasm, there’s a niggling detail that deserves our attention: AI startups often have worse economics than most software startups.

The fact that Anthropic, a leading AI startup that has raised billions of dollars, reportedly had gross margins of 50% to 55% last December underscores the costs of building and running modern AI models, and hints that AI-focused startups have a different valuation profile due to the sheer expense of all that computing power.


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Revenue quality is partially predicated on gross margins — revenue less costs of goods sold — and the better those margins, the better the revenue, all else held equal. Startups have long depended on revenue quality as an explanation for their impressive losses during their scaling years — yes, startups consume lots of cash, but the revenue they generate is pristine in terms of quality, and thus worth quite a lot.

This is, among other reasons, why software companies are frequently valued on a multiple of their revenue instead of their profits. When gross margins are high, strong revenue yields oodles of gross profit. Investors like that. But that’s not a valuation model that you can apply to a company that’s, say, selling groceries.

The conversation around AI gross margins is not new. Back in 2020, venture firm a16z argued that AI startups would have lower gross margins due to “heavy cloud infrastructure usage and ongoing human support.”



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