At a technical level, artificial intelligence seems to be the future of software. AI is showing remarkable progress on a range of difficult computer science problems, and the job of software developers – who now work with data as much as source code – is changing fundamentally in the process.
Many AI companies (and investors) are betting that this relationship will extend beyond just technology – that AI businesses will resemble traditional software companies as well. Based on our experience working with AI companies, we’re not so sure.
We are huge believers in the power of AI to transform business: We’ve put our money behind that thesis, and we will continue to invest heavily in both applied AI companies and AI infrastructure. However, we have noticed in many cases that AI companies simply don’t have the same economic construction as software businesses. At times, they can even look more like traditional services companies. In particular, many AI companies have:
Anecdotally, we have seen a surprisingly consistent pattern in the financial data of AI companies, with gross margins often in the 50-60% range – well below the 60-80%+ benchmark for comparable SaaS businesses. Early-stage private capital can hide these inefficiencies in the short term, especially as some investors push for growth over profitability. It’s not clear, though, that any amount of long-term product or go-to-market (GTM) optimization can completely solve the issue.
Just as SaaS ushered in a novel economic model compared to on-premise software, we believe AI is creating an essentially new type of business. So this post walks through some of the ways AI companies differ from traditional software companies and shares some advice on how to address those differences. Our goal is not to be prescriptive but rather help operators and others understand the economics and strategic landscape of AI so they can build enduring companies.
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