AI valuations are verging on the unhinged

Exhibit A is Mira Murati, formerly the chief technologist of OpenAI, who has vaulted almost overnight into the plutocracy. Her AI startup, Thinking Machines Lab, has reportedly raised $2bn at a $10bn valuation in its first fundraising round, before it has much of a strategy, let alone revenue.

Ms Murati’s success can be explained by her firm’s roster of ex-OpenAI researchers. Tech giants like Meta are offering megabucks for such AI superstars. Yet venture-capital (VC) grandees say that even for less exalted startups, traditional valuation measures such as projected revenue growth, customer churn and cash burn are less sacrosanct than they used to be. 

This is partly because AI is advancing so quickly, making it hard to produce reliable forecasts. But it is also a result of the gusher of investment flowing into generative AI.

The once-reliable measure most at risk of debasement is annual recurring revenue (ARR), central to many startup valuations. For companies selling software as a service, as most AI firms do, it used to be easy to measure. Take a typical month of subscriptions, based on the number of users, and multiply by 12. It was complemented by strong retention rates. 

Churn among customers was often less than 5% a year. As marginal costs were low, startups could burn relatively little cash before profits started to roll in. It was, by and large, a stable foundation for valuations.

Not so for AI startups. The revenue growth of some has been unusually rapid. Anysphere, which owns Cursor, a hit coding tool, saw its ARR surge to $500m this month, five times the level in January. Windsurf, another software-writing tool, also saw blistering growth before OpenAI agreed to buy it in May for $3bn.

But how sustainable is such growth? Jamin Ball of Altimeter Capital, a VC firm, notes that companies experiment with many AI applications, which suggests they are enthusiastic but not committed to any one product. He quips that this “easy-come, easy-go” approach from customers produces ERR, or “experimental run rate”, rather than ARR. Others say churn is often upwards of 20%. It doesn’t help that, in some cases, AI startups are charging based on usage rather than users (or “seats”), which is less predictable.

Add to this the fact that competition is ferocious, and getting more so. However fast an AI startup is growing, it has no guarantee of longevity. Many create applications on top of models built by big AI labs such as OpenAI or Anthropic. Yet these labs are increasingly offering applications of their own. Generative AI has also made it easier than ever to start a firm with just a few employees, meaning there are many more new entrants, says Max Alderman of FE International, an advisory firm.

Even well known AI firms are far from turning a profit. Perplexity, which has sought to disrupt a search business long dominated by Google, reportedly generated revenue of $34m last year, but burned around $65m of cash. That has been no hurdle to a punchy valuation. Perplexity’s latest fundraising round reportedly valued it at close to $14bn—a multiple of more than 400 times last year’s revenue (compared with about 6.5 times for stocks traded on the Nasdaq exchange). 

OpenAI, which torched about $5bn of cash last year, is worth $300bn. The willingness of venture investors to look past the losses reflects their belief that the potential market for AI is enormous and that costs will continue to plummet. In Perplexity’s case, the startup may be a takeover target, too.

In time, trusty old approaches to valuations may come back into vogue, and cooler heads prevail. “I’m the old-fashioned person who still believes I need [traditional measures] to feel comfortable,” says Umesh Padval of Thomvest, another VC firm. For now, just feel the vibes.

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