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With every new wave of technology, there is a corresponding bubble in private markets. Entrepreneurs join the gold rush, chasing the edge offered by innovation, and investors lend out picks and shovels to anyone who promises to deliver outsized returns.

Most will fail. This is the normal for venture investing and a natural product of the high failure rate at early stages.

Consider the dotcom bubble. After a period of historically low interest rates, money poured into internet startups through venture capital. When interest rates rose again and capital dried up, many of these companies found themselves with unsustainable economics and were forced to close down.

This is a pattern that has replayed throughout humanity鈥檚 history. From in the U.K. in the 1840s, to Japan鈥檚 鈥渂ubble economy鈥� in the 1980s, low interest rates and irrational enthusiasm produced businesses that simply weren鈥檛 viable in normal conditions.

While we have exited the ultra-low rate era, returning to more normal interest rates of around 5% (the U.S. averaged 5.42% from 1971 until 2023), it鈥檚 worth noting that VC firms are still sitting on a huge amount of dry powder from that period.

Combined with the incredible enthusiasm around AI, evident in some of the , there are clear signs of a bubble.

The larger that bubble grows, the more the transfer of wealth in venture capital becomes a net-negative for the majority. Considering that, and the already potent hangover from VC excesses in 2021, you would imagine that we鈥檇 all be a bit more cautious.

Crude comparisons allow prices to inflate

In every instance of a bubble, the root cause is the simple idea that if a particular investment seems to be lucrative in one instance, the same will be true elsewhere. For startups, the term is 鈥渃omparables鈥� 鈥� the practice of pricing an investment by looking at similar investments.

The cruder the comparison, the wider the net is cast, and the faster a bubble develops.

Throughout 2021, when deals were being completed at a record pace, investors started relying more on comparables as a shorthand way to price rounds. Specifically, multiples on revenue (similar to price per square foot for real estate) became the go-to approach. This has a number of problems, including the huge incentive to , and suffers from being particularly low-resolution.

For example, fintech startups were understood to be priced based on multiples of up to 30x, while ignoring a lot of scrutiny on the underlying business model, profitability and the specific sector of finance.

The importance of specificity with valuation

Multiples shouldn鈥檛 be used simply to justify investor appetite for a technology, but to reflect a similar estimation of future potential. Valuation, after all, is always based on the future.

In simpler terms, multiples should be considered as a reflection of business model and industry, not technology. For example:

• An AI SaaS tool using the revenue multiple of a hot LLM R&D company might reflect investor appetite to a degree, but it says nothing meaningful about the value of the company.
• An AI SaaS tool in freight logistics using the multiple from another company in that industry would be properly reflecting the calculations about market size, growth potential and risk.

The purpose is to offer up a price which aims to reflect the value of the investment in terms of exit trajectory, not a price which fits investor hype and momentum behind a technology at that particular moment. Focusing too much on the latter is how we end up in procyclical spirals, inflating markets and ultimately crashing them.

is an adviser supporting impact entrepreneurs in emerging markets, and is the head of marketing at , a platform for startup valuation.

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