We use AI to analyze startups and private markets, and around six months ago a previously rare anomaly began showing up almost every day. Our AI began estimating many startup valuations as lower than the capital these startups had recently raised. In other words, our AI thinks more and more startups are worth less than their cash in the bank.
Our first instinct was to look for bugs in the code, assuming there must be a glitch. After all, it didn’t make sense for a business to be valued lower than the capital it had raised. For example, if a company raised $100 million last month, it should logically be worth at least $100 million now – presuming it didn’t burn the cash in a bonfire.
After taking a deeper look, nothing seemed wrong with the AI. The code was fine. How was this possible? Then the realization dawned that the anomaly wasn’t in the code but in the market itself.
It’s important to note that our AI does its best to estimate business valuations independently of fundraising or other exogenous factors. The goal is to estimate the true value of a business and to see beyond hype or up-rounds by potentially overzealous venture capitalists with vested interests in creating paper gains.
There’s an argument that a business is worth whatever a buyer is willing to pay, and there’s some truth to that, but there are also plenty of cases when buyers over-pay, or under-pay. I’ll concede there’s no theoretically perfect price for a business and valuations can be as much art as science, but I hope we can agree there must be some rational basis, or at least a reasonable theory, when estimating what a business is worth. You may have a favorite theory. Your cousin may have a different theory. Our AI has theories too.
Our AI cares a lot about quantifiable indicators of market demand for a startup and what it's selling. The AI also works hard to avoid “hype” indicators like social media echo chambers that can create a lot of noise but no meaningful signal. Taken as a whole, the AI seems to be telling us “no, a company that just raised $100 million may not be worth $100 million, at least not yet.”
That’s because the company may have cash, but it hasn’t necessarily converted that money into meaningful business impact. For example, immediately after being funded a startup still needs to spend the cash, and to see if its spending choices have good effects on its business. If spent wisely, the money increases the business’s value. If spent poorly, the money can vaporize into the ethers.
This explains much of what we’re seeing now. The COVID venture capital bubble inflated the valuations of many startups and pumped them full of cash. Some startups used the funds wisely, but others didn’t. Now, despite having an impressive valuation and a pile of money, a startup may lack customers or revenue, it may have a poor product-market fit, a weak business model, or other tragic flaws. In other words, the AI has revealed a landscape littered with paper Unicorns flapping in the wind.
Most people in innovation circles have been aware of the many down rounds, cutbacks and shutdowns facing the venture capital and startup world this year. For example, the New York Times reported that around 3,200 venture capital-backed companies have gone under this year in what it called a "cash bonfire."
VC firms themselves like OpenView (with around $2.4 billion raised) shut down this year as many others find themselves in varied stages of distress. Even bedrock firms like Sequoia and YCombinator have cut staff and restructured this year... and it isn't just a US problem. It's a global problem. For example, UK-based VC firms like Arix Bioscience and Catamaran Ventures shut down, Indian VC firms saw talent exoduses such as with Orbios, Lightbox, Redbright Partners and the Together Fund, and South Africa's largest VC fund, Naspers, shut down too.
Today a friend forwarded me a fascinating interview of David Friedberg by Louisa Burwood-Taylor that was published in October this year. While it wasn't the sole focus of the article, this section jumped out at me:
"And that’s the other problem. So many investors have been trained in the last 15 years to be momentum investors. You invest in stuff when it’s going up. You don’t invest in stuff when it’s going down. In the last year, the index for growth stocks have declined by 70 to 80%. So everything is down 80%. So all private companies should be down 80%. So if you raised money at an A or a B valuation of a hundred, are you now worth 20? And if you’re worth 20 and you’ve raised 40 million in cash, you’re now worth less than your preference stack. And that’s the wall that everyone’s starting to hit because the investors that would do late-stage investing are really nervous to fund a company that’s now worth less than their preference stack. And you’ve got to go negotiate with the existing investors and recap and restructure.
It doesn’t mean the companies are valueless; it doesn’t mean that there isn’t something good there. It just means that the value now with all public companies, public biotech and growth stocks is worth less than the cash that’s gone in. And so there’s a real kind of reset that’s happening right now."
Despite the loudness of this year's market corrections, startup failures and fund shutdowns, this article was the first time I'd heard anyone outside my team isolate and put their finger precisely on the phenomonon we've been observing through the lens of AI. Even though it's bad news, from a pattern recognition standpoint it's reassuring that other people have noticed it too.
Along these lines, as a career survivor of at least 3 major economic downturns it's exciting to live in a time when, today, AI is capable of identifying gaps between what an investor is willing to pay and startup might actually be worth. What I wouldn't have given to have this capability in 1999!
As AI gets better at separating signal from noise, hype from substance, and bubble from gum, there's at least some hope that VCs and entrepreneurs may be better prepared to navigate the inevitability of turbulent markets. This is especially the case as AI reveals key insights that don't seem to make sense at first, or second, glance, and force us to ask "WTF?".