Business Insider reporter Ben Bergman broke the news this week that Mira Murati is raising about $2 billion for her AI startup, Thinking Machines Lab.
This company is only a few months old, and employs just a handful of people. There are no products yet. So, why would investors put in so much money and value such a young enterprise at around $10 billion?
I think there are two main explanations. Both are based on how venture capital works in the age of generative AI and Big Tech.
The Power Law
First up: This is a very expensive option on Mira’s startup working out spectacularly well. If that happens, a $10 billion valuation could become $100 billion. Or $1 trillion. OpenAI, where Murati used to work, was recently valued at $300 billion. Google is worth almost $2 trillion.
If Thinking Machines Lab turns into a success like these two companies, then your venture capital fund is done for the decade. You made 10x or 100x your investment, so the rest of your portfolio of early startups doesn’t really matter. Those businesses can all fail and you’ve still generated impressive returns.
This is known as the “Power Law,” which states that VC funds cannot achieve success without at least one bet so extraordinary that its gains return the entire value of the fund to investors. One potential example that comes to mind is Accel Partners’ early stake in Facebook.
“Each year brings a handful of outliers that hit the proverbial grand slam,” wrote Sebastian Mallaby in his 2022 book on the subject. “The only thing that matters in venture is to own a piece of them.”
The “Big Tech put”
Most startups don’t work out, though. If Thinking Machines doesn’t do amazingly well, there’s another path where investors can still make money — even if they’ve put what seems like a ridiculously high valuation on this startup.
Sometimes, startups that struggle to sell products and generate revenue still have other valuable assets. Maybe they’ve invented a new tech thing and have patents to back it up. Or they employ talented technical people. Or both. (To be sure, Murati is incredibly talented and she’s hired quite a few experts from her OpenAI days).
In situations like this, big tech companies sometimes swoop in and acquire these startups. When these transactions are mostly for talent, these deals are called acqui-hires.
In the generative AI era, when tech companies are racing for domination, some of these deals have been huge.
Last year, Google agreed to pay $2.5 billion to license Character.AI‘s technology and hire the startup’s two superstar cofounders, along with 20% of the other employees.
Microsoft crafted a similar deal with Inflection, a startup run by Deepmind cofounder Mustafa Suleyman. Amazon did a similar one with Adept.
These types of transactions don’t generate 10x or 100x returns for VCs. But they can get them out of struggling startup investments with little financial damage or sometimes even a relatively healthy gain.
For instance, the Character.ai deal netted investors a return of about 2.5x, BI’s Ben Bergman wrote last year.
Let’s call this the “Big Tech put.”
A put option is a financial contract that gives the owner the right to sell an asset at a specific price by a specific date.
More generally, a put is the idea that if the price of something falls a lot, someone important will step in and support it. Or even more generally, it’s the idea that if the sh*t hits the fan, higher powers will intervene to clean up the mess.
There’s a “Fed put,” which is the belief by investors that the Federal Reserve will step in to buoy markets if prices drop to a certain level.
And now there’s even a “Trump put.” This is the belief that US President Donald Trump will do what it takes to prop up the market. This theory did the rounds recently when he paused global tariffs after the bond market went berserk for a day or 2.
If broader markets can have “put” theories, then why can’t venture capitalists have their very own “Big Tech put”?
I just Googled this phrase by the way, and I couldn’t find anyone using it. So I’m claiming it as my own. I came up with this, OK?
