Key Takeaways

  • Counterintuitive data from CO2's analysis shows companies above $10 billion in market cap are statistically more likely to deliver 10x returns than those below. The odds increase further for companies over $100 billion.
  • This "scale advantage" means larger companies better attract talent, access capital, and compound their competitive edge, making their growth more durable.
  • Avoid investing in pre-revenue AI companies with excessively high valuations; while some succeed, Swisser argues growth investing demands more measured approaches than pure venture capital.
  • Steer clear of "in-between" layers of the AI stack (e.g., between chips and foundational models) as they are fragile and vulnerable to disruption by either side.
  • For application-layer builders, Swisser warns against chasing revenue too fast without first establishing a "real durable advantage" through fundamental innovation.

The Counterintuitive Truth of 10x Returns

Lucas Swisser, co-head of growth investing at CO2, shared an insight that rips up the conventional wisdom for many founders and investors chasing the next big AI play. Most people hunt for tiny startups, hoping for a unicorn. But CO2's deep market data tells a different story.

“What we found in the data is you're actually you're much more likely to get a 10x in the pool as on a percentage basis if you're above $10 billion in market cap than below,” Swisser explained. “You're even more likely if you can get to 100 billion.” This isn't just about total dollars; it's about the percentage chance of a 10x return. Larger companies, already giants, are statistically better bets for outsized growth.

Why? Scale creates a compounding advantage. “When you can get to scale, your advantage tends to compound,” Swisser said. “It tends to be durable and it tends to compound over time. You're a better magnet for talent. You have more access to capital.” This means the rich get richer, and they do it faster, with lower risk. For ambitious builders, this flips the script: instead of founding a tiny startup from scratch, think about how to build within or attach to a rapidly scaling giant.

The AI Stack: Avoid the Death Traps

While the promise of AI is vast, Swisser flagged two major pitfalls for investors and founders. The first is obvious, but still traps many: “pre-revenue companies that come out of the gate at very high valuations.” He acknowledged some will work, but for growth investors (and founders aiming for sustainable scale), “you have to be a little bit more measured.” Don't mistake a hot idea for a durable business.

The more subtle, and perhaps more dangerous, trap lies within the AI stack itself. Swisser laid out the chain: “You've got land, you've got power, you've got the chips, you got the data infrastructure, you got the language model, and you got the apps.” He warned about companies that build their entire business “in between the layers that we've talked about... their entire business model is in between the chip and the model.”

“That's a very very very scary place to be. Fragile place,” Swisser concluded. These "in-between" layers lack durable advantages, making them vulnerable to disruption from either the foundational models above or the infrastructure providers below. If your business exists solely as a bridge, be ready for both ends to build their own.

For application-layer companies, Swisser also pointed to a common mistake: “just trying to drive to revenue too fast... Not thinking about the fundamental innovation that a company has, the fundamental thing that they are working on, and instead just trying to chase customers and chase revenue before there's like some real durable advantage.” Build something truly unique first, then monetize it. Chasing revenue before you have a moat is a fast path to becoming one of those "in-between" layers.