Is there an AI bubble? AI's massive losses are mounting.

As creative AI companies race to develop more advanced models, an important question emerges: Are we in an AI bubble?

Long-time AI watchers are increasingly asking questions, especially as new reports emerge about the surprising pitfalls of OpenAI. According to The Information, the company is on track to lose as much as $5 billion in 2024.

So, are we in a bubble? Is AI over-invested and over-hyped?

I got the answers from Paul Rutzer, founder and CEO of the Marketing AI Institute. Episode 107 of Artificial Intelligence Show.

OpenAI's eye-watering pitfalls

According to one Analysis by informationOpenAI could face losses of up to $5 billion this year. It is based on the projection:

• AI training and inference costs are estimated at $7 billion by 2024.
• $1.5 billion in staff costs for approximately 1,500 employees
• Total operating costs could potentially reach $8.5 billion.
• Estimated revenue between $3-3.5 billion

Other companies like Anthropic are also burning through cash, with an estimated loss of $2.7 billion this year and significantly less revenue than OpenAI.

But there may be more to the story than meets the eye.

The big picture: A calculated risk

While these numbers may sound alarming, Roetzer offers a different perspective:

“As crazy as it sounds, losing $5 billion or more a year doesn't matter in the grand scheme of what they're chasing and the market potential they're chasing,” Reutzer says. ” “$5 billion is nothing.”

Roetzer points out that big tech companies like NVIDIA and Google have R&D budgets that mitigate those losses. And the key is to understand the strategic thinking behind these large-scale investments.

Rutzer points to a recent post by Doug Clinton of Deepwater Asset Management, where he frames AI investment strategies as a modern version of “Pascal's wager.”

Pascal's wager is a philosophical argument put forward by the philosopher and mathematician Blaise Pascal in the 17th century. The original argument essentially broke down belief in God's existence into a rational probabilistic argument, not an emotional or belief-based one.

(The bottom line: even the most skeptical non-believer should logically choose to just live as if God exists, since there is no real harm in being wrong.)

Clinton makes the same argument about AI, framing the thinking as follows:

1. If AI is deep and you invest heavily: You capture hundreds of billions in additional annual revenue.
2. If AI is too much but you invest: You waste money on infrastructure that could be rebuilt.
3. If AI is deep but you don't invest: You lose important ground to the competition.
4. If AI is too much and you don't invest: You save money but miss out on trillion dollar potential markets.

Basically, it has infinite downsides. no Betting on AI. Being right doesn't do you much good. You stand to lose. much more If you are wrong.

This framework explains why tech companies are betting so big on AI.

Tech leader doubles down

Rutzer says recent statements from industry leaders reinforce this strategic approach. He points to a post by David Kahn, a partner at Sequoia, which further breaks down the game theory at work here.

In other words…

“Bubble” behavior is actually very reasonable behavior from the big AI players.

What does this mean for business?

While the risks and investments in AI may seem surprising, they represent a calculated bet on a potentially transformative technology. As Rutzer said, “At the end of the day, it seems like the models are getting bigger and smarter.”

This makes sitting on the edge exceptionally dangerous. Companies that wait to see if AI will be “deep” may be setting themselves back.

For now, all signs point to continued growth and investment in AI. The question for enterprises is not whether to engage with AI, but how quickly and strategically they can do so.