The AI industry faces a significant disconnect between large-scale investments in companies and technology and the real economic impact of those investments.
Here's a takeaway from a couple of new reports. Sequoia Capital And The Economist. Both outlets raise serious questions about whether AI is actually living up to the hype.
And Sequoia even puts a number on it, saying that AI needs to fill $500. A billion Annual income gap to be economically viable.
Are we in an AI bubble?
I got the answers from Paul Roetzer, founder/CEO of the Marketing AI Institute. Episode 104 of Artificial Intelligence Show.
The Sequoia $600-Billion Question
Sequoia's doubts come from a new report by Sequoia partner David Kahn. In it, he recently updated his analysis of the AI industry's revenue expectations.
According to the number of Khans:
- The income gap has grown from a $125 billion “hole” to a staggering $500 billion gap that needs to be filled by income growth.
- This gap represents the difference between current capital expenditures and the income required to justify these investments.
“They're actually looking at how much money is being spent with NVIDIA, and then how much of that is driving actual revenue,” Rutzer says, breaking down Sequoia's logic.
“They're largely looking at product revenue. They're not really factoring in things like cost savings, innovation, productivity.”
so, real The question Sequoia is asking comes down to:
How much of the construction capital expenditure is tied to actual end-user demand, and how much is being built in anticipation of future customer demand?
“And that's a valid question,” Rutzer says. Many leading tech companies are buying AI chips and building data centers on the promise of AI that will eventually generate revenue.
“It's all being driven by this promise of end-user demand,” Rutzer says. “Sequoia is approaching it through the very specific (and understandable) lens of a VC firm trying to find companies like is matching their infrastructure investment with a product that actually creates end-user value.
“From that premise, it makes a bunch of sense,” Rutzer says. But it's also a bit more important than the headlines would have us believe.
Headlines are not the whole story.
It certainly seems to be a trend for headlines not to tell the whole story.
The Economist published an in-depth report on the disconnect between AI hype and its real economic impact. Their reporting shows that while many companies are experimenting with or investing heavily in AI, there is little evidence that AI is increasing productivity at scale.
An important part of their argument is a US Census Bureau report from March 2024 which claims AI adoption rates are surprisingly low — only 5.4% of businesses on their books (as of February 2024).
“I was kind of shocked by it,” Reutzer says, finding it extremely understated.
So he dug into the numbers and methodology some more. And, while the Census Bureau's large sample size of 164,000 businesses may seem impressive at first glance, Rutzer is quick to point out a number of problems with the report.
For one, the questions asked to get at AI adoption numbers were vague.
The survey asked whether businesses use AI in the “production of goods and services,” which Reutzer pointed out is “not the same as adoption of AI,” considering AI's potential in areas such as marketing, HR, or accounting. Ignoring usage.
Also, the person being asked about AI matters.
The bureau doesn't seem to have identified who is the best person in an organization to ask about AI.
“You can bias this data by people just not being sure what's what [the Census Bureau] is asking,” says Rutzer. Not everyone within the company knows what AI is or knows how it's being used.
As a result, it is not accurate to say that AI adoption is only above 5%.
The reality of AI adoption
So, what's the real story when it comes to AI adoption and impact?
“I think it's probably because 5% or less of companies have a real AI roadmap, change management plan, internal academy, AI council, and are actually measuring AI,” Rutzer says. “
But pilot AI technology is a different animal entirely.
“I think there's a huge number where people or teams or departments within organizations are experimenting with it, trying to figure it out, testing the technology, and testing use cases. are,” he says. “They [in my experience] is prevalent.”
So, while the headlines may paint a picture of slow AI adoption and a questionable return on investment, the reality is likely more significant.
In fact, there may be a difference between the current investment and the real value. But the true impact of AI is likely still unfolding — and could be more significant, according to some reports.