9.4 C
London
HomeAI USEFUL TIPSTop AI Researcher Warns of Massive Disruption from Cheap Superhuman AI

Top AI Researcher Warns of Massive Disruption from Cheap Superhuman AI

Related stories

Farewell: Fintech Nexus is shutting down

When we started Fintech Nexus in 2013 (known as...

Goldman Sachs loses profit after hits from GreenSky, real estate

Second-quarter profit fell 58% to $1.22 billion, or $3.08...

What Are the Benefits of Using Turnitin AI Detection?

In today’s digital age, academic integrity faces new challenges...

This Man Can Make Anyone Go Viral

Logan Forsyth helps his clients get hundreds of millions of...

A reclusive AI researcher is sounding the alarm about the profound implications of cheap, superhuman AI. And his insights could change how we think about the future of work and the economy.
Carl Shulman, an independent AI researcher with ties to the Machine Intelligence Research Institute and Oxford University’s Future of Humanity Institute, recently gave a marathon interview on the 80,000 Hours podcast. In it, he outlined a future where artificial general intelligence (AGI) could dramatically reshape our world sooner than we think.
What does it all mean for you?
I got the answers from Marketing AI Institute founder/CEO Paul Roetzer on Episode 105 of The Artificial Intelligence Show.
The coming abundance of intelligence
Shulman’s core argument is simple but profound: We’re moving from a world of scarce intelligence to one of abundant intelligence.
“Right now we have a scarcity of intelligence that is generally as capable as humans, especially human experts,” says Roetzer.
But, thanks to increasingly powerful AI, we are headed towards a world where intelligence is abundant. This shift could have massive implications for the job market.
Roetzer explains:

“If you take a step back and look at a more macro view, the question is: What is the greatest value for AI to be applied? What roles is it most likely to be applied to?”

It could first be the roles that are most expensive to employ because, until recently, they’ve required high levels of (relatively scarce) intelligence to perform.
Think: high-value, high-earning professions like managers, directors, executives, and specialists like lawyers and doctors.
AI that never sleeps
This isn’t just about intelligence. It’s also about productivity. Once AI gets to human levels of intelligence, it then performs its cognitive work ceaselessly.

“AI doesn’t sleep. It doesn’t take time off. It doesn’t spend most of its time and career in education or retirement or leisure,” says Roetzer.

AI can work 8,760 hours per year (full-time employment for 24 hours a day) at 100% efficiency, compared to a human’s typical 2,000-2,200 work hours annually. 
This productivity boost could start at the top of the wage scale and work its way down—reshaping traditional assumptions about the economy as it goes.
Are we ready for this?
Despite the potentially massive disruption on the horizon, Roetzer sees a concerning lack of preparation from leaders in business and society.

“I am increasingly convinced that it is going to be insanely disruptive to jobs in the future of work,” he says. “And yet you look around and there just isn’t that much talk about it.”

Despite plenty of doom and gloom headlines about AI’s impact on jobs, Roetzer doesn’t see enough serious conversation, modeling, or planning being done to actually address the possibility of AI disruption to the economy.
He draws parallels to the early days of AI in marketing. When he started Marketing AI Institute  in 2016, few recognized AI’s transformative potential. Now, he sees a similar blind spot when it comes to AI’s impact on jobs across industries.
In fact, many economists don’t seem to be predicting high levels of disruption. In the 80,000 Hours interview, Shulman suggests this is because the scenario seems too far-fetched based on historical precedent.

“They look at what AI can do today, and then they build their reports, projections, and forecasts based on their understanding of that,” he says. “What they’re not seeming to do is develop a deep understanding of what one to two models out could look like, 12 to 24 months out. What does it look like when we’re at GPT-6 or Gemini 3 or Claude 5?”

Right now, many AI researchers (including Shulman) are operating under the assumption that scaling laws will, at least in the short term, produce rapid and profound advancements in AI capabilities.
However, few economists and businesses leaders seem to be taking these assumptions seriously.
“If they’re only 50 percent right, then we’re in a totally different world,” says Roetzer. “We’re in a totally different world of what jobs look like, and we’re talking about three years from now.”

Latest stories