A Godfather of AI Just Won a Nobel. He Has Been Warning the Machines Could Take Over the World.
Geoffrey Hinton hopes the prize will add credibility to his claims about the dangers of AI technology he pioneered
4 min
The newly minted Nobel laureate Geoffrey Hinton has a message about the artificial-intelligence systems he helped create: get more serious about safety or they could endanger humanity.
“I think we’re at a kind of bifurcation point in history where, in the next few years, we need to figure out if there’s a way to deal with that threat,” Hinton said in an interview Tuesday with a Nobel Prize official that mixed pride in his life’s work with warnings about the growing danger it poses.
The 76-year-old Hinton resigned from Google last year in part so he could talk more about the possibility that AI systems could escape human control and influence elections or power dangerous robots. Along with other experienced AI researchers, he has called on such companies as OpenAI, Meta Platforms and Alphabet -owned Google to devote more resources to the safety of the advanced systems that they are competing against each other to develop as quickly as possible.
Hinton’s Nobel win has provided a new platform for his doomsday warnings at the same time it celebrates his critical role in advancing the technologies fueling them. Hinton has argued that advanced AI systems are capable of understanding their outputs, a controversial view in research circles.
“Hopefully, it will make me more credible when I say these things really do understand what they’re saying,” he said of the prize.
Hinton’s views have pitted him against factions of the AI community that believe dwelling on doomsday scenarios needlessly slows technological progress or distracts from more immediate harms, such as discrimination against minority groups .
“I think that he’s a smart guy, but I think a lot of people have way overhyped the risk of these things, and that’s really convinced a lot of the general public that this is what we should be focusing on, not the more immediate harms of AI,” said Melanie Mitchell, a professor at the Santa Fe Institute, during a panel last year.
Hinton visited Google’s Silicon Valley headquarters Tuesday for an informal celebration, and some of the company’s top AI executives congratulated him on social media.
On Wednesday, other prominent Googlers specialising in AI were also awarded a Nobel Prize. Demis Hassabis, chief executive of Google DeepMind, and John M. Jumper, director at the AI lab, were part of a group of three scientists who won the chemistry prize for their work on predicting the shape of proteins.
Thinking like people
Hinton is sharing the Nobel Prize in physics with John Hopfield of Princeton University for their work since the 1980s on neural networks that process information in ways inspired by the human brain. That work is the basis for many of the AI technologies in use today, from ChatGPT’s humanlike conversations to Google Photos’ ability to recognise who is in every picture you take.
“Their contributions to connect fundamental concepts in physics with concepts in biology, not just AI—these concepts are still with us today,” said Yoshua Bengio , an AI researcher at the University of Montreal.
In 2012, Hinton worked with two of his University of Toronto graduate students, Alex Krizhevsky and Ilya Sutskever, on a neural network called AlexNet programmed to recognise images in photos. Until that point, computer algorithms had often been unable to tell that a picture of a dog was really a dog and not a cat or a car.
AlexNet’s blowout victory at a 2012 contest for image-recognition technology was a pivotal moment in the development of the modern AI boom, as it proved the power of neural nets over other approaches.
That same year, Hinton started a company with Krizhevsky and Sutskever that turned out to be short-lived. Google acquired it in 2013 in an auction against competitors including Baidu and Microsoft, paying $44 million essentially to hire the three men, according to the book “Genius Makers.” Hinton began splitting time between the University of Toronto and Google, where he continued research on neural networks.
Hinton is widely revered as a mentor for the current generation of top AI researchers including Sutskever, who co-founded OpenAI before leaving this spring to start a company called Safe Superintelligence.
Hinton received the 2018 Turing Award, a computer-science prize, for his work on neural networks alongside Bengio and a fellow AI researcher, Yann LeCun . The three are often referred to as the modern “godfathers of AI.”
Warnings of disaster
By 2023, Hinton had become alarmed about the consequences of building more powerful artificial intelligence. He began talking about the possibility that AI systems could escape the control of their creators and cause catastrophic harm to humanity. In doing so, he aligned himself with a vocal movement of people concerned about the existential risks of the technology.
“We’re in a situation that most people can’t even conceive of, which is that these digital intelligences are going to be a lot smarter than us, and if they want to get stuff done, they’re going to want to take control,” Hinton said in an interview last year.
Hinton announced he was leaving Google in spring 2023, saying he wanted to be able to freely discuss the dangers of AI without worrying about consequences for the company. Google had acted “very responsibly,” he said in an X post.
In the subsequent months, Hinton has spent much of his time speaking to policymakers and tech executives, including Elon Musk , about AI risks.
Hinton cosigned a paper last year saying companies doing AI work should allocate at least one-third of their research and development resources to ensuring the safety and ethical use of their systems.
“One thing governments can do is force the big companies to spend a lot more of their resources on safety research, so that for example companies like OpenAI can’t just put safety research on the back burner,” Hinton said in the Nobel interview.
An OpenAI spokeswoman said the company is proud of its safety work.
With Bengio and other researchers, Hinton supported an artificial-intelligence safety bill passed by the California Legislature this summer that would have required developers of large AI systems to take a number of steps to ensure they can’t cause catastrophic damage. Gov. Gavin Newsom recently vetoed the bill , which was opposed by most big tech companies including Google.
Hinton’s increased activism has put him in opposition to other respected researchers who believe his warnings are fantastical because AI is far from having the capability to cause serious harm.
“Their complete lack of understanding of the physical world and lack of planning abilities put them way below cat-level intelligence, never mind human-level,” LeCun wrote in a response to Hinton on X last year.
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Technology and semiconductor shares fell sharply as investors questioned the debt financing the AI infrastructure boom and confronted higher rates and Chinese competition.
Technology and semiconductor shares fell sharply as investors questioned the debt financing the AI infrastructure boom and confronted higher rates and Chinese competition.
4 minA sharp technology sell-off has exposed a new anxiety on global markets: whether the enormous borrowing behind the artificial-intelligence infrastructure boom can generate sufficient returns as interest rates rise and Chinese competition intensifies.
The artificial-intelligence investment boom has powered technology shares, semiconductor manufacturers and data-centre operators to extraordinary heights. Last week, investors were given a reminder that much of this expansion is being built with borrowed money.
The technology-heavy Nasdaq Composite fell 4.5 per cent over the week ended October 16, its weakest performance since late June. Semiconductor shares suffered an even deeper correction, with the Philadelphia Semiconductor Index losing about 11.4 per cent.
The selling spread quickly through global markets. Micron Technology fell 14.6 per cent over the week, while South Korea’s Samsung Electronics and SK Hynix dropped approximately 18 per cent and 24 per cent respectively.
This was more than a routine bout of profit-taking. Investors were reassessing three interconnected risks: higher interest rates, the rapidly expanding debt burden attached to AI infrastructure and the prospect of stronger competition from Chinese memory-chip manufacturers.
The debt behind the AI build-out
Artificial intelligence requires a physical infrastructure network of enormous scale. Data centres must be built, advanced processors purchased, power supplies secured and cooling systems installed before customers begin generating meaningful revenue from them.
Technology companies are increasingly supplementing their own cash with corporate bonds, syndicated loans, equipment leases, private credit and project-level financing.
JPMorgan describes the emerging funding model as a combination of conventional corporate borrowing and project finance tied to individual data-centre assets. In Australia, the Reserve Bank estimates that syndicated loans accounted for approximately three-quarters of data-centre financing during 2025 and 2026 to date.
The numbers are becoming large enough to affect broader credit markets. The Federal Reserve Bank of Dallas has cited Wall Street estimates suggesting AI-related investment-grade bond issuance could approach US$300 billion in 2026 alone.
That borrowing is not automatically problematic. Debt can be an efficient way to finance long-lived infrastructure when future revenue is sufficiently predictable. The concern is that AI technology, customer demand and computing economics are changing much faster than the repayment schedules attached to the assets.
A data centre financed today may carry debt for years. Investors must therefore make assumptions about future utilisation rates, electricity prices, chip performance, customer concentration and the amount businesses will ultimately pay for AI computing.
When interest rates rise, those assumptions become less forgiving.
Inflation changes the calculation
September’s US inflation data intensified the pressure. Consumer prices increased 3.7 per cent over the year, ahead of market expectations and up from 3.4 per cent in August.
The hotter reading reduced confidence that interest rates would fall quickly and pushed bond yields higher. That matters particularly for companies whose valuations depend on profits expected well into the future, as well as highly leveraged infrastructure operators that may need to refinance.
CoreWeave has emerged as a closely watched test case. The specialist cloud-computing company has accumulated substantial debt and lease obligations to build AI capacity, while relying on a concentrated group of large technology customers.
Its shares fell sharply during the sell-off, while the cost of insuring its debt against default climbed. CoreWeave has argued that its contracted backlog more than covers its obligations and that financial markets do not always understand its business model. Nevertheless, its securities have become a proxy for investor confidence in debt-funded AI infrastructure.
Oracle, another major investor in AI computing capacity, fell 8.8 per cent in Friday trading, while CoreWeave dropped 15.5 per cent.
The question is shifting from how much AI infrastructure can be built to how profitably it can be operated.
China adds another source of pressure
Interest rates were not the only catalyst. Reports of advances by Chinese memory-chip producer ChangXin Memory Technologies raised concerns that lower-cost Chinese products could increase competition across the global semiconductor industry.
Memory chips are essential to AI systems, but the industry is historically cyclical. Periods of tight supply and high pricing can encourage rapid investment, eventually producing oversupply and falling margins.
The prospect of a well-funded Chinese competitor expanding its technological capabilities unsettled investors in Samsung, SK Hynix and Micron. It also demonstrated that AI-related demand does not insulate chipmakers from conventional competitive pressures.
That combination—slower rate cuts, more expensive financing and potentially cheaper supply—challenged the assumption that every company exposed to AI would enjoy continually expanding margins.
A reset rather than the end of the boom
The sell-off does not establish that the AI boom is ending. Demand for computing power remains strong, the largest technology companies continue to generate formidable cash flows and many infrastructure projects are backed by long-term customer contracts.
What has changed is the market’s willingness to overlook the price and financing structure of that growth.
The most resilient companies are likely to be those able to fund investment from operating cash flow, secure long-duration customers and demonstrate that new capacity will earn returns comfortably above its financing costs.
More speculative operators may face greater scrutiny, particularly where debt is expensive, customers are concentrated or expected revenue depends on demand that has not yet materialised.
For Australian investors, the lesson extends beyond US technology shares. Local data-centre operators, infrastructure funds, electricity suppliers and property groups are increasingly exposed to the same investment cycle.
Artificial intelligence may remain one of the defining growth themes of the decade. But last week’s correction showed that enthusiasm for the technology does not eliminate financial discipline.
The next stage of the AI boom will be judged not only by computing power, model performance or demand for chips, but by something more traditional: whether the assets being financed can generate enough cash to repay the money borrowed to build them.
Fact box
Nasdaq Composite: Down 4.5 per cent for the week ended October 16, 2026
Philadelphia Semiconductor Index: Down approximately 11.4 per cent
Micron Technology: Down approximately 14.6 per cent
Samsung Electronics: Down approximately 18 per cent
SK Hynix: Down approximately 24 per cent
US September inflation: 3.7 per cent annually
Estimated 2026 AI-related investment-grade issuance: Approximately US$300 billion
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