Google DeepMind CEO Demis Hassabis says AI scaling ‘must be pushed to the maximum’
In a recent discussion at the Axios AI+ Summit in San Francisco, Demis Hassabis, CEO of Google DeepMind, emphasized the critical role of scaling laws in the advancement of artificial intelligence (AI). He asserted that maximizing the scaling of current AI systems is essential to achieving artificial general intelligence (AGI)—a theoretical AI capable of reasoning and understanding at a human level. Hassabis pointed out that while scaling up data and computational resources is crucial, he anticipates that a few additional breakthroughs will be necessary to reach AGI. This perspective comes on the heels of DeepMind’s successful release of Gemini 3, which has garnered significant attention for its capabilities.
The debate surrounding scaling laws reflects a broader discussion within the AI community about the future trajectory of the technology. While Hassabis champions the scaling approach, other prominent figures, such as Yann LeCun, the chief AI scientist at Meta, argue that relying solely on scaling may not be sufficient. LeCun contends that many complex problems do not scale effectively with increased data or computational power. He has announced plans to leave Meta to pursue a startup focused on developing world models—AI systems that understand the physical world through spatial data, rather than just language. This shift highlights a growing concern among AI experts regarding the diminishing returns associated with heavy investments in scaling alone, as companies grapple with the environmental and financial costs of building expansive data centers.
As the race toward AGI intensifies, the contrasting views of Hassabis and LeCun underscore the need for innovative approaches to AI development. While scaling remains a foundational strategy for many leading AI firms, the exploration of alternative methodologies, such as world models, could pave the way for more robust and versatile AI systems. The outcome of this debate will likely shape the future of AI, influencing how companies allocate resources and prioritize research in their quest for more advanced and capable AI technologies.
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Demis Hassabis, Google DeepMind CEO
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Google DeepMind CEO Demis Hassabis says scaling laws are vital to the tech’s progress.
Scaling requires feeding AI models ever more data and more compute.
Some other AI leaders, however, believe the industry needs to find another way.
There’s a debate rippling through Silicon Valley: How far can
scaling laws
take the technology?
Google DeepMind CEO
Demis Hassabis
, whose company just released Gemini 3 to widespread acclaim, has made it clear where he stands on the issue.
“The scaling of the current systems, we must push that to the maximum, because at the minimum, it will be a key component of the final AGI system,” he said at the Axios’ AI+ Summit in San Francisco last week. “It could be the entirety of the AGI system.”
AGI, or
artificial general intelligence
, is a still theoretical version of AI that reasons as well as humans. It’s the goal all the leading AI companies are competing to reach, fueling
huge amounts of spending
on infrastructure and talent.
AI scaling laws suggest that the more data and compute an AI model is given, the smarter it will get.
Hassabis said that scaling alone will likely get the industry to AGI, but that he suspects there will need to be”one or two” other breakthroughs as well.
The problem with scaling alone is that there is a limit to publicly available data, and adding compute means
building data centers
, which is expensive and taxing on the environment.
Some AI watchers are also concerned that the AI companies behind the leading large-language models are beginning to
show diminishing returns
on their massive investments in scaling.
Researchers like
Yann LeCun
, the chief AI scientist at Meta who recently announced he was leaving to run his own startup, believe the industry needs to consider another way.
“Most interesting problems scale extremely badly,” he said at the National University of Singapore in April. “You cannot just assume that more data and more compute means smarter AI.”
LeCun is leaving Meta to work on building world models, an alternative to large-language models that rely on collecting spatial data rather than language-based data.
“The goal of the startup is to bring about the next big revolution in AI: systems that understand the physical world, have persistent memory, can reason, and can plan complex action sequences,” he wrote on LinkedIn in November.
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