Jeff Dean, Google's chief scientist, and three other prominent AI researchers have left the company to establish their own startup, signaling a shift in the AI landscape.
Tech giants are actively recruiting artificial intelligence researchers years before they complete their doctoral programs, highlighting intense competition for AI talent.
On July 13, more than 200 leading economists and AI researchers, including 16 Nobel laureates, signed an open letter organised by the Stanford Digital Economy Lab.
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A new report indicates that Alphabet is expected to lose two additional AI researchers, contributing to concerns about a 'brain drain' within the company's artificial intelligence division.
Alphabet's stock has declined following the departure of two prominent AI researchers from Google, raising concerns about the company's artificial intelligence division.
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Anthropic has released a public version of its Mythos-class AI model, Claude Fable 5, with certain restrictions, including the absence of cybersecurity capabilities. This release comes amidst accusations of 'secret sabotage' regarding its capabilities for AI researchers and developers, and broader discussions about AI regulation and competition with OpenAI's ChatGPT.
Many AI researchers, including Turing Award winners, are concerned that artificial intelligence could soon replace humans and potentially lead to extinction, citing the principle of "misalignment."
The great artist and designer has summoned spiritual leaders, AI researchers and academics to try their hands at ceramics – and debate their wide-ranging positions on where tech is taking humanity
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Google's AI boss Demis Hassabis said the memory market came down to "a few suppliers of a few key components."
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Google DeepMind CEO Demis Hassabis said that the "whole supply chain" for memory chips is constrained.
"You need a lot of chips to be able to experiment on new ideas," Hassabis told CNBC.
Google produces its own TPUs, but Hassabis said that there were still "key components" that were supply-constrained.
The memory shortage takes no prisoners. Even Google isn't immune.
AI companies are duking it out for greater and greater quantities of memory chips. The problem? The industry is heavily supply-constrained. Costs have skyrocketed, products have been tied up, and some companies — especially those in consumer electronics — are increasing prices.
On the AI front, Google DeepMind CEO Demis Hassabis told CNBC that physical challenges were "constraining a lot of deployment." Google sees "so much more demand" for Gemini and its other models than it could serve, he said.
"Also, it does constrain a little bit the research," Hassabis said. "You need a lot of chips to be able to experiment on new ideas at a big enough scale that you can actually see if they're going to work."
Researchers want chips, whether they work at Google, Meta, OpenAI, or other Big Tech companies, and memory is a key component. Mark Zuckerberg said that AI researchers demanded two things beyond money: the fewest number of people reporting to them, and the most chips possible.
Hassabis said that wherever there was a capacity constraint, there was a "choke point."
"The whole supply chain is kind of strained," Hassabis said. "We're lucky, because we have our own TPUs, so we have our own chip designs."
Google has long built TPUs — Tensor Processing Units — for internal use. The company also leases them to external customers through its cloud, which has also put Nvidia on edge.
But even access to their own TPUs won't save Google from having to navigate the highly competitive memory market. "It still, in the end, actually comes down to a few suppliers of a few key components," Hassabis said.
Three suppliers dominate memory chip production: Samsung, Micron, and SK Hynix. These companies are struggling to meet demand for chips from AI hyperscalers without dropping their longtime electronics customers.
It doesn't help that AI companies mainly want a different type of memory chip than PC manufacturers do. Large language model producers want HBM (high-bandwidth memory) chips.
Don't expect Google's spending on AI infrastructure and chips to go down anytime soon. On its fourth-quarter earnings call, the company projected capital expenditures of $175 billion to $185 billion for 2026.
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