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Meta Unveils Plans for Batch of In-house AI Chips

Mark Zuckerberg outside the court where he testified in a landmark trial (Reuters)
Mark Zuckerberg outside the court where he testified in a landmark trial (Reuters)
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Meta Unveils Plans for Batch of In-house AI Chips

Mark Zuckerberg outside the court where he testified in a landmark trial (Reuters)
Mark Zuckerberg outside the court where he testified in a landmark trial (Reuters)

Meta Platforms on Wednesday unveiled a roadmap of four new chips that the company is making in-house, as it rapidly expands its data centers.

Like many big tech companies such as Alphabet and Microsoft, Meta has invested heavily in building a team that can design chips in-house in addition to purchasing off-the-shelf products made by Nvidia and Advanced Micro Devices.

Making chips designed to tackle the specific types of data crunching Meta requires can lead to designs that use less energy and at a better cost.

The new chips are part of the company's Meta Training and Inference Accelerator (MTIA) program and the first of the new chips called the MTIA 300 is in use powering the company's ranking and recommendation systems. The other three will be rolled out this year and in 2027, with the final two chips, the MTIA 450 and 500 being designed to perform inference, the process when an AI model such as the one that powers the ChatGPT app responds to customer queries and requests.

"We see inference demand exploding at the moment and that's what we're currently focused on," Yee Jiun Song, Meta's vice president of engineering, said in an interview.

Meta has had some success with inference chips but has struggled with its long-time ambitions to make a generative AI training chip, capable of building the large models that power AI apps.

Beginning with the MTIA 400, which the company says is on the path to being used in its data centers, Meta has designed an entire system around the chips, which is roughly the size of several server racks and includes a version of liquid cooling.

The company plans to release the new chips at six-month intervals because it is rapidly expanding the number of data centers it uses to run apps like Instagram and Facebook, Song said.

"That is the reality of how quickly our infrastructure is being built out," Song said.

The company said in January it expects capital spending of between $115 billion and $135 billion this year.

Meta contracts Broadcom to help with some elements of the designs, though Song did not specify which chips. The company uses Taiwan Semiconductor Manufacturing Co to fabricate the processors.

In February, Meta signed big deals with Nvidia and AMD to buy tens of billions of dollars worth of chips.

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Technology

Meta, TikTok, X Lose Legal Challenge against UK Watchdog over Online Safety Data Demands

Meta logo (Reuters)
Meta logo (Reuters)
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Meta, TikTok, X Lose Legal Challenge against UK Watchdog over Online Safety Data Demands

Meta logo (Reuters)
Meta logo (Reuters)

Meta, TikTok and X on Friday lost a legal challenge against British regulator Ofcom over the amount of information it is demanding they provide under Britain's nascent online safety regime.

The challenge is one of the first in relation ‌to the ‌2023 Online Safety Act, ‌which ⁠sets tougher standards ⁠for platforms such as TikTok, X and Meta's Facebook and Instagram with the aim to protect children from harmful and illegal content, reported Reuters.

Ofcom has been gradually ⁠implementing the regime, with ‌its requirements backed ‌up by the threat of fines ‌of up to 10% of ‌a company's global turnover for the most serious breaches.

In February, it issued information notices requesting detailed content moderation ‌metrics from the tech companies, including data on how ⁠many ⁠posts were removed or had visibility restricted, and how many users were exposed to harmful content.

Meta, TikTok and X argued the notices create unprecedented regulatory burdens, but their case was rejected by London's High Court.

None of the companies, nor Ofcom, immediately responded to requests for comment.

Technology

US Regulator Bans Chinese Labs from Electronics Testing

A person uses his cellphone on a street in Beijing, China, 29 October 2026. (EPA)
A person uses his cellphone on a street in Beijing, China, 29 October 2026. (EPA)
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US Regulator Bans Chinese Labs from Electronics Testing

A person uses his cellphone on a street in Beijing, China, 29 October 2026. (EPA)
A person uses his cellphone on a street in Beijing, China, 29 October 2026. (EPA)

A US regulator voted Thursday to ban Chinese labs from testing electronic devices ranging from smartphones to cameras for use in the United States.

The move, which the Federal Communications Commission (FCC) says aims to boost security, comes a year after the agency adopted rules to ban test labs "owned or controlled by foreign adversaries."

Around 82 percent of certified devices in the United States are tested in recognized labs in China, the FCC estimates.

"These labs are a critical gateway into American communications infrastructure. And their privileged role gives them access to sensitive and confidential information about electronic products often months before the devices enter the market," the FCC said in a statement.

The ban, which will come into force in 2028, applies to test labs and certification bodies in countries that do not have a mutual recognition agreement with the United States, the agency said.

China does not have such a pact with the FCC, according to its website.

The agency said it began investigating "bad labs" after discovering in 2024 that Chinese tech giant Huawei was running an FCC-recognized Test Lab.

The FCC had previously blacklisted telecommunications equipment made by Huawei and several other Chinese firms over security concerns.

The latest ban would also protect the US technology industry by fighting against the "offshoring of electronics manufacturing, which is often collocated where devices are tested", it added.

Beijing criticized the policy which it said harmed trade between the world's top two economies.

"The US has overstretched the concept of national security, abused state power and unjustifiably cracked down on Chinese companies," Chinese foreign ministry spokesman Guo Jiakun said at a regular briefing on Friday.

"The US actions seriously hinder normal economic and trade exchanges between Chinese and US businesses and run counter to the interests of all parties, including US companies and consumers."

He said Beijing would "take necessary measures" to safeguard its interests.

The FCC has been taking steps to separate Chinese entities from US supply chains.

In March, the regulator barred authorizations of new consumer routers made in foreign countries, also on national security grounds.

Technology

Shock, Vindication, Regret: Scientists React to AI Roiling Life's Work

OpenAI and Anthropic each said one of their AI models had disproved the 'exponential time hypothesis'. Nicolas TUCAT / AFP
OpenAI and Anthropic each said one of their AI models had disproved the 'exponential time hypothesis'. Nicolas TUCAT / AFP
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Shock, Vindication, Regret: Scientists React to AI Roiling Life's Work

OpenAI and Anthropic each said one of their AI models had disproved the 'exponential time hypothesis'. Nicolas TUCAT / AFP
OpenAI and Anthropic each said one of their AI models had disproved the 'exponential time hypothesis'. Nicolas TUCAT / AFP

For decades, three computer scientists built their careers around an idea about the limits of what computers can do.

Artificial intelligence may now have shown it to be wrong: OpenAI and Anthropic each said on Monday one of their AI models had disproved the "exponential time hypothesis" (ETH).

The hypothesis holds that some problems are so hard that computers can solve them only by trying nearly every possible answer. Hundreds of studies in computer science and biology rely on it.

The claim is the latest in a string of AI results that have shaken academia, setting off soul-searching, most notably among mathematicians, over the value of human expertise and the future of their profession.

Russell Impagliazzo and Ramamohan Paturi of the University of California San Diego first proposed the hypothesis around 25 years ago.

Ryan Williams of MIT, a leading researcher in the field, had long publicly doubted it.

The three are now helping organize an independent review of the claims from the AI companies.

They shared their initial reactions with AFP. The interview has been edited for length and clarity.

- 'Surprise' -

Q: How did you feel when you learned that a machine may have overturned this hypothesis?

Ramamohan Paturi: My first reaction was surprise. ETH has shaped a great deal of research over the past three decades. But the research built around it would not simply disappear.

Ryan Williams: I did not fully believe the hypothesis, and I'm on the record as a prominent skeptic. So I felt vindicated.

Russell Impagliazzo: I'm afraid I am less cheerful about this than the others. I would have much preferred it to be discovered by a human who could explain their thought process. And I'd like to have shaken the (actual) hand of the person who disproved ETH.

- 'Major revolution' -

Q: What is left for human researchers facing the machine? What would you tell a student?

Paturi: These breakthroughs extend the frontier of research, but I believe shaping that frontier and moving it forward remain within human capacity and creativity. I would encourage young students to join. They should learn to use these tools while developing their own judgment.

Williams: I think we're starting to experience a major revolution in the field, and it is absolutely worth going into. Some people may not want to work in the field anymore; others may now feel motivated to.

Impagliazzo: I'm less confident. The rate of change is amazing. Until now, the same researchers mostly did both the glamorous part of research, solving, and the less glamorous part, verifying and explaining. Now it looks like much or most of the first will be done by AI, leaving humans the second -- until next year, when AI progresses further. I have no idea what research will be like next year.

- 'Worrisome' -

Q: These results come from models owned by companies, which decide what to publish and when. Does that worry you?

Paturi: Concentrated access to powerful research tools raises serious questions about who can participate, who gets early access and who shapes the research agenda. Publicly releasing a proof is important, but it does not provide equal access to the tools that produced it. A company's announcement cannot substitute for the community's assessment.

Williams: Yes, it is worrisome. It's on the AI companies to be responsible about their power, and I think they're aware of that.

Impagliazzo: When a human solves a problem, they share not just the solution but the thought process that led to it, which is often more valuable than the solution itself. Access to the AI model itself is the closest we can get to that.

- Possibilities -

Q: If confirmed, what would this result change for ordinary people?

Williams: It's still a theoretical result, but it opens up the possibility of much more efficient ways of computing in an enormous variety of scenarios.

Impagliazzo: There will be no immediate consequences for almost all people (as opposed to me and my friends!). But a first breakthrough is usually followed by improvements and generalizations, so this might eventually lead to better data analysis tools in many branches of science. The current result does not represent a security threat, but future extensions might require stronger encryption.