Startups focused on lowering the cost of AI are working with US manufacturers.
AI chips are being made at fabrication facilities in New York and Arizona.
Attempting to compete with Nvidia is daunting, especially when it comes to manufacturing.
Nvidia and most of its competitors don't produce their own chips. They vie for capacity from the world's most advanced chip fabricator: Taiwan Semiconductor Manufacturing Company. Nvidia may largely control which companies get the latest and most powerful computing machines, but TSMC decides how many Nvidia can sell. The relationship between the two companies fascinates the industry.
But the bottom line is that there's no manufacturer better and there's no getting ahead of Nvidia for the types of manufacturing capacity relevant to AI.
Still, a few startups think they can find an advantage amid Nvidia's dominance and the ever-fluctuating dynamics surrounding the island nation of Taiwan by tapping chip fabs in the United States.
Positron AI, founded by Thomas Sohmers in 2023, has designed a chip architecture optimized for transformer models — the kind on which OpenAI's GPT models are built. With faster access to more memory, Sohmers claims Postiron's architecture can compete on performance and price for AI inference, which is the computation needed to produce an answer to a query after a model has been trained.
Positron's system has "woefully less FLOPS" than an Nvidia GPU, Sohmers joked. However, his architecture is intended to compensate for this with efficiency for Positron and its customers.
Smaller fabs are 'hungrier'
Positron's chips are made in Chandler, Arizona, by Intel-owned firm, Altera.
Intel acquired Altera, which specializes in a specific type of programmable chip, in 2015. In 2023, some early Positron employees and advisors came from Altera — bringing relationships and trust. The early partnership has given Positron some small influence over Altera's path and a cheaper, more flexible manufacturing partner.
The cost of AI comes from the chip itself and the power needed to make it work. Cutting costs on the chip means looking away from TSMC, Sohmers says, which currently holds seemingly infinite bargaining power.
"Fundamentally, Positron is trying to provide the best performance per dollar and performance per watt," Sohmers said.
Compared to other industries, AI offers a rare proposition: US production is often cheaper.
"In most other industries, made in the USA actually means that it's going to be more expensive. That's not the case for semiconductors — at least for now," Sohmers said.
Many fabs are eager to enter the AI game, but they don't have the same technical prowess, prestige, or track record, which can make finding customers challenging.
Startups, which often lack the high order volumes that carry market power, are a good fit for these fabs, Sohmers said. These less in-demand fabs offer more favorable terms, too, which Sohmers hopes will keep Positron competitive on price.
"If I have some optionality going with someone that is behind but has the ambition to get ahead, it's always good from a customer or partner perspective," he said, adding, "It gives both leverage."
Taking advantage of US fabs has kept the amount of funding Positron needs within reason and made it easier to scale, Sohmers said.
Positron isn't alone. Fellow Nvidia challenger Groq partners with GlobalFoundries in upstate New York and seeks to make a similar dent in the AI computing market by offering competitive performance at a lower price.
Less inherent trust
It's not all upside though. Some investors have been skeptical, Sohmers said. And as an engineer, not going with the best fab in the world can feel strange.
"You have a lot more faith that TSMC is going to get to a good yield number on a new design pretty quickly and that they have a good level of consistency while, at other fabs, it can be kind of a dice roll," he said.
With a global supply chain, no semiconductor is immune from geopolitical turmoil or the shifting winds of trade policy. So, the advantages of exiting the constantly simmering tension between Taiwan, China, and the US serve as a counterweight to any skepticism.
Positron is also working on sourcing more components and materials in North America, or at least outside China and Taiwan.
Sourcing from Mexico, for example, offers greater safety from geopolitical turmoil. The simpler benefit is that shipping is faster so prototyping can happen quickly.
It's taken a while, but Sohmers said the industry is waking up to the need for more players across the AI space.
"People are finally getting uncomfortable with Nvidia having 90-plus percent market share," he said.
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At CES 2025 in Las Vegas, AMD unveiled a slew of new chips destined for devices ranging from desktops to gaming handhelds. AMD is riding high coming into this year’s CES. The company commanded a 28.7% share of the desktop CPU segment in Q3 2024, up 9.6 percentage points compared to the same quarter the […]
Fresh off of its worst year since going public in 1971, Intel is announcing new chips at CES 2025 that it hopes will turn its fortunes around. The product announcement is Intel’s largest since the company’s board of directors forced out CEO Pat Gelsinger. That’s not the only reason stakes are high. Intel’s 13th- and […]
TikTok parent company ByteDance has big plans to buy Nvidia chips in 2025 — despite U.S. restrictions. ByteDance plans to spend $7 billion on the chips in 2025, according to reporting from The Information, citing inside sources. If ByteDance follows through, it will become one of the world’s top owners of Nvidia chips, despite U.S. efforts […]
AWS plans to reduce spending on ZT Systems as it designs more data-center gear in-house.
AWS has been designing more data-center components itself to improve efficiency.
AWS remains the largest cloud provider, with significant capital expenditures planned for 2025.
Amazon Web Services plans to cut back on one key supplier as it designs more data-center components in-house.
AWS is scaling down its spending with ZT Systems, an AI-infrastructure company that AMD agreed this year to acquire, Business Insider has learned.
A confidential Amazon document from late last year obtained by BI estimated that AWS spent almost $2 billion last year on ZT Systems, which designs and manufactures server and networking products.
The document said some of AWS's "server and networking racks" were "transitioning" to a custom hardware approach where it designs this equipment itself. This change, the document said, has the "potential to impact spend" with ZT Systems.
Two current AWS employees familiar with the relationship also told BI recently that AWS was reducing spending on ZT Systems. One of the people said the cutback could happen in phases over a long period because ZT Systems is tightly integrated with AWS servers. They asked not to be identified because of confidential agreements.
An AWS spokesperson told BI that the company continued to have a business relationship with ZT Systems.
"Across AWS, we are relentless in our pursuit of lower costs and improved performance for customers, and our approach to our infrastructure is no different," the spokesperson said in an email. Spokespeople for AMD and ZT Systems didn't respond to requests for comment.
AWS has in recent years been using more homegrown data-center components, where it sees an opportunity to save costs and improve efficiency. This helps AWS because it doesn't have to buy as much from outside suppliers that mark up their offerings to make a profit. In turn, AWS can reduce prices for cloud customers. AWS now uses various custom data-center components, including routers and chips.
AWS is the world's largest cloud computing provider, so any change in its spending behavior is closely followed by the tech industry. AWS's spending on individual suppliers can fluctuate, and any one change doesn't mean AWS is pulling back on its data-center investments. In fact, Amazon is expected to spend $75 billion on capital expenditures this year, and even more in 2025, mostly on AWS data centers.
AMD agreed to acquire ZT Systems in August for $4.9 billion. The company is best known for designing and manufacturing server racks and other gear to help run data centers.
AWS could still send in-house designs to ZT to be manufactured. AMD has said it plans to sell ZT Systems' manufacturing business after the acquisition closes.
In recent months some AWS employees have discussed concerns about working too closely with ZT Systems since AWS and AMD offer similar AI-chip products, one of the people said.
AWS has for years been a close partner of AMD. The cloud giant sells cloud access to AMD CPUs but hasn't made AMD's new AI chips available on its cloud servers, partly because of low demand, an AWS executive who talked to BI recently said.
It's relatively common these days for big tech companies to design custom hardware. Nvidia, for example, acquired Mellanox for $6.9 billion in 2019 to offer its own data-center networking infrastructure. Other cloud giants, including Google, also design their own chips and networking gear.
AMD said in August that ZT Systems would help "deliver end-to-end data center AI infrastructure at scale."
"AWS and AMD work together closely, as we continue to make AWS the best place to run AMD silicon," AWS's spokesperson told BI.
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Elon Musk's xAI raised $6 billion in its Series C funding round.
Steve Granitz/FilmMagic via Getty Images
Elon Musk's xAI raised $6 billion in its Series C fundraising, the startup announced on Monday.
The round's participants included Sequoia Capital, and Nvidia and AMD were strategic investors.
The AI startup plans to use the cash to ship new products and build out its infrastructure.
Elon Musk's xAI has completed its Series C funding round, raising a total of $6 billion, it revealed in a Monday blog post.
Musk's artificial intelligence company said the participants included a16z, Sequoia Capital, Morgan Stanley, BlackRock, Fidelity, Saudi Arabia's Kingdom Holdings, Oman and Qatar's sovereign wealth funds, California-based Lightspeed Venture Partners, Chicago-based Valor Equity Partners, Dubai-based Vy Capital, and UAE-based tech investor MGX.
xAI added that chipmakers Nvidia and AMD took part as strategic investors and "continue to support xAI in rapidly scaling our infrastructure."
Musk shared the news on his X platform, writing, "A lot of compute is needed." He also tagged xAI in a meme generated by xAI's Grok chatbot that riffed on a famous line from the movie "Jaws."
Musk was likely underscoring the vast amount of processing power needed to train and run AI models, which has fueled enormous demand for microchips and underpinned a roughly eightfold rise in Nvidia stock since the start of 2023.
xAI, founded in March last year, raised $6 billion at a post-money valuation of $24 billion in its Series B round in May. The Wall Street Journal reported in late November that it had raised a further $5 billion at a $50 billion valuation. It appears xAI ultimately raised a bigger round of $6 billion, but the valuation wasn't disclosed.
The startup highlighted its progress since May in its blog post, including its launch of Colossus — the world's largest AI supercomputer powered by 100,000 Nvidia Hopper GPUs, which xAI plans to double in size to 200,000 chips soon.
xAI also released version two of Grok, an application programming interface (API) for developers to build on its platform, its Aurora image generation model for Grok, and Grok on X.
The company said it's training Grok 3 and "focused on launching innovative new consumer and enterprise products that will leverage the power of Grok, Colossus, and X to transform the way we live, work, and play."
Musk's fledgling business said it would use the Series C funds to accelerate its infrastructure growth, ship new products, and speed up its research and development of tech that will enable its "mission to understand the true nature of the universe."
Under Joe Biden's direction, the US Trade Representative (USTR) launched a probe Monday into China's plans to globally dominate markets for legacy chips—alleging that China's unfair trade practices threaten US national security and could thwart US efforts to build up a domestic semiconductor supply chain.
Unlike the most advanced chips used to power artificial intelligence that are currently in short supply, these legacy chips rely on older manufacturing processes and are more ubiquitous in mass-market products. They're used in tech for cars, military vehicles, medical devices, smartphones, home appliances, space projects, and much more.
China apparently "plans to build more than 60 percent of the world's new legacy chip capacity over the next decade," and Commerce Secretary Gina Raimondo said evidence showed this was "discouraging investment elsewhere and constituted unfair competition," Reuters reported.
Broadcom's CEO says he's too busy riding the AI wave to consider a takeover of rival Intel.
In an interview with the Financial Times, Hock Tan said he had no interest in "hostile takeovers."
Broadcom has soared to a $1 trillion market capitalization for the first time thanks to the AI boom.
The chief executive of $1 trillion AI chip giant Broadcom has dismissed the prospect of a takeover bid for struggling rival Intel.
In an interview with the Financial Times, Hock Tan said that he has his "hands very full" from riding the AI boom, responding to rumors that his company could make a move for its Silicon Valley rival.
"That is driving a lot of my resources, a lot of my focus," Tan said, adding that he has "not been asked" to bid on Intel.
The Broadcom boss is also adopting a "no hostile takeovers" policy after Donald Trump blocked his company's offer for Qualcomm in 2018 on national security grounds. Broadcom was incorporated in Singapore at the time.
"I can only make a deal if it's actionable," Tan told the FT. "Actionability means someone comes and asks me. Ever since Qualcomm, I learned one thing: no hostile offers."
Broadcom and Intel have experienced diverging fortunes since the start of the generative AI boom. Broadcom has more than doubled in value since the start of the year to hit a $1 trillion market capitalization for the first time, while Intel has collapsed by more than half to $82 billion.
Broadcom, which designs custom AI chips and components for data centers, hit a record milestone last week after reporting its fourth-quarter earnings. Revenues from its AI business jumped 220% year over year.
Intel, meanwhile, has had a much rougher year. Its CEO Pat Gelsinger — who first joined Intel when he was 18 and was brought back in 2021 after a stint at VMWare — announced his shock retirement earlier this month after struggling to keep pace with rivals like Nvidia in the AI boom.
Gelsinger, who returned to revitalize Intel's manufacturing and design operations, faced several struggles, leading him to announce a head count reduction of roughly 15,000 in August and a suspension of Intel's dividend.
Its challenges have led to several rumors of being bought by a rival, a move that would mark a stunning end to the decades-old chip firm. Buyer interest remains uncertain, however. Bloomberg reported in November that Qualcomm's interest in an Intel takeover has cooled.
Broadcom did not immediately respond to BI's request for comment outside regular working hours.
Microsoft bought more than twice as many Nvidia Hopper chips this year than any of its biggest rivals. The tech giant bought 485,000 Nvidia Hopper chips across 2024 according to reporting from the Financial Times, which cited data from tech consultancy Omdia. To compare, Meta bought 224,000 of the same flagship Nvidia chip this year. […]
Mark Heaps is the chief technology evangelist for Nvidia challenger Groq
Groq
Groq is taking a novel approach to competing with Nvidia's much-lauded CUDA software.
The chip startup is using a free inference tier to attract hundreds of thousands of AI developers.
Groq aims to capture market share with faster inference and global joint ventures.
There is an active debate about Nvidia's competitive moat. Some say there's a prevailing perception of a 'safe' choice when investing billions in a technology, in which the return is still uncertain.
Many say it's Nvidia's software, particularly CUDA, which the company began developing decades before the AI boom. CUDA allows users to get the most out of graphics processing units.
Competitors have attempted to make comparable systems, but without Nvidia's headstart, it has been tough to get developers to learn, try, and ultimately improve their systems.
Groq, however, is an Nvidia competitor that focused early on the segment of AI computing that requires less need for directly programming chips, and investors are intrigued. The 8-year-old AI chip startup was valued at $2.8 billion at its $640 million Series D round in August.
Though at least one investor has called companies like Groq 'insane' for attempting to dent Nvidia's estimated 90% market share, the startup has been building its technology exactly for the opportunity that is coming in 2025, Mark Heaps, Groq's "chief tech evangelist" said.
'Unleashing the beast'
"What we decided to do was take all of our compute, make it available via a cloud instance, and we gave it away to the world for free," Heaps said. Internally, the team called the strategy, "unleashing the beast". Groq's free tier caps users at a ceiling marked by requests per day or tokens per minute.
Heaps, CEO and ex-Googler Jonathan Ross, and a relatively lean team have spent 2023 and 2024 recruiting developers to try Groq's tech. Through hackathons and contests, the company makes a promise — try the hardware via Groq's cloud platform for free, and break through walls you've hit with others.
Groq offers some of the fastest inference out there, according to rankings on Artificialanalysis.ai, which measures cost and latency for companies that allow users to buy access to specific models by the token — or output.
Inference is a type of computing that produces the answers to queries asked of large language models. Training, the more energy-intensive type of computing, is what gives the models the ability to answer. So far, the hardware used for those two tasks has been different.
After the inference service was available for free, developers came out of the woodwork, he said, with projects that couldn't be successful on slower chips. With more speed, developers can send one request through multiple models and use another model to choose the best response — all in the time it would usually take to fulfill just one request.
Roughly 652,000 developers are now using Groq API keys, Heaps said.
Heaps expects speed to hook developers on Groq. But its novel plan for programming its chips gives the company a unique approach to the most crucial element within Nvidia's "moat."
No need for CUDA libraries
"Everybody, once they deployed models, was gonna need faster inference at a lower cost, and so that's what we focused on," Heaps said.
So where's the CUDA equivalent? It's all in-house.
"We actually have more than 1800 models built into our compiler. We use no kernels, and we don't need people to use CUDA libraries. So because of that, people can just start working with a model that's built-in," Heaps said.
Training, he said, requires more customization at the chip level. In inference, Groq's task is to choose the right models to offer customers and ensure they run as fast as possible.
"What you're seeing with this massive swell of developers who are building AI applications — they don't want to program at the chip level," he added.
The strategy comes with some level of risk. Groq is unlikely to accumulate a stable of developers who continuously troubleshoot and improve its base software like CUDA has. Its offering may be more like a restaurant menu than a grocery store. But this also means the barrier to entry for Groq users is the same as any other cloud provider and potentially lower than that of other chips.
Though Groq started out as a company with a novel chip design, today, of the company's roughly 300 employees, 60% are software engineers, Heaps said.
"For us right now, there is a billions and billions of dollars industry emerging, that we can go capture a big share of market in, while at the same time, we continue to mature the compiler," he said.
Despite being realistic about the near-term, Groq has lofty ambitions, which board CEO Jonathan Ross has described as "providing half the world's inference." Ross also says the goal is to cast a net over the globe — to be achieved via joint ventures. Saudi Arabia is on the way. Canada and Latin America are in the works.
Earlier this year, Ross told BI the company also has a goal to ship 108,000 of its language processing units or LPUs by the first quarter of next year — and 2 million chips by the end of 2025, most of which will be made availablethrough its cloud.
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Broadcom's stock surged in recent weeks, pushing the company's market value over $1 trillion.
Broadcom is crucial for companies seeking alternatives to Nvidia's AI chip dominance.
Custom AI chips are gaining traction, enhancing tech firms' bargaining power, analysts say.
The rise of AI, and the computing power it requires, is bringing all kinds of previously under-the-radar companies into the limelight. This week it's Broadcom.
Broadcom's stock has soared since late last week, catapulting the company into the $1 trillion market cap club. The boost came from a blockbuster earnings report in which custom AI chip revenue grew 220% compared to last year.
In addition to selling lots of parts and components for data centers, Broadcom designs and sells ASICs, or application-specific integrated circuits — an industry acronym meaning custom chips.
Designers of custom AI chips, chief among them Broadcom and Marvell, are headed into a growth phase, according to Morgan Stanley.
Custom chips are picking up speed
The biggest players in AI buy a lot of chips from Nvidia, the $3 trillion giant with an estimated 90% of market share of advanced AI chips.
Heavily relying on one supplier isn't a comfortable position for any company, though, and many large Nvidia customers are also developing their own chips. Most tech companies don't have large teams of silicon and hardware experts in house. Of the companies they might turn to design them a custom chip, Broadcom is the leader.
Though multi-purpose chips like Nvidia's and AMD's graphics processing units are likely to maintain the largest share of the AI chip market in the long-term, custom chips are growing fast.
Morgan Stanley analysts this week forecast the market for ASICs to nearly double to $22 billion next year.
Much of that growth is attributable to Amazon Web Services' Trainium AI chip, according to Morgan Stanley analysts. Then there are Google's in-house AI chips, known as TPUs, which Broadcom helps make.
In terms of actual value of chips in use, Amazon and Google dominate. But OpenAI, Apple, and TikTok parent company ByteDance are all reportedly developing chips with Broadcom, too.
ASICs bring bargaining power
Custom chips can offer more value, in terms of the performance you get for the cost, according to Morgan Stanley's research.
ASICs can also be designed to perfectly match unique internal workloads for tech companies, accord to the bank's analysts. The better these custom chips get, the more bargaining power they may provide when tech companies are negotiating with Nvidia over buying GPUs. But this will take time, the analysts wrote.
In addition to Broadcom, Silicon Valley neighbor Marvell is making gains in the ASICs market, along with Asia-based players Alchip Technologies and Mediatek, they added in a note to investors.
Analysts don't expect custom chips to ever fully replace Nvidia GPUs, but without them, cloud service providers like AWS, Microsoft, and Google would have much less bargaining power against Nvidia.
"Over the long term, if they execute well, cloud service providers may enjoy greater bargaining power in AI semi procurement with their own custom silicon," the Morgan Stanley analysts explained.
Nvidia's big R&D budget
This may not be all bad news for Nvidia. A $22 billion ASICs market is smaller than Nvidia's revenue for just one quarter.
Nvidia's R&D budget is massive, and many analysts are confident in its ability to stay at the bleeding edge of AI computing.
And as Nvidia rolls out new, more advanced GPUs, its older offerings get cheaper and potentially more competitive with ASICs.
"We believe the cadence of ASICs needs to accelerate to stay competitive to GPUs," the Morgan Stanley analysts wrote.
Still, Broadcom and chip manufacturers on the supply chain rung beneath, such as TSMC, are likely to get a boost every time a giant cloud company orders up another custom AI chip.
Arm and Qualcomm are locked in a legal battle over a licensing agreement.
REUTERS/ Albert Gea
Arm and Qualcomm are heading to trial this week in Delaware after two years of legal disputes.
The legal battle over a licensing agreement puts Arm in conflict with one of its largest customers.
The trial could have big implications for the entire chip industry, from M&A to IP.
A legal battle between two of the world's biggest chip companies, Arm and Qualcomm, is heading to trial this week — and its outcome could have wide-ranging consequences for the entire industry.
The jury trial in Delaware, starting Monday, is the result of a two-year fight between the two major chip companies. The dispute centers on a licensing arrangement connected to Qualcomm's $1.4 billion acquisition of chip startup Nuvia in 2021.
The fight has put Arm in conflict with one of its largest customers. Qualcomm pays Arm roughly $300 million a year in fees, Reuters reported, citing Stacy Rasgon, a senior analyst at Bernstein Research.
The trial is expected to last until Friday, with each side given 11 hours to present its case. It is set to include testimony from the CEO of Arm, Rene Haas, the chief executive of Qualcomm, Cristiano Amon, and the founder of Nuvia, Gerard Williams.
The legal battle
Arm first filed the lawsuit against Qualcomm in August 2022, alleging a breach of contract and trademark infringement.
The suit revolved around Qualcomm's 2021 acquisition of Nuvia, a chip design startup.
Nuvia had a license to use Arm's architecture to design server chips before Qualcomm acquired it. After the deal closed, Qualcomm reassigned Nuvia engineers to work on a laptop processor. Arm claims that Qualcomm failed to properly transfer the license after the acquisition.
Arm has argued Qualcomm should have renegotiated the licensing agreement because it had different financial terms with each company. Arm, which is majority-owned by SoftBank, has accused Qualcomm of continuing to use its intellectual property in products designed with Nuvia's technology despite not having the required licensing agreements.
In response, Qualcomm has said its existing license with Arm is sufficient and countersued the company, accusing Arm of overstepping its rights. Qualcomm has also said the lawsuit is harming its business and ability to innovate.
Haas addressed the case in a recent interview with The Verge's Alex Heath.
"I can appreciate — because we talk to investors and partners — that what they hate the most is uncertainty," the Arm CEO said. "But on the flip side, I would say the principles as to why we filed the claim are unchanged."
The company has previously said the lawsuit was a last-resort move to protect its intellectual property.
Arm is not seeking monetary damages from Qualcomm but is asking it to destroy any products built using Arm's IP without proper licensing.
Consequences for the chip industry
The trial could have ramifications for IP licensing agreements, mergers and acquisitions, and contract law in the tech industry, wrote Jim McGregor,a principal analyst and partner at TIRIAS Research, in an article for Forbes.
"In addition, it will have an impact on the entire electronics ecosystem, especially each party's supply chains and customer bases," he continued.
Arm and Qualcomm are longtime allies, and the trial is an unusual escalation for two companies so closely tied together.
"It's really not in either of their best interests to go nuclear," Rasgon told The Financial Times. "I think it would make sense to see a settlement — they need each other."
The case could also disrupt a wave of AI computers. Arm said in June that Qualcomm used designs based on Nuvia engineering to create new low-power AI PC chips, which launched earlier this year. Should Arm win the legal battle, it could halt shipments of laptops made by partners — including Microsoft — that contain disputed Qualcomm chips.
Representatives for Arm and Qualcomm did not immediately respond to a Business Insider request for comment.
Intel's co-CEOs discussed splitting the firm's manufacturing and products businesses Thursday.
A separation could address Intel's poor financial performance. It also has political implications.
Intel Foundry is forming a separate operational board in the meantime, executives said.
Intel's new co-CEOs said the company is creating more separation between its manufacturing and products businesses and the possibility of a formal split is still in play.
When asked if separating the two units was a possibility and if the success of the company's crucial, new "18A" process could influence the decision, CFO David Zinsner and CEO of Intel Products Michelle Johnston Holthaus, now interim co-CEOs, said preliminary moves are in progress.
"We really do already run the businesses fairly independently," Holthaus said at a Barclays tech conference Thursday. She added that severing the connection entirely does not make sense in her view, "but, you know, someone will decide that," she said.
"As far as does it ever fully separate? I think that's an open question for another day," Zinsner said.
Already in motion
Though the co-CEOs made it clear a final decision on a potential break-up has not been made, Zinsner outlined a series of moves already in progress that could make a split easier.
"We already run the businesses separately, but we are going down the path of creating a subsidiary for Intel Foundry as part of the overall Intel company," Zinsner said.
In addition, the company is forming a separate operational board for Intel Foundry and separating the operations and inventory management software for the two sides of the business.
Until a permanent CEO is appointed by the board, the co-CEOs will manage most areas of the company together, but Zinsner alone will manage the Foundry business.The foundry aims to build a contract manufacturing business for other chip designers. Due to the sensitive, competitive intellectual property coming from clients into that business, separation is key.
"Obviously, they want firewalls. They want to protect their IPs, their product road maps, and so forth. So I will deal with that part of the foundry to separate that from the Intel Products business." Zinsner said.
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Apple is working with semiconductor company Broadcom on its first server chip designed to handle AI applications, according to The Information, which cited three people with knowledge of the project. Apple is known for designing its own chips — called Apple Silicon and primarily manufactured by TSMC — for its devices. But those chips weren’t […]
Micron Technology will receive more than $6.1 billion after the US Department of Commerce finalized one of the largest CHIPS Act awards ever to "the only US-based manufacturer of memory chips," Vice President Kamala Harris said in a press statement.
Micron will use the funding to construct "several state-of-the-art memory chips facilities" in New York and Idaho, Harris said. The chipmaker has committed to a "$125 billion investment over the next few decades" and promised to create "at least 20,000 jobs," Harris confirmed.
Additionally, Micron "agreed to preliminary terms for an additional investment of $275 million to expand" its facility in Manassas, Virginia, Harris confirmed. Those facilities will mostly be used to manufacture chips for automotive and defense industries, Harris noted.
China's top antimonopoly regulator is investigating Nvidia.
The investigation is related to the company's 2020 acquisition of an Israeli chip firm.
Nvidia's stock fell by 2.2% in premarket trading on Monday.
China's top antimonopoly regulator has launched an investigation into Nvidia, whose shares dropped by 2.2% in premarket trading on Monday following the latest escalation of chip tensions with the US.
The State Administration for Market Regulation said on Monday that it was investigating whether the chipmaker giant violated antimonopoly regulations.
The probe is related to Nvidia's acquisition of Mellanox Technologies, an Israeli chip firm, in 2020. China's competition authority approved the $7 billion takeover in 2020 on the condition that rivals be notified of new products within 90 days of allowing Nvidia access to them.
The US-China chip war has been escalating. Last week, China's commerce ministry said it would halt shipments of key materials needed for chip production to the US. The ministry said the measures were in response to US chip export bans, also announced last week.
Nvidia, which is headquartered in Santa Clara, California, has also faced antitrust scrutiny in the US. The Department of Justice has been examining whether Nvidia might have abused its market dominance to make it difficult for buyers to change suppliers.
Nvidia did not immediately respond to a request for comment from Business Insider made outside normal working hours.
AWS's new AI chips aren't meant to go after Nvidia's lunch, said Gadi Hutt, a senior director of customer and product engineering at the company's chip-designing subsidiary, Annapurna Labs. The goal is to give customers a lower-cost option, as the market is big enough for multiple vendors, Hutt told Business Insider in an interview at AWS's re:Invent conference.
"It's not about unseating Nvidia," Hutt said, adding, "It's really about giving customers choices."
AWS has spent tens of billions of dollars on generative AI. This week the company unveiled its most advanced AI chip, called Trainium 2, which can cost roughly 40% less than Nvidia's GPUs, and a new supercomputer cluster using the chips, called Project Rainier. Earlier versions of AWS's AI chips had mixed results.
Hutt insists this isn't a competition but a joint effort to grow the overall size of the market. The customer profiles and AI workloads they target are also different. He added that Nvidia's GPUs would remain dominant for the foreseeable future.
In the interview, Hutt discussed AWS's partnership with Anthropic, which is set to be Project Rainer's first customer. The two companies have worked closely over the past year, and Amazon recently invested an additional $4 billion in the AI startup.
He also shared his thoughts on AWS's partnership with Intel, whose CEO, Pat Gelsinger, just retired. He said AWS would continue to work with the struggling chip giant because customer demand for Intel's server chips remained high.
Last year AWS said it was considering selling AMD's new AI chips. But Huttsaidthose chips still weren't available on AWS because customers hadn't shown strong demand.
This Q&A has been edited for clarity and length.
There have been a lot of headlines saying Amazon is out to get Nvidia with its new AI chips. Can you talk about that?
I usually look at these headlines, and I giggle a bit because, really, it's not about unseating Nvidia. Nvidia is a very important partner for us. It's really about giving customers choices.
We have a lot of work ahead of us to ensure that we continuously give more customers the ability to use these chips. And Nvidia is not going anywhere. They have a good solution and a solid road map. We just announced the P6 instances [AWS servers with Nvidia's latest Blackwell GPUs], so there's a continuous investment in the Nvidia product line as well. It's really to give customers options. Nothing more.
Nvidia is a great supplier of AWS, and our customers love Nvidia. I would not discount Nvidia in any way, shape, or form.
So you want to see Nvidia's use case increase on AWS?
If customers believe that's the way they need to go, then they'll do it. Of course, if it's good for customers, it's good for us.
The market is very big, so there's room for multiple vendors here. We're not forcing anybody to use those chips, but we're working very hard to ensure that our major tenets, which are high performance and lower cost, will materialize to benefit our customers.
Does it mean AWS is OK being in second place?
It's not a competition. There's no machine-learning award ceremony every year.
In the case of a customer like Anthropic, there's very clear scientific evidence that larger compute infrastructure allows you to build larger models with more data. And if you do that, you get higher accuracy and more performance.
Our ability to scale capacity to hundreds of thousands of Trainium 2 chips gives them the opportunity to innovate on something they couldn't have done before. They get a 5x boost in productivity.
Is being No. 1 important?
The market is big enough. No. 2 is a very good position to be in.
I'm not saying I'm No. 2 or No. 1, by the way. But it's really not something I'm even thinking about. We're so early in our journey here in machine learning in general, the industry in general, and also on the chips specifically, we're just heads down serving customers like Anthropic, Apple, and all the others.
We're not even doing competitive analysis with Nvidia. I'm not running benchmarks against Nvidia. I don't need to.
For example, there's MLPerf, an industry performance benchmark. Companies that participate in MLPerf have performance engineers working just to improve MLPerf numbers.
That's completely a distraction for us. We're not participating in that because we don't want to waste time on a benchmark that isn't customer-focused.
On the surface, it seems like helping companies grow on AWS isn't always beneficial for AWS's own products because you're competing with them.
We are the same company that is the best place Netflix is running on, and we also have Prime Video. It's part of our culture.
I will say that there are a lot of customers that are still on GPUs. A lot of customers love GPUs, and they have no intention to move to Trainium anytime soon. And that's fine, because, again, we're giving them the options and they decide what they want to do.
Do you see these AI tools becoming more commoditized in the future?
I really hope so.
When we started this in 2016, the problem was that there was no operating system for machine learning. So we really had to invent all the tools that go around these chips to make them work for our customers as seamlessly as possible.
If machine learning becomes commoditized on the software and hardware sides, it's a good thing for everybody. It means that it's easier to use those solutions. But running machine learning meaningfully is still an art.
What are some of the different types of workloads customers might want to run on GPUs versus Trainium?
GPUs are more of a general-purpose processor of machine learning. All the researchers and data scientists in the world know how to use Nvidia pretty well. If you invent something new, if you do that on GPU, then things will work.
If you invent something new on specialized chips, you'll have to either ensure compiler technology understands what you just built or create your own compute kernel for that workload. We're focused mainly on use cases where our customers tell us, "Hey, this is what we need." Usually the customers we get are the ones that are seeing increased costs as an issue and are trying to look for alternatives.
So the most advanced workloads are usually reserved for Nvidia chips?
Usually. If data-science folks need to continuously run experiments, they'll probably do that on a GPU cluster. When they know what they want to do, that's where they have more options. That's where Trainium really shines, because it gives high performance at a lower cost.
AWS CEO Matt Garman previously said the vast majority of workloads will continue to be on Nvidia.
It makes sense. We give value to customers who have a large spend and are trying to see how they can control the costs a bit better. When Matt says the majority of the workloads, it means medical imaging, speech recognition, weather forecasting, and all sorts of workloads that we're not really focused on right now because we have large customers who ask us to do bigger things. So that statement is 100% correct.
In a nutshell, we want to continue to be the best place for GPUs and, of course, Trainium when customers need it.
What has Anthropic done to help AWS in the AI space?
They have very strong opinions of what they need, and they come back to us and say, "Hey, can we add feature A to your future chip?" It's a dialogue. Some ideas they came up with weren't feasible to even implement in a piece of silicon. We actually implemented some ideas, and for others we came back with a better solution.
Because they're such experts in building foundation models, this really helps us home in on building chips that are really good at what they do.
We just announced Project Rainier together. This is someone who wants to use a lot of those chips as fast as possible. It's not an idea — we're actually building it.
Can you talk about Intel? AWS's Graviton chips are replacing a lot of Intel chips at AWS data centers.
I'll correct you here. Graviton is not replacing x86. It's not like we're yanking out x86 and putting Graviton in place. But again, following customer demand, more than 50% of our recent landings on CPUs were Graviton.
It means that the customer demand for Graviton is growing. But we're still selling a lot of x86 cores too for our customers, and we think we're the best place to do that. We're not competing with these companies, but we're treating them as good suppliers, and we have a lot of business to do together.
How important is Intel going forward?
They will for sure continue to be a great partner for AWS. There are a lot of use cases that run really well on Intel cores. We're still deploying them. There's no intention to stop. It's really following customer demand.
Is AWS still considering selling AMD's AI chips?
AMD is a great partner for AWS. We sell a lot of AMD CPUs to customers as instances.
The machine-learning product line is always under consideration. If customers strongly indicate that they need it, then there's no reason not to deploy it.
And you're not seeing that yet for AMD's AI chips?
Not yet.
How supportive are Amazon CEO Andy Jassy and Garman of the AI chip business?
They're very supportive. We meet them on a regular basis. There's a lot of focus across leadership in the company to make sure that the customers who need ML solutions get them.
There's also a lot of collaboration within the company with science and service teams that are building solutions on those chips. Other teams within Amazon, like Rufus, the AI assistant available to all Amazon customers, run entirely on Inferentia and Trainium chips.
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"We are very driven toward 'no wafer left behind,'" Naga Chandrasekaran, the chief global operations officer, said at the UBS Global Technology and AI Conference on Wednesday.
But Intel needs a "no capital left behind" mindset, he added.
Chandrasekaran, who joined Intel this year after two decades at Micron, said that Intel's strategy of producing excess wafers in the hope that there will be demand may have worked when it was closer to a monopoly.
Intel was Silicon Valley's dominant chipmaker in the 2000s. But it has lost ground to AI king Nvidia, Samsung, and several Taiwanese and American players over the years, missing out on skyrocketing artificial intelligence demand. Companies like Microsoft and Google have been designing their own chips, further limiting Intel's market.
Intel's share price has dropped almost 50% this year as it has faced multiple challenges, including billions in losses, sweeping layoffs, and buyouts.
Chandrasekaran and Intel's interim co-CEO David Zinsner, who also participated in Wednesday's fireside talk, said that the company needs to be more mindful of capital spending and operating expenses.
"We're going line by line through this stuff and he's challenging everything and we're picking off things," Zinsner said of Chandrasekaran's strategy. "You've got to absolutely think about every dollar going to capital and scrutinizing it for sure."
The company said in its most recent annual report that it expects continued high capital expenditures "for the next several years" amid an expansion. Intel spent $25.8 billion on capital expenditures last year, up from $18.7 billion two years ago.
On Wednesday, the execs also said that Intel would stick to its current financial forecast and that they were not worried about the impact of the incoming Trump administration.
The company is set to get a $7.9 billion CHIPS Act grant, which is mostly awarded in tax credits, as part of a government program to boost the American semiconductor industry. The Commerce Department told The New York Times that Intel was receiving less than the $8.5 billion originally promised because it also received a separate grant of $3 billion to produce chips for the military.
Trump's tariff threats are not publicly ruffling the Intel executives.
"We have good geographic dispersion of our factories. We can move things around based on what we need," Zinsner said.
Bloomberg and Reuters reported Wednesday that the chipmaker is considering at least two people to replace Gelsinger, who abruptly retired on Sunday after clashing with Intel's board over turnaround plans. Candidates include Lip-Bu Tan, a former Intel board member, and Matt Murphy, the CEO of Marvell Technology.
Amazon is betting on its own chips and supercomputers to forge ahead with its AI ambitions.
Noah Berger/Getty Images for Amazon Web Services
AWS unveiled a new AI chip and a supercomputer at its Re: Invent conference on Tuesday.
It's a sign that Amazon is ready to reduce its reliance on Nvidia for AI chips.
Amazon isn't alone: Google, Microsoft, and OpenAI are also designing their own AI chips.
Big Tech's next AI era will be all about controlling silicon and supercomputers of their own. Just ask Amazon.
At its Re: Invent conference on Tuesday, the tech giant's cloud computing unit, Amazon Web Services, unveiled the next line of its AI chips, Trainium3, while announcing a new supercomputer that will be built with its own chips to serve its AI ambitions.
It marks a significant shift from the status quo that has defined the generative AI boom since OpenAI's release of ChatGPT, in which the tech world has relied on Nvidia to secure a supply of its industry-leading chips, known as GPUs, for training AI models in huge data centers.
While Nvidia has a formidable moat — experts say its hardware-software combination serves as a powerful vendor lock-in system — AWS' reveal shows companies are finding ways to take ownership of the tech shaping the next era of AI development.
Putting your own chips on the table
Amazon is pushing forward with its own brand of chips called Trainium.
Noah Berger/Getty Images for Amazon Web Services
On the chip side, Amazon shared that Trainium2, which was first unveiled at last year's Re: Invent, was now generally available. Its big claim was that the chip offers "30-40% better price performance" than the current generation of servers with Nvidia GPUs.
That would mark a big step up from its first series of chips, which analysts at SemiAnalysis described on Tuesday as "underwhelming" for generative AI training and used instead for "training non-complex" workloads within Amazon, such as credit card fraud detection.
"With the release of Trainium2, Amazon has made a significant course correction and is on a path to eventually providing a competitive custom silicon," the SemiAnalysis researchers wrote.
Trainium3, which AWS gave a preview of ahead of a late 2025 release, has been billed as a "next-generation AI training chip." Servers loaded with Trainium3 chips offer four times greater performance than those packed with Trainium2 chips, AWS said.
Matt Garman, the CEO of AWS, told The Wall Street Journal that some of the company's chip push is due to there being "really only one choice on the GPU side" at present, given Nvidia's dominant place in the market. "We think that customers would appreciate having multiple choices," he said.
It's an observation that others in the industry have noted and responded to. Google has been busy designing its own chips that reduce its dependence on Nvidia, while OpenAI is reported to be exploring custom, in-house chip designs of its own.
But having in-house silicon is just one part of this.
The supercomputer advantage
AWS acknowledged that as AI models trained on GPUs continue to get bigger, they are "pushing the limits of compute and networking infrastructure."
Amazon has a close partnership with OpenAI rival Anthropic.
Noah Berger/Getty
With this in mind, AWS shared that it was working with Anthropic to build an "UltraCluster" of servers that form the basis of a supercomputer it has named Project Rainier. According to Amazon, it will scale model training across "hundreds of thousands of Trainium2 chips."
"When completed, it is expected to be the world's largest AI compute cluster reported to date available for Anthropic to build and deploy their future models on," AWS said in a blog, adding that it will be "over five times the size" of the cluster used to build Anthropic's last model.
The supercomputer push follows similar moves elsewhere. The Information first reported earlier this year that OpenAI and Microsoft were working together to build a $100 billion AI supercomputer called Stargate.
Of course, Nvidia is also in the supercomputer business and aims to make them a big part of its allure to companies looking to use its next-generation AI chips, Blackwell.
AWS made no secret that it remains tied to Nvidia for now. In an interview with The Wall Street Journal, Garman acknowledged that Nvidia is responsible for "99% of the workloads" for training AI models today and doesn't expect that to change anytime soon.
That said, Garman reckoned "Trainium can carve out a good niche" for itself. He'll be wise to recognize that everyone else is busy carving out a niche for themselves, too.
