Inside a sprawling lab at Google headquarters in Mountain View, California, hundreds of server racks hum across several aisles, performing tasks far less ubiquitous than running the world’s dominant search engine or executing workloads for Google Cloud’s millions of customers.
Instead, they’re running tests on Google’s own microchips, called Tensor Processing Units, or TPUs.
Originally trained for internal workloads, Google’s TPUs have been available to cloud customers since 2018. In July, Apple revealed it uses TPUs to train AI models underpinning Apple Intelligence. Google also relies on TPUs to train and run its Gemini chatbot.
“The world sort of has this fundamental belief that all AI, large language models, are being trained on Nvidia, and of course Nvidia has the lion’s share of training volume. But Google took its own path here,” said Futurum Group CEO Daniel Newman. He’s been covering Google’s custom cloud chips since they launched in 2015.
Google was the first cloud provider to make custom AI chips. Three years later, Amazon Web Services announced its first cloud AI chip, Inferentia. Microsoft‘s first custom AI chip, Maia, wasn’t announced until the end of 2023.
But being first in AI chips hasn’t translated to a top spot in the overall rat race of generative AI. Google’s faced criticism for botched product releases, and Gemini came out more than a year after OpenAI’s ChatGPT.
Google Cloud, however, has gained momentum due in part to AI offerings. Google parent company Alphabet reported cloud revenue rose 29% in the most recent quarter, surpassing $10 billion in quarterly revenues for the first time.
“The AI cloud era has completely reordered the way companies are seen, and this silicon differentiation, the TPU itself, may be one of the biggest reasons that Google went from the third cloud to being seen truly on parity, and in some eyes, maybe even ahead of the other two clouds for its AI prowess,” Newman said.
‘A simple but powerful thought experiment’
In July, CNBC got the first on-camera tour of Google’s chip lab and sat down with the head of custom cloud chips, Amin Vahdat. He was already at Google when it first toyed with the idea of making chips in 2014.
Amin Vahdat, VP of Machine Learning, Systems and Cloud AI at Google, holds up TPU Version 4 at Google headquarters in Mountain View, California, on July 23, 2024.
Marc Ganley
“It all started with a simple but powerful thought experiment,” Vahdat said. “A number of leads at the company asked the question: What would happen if Google users wanted to interact with Google via voice for just 30 seconds a day? And how much compute power would we need to support our users?”
“We realized that we could build custom hardware, not general purpose hardware, but custom hardware — Tensor Processing Units in this case — to support that much, much more efficiently. In fact, a factor of 100 more efficiently than it would have been otherwise,” Vahdat said.
Google data centers still rely on general-purpose central processing units, or CPUs, and Nvidia’s graphics processing units, or GPUs. Google’s TPUs are a different type of chip called an application-specific integrated circuit, or ASIC, which are custom-built for specific purposes. The TPU is focused on AI. Google makes another ASIC focused on video called a Video Coding Unit.
The TPU, however, is what set Google apart. It was the first of its kind when it launched in 2015. Google TPUs still dominate among custom cloud AI accelerators, with 58% of the market share, according to The Futurum Group.
Google coined the term based on the algebraic term “tensor,” referring to the large-scale matrix multiplications that happen rapidly for advanced AI applications.
With the second TPU release in 2018, Google expanded the focus from inference to training and made them available for its cloud customers to run workloads, alongside market-leading chips such as Nvidia’s GPUs.
“If you’re using GPUs, they’re more programmable, they’re more flexible. But they’ve been in tight supply,” said Stacy Rasgon, senior analyst covering semiconductors at Bernstein Research.
The AI boom has sent Nvidia’s stock through the roof, catapulting the chipmaker to a $3 trillion market cap in June, surpassing Alphabet and jockeying with Apple and Microsoft for position as the world’s most valuable public company.
“Being candid, these specialty AI accelerators aren’t nearly as flexible or as powerful as Nvidia’s platform, and that is what the market is also waiting to see: Can anyone play in that space?” Newman said.
Now that we know Apple’s using Google’s TPUs to train its AI models, the real test will come as those full AI features roll out on iPhones and Macs next year.
Broadcom and TSMC
It’s no small feat to develop alternatives to Nvidia’s AI engines. Google’s sixth generation TPU, called Trillium, is set to come out later this year.
Google showed CNBC the sixth version of its TPU, Trillium, in Mountain View, California, on July 23, 2024. Trillium is set to come out later in 2024.
Marc Ganley
“It’s expensive. You need a lot of scale,” Rasgon said. “And so it’s not something that everybody can do. But these hyperscalers, they’ve got the scale and the money and the resources to go down that path.”
The process is so complex and costly that even the hyperscalers can’t do it alone. Since the first TPU, Google’s partnered with Broadcom, a chip developer that also helps Meta design its AI chips. Broadcom says it’s spent more than $3 billion to make these partnerships happen.
“AI chips — they’re very complex. There’s lots of things on there. So Google brings the compute,” Rasgon said. “Broadcom does all the peripheral stuff. They do the I/O and the SerDes, all of the different pieces that go around that compute. They also do the packaging.”
Then the final design is sent off for manufacturing at a fabrication plant, or fab — primarily those owned by the world’s largest chipmaker, Taiwan Semiconductor Manufacturing Company, which makes 92% of the world’s most advanced semiconductors.
When asked if Google has any safeguards in place should the worst happen in the geopolitical sphere between China and Taiwan, Vahdat said, “It’s certainly something that we prepare for and we think about as well, but we’re hopeful that actually it’s not something that we’re going to have to trigger.”
Protecting against those risks is the primary reason the White House is handing out $52 billion in CHIPS Act funding to companies building fabs in the U.S. — with the biggest portions going to Intel, TSMC, and Samsung to date.
Processors and power
Google showed CNBC its new Axion CPU,
Marc Ganley
“Now we’re able to bring in that last piece of the puzzle, the CPU,” Vahdat said. “And so a lot of our internal services, whether it’s BigQuery, whether it’s Spanner, YouTube advertising and more are running on Axion.”
Google is late to the CPU game. Amazon launched its Graviton processor in 2018. Alibaba launched its server chip in 2021. Microsoft announced its CPU in November.
When asked why Google didn’t make a CPU sooner, Vahdat said, “Our focus has been on where we can deliver the most value for our customers, and there it has been starting with the TPU, our video coding units, our networking. We really thought that the time was now.”
All these processors from non-chipmakers, including Google’s, are made possible by Arm chip architecture — a more customizable, power-efficient alternative that’s gaining traction over the traditional x86 model from Intel and AMD. Power efficiency is crucial because, by 2027, AI servers are projected to use up as much power every year as a country like Argentina. Google’s latest environmental report showed emissions rose nearly 50% from 2019 to 2023 partly due to data center growth for powering AI.
“Without having the efficiency of these chips, the numbers could have wound up in a very different place,” Vahdat said. “We remain committed to actually driving these numbers in terms of carbon emissions from our infrastructure, 24/7, driving it toward zero.”
It takes a massive amount of water to cool the servers that train and run AI. That’s why Google’s third-generation TPU started using direct-to-chip cooling, which uses far less water. That’s also how Nvidia’s cooling its latest Blackwell GPUs.
Despite challenges, from geopolitics to power and water, Google is committed to its generative AI tools and making its own chips.
“I’ve never seen anything like this and no sign of it slowing down quite yet,” Vahdat said. “And hardware is going to play a really important part there.”
Traders work on the floor at the New York Stock Exchange (NYSE) in New York City, U.S., Nov. 26, 2025.
Brendan McDermid | Reuters
The U.S. stock market was closed Thursday stateside for Thanksgiving Day and will reopen on Friday until 1 p.m. ET.
With approximately just 3 hours of trading left for the month, major U.S. indexes are looking to end November in the red, based on CNBC calculations.
As of Wednesday’s close, the S&P 500 was down 0.4% month to date, the Dow Jones Industrial Average 0.29% lower during the same period and the Nasdaq Composite retreating 2.15%, vastly underperforming its siblings as technology stocks stumbled in November.
Unless there’s a huge jump in stocks during the shortened trading session on Friday stateside — which might not be an unequivocally positive move since it would raise more questions about the market’s sustainability — that means the indexes are on track to snap their winning streaks. The S&P 500 and Dow Jones Industrial Average have risen in the past six months, and the Nasdaq Composite seven.
It will also mark a divergence from the historical norm. The S&P 500 has advanced an average of 1.8% in November since 1950, according to the Stock Trader’s Almanac. And in the year following a U.S. presidential election, it typically rises 1.6%.
But it’s not been a typical post-presidential election year. It’s hard to see the market, in the coming months, or even years, moving according to any historical trajectory.
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And finally…
An operator works at the data centre of French company OVHcloud in Roubaix, northern France on April 3, 2025.
It’s unlikely that Europe will lead in building facilities for AI hyperscalers or for the training of AI — that race is considered all but won — but the general consensus is that it could excel in smaller, cloud-focused and connectivity-style facilities.
Europe has “a lot of constraints, but, actually, the more difficult something is to replicate, the more long-term value what you’ve got has,” said Seb Dooley, senior fund manager at Principal Asset Management.
A key rotation away from artificial intelligence stocks may be underway in the market.
According to Astoria Portfolio Advisors’ John Davi, a broader range of stocks are getting a “green light” because liquidity is returning to the system.
“The Fed cut rates four times last year. They cut rates twice already. They’re going to go again whether its December [or] January,” the firm’s CEO and chief investment officer told CNBC’s “ETF Edge” this week. “Historically whenever the Fed cuts interest rates, usually that’s a turn of a new cycle. Market leadership does tend to change quietly.”
He lists the latest performance in areas ranging from emerging markets to industrials. The iShares MSCI Emerging Markets ETF, which tracks the group, is up 17% over the past six months as of Wednesday’s close. The Industrial Select Sector SPDR Fund is up 9% over the same period.
“I think they can be a good offset to what’s an expensive large cap tech position, which dominates most portfolios,” he added. “We’re living in a structurally higher inflation world. The Fed is cutting rates like, why do you want to take so much risk in just seven stocks?” and
Sophia Massie, CEO of ETF-issuer LionShares, is also wary of going all-in on the AI trade.
“I think analysts have an idea of how much value AI will add to our economy. I don’t think we really understand how that’s going to play out between different companies yet,” Massie said in the same interview. “So, I have this sense that right now, we’re pricing in this probability that… one company may be the one that dominates, dominates AI and ends up being a big player in the future.”
When ChatGPT launched in 2022, Google was caught flatfooted, but the launch of Gemini 3 and the Ironwood AI chip this month has experts raving about Alphabet’s AI comeback.
Google kicked off November by unveiling Ironwood, the seventh generation of its tensor processing units, or TPUs, that the company says lets customers “run and scale the largest, most data-intensive models in existence.” And last week, Google launched Gemini 3, its latest artificial intelligence model, saying it requires “less prompting” and provides smarter answers than its predecessors.
Salesforce CEO Marc Benioff captured the excitement around Gemini 3 with a Sunday post on X, saying that despite using OpenAI’s ChatGPT daily for three years, he wasn’t going back after two hours of using Gemini 3.
“The leap is insane,” wrote Benioff, whose company has partnerships with Google, OpenAI and other frontier AI model providers. “Everything is sharper and faster. It feels like the world just changed, again.”
Most tech stocks were down to start the week, except for one: Alphabet.
Shares of the Google parent surged more than 5% on Monday, adding to last week’s gain of more than 8%. Warren Buffett’s Berkshire Hathaway revealed earlier this month that it owns a $4.3 billion stake in Alphabet as of the end of the third quarter.
Alphabet shares are up nearly 70% this year and have outperformed Meta’s by more than 50 percentage points this year, and last week, Alphabet’s market cap surpassed Microsoft’s.
All of this came despite Nvidia reporting stronger-than-expect revenue and guidance in its third-quarter earnings last week.
“You may be asking why almost all of the AI stocks we cover are selling off after such good news from Nvidia,” Melius Research analyst Ben Reitzes wrote in a note Monday, referring to Nvidia’s positive third quarter earnings last week. “There is one real reason for worry and it is the ‘AI comeback’ of Alphabet.”
But while Google appears to have regained the edge, its lead over rivals remains razor thin in the gruelingly competitive AI market, experts said.
Sundar Pichai, chief executive officer of Alphabet Inc., during the Bloomberg Tech conference in San Francisco, California, US, on Wednesday, June 4, 2025.
David Paul Morris | Bloomberg | Getty Images
Putting the pieces together
With Gemini 3 and Ironwood, Google CEO Sundar Pichai appears to have finally put the pieces together for the company’s AI offerings, said Michael Nathanson, co-founder of equity research firm Moffett Nathanson. Google is serving a broad range of customers from consumers to enterprise, something the company initially struggled to do after the arrival of ChatGPT.
“Three years ago, they were seen as kind of lost and there were all these hot takes saying they lost their way and Sundar is a failure,” Nathanson said. “Now, they have a huge leg up.”
The company had a number of AI product mishaps in its initial attempts to catch up with OpenAI. In 2024 alone, Google had to pull its image generation product Imagen 2 for several months after users discovered a number of historical inaccuracies. The launch of AI Overviews caused a similar reaction when users discovered it gave faulty advice, which the company later remedied with additional guardrails.
“There was a lot of fumbling, and they were scrambling,” said Gil Luria, managing director at technology research firm DA Davidson. “But they had the tech in the pantry, and it was just a matter of getting it all together and shipped.”
Of particular note is how quickly Google launched Gemini 3 after the spring release of Gemini 2.5, which was already considered an impressive model. The hyper-realistic image generation features of Nano Banana is another notch in Google’s belt. After the company initially launched the image generation tool, Gemini shot to the top of the Apple App Store in September, dethroning ChatGPT.
And after the launch of Gemini 3, Google released Nano Banana Pro last week.
Google’s ownership of YouTube and all the content on the video platform gives the company an edge when it comes to training models for image and video generation.
“The amount of video and current data that Google has, that’s really a huge competitive advantage,” said Mike Gualtieri, vice president and principal analyst for Forrester Research. “I don’t see how OpenAI and Anthropic can overcome that.”
Additionally, Google has successfully incorporated its AI models into its enterprise products, driving sales for the company’s cloud unit. In its third quarter earnings results last month, Google reached its first $100 billion quarter, boosted by its cloud growth. The company’s cloud unit, which houses its AI services, showed solid growth and a $155 billion backlog from customers.
And it’s not just the AI models. Google is also garnering attention with its AI chips.
Google says Ironwood is nearly 30 times more power efficient than its first TPU from 2018. Google’s ASIC chips are emerging as the company’s secret weapon in the AI wars and have helped it notch recent deals worth billions with customers such as Anthropic.
After a reportsaid that Meta could strike a deal with Google to use its TPUs for the social media company’s data centers, Nvidia saw its stock drop 3% on Tuesday, prompting the chipmaker to post a response on social media.
With the rise of Google’s TPUs, Nvidia may no longer have the AI chips market cornered.
“The advantage of having the whole stack is you can optimize your model to work specifically well on a TPU chip and you’re building everything to a more optimally designed,” said Luria.
The company’s ability to serve AI enterprise customers with its TPUs and Google Cloud offerings as well as its incorporation of Gemini 3 throughout its consumer products is driving Wall Street’s enthusiasm.
Experts who spoke with CNBC said the competitive landscape is broader than just one AI winner, but they added that it’s become increasingly expensive for multiple companies to prove success.
Tight competition
Despite these wins, Google is still in fierce competition with other AI companies, experts said.
“Having the state of the art model for a few days doesn’t mean they’ve won to the extent that the stock market is implying,” Luria said, pointing to Anthropic’s new Opus 4.5 model launched Monday.
Earlier this month, OpenAI also announced two updates to its GPT-5 model to make it “warmer by default and more conversational” as well as “more efficient and easier to understand in everyday use,” the company said.
“The frontier models still seem to be neck and neck in some ways,” Forrester Research’s Gualtieri said.
The competitive edge will likely go to the companies willing to spend more money given the expenses of the AI race, experts said. In their earnings reports last month, Alphabet, Meta, Microsoft and Amazon each lifted their guidance for capital expenditures. They collectively expect that number to reach more than $380 billion this year.
“These companies are spending a lot of money assuming there’s gonna be a winner take all when in reality we may end up with frontier models being a commodity and several will be interchangeable,” Luria said.
For Google, maintaining a lead in AI won’t be without challenges.
Company executives told employees earlier this month that Google has to double its serving capacity every six month to meet demand for AI services and run its frontier models, CNBC reported last week.
“The competition in AI infrastructure is the most critical and also the most expensive part of the AI race,” Google Cloud Vice President Amin Vahdat told employees.
Although Google’s in-house TPUs have gotten increased attention as viable alternatives to Nvidia’s Blackwell chips, Nvidia still holds more than 90% of the AI chip market.
In its post on Tuesday, Nvidia pointed out that its chips are more flexible and powerful than ASIC chips, like Google’s Ironwood, which are typically designed for a single company or function.
And despite getting Salesforce’s Benioff to switch to Gemini, Google also has a lot of catching up to do with its consumer chat product, experts said, citing hallucinations and lower user numbers than OpenAI’s.
The Gemini app has 650 million monthly active users and AI Overviews has 2 billion monthly users, Google said last month. OpenAI, by comparison, said in August that ChatGPT hit 700 million users per week.
“Yes, Google has got its act together,” Luria said. “But that doesn’t mean they’ve won.”