Alphabet chief executive Sundar Pichai said Google Cloud is giving up some near-term sales so Google can keep enough compute for frontier AI, in a Big Technology Podcast interview with Alex Kantrowitz published on 20 October 2026. “Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development,” Pichai said, according to the transcript Big Technology published.
He also said Gemini 4 Argon reached almost 25 trillion tokens in its first 72 hours, “about three times what Gemini 3.0 had in the first 72 hours,” and that Google is “capacity constrained as we are serving it.”
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The key insight: As we read it, Pichai described compute as the constraint Google allocates first to its own frontier models and then to its businesses, including the Cloud customers who would otherwise buy it.
Asked how Google balances Cloud’s demand to sell compute against researchers’ need for it, Pichai said: “Ground zero for us is making sure we are investing in the compute that’s needed to be at the AI frontier.” He described the order of allocation: “The way I think about compute allocation is making sure Google DeepMind, both for research and frontier model development, is cutting edge in terms of compute access. After that, we plan for all our businesses.”
He said frontier compute is “non-negotiable, and everything else works on top,” and that lead times are long: “The compute we have today is a function of decisions I made in 2024.”
Kantrowitz relayed Ben Horowitz’s account that Andy Jassy had said compute was sold out until 2028. Pichai answered: “If today a customer is coming out and asking us for compute in ’27, that is incredibly difficult to do.” He added that Google had “leased some short-term compute to accommodate some customers” last year.
Gemini 4 Argon tokens in the first 72 hours after launch, as Sundar Pichai told the Big Technology Podcast: almost 25 trillion, about three times Gemini 3.0’s first 72 hours. The Gemini 3.0 circle is our arithmetic from his ‘about three times’; Pichai did not give that figure.
What Was Posted on X
New: I spoke with @sundarpichai about Google’s commitment to the AI frontier, how close we are to RSI, balancing DeepMind’s compute needs vs. Google Cloud profits, and much, much more.
Full Big Technology Podcast episode here –>
Chapters:
0:00 Introduction 1:14 Googleโs Frontier AI Strategy 3:47 Do Frontier Models Really Matter 6:35 The Future of AI Agents 7:39 Recursive Self-Improvement 12:58 World Models and the Path to AGI 14:56 Googleโs Compute Dilemma and AI Infrastructure 19:36 Is AI Overbuilt 23:35 The Future of the AI Interface 31:42 Googleโs Answer to Muse and Dots 35:03 Apple, Space Data Centers, and AI Coding 41:42 Optimism and AIโs Future
Alex Kantrowitz, host of the Big Technology Podcast, posted the episode on X on 20 October 2026. He wrote that he spoke with Sundar Pichai about Google’s commitment to the AI frontier, how close we are to RSI and balancing DeepMind’s compute needs against Google Cloud profits, among other topics. The quotes in this piece come from Big Technology’s published transcript.
Is AI Overbuilt?
Pichai rejected the comparison with the fibre bubble on his own usage: “this is not like fiber. It’s not dark. We are using it. We are capacity constrained on a daily basis.”
He did not rule out an overbuild across the economy. “If your question is, can there be an overbuild? Yes, because you can have some constrained components,” he said, listing land and “2,000 components or more” and adding: “There could be external shocks to the economy. There are macro risks.” Asked about Google’s own plan, he said: “am I confident that the compute we are planning for, we will have uses for it given enough time? Absolutely, yes.”
Muse, Agents, Anthropic and Apple
On Meta’s Muse. Asked where Google’s equivalent of Muse or Dots is, Pichai said: “Yes, we are going to bring personal agents to our consumers. It’ll work in the Gemini app. It’ll work in Google Search. It’ll work in Gmail.” He said it will be “powered by Argon.”
On Anthropic. Pichai said: “We’ve always tried to identify and partner with companies which would be at the cutting edge of technology. We’ve done that with SpaceX. We’ve done that with Anthropic.”
On Apple. On licensing Gemini for Siri: “There’s nothing specific to comment, but it’s a partnership. It’s working well.”
Update, 21 October: The Information Reports Gemini 4 Carbon Is in the Works
The Information reported in a post on X on 20 October that “Google is developing a model codenamed Gemini 4 Carbon that appears to improve on last week’s Gemini 4 Argon release, as competition with Anthropic and OpenAI intensifies.” The full article is behind The Information’s paywall and we could not read it, so this box goes no further than that post.
Other posts on X claim Carbon has already entered early access with select partners. Those are relays that do not cite a named source, and The Information’s post does not mention early access, so we treat that claim as unconfirmed.
Our Muse piece reported 10.2 million US downloads in Muse’s first 40 days, according to TechCrunch, citing Sensor Tower, the product Kantrowitz asked Pichai about. And our piece on Anthropic’s draft prospectus reported that Anthropic is on the hook for $111 billion of Google cloud computing contracts, the Wall Street Journal reported. As we read it, that sets Pichai’s description of Anthropic as a partner beside a large committed buyer of Google Cloud compute.
How Alphabet Makes Money, Visualized
Pichai’s trade-off sits inside Alphabet’s income statement. Our video walks through how Alphabet makes money, from Search and YouTube to Google Cloud.
How Alphabet (Google) Makes Money: 2025 Income Statement, Visualized (The Business Engineer on YouTube)
The Structural Read
As we read it, the interview names an order of priority: Google DeepMind first for research and frontier model development, then the businesses, which plan against demand signals and committed contracts.
As we read it, Pichai’s answer on overbuild has two halves: Google is capacity constrained today, and an economy-wide overbuild remains possible if components bottleneck or external shocks arrive.
As we read it, the token figure is the demand side of the same answer: Pichai said Argon reached almost 25 trillion tokens in 72 hours and that Google is capacity constrained serving it.
Sundar Pichai, Big Technology Podcast, 20 October 2026
“Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development.”
Three Implications
ALLOCATION Pichai said compute for Google DeepMind’s research and frontier model development comes first, and that Google plans for its businesses after that.
LEAD TIMES Pichai said the compute Google has today is a function of decisions he made in 2024.
DEMAND Pichai said a customer asking Google for compute in 2027 today would find that incredibly difficult to accommodate.
The framework puts it this way: “Training builds the brain. Inference is the brain working.”
As we read it, Pichai’s trade-off fits that split on his own description: compute held for frontier model development builds the next brain, while Cloud customers buy compute to run theirs. The framework was published in March 2026 and does not discuss this interview.
What Is Not Established
Pichai gave no figure for how much revenueGoogle Cloud has forgone, and no split of compute between DeepMind and Cloud customers. The token count is his figure and we have not checked it independently. The interview was recorded at Google’s Mountain View headquarters, Big Technology wrote; the quotes here are from its lightly edited transcript.
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The State of the Inference Economy
A Business Engineer map of how AI moved from the training era to the inference era, and who pays for it.
Sundar Pichai told the Big Technology Podcast that Google Cloud is forgoing short-term revenues to support frontier AI development, that Google is capacity constrained serving Gemini 4 Argon, and that AI infrastructure is “not like fiber.” As we read it, the interview puts a name on a trade-off every hyperscaler faces: compute sold to customers today versus compute held back for its own models.
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A note on sourcing. This piece is built on the transcript of the Big Technology Podcast interview with Sundar Pichai as published by Big Technology, Alex Kantrowitz’s post on X, and our own earlier coverage, which cites its sources. Nothing here is legal, financial or investment advice.
Gennaro Cuofano is a CRO and tech executive who has worked in AI since late 2015, starting with bringing NLP, natural-language generation, voice and chatbot products to the marketing industry. His background is in law and finance: he holds a Master's degree in Law and an International MBA with an emphasis on corporate finance (LUISS Business School and the University of San Diego, 2012), and worked as a financial analyst at a real-estate investment firm in San Diego and as an assistant controller. His work focuses on business model strategy, business engineering and, more broadly, structural analysis: how companies make money, read from their own filings. He created FourWeekMBA and leads research there and at The Business Engineer, his newsletter on AI and business strategy, with over 95,000 subscribers and more than 1,000 published analyses. His writing has also appeared on Entrepreneur (for example, How to Digitize a Family Business in Europe) and HackerNoon. Find him on LinkedIn and Substack. See how we source.
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