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Google Starts Selling TPUs, Big Tech Aims to Produce "Low-Cost Tokens" with AI Chips

Google has begun selling its proprietary TPU chips and AI computing hardware directly to third-party data centers and clients, marking a strategic shift. Previously only accessible via cloud rentals, TPUs are specialized processors designed for the matrix and tensor operations central to AI models. By combining thousands into supercomputing clusters managed by CPUs, Google achieves high-efficiency AI processing. This move enables Google’s Gemini AI to offer competitive token pricing, challenging rivals like OpenAI. It also signals a broader industry trend where AI compute is becoming a commoditized resource like electricity. While NVIDIA remains dominant with its CUDA ecosystem and high-performance GPUs, the focus is shifting from raw power to cost efficiency and system integration. Google’s approach mirrors NVIDIA’s by selling an entire ecosystem—hardware, software, and data center expertise—rather than just chips. This threatens NVIDIA’s grip on the mid-range inference market, where lower-cost, efficient solutions are increasingly demanded. Similarly, cloud providers like Huawei Cloud and Alibaba Cloud in China are developing their own AI chip ecosystems (e.g., Ascend, Zhenwu), packaging chips, clusters, and tools into full-stack solutions. They aim to reduce token costs and capture market share through integrated systems. In summary, the AI infrastructure race is evolving from a competition for the strongest chips to a contest for the most efficient and cost-effective systems. Google’s TPU sales highlight this transition, emphasizing that future success lies in delivering affordable, scalable AI compute as a foundational service.

marsbit06/24 10:22

Google Starts Selling TPUs, Big Tech Aims to Produce "Low-Cost Tokens" with AI Chips

marsbit06/24 10:22

Google TPU Shipments Revised Up by 50%

Recent industry research indicates a significant upward revision in the shipments of Google's TPU (Tensor Processing Unit) chips. Previous expectations for 2027 were set at around 10 million units, but new estimates now point to 15 million units, a 50% increase. This substantial boost directly translates to higher demand across the entire supporting supply chain. Google's TPU clusters utilize a standardized all-optical interconnect architecture. Consequently, key hardware components are deeply integrated and scaled in fixed ratios with the chips. The 15 million TPU target will drive corresponding demand increases for NPO optical engines (roughly a 1:1 match), 1.6T optical modules, OCS optical switches, high-end server power supplies, fiber optics & MPO connectors, and liquid cooling solutions. Among these, liquid cooling is highlighted as the sector experiencing the most significant transformation and offering the most stable potential for excess returns. As next-generation TPU chips reach power levels where traditional air cooling is insufficient, liquid cooling becomes essential. 2026 is forecasted as the first year of substantial adoption for Google's liquid cooling solutions. This shift, coupled with delivery and capacity bottlenecks faced by incumbent overseas manufacturers, is creating a prime window for domestic Chinese suppliers to enter and secure Google's core supply chain. The market size for Google-specific liquid cooling is projected to potentially triple from a baseline of hundreds of billions to around 300 billion units by 2028. The logic for the fiber optic sector is also being rewritten. Once considered a cyclical commodity tied to telecom operator procurement, fiber is now a strategic and scarce resource for AI Data Centers (AIDC). A severe supply-demand imbalance, driven by the long lead time for preform production (18-24 months) and surging demand from cloud giants, is supporting strong performance. Chinese fiber manufacturers are well-positioned to capture a significant share of global AIDC demand, with exports potentially reaching 200-300 million core kilometers in 2026. Overall, the investment focus within the AI computing industry is shifting from pure "chip performance speculation" towards the more certain incremental growth in computing infrastructure and its supporting ecosystem. The upward revision in Google TPU shipments, along with the potential for further doubling by 2028, is seen as solidifying performance visibility for the entire supporting supply chain over the next two years.

marsbit06/17 00:25

marsbit06/17 00:25

When Google Also 'Prints Stocks' to Build AI, Whose Narrative is Shattering the High Valuations of Neocloud?

Google has announced its first equity financing since 2005, a series of moves totaling $80 billion that signal a strategic challenge to Nvidia's GPU dominance in the AI compute market. This impacts "Neocloud" companies like CoreWeave, Nebius, and IREN, whose valuations are heavily tied to Nvidia's perceived uniqueness. Google's three-part strategy involves: launching new TPU chips (TPU 8t/8i) and selling them to third parties for the first time; forming a $25 billion compute-as-a-service joint venture with Blackstone; and raising ~$50 billion in new equity (part of an $80B package) to fund AI infrastructure, underscoring the massive capital demands even for tech giants. This marks a divergence from Microsoft's path. Microsoft, lacking a mature in-house AI chip, relies heavily on outsourcing to Neocloud providers using Nvidia GPUs. Google, with its proprietary TPU, is pursuing vertical integration—building its own data centers, selling chips, and competing directly with Neocloud services. While Neocloud firms have strong near-term revenue from locked-in Nvidia GPU contracts (e.g., CoreWeave's ~$100B backlog), Google's moves undermine their long-term valuation narrative based on Nvidia's sole supremacy and perpetual supply shortage. TPU performance claims and adoption by firms like Anthropic add credibility to Google's alternative. The AI compute market is transitioning from a uniform seller's market to a layered one: top AI labs are diversifying their hardware stacks; hyperscalers are pursuing different chip strategies; and financing costs will become a critical differentiator, favoring players like Google with lower capital costs. Key metrics to watch include the progress of the Google-Blackstone JV, expansion of the TPU customer base beyond Anthropic, and potential shifts in Microsoft's sourcing strategy. If Google succeeds on these fronts, the Neocloud investment thesis will require significant reassessment.

marsbit06/03 07:04

When Google Also 'Prints Stocks' to Build AI, Whose Narrative is Shattering the High Valuations of Neocloud?

marsbit06/03 07:04

Jensen Huang Publicly Challenges Google and Amazon, Is the Chip Business Entirely Sustained by Anthropic?

In a candid interview, Nvidia CEO Jensen Huang challenged competitors like Google and Amazon, admitted past strategic errors, and criticized U.S. export controls on AI chips. He framed Nvidia’s role as turning “electricity in, tokens out,” emphasizing the complexity and value of AI inference. Huang dismissed rival custom chips like Google’s TPU and Amazon’s Trainium as inflexible and niche, claiming their growth relies heavily on clients like Anthropic. He also acknowledged missing early investment opportunities in OpenAI and Anthropic. On China, Huang warned that export restrictions risk pushing the country toward self-sufficiency and could cost U.S. leadership in AI. Finally, he explained Nvidia’s acquisition of Groq as a move to serve premium, low-latency token markets. Throughout, Huang emphasized ecosystem trust and Nvidia’s central role in global AI infrastructure.

marsbit04/21 07:00

Jensen Huang Publicly Challenges Google and Amazon, Is the Chip Business Entirely Sustained by Anthropic?

marsbit04/21 07:00

Pichai's 10-Year Tenure as Google CEO: Lows, Reversals, and Regrets

In a wide-ranging interview marking his 10-year anniversary as Google CEO, Sundar Pichai reflects on the company's journey in AI, from being an early innovator with the Transformer architecture to its current leadership position. Pichai addresses the "missed opportunity" narrative, explaining that internal versions of models like LaMDA (a precursor to ChatGPT) existed but were not released due to higher safety thresholds and early "toxicity" issues. He emphasizes that its research was always product-driven, and attributes OpenAI's success to a fortunate combination of factors, including identifying the coding use case early. Looking forward, Pichai asserts that search will not die but will evolve into an "agent manager," where users command AI to complete tasks. He reveals Google's massive capital expenditure, projected to reach $175-185 billion in 2026, is a testament to its belief in the AGI curve. However, he warns of a major supply crunch in 2026, citing critical bottlenecks in wafer capacity, memory, and even a shortage of electricians as fundamental constraints. Pichai also discusses Google's "hidden gems," including early-stage projects like space-based data centers, quantum computing (which he believes will excel at simulating nature), and robotics. He shares a regret: not investing more aggressively in Waymo earlier. Internally, Pichai reveals he personally spends at least an hour each week allocating scarce computing resources (TPU time), which has become the company's most critical allocation decision. He predicts that by 2027, business forecasting at Google will be fully automated by AI agents, marking a major shift in how work is done.

marsbit04/10 00:36

Pichai's 10-Year Tenure as Google CEO: Lows, Reversals, and Regrets

marsbit04/10 00:36

Anthropic's Revenue Surpasses $30 Billion, Signs 3.5 Gigawatt Computing Power Agreement with Google and Broadcom

Anthropic announced its annualized revenue has surpassed $30 billion, a more than threefold increase from $9 billion at the end of 2025. The number of enterprise customers spending over $1 million annually doubled to 1,000 in less than two months. Growth is largely driven by Claude Code, launched in May 2025, which reached over $2.5 billion in annualized revenue by February 2026. In a related move, Broadcom disclosed in an SEC filing that Anthropic will secure approximately 3.5 gigawatts of next-generation TPU compute power starting in 2027, marking its largest single compute commitment to date. This is part of an expanded partnership involving Google and Broadcom, which will design and supply the new TPUs. Unlike OpenAI’s capital-intensive Stargate project, which involves building owned-and-operated data centers, Anthropic adopts a capital-light strategy—relying on long-term compute agreements with cloud providers (AWS, Google Cloud, and Microsoft Azure) without owning physical infrastructure. This multi-cloud, multi-chip approach offers flexibility but may entail higher long-term costs. Anthropic expects a $14 billion loss in 2026 despite ~40% gross margins.

marsbit04/07 07:29

Anthropic's Revenue Surpasses $30 Billion, Signs 3.5 Gigawatt Computing Power Agreement with Google and Broadcom