Thrilled to share a big milestone from our AI & Infrastructure team at @Google. Today we’re expanding our 8th generation TPU portfolio with: • TPU 8i Max: Built for massive context windows and the highest compute density per rack • TPU 8tx: Purpose-built for secure, confidential inference This marks the first time we’ve introduced four distinct TPU systems in a single generation, in one year. For developers, hardware is only as good as the software stack. And we want you to be able to build with TPUs as easily and efficiently as we do at Google. You can now: ↳ Run native PyTorch & Hugging Face models via TorchTPU (zero code rewrites) ↳ Cut KV transfer latency >90% with open-source TPU Sync ↳ Eliminate idle compute waste with Agent Compute Engine Read more about our newest TPUs on our blog: I look forward to connecting with builders, researchers, engineers and organizations pushing the boundaries of AI and computing!

Today we’re releasing GDPx, a benchmark for measuring agentic capabilities across the most valuable domains. GDPx is a true frontier-level benchmark: even the best model scores just 12% (1/7)🧵

🇺🇸 For a deployed service member, missing family moments adds up. Baby’s first step. The first performance. Sharing a laugh. In honor of National Day of the Deployed, we’re excited to partner with @USArmy and @The_USO to bring Google Beam to 20 locations so deployed service members can experience lifelike, 3D video connections with their loved ones. 🎥 Watch the docuseries below

Sundar Pichai on Gemini 4 and the AI compute crunch Even Google can’t get enough compute. I sit down with Google and Alphabet CEO @sundarpichai shortly after the wider release of Gemini 4 Argon to discuss its early reception, where he still wants the model to improve, and what it takes to keep Google at the frontier of AI. Pichai explains the recent leadership shakeup at Google DeepMind and why building frontier models has become an “all-consuming” job. We also get into Google’s answer to the personal agent race, how he predicts Gemini will do more work on your behalf, and whether agents will finally bring Search, Gmail, and Gemini together. I ask him about Google turning free cash flow negative, the risks in the AI infrastructure buildout, and how he plans for demand years before knowing what the models will be capable of. He explains the signals giving him confidence to keep spending and how he uses AI agents to follow model development and compute usage inside Google. Timestamps: 00:00 Gemini Argon launch reception 01:49 Model access and safety 06:20 The AI model race 08:35 Agentic breakthroughs and 2027 11:34 Recursive self-improvement 15:41 DeepMind leadership changes 19:52 Google's AI compute investments 25:20 Is AI spending sustainable? 33:18 How Sundar uses AI agents 35:35 Gemini's personal agent plans 38:37 How AI agents change the web 43:52 Google's space data centers 47:38 Waymo's expansion strategy 50:26 Wing's drone delivery plans 51:36 AI safety and regulation 55:58 Google's quantum computing bet YouTube: Spotify: Substack: Apple Podcasts: This episode's presenting sponsor is @hyperagentapp. Thanks to the show’s premiere sponsors: @Atlassian, @meetgranola, and @mercury.

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 Google’s 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 Google’s Compute Dilemma and AI Infrastructure 19:36 Is AI Overbuilt? 23:35 The Future of the AI Interface 31:42 Google’s Answer to Muse and Dots 35:03 Apple, Space Data Centers, and AI Coding 41:42 Optimism and AI’s Future

Gemini 4 Argon built a 3D periodic table synced to Tom Lehrer’s song, “The Elements.” Created in @Antigravity, it animates orbiting electrons, atomic data, and light spectra.