Google / Gemini

Mozilla unveiled PACT, an anonymous credential scheme that lets sites enforce per-visitor rate limits without device attestation or identity checks. The core bet: sites don't need to know who you are or what hardware you run—they just need proof you haven't exceeded a rate cap. The post calls out Google's abandoned Web Environment Integrity and Apple's deployed Private Access Tokens, arguing the former would kill browser competition and the latter ties web access to expensive hardware. The article cuts off mid-sentence before explaining what scarce resource PACT would anchor to instead of hardware. The direction is sound, but the missing piece is the whole game.

John Jumper, 2024 Nobel laureate in chemistry for AlphaFold, is leaving Google DeepMind after nearly 9 years to join Anthropic. He led the AlphaFold team just six months after his PhD. Bloomberg notes he was also a key member of Google's coding-tools team, which has struggled with enterprise sales. The same week, Character AI co-founder Noam Shazeer left DeepMind for OpenAI.

Noam Shazeer announced on X that he is joining OpenAI, calling it a difficult decision and expressing pride in his work at Google. The post does not disclose his role, start date, or focus area.

Noam Shazeer has left Google and joined OpenAI. Google paid $2.7 billion to bring him back two years ago. The post doesn't disclose his role at OpenAI or when the move happened.

Google's developer blog posted three architectural patterns for mixing A2UI's declarative native rendering with MCP Apps' iframe-based custom UIs. The key idea: use A2UI's JSON payloads for standard components so the host app renders them natively, and reserve iframes only for complex custom logic—avoiding the visual inconsistency and performance hit of iframe-heavy pages. Pattern 1 lets an MCP server return A2UI JSON directly, bypassing iframes entirely. Patterns 2 and 3 are only mentioned by name in the post; the article doesn't spell out their details or code. A recipe demo shows both panels rendered by A2UI with data fetched from an MCP server. The team is considering an MCP extension to simplify adoption and is collecting feedback on GitHub.

Google put Gemini into a $99.99 Home Speaker that lets you correct mid-sentence and keeps a conversation going without re-waking. Premium features like free-flowing chat and Nest camera summaries require a $10/month or $100/year Home Premium subscription. Pre-orders open now, shipping this month.

Google and industry partners launched ARD, an open spec that lets agents find, verify, and connect to tools and other agents across organizational boundaries. Organizations publish a catalog under their own domain; registries crawl and index those catalogs so agents can search by plain-language intent. The spec provides verifiable trust metadata before a direct connection is made. Google Cloud already ships Agent Registry inside Gemini Enterprise Agent Platform, with governance features like namespaced URNs and egress policies. The post does not disclose pricing or a GA date.

Google Home Speaker goes on sale June 24 at $49.99, the first new Google smart speaker since the 2020 Nest Audio. It runs Gemini for Home, so the assistant can hold a conversation and follow context without repeating 'Hey Google' every time. The design is a compact fabric-wrapped puck in black or white, with a physical mic mute switch. The post doesn't detail audio specs or Matter/Thread support — it positions this as a Gemini entry point for the home, not a Nest Audio replacement for music. At $50 the price is aggressive, but real-world latency and accuracy of Gemini on-device are still open questions.

Hugging Face released Discover Tool, a reference implementation of the Agentic Resource Discovery (ARD) spec. ARD is an open draft co-developed by Microsoft, Google, GoDaddy, Hugging Face, and others. It lets agents find MCP tools, A2A agents, or skills at runtime via natural-language search instead of hardcoding each one. Hugging Face's implementation wraps the Hub's existing semantic search and Agent Skills into an ARD catalog, exposed as a REST API and an MCP Tool. The post does not disclose pricing, search latency, or accuracy figures.

Gary Marcus argues OpenAI's moat is gone, citing three data points: market share fell below 50% for the first time as Google eats into it; Microsoft is exploring DeepSeek over OpenAI for Copilot; and audited 2025 financials show $13.07B revenue against $34B in costs—losses up nearly 8x year-over-year. Marcus says pure LLM businesses lack stickiness since regular users see no difference between ChatGPT and Gemini. He also notes Washington may inadvertently help OpenAI by targeting Anthropic with export controls, but stands by his prediction that OpenAI will be acquired, with Elon Musk as a dark-horse bidder.

Google DeepMind published an AI Control Roadmap, a framework for building and managing advanced AI deployed inside Google. It takes a defense-in-depth approach, adding system-level safety layers on top of model alignment so protections hold even when alignment is imperfect.

ChatGPT still leads with 1.1B monthly users, but its share just dipped below 50% for the first time. Gemini has 662M, Claude 245M. The post doesn't disclose exact share figures, methodology, or the measurement window—worth waiting for more detail.

Google filed a lawsuit against a China-based cybercrime ring called Outsider Enterprise, accusing it of using Gemini to build 131 software toolkits that mass-produce fake sites impersonating Google, USPS, and E-ZPass. In just two weeks this May, the group sent 2.5 million phishing texts to Android users, linking to 9,000 fake sites. Google says this is its first coordinated takedown with the FBI and carriers AT&T, T-Mobile, and Verizon. The FBI reported roughly $893 million in AI-linked fraud losses last year; Google estimates hundreds of thousands of victims here and millions of dollars in losses. The post does not name specific defendants or their locations.

Google's Android security lead René Mayrhofer quit over the company's AI contracts with the U.S. Department of Defense. In his farewell letter, he said management quietly dropped carbon-neutral goals and bypassed internal discussion to sign deals allowing AI use for military operations and intelligence. He fears the tech could be used for mass surveillance against his own family. Google had pledged in 2018 not to use AI for weapons, but removed that restriction in February 2025.

A user tested MTP speculative decoding with Gemma 4 Heretic models in llama.cpp and found assistant model selection is everything. A 26B Q8 jumped from 30 t/s to 62 t/s; a 12B Q4 went from 12 t/s to 54 t/s. Two GGUFs with the same name aren't always identical. Unquantized assistants consistently beat Q4/Q8 assistants by roughly 10 t/s. Draft count of 1 gave the best results across the board. Always check logs to confirm MTP actually initialized—otherwise you're benchmarking the base model by accident.

David Curlewis argues that Google, Anthropic, and OpenAI are all touting volume metrics like 'percent of code written by AI,' which is just lines-of-code counting with better PR. He contrasts earlier outcome claims (Copilot made tasks 55% faster) with today's unfalsifiable adoption numbers that rise regardless of real improvement. The post walks through conflicting research: METR first found experienced devs 19% slower with AI, then walked it back and abandoned the study design; an NBER survey of ~6,000 execs found ~90% reporting no measurable productivity impact. Anthropic simultaneously claims '8x more code' and published an RCT showing 17% lower comprehension with no significant productivity gain. Curlewis worries these numbers are driving layoffs—Block cut 40% of staff, Atlassian cut 10%, both explicitly citing AI as the rationale.

Google open-sourced DiffusionGemma, a 26B MoE model that activates only 3.8B parameters at inference. Instead of generating tokens one by one, it drafts 256-token blocks in parallel, hitting 1,000+ tokens/sec on an H100—up to 4× faster than autoregressive models. Output quality is lower than standard Gemma 4, so Google still recommends the autoregressive version for production. It ships under Apache 2.0, fits quantized on consumer GPUs with 18GB VRAM, and targets latency-sensitive nonlinear tasks like inline editing and code completion.

Google open-sourced DiffusionGemma, a 26B MoE diffusion text model that activates only 3.8B parameters at inference and fits in 18GB VRAM after quantization. It denoises 256 tokens in parallel—like a printing press instead of a typewriter—hitting 1,000+ tokens/s on an H100 and 700+ on an RTX 5090, roughly 4× faster than a comparable autoregressive model. Bidirectional attention enables real-time self-correction; after fine-tuning, Sudoku accuracy jumped from 0% to 80%. Quality still trails Gemma 4, and Google positions it as an experimental “racehorse” for speed-sensitive local use. Released under Apache 2.0, weights available on Hugging Face.

A group of independent musicians is suing Google, claiming it trained its Lyria music model on YouTube creators' uploads. Google's response is evasive: it says the terms of service allow it, but refuses to confirm or deny whether it actually did. The post doesn't disclose Lyria's training data sources or scale.

A German court ruled that false answers from Google's AI Overviews count as Google's own speech, making the company legally liable for hallucinations. Gary Marcus calls the decision potentially huge if other countries follow. The post links to a report on the-decoder.com; the case details, plaintiff, and damages are not disclosed in the body.