Google / Gemini

Google moved Gemini 3.6 Flash and 3.5 Flash-Lite to GA. 3.6 Flash costs $1.50/$7.50 per 1M input/output tokens — cheaper than 3.5 Flash — and uses fewer tokens and turns on complex agentic and multimodal tasks, with better code generation and instruction following. 3.5 Flash-Lite is the fastest, cheapest 3.5 model at $0.30/$2.50 per 1M tokens, built for high-throughput work. Both keep the 1M-token context window, 64k max output, and Computer Use support. The post includes migration steps and code samples but no benchmark scores.

Alphabet's Q2 free cash flow dropped to $6.9bn, down $6bn year-on-year, driven by heavy AI infrastructure spending. Capex hit $19bn, up 45% YoY, mostly on servers and data centers. CEO Sundar Pichai said AI products are generating revenue but gave no figures. Cloud grew 28%, yet profits are getting eaten by investment—near-term returns remain unclear.

Alphabet's latest earnings gave nervous investors some relief. Google Cloud revenue hit $24.8B, up 82% year-over-year and well above the $22.46B Wall Street expected. The jump was driven by enterprise adoption of AI solutions and AI infrastructure. Last quarter grew 63% to $20B, so the acceleration is real. One caveat: the post doesn't break out how much came from AI training vs. inference vs. traditional cloud services, and margin details are missing.

Alphabet burned through $6bn in free cash flow last quarter as capex hit $28bn, driven by data centers and custom TPU chips. CEO Pichai said cloud growth is literally constrained by available compute capacity, so they have no choice but to keep building. Revenue still rose 14% to $97bn, with search and ads holding up, but the cash burn sent shares down 4% after hours. The article doesn't provide an updated full-year capex target, only that spending won't slow in H2.

Google raised its full-year capex guidance from $180B–$200B to $195B–$205B, driven by servers, data centers, and networking. CEO Sundar Pichai cited strong AI demand and said the company is accelerating infrastructure buildout for cloud and search. The figure is a budget ceiling, not a committed spend, but the direction is clear: Google is doubling down on AI infrastructure.

Sundar Pichai posted Alphabet Q2 numbers. Revenue up 24% YoY, Google Cloud surged 82%. Gemini app hit 950M MAU, model API throughput reached 22B tokens/min driven by Flash. Gemini Enterprise is in 90% of Fortune 100. The post doesn't disclose margins or capex—I'd discount profitability until those land.

Google released Tunix, a JAX post-training library that tackles TPU idle time during agentic RL training. The core fix is an async rollout engine that decouples trajectory generation from training: when one agent waits on a tool call, inference immediately switches to another active trajectory. Completed trajectories stream into a dynamic producer-consumer pipeline and get grouped on the fly for algorithms like GRPO, so the trainer never starves. Tunix also ships lightweight RL-specific instrumentation that correlates high-level loop metrics with TPU timelines. It integrates with vLLM-TPU and SGLang-Jax. The post doesn't disclose open-source repo links, benchmark numbers, or concrete throughput gains—worth waiting for real-world results before getting excited.

Google DeepMind released three new models: Gemini 3.6 Flash, 3.5 Flash-Lite, and the security-focused 3.5 Flash Cyber.

Google DeepMind released Gemini 3.5 Flash Cyber, fine-tuned from 3.5 Flash to find, verify and patch vulnerabilities quickly. With multiple calls, it approaches larger models on benchmarks such as CyberGym.

A VentureBeat survey of 107 enterprises finds a wide agent security gap: 54% have had a confirmed incident or near-miss, yet only 32% give each agent its own scoped identity. Most agents share API keys or human credentials. The security stack is dominated by model-provider guardrails from OpenAI, Google, and Anthropic; dedicated agent-security vendors barely register. Satisfaction with this borrowed stack averages 4.2/5, but two-thirds plan to switch tooling within a year. Only 30% isolate high-risk agents, and isolation drops as company size grows—larger firms hit a 63% incident rate with just 20% isolation. Spending is a thin slice of the security budget, and only a third believe their defenses are ahead of AI-enabled attackers.

Google added a 'Connected Apps' feature to AI Mode, starting with Instacart, Canva, and YouTube. Users can link their accounts and let AI Mode take action across apps—like adding a grocery list straight to an Instacart cart. The move puts AI Mode in direct competition with ChatGPT and Claude, both of which already support app integrations. The post doesn't say whether this is a gradual rollout or available to all, and doesn't name future app partners.

The EU is ordering Google to give rival search engines and AI assistants comparable access to Android and some Search data under the DMA. This means third-party AI assistants could get system-level access on Android similar to Gemini. The post doesn't spell out which Search data must be shared or through what API. Google will likely appeal, so timing and enforcement remain uncertain.

The author got Google's Gemma 4 26B MoE model running on a dual Xeon E5-2690 v2 server from 2013 with no GPU, costing under $300. The CPUs only support AVX1, but ik_llama.cpp's optimized kernels require AVX2, causing silent gibberish output. Claude diagnosed that the graph builder unconditionally emitted MOE_FUSED_UP_GATE ops while the dispatcher had no matching case, leaving ~240 tensors per forward pass reading uninitialized memory. After the fix, decode reaches ~5.2 tokens/sec and prompt eval ~16 tokens/sec. A PR is open but not yet merged. The post doesn't disclose quantized model memory usage or power draw.

Hachette, Cengage, Elsevier, and author Scott Turow filed a class action against Google, alleging it trained Gemini on copyrighted works without permission. The suit also claims Google removed or altered copyright info to conceal the source of training data. This is the latest in a wave of publisher lawsuits against AI firms; two early California rulings have favored AI companies under fair use.

Demis Hassabis posted on X calling for an independent standards body to test frontier models and set release best practices, modeled on FINRA. Labs would voluntarily submit models 30 days before release; once the protocol proves effective, it would become mandatory for US deployment. The post doesn't spell out a timeline, funding source, or enforcement authority. This reads more like a position paper than a concrete plan for now.

Google demoed Pixel 10's on-device AI at I/O India, running Gemma 4 E2B natively on the Tensor G5 TPU with no data leaving the device. Showcases included offline travel planning, image recognition, audio transcription, and a Functional Gemma model that controls phone functions like Wi-Fi via voice or text. The Tensor SDK is now open for beta sign-up, offering 100+ classical ML models and precompiled small models. The post does not disclose parameter counts, latency, or power figures.

Gemini 3.5 Live Translate processes raw audio streams directly and preserves the speaker's tone, rhythm, and pitch. Southeast Asian super-app Grab is exploring it for cross-language driver-passenger calls—Grab users make over 10 million voice calls per month. Developers can integrate via the Gemini Live API with LiveKit, Fishjam, Pipecat, or Vision Agents. LiveKit already demonstrated real-time multilingual understanding in virtual meeting rooms; Software Mansion used the MoQ protocol to break through streaming bottlenecks; VisionAgents AI showed dynamic language switching. Developers can try it now in Google AI Studio and grab Cookbook sample code.

Demis Hassabis wants the US to lead an international body, akin to CERN, for testing frontier AI models. The article is paywalled, so details on structure, funding, or timeline are not disclosed. The title confirms he's calling for US leadership and a focus on safety testing of frontier models.

Microsoft, Amazon, and Google datacenters emitted 104 million tonnes of CO₂ in 2025—equal to 33% of France's national total. Microsoft accounted for nearly half, driven by AI infrastructure expansion. All three have walked back clean-energy pledges: Amazon and Google dropped 24/7 carbon-free targets, and Microsoft's carbon-offset contracts were found to overstate impact. These figures are self-reported, so real emissions are likely higher.

An FT investigation found OpenAI and Google sold model access via Microsoft Azure and Google Cloud to at least eight Chinese companies on the US Entity List, including Huawei, SenseTime, Yitu, and iFlytek. Sales went through overseas subsidiaries or third-party resellers. Both companies say they didn't violate export controls, but internal documents obtained by FT show some sales teams knew the customers' backgrounds and kept the deals going. The core tension: whether cloud-based model access counts as an 'export' is still a legal gray area.