Google’s Gemini AI assistant is hitting the road in millions of vehicles

Financial Times testing found safety controls on open AI models from Meta and Google could be stripped in minutes, raising governance concerns.

Today, I’m talking with Google and Alphabet CEO Sundar Pichai, in a conversation we recorded just after the Google I/O developer conference. This is the fifth year Sundar and I have sat down after I/O, and it’s become one of my favorite Decoder traditions. There’s always a lot of news at I/O, and this year was no exception — Google has powerful new Gemini models, it’s putting AI agents in everything, and it’s making huge changes to Search on both the web and YouTube that will once again reshape the information ecosystem. That’s a lot to talk about, and Sundar and I got into all of it. But I also realized it’s been a long time since I’d asked Sundar the Decoder questions about structure and decision making, so I started there. You’ll hear Sundar say he realized he needed to rethink how Google worked a few years ago in response to ChatGPT, and he made a lot of executive changes and big decisions to get the company in a more aggressive posture. Verge subscribers, don’t forget you get e

The adoption of SynthID by major AI players suggests a shift towards unified standards for AI content verification, impacting industry norms. The post Google expands SynthID watermarking to OpenAI, Kakao, and ElevenLabs for AI content transparency appeared first on Crypto Briefing.

Midnight launched its federated mainnet in March 2026 with Google and Vodafone as partners. Inside Cardano's privacy sidechain and why almost no one noticed.

Encouraging LLMs to express uncertainty could enhance trust and reliability in AI applications, impacting fields like finance and beyond. The post Google paper advocates for LLMs to express uncertainty clearly appeared first on Crypto Briefing.

Google has introduced Agent Executor, an open source runtime aimed at helping enterprises run AI agents more reliably at scale, as attention shifts from building agent prototypes to managing the operational challenges of putting them into production. To address those production-related challenges, the runtime, according to the company, comes with capabilities that are geared towards supporting long-running and distributed agent workflows. Typically, long-running agent workflows are AI-driven tasks that execute over extended periods, from minutes to days, often involving multiple steps, system interactions, pauses for human input, or recovery from interruptions before reaching completion. For such workloads, the runtime includes support for durable execution, allowing workflows to resume after outages or human approvals, along with secure sandboxing for isolating agent components, session consistency controls for distributed workflows, and connection recovery features intended to preser

Google has introduced Agent Executor, an open source runtime aimed at helping enterprises run AI agents more reliably at scale, as attention shifts from building agent prototypes to managing the operational challenges of putting them into production. To address those production-related challenges, the runtime, according to the company, comes with capabilities that are geared towards supporting long-running and distributed agent workflows. Typically, long-running agent workflows are AI-driven tasks that execute over extended periods, from minutes to days, often involving multiple steps, system interactions, pauses for human input, or recovery from interruptions before reaching completion. For such workloads, the runtime includes support for durable execution, allowing workflows to resume after outages or human approvals, along with secure sandboxing for isolating agent components, session consistency controls for distributed workflows, and connection recovery features intended to preser

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