All gas, no brakes

Google rolls out its full AI arsenal

Google just reminded everyone it’s still very much in the AI race—in fact, it’s betting the company on AI. Now that the dust has settled on I/O this week, here are four major highlights from the mega-event ▶️ 

• Search that does the Googling for you: AI Mode is rolling out across the US, turning queries into research assistants. It breaks questions into subtopics, pulls in web results, and builds curated responses, complete with follow-ups and personalized context.

• Smarter reasoning: Gemini 2.5 introduces “Deep Think” for complex problem-solving.

• Cinematic generation: Veo 3 and Imagen 4 enable rich video and image generation—with audio, sound effects, and dialogue baked in.

• NotebookLM goes mobile: One of Google’s most popular AI tools is now mobile first, offering offline audio summaries, real-time interaction with AI podcast hosts, and a new video overview feature coming soon.

Can OpenAI make the next iPhone?

OpenAI has acquired io, the AI hardware startup started by famed former Apple designer Jony Ive, in a $6.5 billion deal.

So what are these two big names building together? Details are sparse, but here’s what we know so far:

• The product will be pocket sized, contextually aware, screen free…and it’s not eyewear, OpenAI head Sam Altman told his team.

• Altman said he aims to release the first device by late 2026.

Altman suggested that this acquisition could increase OpenAI’s value by $1 trillion (with a T), kicking off a family of high-tech devices—just as the iPod did in the 2000s. In his memo to the OpenAI team, Altman said this deal gives the company “the chance to do the biggest thing we’ve ever done.”

Mistral’s coding model makes a strong entrance

This week, Mistral launched Devstral, a new open-weight model trained for software engineering tasks. Unlike some recent LLMs optimized for general use, Devstral is built to tackle real coding problems: issue resolution, pull request handling, and bug-fixing across diverse codebases.

What’s under the hood: Devstral was fine-tuned on datasets similar to those behind SWE-Bench, the benchmark for evaluating coding agents on GitHub issues. And it’s performing well—beating other open models like Google’s Gemma 3 27B and DeepSeek’s V3 on SWE-Bench Verified.

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