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Hassabis steps back from DeepMind leadership

Image via TechCrunch

Saturday, August 8, 2026

Hassabis steps back from DeepMind leadership

Demis Hassabis is stepping back from running Google DeepMind day-to-day to focus on AGI strategy, with Koray Kavukcuoglu taking over leadership. OpenAI is building a premium screen-free smart speaker powered by ChatGPT Voice, entering the consumer hardware market for the first time.

Top Stories

1
Hassabis hands off DeepMind in a sweeping Google AI reshuffle

Google Blog

Demis Hassabis is stepping back from operational leadership at Google DeepMind to focus on AGI strategy as Chief Scientist of Alphabet, with Koray Kavukcuoglu taking over as SVP to lead Gemini development and frontier AI research. The reshuffle also includes Jeff Dean's departure after 27 years to launch an independent ML-focused public benefit corporation.

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2
OpenAI's foray into consumer hardware has been leaked and is materializing through a screen-free, battery-powered smart speaker

Bloomberg

OpenAI is launching a consumer smart speaker with camera and mechanical components, priced at $300+, to deliver contextual ChatGPT Voice experiences and expand its hardware ecosystem beyond software.

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3
AI Hype and Tech Layoffs

New York Times

Tech executives are using AI as cover for layoffs driven by older cost-cutting and strategy decisions, conflating genuine AI disruption with pre-planned workforce reductions to reshape their narratives around industry transformation.

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4
Meta's ad system approved AI-generated child sexual abuse imagery

Wired

Meta's automated ad review system approved and ran dozens of paid ads containing AI-generated child sexual abuse material over nine months, with some reaching thousands of accounts across multiple countries. The failure occurred despite Meta's stated policies and new AI detection tools, highlighting significant gaps in platform safety mechanisms for paid advertising.

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5
Meta Releases Muse Code

Meta AI Research

Meta launched Muse Code, a terminal coding agent powered by the new Muse Spark 1.2 model, featuring persistent subagents and crash-resistant design for tackling complex, long-running software engineering tasks across large codebases. The model was co-trained with its agent environment and uses self-improvement loops to handle extended workflows like iterative GPU kernel optimization.

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