June 27, 2025
Meta poaches four OpenAI researchers for superintelligence unit
Good morning, AI enthusiasts. Meta’s recruiting blitz just scored a major haul, with four OpenAI researchers jumping ship to Zuck’s new superintelligence team. OpenAI CEO Sam Altman has shown confidence in retaining staff despite $100M offers, but Meta’s deep pockets are clearly talking — and its new unit is starting to take shape in a big way.
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In today’s AI rundown:
Meta poaches four OpenAI researchers
Google’s Gemma 3n brings powerful AI to devices
Convert lecture videos into detailed study materials
Anthropic studies Claude’s emotional support
4 new AI tools & 4 job opportunities
LATEST DEVELOPMENTS
Image source: Midjourney / The Rundown
The Rundown:
Meta has reportedly successfully recruited four OpenAI researchers for its new superintelligence unit, including three from OAI’s Zurich office and one key contributor to the AI leader’s o1 reasoning model.
The details:
Zuckerberg personally recruited Lucas Beyer, Alexander Kolesnikov, and Xiaohua Zhai, the trio that established OpenAI’s Zurich operations last year. Meta also landed Trapit Bansal, a foundational contributor to OpenAI's o1 reasoning model who worked alongside co-founder Ilya Sutskever.
Sam Altman said last week that Meta had offered $100M bonuses in poaching attempts, but “none of OpenAI’s best people” had taken the offer. Beyer confirmed on X that the Zurich trio was joining Meta, but denied the reports of $100M signing bonuses, calling them “fake news”.
Meta’s hiring spree comes after its $15B investment in Scale AI and poaching of its CEO Alexandr Wang to lead the new division.
Why it matters:
Meta’s new superintelligence team is taking shape — and despite Altman’s commentary last week, at least four of his researchers are willing to make the move. With an influx of new talent from top labs and a clear willingness to spend at all costs, Meta’s first release from the new unit will be a fascinating one to watch.
The Rundown:
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With Airia, you’ll experience:
Built-in AI security safeguards for runtime prompts, data leakage, and prompt injections
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Image source: Google DeepMind
The Rundown:
Google launched the full version of Gemma 3n, its new family of open AI models (2B and 4B options) designed to bring powerful multimodal capabilities to mobile and consumer edge devices.
The details:
The new models natively understand images, audio, video, and text, while being efficient enough to run on hardware with as little as 2GB of RAM.
Built-in vision capabilities analyze video at 60 fps on Pixel phones, enabling real-time object recognition and scene understanding.
Gemma’s audio features translate across 35 languages and convert speech to text for accessibility applications and voice assistants.
Gemma’s larger E4B version becomes the first model under 10B parameters to surpass a 1300 score on the competitive LMArena benchmark.
Why it matters:
The full Gemma release is another extremely impressive launch from Google, with models continuing to get more powerful despite shrinking in size for consumer hardware. The small, open model opens up limitless intelligent on-device use cases.
The Rundown:
In this tutorial, you will learn how to use the new video input feature of Google's Gemini to transform lecture videos into detailed notes and interactive quiz sessions to improve your study experience.
Step-by-step:
Go to Google's Gemini app and upload your lecture video.
Use this prompt: “Analyze this lecture video and provide: detailed outline, comprehensive notes, formulas/examples, and timestamps for each topic”
Follow up by requesting it to create a comprehensive quiz, plus answer keys with explanations
Ask it to code an interactive quiz based on this lecture content, and to include a hint button for when help is needed
Pro tip:
Save all materials in one document and repeat this process for multiple lectures to build your complete course study library.
The Rundown:
Lockheed Martin’s AI takeoff began with data cleanup — cutting its tools by 50% and replacing them with a single unified system that runs on IBM’s watsonx.data to drive faster, smarter engineering.
Learn how Lockheed Martin leveraged watsonx to:
Help 10,00 engineers build scalable AI products in its AI Factory
Respond to employee questions using AI, boosting accuracy by 20%
Overcome data silos to create a single accessible, connected data environment
The Rundown:
