This Week in AI: Meta’s Superintelligence Push, xAI’s Ten Billion-Dollar Raise, Genesis AI’s Robotics Ambitions, Microsoft Restructuring, Amazon’s Million-Robot Milestone, and Google’s AlphaGenome Update
Major developments across the AI sector include new labs, massive funding rounds, layoffs to retool talent, and advances in robotics and generative biology
Meta has launched a new division called Superintelligence Labs, led by Alexandr Wang and Nat Friedman, bringing onboard top researchers including Shengjia Zhao, co-creator of ChatGPT.
The lab is part of a restructuring that splits Meta’s AI work into four teams (TBD Lab, infrastructure, products, and the Fundamental AI Research lab) and is intended to accelerate progress toward artificial general intelligence.
Some key original team members are already departing.
Meta has also made a strategic investment in Scale AI, acquiring a forty-nine percent stake for about fourteen point three billion dollars, while keeping Scale partly independent.
Elon Musk’s xAI has closed a landmark ten-billion-dollar funding round, a mix of debt and equity, to expand its AI infrastructure, improve its Grok platform, and build new data centres and custom AI chips.
This positions xAI as a serious player in the race against OpenAI and others.
Genesis AI has emerged from stealth with one hundred and five million dollars in seed funding, co-led by Eclipse Ventures and Khosla Ventures, aiming to build a universal robotics foundation model.
Its approach leverages ultra-fast physics simulations—claimed to be hundreds of thousands of times faster than real-time—to train robots capable of operating in diverse and unpredictable environments.
Microsoft is undergoing significant workforce reductions, having laid off about nine thousand employees globally in one round and over fifteen thousand over the course of 2025.
The company is pushing to build out its own AI models, diversify beyond existing partnerships, and invest heavily in infrastructure, including new chip-cluster capacity.
Remaining staff are being urged to integrate AI tools into their work and adapt to new performance expectations.
Amazon has reached its one millionth robot deployment milestone in its global warehouse network spanning over three hundred fulfilment centres.
The company also unveiled a new generative AI foundation model, DeepFleet, designed to improve the efficiency of its robotic fleet by about ten percent—optimising robot travel, reducing fleet congestion, and speeding up deliveries.
Google has made updates in its AI biology and genomics efforts, notably with an announcement around AlphaGenome—its generative biology initiative.
While full technical details remain emerging, the project is seen as part of Google’s strategy to apply AI beyond text, images, and code, into scientific discovery and biotechnology.
These major stories underline a broader shift in the AI industry: companies are racing to acquire talent, invest in infrastructure, and shift organisational structure.
The balance between layoffs, ambitious research labs, and practical robotics deployment reveals both opportunity and tension in global AI development.
The lab is part of a restructuring that splits Meta’s AI work into four teams (TBD Lab, infrastructure, products, and the Fundamental AI Research lab) and is intended to accelerate progress toward artificial general intelligence.
Some key original team members are already departing.
Meta has also made a strategic investment in Scale AI, acquiring a forty-nine percent stake for about fourteen point three billion dollars, while keeping Scale partly independent.
Elon Musk’s xAI has closed a landmark ten-billion-dollar funding round, a mix of debt and equity, to expand its AI infrastructure, improve its Grok platform, and build new data centres and custom AI chips.
This positions xAI as a serious player in the race against OpenAI and others.
Genesis AI has emerged from stealth with one hundred and five million dollars in seed funding, co-led by Eclipse Ventures and Khosla Ventures, aiming to build a universal robotics foundation model.
Its approach leverages ultra-fast physics simulations—claimed to be hundreds of thousands of times faster than real-time—to train robots capable of operating in diverse and unpredictable environments.
Microsoft is undergoing significant workforce reductions, having laid off about nine thousand employees globally in one round and over fifteen thousand over the course of 2025.
The company is pushing to build out its own AI models, diversify beyond existing partnerships, and invest heavily in infrastructure, including new chip-cluster capacity.
Remaining staff are being urged to integrate AI tools into their work and adapt to new performance expectations.
Amazon has reached its one millionth robot deployment milestone in its global warehouse network spanning over three hundred fulfilment centres.
The company also unveiled a new generative AI foundation model, DeepFleet, designed to improve the efficiency of its robotic fleet by about ten percent—optimising robot travel, reducing fleet congestion, and speeding up deliveries.
Google has made updates in its AI biology and genomics efforts, notably with an announcement around AlphaGenome—its generative biology initiative.
While full technical details remain emerging, the project is seen as part of Google’s strategy to apply AI beyond text, images, and code, into scientific discovery and biotechnology.
These major stories underline a broader shift in the AI industry: companies are racing to acquire talent, invest in infrastructure, and shift organisational structure.
The balance between layoffs, ambitious research labs, and practical robotics deployment reveals both opportunity and tension in global AI development.
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