Google Research has published TurboQuant, an algorithm that cuts the memory cost of running large AI models by at least sixfold - with no accuracy penalty and no retraining required. Memory chip stocks sold off sharply. The sell-off misread what the research actually says.

OpenAI has shut down Sora, its AI video platform, roughly 15 months after launch - taking down with it a blockbuster licensing deal with Disney and a planned $1 billion investment. Reuters confirmed no money ever changed hands. The manner of the shutdown, as much as the decision itself, reveals how fragile the Big Tech-Hollywood AI partnership model always was.

Tesla, Figure AI, Boston Dynamics, and 1X have each crossed from prototype to production-ready product within months of one another. The competition is no longer about which robot looks most human. It is about which company can scale.

In 2025, just ten companies absorbed 41% of all U.S. venture dollars - a concentration level with no precedent in a decade. The headline figures flatter a market that is quietly contracting at its base, where deal counts have hit a six-year low and seed funding is falling. The question is not whether AI deserves capital. It is whether this degree of gravitational pull leaves room for anything else.

A Harvard Business School working paper analyzing nearly all U.S. job postings from 2019 to 2025 is the most rigorous accounting yet of generative AI's labor market impact. The headline numbers are striking - but three separate research teams find reasons for both alarm and restraint.

A research preview unveiled at NVIDIA GTC shows HD video generated in under 100 milliseconds, a latency drop so sharp it changes what video AI is, not just how fast it runs. The creative and safety implications are profound.