The battle for top tech talent in Silicon Valley has escalated again. On February 26, 2026, according to 36 Kr, The Information reported that OpenAI has successfully lured renowned AI researcher Ruoming Pang from Meta.

This AI expert, who once held key positions at Apple and Meta, finally decided to join Sam Altman's team after being intensely courted by OpenAI for several months.

Key Highlights: A Top-Level Career Spanning Apple, Meta, and OpenAI

Ruoming Pang's career has almost connected the most prestigious AI laboratories in Silicon Valley:

  • Former AI Leader at Apple: Before joining Meta, Ruoming Pang led Apple's AI model team, playing a key role in Apple's strategy for generative AI.

  • Meta's High-Value Recruitment: About seven months ago, Ruoming Pang had just moved from Apple to Meta. It was reported that Meta offered him a compensation package worth over $200 million (paid annually).

  • OpenAI's Months-Long Victory: Despite his high value, OpenAI eventually convinced him to officially leave Meta last week through persistent efforts.

Industry Impact: Intelligence Bidding Beyond Computing Power

In the sprint toward AGI (Artificial General Intelligence), the movement of top researchers often signals changes in technological trends:

  • Model Efficiency and Architecture: Ruoming Pang's deep background in model optimization and efficient inference is a key support for OpenAI to handle massive inference demands.

  • Talent Magnet Effect: Although OpenAI recently faced some core members leaving, it still maintains an industry-leading talent density through continuous recruitment of experienced experts from Meta, Google, and other big companies.

Next Article: Storage Enters the "Inference Era"

At the same time as Ruoming Pang switched jobs, the underlying hardware of AI also experienced a major transformation. SK Hynix and SanDisk jointly launched the global standardization process for the next-generation storage "HBF (High Bandwidth Flash)" on February 25. This storage solution specifically designed for the AI inference era aims to break the data throughput bottleneck in large model operations.