finance
Nvidia CEO’s Vision Propels AI Startup to Prominence
The chief executive of Nvidia recently highlighted a burgeoning artificial intelligence firm, predicting its potential to achieve a trillion-dollar valuation. This endorsement significantly boosted the company's stock market performance.
6/18/2026, 4:15:34 AM
Jensen Huang, the head of the semiconductor giant Nvidia, has recently drawn attention to Recursion Pharmaceuticals, an artificial intelligence company specializing in drug discovery. Huang’s forecast of Recursion joining the ranks of trillion-dollar enterprises has reportedly fueled a substantial surge in the pharmaceutical company's stock value, according to Yahoo Finance.
Recursion Pharmaceuticals, listed on the Nasdaq, saw its shares climb by more than 70% following Huang's remarks at Nvidia’s latest GTC AI conference in San Jose. The market capitalization of Recursion Pharmaceuticals surpassed $4 billion after this notable increase in share price.
Huang’s comments underscored the accelerating influence of artificial intelligence across various industrial sectors, particularly in the realm of biotechnology and drug development. He emphasized the transformative capacity of AI to revolutionize traditional scientific research and discovery processes.
Nvidia has further demonstrated its confidence in Recursion, having invested $50 million in the company last year. This investment signals Nvidia's strategic interest in the application of AI within the pharmaceutical industry and its belief in Recursion's innovative approach.
The collaboration between the two companies extends beyond financial backing. Recursion Pharmaceuticals is leveraging Nvidia's advanced computing infrastructure, including its DGX Cloud, to bolster its drug discovery initiatives. This technological synergy aims to accelerate the identification and development of new therapeutic compounds.
Recursion operates several laboratories in Salt Lake City, Utah, equipped with robotics and automation to conduct numerous experiments daily. These facilities are designed to generate vast datasets, which are then analyzed by machine learning algorithms to identify potential drug candidates more efficiently than conventional methods.