SAN FRANCISCO (AP) — Nvidia Inc. on Wednesday reported a surge in profit and beat Wall Street expectations, buoyed by the chip-making advantages that have made the company a symbol of the artificial intelligence boom.
Net income increased more than seven times from the same period a year ago, and soared to $14.88 billion in the first quarter ended April 28 from $2.04 billion in the same period a year ago. Revenue more than tripled to $26.04 billion from $7.19 billion a year ago.
The company reported adjusted earnings per share of $6.12, excluding one-time items, well above the $5.60 expected by Wall Street analysts, according to FactSet. The company also announced a 10-for-1 stock split. The company notes that this move will make its stock more accessible to employees and investors.
The company also increased its dividend from 4 cents to 10 cents per share.
Nvidia shares rose more than 4% to $991.85 in after-hours trading. The company's shares have risen more than 200% over the past year.
The Santa Clara, California-based company built an early lead in the hardware and software needed to customize its technology for AI applications, in part because founder and CEO Jensen Huang began steering the company toward technology that was then seen as immature more than a decade ago. The company also makes chips for games and cars.
The company currently ranks third in market capitalization on Wall Street, behind Microsoft and Apple.
“Nvidia has once again defied gravity,” eMarketer analyst Jacob Born said in the quarterly report. Many tech companies are looking to reduce their reliance on Nvidia, which has achieved a level of AI hardware dominance comparable to pioneers like Intel Corp., but “we're not there yet,” he added.
Demand for generative AI systems that can write documents, create images and act as increasingly lifelike personal assistants has driven astronomical sales of Nvidia's dedicated AI chips over the past year. Tech giants Amazon, Google, Meta and Microsoft have all signaled they will need to invest more in the chips and data centers needed to train and operate their AI systems in the coming months.
