How Nvidia used the growing need for powerful GPUs in AI, self-driving cars, and data centers to rapidly expand, after missing out on smartphone chips
Ian King / Bloomberg : Tweets: @ericnewcomer , @ericnewcomer , @labboudles , and @mcbridesg Tweets: Eric Newcomer / @ericnewcomer : Nvidia net income 2012: $740.4M 2013: $611.3M 2014: $825.3M 2015: $780.4M Current 12 trailing: $1.26B https://www.bloomberg.com/... @ianmking Eric Newcomer / @ericnewcomer : Nvidia = best performer on the Nasdaq 100 Stock Index this year https://www.bloomberg.com/... pic.twitter.com/sMWCxU2GOD Leila Abboud / @labboudles : Really good @ianmking piece on chip maker of the moment Nvidia and its super-driven founder Jen-Hsun Huang http://www.bloomberg.com/... Sarah G McBride / @mcbridesg : The time your parents accidentally sent you to reform school in Kentucky and you then founded Nvidia. By @ianmking http://www.bloomberg.com/... http://twitter.com/...
Context & Ripple Effects
Ian King's Bloomberg profile lands at the pivot point of Nvidia's history: having already lost the smartphone-silicon race, the company rerouted its GPU business toward AI, self-driving cars, and data centers, lifting trailing net income to $1.26B from roughly $780M the prior year and making the stock the Nasdaq 100's best performer of 2016.
The piece reads differently in hindsight because the arc kept extending. Huang's decade-old bet on general-purpose GPU computing became the engine of the AI boom he was then chasing, his 70%-plus grip on the GPU market held even as competition exploded, and seven years later the stock tripled in 2023, the strongest major-stock year in a decade as generative AI made his earlier bets look prescient.
First-order effects
- Nvidia's financials inflect immediately: trailing net income jumps to $1.26B from $780.4M, reversing the flat-to-down years of 2013-2015 ($611.3M-$825.3M) as AI, autonomous-driving, and data-center buyers displace the company's traditional customer base.
- Jensen Huang converts a strategic failure — losing smartphone chips — into a focused identity, with the company now defined by accelerated computing rather than mobile.
Second-order effects
- Rival chipmakers are pulled into the same AI-compute market, forcing them to contest a segment where Huang has already entrenched a dominant GPU share, per the Fortune profile.
- Data-center and automotive customers begin competing for constrained GPU capacity, shifting bargaining power toward Nvidia and making its product roadmap a dependency for anyone building AI systems.
Third-order effects
- If the compounding pattern holds, accelerator supply becomes a structural bottleneck for the whole AI economy — the dynamic that culminated in the 2023 generative-AI surge Huang described in his CNBC interview, when Nvidia outperformed every S&P 500 stock.
- The industry drifts toward consolidation around a single vendor's hardware-plus-software ecosystem, raising the long-term stakes for regulators watching US-China trade tensions that Huang flagged in 2023.
The trend: Nvidia's run from smartphone also-ran to the world's most valuable-performing stock shows how early platform bets compound once AI turns accelerated computing into the economy's default purchase.