The National Basketball Association is preparing for one of the most significant officiating changes in modern professional sports, with Commissioner Adam Silver confirming that artificial intelligence will soon automate a category of referee decisions currently responsible for some of the league’s most disruptive stoppages.

Speaking on ESPN’s The Pat McAfee Show, Silver said the NBA plans to introduce an AI-powered camera system capable of instantly determining possession calls such as out-of-bounds rulings, removing those judgments from on-court referees and sharply reducing replay reviews.

The shift mirrors the adoption of Hawk-Eye technology in professional tennis, where electronic line-calling has largely replaced human judges in determining whether balls land in or out. Silver suggested the NBA envisions a similar model for objective calls.

“We’re going to move to a system like that where that whole category of calls will be automatic,” Silver said. “It’s going to be Laker ball, Knick ball, whatever it is.”

The planned system would rely on cameras positioned throughout arenas to track player movement and ball contact in real time. Instead of referees huddling around replay monitors for several minutes while fans wait, possession rulings could be delivered almost instantly by the automated system.

The NBA has spent years trying to balance officiating accuracy with entertainment flow. Expanded replay review and coach’s challenges have improved precision in some areas, but they have also lengthened games and generated mounting frustration among viewers, broadcasters, and teams over repeated interruptions.

Silver’s comments indicate that the league now sees AI not merely as a support tool, but as a direct replacement for human judgment in narrowly defined categories where machine vision can outperform referees in speed and consistency.

The league appears to be drawing a sharp distinction between objective and subjective officiating. Out-of-bounds calls, goaltending reviews, and timing determinations can be measured through tracking systems and visual analysis. Fouls, however, remain more complicated because they involve context, intent, positioning, and varying levels of contact.

Silver emphasized that referees would still be essential for interpreting physical play.

“There’s often contact on every play, but that doesn’t mean there’s a foul on every play,” he said. “That’s something that can’t just be done on camera.”

That distinction is important because officiating crews remain central to how the NBA manages game flow, player conduct, and competitive balance. Fully automating foul decisions would likely trigger major resistance from players, coaches, and fans who already debate the consistency of officiating standards across games and playoff series.

A broader transformation is going on across global sports, where AI and computer vision are increasingly being integrated into officiating and performance analysis.

Professional tennis has already normalized automated line calling. Soccer has introduced semi-automated offside technology. Baseball continues experimenting with automated strike zones in the minor leagues. Cricket, Formula One, and the NFL have all expanded their use of real-time tracking and replay systems.

For the NBA, the technology push comes at a time when the league is also investing heavily in data analytics, player tracking, and media innovation as it competes for younger audiences accustomed to faster digital experiences.

But reducing replay interruptions carries commercial implications as well. Faster games improve broadcast pacing, reduce viewer fatigue, and help streaming partners retain audiences in an increasingly fragmented media environment.

Silver did not provide a launch date, but his comments suggest implementation may come sooner rather than later.

“It will be fairly quickly,” he said.

The challenge for the NBA will be ensuring that the technology is trusted by teams and fans during high-pressure moments, particularly in playoff games where possession rulings can decide outcomes.

Even if AI removes some of the league’s most controversial replay reviews, it is unlikely to eliminate debates over officiating entirely. In basketball, the most contentious decisions are often not whether the ball touched a player’s fingertip, but whether contact warranted a whistle in the first place.

That means the next era of NBA officiating may involve a hybrid model: machines handling precision, humans handling interpretation.

Chinese technology giant ByteDance is moving deeper into the global artificial intelligence infrastructure race with plans to develop its own central processing units, Reuters reports, citing people familiar with the matter.

The parent company of TikTok is developing proprietary CPUs to support its expanding AI infrastructure needs, according to people familiar with the matter, as soaring chip prices, tightening supply, and geopolitical uncertainty force major technology companies to seek greater control over their computing stacks.

The shift highlights how the AI boom is rapidly evolving beyond Nvidia’s graphics processors and into a broader battle over the foundational chips powering next-generation computing.

For the past several years, the AI surge has largely revolved around graphics processing units, or GPUs, particularly those made by Nvidia, which dominate the training of large AI models. But as companies increasingly deploy AI systems into commercial products and services, attention is shifting toward “inference” computing, where trained AI models perform real-time tasks for users.

That shift is dramatically increasing demand for CPUs, which manage memory allocation, networking, workload orchestration, and data processing inside AI data centers.

Industry executives say the demand surge has created an emerging supply crunch.

The move by ByteDance places it alongside global hyperscalers such as Amazon, Microsoft, and Google, all of which are aggressively developing custom processors to reduce dependence on external suppliers and lower the enormous cost of scaling AI infrastructure.

Sources familiar with ByteDance’s plans said the Beijing-based company intends to deploy the CPUs across its own servers and data centers as it prepares a large-scale expansion of agent-based AI products, including its Coze platform and other internal artificial intelligence systems.

The company is reportedly evaluating two chip architecture paths simultaneously. One design is based on technology from Arm Holdings, while another uses the open-source RISC-V instruction set architecture.

The dual-track approach is borne out of the uncertainty facing many technology companies attempting to build custom silicon. Arm-based chips benefit from a mature software ecosystem and widespread adoption across cloud infrastructure, while RISC-V offers greater flexibility and lower licensing costs, making it increasingly attractive to Chinese technology firms seeking technological independence.

People familiar with the matter said ByteDance has approached several external partners to assist with both chip design and manufacturing coordination. Securing foundry capacity has become a critical challenge as semiconductor manufacturers struggle to meet exploding demand from AI companies worldwide.

ByteDance is making its move as the global AI infrastructure market is entering a new phase where the economics of inference computing are beginning to rival, and in some cases exceed, the importance of model training. While training frontier AI systems requires immense bursts of GPU power, inference workloads require sustained, large-scale deployment across millions of users and devices.

That creates enormous demand for CPUs capable of coordinating AI systems efficiently and cheaply.

The market dynamics are already reshaping the semiconductor industry. Intel and AMD, whose dominance had appeared threatened by Nvidia’s rise, are now benefiting from renewed investor interest as CPUs regain importance inside AI data centers. Intel warned earlier this year that Chinese customers faced server CPU lead times stretching up to six months, while AMD CEO Lisa Su recently described the global CPU market as “tight,” with demand significantly exceeding expectations.

Sources said ByteDance has experienced substantial increases in server CPU pricing in recent months, with some products rising between 10% and 35% quarter-on-quarter. Those cost pressures are said to have accelerated the company’s internal chip efforts.

The development also points to China’s broader push toward semiconductor self-sufficiency. As Washington continues tightening restrictions on advanced semiconductor exports to China, major Chinese technology firms are increasingly seeking to reduce dependence on U.S. suppliers. Export controls introduced since 2022 have already restricted Chinese access to Nvidia’s most advanced AI chips due to concerns about potential military applications.

Although ByteDance’s CPU project is primarily commercially driven, it aligns with Beijing’s wider objective of strengthening domestic semiconductor capabilities amid escalating technology tensions with the United States.

The move comes as Nvidia itself attempts to expand beyond GPUs. Nvidia CEO Jensen Huang recently said the company’s new “Vera” CPU platform could give the chipmaker access to a $200 billion market, highlighting how central processors are becoming increasingly important in the next phase of AI development.

Nvidia has also begun integrating CPUs more deeply into its AI systems to create vertically integrated computing platforms capable of handling both training and inference workloads.

Custom chip development allows companies to optimize performance for specific workloads, reduce long-term procurement costs, and lessen dependence on external suppliers during periods of shortage or geopolitical disruption.

But building advanced processors remains a high-risk undertaking. Even for well-funded technology giants, designing competitive chips requires deep engineering expertise, sophisticated software integration, and reliable access to advanced manufacturing nodes.

Many custom silicon projects fail to achieve broad deployment because of technical complexity, escalating development costs, and software compatibility challenges.