HomeBusinessAI shares need to buy 1 stock split before they rise 450%,...

AI shares need to buy 1 stock split before they rise 450%, according to a Wall Street expert

Philip Panaro is the founder and former CEO of Boston Consulting Group (BCG) Platinion, a division of BCG that offers technology consulting services. Panaro told Schwab Network during an interview in November Nvidia (NASDAQ: NVDA) could reach $800 per share by 2030 thanks to its leadership in artificial intelligence (AI) accelerators. This forecast implies an upside of approximately 450% from the current stock price of $145.

Of course, Nvidia is one of the hottest stocks on the market. The stock price has risen more than 900% since the launch of ChatGPT in late 2022 led to an exponential increase in demand for AI infrastructure. The company executed a 10-for-1 stock split earlier this year to offset that price increase, and if Panaro is right, another split could be in the offing.

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Here’s what investors need to know.

Nvidia has a 98% market share in data center graphics processing units (GPUs), chips used to accelerate complex data center workloads such as training machine learning models and running artificial intelligence applications. One reason for that dominance is superior chip performance. Nvidia regularly achieves the highest scores on the MLPerfs, objective tests that benchmark the capabilities of AI systems.

But there’s another reason why Nvidia accounts for virtually all GPU sales in data centers: It has spent the better part of the last two decades building an extensive software ecosystem. In 2006, Nvidia introduced its CUDA programming model, a platform that now includes hundreds of code libraries and pre-trained models that streamline the development of AI applications for use cases ranging from autonomous cars and robots to conversational agents and drug discovery.

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In addition, Nvidia has branched out into other hardware verticals, such as central processing units (CPUs) and networking equipment. Indeed, Nvidia has a leading position in InfiniBand networking, currently the most popular connectivity technology for back-end AI networks. The ability to integrate hardware components into a cohesive computing system allows Nvidia to build data centers with the lowest total cost of ownership, said CEO Jensen Huang.

Here’s the big picture: Competing with Nvidia is extremely difficult. The GPUs are not only the fastest AI accelerators on the market, but are also supported by the most robust software development platform. And Nvidia has another important advantage in vertical integration. Although the company has more pricing power than its competitors, Nvidia systems are therefore less expensive when it comes to direct and indirect costs.

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