You Should Be Buying This Artificial Intelligence (AI) Stock Hand Over Fist Before It Soars

Nvidia‘s (NASDAQ: NVDA) stunning artificial intelligence (AI) surge has left investors and analysts in awe. Shares of the chipmaker, which originally made its name as a manufacturer of graphics cards for personal computers (PCs), have jumped nearly sixfold since the beginning of 2023.

However, this big jump has created doubts in certain corners of Wall Street that Nvidia stock may be in a bubble. From comparisons with the dot-com bubble of 1999 to a potential decline in AI-related demand for its chips, to its expensive valuation, there are multiple reasons why some believe that Nvidia is a bubble waiting to burst.

But a closer look at the AI market in general and Nvidia in particular will illustrate why the company is far from being in a bubble.

Why it isn’t right to call Nvidia, and AI, a bubble

A stock market bubble is a “significant run-up in stock prices without a corresponding increase in the value of the businesses they represent.” In a bubble, the valuation of a company is based on speculation instead of the actual fundamentals.

However, if you take a closer look at how AI is driving productivity gains across multiple industries, it will become easier to understand that the adoption of this technology should ideally continue gaining momentum. For instance, Meta Platforms says that the integration of AI tools has led to an impressive jump of 32% in returns delivered by ad campaigns. Meanwhile, customer service associates are reportedly witnessing a 14% increase in productivity thanks to AI.

Factories, on the other hand, are expected to witness a 30% to 50% jump in productivity in the future by integrating AI, according to Bain & Company. Investment bank UBS believes that AI could drive productivity growth of 2.5% this year, ahead of the Federal Reserve’s estimate of 1.5%. Over the next three years, UBS is expecting AI to deliver 17% of productivity gains.

Nvidia’s chips are going to play a central role in driving these productivity gains across different industries. That’s because AI models need to be trained using millions and billions of parameters before…

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