Nvidia (NASDAQ: NVDA) has been impressing investors and analysts alike with its remarkable surge in artificial intelligence (AI) technology. The chipmaker, originally recognized for producing graphics cards for PCs, has seen its stock price skyrocket almost six times since the start of 2023.
Despite this significant increase, there are concerns in certain areas of Wall Street regarding the possibility of Nvidia’s stock being overvalued. Comparisons to the dot-com bubble of 1999, the potential decline in AI-related chip demand, and the costly valuation all contribute to the belief that Nvidia could be a bubble waiting to pop.
However, a deeper analysis of the AI market and Nvidia’s position in it demonstrates that the company is far from being a bubble.
Why Nvidia and AI are Not in a Bubble
A stock market bubble is characterized by a “substantial rise in stock prices that is not justified by the underlying value of the businesses they represent.” In a bubble scenario, a company’s valuation is driven by speculation rather than fundamental factors.
When examining how AI is driving efficiency improvements across various industries, it becomes evident that the adoption of this technology should logically continue to gather momentum. For example, Meta Platforms has reported a remarkable 32% increase in campaign returns due to the integration of AI tools. Additionally, customer service agents are experiencing a 14% boost in productivity attributed to AI.
According to Bain & Company, factories are projected to witness a 30% to 50% increase in productivity through AI integration in the future. UBS predicts that AI could contribute to a 2.5% productivity growth this year, surpassing the Federal Reserve’s estimate of 1.5%. UBS anticipates that AI could deliver 17% of productivity gains over the next three years.
Nvidia’s chips will play a pivotal role in driving these productivity enhancements across diverse sectors. This is because AI models require extensive training with millions and billions of parameters before they can be operationalized. These models, known as large language models (LLMs), are being deployed in various industries…
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