Should You Forget Nvidia and Buy 2 Other Artificial Intelligence (AI) Stocks Instead?

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Nvidia's (NASDAQ: NVDA) stock has soared about 27,340% over the past 10 years. The explosive growth of its data center business, which sells high-end GPUs for processing complex artificial intelligence (AI) tasks, fueled a large portion of those gains and turned it into a linchpin and bellwether of the booming AI market.

The market's demand for Nvidia's data center GPUs is still outstripping its supply as more companies upgrade their servers to handle the latest AI applications. Its revenue surged 126% in fiscal 2024 (which ended in January 2024), and analysts expect it to continue growing at a compound annual growth rate (CAGR) of 57% from fiscal 2024 to fiscal 2027 as its earnings per share (EPS) rises at a CAGR of 345%.

A digital illustration of the letters AI on a circuit board.
Image source: Getty Images.

Based on those rosy expectations, Nvidia's stock doesn't seem overvalued at 31 times next year's earnings. But its long-term growth could still be throttled by competition from cheaper chipmakers, the development of first-party AI accelerator chips, tighter U.S. export curbs against Chinese companies, and a recent antitrust probe in China.

So while Nvidia might still be a great play on the AI market, investors should recognize those potential risks and keep a close eye out for other promising AI stocks. Let's check out two of those other names -- Innodata (NASDAQ: INOD) and Taiwan Semiconductor Manufacturing (NYSE: TSM) -- and see if they're worthwhile alternatives to Nvidia.

A small-cap hypergrowth stock: Innodata

Innodata went public in 1993, and it was considered a slow-growth IT services and enterprise software company for many years. However, its stock surged from around $1 at the end of 2019 to nearly $35 as of this writing. That massive rally was driven by the rollout of its new generative AI training services for five of the "Magnificent Seven" companies.

In the past, Innodata mainly provided business process, technology, and consulting services -- along with software tools for managing and distributing digital data -- for the government, aerospace, defense, financial services, and tech sectors. From 1994 to 2019, its revenue only grew at a CAGR of 6% as it struggled to grow in the shadow of larger companies like IBM and Microsoft.

But over the past few years, many of those tech leaders struggled to efficiently prepare large amounts of high-quality data for their new AI applications. Many large companies reportedly spent 80% of their time preparing their data for an AI project and the remaining 20% on actually training the AI algorithms. To address those inefficiencies, Innodata rolled out a suite of task-specific microservices for preparing custom data for AI applications in 2018.