Developing and maintaining advanced artificial intelligence systems can be financially challenging. Likewise, spending on the much-needed AI infrastructure is not expected to slow soon. That’s because big tech companies are expected to spend more than $500 billion by early next decade as the focus shifts to running AI models rather than training them.
High-end technology, such as GPUs and TPUs, are necessary for building and running AI models and training big AI models. These parts are costly as they cost thousands of dollars and require frequent maintenance and upgrading. Operational costs are further increased by the processing and storage capacity needed to handle large datasets for model training.
In addition to hardware, companies must contend with personnel expenses since hiring and keeping specialized AI talent such as researchers, engineers, and data scientists comes with extremely competitive pay that is frequently greater than that of other IT industries.
Hyperscale companies or the big tech giants in the US are on course to spend $371 billion in building out data centers and other computing resources in 2025. According to a new study by Bloomberg Intelligence, the amount is expected to increase by over $525 billion by 2032. The surge starkly contrasts initial concerns of a shift of focus into developing cost-effective AI models following DeepSeek revelations.
“Capital spending growth for AI training could be much slower than our prior expectations,” Mandeep Singh, an analyst with Bloomberg Intelligence, wrote in the report. But the immense amount of attention on DeepSeek, he wrote, will likely push tech firms to “increase investments” in inference, making it the fastest-growing segment in the generative AI market.
The introduction of the DeepSeek models raised concerns about the necessity of funding AI infrastructure, but it also increased interest in reasoning models, which demand higher inference costs. Bloomberg analysis predicts that by 2032, training-related expenditures will account for only 14% of hyperscalers’ AI budgets, down from over 40% this year. On the other hand, about half of all AI spending that year may come from inference-driven initiatives.
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Our Methodology
For this article, we selected AI stocks by going through news articles, stock analysis, and press releases. These stocks are also popular among hedge funds in Q4 2024.
Why are we interested in the stocks that hedge funds pile into? The reason is simple: our research has shown that we can outperform the market by imitating the top stock picks of the best hedge funds. Our quarterly newsletter’s strategy selects 14 small-cap and large-cap stocks every quarter and has returned 373.4% since May 2014, beating its benchmark by 218 percentage points (see more details here).
9. DXC Technology Company (NYSE:DXC)
Number of Hedge Fund Holders: 24
DXC Technology Company (NYSE:DXC) is an information technology services and solutions company. It offers analytics services and an extensive partner ecosystem that helps customers gain insights and automate operations. On March 17, Portuguese fashion brand Parfois selected the company to provide solutions for enhancing customer experience through data-driven personalization.
The collaboration entails the deployment of the Snowflake data platform to assess customer information utilizing predictive AI models. It will also include the setup of an analytics platform, data engineering, development of AI models, and business intelligence dashboards. The agreement highlights DXC’s expertise in data analytics and AI, specifically the execution of Snowflake’s data platform, which will facilitate predictive AI models and provide Parfois with real-time customer insights. This collaboration underscores DXC Technology Company’s (NYSE:DXC) ongoing progress in acquiring new businesses within their primary service areas and assisting organizations with IT modernization.
8. NICE Ltd. (NASDAQ:NICE)
Number of Hedge Fund Holders: 28
NICE Ltd. (NASDAQ:NICE) is a technology company that provides cloud platforms for AI-driven digital business solutions. It offers CXone, a native cloud open platform, and Enlighten, an AI engine for the customer engagement market. On March 17, the company launched CXone Mpower Orchestrator, a new solution that automates customer service workflows with agentic AI.
The solution reduces cost and accelerates resolution across various back-office operations. Orchestrator integrates third-party apps, enterprise-wide workflows, and AI-driven insights into a single, automated, efficient framework. Additionally, it proactively finds and applies enhancements throughout the whole service ecosystem by dynamically analyzing, forecasting, and optimizing operations. The AI-powered solution is a significant breakthrough that improves NICE Ltd.’s (NASDAQ:NICE) standing in the cutthroat market for customer experience platforms and may lead to both an increase in the wallet share of current customers and the acquisition of new ones.