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Learn moreThe U.S. has between 3,000 and 4,700 active data centers, with plans to build another 1,500. Globally, there are about 11,800, with plans to add about 100 gigawatts of new capacity by 2030. All of which creates substantial opportunity for beaten-down stocks such as ASML Holding (ASML). However, to run all of those data centers, the world needs advanced AI chips. Today's most advanced data-center AI processors, manufactured on 4nm, 5nm, and certain 7nm processes, depend heavily on ASML's lithography technology. And as the industry moves toward 2nm production, that dependence on ASML's EUV systems will become even more important.
Some of those companies include Samsung Electronics (SMSN.L.IX), Taiwan Semiconductor Manufacturing (TSM), and Intel (INTC). All of which are moving forward with ASML's High-Numerical Aperture extreme ultraviolet lithography systems, better known as High-NA EUV. In short, AI must have powerful chips to run. And those chips are becoming much harder to manufacture, with ASML producing the machines that make their production possible.
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Weakness Is Also an Opportunity
That all makes ASML even more attractive moving forward.
Granted, there are calls for the AI industry to slow down from Anthropic CEO Dario Amodei, which sent ASML down more than $102 a share. But I believe that weakness will be short-lived. Along with Amodei, OpenAI CEO Sam Altman and Elon Musk believe he's right. "I agree with Dario that we need to pace the frontier," Altman posted on X. "Committing to having independent evaluators with employee-like access is a great idea, and we will do the same." Musk wrote, "Dario is right." But is it likely to happen? Probably not, especially with President Trump rejecting the idea of slowing down and China's pushback against the slowdown idea.
So, What Exactly Is High-NA EUV?
ASML's existing EUV lithography equipment already plays an important role in producing many of today's most advanced semiconductors. High-NA EUV is the next major step forward. The technology is designed to print smaller and more complex patterns onto silicon wafers with greater precision. This could allow chipmakers to fit more transistors onto each chip. If that happens, we could see faster performance and greater computing power.
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