AI Stocks Beyond Nvidia: Investing in the AI Value Chain | Hugo Investing

When people ask me about investing in artificial intelligence, the conversation often starts with Nvidia. It is an understandable starting point. Powerful chips are central to AI.

But I also like to ask another question: what else needs to work for AI to work?

A chip needs connections. A data centre needs to move huge amounts of information. And before any of that can happen, manufacturers need machines that can produce chips with remarkable accuracy.

Looking at these less visible parts of the industry helps me understand the bigger picture. Marvell Technology and Lam Research are two useful examples.

Who Supplies the Tools?

The gold rush is a familiar comparison in investing. Some people searched for gold; others supplied the picks and shovels.

AI has its own suppliers of tools. They provide the equipment and technology that different businesses need, regardless of which AI application becomes the best known.

I find that an interesting angle, but it is no shortcut to investment success. Suppliers still face competition, changing customer budgets and the risk that a new technology replaces their products.

The comparison is a starting point for understanding a business, rather than a reason to buy its shares.

Key Market Players Behind the Scenes

Marvell: Connecting the Computing Power

Imagine having a powerful engine but no way to transfer its power to the wheels. A data centre can face a similar problem: fast chips are only useful if information can move between them quickly enough.

Marvell develops technology that helps data move between different parts of computing systems. It also designs chips for specific customer needs and provides technology that sends data using light.

What interests me here is the importance of the connections. AI is often described in terms of computing power, but getting information to the right place is part of the challenge too.

Marvell helps explain why the story extends beyond the processor itself.

Lam Research: Making the Chips Possible

Lam Research takes us to the factory floor.

Making a chip involves adding extremely thin layers of material, carefully removing parts of those layers and cleaning the surface. These steps are repeated many times to create the tiny structures inside a chip.

Lam Research supplies equipment used in these processes.

Many Dutch investors will know ASML. Its machines use light to create patterns on the thin discs, called wafers, on which chips are made. But that is one part of a much longer production process. Lam Research provides equipment for other essential steps.

For me, this is a reminder of how much specialist knowledge sits behind something we take for granted. We see the phone, computer or AI application. We rarely see the machines that helped make it possible.

A Good Business and a Good Investment Are Different Questions

This is where I think it helps to slow down.

Understanding why a company matters does not tell us whether its shares will deliver a good return.

A business may grow, but investors may already expect that growth. Profits may rise, but less than the market hoped. A useful technology may also attract strong competitors.

Core Evaluation Criteria

I therefore see three separate questions:

• Does the technology serve a real need?
• Can the company turn that need into lasting profits?
• What expectations are already reflected in the share price?

The answers do not always point in the same direction.

The chip industry also goes through periods of stronger and weaker demand. Customers can delay factory projects or reduce spending. Trade restrictions and tensions between countries can affect where equipment and chips may be sold.

Being essential to a process does not make a business free from risk.

Why I Look Beyond the Headlines

What draws me to these examples is the chance to understand how an industry fits together.

Marvell helps connect computing systems. Lam Research helps manufacturers produce chips. Both sit behind the AI tools that attract most of the attention.

For me, that is one of the most engaging parts of following markets: looking beyond a familiar name and finding out what a company actually contributes.

Questions That Matter Long Term

Key Inquiries:

How does it earn its money? Why do customers choose it? What could change that?

Those questions remain useful long after the latest AI headline has passed.

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