Broadcom Inc. (AVGO) delivered the kind of AI revenue growth that turns heads—$6.5 billion in AI chip sales last quarter, up roughly 74% year over year. But here's the thing: not all AI growth stories are created equal. When you look at how Broadcom is actually making that money, you start to see risks that Nvidia Corp. (NVDA) investors don't really lose sleep over.

The Custom Silicon Trade-Off

Broadcom's AI explosion is real. The company just posted about $6.5 billion in AI-related revenue for its third quarter and is forecasting that figure could nearly double to around $8.2 billion next quarter. Those are eye-popping numbers by any standard.

But the revenue isn't coming from selling standardized chips to everyone who wants them. Instead, Broadcom is designing custom AI accelerators and bespoke silicon tailored specifically for individual hyperscale customers. Think of it as haute couture versus ready-to-wear—impressive craftsmanship, but each piece takes more work and comes with different economics.

That custom approach is driving serious momentum, but it's also eating into margins. Broadcom's own CFO acknowledged that rising AI revenue carries lower gross margins compared to the company's other product lines, and that mix shift is expected to squeeze overall profitability going forward. Analysts have flagged this margin pressure as a tangible drag on the bottom line.

The Concentration Question

Here's where things get interesting. Most of Broadcom's AI pipeline flows through a small group of hyperscale clients—Alphabet Inc. (GOOGL), Meta Platforms Inc. (META), and a few other major cloud players. The company's CEO has talked openly about substantial AI order backlogs tied to just a handful of buyers, and analysts estimate that four major customers could generate roughly $10 billion in AI revenue starting next fiscal year.

When that much revenue depends on so few relationships, you're exposed. If one of those deals slows down, gets renegotiated, or hits an execution snag, the impact ripples through the entire business model. It's concentration risk in its purest form.

Now contrast that with Nvidia. Its AI business is built on standardized GPU platforms like the H100 and broader data center architectures that serve a much wider customer base. We're talking enterprise clients, cloud service providers, research institutions—demand coming from all directions. Nvidia's data center revenue accounts for the lion's share of total sales, and the company commands a dominant position in the GPU AI accelerator market precisely because its products work for so many different use cases.

Why This Difference Matters

Nobody's saying Broadcom's AI growth isn't legitimate—it absolutely is, and it's scaling fast. But the structure of that growth matters as much as the headline numbers. The custom silicon model that Broadcom has embraced is fundamentally different from Nvidia's standardized platform approach.

Because so much of Broadcom's AI revenue is tied to bespoke projects for a few large customers, execution risk runs higher. Any single customer relationship carries outsize importance. Meanwhile, Nvidia's broader adoption spreads demand across hundreds of customers, which means no single deal can make or break a quarter. That diversification is exactly what Nvidia bulls point to when they talk about the durability of the company's AI franchise.

The irony is that both companies are winning in AI, just in completely different ways. One built a platform everyone can use. The other is building exactly what a few giants need. Both strategies can work—but they come with very different risk profiles attached.