Driven by explosive growth in artificial intelligence, the world’s largest chipmakers generated more than $400 billion in combined sales in 2025, with expectations for an even bigger year ahead. Demand for computing power remains, in the words of CEOs and analysts alike, insatiable.

But for traders and investors, this is no longer just a growth story.

It’s a story about where opportunity shifts as the AI chip boom matures—and where risk quietly builds beneath the surface.

From Training to Inference: A Critical Shift in the AI Chip Boom

The first leg of the AI revolution was defined by training massive models. That phase overwhelmingly favored Nvidia, whose advanced GPUs became the picks and shovels of the digital gold rush.

Now the battleground is changing.

The next phase of the AI chip boom is about inference—the process of serving real-time answers from trained models. Inference workloads are more diverse, more cost-sensitive, and more open to competition.

Recent moves in the space underscore the transition. Nvidia’s licensing deal with Groq highlights a market that is increasingly focused on speed, efficiency, and cost-per-inference rather than raw training dominance.

Competition Is No Longer Theoretical

Nvidia is still the leader—but it is no longer alone.

• Alphabet continues to expand the adoption of its custom Tensor Processing Units (TPUs)
• Amazon is pushing Trainium and Inferentia chips deeper into its cloud ecosystem.
• Microsoft has signaled major data-center expansion over the next two years
• AMD is preparing a 2026 GPU launch positioned as its first significant challenge to Nvidia’s AI processors
• Broadcom and AI labs are increasingly partnering on custom silicon

This is where the narrative becomes more nuanced. The pie may keep growing—yet the slices can change quickly when customers move to custom designs, when cloud providers bundle chips with services, and when inference opens new competitive lanes.

Supply Constraints: The Quiet Risk in the AI Chip Boom

While demand headlines dominate, supply constraints are becoming the defining friction point—especially for data center buildouts.

AI infrastructure is running into shortages of:

• Electrical transformers and gas turbines
• Power generation capacity and grid availability
• High-bandwidth memory (HBM) chips
• Advanced silicon substrates and specialty packaging inputs

Inference workloads are increasingly memory-bound, meaning performance is constrained not only by processor power but by how quickly data can be moved and stored at scale.

For traders, these constraints matter because bottlenecks can cap near-term upside—even when demand is real—and they can create second-order opportunity in suppliers that solve the bottleneck.

Financing, Expectations, and the Risk of a Growth Scare

Another underappreciated variable in the AI chip boom is financing.

Markets have grown used to outsize quarter-to-quarter revenue growth. That’s a fragile condition. When expectations get extreme, even “good news” can trade poorly if it implies deceleration.

We already saw a preview of this dynamic when investors broadly sold AI stocks over concerns that the financing behind infrastructure spending might not be as durable as previously believed.

The AI story doesn’t end if spending slows—but the way it trades can change overnight.

What Traders Should Watch Next

The AI trade is not over. But it is evolving.

Rather than predicting headlines, traders should watch where leadership, margins, and risk are shifting:

• Relative strength between Nvidia and emerging challengers
• HBM and packaging signals that indicate supply is loosening or tightening
• Capex guidance from hyperscalers and data-center operators
• Market reactions to earnings beats (does it rally—or sell off?)
• Any sign of slowing growth in the highest-expectation names

Opportunity doesn’t disappear—it migrates.

And in markets driven by scale, expectation, and capital intensity, structure matters more than stories.