The Jensen Effect…
Every technological boom eventually runs into a bottleneck.
For the oil industry, it was pipelines.
For the internet, it was bandwidth.
And for artificial intelligence…
It might be memory.
This week, investors were watching Nvidia’s big GTC conference, where CEO Jensen Huang outlined the next phase of the AI boom.
Yes — the man in the leather jacket.
But interestingly, the stocks moving the most ahead of Huang’s speech lately haven’t been Nvidia.
They’ve been the companies supplying memory chips.
Here’s what’s happening ⇩
The Quiet Movers
While the spotlight stays on Nvidia, a few of the companies feeding the AI machines have been catching a bid.
➝ Micron MU ( ▲ 4.89% )
➝ Sandisk SNDK ( ▲ 8.54% )
➝ Western Digital WDC ( ▲ 4.62% )
➝ Seagate STX ( ▲ 4.68% )
These companies produce the memory and storage hardware that AI systems rely on to move massive amounts of data.
AI Infrastructure / Networking
➝ Marvell Technology MRVL ( ▲ 4.48% )
➝ Amphenol APH ( ▲ 1.74% )
These firms build connectivity and networking components that link AI chips and servers together inside massive data centers.
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The Stock Getting Extra Attention
One name drawing particular interest lately: Sandisk $SNDK ( ▲ 4.75% ) .
The stock has become something of a momentum favorite among retail traders, after delivering a staggering ~1,200% gain over the past year.
Even after recent volatility, Sandisk is still up roughly 170% this year, keeping it near the top of the S&P 500’s performance rankings.
Meanwhile, another potential catalyst is approaching.
Micron reports earnings on Wednesday, and several Wall Street analysts have already started raising price targets ahead of the release.
Over the weekend, Micron announced it completed the acquisition of a new manufacturing site in Taiwan, expanding its ability to produce advanced memory chips.
Moves like this highlight how quickly demand for AI hardware is expanding.
When companies start adding new manufacturing capacity, it usually means the industry expects demand to stay strong.
Building the next generation of AI systems isn’t only about designing better GPUs, but also about expanding the entire supply chain around them.
Memory, storage, networking, power, and cooling are all becoming critical parts of the equation.
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The Speed Bumps
Training large AI models requires enormous computing power.
But compute alone isn’t enough.
These systems also need massive amounts of memory to move and store data.
Think of GPUs as the engine of AI.
Memory is the fuel line.
If data can’t move fast enough between chips, the entire system slows down.
A Pattern Traders Remember
This isn’t the first time memory stocks have reacted to Huang’s comments.
Back in January, during Nvidia’s keynote at the Consumer Electronics Show in Las Vegas, Huang pointed to memory bandwidth as one of the main constraints slowing the AI build-out.
The market responded immediately.
The following day:
→ Sandisk surged nearly 30%
→ Western Digital and Seagate posted double-digit gains
→ Micron rallied alongside them
In other words, when Huang highlights a constraint in the AI system… traders start bidding up the companies solving it.
That’s why memory stocks are getting attention again just ahead of Nvidia’s conference.
Investors are watching to see whether memory once again becomes part of the conversation.
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The Big Question
Before the keynote, traders were already positioning for what Jensen Huang might say.
A few themes were at the top of the list:
→ AI Inference
The shift from training AI models to actually running them at scale across real-world applications.
→ Nvidia’s Expanding Stack
Beyond GPUs — including CPUs, networking, and full data-center systems.
→ AI Infrastructure Spending
Confirmation that hyperscalers like Microsoft, Meta, Amazon, and Google are still pouring billions into AI compute.
→ Efficiency Gains
How Nvidia plans to deliver more performance with less power, hardware, and cost.
Because at this stage of the AI boom, the question isn’t just how powerful the chips are.
It’s how efficiently the entire system can run.
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