MY BIGGEST TAKEAWAY FROM NVDA EARNINGS
The real story from Nvidia earnings wasn’t the revenue beat. It wasn’t the guide. It wasn’t even the China write-down. It was the $4.96B in networking revenue.
That number -- blowing past the $3.45B estimate -- is the loudest signal we’ve gotten yet that the center of gravity in AI compute has shifted. Not away from Nvidia, but within it. From training to inference. From chip to system.
This is the regime change the market hasn’t fully priced: inference isn’t a tail-end workload anymore. It’s becoming the default mode of AI at scale. And the economics, the architecture, the physical infrastructure of that shift all tilt back toward one conclusion -- this isn’t about GPUs alone. This is about the pipes. The fabric. The connective tissue. And right now, Nvidia owns that, too.
What they’re building isn’t a product line. It’s a substrate. Blackwell isn’t just replacing Hopper -- it’s replacing the idea that AI progress happens in discrete steps. When 70% of data center compute revenue already comes from a chip architecture launched just months ago, that’s not a product cycle. That’s cadence. And the fact that $MSFT has already deployed tens of thousands of GB200s with plans to scale to hundreds of thousands means this isn’t theoretical demand. It’s committed capital. It’s forward-positioned infrastructure. It’s factory-scale deployments being rolled out to meet the explosion in real-world token usage -- 100 trillion tokens processed in a single quarter. A 5x YoY surge.
You don’t build for that with old assumptions. You build with bandwidth. With latency minimization. With full-stack integration from silicon to rack. Which is why Nvidia’s networking business didn’t just outperform -- it became the heartbeat of the AI factory model. Spectrum-X, NVLink, custom interconnects -- they’re no longer supplementary. They’re elemental.
And here’s where the impact spills out to the rest of the ecosystem. Because once the market wakes up to inference at scale, it doesn’t just re-rate Nvidia. It elevates every company within the stack
Start with $Broadcom(AVGO)$ . You don’t move trillions of tokens per quarter without silicon photonics, custom switch ASICs, and hardened firmware stacks that Broadcom dominates. Their position inside $Meta Platforms, Inc.(META)$ , $Microsoft(MSFT)$ , $Alphabet(GOOGL)$ & $Amazon.com(AMZN)$ networking is no longer optional -- it’s foundational. The scale Nvidia just outlined will require not just horizontal expansion, but vertical reengineering of every signal pathway in the data center. And Broadcom is already embedded in those redesigns.
Then look at $Astera Labs, Inc.(ALAB)$ . Because when inference becomes the primary driver of compute -- and not just isolated inference, but persistent, agent-driven, multimodal reasoning -- you don’t just need better chips. You need better bridges. More memory lanes. Smarter cables. Lower latency interconnects that can be custom-fitted to rack-scale deployments. And that’s exactly where Astera Labs plays.
The ramp in inference workloads doesn’t just validate Nvidia. It crystallizes the bull case for companies like Astera. Because it’s no longer enough to have raw GPU horsepower. You need coherent, synchronized systems where the memory interface, PCIe lanes, CXL switches, and Smart Cable Modules aren’t bottlenecks -- they’re enablers. Astera’s core product portfolio isn’t solving yesterday’s problems -- it’s solving the precise architectural gaps Nvidia just said will define tomorrow.
And that’s what makes this earnings call so pivotal. They showed us that inference isn’t just scaling -- it’s scaling first. That the networking layer isn’t a cost center -- it’s the multiplier. That the export hit from China -- an $8B revenue gap -- can be fully absorbed because the demand curve in the U.S., in Europe, in the Middle East is no longer tied to consumer devices or discretionary spend. It’s tied to sovereign infrastructure.
So yes -- the takeaway from Nvidia earnings was that inference is exploding. But the implication is bigger: the AI economy is restructuring around sustained, connected inference as the dominant workload, and the companies that build the hardware highways for that data to travel aren’t just participating.
They’re becoming indispensable -- because the engines of the Fourth Industrial Revolution aren’t just turning. They’re humming.
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