NVidia Got DeepSeeked

NVidia doesn’t get got. They go get.

Well, until today.

NVidia, the titan of AI hardware, experienced a record-setting day today — just not the one CEO Jensen Huang dreamt of when his head nestled into his pillow last night.

What happened?

A wildcard: DeepSeek.

DeepSeek, a competitor to ChatGPT (among others) launched their groundbreaking R1 AI model.

The result?

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NVIDIA suffered the largest single-day market cap wipeout in history.

A cool $589 billion in value vanished in mere hours…

And NVDA closed down about 17% for the day.

So, did today mark the end of NVIDIA's dominance?

Or is this just another chapter in the winner-take-all AI arms race?

To answer those questions we need to know to the story behind the story…

Billed as the most efficient AI model ever… R1 promises to deliver better results with significantly less computational power.

DeepSeek claims to have developed an AI algorithm that achieves dramatically better results with fewer computational resources.

On the surface, it’s an incredible breakthrough in optimization.

But dig deeper, and the story is a little more complex.

Scale AI’s founder and CEO Alexandr Wang revealed that DeepSeek’s "efficiency" relied on 50,000 NVIDIA H100 GPUs.

With one H100 GPU priced at approximately $25,000 that adds up to an equivalent $1.25 billion hardware investment…

Not the $6 million DeepSeek claimed.

Moreover, questions swirl around whether DeepSeek circumvented U.S. export sanctions to China acquire this hardware.

Despite the implications…

Silicon Valley is scrambling to unpack what DeepSeek has accomplished because there seems to be little doubt…

Their algorithm operates at a fraction of the cost of competing models and can run on cheaper hardware.

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But DeepSeek’s “breakthrough” doesn’t spell doom for NVIDIA…

Because in the longue durée of tech — the next few years — the AI mafia still want compute.

Today’s event underscores the insatiable future demand for GPUs as adoption increases as access to technology gets cheaper.

As AI adoption skyrockets, inference workloads—the application of trained AI models—are set to grow exponentially.

Jensen Huang has long argued that inference workloads will eventually eclipse training.

In a press release following the market cap wipeout, Huang put a positive spin on the day’s events:

We wrote about this before.

AI models are becoming larger, smarter, and more capable of handling complex tasks.

Advances like "chain-of-reasoning" and multi-agent systems mean that inference doesn’t just grow—it explodes.

One simple prompt can now generate 10, 100, or even 1,000 times more inferences, creating massive demand for GPUs.

Even with algorithmic optimizations, NVIDIA’s hardware remains the gold standard for scaling solutions globally.

Whether you see today as a "Sputnik Moment" as Marc Andreessen did...

It's clearly a wake-up call for the AI industry…

Because it showcases the growing importance of algorithmic innovation — which can be done both on cheap hardware or by AI — while reaffirming the foundational role of hardware scalability.

There’s never a dull day in the race for Artificial General Intelligence (AGI).

In the coming months…

AI Billionaires like Elon Musk, Sam Altman, and Mark Zuckerberg will pour trillions into their visions of the future…

As they aim for the worlds first 1,000,000 GPU cluster.

Their next big breakthrough may be just around the corner…

But the long-term trajectory has’t changed: AI is on an exponential rise, and NVIDIA GPUs are central to progress for the next few years.

Every day it becomes clearer AGI isn’t a question of "if" — only "when."

Always be prospering,

socrAItes

Publisher, Sage Research