To The Moon

Two very different signals around $NVIDIA(NVDA)$ emerged this week.

Michael Burry, known for “The Big Short,” reportedly closed his December 2026 put options on $NVIDIA(NVDA)$ and $Palantir Technologies Inc.(PLTR)$, effectively narrowing his near-term bearish exposure. That does not necessarily mean he has turned bullish on AI stocks, but it does suggest he is shrinking his short front line.

At nearly the same time, $NVIDIA(NVDA)$ CEO Jensen Huang used the $Goldman Sachs(GS)$ Communacopia + Technology Conference to make the opposite case: the AI buildout is still in its early stages, and global AI infrastructure investment could reach US$3 trillion to US$4 trillion by 2030.

For investors, the message is clear: Burry is reducing some near-term downside exposure just as Nvidia is arguing that the AI spending cycle still has years of runway ahead.

Burry Steps Back, But the Bigger Debate Remains

Burry’s decision to exit his Dec. 2026 $NVIDIA(NVDA)$ and $Palantir Technologies Inc.(PLTR)$ puts looks more like position adjustment than a full reversal of his broader concerns. The market takeaway is less “Burry turns bullish” and more “Burry is becoming more selective about where and when to press the short side.”

That matters because Nvidia is still at the center of one of the market’s biggest debates: is the AI boom still expanding, or are expectations already too high?

Jensen Huang’s Core Message: AI Infrastructure Could Be a Multi-Trillion-Dollar Market

Huang’s most important point was scale. $NVIDIA(NVDA)$ believes the world is building an entirely new computing layer for AI, and by 2030, total investment in that infrastructure could reach US$3 trillion to US$4 trillion.

He also outlined three major growth drivers behind Nvidia’s next phase:

1. Broad model support
   $NVIDIA(NVDA)$ wants to power closed-source, open-source, and frontier AI models, allowing it to benefit regardless of which model ecosystem wins.

2. A broader customer base
   Demand is no longer coming only from traditional hyperscalers. $NVIDIA(NVDA)$ is increasingly selling into enterprise self-built clusters, regional cloud providers, neoclouds, and sovereign AI projects.

3. A larger share of customer capex
   $NVIDIA(NVDA)$ is designing more of the full AI system stack—from chips to networking to rack-scale systems—so it can capture a greater share of customer infrastructure spending.

This is visible in the average system values management cited:

• Hopper: ~US$18,000

• Blackwell: ~US$25,000

• Vera Rubin: ~US$40,000

In other words, $NVIDIA(NVDA)$ is no longer just selling GPUs. It is selling more of the AI factory.

Demand Still Looks Stronger Than Supply

Huang also said demand for $NVIDIA(NVDA)$’s GB-series NVL72 rack systems continues to grow, with sequential growth of around 27%.

Management still expects revenue to grow by around 70% year on year, and suggested that if supply were not a constraint, market demand growth could exceed 100%.

That is a crucial point for investors. Nvidia’s problem is not a lack of customers—it is whether the industry can provide enough chips, packaging, memory, power, and data-center capacity to keep up.

So the bull case remains intact, but supply constraints are still a real limiting factor.

Nvidia Wants AI Compute to Become an Infrastructure Asset

One of the more interesting takeaways from Huang’s remarks was that $NVIDIA(NVDA)$ is working with financial institutions to make AI systems more like financeable infrastructure assets.

The idea is to turn AI compute from a pure technology purchase into something that can be financed, pledged as collateral, or leased.

If that model scales, it could help customers fund larger AI deployments without relying entirely on their own balance sheets. That would not just support Nvidia hardware demand—it could also make AI infrastructure a more investable asset class.

Physical AI Is the Next Commercialization Layer

Huang also mapped out $NVIDIA(NVDA)$’s view of physical AI commercialization in stages:

• Autonomous driving

• Mobile robots and logistics

• General-purpose robotic arms

• Telecom and edge computing

This matters because it shows Nvidia’s long-term growth story is expanding beyond today’s data-center training and inference market. The company sees AI moving from software and cloud workloads into the physical world.

Cybersecurity Could Become Another Major AI Market

Another key theme was cybersecurity.

Huang described security as one of the most important AI application markets after programming. As AI becomes more capable of writing and understanding code, it also becomes more relevant in identifying vulnerabilities, monitoring threats, and defending systems in real time.

That opens another potential growth avenue for $NVIDIA(NVDA)$ beyond core model training.

What It Means for NVDA Stock

The contrast is what makes this story interesting.

On one side, Burry is cutting some near-term bearish exposure. On the other, $NVIDIA(NVDA)$ is still presenting one of the market’s biggest long-term growth narratives: multi-trillion-dollar AI infrastructure, rising system value, broader customers, strong rack demand, financeable compute, physical AI, and cybersecurity.

So the real question for investors is no longer whether AI spending is growing.

It is this:

Can Nvidia continue capturing enough of that growth to justify the market’s already-high expectations?

That is likely to remain the key debate around NVDA.

🪙 Tiger Coins Interaction

💬 POLL | Which side of the NVDA debate are you leaning toward?

🚀 A. AI runway is still long — Nvidia’s US$3–4T infrastructure opportunity matters more
📈 B. Demand is the key — Supply constraints are the bigger issue, not customer appetite
🤖 C. New growth engines matter most — Physical AI, financeable compute and cybersecurity could expand the story
⚠️ D. Expectations are already high — Strong growth may already be reflected in NVDA’s valuation

Vote in the poll and share your view in the comments — useful insights can earn Tiger Coins! 🎁

Do you think Burry closing his near-term NVDA puts makes the stock look more attractive, or are you still cautious about Nvidia’s valuation?

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