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NVIDIA Before GTC 2026: Is the AI King Still Cheap Enough to Buy?

NVIDIA's annual GTC conference starts tomorrow, Monday, March 16, 2026, and the market is treating it the way sports fans treat a championship game.

This is the Super Bowl of AI.

That nickname is not just media fluff. NVIDIA's own GTC page says the event runs March 16–19 in San Jose and will focus on inference, agentic AI, physical AI, and AI factories. Jensen Huang's keynote is scheduled for Monday, March 16, from 8–11 a.m. PT.

That matters because GTC is not an ordinary product event anymore. It is where the market tries to figure out whether NVIDIA is still one step ahead of everybody else in the AI stack.

And right now, the question has become more specific.

Not "is AI still big?"

Not "do hyperscalers still spend?"

But this:

Can NVIDIA stay dominant as AI shifts from training to inference, and from one-shot models to agentic systems that require far more orchestration, memory movement, networking, and real-time compute?

That is why Jensen Huang's keynote matters so much tomorrow.

Investors are expecting:

a new inference-focused chip or platform discussion,
updates tied to agentic AI,
and a broader roadmap that proves NVIDIA is not just selling GPUs, but owning the operating system of the AI economy.

And because this is Cheap Investor territory, we have to ask the real question:

At roughly $180 a share and a market cap above $4.5 trillion, is NVIDIA still cheap enough to matter… or are investors already paying for all the good news before Jensen even walks on stage?

Scoreboard: where NVIDIA stands heading into GTC

Let's start with the hard numbers.

As of the latest finance data, NVIDIA (NVDA) is trading around $180.25, with a market cap of about $4.53 trillion and a trailing P/E of roughly 45.6x.

That is obviously not a deep-value multiple.

But NVIDIA is also obviously not a normal business.

Reuters reported after NVIDIA's latest earnings that the company guided fiscal Q1 2027 revenue above analyst expectations, even as Wall Street continued to debate whether customer concentration and in-house chip development at major cloud customers could eventually narrow its edge. Reuters also noted that two customers accounted for 36% of sales in the just-ended fiscal 2026, versus 34% from three customers the year before.

That concentration matters.

But so does the size of the spending pool.

Reuters said big tech companies are collectively expected to spend at least $630 billion this year on AI-related infrastructure.

That means NVIDIA is heading into GTC with three things at once:

enormous scale,
extraordinary expectations,
and a market that still wants proof the next phase of AI belongs to NVIDIA too.

The real reason GTC 2026 matters

The market already knows NVIDIA won the first phase of the AI boom.

That first phase was mostly about training.

Buy giant clusters.
Buy high-end GPUs.
Build capacity as fast as possible.
Race to the frontier.

Now the conversation is changing.

The next phase is increasingly about:

inference
agentic AI
network efficiency
power efficiency
software orchestration
and the economics of running AI continuously, not just training it once.

That is why tomorrow's keynote matters.

Reuters reported that investors are looking for NVIDIA to unveil products and partnerships aimed at defending its lead as competition rises not just from AMD or Intel, but from customers like Meta and OpenAI that are designing more of their own silicon. Reuters also said NVIDIA still holds roughly 90% share across training and inference, but the company is under pressure to prove its reinvestment strategy and full-stack roadmap can keep competitors boxed out.

So the conference is not just a celebration.

It is a moat inspection.

The "inference chip" angle is bigger than it sounds

A lot of investors still think the AI chip war is just about the biggest training GPU.

That is old thinking.

Inference is where the market is heading, because inference is where AI becomes an always-on service rather than a one-time model-building exercise.

Business Insider reported analysts are focused on whether NVIDIA unveils a new inference chip, possibly designed with a different memory architecture to improve efficiency for the fast-growing inference market. The same coverage pointed to Rubin Ultra, future Feynman products, and co-packaged optics as part of the broader roadmap investors expect to hear about.

Reuters separately reported that NVIDIA recently acquired Groq for $17 billion to strengthen its inference position and may use GTC to emphasize inference, orchestration, and the infrastructure demands of agentic AI.

That matters because training is not the only profit pool anymore.

In fact, there is a real argument that inference becomes the more durable one.

Why?

Because once models are trained, enterprises still have to run them.
At scale.
Cheaply.
Fast.
Securely.
And often across millions or billions of requests.

That turns the AI race from a pure hardware land grab into a system-efficiency race.

And NVIDIA wants to own that too.

Agentic AI is the second giant theme

NVIDIA's own GTC materials explicitly list agentic AI as one of the major conference pillars.

That is not a buzzword accident.

Agentic AI means systems that do not just answer prompts, but plan, reason across steps, use tools, call APIs, coordinate sub-models, and operate more autonomously.

That creates a much heavier infrastructure burden.

More orchestration.
More memory movement.
More networking.
More inference.
More software tooling.

Reuters said GTC is expected to showcase advances not only in AI chips, but also in CUDA, AI agents, robotics, networking, and infrastructure. Wired separately reported NVIDIA is expected to launch an open-source AI agent platform called NemoClaw, aimed at helping enterprises deploy AI agents for workplace tasks. I want to be careful here: Wired is not a primary source, so I would treat the exact product name as not yet official until NVIDIA says it on stage. But it does reinforce the broader point that NVIDIA is pushing hard into the agent layer, not just the hardware layer.

That is strategically important.

Because if the AI stack shifts upward toward agent frameworks and orchestration, NVIDIA cannot afford to be "just the GPU company."

It needs to be the company that makes the whole thing work.

And that is exactly the story GTC is designed to tell.

Deep dive: what NVIDIA actually is now

The lazy version of the NVIDIA thesis is: "It sells AI chips."

The better version is:

NVIDIA is trying to be the full-stack infrastructure platform for accelerated computing.

That stack includes:

GPUs
CPUs
networking
interconnects
software
orchestration
robotics
AI frameworks
and increasingly, enterprise tooling around AI deployment.

That is why GTC sessions and marketing language now emphasize not just chips, but AI factories, agentic AI, and physical AI.

This is a crucial distinction for investors.

Because if NVIDIA were only a chip vendor, the valuation would be much harder to defend.

But if NVIDIA is becoming the operating system for AI infrastructure, then the multiple starts to make more sense.

The market is not paying 45x trailing earnings for a commodity semiconductor company.

It is paying that multiple because it believes NVIDIA can capture value at multiple layers of the stack.

The networking war may be the hidden story of GTC

One underappreciated angle here is that the next phase of AI is not just compute-bound.

It is increasingly network-bound.

As clusters scale, the bottleneck is often no longer just the chip. It is how quickly data moves between chips, servers, and racks.

That is why Reuters said investors are watching not just for chip updates, but for advances in networking and infrastructure. Business Insider also flagged co-packaged optics as a likely roadmap topic.

This matters because agentic AI and inference-heavy workloads create a different performance profile than brute-force training alone.

If NVIDIA can make the case that:

it owns the GPU,
it owns more of the inference pipeline,
it owns the interconnect roadmap,
and it owns the software layer,

then the moat is wider than most valuation models capture.

That is the bullish interpretation.

The bearish interpretation is different:
that each new layer NVIDIA enters creates more complexity, more capital intensity, and more places where rivals can attack.

Tomorrow's keynote is really about which interpretation wins.

Data section: why the Street still leans bullish

Despite all the noise, analyst sentiment remains very strong.

Investopedia reported that among the analysts it tracked ahead of GTC, 12 of 13 rated NVIDIA a buy-equivalent, with average price targets implying roughly 45% upside from recent levels.

That sounds aggressive, but it also tells you something about expectations.

Wall Street is not gathering around GTC wondering whether NVIDIA is in trouble.

Wall Street is gathering around GTC wondering whether NVIDIA can extend its lead enough to justify staying one of the most expensive large-cap stocks on Earth.

That is a very different problem.

And even the competitive concerns come with nuance.

Reuters noted that custom chips from cloud giants are a real risk, but also emphasized that NVIDIA's challenge is to keep proving its ecosystem remains harder to replace than bulls and bears alike sometimes assume.

That is why the conference matters more than a normal product launch.

It is a defense of the ecosystem.

Is NVIDIA cheap?

Now the part that matters.

At $180.25 and about 45.6x trailing earnings, NVIDIA is not cheap in the classic sense.

Nobody should pretend otherwise.

This is not a low-multiple bargain.
This is not a cigar-butt turnaround.
This is not a hated stock with hidden assets.

So why is it even a Cheap Investor discussion?

Because there are two kinds of cheap.

The first is statistically cheap.
The second is cheap relative to future dominance.

NVIDIA is only interesting if you believe the second one.

The bull case says NVIDIA may still be underpriced because:

AI infrastructure spending is still exploding,
inference is becoming another giant profit pool,
agentic AI increases stack complexity in NVIDIA's favor,
and the company keeps moving upward into higher-value software and orchestration layers.

The bear case says the opposite:

expectations are extreme,
custom silicon risk is real,
the stock already discounts years of leadership,
and every GTC now has to be spectacular just to keep the multiple from compressing.

My Cheap Investor answer is simple:

NVIDIA is not cheap on current numbers.
It may still be cheap if GTC proves NVIDIA owns the next phase of AI as thoroughly as it owned the first one.

That is the whole bet.

The unique angle: GTC is less about products than about time horizon

Most investors think GTC is about what launches tomorrow.

I think that is too short-term.

GTC is really about extending the duration of the NVIDIA story.

If Jensen Huang can convince the market that:

inference is another multiyear wave,
agentic AI multiplies infrastructure demand,
networking and orchestration deepen the moat,
and the roadmap beyond Blackwell remains intact,

then NVIDIA does not need to surprise on one chip.

It just needs to prove that the AI capex cycle has more legs than the market feared.

That is why this event matters.

It is not just about what NVIDIA sells next quarter.

It is about how long investors are willing to underwrite extraordinary growth.

Bull, base, and bear

Bull case

Jensen unveils a compelling inference roadmap, agentic AI tooling lands well, and NVIDIA reinforces the view that it remains the center of gravity for the full AI stack. In that scenario, GTC becomes another proof point that the company is still early in monetizing inference, orchestration, robotics, and AI factories. That would make the current valuation easier to defend, even if it still looks expensive on paper.

Base case

GTC is solid, but not shocking. NVIDIA says many of the right things, updates the roadmap, and investors come away reassured but not euphoric. In that case, the stock may trade more on broader macro conditions, oil, rates, and follow-through from hyperscaler capex rather than on an immediate post-keynote pop.

Bear case

The company talks a big game on inference and agentic AI, but the announcements feel incremental relative to what the market already expects. If that happens, the stock could sag simply because the bar is so high. A company with a $4.5 trillion market cap and a 45x trailing P/E does not get much credit for "pretty good." It needs "obviously category-defining."

Action plan for bargain hunters

This is not a chase-the-keynote setup for everyone.

For conservative investors, it probably makes more sense to wait for the event and judge the substance rather than gamble on the reveal.

For moderate investors, NVIDIA still looks like a scale-in name, not an all-at-once name. The right framework is not "buy because GTC is tomorrow." It is "buy if GTC extends the duration of the thesis."

For aggressive investors, the real opportunity is in the gap between what the market expects and what Jensen actually delivers. If the market is underestimating the importance of inference or agentic AI orchestration, the stock can still move meaningfully even from this size.

In plain English:

Do not buy NVIDIA because the conference is famous.
Buy it only if you believe tomorrow proves the next AI phase belongs to NVIDIA too.

Cheap Investor checklist

Here is what I would watch in the keynote and follow-up coverage.

First, whether Jensen gives a concrete update on a new inference-focused chip or architecture. That is one of the market's biggest expectations heading into the event.

Second, whether NVIDIA frames agentic AI as a software opportunity, a systems opportunity, or both. The distinction matters because higher software attachment would support the premium multiple.

Third, whether there is fresh detail on Rubin Ultra, Feynman, or other forward roadmap items. Investors do not just want the next chip; they want visibility beyond it.

Fourth, whether NVIDIA highlights networking, co-packaged optics, or interconnect innovation. That may be the hidden moat extension the market is not fully modeling.

Fifth, whether any new partnerships reinforce customer dependence on NVIDIA's broader stack, not just its accelerators. Reuters specifically said partnerships are expected to be part of the story.

Bottom line

NVIDIA heads into GTC 2026 as the biggest company in the market, one of the most expensive mega-caps in the world, and still one of the highest-conviction AI names on Wall Street. The stock sits around $180, the market cap is above $4.5 trillion, and the conference itself is explicitly centered on inference, agentic AI, and the next wave of AI infrastructure.

So here is the Cheap Investor verdict:

NVIDIA is not cheap in the normal way.
It is only cheap if tomorrow proves the company is still extending its moat faster than the market can price it.

That is why all eyes are on Jensen Huang.

Because tomorrow is not just about a keynote.

It is about whether NVIDIA can convince investors that the first AI wave was only the opening act.

Disclaimer: This editorial is for informational purposes only and should not be considered investment advice. Always conduct independent research before making financial decisions.

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Minggu, 15 Maret 2026

NVIDIA’s Super Bowl Starts Tomorrow: Is GTC 2026 the Next Leg Higher?

NVIDIA's Super Bowl Starts Tomorrow: Is GTC 2026 the Next Leg Higher?