NVDA, Nvidia: Overvalued Bubble or the First Stock of a New Paradigm?

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 I strongly recommend reading this article all the way to the end; your money is precious, and knowledge is what protects it.

Nvidia: Overvalued Bubble or the First Stock of a New Paradigm?

If you scroll through financial media right now, you see the same refrain over and over again:
“Nvidia is a bubble.”

Famous traders announce short positions. Commentators insist the valuation is “detached from reality.” Historical charts of Cisco and other dot-com leaders are posted next to Nvidia’s parabolic move as a warning.

At the same time, Nvidia has effectively become the central engine of U.S. tech equities. Its daily movement often dictates the tone of the entire Nasdaq — if Nvidia trades up, the market “feels” risk-on; if Nvidia trades down, risk appetite contracts.

So the simple question that everyone is dancing around is this:

Is Nvidia just “too expensive”… or are we watching the market slowly rewrite how it prices a company that sits at the core of the AI age?

My view is clear:
Nvidia is not “overvalued” in the traditional sense; the problem is that most investors are using an outdated framework to judge it. The market is being forced to adopt a new mental model.

The World Has Changed: Information, Participants, and Market Reflexivity

Classic bubble narratives assume a world where:

  • Information spreads slowly

  • Only a small professional elite understands the technology

  • Retail arrives late at the top, driven purely by hype

That is not the environment we live in anymore.

Today:

  • Information is global, real-time, and almost free. Analyst notes, earnings calls, and even proprietary-looking decks circulate on X and Telegram within minutes.

  • The number of semi-professional traders (people with a normal job but institutional-level tools) has exploded.

  • Even casual investors now understand basic AI terminology: GPUs, training vs inference, data centers, model parameters, and so on.

When a well-known macro or equity trader announces a short on Nvidia today, the information is instantly:

  • Arbitraged

  • Debated

  • Copied

  • Faded

All in public. There is very little “secret smart money” left in a name this widely followed. That doesn’t make Nvidia safe, but it makes the classical idea of an unseen bubble far less relevant.

In other words: when “Nvidia is a bubble” is already consensus small talk, a big chunk of that thesis is already priced in.

AI Is at Its “Electricity Moment”

To understand Nvidia, you have to zoom out.

Electricity did not stay a niche product for lighting a few streets. It became an invisible layer under:

  • Manufacturing

  • Transportation

  • Communication

  • Computing

It stopped being a “sector” and became infrastructure for everything else.

AI is on the same trajectory:

  • It starts with obvious use cases: search, chatbots, code assistants.

  • It spreads into less visible but more powerful layers: logistics, finance, industrial optimization, biotech, robotics.

  • Eventually it becomes embedded — not as “AI apps,” but as background intelligence inside nearly all digital and physical systems.

If you look at Nvidia as “just another semiconductor cyclical,” of course the current valuation feels insane.
If you look at Nvidia as the company building and operating the core hardware + software grid for AI, the conversation changes completely.

Nvidia Is Not Just a Chip Company — It’s a Platform with a Different Attitude to Risk

The most important conceptual mistake people make is treating Nvidia like a standard chip vendor:

  • “They sell GPUs, others will catch up, margins mean-revert.”

In reality, Nvidia is:

  • A hardware leader in AI accelerators

  • A software and tooling monopoly in the form of CUDA and its libraries

  • An ecosystem standard around which a huge amount of AI R&D and production workloads are already built

That alone gives Nvidia a very different profile. But there’s a second, deeper point that often gets ignored — and this is where the behavior of its competitors really matters.

Competitors Are Diversifying. Nvidia Is Attacking Straight Ahead.

Look at the companies we used to think of as Nvidia’s “natural” rivals: Intel, AMD, and even some of the large cloud providers building their own AI chips.

What are they doing?

  • Intel is trying to reboot itself as a foundry player, rebuild its CPU franchises, defend server share, and chase AI accelerators at the same time. It is effectively managing a portfolio of battles.

  • AMD is building solid AI accelerators and has made real progress — but AMD is also balancing:

    • Client CPUs

    • Server CPUs

    • Gaming / console chips

    • Embedded and semi-custom designs

On paper, these are competitors. In practice, their corporate behavior tells a different story:

They clearly feel the weight and risk of going head-to-head with Nvidia in its strongest lane, so they frame their strategy as “balanced portfolios” and “diversified compute roadmaps.”

To be fair, from their perspective this may not be cowardice but a long game: quietly reducing ecosystem-wide dependence on Nvidia inside their own platforms and cloud environments, so that over time they claw back bargaining power and margin without an immediate frontal war.

Still, the contrast in posture is obvious.

Nvidia, meanwhile, is doing almost the opposite of diversification:

  • It is doubling, tripling, and quadrupling down on AI infrastructure.

  • Its roadmap is not “let’s be okay at everything,” but “let’s stay terrifyingly good at the one thing that will dominate the next decade: accelerated compute for AI.”

This divergence in strategy is critical:

  • Intel and AMD behave like portfolio managers of technology risk — spreading bets, hedging, avoiding an all-or-nothing fight.

  • Nvidia behaves like a single-themed, high-conviction fund that went all-in on AI infrastructure years ago and keeps pressing the advantage.

From a valuation perspective, that difference in attitude and focus matters as much as the difference in products.

The Ecosystem Lock: Why “Just Compete” Is Not That Simple

There’s also a structural reason why “just compete with Nvidia” is much harder than it sounds on paper.

Nvidia has built:

  • A deeply entrenched software ecosystem (CUDA, cuDNN, TensorRT, etc.)

  • Long-running relationships with researchers, labs, hyperscalers, and enterprises

  • A de facto standard for training large models

For a competitor to truly dislodge this, they must:

  1. Match or exceed Nvidia’s raw hardware performance

  2. Offer a comparable or better software stack

  3. Convince thousands of teams to migrate and re-optimize their models and workflows

  4. Do all of this while the AI field itself is evolving at high speed

That’s not a normal technology race. It’s more like trying to replace both Windows + x86 at the peak of their dominance, while the entire world is rewriting software on top of them.

So when Intel and AMD talk about diversified portfolios, it’s not only risk aversion; it’s also a quiet acknowledgement that the cost of a full-scale frontal war against Nvidia’s ecosystem is enormous.

Paradoxically, this competitor hesitation — or slow, indirect long game — is one of the strongest confirmations that Nvidia’s position is structurally different from a typical semiconductor name.

“But the Numbers Are Crazy”… or Are We Pricing a Different Animal?

When people say “Nvidia’s numbers are crazy,” what they often mean is:

  • “These growth rates can’t last forever”

  • “These margins are unsustainable”

  • “No chip company can deserve this multiple”

All of that is emotionally reasonable. But there are two layers that matter more:

  1. The absolute level of AI infrastructure spend
    Global capex on AI data centers and accelerated compute is already in the hundreds of billions of dollars and is still in the early stages of the curve. If AI really is the next electricity-like layer, then we are not in the late innings — we’re closer to the second or third inning.

  2. Who captures what share of that spend
    If Nvidia maintains a dominant share of AI accelerator revenue and continues to monetize its software stack and platform advantages, then traditional semiconductor multiples simply don’t apply. We are closer to a “platform + tollbooth + quasi-standard” company than to a commodity supplier.

So yes, the stock looks expensive against old models.
But those models were built for companies that:

  • Sold into slower, more predictable device cycles

  • Had clear, competitive peers with similar strategic focus

  • Did not sit at the center of a new general-purpose technology wave

Nvidia simply does not fit that template.

Why the Short Thesis Keeps Failing (So Far)

We keep seeing headlines like:

  • “Famous macro fund shorts Nvidia”

  • “Hedge fund calls the top in AI”

The logic is usually:

  1. The stock has gone up “too far, too fast.”

  2. The valuation looks stretched on any historical semiconductor metric.

  3. AI spending must slow, and when it does, Nvidia will de-rate hard.

This might eventually be directionally true — nothing grows in a straight line forever. But turning this into a profitable short is a different game.

1. The path can kill you before the destination validates you

Even if Nvidia eventually corrects 50%, the stock can easily move another 50–100% up before that happens. A short built on valuation alone may not survive the path, even if the final thesis is “right.”

2. The market is repricing a regime, not a quarter

In Nvidia’s case, the market is not just pricing the next few quarters of AI demand. It is:

  • Pricing the possibility that AI becomes embedded everywhere

  • Pricing the possibility that Nvidia remains the default supplier of that infrastructure

  • Pricing the structural weakness or hesitation of its competitors

That is a regime repricing, not a one-off bubble in units shipped.

3. “Everyone knows it’s expensive” is a terrible short setup

Overvaluation that nobody sees is a great short.
Overvaluation that everyone talks about is often the most persistent kind, because it becomes part of the narrative:

  • Bulls say: “Yes, it’s expensive, but it deserves it.”

  • Bears say: “It’s obviously a bubble,” but are often too early.

Meanwhile, the stock grinds higher as the framework shifts.

The Real Risk: Misunderstanding the Regime, Not Missing the Top Tick

From my perspective, the main risk around Nvidia is not “what if I buy and it drops 30%.” Volatility in a name like this is almost guaranteed.

The real risk is deeper:

Using a 20th-century semiconductor mental model to judge a 21st-century AI infrastructure platform.

If you think in terms of:

  • “Peak cycle”

  • “Mean-reverting margins”

  • “Normalizing to industry averages”

…you will constantly see Nvidia as a stock that is “about to collapse any minute now,” and every pullback will look like the beginning of the end.

If you instead think in terms of:

  • Platform dominance

  • Ecosystem lock-in

  • Competitor hesitation and portfolio diversification

  • Long-duration AI infrastructure spend

…the same facts support a very different conclusion: Nvidia might be the first major equity of a new paradigm, and the market is still in the process of discovering what that is worth.

Three Scenarios to Keep in Mind (and Where My Bias Sits)

To avoid being trapped in a single narrative, it helps to hold at least three parallel scenarios in your head:

1. Base case – structural winner with normalizing growth

  • AI becomes a widely adopted, electricity-like layer of the economy, but adoption is uneven and cyclical.

  • Nvidia keeps its leadership, but competitors (including custom chips from hyperscalers) slowly chip away at share.

  • Revenue and earnings still grow, but growth rates cool and valuation multiples compress somewhat.
    Result: Long-term chart is still up and to the right, but with violent drawdowns and a lower forward return than the last few years.

2. Bull case – Nvidia as the enduring AI grid

  • AI infrastructure capex exceeds current expectations and remains elevated for longer.

  • Nvidia maintains a dominant share in accelerators and successfully monetizes more of its software, networking, and platform layers.

  • Its de facto standard status proves sticky despite ongoing competition.
    Result: Today’s valuation looks high on old metrics but proves reasonable or even cheap in hindsight. Nvidia becomes one of the defining compounders of this era.

3. Bear case – CAPEX slows and the moat leaks

  • Global AI capex slows due to macro, politics, or simple digestion of earlier investment.

  • Hyperscalers and chip competitors quietly win more workloads with in-house and alternative solutions.

  • Regulation and pricing pressure compress margins and force the market to value Nvidia more like a mature hardware supplier.
    Result: The stock can still be a good business, but the multiple unwinds meaningfully; for new buyers at peak narrative, the risk–reward is poor.

My personal view sits somewhere between the base and bull cases — I do see Nvidia as a structural winner of the AI era. But that conviction doesn’t erase the bear case. For portfolio construction and position sizing, all three worldlines matter more than any single story.

So What Should an Investor Actually Do?

None of this means “buy Nvidia at any price” or “Nvidia can never go down.” That would be absurd.

What it does mean, at least in my view, is:

  1. Stop pretending this is just another chip cycle
    Treating Nvidia like a regular semiconductor name is intellectually comfortable but strategically dangerous. Its role in AI infrastructure — and its willingness to attack that role head-on while rivals diversify — puts it in a different bucket.

  2. Expect extreme volatility as the cost of exposure
    Big winners in new paradigms almost never travel in straight lines. If you want the upside of this story, you must emotionally budget for very ugly drawdowns.

  3. Separate valuation discomfort from structural damage
    Feeling that the stock is “too expensive” is not a thesis. Structural damage would look like:

    • A credible alternative platform taking meaningful share and winning hearts and minds of developers

    • A collapse or reversal in AI infrastructure spending

    • Regulatory or geopolitical shocks that directly attack Nvidia’s ability to ship and monetize its products

  4. Size positions so you are not forced to sell at the worst time
    The right question is not “Is Nvidia a buy or a sell?” but “How large can my position be while still allowing me to survive a brutal drawdown without panicking?”

Final Thoughts: Paying the Price for a New Mental Model

In the end, I see Nvidia’s current valuation as the price the market is paying to update its own thinking.

Past generations struggled to value:

  • Electricity

  • The early internet

  • Cloud platforms

Each time, people tried to force the new reality into old templates — and each time, the templates eventually broke.

Today, AI is at its electricity moment, and Nvidia is the company that:

  • Builds the “power plants” (data center GPUs / accelerators)

  • Owns much of the “grid” (software stack, ecosystem, partnerships)

  • Keeps attacking straight ahead, while some of its presumed competitors quietly diversify and play a slow, indirect long game around it

If those facts remain broadly true over the next decade, many of today’s “overvaluation” arguments will probably look, in hindsight, like artifacts of a dying framework.

This article is for informational and educational purposes only and does not constitute financial or investment advice; any decisions you make with your money are entirely your own responsibility.

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