To The Moon

The recent drop in $NVIDIA(NVDA)$ stock does seem to reflect growing worry that the firm’s near-monopoly in AI hardware could erode if hyperscalers diversify away from GPUs. The report that Meta Platforms is in talks to use Google’s TPUs in its data centers highlights exactly that risk.

Below is a breakdown of the situation — with our take on whether the stock drop is justified, and what might be a reasonable long-term price target for Nvidia if you believe in its long-term strategy.

My Entry Target for Nvidia (NVDA) is at $120.90 which I feel that it have been a good entry price.

Why The Stock Drop Can Be Explained By Investor Concern

Potential loss of a big customer’s GPU demand. According to media reports, Meta may begin deploying Google’s TPUs by 2027 — and even start renting TPUs through Google Cloud as soon as next year.

Supply-diversification reduces reliance on a single supplier (Nvidia). For large “hyperscalers” with massive AI compute needs, having only one supplier (especially under historically strained supply) is risky. Alternative accelerators — like TPUs — offer redundancy, potentially lower cost, or better power efficiency, which strengthens their appeal.

Market re-pricing reflects possible margin/market-share pressure. News that a leading buyer might shift to TPUs triggered a steep drop (on one day alone, Nvidia lost tens of billions in market value).

A structural risk, not just short-term noise. The adoption of specialized AI accelerators (ASICs, custom chips) rather than general-purpose GPUs reflects a broader industry trend — not a one-off event. This could gradually chip away at GPU-led growth for training and inference.

It is rational for investors to worry that NVDA’s dominance might be challenged. That said — “near-monopoly” does not mean “untouchable.”

Why Nvidia Might Still Hold Up — And Why Some Investors Remain Bullish

GPUs remain more flexible than ASICs/TPUs. GPUs such as Nvidia’s are general-purpose and support a wide range of workloads, not just AI. ASICs/TPUs are often optimized for specific workloads; while efficient, they may lack the flexibility needed for novel/future/edge use cases. This flexibility remains a strategic advantage for Nvidia.

Nvidia has scale, ecosystem, and a strong track record. Their AI GPUs (like the H100/H200 generation) are deeply embedded across multiple data centers globally. Many AI teams and cloud providers have built their software stack (frameworks, pipelines) around Nvidia’s hardware. That kind of ecosystem effect is hard to displace quickly.

Even some analysts are not panicking. As noted recently, some Wall Street analysts reiterated bullish ratings on Nvidia despite the market jitters. For instance, a $240–$270 price target was mentioned as still plausible under a “buy” case.

Not all workloads are suitable for TPUs/ASICs. For cutting-edge model development, research, or workloads requiring flexibility (e.g., model exploration, mixed workloads), GPUs may remain the preferred (or only practical) option for some time.

Hence, while the risk is real, it does not necessarily mean a Come-to-Jesus moment for Nvidia — there is still a strong case for outperformance if it adapts well and keeps commanding a large share of AI infrastructure demand.

What A “Good” Long-Term Price Target Might Be

Given the mix of risks and strengths, here are a few scenarios and corresponding rough price targets. These assume a 2–5 year time horizon (i.e. long-term).

Scenario / Assumption

What Happens / Nvidia Position

Implied Price Target (on current price ~ US$178)

Base case — modest slowdown

Some demand shifts to TPUs/ASICs, but GPUs remain dominant for new models, mixed workloads, and flexibility. Nvidia maintains leadership, continues winning new customers, incremental margin pressure but steady growth.

US$240–260 — implies ~35–45% upside

Bullish / strategic execution

Nvidia successfully expands GPU market — new products (e.g. next-gen GPUs), expands software stack, captures new workloads (edge AI, generative AI, inference + training), possibly new segments beyond datacenter. Alternative chips supplement but don’t significantly displace Nvidia volume.

US$270–300+ — up to ~50–70% upside

Cautious / modest contraction

Gradual but steady loss of GPU share to TPUs/ASICs among large hyperscalers; growth slows, but Nvidia retains niche + high-margin workloads.

US$200–220 — limited upside, some downside risk if margin pressure worsens

Indeed, as noted above, some analysts are already in the $240–$270 range despite recent headlines.

Given current dynamics, a $240–260 price target seems a reasonable “base-case” long-term goal. If Nvidia successfully executes — and the broader AI infrastructure demand continues to grow — $270–300+ becomes a plausible “bullish” outcome.

Key Risks / What to Watch

Before treating those targets as “safe,” these uncertainties should be factored in:

If hyperscalers like Meta fully pivot to TPUs or custom ASICs, the shift could reduce Nvidia’s GPU demand — especially for inference workloads where cost, energy and efficiency matter more.

The competitive field is not limited to Google: other cloud providers or chip designers (ASIC specialists) may enter, increasing pressure on pricing and margins.

If supply-side cheaper alternatives mature (e.g. more advanced TPUs, ASICs, specialized AI chips), Nvidia may face both demand and price pressure, squeezing margins.

Macro-economic / regulatory / geopolitical risks remain — AI hardware is capital intensive, and any interest-rate, chip-supply chain or export-control shock could impact capital spending by data-center operators.

Our View: Long-Term Strategy on Nvidia

If you were building a long-term investment strategy today:

You might need to treat Nvidia not as a “forever monopoly” but as a market-leader in transition. They still have arguably the best combination of hardware performance + flexibility + ecosystem breadth.

We might want to see the next 2–5 years as critical: if Nvidia adapts — by continuing GPU innovation, deepening software and tooling, expanding into new AI-heavy workloads (inference, enterprise AI, edge AI, generative AI, high-performance computing) — then it could justify $270–300+.

But I would also spread risk: since competition is real, I would not bet everything on Nvidia alone — maybe consider diversified exposure in AI infrastructure (chips, software, cloud providers, services).

This is why my portfolio also consists of $Alphabet(GOOGL)$, $Advanced Micro Devices(AMD)$ and $Amazon.com(AMZN)$.

As I hold today, I would consider $240–260 as a conservative long-term “core-position” target, and treat any dip below $170–180 as potential buying opportunity (assuming underlying fundamentals hold).

In the next section, we ran a full, compact valuation exercise for NVIDIA (NVDA): a DCF under three scenarios (Bear / Base / Bull), a simple P/E multiple check, and a competitive-threat stress test that simulates what happens if hyperscalers materially shift away from Nvidia GPUs.

We used Nvidia’s recent reported FCF, shares, cash and debt, and current price as model inputs. Sources for the inputs are cited after the results.

Key inputs (sources)

Starting Free Cash Flow (FCF): $60.85B (FY2025).

Shares outstanding (diluted): 24.3 billion.

Cash & short-term investments (mrq): ~$60.6B; Total debt (mrq): ~$10.8B — used to convert EV → equity.

Current market price (used for comparison): US$177.82 (Nov 25–26, 2025 snapshot).

DCF assumptions (5-year explicit forecast + Gordon terminal)

Projection horizon: 5 years of discrete FCF projections, then a terminal value via Gordon Growth.

Enterprise value → equity: EV + cash − debt.

Scenarios (growth = annual FCF growth for first 5 years / discount rate / terminal growth):

• Bear: growth 10% / discount 9% / terminal growth 2%

• Base: growth 18% / discount 8% / terminal growth 3%

• Bull: growth 25% / discount 7% / terminal growth 3.5%

DCF results (per-share equity value)

Interpretation: at current price ~$178, the market is pricing Nvidia roughly in line with a very optimistic / bull DCF (or a P/E that implies similar expectations). The Base case DCF implies a material downside vs present market price; Bear and stress cases are far below today's market price.

Simple P/E check

Using reported net income FY2025 ≈ $72.88B and diluted shares 24.3B → EPS ≈ $3.00.

Price targets by multiple (EPS × multiple):

• P/E 30 → $90

• P/E 45 → $135

• P/E 60 → $180

So a market price near $178 implies investors are valuing Nvidia at roughly P/E ≈ 60 on FY2025 EPS — a very rich multiple that embeds high growth expectations.

Competitive-Threat Stress Test (Example)

Simulated scenario: sustained demand loss from hyperscalers (Meta/others adopt TPUs/ASICs), lower growth & higher risk:

• Assumptions: growth 6% (5 yrs), discount 10%, terminal growth 1.5% → DCF per-share ≈ $38.1.

That shows how quickly valuation collapses if growth stalls and perceived risk (discount rate) rises — the market is sensitive to these shifts.

Short Takeaways & Strategy Guidance

Market is pricing in the Bull outcome. Current price (~$178) is close to my Bull-case DCF (~$183.6) and an implied P/E ≈ 60 — meaning investors expect sustained, very strong FCF growth and structural moat to persist.

Risk is asymmetric if hyperscalers materially diversify. The stress test shows a large downside if big buyers (Meta, Microsoft, OpenAI customers) materially cut GPU demand or if pricing/efficiency advantages shift to TPUs/ASICs. Recent reports that Meta is exploring Google’s TPUs are exactly the kind of event that would lower embedded growth assumptions.

A “good” long-term price target depends on your risk profile:

• Conservative/core long-term target: $90–$135 (P/E 30–45) — aligns with base/bear outcomes.

• Base-case DCF fair value: ~$99 per share (my middle-of-the-road DCF).

• Bull / execution succeeds: $180–$300+ (market already prices toward the low end of this range; higher targets assume sustained >20% FCF growth and multiple expansion).

• Put simply: $240–260 (quoted earlier by some analysts) is an optimistic/price-appreciation target that requires Nvidia to keep winning share AND avoid material displacement by TPUs/ASICs.

How I Would Position: If you want long-term exposure but want to avoid paying full bull expectations:

Consider a core position sized to a base DCF target (~$90–$100) and add opportunistically on pullbacks below $170 (my earlier quick rule) — or buy call spreads if you want upside with limited capital. (Not investment advice — adapt to your portfolio & risk tolerance.)

Watch these signals closely:

• (a) large long-term commitments by hyperscalers to non-Nvidia accelerators (TPU purchase agreements);

• (b) Nvidia gross margins & FCF conversion;

• (c) adoption curves of Blackwell/Vera Rubin vs Google TPU performance and TCO;

• (d) major deals with OpenAI, Microsoft, Anthropic, etc. — any multi-year compute contracts can materially change assumptions.

Technical Analysis - Nvidia Expected Move and Possible Option Play

With the expected move for Nvidia at near $179, the market is in a "digestion" phase following its November earnings beat. The narrative has shifted from "will they beat?" to "can supply keep up?" CEO Jensen Huang confirmed that Blackwell chips are "sold out" well into 2025, signaling demand far outstrips supply. However, the stock's muted reaction reflects valid concerns over gross margin compression (dipping from 75% to ~73.4%) and the sheer scale required to maintain 90%+ growth rates.

Technically, $179 acts as a pivot. The "easy money" momentum trade has paused, replaced by a need for execution on the Blackwell production ramp. For investors, this volatility contraction offers a chance to position for the next leg up (likely targeting $200+) without chasing peak premiums. The "expected move" implied volatility has likely crushed post-earnings, making long options cheaper than they were weeks ago.

The "Support Accumulator" Play: Bull Put Spread (Credit Spread)

Why: Takes advantage of the $170 support level. You profit as long as Nvidia doesn't crash, effectively letting the market pay you to wait.

The Trade: Sell a Put at a support level (e.g., $165) and Buy a lower strike Put (e.g., $160) for protection.

Benefit: Generates immediate income (credit). If NVDA stays above $165, you keep the full profit.

Summary

Nvidia’s stock recently faced a sharp pullback following reports that Meta Platforms is in advanced talks to rent and eventually deploy Google’s Tensor Processing Units (TPUs) in its data centers. This development rattled investors because Meta is one of Nvidia’s largest customers, reportedly planning to spend billions on Google's custom silicon instead of relying exclusively on Nvidia's GPUs.

The market reaction reflects a growing fear that Nvidia’s "moat" is narrowing. While Nvidia currently controls over 90% of the AI accelerator market, the Meta-Google talks signal that "hyperscalers" (Big Tech firms) are aggressively seeking alternatives to reduce costs and supply bottlenecks. If Meta—an industry bellwether—successfully transitions workloads to Google’s TPUs, it could validate the technology for other enterprise giants, potentially cracking Nvidia’s monopoly sooner than anticipated.

Are Investor Concerns Valid? (Market Share & Over-Reliance)

Yes, the decline is directly linked to valid concerns about customer concentration and structural shifts in the AI market:

The "Over-Reliance" Risk: A massive portion of Nvidia’s revenue (estimated at nearly 40%) comes from just a handful of companies: Microsoft, Meta, Alphabet, and Amazon. If even one of these giants shifts just 20–30% of their compute to in-house chips (like Google’s TPU or AWS Trainium), Nvidia’s revenue growth could decelerate significantly.

Cost Efficiency vs. Raw Power: Nvidia’s GPUs (like Blackwell) are the "Ferraris" of AI—powerful but expensive and energy-hungry. For running established models (inference), tech giants prefer "sedans"—cheaper, more efficient custom chips like TPUs. Investors fear that as the AI market moves from training (building models) to inference (using models), Nvidia's expensive chips will become less essential for daily operations.

Long-Term Strategy & Price Targets

Despite the Meta news, Nvidia remains the undisputed leader in training next-generation models. For a long-term strategy (looking toward 2026/2027), analysts are balancing these competitive risks against the continuing AI boom.

Analyst Consensus Price Targets (12-18 Months):

Bull Case: $300 - $350. Rationale: The AI boom is still in its early innings ("the first 1%"). Nvidia’s upcoming Rubin platform (scheduled for 2026) re-establishes a performance lead so large that competitors like Google cannot catch up, forcing Big Tech to keep buying Nvidia for their most advanced tasks.

Base Case: $220 - $250. Rationale: Nvidia grows, but at a slower pace. The company successfully co-exists with custom silicon, maintaining the "premium" tier of the market while losing the lower-end "inference" market to TPUs and other custom chips.

Bear Case: $160 - $180. Rationale: Rapid commoditization. If Meta and OpenAI successfully migrate major workloads to non-Nvidia hardware faster than expected, Nvidia's margins (currently ~75%) could compress, dragging the stock price down to historical valuation norms.

Strategic Takeaway: A price entry near $170–$180 is viewed by many institutional analysts as a strong support level, offering a "safety margin" against competitive risks.

Appreciate if you could share your thoughts in the comment section whether you think holding Nvidia for long term would be a good strategy and can do option play for Nvidia recent movement.

@TigerStars @Daily_Discussion @Tiger_Earnings @TigerWire @MillionaireTiger appreciate if you could feature this article so that fellow tiger would benefit from my investing and trading thoughts.

Disclaimer: The analysis and result presented does not recommend or suggest any investing in the said stock. This is purely for Analysis.