Discuss NVIDIA's scale advantage

Charlie Munger said that the importance of scale advantage in determining which companies win and which lose is absurdly large.
The sources of scale advantage can be broadly categorized into several layers:

1. The experience curve (learning by doing): The more complex tasks a company performs, the more familiar the processes become, the higher the yield, and the less waste is generated. Driven by competition and incentives, companies continuously iterate, making the same task increasingly efficient, with unit costs decreasing as cumulative production increases.
2. Geometry/physical laws (bigger is cheaper): For example, in the case of a cylindrical storage tank, the surface area (and thus the amount of steel required) grows by the square, while the capacity grows by the cube. Therefore, the larger the size, the more capacity each unit of material can support—this is the essence of scale advantage in many industries, rooted in the hard laws of the physical world.
3. Threshold resources (small players can't afford them): In the early days of TV advertising, nationwide network placements were expensive and couldn't be "half-bought." Only leading companies could afford them, and they were the most effective marketing tool at the time. As a result, already-large brand companies enjoyed a massive tailwind.
4. Information/trust advantage (familiarity is safer): When faced with a familiar brand versus an unfamiliar one—especially for products that are ingested, worn, or risk-sensitive—most people would rather pay a little more than save a small amount by putting something uncertain into their bodies. "Being recognized and trusted" is itself a moat.
5. Social proof: Seeing others buy and praise a product makes people more willing to follow suit, either subconsciously or rationally. No one wants to be the "odd one out."
6. Distribution density (ubiquitous availability): A distribution network like Coca-Cola's, which is "available almost everywhere," is the result of long, hard-fought battles by large companies. Once established, it's difficult for newcomers to disrupt.
7. Winner-takes-all (positive feedback): In some industries, scale → advantage → even greater scale creates a strong positive feedback loop (e.g., reader base → advertising → content → more readers), ultimately leading to winner-take-most outcomes.
8. Deeper specialization: When a company reaches sufficient scale, it can break down functions into finer segments, with each person focusing on a small part and perfecting it, further enhancing organizational capabilities.
   However, scale also has clear downsides:
   • Narrow and specialized can outperform broad and general: A more vertical positioning can achieve more precise information, cost-effective distribution, and more efficient reach, thereby defeating "broad and general" approaches.
   • Bureaucracy and territorialism: As organizations grow, they tend to develop hierarchies, processes, and an instinct to "protect turf." Work is often misjudged as "completed when passed on" (from my inbox to someone else's inbox) rather than "completed when results are delivered."
   • Corruptive 默契 and internal friction: Departments easily form unwritten rules—"you don't mess with me, and I won't mess with you"—ultimately piling up unnecessary management layers and costs, slowing decision-making, and leaving the company vulnerable to more agile competitors.
   • Outsiders leading insiders, expanding beyond the circle of competence: Placing inexperienced people in unfamiliar roles and using scale confidence to venture beyond one's circle of competence can lead to disastrous consequences.
   • Information silos (bad news doesn't rise): If bad news is unwelcome, people around will only report good news, leaving the organization living in an unrealistic "silo" and drifting toward absurdity amid prosperity.
   Final note: Scale advantage is a powerful weapon, but it always comes with the "curse of bureaucracy." What excellent companies must do is leverage the positive feedback of scale while using systems and culture to continuously counteract its side effects.

NVIDIA's scale advantage: The core isn't "selling more chips" but "platform + system delivery," crystallizing scale into:
"Integrated hardware-software platform + rack/system-level delivery capability."
The larger the scale, the stronger the system; the stronger the system, the more it can handle larger-scale training and inference demands, creating a powerful positive feedback loop. Its current high gross margins and market share confirm this.

1. The experience curve (learning by doing)
   NVIDIA's "experience curve" isn't just about manufacturing but extends to three areas: performance, engineering, and delivery:
   • Long-term accumulation of kernel/operator/communication optimizations: With massive model training and inference optimized within the CUDA ecosystem and library layers (e.g., cuDNN, TensorRT, CUDA-X Libraries), the "same training/inference tasks" run faster and more cost-effectively over time.
   • Cluster-level tuning and delivery experience: Evolving from "selling GPUs" to "delivering full data center-level solutions," the experience curve expands from chips to systems and operations. The FY26 Q3 CFO commentary also mentioned the business model's shift toward Blackwell full-stack data center solutions and explained how gross margins vary with architecture and cost structures.
2. Geometry/physical laws (bigger is cheaper)
   The reality of AI training is: the larger the scale, the greater the need for low-latency interconnects, higher bandwidth, and more stable power and cooling. NVIDIA has productized this "physical law" into rack-level systems:
   • GB200 NVL72: 36 Grace CPUs + 72 Blackwell GPUs, forming a 72-GPU NVLink domain, touted to "work like one giant GPU." This isn't about "selling more GPUs" but about using physics and systems engineering to solidify scale advantage into product form.
3. Threshold resources (small players can't afford them)
   NVIDIA's "threshold resources" in the modern context are:
   • Capital and organizational capabilities for advanced process/packaging/system delivery chains (design, validation, supply chain, software support, global delivery, on-site engineering).
   • The result: It maintains overwhelming dominance in the AI Accelerator market (85.2% share per IDC CY2Q25 data), with scale itself further enhancing its priority and bargaining power.
4. Information/trust advantage (familiarity is safer)
   Enterprise customers are highly risk-sensitive when "running core business on compute":
   • Buying the "most proven, most mature ecosystem, most certain to deliver" solution is more important than saving a little on cost.
   • NVIDIA's sustained high gross margins (FY26 Q3 GAAP/Non-GAAP gross margin of 73.4%/73.6%, with guidance for even higher next quarter) essentially reflect this "certainty premium."
5. Social proof
   In technology procurement, "peers are adopting it" significantly reduces decision friction:
   • You can interpret IDC's market share data as a form of "industry consensus voting." When a solution becomes the de facto standard, the cost of opposition within an organization rises.
6. Distribution density (ubiquitous availability)
   NVIDIA's "distribution density" isn't about convenience store placements but:
   • Cloud availability + OEM/integrator ecosystem + data center-level delivery capability.
   Products like the GB200 NVL72 rack-level system inherently represent density advantages in delivery chain coverage and maturity.
7. Winner-takes-all (positive feedback)
   NVIDIA's strongest positive feedback lies in:
   • Software platform → developer/operator habits → more deployments → more optimizations → stronger platform. Libraries like CUDA-X, cuDNN, and TensorRT continuously "solidify" performance and engineering certainty, amplifying platform advantage.
   • The market also leans toward winner-take-most: AI Accelerator share remains extremely high per IDC data.
8. Deeper specialization
   As NVIDIA evolves from a "chip company" to a "full-stack data center supplier," internal specialization becomes finer:
   • Architecture, networking, compilers, libraries, systems, cooling, power, operations toolchains, and industry solutions advance in synergy, creating organizational capabilities difficult to replicate piecemeal.
   • The CFO's comments on "platform transformation," "cost structure improvements," and "new product introductions" reflect this division and collaboration to some extent.

Scale backlash: NVIDIA can't escape the "curse of scale"
The larger the scale, the more complex the system; once complexity rises, backlash becomes more visible. NVDA must watch three areas:
A) Bureaucracy/collaboration complexity: More evident after shifting from "selling GPUs" to "delivering racks"
System-level delivery dramatically increases complexity—any hiccup (data center cooling, power, integration) can impact delivery and reputation. The cooling solution debates and complexities during Blackwell rack deployments exemplify the friction of "bigger scale, more complex systems."
B) "Narrow and specialized" counterattacks: Portable software layers + custom ASICs
• Modular (cross-chip execution, reducing rewrite costs), as reported by Reuters, can be seen as a "narrow and specialized leverage point": If software portability improves significantly, NVIDIA's strongest positive feedback could weaken.
• Meanwhile, large customers developing custom ASICs/diversifying supply chains to cut costs and bargaining power could make long-term share declines a consensus talking point (e.g., some institutions' 2030 share decline expectations).
C) Information silos and overconfidence
The stronger the position, the greater the need to guard against:
• Internal echo chambers of good news and overextrapolated external demand;
• Or expansions with poor returns made to sustain the "full-stack platform narrative."

Investor perspective: Translating "scale advantage" into monitorable metrics
The most practical approach is tracking whether the "flywheel keeps accelerating" and whether "backlash is materializing."
Signals of continued flywheel acceleration:
• High and stable gross margins (strong pricing power and delivery capabilities).
• Sustained high growth in data center revenue and smooth platform migration (from HGX/Hopper to Blackwell full-stack solutions).
Signals of materializing backlash:
• Frequent delivery/integration issues hindering volume (side effects of rising system complexity).
• Significant improvements in software portability, making it easier for customers to switch chips (undermining winner-take-most feedback).
• Trend-based market share declines and reduced pricing power (compare third-party share data and industry expectations).

"NVIDIA's scale advantage is essentially turning scale into a platform; NVIDIA's risks also stem from platforms growing more complex. Investing is about watching whether the flywheel keeps spinning and whether complexity starts biting back."