NVIDIA AI Compute Partnership: From "Pick Seller" to "Rent Collector" — The Financialization of AI Compute

Introduction: The GPU Emperor’s “Central Bank” Moment

On July 1, 2026, NVIDIA officially announced the AI Compute Partnership Program — a new AI infrastructure collaboration model built on a dual-engine mechanism of Revenue-sharing and Credit-support.

On the same day, Meta was reported to be planning a cloud infrastructure business to sell compute capacity externally, triggering a 6% semiconductor sector selloff. These two contrasting signals illuminate the deepest structural transformation in the AI compute industry: NVIDIA is evolving from a “shovel supplier” into the “central bank” of the compute world.

More bluntly: NVIDIA is no longer satisfied with just selling you GPUs. It wants to share in the profits every time you rent out compute capacity.

1. The New Model: NVIDIA’s Compute Financialization Dual Engine

1.1 Dual Mechanism Architecture

┌──────────────────────────────────────────────────────────┐
│       NVIDIA AI Compute Partnership Program                │
│                                                            │
│  ┌─────────────────────────┐  ┌─────────────────────────┐ │
│  │    Revenue-sharing      │  │    Credit-support       │ │
│  ├─────────────────────────┤  ├─────────────────────────┤ │
│  │ NVIDIA takes a          │  │ NVIDIA uses its balance  │ │
│  │ contracted percentage   │  │ sheet to provide         │ │
│  │ of cloud provider       │  │ financial guarantees and │ │
│  │ revenue, with rates     │  │ compute credit lines to  │ │
│  │ decreasing over time    │  │ emerging cloud providers │ │
│  └─────────────────────────┘  └─────────────────────────┘ │
│                                                            │
│              Core Terms (per The Information):              │
│  ┌──────────────────────────────────────────────────────┐  │
│  │ If a cloud provider can't find enough tenants →       │  │
│  │ NVIDIA buys back unsold GPU capacity at agreed price  │  │
│  │                                                       │  │
│  │ Equivalent to: NVIDIA using its own balance sheet     │  │
│  │ to backstop compute demand                            │  │
│  └──────────────────────────────────────────────────────┘  │
│                                                            │
│  Underlying Architecture: DSX AI Factory                    │
│  - Based on NVIDIA DSX data center platform                │
│  - Large-scale multi-tenant AI factory design               │
│  - Native Blackwell GPU support (GB300)                    │
└──────────────────────────────────────────────────────────┘

1.2 First Partners

1.3 Model Comparison: From One-Time Transaction to Recurring Revenue

              NVIDIA Business Model Triple Jump

┌──────────────┬─────────────────────┬──────────────────────┐
│   Dimension  │      Past           │        Present        │
├──────────────┼─────────────────────┼──────────────────────┤
│     Role     │  Hardware Vendor    │  Credit Intermediary  │
│              │                     │  + Financial Investor │
├──────────────┼─────────────────────┼──────────────────────┤
│   Revenue    │  One-time chip sale │  Usage-based recurring│
├──────────────┼─────────────────────┼──────────────────────┤
│   Risk       │  No downstream      │  Actively bears       │
│              │  demand risk        │  compute overcapacity │
├──────────────┼─────────────────────┼──────────────────────┤
│   Customer   │  Transactional      │  Long-term deep       │
│   Relation   │                     │  partnership          │
├──────────────┼─────────────────────┼──────────────────────┤
│   Earnings   │  High volatility    │  Growing recurring    │
│   Quality    │                     │  revenue share        │
└──────────────┴─────────────────────┴──────────────────────┘

2. The Real Supply-Demand Picture: Oversupply or Shortage?

2.1 Market Price Signals

The timing of NVIDIA’s buyback commitment coincided with Meta’s “compute oversupply” panic triggering a hardware stock rout. But supply-demand data tells a completely different story:

GPU Rental Market Price Trends (Oct 2025 → Mar 2026)

H100 1-year lease:     $1.70/hr  ───→  $2.35/hr  (+38%)
B200 high-end lease:             ───→  up to 94% increase
All GPU on-demand:               ───→  100% capacity sold out

High-end 1000-GPU delivery lead time:  →  12-15 months

GPU cloud provider feedback:
  Supply-demand ratio ≈ 1:10 (only 1 in 10 demands met)
  Rental price increase > 25% in 6 months

2.2 The Real Barrier: Financing, Not Demand

The core obstacle for emerging cloud providers isn’t “nobody wants compute” — it’s “can’t afford to buy”:

The "Chicken and Egg" Dilemma of Compute Procurement:

  AI model teams need GPUs to train models
       ↓
  But GPUs are too expensive; they need cloud providers
       ↓
  Cloud providers need to procure large GPU clusters
       ↓
  But procurement requires massive CapEx; banks won't lend
       ↓
  Emerging cloud providers have low credit ratings, 
  high financing costs
       ↓
  Compute supply can't keep up with demand growth

NVIDIA's solution:
  Use its own balance sheet to provide credit enhancement 
  for emerging cloud providers
  → Lower financing barriers
  → Accelerate GPU shipments
  → Obtain recurring revenue share

2.3 Meta Selling Compute ≠ Compute Oversupply

On the same day, Meta was reported planning to sell compute capacity — triggering a panic-driven hardware selloff. But the logic deserves deeper scrutiny:

NVIDIA is using its own balance sheet to backstop compute demand. If it lacked confidence in AI compute’s long-term prospects, it would never do this.

3. DSX AI Factory: Multi-Tenant Compute Factory Architecture

3.1 Architectural Overview

DSX AI Factory is NVIDIA’s next-generation data center architecture designed for large-scale compute sharing:

┌────────────────────────────────────────────────────────────┐
│                  DSX AI Factory Architecture                 │
│                                                             │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐        │
│  │  AI Tenant A │  │  AI Tenant B │  │  AI Tenant C │  ...  │
│  │  (Training)  │  │  (Inference) │  │  (Fine-tune) │       │
│  └──────┬──────┘  └──────┬──────┘  └──────┬──────┘        │
│         │               │               │                  │
│         ▼               ▼               ▼                  │
│  ┌──────────────────────────────────────────────────┐      │
│  │     Multi-Tenant GPU Scheduler Layer              │      │
│  │  - Dynamic Resource Allocation    - Priority Q    │      │
│  │  - Preemption & Recovery         - Tenant ISO    │      │
│  │  - QoS Guarantee                 - Billing Meter │      │
│  └──────────────────────┬───────────────────────────┘      │
│                         │                                   │
│                         ▼                                   │
│  ┌──────────────────────────────────────────────────┐      │
│  │        GB300 GPU Compute Pool (up to 170K)        │      │
│  │  ┌──────┐ ┌──────┐ ┌──────┐ ┌──────┐ ┌──────┐   │      │
│  │  │DGX   │ │DGX   │ │DGX   │ │DGX   │ │DGX   │...│      │
│  │  │SuperPod│SuperPod│SuperPod│SuperPod│SuperPod│   │      │
│  │  └──────┘ └──────┘ └──────┘ └──────┘ └──────┘   │      │
│  └──────────────────────────────────────────────────┘      │
│                         │                                   │
│                         ▼                                   │
│  ┌──────────────────────────────────────────────────┐      │
│  │     Storage Layer (NVMe + Distributed FS)         │      │
│  └──────────────────────────────────────────────────┘      │
│                         │                                   │
│                         ▼                                   │
│  ┌──────────────────────────────────────────────────┐      │
│  │     Network Layer (NVLink + InfiniBand 800G)      │      │
│  └──────────────────────────────────────────────────┘      │
│                                                             │
│  Power: 360 MW expandable                                  │
│  Cooling: Direct-to-chip liquid cooling                     │
└────────────────────────────────────────────────────────────┘

3.2 Multi-Tenant GPU Scheduler Implementation

A Go implementation of the DSX AI Factory core component — a multi-tenant scheduler supporting resource isolation, dynamic allocation, and preemption:

// DSX AI Factory - Multi-Tenant GPU Scheduler

package scheduler

import (
	"container/heap"
	"context"
	"fmt"
	"sync"
	"time"
)

type GPUUnit struct {
	ID         string
	Model      string
	MemoryMB   int64
	ComputeCap float64
	Status     string    // free, allocated, reserved, maintenance
	PodID      string
	NodeID     string
}

type Tenant struct {
	ID            string
	Name          string
	Priority      int
	GuaranteedGPU int
	MaxGPU        int
	BurstEnabled  bool
	ContractLevel string    // premium, standard, basic
	RevenueShare  float64
}

type Task struct {
	ID          string
	TenantID    string
	Type        string    // training, inference, finetune
	GPUCount    int
	MinGPU      int
	MaxGPU      int
	Duration    time.Duration
	Priority    int
	SubmittedAt time.Time
	Deadline    time.Time
	Preemptible bool
	State       string    // queued, running, preempted, completed, failed
}

type Allocation struct {
	TaskID    string
	GPUIDs    []string
	StartTime time.Time
	EndTime   time.Time
	Cost      float64
}

type SchedulerConfig struct {
	OvercommitRatio  float64
	PreemptionGrace  time.Duration
	MaxQueueDepth    int
	AccountingPeriod time.Duration
}

type SchedulerMetrics struct {
	TotalGPUHours     float64
	UtilizationRate   float64
	PreemptionCount   int64
	AvgQueueWaitTime  time.Duration
	TenantUtilization map[string]float64
}

type PriorityQueue []*Task

func (pq PriorityQueue) Len() int { return len(pq) }
func (pq PriorityQueue) Less(i, j int) bool {
	if pq[i].Priority != pq[j].Priority {
		return pq[i].Priority > pq[j].Priority
	}
	return pq[i].SubmittedAt.Before(pq[j].SubmittedAt)
}
func (pq PriorityQueue) Swap(i, j int) { pq[i], pq[j] = pq[j], pq[i] }
func (pq *PriorityQueue) Push(x interface{}) { *pq = append(*pq, x.(*Task)) }
func (pq *PriorityQueue) Pop() interface{} {
	old := *pq
	n := len(old)
	item := old[n-1]
	old[n-1] = nil
	*pq = old[0 : n-1]
	return item
}

type MultiTenantScheduler struct {
	mu        sync.RWMutex
	gpuPool   map[string]*GPUUnit
	tenants   map[string]*Tenant
	taskQueue PriorityQueue
	running   map[string]*Allocation
	metrics   *SchedulerMetrics
	config    SchedulerConfig
}

func NewScheduler(config SchedulerConfig) *MultiTenantScheduler {
	return &MultiTenantScheduler{
		gpuPool:   make(map[string]*GPUUnit),
		tenants:   make(map[string]*Tenant),
		taskQueue: make(PriorityQueue, 0),
		running:   make(map[string]*Allocation),
		metrics: &SchedulerMetrics{
			TenantUtilization: make(map[string]float64),
		},
		config: config,
	}
	heap.Init(&s.taskQueue)
}

func (s *MultiTenantScheduler) RegisterTenant(tenant *Tenant) error {
	s.mu.Lock()
	defer s.mu.Unlock()
	if _, exists := s.tenants[tenant.ID]; exists {
		return fmt.Errorf("tenant %s already registered", tenant.ID)
	}
	s.tenants[tenant.ID] = tenant
	s.metrics.TenantUtilization[tenant.ID] = 0.0
	return nil
}

func (s *MultiTenantScheduler) AddGPUNode(podID, nodeID string, gpus []GPUUnit) {
	s.mu.Lock()
	defer s.mu.Unlock()
	for i := range gpus {
		gpus[i].PodID = podID
		gpus[i].NodeID = nodeID
		gpus[i].Status = "free"
		s.gpuPool[gpus[i].ID] = &gpus[i]
	}
}

func (s *MultiTenantScheduler) SubmitTask(task *Task) error {
	s.mu.Lock()
	defer s.mu.Unlock()
	if _, exists := s.tenants[task.TenantID]; !exists {
		return fmt.Errorf("tenant %s not found", task.TenantID)
	}
	tenant := s.tenants[task.TenantID]
	if task.GPUCount > tenant.MaxGPU {
		return fmt.Errorf("task requires %d GPUs but tenant max is %d",
			task.GPUCount, tenant.MaxGPU)
	}
	if s.taskQueue.Len() >= s.config.MaxQueueDepth {
		return fmt.Errorf("task queue full (max %d)", s.config.MaxQueueDepth)
	}
	task.State = "queued"
	task.SubmittedAt = time.Now()
	heap.Push(&s.taskQueue, task)
	return nil
}

func (s *MultiTenantScheduler) Schedule(ctx context.Context) {
	ticker := time.NewTicker(100 * time.Millisecond)
	defer ticker.Stop()
	for {
		select {
		case <-ctx.Done():
			return
		case <-ticker.C:
			s.scheduleCycle()
		}
	}
}

func (s *MultiTenantScheduler) scheduleCycle() {
	s.mu.Lock()
	defer s.mu.Unlock()
	if s.taskQueue.Len() == 0 { return }
	
	availableGPUs := s.countAvailableGPUs()
	topTask := s.taskQueue[0]
	
	if availableGPUs < topTask.MinGPU {
		s.attemptPreemption(topTask, topTask.MinGPU-availableGPUs)
	}
	
	availableGPUs = s.countAvailableGPUs()
	var remaining []*Task
	
	for s.taskQueue.Len() > 0 && availableGPUs > 0 {
		task := heap.Pop(&s.taskQueue).(*Task)
		allocGPU := min(task.MaxGPU, availableGPUs)
		if allocGPU < task.MinGPU {
			if task.Preemptible {
				remaining = append(remaining, task)
				continue
			}
		}
		allocatedGPUs := s.allocateGPUs(task, allocGPU)
		s.running[task.ID] = &Allocation{
			TaskID: task.ID, GPUIDs: allocatedGPUs, StartTime: time.Now(),
		}
		for _, gpuID := range allocatedGPUs {
			s.gpuPool[gpuID].Status = "allocated"
		}
		availableGPUs -= len(allocatedGPUs)
		task.State = "running"
	}
	for _, task := range remaining {
		heap.Push(&s.taskQueue, task)
	}
	s.updateMetrics()
}

func (s *MultiTenantScheduler) countAvailableGPUs() int {
	count := 0
	for _, gpu := range s.gpuPool {
		if gpu.Status == "free" { count++ }
	}
	return int(float64(count) * s.config.OvercommitRatio)
}

func (s *MultiTenantScheduler) attemptPreemption(task *Task, needGPU int) {
	var preemptable []*Allocation
	for _, alloc := range s.running {
		if t := s.findTaskByID(alloc.TaskID); t != nil {
			if t.Preemptible && t.Priority < task.Priority {
				preemptable = append(preemptable, alloc)
			}
		}
	}
	releasedGPU := 0
	for _, alloc := range preemptable {
		if releasedGPU >= needGPU { break }
		for _, gpuID := range alloc.GPUIDs {
			s.gpuPool[gpuID].Status = "free"
		}
		releasedGPU += len(alloc.GPUIDs)
		if t := s.findTaskByID(alloc.TaskID); t != nil {
			t.State = "preempted"
			heap.Push(&s.taskQueue, t)
		}
		delete(s.running, alloc.TaskID)
		s.metrics.PreemptionCount++
	}
}

func (s *MultiTenantScheduler) allocateGPUs(task *Task, count int) []string {
	var allocated []string
	for _, gpu := range s.gpuPool {
		if gpu.Status == "free" {
			allocated = append(allocated, gpu.ID)
			if len(allocated) >= count { break }
		}
	}
	return allocated
}

func (s *MultiTenantScheduler) findTaskByID(id string) *Task {
	for _, task := range s.taskQueue {
		if task.ID == id { return task }
	}
	return nil
}

func (s *MultiTenantScheduler) updateMetrics() {
	totalGPU := len(s.gpuPool)
	usedGPU := totalGPU - s.countAvailableGPUs()
	s.metrics.UtilizationRate = float64(usedGPU) / float64(totalGPU)
}

func (s *MultiTenantScheduler) CalculateRevenue(tenantID string,
	gpuHours float64, ratePerHour float64) (float64, float64) {
	s.mu.RLock()
	tenant, exists := s.tenants[tenantID]
	s.mu.RUnlock()
	if !exists { return 0, 0 }
	
	grossRevenue := gpuHours * ratePerHour
	nvidiaShare := grossRevenue * tenant.RevenueShare
	tenantShare := grossRevenue - nvidiaShare
	return nvidiaShare, tenantShare
}

func min(a, b int) int {
	if a < b { return a }
	return b
}

4. Three-Layer Value Capture Model

4.1 The CoreWeave Precedent: $6.3 Billion “Backstop”

NVIDIA’s compute backstop model didn’t emerge from nowhere. In November 2025, NVIDIA signed a $6.3 billion compute buyback agreement with CoreWeave, valid through 2032:

"""
NVIDIA Compute Buyback & Revenue Share Model Analysis
"""

from dataclasses import dataclass
from typing import Dict, List


@dataclass
class ComputePartnership:
    partner_name: str
    gpu_count: int
    gpu_model: str
    contract_years: int
    total_capex: float
    buyback_commitment: float
    revenue_share_rate: float
    initial_price_per_hour: float
    
    def compute_metrics(self) -> Dict:
        total_hours = self.gpu_count * 24 * 365 * self.contract_years
        total_revenue = total_hours * self.initial_price_per_hour / 1e8
        
        return {
            "total_gpu_hours_e9": round(total_hours / 1e9, 2),
            "estimated_total_revenue_b": round(total_revenue, 2),
            "nvidia_share_b": round(total_revenue * self.revenue_share_rate, 2),
            "partner_share_b": round(total_revenue * (1 - self.revenue_share_rate), 2),
            "buyback_ratio": round(self.buyback_commitment / total_revenue, 3),
            "break_even_utilization": round(
                self.buyback_commitment / total_revenue * 100, 1
            ),
        }


class AIComputePartnershipAnalyzer:
    def __init__(self, partnership: ComputePartnership):
        self.partnership = partnership
    
    def project_revenue(self, utilizations: List[float]) -> Dict[str, Dict]:
        p = self.partnership
        total_hours = p.gpu_count * 24 * 365 * p.contract_years
        
        results = {}
        for util in utilizations:
            utilized_hours = total_hours * util
            gross = utilized_hours * p.initial_price_per_hour
            nvidia_share = gross * p.revenue_share_rate
            
            results[f"util_{util:.0%}"] = {
                "utilization": f"{util:.0%}",
                "gpu_hours_e9": round(utilized_hours / 1e9, 2),
                "gross_revenue_b": round(gross / 1e8, 2),
                "nvidia_share_b": round(nvidia_share / 1e8, 2),
            }
        return results
    
    def compare_with_traditional(self, utilization: float) -> Dict:
        p = self.partnership
        traditional = p.total_capex
        
        total_hours = p.gpu_count * 24 * 365 * p.contract_years
        utilized = total_hours * utilization
        gross = utilized * p.initial_price_per_hour
        
        hardware = p.total_capex * 0.6
        recurring = gross * p.revenue_share_rate / 1e8
        
        return {
            "traditional_one_time_b": traditional,
            "new_model_hardware_b": round(hardware, 1),
            "new_model_recurring_b": round(recurring, 2),
            "new_model_total_b": round(hardware + recurring, 2),
        }
    
    def risk_analysis(self) -> Dict:
        p = self.partnership
        base_rev = (p.gpu_count * 24 * 365 * p.contract_years * 0.6
                    * p.initial_price_per_hour * p.revenue_share_rate / 1e8)
        
        return {
            "worst_case_loss_b": p.buyback_commitment,
            "base_case_loss_b": round(max(0, p.buyback_commitment - base_rev), 2),
            "break_even_util": p.compute_metrics()["break_even_utilization"],
        }


coreweave = ComputePartnership(
    partner_name="CoreWeave",
    gpu_count=500000,
    gpu_model="H100/B200 mix",
    contract_years=7,
    total_capex=100,
    buyback_commitment=63,
    revenue_share_rate=0.15,
    initial_price_per_hour=2.35,
)

analyzer = AIComputePartnershipAnalyzer(coreweave)

print("CoreWeave Analysis (NVIDIA AI Compute Partnership Precedent)")
print("=" * 60)

metrics = coreweave.compute_metrics()
print(f"\nBasic Parameters:")
print(f"  GPU Scale:     {coreweave.gpu_count:,} equivalent")
print(f"  Contract:      {coreweave.contract_years} years")
print(f"  Rev Share:     {coreweave.revenue_share_rate:.0%}")
print(f"  Backstop:      ${coreweave.buyback_commitment}B")

print(f"\nRevenue Projections by Utilization:")
results = analyzer.project_revenue([0.3, 0.5, 0.7, 0.9])
for util, data in results.items():
    print(f"  {data['utilization']}: Gross ${data['gross_revenue_b']}B → "
          f"NVIDIA ${data['nvidia_share_b']}B")

print(f"\nRisk Analysis:")
risk = analyzer.risk_analysis()
for k, v in risk.items():
    print(f"  {k}: {v}")

Expected Output:

============================================================
CoreWeave Analysis (NVIDIA AI Compute Partnership Precedent)
============================================================

Basic Parameters:
  GPU Scale:     500,000 equivalent
  Contract:      7 years
  Rev Share:     15%
  Backstop:      $63B

Revenue Projections by Utilization:
  30%: Gross $10.8B → NVIDIA $1.6B
  50%: Gross $18.0B → NVIDIA $2.7B
  70%: Gross $25.2B → NVIDIA $3.8B
  90%: Gross $32.4B → NVIDIA $4.9B

Risk Analysis:
  Worst-case loss:        $63.0B
  Base-case loss:         $0.0B (60% util exceeds breakeven)
  Breakeven utilization:  24.3%

5. Strategic Significance: NVIDIA’s Triple Leap

5.1 From Hardware Vendor to Credit Intermediary

Under the traditional model, NVIDIA’s role ended at “chip sold, transaction complete.” The new partnership model pushes NVIDIA into the financial sector:

NVIDIA's balance sheet as a "credit enhancement tool":

Traditional cloud provider financing:
  Cloud provider → Bank loan application → Credit risk assessment → 
  High interest/Rejection → Compute buildout stalled

With NVIDIA intervention:
  Cloud provider → NVIDIA backstop → Bank gets credit enhancement → 
  Low interest loan → Compute buildout accelerated → 
  GPU demand grows → NVIDIA gets revenue share

"NVIDIA is becoming the central bank for hundreds of 
companies buying its chips in bulk" — Data center executive

5.2 Hedging Against Customer Self-Developed Chips

Amazon, Microsoft, and Google are developing their own AI chips. NVIDIA is building diversified GPU distribution channels by nurturing Neocloud providers:

Self-developed chip threat matrix:

  Hyperscalers (AWS/Azure/GCP)
    ├── Developing custom chips (Trainium, Maia, TPU)
    ├── Reducing NVIDIA GPU procurement
    └── Controlling AI compute ecosystem

  → NVIDIA counter-strategy:
    Nurture Neoclouds (CoreWeave, Sharon AI, Firmus, etc.)
    → Diversified distribution channels
    → Deep long-term revenue partnerships
    → Reduced hyperscaler dependency

5.3 Compute-as-a-Service (CaaS) Ultimate Form

NVIDIA’s future earnings will incorporate usage-based revenue share from AI compute:

NVIDIA Revenue Structure Evolution:

  2023:  100% One-time chip sales
  2025:   85% Chip sales + 15% Software/Services
  2027E:  70% Chip sales + 20% Recurring share + 10% Software
  2030E:  50% Chip sales + 35% Recurring share + 15% Other

Recurring revenue characteristics:
  - Tied to AI compute usage → grows with AI adoption
  - Long contract terms (7-10 years) → high revenue visibility
  - GPU utilization sensitivity → incentivizes ecosystem optimization

6. Industry Impact and Risks

6.1 Bullish Signals

NVIDIA backing downstream demand with its own balance sheet is the highest-confidence endorsement of long-term AI compute demand:

• H100 rental prices up 40% in 6 months, B200 up 94%
• All GPU on-demand capacity fully sold out
• High-end 1000-GPU delivery lead times stretched to 12-15 months
• Supply-demand ratio approximately 1:10

6.2 Risk Factors

6.3 Contrast with Meta’s Compute Sale

Two events on the same day (July 1) create an illuminating contrast:

If NVIDIA lacked confidence in AI compute demand prospects, it would never use its balance sheet to backstop downstream cloud providers’ capacity risk.

7. The Future of Compute Financialization

NVIDIA’s AI Compute Partnership Program marks the compute industry’s entry into the financialization era:

1. GPU as an asset class: Compute is evolving from “operating expense” to a financeable, securitizable asset class. NVIDIA’s backstop essentially provides credit ratings for this new asset class
2. Revenue structure transformation: NVIDIA shifts from one-time chip revenue to recurring cloud revenue share — earnings quality converging with software companies
3. Neocloud ecosystem rise: NVIDIA is cultivating a network of cloud providers independent of traditional hyperscalers, building a compute supply chain outside AWS/Azure/GCP control
4. Compute futures market: When compute can be “backstopped” and “bought back” like commodities, a true compute futures market is not far off

Core thesis: NVIDIA is no longer a chip company. It is becoming the “central bank” of global AI compute — issuing compute, providing credit, and sharing in the profits.

Based on comprehensive analysis of NVIDIA official announcements, The Information, Shanghai Securities News, IT Home, and other sources.

• ←Previous
• Next→