英文【GEP】为什么人工智能巨头需要重新制定供应链战略

Why AI Hyperscalers Need to Recode Their Supply Chain StrategyWHY AI HYPERSCALERS NEED TO RECODE THEIR SUPPLY CHAIN STRATEGY2The AI industry has spent the past few years engaged in a high-stakes infrastructure arms race — one defined by billion-dollar bets, rapid technological leaps, and an unrelenting demand for computational power. The companies that can build the fastest, most powerful AI systems have long believed they will emerge as the industry’s dominant players. And until recently, that belief seemed indisputable.Venture capital poured in. Tech giants doubled down. Governments got involved. The $500 billion Stargate Project in the U.S, an unprecedented investment in AI infrastructure, made it clear that the race to scale AI isn’t just about private competition—it was about national strategy. The fundamental equation seemed simple:•Maximize Raw Performance—The sheercomputational muscle needed to train ever-larger AImodels.•Maximize Deployment Speed—The ability to scalenew models at an industrial pace.To sustain this, an immense, highly specialized, and rigid physical supply chain emerged—what insiders now call the “Supply Chain of AI”. This infrastructure isn’t just about silicon chips or cloud storage; it’s a sprawling ecosystem of data centers, power generation, fiber optic networks, and high-performance cooling systems, all working in tandem to sustain AI’s insatiable appetite for compute power.But as 2025 unfolds, the assumptions underpinning this strategy are beginning to crack. Two new imperatives are reshaping the industry’s approach to AI infrastructure:•Agility—The ability to pivot instantly in response tonew breakthroughs, regulatory shifts, orunexpected constraints.•Cost Efficiency—Ensuring that AI inference(the execution of AI models at scale) can remaineconomically viable, rather than a black hole ofoperational costs.The problem? The infrastructure built to maximize performance and speed was never designed to be flexible or cost-efficient. Vertical integration—where a company owns and controls everything from chips to data centers—has long been seen as the optimal approach. But now, as the financial realities of AI inference come into focus, the industry is being forced to rethink whether a more externalized, modular supply chain might offer a better path forward.Achieving that balance isn’t a simple choice between owning infrastructure or outsourcing it. The trade-offs will vary across different components of the supply chain, from compute hardware to energy sourcing. A rigid, one-size-fits-all approach is no longer feasible—what’s needed instead is a portfolio strategy, one that optimizes for speed and performance while maintaining enough flexibility and cost control to sustain AI’s long-term growth.Four Core ElementsSix Hidden ElementsData Center ConstructionData Center Infrastructure EquipmentPower GenerationTelecom InfrastructureCompute HardwareReal EstateAI Training Data4AI Chips3AI Models2AI Talent15791086The Supply Cha