The existential threat to major AI labs may not be total collapse—it's stagnation. Consider the capital demands: sustaining current development trajectories requires approximately $100 billion over the next 2-3 years. Yet here's the paradox: LLM competitive advantage has an absurdly short shelf life, often under 100 days before models become outdated. Test this yourself—would you rely on ChatGPT from twelve months ago for complex tasks? Claude from a year prior? The quality gap is stark. This creates a relentless treadmill: continuous massive investment required just to stay competitive, yet rapid iteration makes yesterday's breakthrough today's commodity. The real risk isn't insolvency through failure; it's the inability to sustain the capital intensity required to maintain relevance in an industry where technological moats erode faster than they're built.