The AI Bubble Is on the Verge of Bursting (wlockett.medium.com)

A recent opinion piece argues that the AI investment bubble is on the verge of bursting — perhaps already beginning to pop — driven by hyper-inflated valuations, pervasive hype, and deep entanglement of AI bets with broader financial systems. The author points to high-profile moves like Meta’s all-in AI push (despite a stagnating core product) as symptomatic: large firms are pouring capital into compute-heavy model building to chase growth, creating an ecosystem that looks fragile if sentiment or capital dries up. The argument is that predicting the precise collapse timing is impossible, but emerging signs over the past week suggest a correction may be underway. For the AI/ML community this matters because a downturn would rewire incentives and resource flows. Expect potential funding pullbacks, hiring slowdowns, and more scrutiny on ROI for giant models — driving attention toward efficiency, reproducibility, and deployment-ready research. Technically, that shifts emphasis from raw scale to compute- and parameter-efficient architectures, better benchmarks, model distillation, and energy/latency tradeoffs; open-source and edge-oriented approaches could outcompete prestige, scale-first models. The piece warns of systemic risk too: market retrenchment could disrupt long-term research and infrastructure projects, so teams should prioritize robust evaluation, cost-aware design, and real-world utility over novelty for its own sake.