Wall Street Blows Past Bubble Worries to Supercharge AI Spending Frenzy (www.wsj.com)

Wall Street is accelerating AI investments despite mounting “bubble” warnings, turning caution into a spending spree across trading desks, risk teams, and client services. Banks and hedge funds are pouring money into foundation models (private LLMs), custom fine-tuning, vector stores, MLOps pipelines and real‑time inference stacks to squeeze latency and accuracy gains in research, automated trading, compliance monitoring and customer engagement. That rush is driving big contracts for cloud providers and demand for accelerators (NVIDIA H100-class GPUs and emerging ASICs), plus expanded budgets for data engineering, labeling and model-governance tooling. The significance for the AI/ML community is twofold: immediate commercial tailwinds plus sharper technical constraints. On the positive side, surging enterprise adoption funds innovation in scalable serving, retrieval-augmented generation, privacy-preserving fine‑tuning and explainability. But it also concentrates demand for scarce talent, raises inference and storage cost pressures, and escalates model-risk and regulatory scrutiny—forcing banks to invest in robust auditability, synthetic-data controls, and low-latency, secure on‑prem or hybrid deployments. For startups and infrastructure vendors this means bigger opportunities and consolidation pressure; for researchers it accelerates production-focused work on efficiency, safety and compliance rather than purely exploratory models.