Altruist’s February 10th launch of Hazel AI’s tax planning capabilities sent shockwaves through global financial markets, with wealth management stocks plummeting 6-11% across US and UK exchanges. LPL Financial dropped 8.3%, Charles Schwab fell 7%, and Morgan Stanley declined 2.4% as the AI agent’s ability to generate personalized tax strategies “within minutes” triggered what analysts are calling the sector’s first major AI displacement event.

The market reaction underscored a critical infrastructure reality: AI agents are moving beyond demos into production workflows that directly threaten traditional business models. Unlike previous AI hype cycles focused on capabilities, Hazel represents operational deployment where autonomous analysis replaces billable human hours, fundamentally altering the economics of professional services.

OPAQUE Systems raised $24 million in Series B funding at a $300 million valuation to advance its Confidential AI platform, addressing what CEO Aaron Fulkerson calls the “trust chasm” blocking enterprise AI adoption. The UC Berkeley RISELab spinout has attracted customers including ServiceNow and Anthropic by providing verifiable privacy guarantees for AI workloads running on sensitive data.

The round reflects growing recognition that traditional security approaches fall short when enterprises attempt to scale AI beyond pilots. According to Gartner, Confidential AI techniques are becoming essential for securing GenAI workflows, while companies like NVIDIA, AMD, Intel, and all major hyperscalers have endorsed the category within the past year.

Former GitHub CEO Thomas Dohmke has secured a record-breaking $60 million seed round for Entire, the largest seed investment ever for a developer tools startup. The round, led by Felicis Ventures at a $300 million valuation, directly addresses what Dohmke calls the “agent boom” crisis—where massive volumes of AI-generated code overwhelm traditional development workflows.

This infrastructure play comes as enterprises grapple with a fundamental mismatch between legacy development processes and AI agent capabilities. While coding agents like GitHub Copilot have accelerated code generation, the industry lacks essential infrastructure to manage, review, and coordinate the output from multiple AI systems.