When Andrew Ng speaks, the AI world listens. He co-founded Google Brain, built Coursera into a platform serving 120 million learners, led AI at Baidu with 1,300 researchers, trained more than 8 million students, and now manages a $370 million AI fund. His track record gives him unique credibility.
Recently, Ng told his followers that the next big AI wave will not come from ever-larger models, but from Agentic AI—autonomous systems that can plan, reflect, critique, and improve their own work. He predicts the market will grow 13×, from $5.1 billion today to $69 billion by 2032, making it one of the most important technologies of the decade.
For financial services, where precision and trust are paramount, the implications are transformative.
What Is Agentic AI?
Traditional AI works like a calculator: ask a question, get one answer. Agentic AI, by contrast, behaves more like a team of analysts working through a problem. It relies on four key design patterns
Reflection – Critiques and revises its own outputs.
Tool Use – Connects to APIs, databases, and trading systems.
Planning – Breaks complex processes into smaller steps.
Multi-Agent Collaboration – Coordinates like a project team.
This makes workflows iterative and adaptive: outlining, researching, drafting, critiquing, and refining. In financial services, where mistakes can cost billions, this reliability is a breakthrough.
Pull Quote
“Agentic AI acts more like a team of analysts than a calculator.”
JPMorgan: A Case Study in Agentic AI at Scale
No financial institution has leaned into AI harder than JPMorgan. In 2025, it allocated $18 billion to technology, with agentic AI central to that spend.
The results:
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This insight was originally published in the first issue of FinScale Magazine by TrialScale. Download the magazine to keep reading.