Ken Griffin described the part of agentic AI that should worry every quant fund A researcher can spend weeks finding one hypothesis, coding it, backtesting it, and deciding whether it survives. An agentic system can compress most of that loop into hours The real advantage is not one better strategy It is continuous strategy discovery One agent scans markets for anomalies. Another turns the strongest signals into hypotheses. A separate backtest agent tests them, then a validation layer kills anything that does not clear the thresholds The system keeps producing new candidates while old edges decay That is the part traditional quant teams spend millions building around their researchers The missing piece is memory If every failed strategy disappears into a log file, the next agent can waste time rediscovering the same dead idea. A real research system should preserve what failed, why it failed, and which market regime killed it The video shows why agentic research changes the economics of quant work The article below breaks down the full continuous strategy discovery stack, including parallel market scanning, hypothesis generation, backtesting, validation, deployment, risk controls, and the agent architecture that keeps the loop running