@rohanpaul_ai: Finance may be the most suited to massive agent swarms because burning huge amounts of compute can still make economic …

X AI KOLs Timeline News

Summary

The article discusses how finance is well-suited for massive AI agent swarms because high compute costs can be justified by the extreme value of differentiated investment insights in hedge funds.

Finance may be the most suited to massive agent swarms because burning huge amounts of compute can still make economic sense when a single differentiated investment insight is extremely valuable. "Imagine if every portfolio manager at a hedge fund had 10,000 agents that were just kind of fraternizing, talking about ideas, reading through the notes, pontificating, and then at the end of, you know, 24 hours of endless, you know, debate just gave you one idea. And the reason you're able to do that is because investors are happy to pay $50,000 for one really good idea." - Gabe Stengel, co-founder of Rogo ---- From "Invest Like The Best" YouTube channel @patrick_oshag (full video link in comment)
Original Article
View Cached Full Text

Cached at: 09/23/26, 06:04 AM

Finance may be the most suited to massive agent swarms because burning huge amounts of compute can still make economic sense when a single differentiated investment insight is extremely valuable.

“Imagine if every portfolio manager at a hedge fund had 10,000 agents that were just kind of fraternizing, talking about ideas, reading through the notes, pontificating, and then at the end of, you know, 24 hours of endless, you know, debate just gave you one idea. And the reason you’re able to do that is because investors are happy to pay $50,000 for one really good idea.”

  • Gabe Stengel, co-founder of Rogo

From “Invest Like The Best” YouTube channel @patrick_oshag (full video link in comment)

Similar Articles

Swarm Scaling (13 minute read)

TLDR AI

This article analyzes the capabilities and scaling dynamics of large AI agent swarms, citing OpenAI's recent examples, and discusses their potential as a new form of inference scaling.