The most dangerous question in the AI age sounds pragmatic:
“Where do we put the humans when AI does the work?”
It isn’t pragmatic. It’s diagnostic.
It reveals a hidden premise: humans are primarily functions—and “not being needed” becomes an existential defect.
AI doesn’t just change jobs. It relocates the bottleneck: from output to judgment—criteria formation, accountability, refusal, and the ability to stay coherent under uncertainty.
The most dangerous question in the AI age sounds pragmatic:
“Where do we put the humans when AI does the work?”
It isn’t pragmatic. It’s diagnostic.
It reveals a hidden premise: humans are primarily functions—and “not being needed” becomes an existential defect.
AI doesn’t just change jobs. It relocates the bottleneck: from output to judgment—criteria formation, accountability, refusal, and the ability to stay coherent under uncertainty.
Multi-agent AI systems are really fascinating because they move beyond single-model intelligence. Instead of one AI making all decisions, multiple agents work together—sharing information, negotiating, and even competing—to solve complex problems more efficiently. This approach mirrors real-world systems, from traffic management to financial trading, where multiple actors interact dynamically.
The future of AI is likely to be collaborative and distributed, and understanding multi-agent systems gives you a front-row seat to that evolution. For structured learning and practical applications in AI, platforms like https://www.icertglobal.com/
offer resources to explore these concepts hands-on.
The most dangerous question in the AI age sounds pragmatic:
“Where do we put the humans when AI does the work?”
It isn’t pragmatic. It’s diagnostic.
It reveals a hidden premise: humans are primarily functions—and “not being needed” becomes an existential defect.
AI doesn’t just change jobs. It relocates the bottleneck: from output to judgment—criteria formation, accountability, refusal, and the ability to stay coherent under uncertainty.
Essay here:
👉 https://open.substack.com/pub/leontsvasmansapiognosis/p/the-most-dangerous-question-in-the
— Leon Tsvasman
The most dangerous question in the AI age sounds pragmatic:
“Where do we put the humans when AI does the work?”
It isn’t pragmatic. It’s diagnostic.
It reveals a hidden premise: humans are primarily functions—and “not being needed” becomes an existential defect.
AI doesn’t just change jobs. It relocates the bottleneck: from output to judgment—criteria formation, accountability, refusal, and the ability to stay coherent under uncertainty.
Essay here:
👉 https://open.substack.com/pub/leontsvasmansapiognosis/p/the-most-dangerous-question-in-the
— Leon Tsvasman
Multi-agent AI systems are really fascinating because they move beyond single-model intelligence. Instead of one AI making all decisions, multiple agents work together—sharing information, negotiating, and even competing—to solve complex problems more efficiently. This approach mirrors real-world systems, from traffic management to financial trading, where multiple actors interact dynamically.
The future of AI is likely to be collaborative and distributed, and understanding multi-agent systems gives you a front-row seat to that evolution. For structured learning and practical applications in AI, platforms like https://www.icertglobal.com/
offer resources to explore these concepts hands-on.