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TechPulse take: The article argues that scaling agentic AI requires solving systemic infrastructure challenges—specifically cost control, risk management, and efficient resource orchestration—rather than just improving model accuracy. This perspective aligns with the "value-to-cost problem" highlighted by Gartner regarding high cancellation rates for enterprise AI projects. By drawing parallels between biological evolution and enterprise adoption, the piece emphasizes that sustainable AI growth depends on building robust support systems around the core intelligence.

Sources coveringGDELT, LessWrong, MIT News, AI Alignment Forum

What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

Intelligence Is A Systems Problem And Always Has Been

GDELTAugust 21, 20265 min read

Bratin Saha, CEO of NTT DATA AIVista, a wholly owned subsidiary of NTT DATA that builds agentic AI capabilities for regulated enterprises. The agentic AI market has a value-to-cost problem. Gartner projects that more than 40% of agentic AI projects will be canceled by the end of 2027 because of cost, unclear value and inadequate risk controls. One of the most consequential technologies in human history is struggling to reach enterprise scale. That should not surprise us. Nature ran a similar experiment 540 million years ago, and enterprises are only beginning to absorb its lessons. Half a billion years ago, life on…

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