Don't Default To The Biggest AI Model: Agentic Systems Deserve Better [Forbes]
Goldman Sachs Group, Inc. (The) (GS)
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Source: Forbes
There's an unspoken assumption driving most enterprise AI deployments today: Bigger is better. When teams build agentic AI systems (the kind that autonomously execute multistep workflows, call APIs, extract data and make decisions), they reflexively reach for the largest language model available. I understand the instinct. These frontier models are extraordinary. But here's what I've learned building AI systems in production: Most of what an AI agent does in the real world doesn't require that power. Most agentic subtasks are repetitive, narrowly scoped and non-conversational: extracting structured data from documents, parsing user intent into a predefined category, generating a tool call with the right JSON parameters or summarizing text into three bullet points. You don't drive an 18-wheeler to pick up groceries. And you shouldn't burn frontier-model compute on tasks a well-tuned 7-billion-parameter model handles just as reliably at a fraction of the cost, latency and energy.
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