JBS Dev, 모델 성능보다 AI 비용과 이식성 우선 제안
JBS Dev 사장 Joe Rose는 기업이 기존 클라우드 도구를 활용해 에이전트형 워크로드를 구축하고 비용 지속 가능성에 초점을 옮겨야 한다고 주장했습니다.
joe rose, president of strategic technology provider jbs dev, challenged the vendor narrative that requires massive data lakes before adopting generative and agentic ai. he argued that modern large language models can effectively process imperfect data, citing a client example where ai reconciled messy medical billing records involving pdfs, images, and mismatched fields. rose emphasized that while a human in the loop is necessary for guardrails, automation rates can grow incrementally. he predicted that future ai discussions will center on reducing costs and enabling models to run on laptops rather than data centers, citing diminishing returns from training on existing internet data.