The urge to make the most of AI is widespread, but organizations might be unsure about the scale and scope of the models they need to deliver on what the technology can offer. Smaller AI models can be useful for fine-tuned tasks and purposes, though there may be some tradeoffs.
Amol Ajgaonkar joined Pierre DeBois, CEO of Zimana Analytics, to explore what makes sense when deploying smaller AI models.
At the time of the episode recording, Ajgaonkar was CTO of product innovation at Insight Enterprises. He is no longer with the company.
In the discussion, DeBois and Ajgaonkar talked about less obvious differences between working with an LLM versus smaller AI models, if an AI model can be “too small” to deliver results and whether or not smaller AI models actually deliver cost savings.
They also worked their way through the Questionable Ideas tabletop exercise, where they served as interim executives for a fictional company populated with tech hype-loving goblins, old-school kobold engineers and chaos-driven gremlins.
Senior Editor, InformationWeek
Joao-Pierre S. Ruth edits stories for InformationWeek as well as reports on C-suite tech leaders across a multitude of industries and tech disciplines. He also hosts the InformationWeek podcast, which brings together one CIO or CTO with a business-operations executive to discuss their different approaches to addressing shared challenges. He has been a journalist for more than 25 years, reporting on business and technology first in New Jersey, then covering the New York tech startup community, and later as a freelancer for such outlets as TheStreet, Investopedia, and Street Fight.
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