Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders.<\/em> Subscribe Now<\/em><\/p>\n\n\n\n Deciding on AI models is as much of a technical decision and it is a strategic one. But choosing open, closed or hybrid models all have trade-offs.<\/p>\n\n\n\n While speaking at this year\u2019s VB Transform, model architecture experts from General Motors, Zoom and IBM discussed how their companies and customers consider AI model selection.<\/p>\n\n\n\n Barak Turovsky, who in March became GM\u2019s first chief AI officer, said there\u2019s a lot of noise with every new model release and every time the leaderboard changes. Long before leaderboards were a mainstream debate, Turovsky helped launch the first large language model (LLM) and recalled the ways open-sourcing AI model weights and training data led to major breakthroughs.<\/p>\n\n\n\n \u201cThat was frankly probably one of the biggest breakthroughs that helped OpenAI and others to start launching,\u201d Turovsky said. \u201cSo it\u2019s actually a funny anecdote: Open-source actually helped create something that went closed and now maybe is back to being open.\u201d<\/p>\n\n\n\n Factors for decisions vary and include cost, performance, trust and safety. Turovsky said enterprises sometimes prefer a mixed strategy \u2014 using an open model for internal use and a closed model for production and customer facing or vice versa.\u00a0<\/p>\n\n\n\n \n
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