AI Model Debate: GM, Zoom, and IBM Leaders Unpack Open vs. Closed Systems for Enterprise Success

In a recent discussion at the VB Transform event, AI leaders from General Motors (GM), Zoom, and IBM shared their insights on the critical decision of choosing between open, closed, and hybrid AI models for enterprise applications. The choice, they emphasized, is not just a technical one but a strategic move that can shape a company's future in the rapidly evolving AI landscape.

Barak Turovsky, GM's first Chief AI Officer, highlighted the constant buzz around new model releases and leaderboard shifts. Reflecting on his experience launching one of the earliest large language models (LLMs), Turovsky noted how open-sourcing AI model weights and training data has historically driven major breakthroughs in the field, fostering innovation through collaboration.

Representatives from Zoom and IBM echoed the sentiment that selecting an AI model involves weighing significant trade-offs. Closed models often provide enhanced security and control, making them appealing for enterprises handling sensitive data. However, they can lack the flexibility and community-driven advancements seen in open models.

The discussion also touched on the growing relevance of hybrid approaches, which combine elements of both open and closed systems. This strategy allows companies to balance security needs with the benefits of community innovation, tailoring solutions to specific use cases within their organizations.

As enterprises move beyond experimentation with AI—shifting from chatbots to intelligent, autonomous agents—these leaders stressed the importance of aligning model selection with long-term business goals. The right choice can enhance efficiency, reduce costs, and position a company as a leader in adopting transformative technology.

For businesses navigating this complex terrain, the insights from GM, Zoom, and IBM serve as a reminder that there is no one-size-fits-all solution. Each model type offers unique advantages and challenges, requiring careful consideration of technical, strategic, and ethical factors in the decision-making process.