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AI's easy on-ramp has become a costly exit problem for enterprises, says Red Hat
Red Hat argues that AI's easy on-ramp through frontier model providers has become a costly exit problem as enterprises scale inference. Red Hat's Stephen Watt says companies should start on a frontier model like OpenAI or Anthropic but would be unwise to stay once token economics get expensive at scale. The company positions horizontal cloud, a shared foundation for running workloads, at the center of AI strategy. Red Hat's vLLM Semantic Router directs inference requests to purpose-trained open-weight models to improve accuracy and lower cost.
IBM's enterprise AI strategy makes trust and control the production test
This SiliconANGLE feature examines IBM's enterprise AI strategy, which centers on trust, governance, and control rather than chasing frontier models. As companies move from pilots to production, the test is whether platforms can bring automation, trusted data, and operational control into complex environments. theCUBE Research analyst John Furrier frames watsonx, hybrid cloud, and governance as IBM's path to becoming a trusted execution layer. IBM's Red Hat foundation supports its hybrid cloud approach.
SAP recasts Joule as the front door to autonomous enterprise AI
At Sapphire 2026, SAP introduced what it calls the Autonomous Enterprise, a suite of AI tools and agents built on the new SAP Business AI Platform. The company recast Joule, its generative AI assistant, from a chat interface into an engagement layer that routes users to the right assistant or agent across business domains. SAP also launched Joule Studio so customers can build their own agentic AI experiences.