AI’s future starts to look surprisingly old-school: theCUBE’s KubeCon day one keynote analysis
The cloud-native community that turned Kubernetes into an enterprise standard is now being asked to carry artificial intelligence into production. As the underlying technology looks more alike from one company to the next, the hunt for domain-specific AI value is spotlighting how AI is put to work – and how to do so safely. That […] The post AI’s future starts to look surprisingly old-school:…
As the cloud-native community that spearheaded Kubernetes' journey to enterprise standardization turns its attention towards artificial intelligence, a striking resemblance emerges in the underlying technology. At KubeCon + CloudNativeCon NA's day one keynote, theCUBE's John Furrier, co-founder and executive analyst of theCUBE Research, observed that AI is now being deployed in production environments, much like microservices before it.
Furrier explained that the cloud-native community's existing foundation of control planes, scalable data layers, and telemetry has proven useful for AI, revealing a convergence of challenges for both domains. Enterprises are now looking to standardize on Kubernetes for AI, leveraging its maturity to support AI factories and agents, which involve coding and tasks.
The focus is shifting from maximizing token consumption to securing and governing AI agents, measuring business value, and minimizing the risk of poorly instructed agents causing trouble. Perimeter-style security is expected to make a comeback, alongside a move away from token consumption as a measure of AI value, towards tangible business outcomes.
Furrier emphasized that enterprises will need to define the limits of AI agents' freedom within real systems, fostering a return to a principle of "Build something, show me what you built, present it for value."
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