Open-weight AI is having its Kubernetes moment

In one line

Open-weight models are becoming the foundation for the next AI ecosystem. The US should compete in it, not wall itself off.

Opening excerpt

In 2013 I co-founded Mesosphere, an open source cloud-native software company. We built on Apache Mesos, which my co-founder Ben Hindman had helped create at UC Berkeley. We later built DC/OS (Data Center Operating System) around Mesos, released it as open source, and commercialized it through an enterprise distribution with support and proprietary features.

After several years of massive growth, Kubernetes disrupted us. It was newer, fully open source, and it quickly galvanized the cloud-native community. Many of the world’s best distributed-systems and infrastructure engineers bet their careers on it, and even some of our most loyal community members changed horses.

Once that happened, innovation moved to Kubernetes. Whatever the platform was missing, someone started building: networking, storage, observability, deployment tools, policy engines. A large number of startups were created, and legacy vendors joined in as well. Almost every component required to run Kubernetes in production became available as open source.

(Excerpted from the original; full article via the source link below.)

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