GoKawiilOpen-weight models are becoming the foundation for the next AI ecosystem. The US should compete in it, not wall itself off.
I have seen a version of this story before.
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. Cloud providers and companies including Mesosphere/D2iQ, Rancher, Red Hat and Nutanix then built businesses around integration, enterprise features, support and operations.
Kubernetes did not win simply because its repository was public. It became a neutral substrate that engineers, cloud providers and enterprise vendors could all extend to fit their customers’ needs. Common interfaces and vendor-neutral governance gave everyone confidence that they could build on it.
The lesson I took away wasn’t that open source always wins. It was that once an open platform that people can customize becomes the industry’s center of gravity, no single vendor can match the combined rate of innovation around it.
I think AI is approaching the same point.
Open weights turn a model into a platform
First, a terminology note. Most models we casually call “open source” are more accurately described as open-weight. You can download and modify the trained parameters, but the training data and complete training process usually aren’t available. That falls short of the Open Source Initiative’s definition of open source AI. The distinction matters. It doesn’t, however, prevent an ecosystem from forming around the artifact people can run and modify.
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