
Globex
Open-source infrastructure for the next generation of AI
Globex is an open-source AI project focused on making advanced machine learning infrastructure more accessible, inspectable, and useful to the broader developer community. The project brings together models, tooling, and infrastructure intended to help researchers and engineers experiment with capable AI systems without depending entirely on closed platforms.
Chapter
Role
Founder & Project Lead
Status
Current
Timeline
2022
-
Present
Venture
Founding
Crowd Sourced
Team Size
11-50
Founded
2022
Website
Why Open Source
The early development of modern AI was shaped by an unusual concentration of capability. A relatively small number of organizations had access to the compute, data, infrastructure, and research teams required to build increasingly capable systems.
That concentration produced remarkable progress, but it also created a question we felt was worth exploring: what happens when the infrastructure required to understand and build with advanced AI becomes available to more people?
Globex started from that question.
The goal was never to reproduce a closed AI company in public. We wanted to explore a different model: build useful infrastructure in the open, let developers inspect how it works, allow researchers to modify it, and create an environment where improvements could come from a community rather than a single organization.
The first project was intentionally modest. We released a small set of tools, documented the underlying decisions, and invited developers to experiment with them.
The response changed the direction of the project.
People began using the software in ways we had not anticipated. Researchers adapted components for their own experiments. Developers contributed fixes and optimizations. Others built entirely new applications on top of the work.
The most important realization was that the project was not simply producing software.
It was creating a shared technical foundation.
Building in Public
Open source changes the way a company or project has to make decisions.
When development happens privately, a team can optimize around its own assumptions. When the work is public, those assumptions are tested immediately by people with different requirements, different architectures, and different ideas about what the system should become.
We treated that constraint as an advantage.
Instead of trying to design the entire system in advance, we focused on building a strong core and making the boundaries around it understandable. Documentation became part of the product. Reproducibility became a product requirement. Interfaces were designed not only for our own engineers, but for people we would never meet.
This also meant being deliberate about what not to build.
The project did not need to become an all-purpose AI platform. It needed to make a few important capabilities accessible and extensible enough that other people could build on them.
As the community grew, the work became increasingly collaborative. Contributions came from independent developers, researchers, and engineering teams working on very different problems. Some improvements were small and practical. Others changed fundamental assumptions about how components should interact.
That created a different kind of development loop.
A feature was proposed, tested, challenged, modified, and often improved by someone outside the original team. The resulting system was less representative of any single person's vision and more representative of the problems the community was actually encountering.
That became the central operating principle of Globex: build the foundation, make it understandable, and let other people take it further.
The Open Frontier
AI infrastructure is moving quickly, but speed alone is not the objective.
As models become more capable, the surrounding infrastructure becomes increasingly important. Researchers need systems they can inspect. Developers need tools they can adapt. Organizations need to understand the technology they are building on rather than treating it as an opaque dependency.
Open source cannot solve every problem in AI. Training frontier models requires resources that remain difficult to reproduce, and openness introduces its own challenges around governance, security, maintenance, and responsible deployment.
But those constraints make the underlying question more important, not less.
Globex is exploring what an open technical layer for AI can look like when accessibility, transparency, and extensibility are treated as first-class design requirements.
The long-term opportunity is larger than any individual repository or model.
It is to help create an ecosystem where more people can understand the technology, contribute to it, and build new things with it.
Takeaway
The most valuable infrastructure is infrastructure other people can build upon.

