The Lede

The open source AI movement has made significant progress in producing transparent and widely usable machine learning models and systems. However, proponents of open source AI face an uphill battle in fully democratizing access to AI due to the substantial resources required for activation—compute, post-training, deployment, and oversight. Unlike software, AI models require significant computational power and specialized infrastructure, which only a few actors can currently provide.

Background & Context

The concept of open source AI is not new, but it has gained significant attention in recent years. Proponents argue that open source AI can democratize access to AI technology, making it more accessible and transparent. However, critics claim that closed-source, for-profit models will dominate the AI landscape due to their scale and funding advantages. The debate surrounding open source AI has sparked a discussion about the future of AI development and the role of public infrastructure and institutions in ensuring that AI is accessible, sustainable, and transparent.

Deep Dive

One of the key challenges facing open source AI is the substantial resources required for activation. Building foundation models is expensive, and non-AI companies looking to build AI features will often outsource their intelligence layer to a company that specializes in it. This means that the average company cannot scale large language models (LLMs) or produce novel results the same way a well-capitalized team of talented researchers can. Additionally, open source AI models may not be safe, as Mad scientists cooking up intelligence on their cinderblock-encased GPUs may not align their models with general human interests. On the other hand, proponents argue that community-driven innovation will prevail, and that public AI infrastructure and institutions are needed to ensure that models are accessible, sustainable, and transparent.

Expert Angle

According to Dean Ball, author of 'The United States Must Win The Global Open Source AI Race,' open source AI cannot compete with the resources at industry labs. 'Building foundation models is expensive, and non-AI companies looking to build AI features will outsource their intelligence layer to a company that specializes in it,' Ball said. However, Ball also believes that community-driven innovation will prevail, and that public AI infrastructure and institutions are needed to ensure that models are accessible, sustainable, and transparent. 'We need to create a public AI infrastructure that allows for the development and deployment of AI models that serve the public interest,' Ball said.

What Comes Next

The debate surrounding open source AI is far from over, and it is likely that the winner of the AI race will be a closed-source, for-profit model. However, open source advocates argue that community-driven innovation will prevail, and that public AI infrastructure and institutions are needed to ensure that models are accessible, sustainable, and transparent. As the AI landscape continues to evolve, it is essential to consider the implications of open source AI and the role of public infrastructure and institutions in ensuring that AI is developed and deployed in the public interest.