Open source vs. commercial API-based: the philosophical frictions of AI foundation models

The rapidly evolving landscape of AI foundation models is marked by two major philosophical frictions: the dichotomy between massively large and smaller models, and the tension between open-source and commercial API-based distribution. Foundation models, which serve as a general-purpose basis for a diverse range of AI applications, are pre-trained on extensive data and can be fine-tuned for specific tasks. Large-scale models, such as OpenAI's GPT-3 and Google's BERT, traditionally outperform smaller models but are now being challenged by compact models like LLaMA, which offer comparable performance with greater efficiency. These smaller models are more accessible for deployment on devices with limited resources. Another significant debate centers around the distribution of these models, with commercial APIs like GPT-4 and LaMDA contrasted against open-source options like Dolly 2 and Stable Diffusion, raising issues of fairness, safety, and accessibility. The interplay between model size and distribution method has created distinct factions, with large models typically aligned with commercial APIs and smaller models often associated with open-source approaches. These frictions are expected to continue influencing the development, distribution, and adoption of foundation models, necessitating careful consideration by stakeholders as the technology advances.

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