Open Source vs. Proprietary AI A Tussle for the Future of Artificial Intelligence

AI models and underlying hardware are possibly the hottest AI properties in town today. Considering open-source models are nowhere near as advanced and capable as private ones, the list of companies notably missing from the AI Alliance, OpenAI, Microsoft, NVIDIA, Google, DeepMind, Amazon, Anthorpic, Tesla, and multiple other AI bigwigs, is telling of the open-source and non-open-source divide.

OpenAI CEO Sam Altman and former OpenAI chief scientist Ilya Sutskever were asked by a person in the audience during a discussion at the Tel Aviv University in June this year whether open-source large language models (LLMs) can match GPT-4 without additional technical advances.

“Am I wasting my time installing Stable Vicuna 13 billion-plus wizard/ Am I wasting my time, tell me?” asked Open Source AI researcher Ishay Green, leaving Altman at a loss for words and Sutskever speechless for 12 seconds. Here’s what Sutskever answered:

“To the open-source vs. non-open-source models question, you don’t want to think about it in binary black and white terms where, like, there is a secret source that will never be rediscovered. What I will say, or whether GPT-4 will ever be reproduced by open-source models — perhaps one day it will be, but when it will be, there will be a much more powerful model in the companies. So, there will always be a gap between open-source models and their private models. And this gap may even be increasing this time. The amount of effort, engineering, and research it takes to produce one such neural net keeps increasing. And so even if there are open source models, they will be less and less produced by small groups of dedicated researchers and engineers, and it will be from the providence of a company, a big company.”

Well, sure. A strong financial backing can help companies attain a technological headstart and thus a competitive edge, Nate MacLeitch, founder and CEO of QuickBlox, opined. Sundeep Reddy Mallu, SVP at Gramener, assessed that there is “at least a 3X gap between open-source and closed-source AI models today. AI model building benefits from access to large computational power, immense granularity of data, and minimal guardrails on what can be done with it.”

Still, open-source models can play at their strengths, Narayanan said. “Open and closed-source AI models each have their strengths, typically excelling in distinct areas due to their inherent characteristics and approaches. The technological gap between these models varies: open-source models often lead in innovation and community-driven improvements, while proprietary models may offer unique,  specialized capabilities and robust support.”