Open-Source vs. Closed-Source AI Models in the Contemporary Landscape

The current discourse surrounding artificial intelligence (AI) is heavily influenced by the ongoing debate between open-source and closed-source AI models, particularly in the realm of advanced AI model development. Leading AI developers such as OpenAI, Google DeepMind, Anthropic, and Cohere predominantly maintain proprietary ownership over their cutting-edge AI models. Conversely, a select few, including Meta and emerging startup Mistral, have opted to release their state-of-the-art model weights to the public domain.

Presently, the top-performing foundation models, exemplified by OpenAI's GPT-4, remain closed-source. However, proponents of open-source AI contend that the performance gap between closed and open models is narrowing, positing that open models are poised to surpass closed models in performance, potentially as early as the upcoming year.

Contrary to this perspective, we anticipate that premier closed models will persist in significantly outperforming their open counterparts in 2024 and beyond. The landscape of foundation model performance represents a dynamic frontier, with Mistral recently announcing plans to open-source a GPT-4-level model in 2024, eliciting enthusiasm within the open-source community. Nevertheless, considering OpenAI's release of GPT-4 in early 2023, Mistral's forthcoming model is expected to lag behind the latest advancements.

Indeed, the endeavor to establish a new performance frontier entails substantial investment, exemplified by OpenAI's projected $2 billion expenditure on GPT-5 development. Meta, as a publicly traded entity, faces shareholder expectations, with open-source model releases unlikely to yield direct revenue. While Meta's investment in AI model development, such as Llama 2, may justify strategic benefits, the prospect of channeling billions into open-source endeavors without tangible returns remains dubious.

Similarly, burgeoning entities like Mistral confront the challenge of reconciling substantial investment in AI model advancement with the absence of a viable revenue model for open-source foundation models. The practicality of freely distributing models developed at considerable expense is questionable, particularly given the prevailing market dynamics exemplified by recent pricing pressures on Mistral's Mixtral model.

In light of these considerations, it's conceivable that companies like Mistral may pivot towards maintaining proprietary ownership over their most advanced models to facilitate monetization opportunities amidst escalating investment in AI model development.