Reevaluating the Role of Private Foundation Models in the LLM Race
1. Pricing Dynamics: Concerns about predatory pricing by big players are prevalent. Yet, it's unlikely these companies would adopt such strategies, as they risk driving users away. History shows us that as infrastructure becomes more cost-effective and competition increases, prices tend to decrease, not rise. In specific cases like GitHub CoPilot, high pricing could be a barrier. Still, the real question is whether cutting costs is a viable strategy for maintaining competitiveness.
2. Competitive Edge through 'Secret Sauce': Claiming a unique advantage through a proprietary model essentially positions a company against giants like Microsoft and Google. This advantage, often based on exclusive data and tailored adjustments, might not be sustainable. Moreover, any minor enhancements might be imperceptible to users, leading to a situation akin to building a slightly better version of an existing solution.
3. Focus and Resource Allocation: Dedicating extensive resources to train or fine-tune your open source model might detract from developing other innovative features. In the vast landscape of software and AI, unique functionalities surrounding your product can be more valuable. Without these, a product risks being reduced to a mere interface for an enhanced open-source model.
4. The Quality of Paid Models: While open-source models benefit from a vast community of contributors, the quality of output from a select, highly compensated team at a private firm could be superior. If the top 0.01% of contributors from the open-source community were offered significant compensation by leading tech firms, it's likely they would accept. This suggests that while the open-source community is larger, private firms may have a smaller, yet more distinguished team.
5. Evolving Benchmarks and Release Cycles: There's a common belief that open-source models are quickly catching up to industry benchmarks. However, this perspective doesn't fully account for the differing update cycles between open-source and private foundation models. Open-source models often undergo continual, incremental updates, leading to more frequent but smaller advancements. In contrast, private foundation models like GPT are typically updated in more significant leaps, scheduled at less frequent intervals. This means that when benchmarks are conducted, they often compare the latest open-source models against slightly older versions of private models. Consequently, the benchmarks may not accurately reflect the current capabilities of private models, which could be undergoing substantial advancements behind the scenes, unknown to the public. This discrepancy in update cycles and the nature of advancements can skew perceptions of the actual progress and capabilities of private foundation models compared to their open-source counterparts.
The intention here isn't to undermine open-source models but to present counterarguments to the predominant trend of gravitating towards them. It's important to maintain a balanced perspective and recognize the potential of private foundation models in the evolving landscape of LLMs.