Open source AI models can't improve in the same decentralized way as software like Linux. While the community can fine-tune and optimize, the primary driver of capability—massive-scale pre-training—requires centralized compute resources that are inherently better suited to commercial funding models.

The collective innovation pace of the VLLM open-source community is so rapid that even well-resourced internal corporate teams cannot keep up. Companies find that maintaining an internal fork or proprietary engine is unsustainable, making adoption of the open standard the only viable long-term strategy to stay on the cutting edge.

The excitement around AI often overshadows its practical business implications. Implementing LLMs involves significant compute costs that scale with usage. Product leaders must analyze the ROI of different models to ensure financial viability before committing to a solution.

Building software traditionally required minimal capital. However, advanced AI development introduces high compute costs, with users reporting spending hundreds on a single project. This trend could re-erect financial barriers to entry in software, making it a capital-intensive endeavor similar to hardware.

Reports of OpenAI's massive financial 'losses' can be misleading. A significant portion is likely capital expenditure for computing infrastructure, an investment in assets. This reflects a long-term build-out rather than a fundamentally unprofitable operating model.

Unlike traditional SaaS, achieving product-market fit in AI is not enough for survival. The high and variable costs of model inference mean that as usage grows, companies can scale directly into unprofitability. This makes developing cost-efficient infrastructure a critical moat and survival strategy, not just an optimization.

Historically, a developer's primary cost was salary. Now, the constant use of powerful AI coding assistants creates a new, variable infrastructure expense for LLM tokens. This changes the economic model of software development, with costs per engineer potentially rising by dollars per hour.

While OpenFold trains on public datasets, the pre-processing and distillation to make the data usable requires massive compute resources. This "data prep" phase can cost over $15 million, creating a significant, non-obvious barrier to entry for academic labs and startups wanting to build foundational models.

VLLM thrives by creating a multi-sided ecosystem where stakeholders contribute for their own self-interest. Model providers contribute to ensure their models run well. Silicon providers (NVIDIA, AMD) contribute to support their hardware. This flywheel effect establishes the platform as a de facto standard, benefiting the entire ecosystem.