The gap between open weights LLMs and closed source LLMs

Terminology: “Open Source” vs “Open Weights”

• Several comments note the article conflates open source with open weights; some see this as an important distinction (reproducible pipeline, licensing, training data), others dismiss it as semantics since users mainly care about modifiable, locally runnable models.
• Some propose neutral terms like “available weights,” but there’s skepticism anyone will adopt them.

Persistence, Control, and Regulation

• Strong emphasis that open-weight models, once released, are hard to “take back,” unlike API models that can be deprecated or accounts shut off.
• Counterpoint: governments can still criminalize usage and deter most people via enforcement and penalties, though others argue such laws are hard to meaningfully enforce (citing piracy, drugs, censorship).
• Concern that propaganda about “harmful” open models could justify bans and OS-level restrictions; others stress civic resistance and open tools as defenses.

Sources of Open Models and Geopolitics

• Many note most strong open-weight models now come from Chinese labs.
• Debate over whether this is philanthropy, marketing, national strategy, or some mix.
• Some argue Chinese models rely heavily on distillation from US frontier models; others point to published research and claim this “copy-only” view underestimates Chinese innovation and hardware capabilities.
• Worry that both US and Chinese governments could eventually restrict open releases once they perceive serious strategic value.

Gap Between Open and Closed Models

• Coding is seen as the area where open weights are closest; some benchmarks show small gaps, and users report open models being “good enough” for many software tasks.
• For domains like legal, biomedical, and high-safety applications, many think frontier closed models still lead.
• Several predict a stabilizing lag: open models trail by the time needed to extract data from frontier models and retrain.

Economics, Sustainability, and Distributed Training

• Training frontier-scale models requires massive capital; concern that open releases rely on a few private actors and could stop.
• Ideas floated: community funding, “fabless” training companies licensing to inference providers, SETI@home-style distributed training, and federated learning.
• Others argue practical constraints (VRAM, bandwidth, reliability) limit large-scale distributed training, though small or specialized models may be feasible.