The current balance of power in open models
- ID
- 26791
- Status
- summarized
- Published
- 21 Sep 2026, 7:56 PM
- Fetched
- 21 Sep 2026, 8:53 PM
- Provider
- Interconnects
- Category
- research-analysis
- Original URL
- https://www.interconnects.ai/p/the-current-balance-of-power-in-open
- Source URL
- https://www.interconnects.ai/feed
Summary
- Score
- 7.0
- Created
- 21 Sep 2026, 8:55 PM
- Tags
- Audience
- developersai_ml_learnersvibe_coderssaas_founders
What happened
Nathan Lambert published his Congressional briefing on the state of open-weight models in U.S.-China competition, noting that since April 2025 Chinese AI companies have been the clear leader in open-weight releases. He distinguishes open-weight (weights + license + inference code, e.g., Llama, Qwen, DeepSeek) from true open-source (also includes training code and data, e.g., AI2's Olmo, OpenAthena's Marin, EleutherAI's Pythia), and highlights that GLM-5.2 and Kimi K3 have driven a step change in commercial viability of open models.
Why it matters
If you are selecting open models for production or fine-tuning, the practical reality is that the strongest open-weight options are now Chinese (GLM-5.2, Kimi K3, Qwen, DeepSeek), which carries licensing, data governance, and geopolitical considerations—especially relevant in Malaysia/SEA where U.S.-export-control dynamics and China-model adoption both hit close to home. Builders should evaluate whether their use case needs true open-source reproducibility (US-led: Olmo, Marin) or whether open-weight suffices, and factor in license terms and potential regulatory shifts before committing.
Discussion angle
For a Malaysia/SEA builder community: which open-weight models are you actually deploying, and are you factoring in the license restrictions and geopolitical risk of relying on Chinese-led models like Qwen or DeepSeek versus US-led open-source like Olmo?