Comparative analysis of development trajectories for open vs closed AI models. Lambert argues these two categories follow distinct improvement curves, with different implications depending on use cases where marginal intelligence gains create value.
Nathan Lambert analyzes AI trends for May 2026: Gemini Flash 3.5, Mythos model, open-closed balance, America's open-source surge, and emerging power struggles in the ecosystem.
Busy month with multiple flagship releases: Gemma 4, DeepSeek V4, Kimi K2.6, MiMo 2.5, GLM-5.1. Nathan Lambert also covers CAISI's V4 assessment of these open-source models.
Analysis of China's high-participation, open-first AI ecosystem. Reflections on compounding effects and innovation dynamics within this decentralized model.
Nathan Lambert shares observations from visits to China's leading AI labs. The article documents approaches, capabilities, and strategies of local players competing globally.
Nathan Lambert critiques the term 'distillation attacks' as a misnomer for current knowledge extraction practices from AI models. He questions the alarmist framing of the phenomenon.
Analysis of complex factors behind the performance gap between open-source and proprietary models on benchmarks. Examines how this dynamic will evolve.
Nathan Lambert shares his predictions on open-source models for mid-2026, focusing on the open-closed gap. He analyzes expected market trends for open models versus proprietary solutions.
Nathan Lambert critiques exaggerated fears around open-weight models, calling them myths. The article debunks alarmist arguments about open-source AI risks.
Google releases Gemma 4, an open-source model. The article examines success factors for open models beyond benchmark scores: ecosystem, documentation, community, and deployment ease.
Review of new models and organizations: Nemotron Super (NVIDIA), Sarvam (Indian models), Cohere Transcribe (transcription). Overview of latest open-source releases and emerging initiatives.
Nathan Lambert argues self-improvement in AI models is real but doesn't trigger fast takeoff. Improvement is gradual with diminishing returns, contradicting exponential growth scenarios.
Nathan Lambert examines the industrialization of open-source language models: market dynamics, capability progression, adaptation strategies, and growing confusion among players facing sector consolidation.
Allenai releases Olmo Hybrid, exploring novel LLM architectures. The article covers advances in open-source post-training tools and future directions in model design.
Qwen 3.5, GLM 5, and MiniMax 2.5: Chinese labs release latest frontier models. Interconnects covers recent advances from major Chinese AI players.
Nathan Lambert examines the actual importance of distillation for Chinese LLMs, responding to Anthropic's post on 'distillation attacks.' The article investigates whether model distillation remains a critical technique for Chinese developers facing restrictions on closed-model access.
Nathan Lambert analyzes the persistent gap between open-source and proprietary models. He examines distillation, innovation cycles, winning strategies for open models, specialization, and structural gaps in the industry.
Nathan Lambert examines model comparison in 2026, discussing Opus 4.6 and Codex 5.3. He questions the relevance of traditional benchmarks as model capabilities evolve rapidly and proposes reflection on new evaluation methods.