OpenAI is running a parallel strategy of direct product deployment and policy positioning. The company shipped GPT-6 Astra to paying customers in legal document drafting, video production, and customer service automation, extracting measurable productivity gains: Harvey's lawyers get structured context-aware drafts, invideo triples color correction speed, Ringg cuts call-handling costs by 90 percent versus GPT-4.1. Simultaneously, Altman addressed the UN Security Council on AI safety and international cooperation while the company extended its Daybreak cyber program to Ukraine and marked two years of academy training initiatives. Google DeepMind added server-side memory to Private AI Compute and released Gemini 3.8 text-to-speech. Anthropic announced enzyme discovery using Claude for scientific research. GitHub shipped diff rendering for massive pull requests in Copilot. The announcements collectively reveal where the money is moving: enterprise workflow automation with quantified ROI, not research papers or capability benchmarks. MIRI published a policy position on superintelligence regulation. AMD and Hugging Face published technical content on RL debugging and robotics simulation. PrismML is targeting edge inference on mobile hardware. The signal is clear: the labs with shipping products and paying customers are talking about cost reduction and specific business outcomes. The labs with policy positions are talking about regulation. The labs with infrastructure plays are talking about technical infrastructure. Nobody is talking about AGI timelines or alignment breakthroughs, which means either they are not happening at the pace the previous cycle suggested, or the labs have learned to separate public positioning from internal research priorities.
Sloane Duvall
A curated reference of models from major AI labs, with open/closed weight status, input modalities, and context window size. American labs tend towards closed weights models and Chinese labs tend toward open weights models.
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