OpenAI's announcements today map a strategy of vertical integration across the AI value chain: monetizing user attention through ad products, embedding itself into enterprise workflows via ChatGPT Work, and building political capital through national security partnerships and educational initiatives. The company is simultaneously pushing regulatory capture, framing safety concerns as justification for pacing model development while launching ChatGPT for Teens to expand the user base it monetizes. Meanwhile, infrastructure players are fragmenting around different optimization targets. AMD is publishing reinforcement learning benchmarks on its MI355X hardware, signaling a push into the training market where it competes directly with NVIDIA on cost and performance. Hugging Face is publishing work on agent memory efficiency and embedding models, positioning itself as the open-source alternative for teams that don't want to depend on OpenAI's closed APIs. AWS is bundling OpenAI's Bedrock integration alongside its own infrastructure services, taking a platform approach. Anthropic, by contrast, is staying quiet on infrastructure and instead publishing results on protein design and chemistry, a bet that domain-specific applications will drive adoption independent of the model wars. The real tension: OpenAI is expanding horizontally into ads, education, and government while simultaneously arguing it needs to slow down for safety reasons. Everyone else is either building around it, competing on cost and openness, or finding vertical niches where model choice matters less than application fit.
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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