Samsung's deployment of ChatGPT Enterprise and Codex across its workforce represents the kind of scale that matters for OpenAI's revenue model, not a proof of concept, but a global rollout at a company with over 250,000 employees. Meanwhile, NVIDIA is solving a different half of the same problem. Its higher-temperature cooling systems don't generate headlines about capabilities; they generate margins. By allowing liquid cooling at 45 degrees Celsius instead of lower thresholds, NVIDIA reduces the operational cost of running the machines that Samsung's employees will be querying. The efficiency gain is concrete and measurable, which is why it matters more than most infrastructure announcements. IBM's Wimbledon deal sits in a different category entirely, a consumer-facing application that polishes the brand and generates use-case material for sales conversations, but doesn't move the underlying competitive needle. The real story across these three is vertical integration of advantage: OpenAI captures the software layer and enterprise lock-in, NVIDIA owns the hardware economics that make that software affordable to run at scale, and everyone else is racing to find a defensible position in between.
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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