"Inference and pre-training each require large, talent-dense organizations that deploy significant CapEx to compete on optimization, scale, and generality. In traditional software, you don't need massive capex to be market competitive, and this is why startups with good product sense have historically fared well. Our bet is that post-training is more open-ended and lends itself to a business model that is closer to traditional software, where taste, judgment, and small teams will produce better products than legacy incumbents. Across a variety of domains spanning knowledge work, search, design, and so on, we expect this new working model of intense data-training codesign to outperform the data vendor outsourcing at frontier labs.
As such, we expect to see a rich ecosystem of open-weight developers post-training domain-specific models that are Pareto optimal for the large chunks of GDP that have thus far been untouched by AI. These open-weight developers will benefit from open-source software, deploy on existing inference provider networks, and build upon a growing catalog of open base models. Open-weights will be an essential component of each enterprise’s intelligence stack."
As such, we expect to see a rich ecosystem of open-weight developers post-training domain-specific models that are Pareto optimal for the large chunks of GDP that have thus far been untouched by AI. These open-weight developers will benefit from open-source software, deploy on existing inference provider networks, and build upon a growing catalog of open base models. Open-weights will be an essential component of each enterprise’s intelligence stack."
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