Emerging companies competing in AI markets must either invest heavily in compute and datasets to build foundation models and refine them into agents for specific uses, or license pre-trained models and lease compute to concentrate on end-user applications. Many firms cannot raise enough capital to overcome entry barriers in foundation model markets, so relying on third-party foundational capabilities creates dependencies that can threaten control over inputs and intellectual property. Building both foundation models and applications diffuses innovation capability and slows product development. Focusing from the start on consumer-facing products accelerates development while still allowing leading labs with frontier models to gain advantages when they have competing application presence. Collaboration can integrate emergent application products with foundation model developer capabilities, potentially enhancing competition even with consolidation effects.
"To compete in artificial intelligence (AI) markets, emerging companies must choose one of two routes: the capital-intensive route entails buying compute and datasets to build in-house foundation models and refining them into agents for specific use cases. Alternatively, emergents can license pre-trained models and lease compute to focus on developing applications for the end user, whether that is a solo software developer or an entire business domain."
"Of course, not all emergents can meet the fundraising expectations to build their own infrastructure and thus overcome the entry barrier in the foundation model market. But because agentic applications are in effect task-based properties of a foundation model, giving up in-house capability creates dependencies with a third-party's foundational capabilities. Naturally, most emergents do not want to cede control over these inputs to protect their competitive edge and the prospective value of their intellectual property."
"But to build both models and applications is to diffuse innovation capability at the cost of product development. If an emergent focuses from the beginning only on developing a consumer-facing product, they spare resources pre-training their own in-house models and thus accelerate product development on the application side. At the same time, if the leading AI labs with frontier models have presence in applications with their own competing products, they enjoy a competitive advantage over their peers owing to their foundational capabilities."
"This dynamic necessitates a type of collaboration that integrates the leading products of emergents with the capabilities of foundation model developers. This synergy occurs in circumstances where two firms enter a"
#ai-market-competition #foundation-models #agentic-applications #compute-and-datasets #licensing-vs-in-house-development
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