#Gate13周年现场直击

$1.5 billion bet on AI deployment, OpenAI no longer just sells models

It is reported that OpenAI is in talks to invest in a joint venture established with a private equity firm, with a maximum scale of $1.5 billion. The project is internally codenamed "DeployCo," with an estimated valuation of about $10 billion, and is expected to complete funding as early as early May.

As AI model capabilities continue to break through and performance keeps reaching new limits, the real bottleneck is no longer "can it be built," but "can it be used." In other words, the issue with AI is shifting from a technical problem to a deployment problem.

DeployCo's design directly responds to this issue. Instead of simply selling models, OpenAI chooses to bring in private equity firms, leveraging capital and customer networks to embed AI directly into a large number of enterprises it controls: providing technology and deployment capabilities on one hand, and transforming specific business processes through methods like "frontline deployment engineers" on the other. Meanwhile, the private equity firms are expected to invest around $4 billion, with a five-year funding arrangement and a minimum return guarantee of about 17.5%, making this model both scalable and reducing capital-side uncertainty.

More importantly, this is not an isolated attempt. Whether it’s the similar collaborations being promoted by Anthropic or the validated "embedded engineer" path by Palantir, they all point to the same trend: the core competitiveness of AI is shifting from model capability to the ability to transform enterprise organizations.

The recent frequent mention of AI model deployment capabilities has become a new battleground for AI companies to win. The transformation from "building" to "using" AI is essentially a reorganization of organizational capabilities, not just a technological breakthrough. Currently, global AI model parameters continue to surpass the trillion-level, but the enterprise deployment rate remains below 5%. The core contradiction lies in the structural gap between the technological advancement and organizational lag. Bridging this gap and truly integrating AI into specific enterprise-level application scenarios may be a key issue for AI leaders to consider. Everyone can also pay attention to which AI tokens are trending toward this direction. Stay tuned!