OpenAI and Zapier accelerate each other
Mike Knoop, co-founder of Zapier, on Zapier's LLM-powered future
The immediate win in the OpenAI and Zapier partnership is distribution turning into product improvement on both sides. OpenAI gave Zapier a huge new front door through ChatGPT, while Zapier gave OpenAI a fast way to make chat useful by letting it search, write, and update across thousands of apps. That created a tight loop where more users produced more real workflows, which improved the model for how AI should call software in practice.
-
Zapier built its Natural Language Actions API as a universal layer over thousands of app actions. Instead of asking a developer to wire every API field manually, Zapier lets the model send a plain language instruction, translates that into parameters, executes the call, and returns a trimmed result safe for another model step. That made ChatGPT plugins much more practical, much faster.
-
The partnership also solved a core weakness in early LLMs. Models could reason in text, but they were frozen after training and could not act inside live software. Zapier supplied that missing execution layer across 5,000 plus apps, so OpenAI gained utility fast, and Zapier got the fastest growing integrations in its history.
-
The longer arc already points to separation of roles. OpenAI is optimized to build general purpose models and chat surfaces, while Zapier is optimized to orchestrate long tail workflows for non developers, with controls, approvals, and deterministic steps around the model. That is why Zapier later framed its edge as orchestration, not just chat plugins.
This heads toward a world where OpenAI supplies reasoning and Zapier supplies the work graph around it. As models improve, more value shifts from getting a chatbot to answer a question toward getting software to take safe action across many systems, invisibly and at scale. That favors Zapier if it keeps owning orchestration, governance, and the long tail of app connections.