Copy.ai Focuses on Multi-Model Workflows
Chris Lu, co-founder of Copy.ai, on the future of generative AI
This reveals that Copy.ai is trying to win at the workflow layer, not the model layer. The product value is in taking the best available model at any moment, then plugging it into repeatable business tasks like account research, outbound email drafting, CRM updates, and content production. That lets model competition work in Copy.ai’s favor, because better and cheaper models immediately improve the product without requiring Copy.ai to build foundation models itself.
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Copy.ai had already moved beyond being a simple GPT wrapper by late 2022, running 20 to 30 fine-tuned models and using product interaction data, like saves and rewrites, to retrain models for specific tasks. The moat was starting to come from workflow data and testing speed, not exclusive access to one model vendor.
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By 2024, that logic became operational. Copy.ai described switching between GPT-4, Claude, and potentially open source models based on whichever was cheaper, faster, and good enough for a given job. In practice, that means the platform behaves like a routing layer for AI work across sales, marketing, and research tasks.
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The comparison is less to OpenAI and more to software layers built on commoditizing infrastructure. Early AI writing tools resold model output inside a better user workflow, similar to how developer tools repackaged AWS. After ChatGPT compressed the standalone writing market, Copy.ai repositioned into enterprise GTM workflows where integration into Salesforce, HubSpot, and internal systems matters more than owning the underlying model.
The direction from here is toward multi-model workflow software that captures business context and automates larger chunks of work. As models keep getting cheaper and better, the companies that win are likely to be the ones that can turn those improvements into faster prospecting, better personalization, and measurable revenue outcomes inside the systems enterprises already use.