LLMs are already transforming Zapier's business. They're seeing 100x productivity improvements on internal processes that drive user acquisition, deflecting a large volume of customer support requests by improving the handling of user-facing error messages. But most interesting is that Zapier views LLMs as the "escape hatch" UX paradigm that will enable it to grow from 10M users today, to 100M users over the next decade.

Topics Covered:

(1:27) Mike's journey to taking the role of Head of Zapier AI

(4:16) Fast internal LLM adoption at Zapier

(6:54) What were the specific use cases driving the growth?

(11:05) If AI/bots are the ones trying to do tool discovery, will that change the role of SEO?

(13:09) Is it also important to vendors to optimize their apps to ensure Zapier is picking them to accomplish a given tool?

(14:12) Use case of handling customer interactions; what have you seen there?

(16:46) Summarization of customer info... will this be owned by Intercom/Zendesk, or should it exist as an independent capability?

(19:04) How do you think about the vision for Zapier in this new world?

(22:30) Where do you see LLMs adding most value?

(27:00) Do you think you need to capture users earlier in the funnel?

(28:18) What problems have you encountered building these LLM-powered features, solved or unsolved?

(35:52) Any tools you've found valuable to handle fine tuning, quality management, etc.?

(40:07) How do you actually evaluate the outputs / measure similarity?

(45:16) Do AI agents necessitate lower latency?

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