A product discussed on AI Engineer.

AI in GTM at Notion — Flora Liu
Aug 26, 2026 · 21:15
Flora Liu, an engineer on Notion's GTM team, argues GTM is now a distributed systems problem, not marketing ops, and details how Notion built a unified decisioning system across self-serve and sales-assist. Workflows reduce to four questions — what do we know, what should happen next, how to execute safely, did it work — built as Know, Decide, Act, Learn layers. Snowflake computes the profile, DynamoDB serves it in milliseconds, and Notion is the substrate where humans and agents operate on the same context; signals become Temporal workflows. Agents research and draft, humans approve customer actions, and contact-sales forms are untrusted input. After 13 weeks, enterprise reps log more qualified opportunities, and context-aware recommendations made users 63% more likely to take next step.

Reverse-Engineering the AI Buyer — Aliisa Rosenthal, Acrew Capital
Aug 26, 2026 · 19:10
Aliisa Rosenthal, who helped take OpenAI's enterprise business from a couple million to several billion in revenue, argues founders should build the automated sales machine before hiring humans. ChatGPT launched with no enterprise features; nine months later they shipped an expensive product, but self-serve cannibalized it four months on. Advice: capture phone numbers at signup, reply to every inbound (10,000 a day), never give buyers homework, avoid pilots via reference calls, data evals, or 90-day opt-outs. $60 per user per month was too high; a low base fee plus usage spread. Automate security, don't pull up-market, hire AI-native sellers when buyers need contact, and use forward deployed engineers sparingly—scarce but sticky. First 10 customers should be handpicked design partners, and POCs aren't self-serve.
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