ai-hypergrowth-gtmlisted

# AI Hypergrowth GTM Use this skill to turn the AI Sales-Led Hypergrowth research corpus into concrete GTM decisions. Keep the answer operational: diagnose the current motion, identify the highest-leverage pattern, stress-test platform/distribution risk when relevant, and propose the next sales/pilot/proof step. Source basis: Jina Reader exports of `https://sevaustinov.me/hypergrowth-research/`, saved in `references/` on May 19, 2026. ## Core Workflow 1. Identify the current stage: pre-product discovery, first design partners, founder-led sales, pilot conversion, first sales hire, or expansion. 2. Name the active wedge: buyer, painful workflow, measurable output, current human/labor cost, and why this can reach meaningful ARR before broadening. 3. If the idea touches agent infrastructure, coding agents, assistant operating systems, workflow entry points, generic MCP/tooling, or platform-owned data, run the platform-risk screen in `references/distribution-endgame.md` before recommending a wedge. 4. Apply the growth laws and sales laws below. Do not give generic SaaS advice if one of these laws gives a sharper move. 5. Load detailed references only as needed: - Full map: `references/source-index.md` - Fast case selector: `references/case-metaprinciples.md` - Platform/distribution risk: `references/distribution-endgame.md` - Company cases: `references/companies/<company>.md` - Growth law details: `references/growth-laws/<law>.md` - Sales law details: `refer