# AI Hypergrowth Gtm > Apply AI sales-led hypergrowth patterns to startup GTM, enterprise sales, wedge selection, paid pilots, design partners, pricing, founder-led sales, practitioner adoption, upmarket expansion, and agent-infrastructure platform-risk analysis. Use when evaluating or designing B2B AI startup growth strategy, first enterprise deals, sales motion, pilot offer structure, ICP validation, demo strategy, proof-building, labor-replacement pricing, or whether a product wedge survives Google/Microsoft/Apple/OpenAI/Anthropic distribution pressure. - Skill: `swiftadviser/ai-hypergrowth-gtm` (Agent Skill, multi-file: 51 files) - Install (CLI): `npx skillmds@latest add swiftadviser/ai-hypergrowth-gtm` - Raw SKILL.md: https://api.skillmd.com/api/skills/swiftadviser/ai-hypergrowth-gtm/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Marketing & Growth - Author: SwiftAdviser (https://skillmd.com/u/swiftadviser) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/swiftadviser/ai-hypergrowth-gtm --- # 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/.md` - Growth law details: `references/growth-laws/.md` - Sales law details: `references/sales-laws/.md` - Matrices: `references/data/pattern-matrix.md`, `references/data/comparison-tables.md` 6. Output a concrete recommendation: target buyer, offer, pilot terms, proof metric, demo input, pricing anchor, platform-risk verdict when relevant, and next 5-10 founder actions. ## Reference Loading Rules - Start with `references/case-metaprinciples.md` when choosing which company cases matter. It maps each case to the transferable principle, when to load it, and what not to overgeneralize. - Load the full company file after selecting 1-3 matching cases; do not answer a serious GTM question from the summary table alone. - Load `references/distribution-endgame.md` whenever the product could be described as generic agent infrastructure, generic coding-agent workflow, agent OS, agent runtime, tool marketplace, connector layer, eval/observability layer, or "AI for work" surface. - For agent-infrastructure ideas, default to the inversion question: what will the distribution owner not do, not be trusted to do, or not be economically incentivized to do? ## Growth Laws 1. **Start with the wedge that prints value.** Pick one workflow with obvious economic value, not a horizontal platform. Scale the wedge hard before broadening. Use `references/growth-laws/wedge-clarity.md`. 2. **Win the buyer the market follows.** In prestige-sensitive categories, do not start mid-market and hope to move up. Use a top-tier buyer, design partner, or anchor logo to create a trust cascade. Use `references/growth-laws/prestige-first.md`. 3. **Domain-expert GTM beats generic sales.** Prefer sellers, solutions leads, and CS people who have done the buyer's job. A lawyer selling to lawyers or clinician selling to clinicians beats a generic AE memorizing a product. Use `references/growth-laws/domain-expert-gtm.md`. 4. **Build proof that cannot be argued with.** Engineer paid pilots/design partners with pre-agreed metrics, baselines, timelines, and conversion terms before scaling GTM. Use `references/growth-laws/proof-before-scale.md`. 5. **Price against labor cost, not software alternatives.** Anchor to the human work being replaced or compressed: hours saved, headcount avoided, agency/process cost removed, or outcome delivered. Use `references/growth-laws/labor-budget-pricing.md`. 6. **Build expansion into product logic, not sales motion.** Land in one workflow/team, then expand because usage, outcomes, seats, data, departments, or resolved cases naturally grow. Use `references/growth-laws/expansion-flywheel.md`. Supplemental growth patterns exist for trust architecture, high-touch implementation, non-black-box design, ICP discovery filters, founder timing, and product arc. Load the matching files under `references/growth-laws/` when the problem touches those topics. ## Platform-Risk Law **Distribution eats generic agent infrastructure.** If Google, Microsoft, Apple, OpenAI, Anthropic, or a cloud/platform owner can ship the feature into the workflow entry point, assume the base layer will be commoditized. The survivable wedge is usually a neutral, workflow-specific, compliance-sensitive, budget-owned control point that the platform owner cannot or will not own cleanly. Use `references/distribution-endgame.md`. ## Sales Laws 1. **Founders sell every deal until the motion is proven.** Do not hire a VP Sales to discover the motion. Founders validate ICP, pricing, objections, demo, success metrics, and deal structure first. Use `references/sales-laws/founder-sells-first.md`. 2. **Demo against their own work, not sample data.** Use the prospect's documents, workflows, public artifacts, tickets, filings, calls, or ecosystem data. Generic AI demos create distance. Use `references/sales-laws/demo-against-own-work.md`. 3. **Willingness to pay is a signal, not enthusiasm.** Ask directly what they would pay. Specific high-dollar urgency from a budget owner matters; "interesting, maybe later" is a weak signal. Use `references/sales-laws/wtp-as-qualification.md`. 4. **Use paid pilots with pre-agreed exit conditions.** A strong pilot has fixed scope, fixed time, success metrics, baseline, full-contract pricing, and a conversion decision before it starts. Use `references/sales-laws/paid-pilot-structure.md`. 5. **The hardest objector may be the best champion.** The person pushing back often has the most operational risk, authority, and accountability. Convert them with proof and they can move the deal. Use `references/sales-laws/objector-as-champion.md`. 6. **Sell to practitioners first, then let them pull procurement.** Solve personal pain, embed where work happens, and let practitioner dependency create internal pull toward budget owners. Use `references/sales-laws/practitioner-pull.md`. Supplemental sales patterns exist for economic-buyer pre-close, peer-reference hierarchy, and multi-year contracts as switching costs. Load the matching files under `references/sales-laws/` when needed. ## Offer Patterns Use these as starting shapes, then adapt them to the user's product: - Design partner: `You get for <10-20% of expected TCV> in <4-8 weeks>; if is not met, you get your money back or no full rollout.` - Paid pilot: `One workflow, one owner, one baseline, one success metric, one timebox, full-contract price agreed before kickoff.` - Labor pricing: `Current cost is ; AI cost is <10-15x cheaper or outcome-aligned>; buyer pays only when value is delivered when possible.` - Demo: `Bring the prospect's own work into the product: their docs, tickets, calls, filings, data, codebase, community, or public footprint.` - Expansion: `Start with one team or workflow; expand when usage/output naturally crosses departments, seats, cases, data volume, or customer-facing surface area.` ## Useful Company Cases - **Sierra**: design partners, 10-20% TCV upfront, outcome pricing, Fortune 500 CX, founder credibility. See `references/companies/sierra.md`. - **Harvey**: B2C2B legal adoption, prestige-first Big Law, personalized PACER demos, legal-engineer GTM. See `references/companies/harvey.md`. - **Decagon**: 100+ founder interviews, hard WTP filter, 4-week paid pilots, support automation. See `references/companies/decagon.md`. - **Gong**: founder-led ICP pivot, alpha trial-close, pilots as conversion engine, category creation. See `references/companies/gong.md`. - **Glean**: founder network design partners, paid POCs, adoption-data expansion. See `references/companies/glean.md`. - **Abridge**: clinical trust architecture, physician-founder sales, health-system proof, Epic distribution. See `references/companies/abridge.md`. - **Legora**: paid lawyer interviews, live-demo FOMO, reliability pause before scaling. See `references/companies/legora.md`. - **Deel, Wiz, Ramp, Moveworks, Writer, Hebbia, Cognition, Incident.io, Intercom/Fin, Listen Labs**: load their company files when the user's situation matches payroll/compliance, security, finance, IT support, enterprise writing, research, software engineering, incident management, support, or research automation. ## Output Contract For strategy asks, answer in this shape: 1. **Diagnosis**: the current motion and likely bottleneck. 2. **Best-matching pattern**: cite the relevant law and 1-3 company cases. 3. **Concrete offer**: buyer, workflow, pilot terms, success metric, price anchor, and demo input. 4. **Next actions**: founder-owned steps for the next 1-2 weeks. 5. **Risks**: the main anti-pattern to avoid. For agent-infrastructure or agent-workflow ideas, include a platform-risk verdict inside **Risks**: likely commoditized, survivable with reframing, or structurally defensible. Avoid broad inspiration. The value of this skill is applying specific observed mechanics to the user's current GTM decision.