---
title: "SaaStr: Six AI Questions Founders Should Ask Themselves"
author: "Unique Research"
sourcePublication: "Unique Research (FFCAP English library)"
originalPublishedAt: "2024-09-20T16:30:00+08:00"
canonical: "https://ffcap.cn/en/research/20240920a05zmn00"
source: "https://ffcap.cn/en/research/20240920a05zmn00"
language: "en"
cnSourceUrl: "https://view.inews.qq.com/a/20240920A05ZMN00"
---

# SaaStr: Six AI Questions Founders Should Ask Themselves

_Unique Research · Insight · September 20, 2024 · Source framing: Base10 Partners (TJ Nahigian & Luci Fonseca) via SaaStr_

GenAI is a platform shift — models take text/image/audio/video/code and create new content. Base10 maps incumbents vs startups across platform / infra / apps and poses six founder questions.

## Pace & capital

AI patents in 2021 were ~30× the level six years prior. VC: despite a hard raise climate, AI is hot — last year’s H1 alone >$4B into AI; this year already ~5× last year’s full-year AI capital.

## Stack map

**Platform / model:** Google, Meta; OpenAI, Hugging Face, Cohere — build in-house and stitch third-party tools.  
**Infrastructure:** tune, integrate, scale models economically (e.g. LangChain).  
**Applications:** horizontal + vertical — Notion, Gorgias (incumbents); Jasper, Copy.ai, Harvey (startups). App-layer retention is hard: enterprise trial budgets thin; prosumer churn high; copycats muddy brand stories.

## Incumbent plays

Google: invented Transformers, open-sourced path, then repositioned as AI company with Bard under OpenAI pressure. Meta: ads + feed/content models + years of monetization muscle. Microsoft: partner/outsource mastery — early OpenAI partnership ($1B then +$10B) with preferential access/rights. Nvidia: GPU demand explosion; recent revenue >+100%; top-five market cap. Earnings-call AI mentions up ~100× in two years. App layer: almost every company has an AI strategy; GitHub Copilot writing >half of some engineers’ code; ServiceNow embeds GenAI for summary / text-to-code. Many incumbents experiment in-house with open tools — know this if you sell to them.

## Moats that matter now

Proprietary data, best workflows, deep customization that wins. Early in the AI platform shift, Base10 argues **incumbents capture most value** via distribution, data, and workflows.

**Casetext:** 11 years into law-firm software; growth stalled ~$10M ARR; shipped GPT-powered Co-counsel — $0→$9M ARR in nine months; acquired ~$650M cash. Hard for a greenfield startup to clear law-firm legal review.  
**Notion:** first AI feature wave — pricing path toward ~$100M ARR AI; rolled to 30M users incl. growing enterprise; NotionAI sits in daily tools/workflows with private data; GenAI treated as foundational (like electricity), returning to “democratize tool creation.”  
**Gorgias:** Shopify-centric CS ticketing; GenAI automation add-on priced at ~half core; tickets automated 7%→18% with 50% goal; 10k+ merchant distribution + training data.

## Business-model shifts

ARPU / ACV / LTV up via AI add-ons (sub or per-transaction). Costs down (medical billing, QA) without matching price cuts → margin/LTV up. Retention rises where data + distribution + fit exist. Cloud/mobile history: Amazon (AWS) and Meta (mobile) captured more value than prior incumbents expected — similar pattern likely for GenAI; startups must be more strategic about positioning.

## Six questions

**For new GenAI company builders (3):**

1.  Which niche/market/vertical can you _absolutely_ dominate — not nibble — without head-on incumbent war?
2.  Retention is existential — how do you substantially improve the product for new GenAI expectations and PMF?
3.  How is GenAI in the core architecture, not a bolt-on? Best GenAI companies barely say “GenAI” — it drives everything. Build a long-term business.

**For incumbent founders seeking GenAI value (3):**

1.  Return to original mission — which 1–3 customer problems were previously tech-impossible? How will you concentrate resources?
2.  Walk every P&L / dashboard metric — how can GenAI improve cost, LTV, conversion, etc.?
3.  Innovation pace is extreme — how do culture and org keep experiment velocity matched to the platform shift?

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Editorial English edition published on UniqueCapital: https://ffcap.cn/en/research/20240920a05zmn00
On-site reading page: https://ffcap.cn/en/research/20240920a05zmn00