Startup & Founder, in order.

Open questions 27 open

• Agentic Technical Debt
  • How long does a CLAUDE.md remain accurate as a codebase evolves? The playbook gestures at session-by-session updates; no data on rot rate.
• AI Investment Story, Not Efficiency Story
  • **Is the classification driving the result?** "AI company" is Emergence's label. If AI companies are disproportionately *younger* (more likely pre-revenue-inflection) than the non-AI cohort at the same revenue band, some of the RPE gap is an age/stage artifact, not an AI effect. The report doesn't publish a stage-matched comparison.
  • **Tail vs. mean gap.** No data here on the deliberately-lean solo-founder tail's RPE specifically — the lean-unicorn claim lives in that tail, which the population medians can't isolate. *(Partly informed: [[emergent]], a celebrated lean-tail exhibit, checks in at ~$600K/head at $120M ARR — **below** this cohort's $100M+ top-decile AI figure ($960K), suggesting the tail's *scaled* RPE is less exceptional than the low-headcount snapshots imply. One `vendor-claim` datapoint, not a cohort.)*
  • **Which instrument is right for the *frontier* AI-native subset?** Two `empirical`-tagged sources disagree in *direction* — cap-table financials say AI companies earn ~39% less per head, a founder survey says AI-natives clear $400K/head at 55% and grow 156%. The disagreement is confounded by instrument (measured vs self-reported) *and* reference class (matched-band AI-vs-non-AI vs AI-native-vs-all-startups). Only a matched-segment, financial-data RPE study of the deliberately-lean AI-native frontier *specifically* — not the broad "AI company" label — would settle whether the survey optimism or the cap-table pessimism describes that tail. (ICONIQ's fourth reading adds a forward *trajectory* — RPE projected +84% by 2027 — but it too is self-report, and projected, so it deepens the survey-side optimism rather than adjudicating it.)
• AI Product Economics Maturation
  • FDEs are monetized fragmentedly (bundled / separate PS fees / hybrid) and comped on retention. Does a dominant FDE monetization model emerge, and does the "Revenue Driver" self-framing (38%) survive a margin analysis — i.e. are FDEs actually accretive, or a services drag reclassified as growth?
• AI-Native Startup Lifecycle
  • The 42% "built-something-nobody-wanted" CB Insights figure is from a pre-AI era; the playbook predicts the rate will climb but doesn't cite a 2026 measurement.
  • Tension with HBR's accountability findings (above) is unresolved. The playbook's orchestration framing reads as the exact framing HBR's experimental conditions tested *against*.
• Compounding Data Moat
  • Is the "two-year replication window" claim defensible empirically, or aspirational? The playbook does not cite measurement.
  • The data-flywheel argument has been made for SaaS for 15 years. What's actually different in the AI-native version? Probably: the data improves the *model* in addition to the product, but the playbook doesn't make this distinction precisely.
  • The "customers build APIs on top of you" lock-in is structurally similar to platform plays (Salesforce AppExchange, Shopify apps). Is the moat type really new, or just newly accessible to lean startups?
• Founder as Agent Orchestrator
  • The playbook claims non-technical founders can now build production software, but it does not address the architectural-judgment recursion problem ([[agentic-technical-debt]]): non-technical founders may not have the vocabulary to write effective CLAUDE.md. How does that scale?
  • How does the orchestration role change the founder's *decision* burden? Fewer hands-on tasks but more parallel agent oversight; net cognitive load is unclear and may be higher (see [[ai-brain-fry]]).
  • Anthropic publishes both the playbook's anthropomorphic framing *and* HBR-aware accountability work (auto-mode, alignment) simultaneously without engaging the framing literature directly. The synthesis in [[wiki/derived/orchestration-vs-employee-framing-reconciliation]] reconciles the tension at the operational level — *orchestration as workflow design* preserves accountability; *orchestration as mental model of agents-as-coworkers* does not — but the open question of why the playbook's marketing language doesn't reflect Anthropic's own framing-discipline work remains.
• Founder-Led Sales Discipline
  • Where exactly does "until PMF" end, and what's the first thing a founder *should* hand off (AE? agent? both)? Glasgow still does it post-Series-B, suggesting the boundary is fuzzy.
  • Does Glasgow's anti-offload stance generalize, or is it specific to high-trust, mission-critical enterprise sales (ERP) where "they're buying *you*" — would a PLG/SMB motion delegate to agents far earlier?
• Narrow Wedge into a Legacy Market
  • The wedge-flip shows the first wedge can be wrong. What's the fastest signal that a wedge converts to the core vs. merely sells — Campfire took ~3 months; can it be read sooner?
• Printing Press Software Democratization
  • Is domain-expert-as-builder actually happening at scale in 2026? Anecdotes (shop owners, microcontroller hobbyists) yes; primary-job software building by non-engineers, less clear. *(Partly answered: [[returns-to-expertise|Anthropic's 400K-session study]] finds non-software occupations reach verified success in code-producing sessions within ~7pp of software engineers — the strongest evidence yet that the claim holds, at least within Claude Code's user base. Market-scale corroboration: [[emergent]] reports **200K+ non-technical paying customers** — trucking companies, factories, and construction businesses building their own ERPs, property managers building CRM tools ([[raw/emergent-unicorn-series-c|TechCrunch, July 2026]], `vendor-claim`) — Boris's "the accountant writes the accounting software" observed as a paying market, not just inside one vendor's telemetry.)*
  • Boris's "accountant writes accounting software" — does that result in 10K narrow tools that don't interoperate? What's the integration story?
• Problem-Solution Fit Discipline
  • Does asking an AI to argue against an idea actually produce disconfirming evidence at the same rigor as confirming evidence, or does the model still bias toward the framing the founder presents? Worth measuring.
  • The playbook recommends "ask Claude to make the most compelling argument for why a competitor would succeed while you do not." How does this interact with Anthropic's published [[claude-character-as-product|character training]] (sycophancy resistance, devil's-advocate willingness)?
  • Has anyone measured 2026 startup failure rates with AI-built products? The "42% will climb" claim is asserted without measurement.
• Product Velocity as Moat
  • "Never had anyone outgrow Campfire" — is that survivorship (they haven't hit true enterprise scale yet) or a real claim that velocity closes the breadth gap faster than customers grow into it?
• Seven Powers Applied to AI
  • Is "switching cost" really collapsing in practice, or just in narrative? Anthropic's own retention numbers, Salesforce churn, etc. would test this.
  • Counter-positioning — explicitly the "incumbent can't follow" power — should *amplify* under AI. Is anyone running this play deliberately?
• Zero-Friction Scope Creep
  • The playbook recommends written scope but offers no template or worked example. How specific does "what we deliberately don't do" need to be to actually block requests?
  • Is there a measurable threshold where scope creep crosses into outright pivot territory? The playbook gestures at "losing direction" without a metric.
  • How does this interact with [[ai-native-product-cadence|Cat Wu's]] 1-day shipping cadence? Anthropic's internal practice ships fast but with strong product judgment; how does that judgment translate for a first-time founder?