Interview Prep 09 — AI Startup CTO

Leads tech + product + team for an AI-native company: architecture, moat, unit economics, hiring, fundraising. Phase: 15 (+ all of 5–14 as the technical base).

1. Core concepts

  • The stack layers; the model commoditizes wrappers; build where the model is an input.
  • AI-native design (own the workflow + system of record; not a wrapper) (Phase 15.03).
  • Moats: data flywheel / integration / domain / distribution; "can OpenAI build this?" (Phase 15.06).
  • Unit economics: cost per resolved task; engineer 70–80% margin; price to margin/value (Phase 15.05).
  • Build vs buy; enterprise readiness (SOC 2/DPA/SSO); fundraising story.
  • The full technical base (RAG/agents/serving/eval/security) — you must reason across all of it.

2. System design questions

  1. Architect the company's LLM platform (gateway + RAG + agents + eval + security + cost) (diagrams/18).
  2. Design for build-vs-buy across the stack (build the moat, buy commodities).
  3. Architect for enterprise (multi-tenant isolation, residency, on-prem option) (Phase 15.08).

3. Debugging questions (business + technical)

  • Negative gross margin at scale → cost per resolved task; routing/caching; price to margin (template 05).
  • Product loved but won't sell → vitamin not painkiller; discovery (Phase 15.01).
  • Enterprise deals stall → enterprise readiness gaps (SOC 2/DPA/SSO).
  • "Can OpenAI build this?" has no answer → no moat; redesign (Phase 15.06).

4. Coding / technical-judgment exercises

5. Architecture exercises

  • Draw the startup product architecture (workflow + platform + moat).
  • Decide the deployment-model spectrum per customer tier (SaaS/VPC/on-prem).

6. Model selection questions

  • Set the org's model strategy (hybrid commercial + self-host; routing; re-eval cadence).
  • When to fine-tune vs RAG vs prompt across the product (the ladder).

7. Production incident questions

  • A data leak / cross-tenant exposure → IR + disclosure + fix + trust recovery (template 04).
  • A provider price hike threatens margins → routing/portability; self-host hot paths.
  • A safety incident goes public → guardrails (fail closed), red-team, governance.

8. Portfolio project

A startup-grade platform + product narrative: the Capstone (gateway + RAG + agents + eval + cost + security) framed as a product — opportunity map, MVP spec, unit-economics model, moat strategy, and a 3-minute technical demo. README that doubles as an investor/demo narrative (Phase 15.09).

9. 30-minute drill

  • 5 min: where to play (layer/category) + "can OpenAI build this?".
  • 10 min: architect the product platform (whiteboard) + build-vs-buy.
  • 10 min: unit economics (cost per resolved task → margin) + pricing.
  • 5 min: enterprise readiness + fundraising story.

10. 2-hour take-home

Produce a technical+product brief for an AI startup idea: opportunity map (10-dimension score), AI-native design (owned workflow + moat), an MVP spec, a unit-economics model proving margin, and an architecture diagram. End with the "can OpenAI build this?" answer.

11. Senior-level (CTO) expectations

  • Lead/Staff: builds the platform; sound architecture + cost/eval/security.
  • CTO: sets technical strategy, build-vs-buy, the moat, unit economics, enterprise readiness, hiring, and the fundraising technical narrative; reasons across the entire stack (5–14) and the business; survives technical diligence.
  • Signal: every architectural decision is justified by cost/latency/reliability/safety and moat/economics — and there's a working demo with numbers.

Back to: interview-prep/ · Build the Capstone — it's the portfolio centerpiece for this and every role.