Role

Hire your AI CTO

Product, engineering, and infra — run by a technical partner you talk to, not a contractor you chase.

Your AI CTO is the technical leader of your one-person company. It translates product direction into a shipping roadmap, writes and reviews code, owns infrastructure decisions, runs incidents, and hires subordinate AI engineers when scope expands. You bring the 'what'; the CTO owns the 'how'.

Hire your AI CTOSee all roles
Free to startNo credit card requiredUpdated Apr 2026

What your AI CTO does

01Translate product vision into a weekly engineering priority list with acceptance criteria
02Write code directly for features, fixes, and infrastructure changes
03Review every pull request before merge — security, performance, maintainability
04Own the architecture decisions and write them down in an ADR log
05Run incident response when production breaks, including postmortems
06Manage infra and cost — hosting, databases, CDNs, observability, CI/CD
07Coordinate with the AI CMO on what's ship-ready for launch vs still in beta
08Escalate the handful of decisions a non-technical founder should weigh in on (pricing infra, data residency, security posture)

Workflows on autopilot

Weekly sprint plan
Reads product roadmap, open bugs, and customer feedback. Writes a 5-item sprint with owner (self or subordinate agent) and acceptance criteria.
PR review gate
Every pull request — including its own — passes a structured review: correctness, tests, security, performance, docs. Bad PRs get rewritten, not merged.
Incident response
When monitoring fires, pages the founder if P0, investigates root cause, ships the fix, writes a postmortem, adds a regression test.
Monthly infra review
Audits cost, performance, and security. Rightsizes compute. Removes dead dependencies. Rotates secrets. Updates the ADR log.
Ship-ready gating
Before a launch goes to the CMO, the CTO confirms: feature flag off, docs written, telemetry in place, rollback plan exists.
Technical debt sprints
Once a quarter, blocks a week for migrations, test coverage, and dependency upgrades — so the debt never compounds past the danger line.

Without vs With a AI CTO

Without
  • —You hire a contractor who ships in two weeks and disappears when it breaks
  • —You read tutorials at midnight trying to fix a database issue
  • —Your codebase becomes a liability nobody understands
  • —Every launch has a last-minute fire because the feature wasn't really ready
  • —You spend $25K/month on an engineering contractor
With Tycoon
  • ✓The AI CTO is always available and owns the code after it ships
  • ✓The CTO pages you only for decisions, handles the rest directly
  • ✓The CTO maintains an ADR log, inline comments, and a readable architecture doc
  • ✓The CTO enforces a ship-ready gate before anything goes to marketing
  • ✓The AI CTO runs for $100-$400/month and ships more

A day in the life of your AI CTO

07:15
Reads overnight monitoring and error logs. Fixes two P3 bugs before the founder is awake.
09:00
Reviews a draft PR from the subordinate AI engineer. Rewrites the test suite and explains why in a comment.
10:30
Writes the pricing-page update: feature flag, backend schema migration, and Stripe integration. Opens PR.
13:00
Debugs a customer-reported checkout failure. Finds the race condition, ships the fix, adds a regression test, messages support.
15:00
Runs a dependency audit. Updates three packages, pins one behind a feature flag pending next week's review.
17:00
Writes the week's ADR: why we're moving to Postgres from SQLite. Ships it to the ADR log and pings the CEO.
21:30
Heartbeat: confirms deploys green, no alerts pending, hands off to the monitoring skill for overnight.

Tools your AI CTO uses

GitHub for source control, PRs, Actions, and issue trackingLinear or Notion for product roadmap and specsVercel, Fly.io, Cloud Run, or Railway for deployPostgres, MongoDB, or the platform's managed database for dataSentry, Datadog, or Axiom for observabilityStripe and webhook infrastructure for billingClaude Code / Codex for direct code executionTycoon skill marketplace for security review, migration, and platform-specific skills
FAQ

Frequently asked questions

Clear answers about wallet credit, usage, subscriptions, and how Tycoon charges for work.

Can an AI CTO actually write production code?

Yes — and it's how founders like Matthew Gallagher (Medvi) and Pieter Levels operate. The AI CTO in Tycoon writes code directly using Claude Code as the execution runtime, the same tooling professional engineering teams are adopting in 2026. It opens pull requests, runs the test suite, ships to staging, and only merges after passing its own review gate. For non-technical founders this is the highest-leverage hire available: you get production-grade engineering without managing a human engineer or chasing a contractor. For technical founders it's a force multiplier — you pair with it instead of writing everything yourself.

What if I'm not technical? Can I still direct an AI CTO?

Yes, and you should. You direct the CTO in product language: "users are complaining checkout is slow," "we need to add a second-model option to pricing," "make onboarding require fewer clicks." The CTO translates that into technical work and makes the architectural calls. It escalates to you only the handful of decisions a founder should weigh in on: data residency, security posture, user-visible tradeoffs. Non-technical founders running Tycoon typically feel shockingly capable in the first week — the CTO is the layer that used to require a technical cofounder.

How does this compare to hiring a fractional CTO?

A fractional human CTO costs $5K-$15K/month for 10-20 hours/week of availability. They're excellent for strategic advice and network intros. They're not going to write your code, review every PR, or fix a 2am outage. An AI CTO is the opposite end: always available, ships code directly, covers the execution layer. Many successful Tycoon founders run both — the AI CTO owns execution; a fractional human advises on strategic technical decisions (should we self-host LLMs? is this the right moment to rebuild auth?) for 2-3 hours a month. Combined cost is still a fraction of a full-time senior engineer.

How is this different from Claude Code or Cursor alone?

Claude Code and Cursor are excellent execution tools — but they're pair programmers you drive. An AI CTO is a peer you delegate to. It owns the weekly sprint, the PR review gate, the ADR log, the incident rotation, and the ship-ready checklist. Under the hood it uses Claude Code for code execution, but the difference is the management layer: scope, autonomy, memory, cadence, and coordination with the rest of your AI team (CMO, COO, CFO). Without that layer you're still the engineering manager. With it, you're the product owner.

What if the AI CTO ships a bug to production?

Same thing that happens with any good engineering team: monitoring catches it, the CTO investigates, ships the fix, writes a postmortem, adds a regression test, and updates the pre-merge review checklist so it can't repeat. For the founder, the visible loop is: alert → message in your chat → fix deployed → summary — usually within an hour. The postmortem is automatic. Compare this to a contractor outage at 2am where you're blocked until Monday. Tycoon's AI CTO runs on the same 24/7 availability model as the rest of your AI team.

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