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What a Fractional CTO Actually Does in an AI Startup

Most founders picture a fractional CTO as a part-time engineer with a fancy title. They imagine someone who reviews pull requests on Tuesdays and shows up to sprint planning when available.

That’s not what the job is. At least not the version that moves companies forward.

The real scope

A fractional CTO in an AI startup fills the gap between where your technical team operates and where your product and business decisions get made. You’re not hiring for execution capacity — you have engineers for that. You’re hiring for judgment.

That means scope decisions. When your team wants to build a custom vector search from scratch and I think you should use an existing embedding service and ship two months sooner, I say so. When you’re about to hire a senior backend engineer and what you actually need is a fine-tuning specialist, I flag it before you spend four months on the wrong hire.

What changes when AI is in the stack

AI products add a layer of decision-making that most early-stage teams haven’t faced before. Which model do you use? Do you fine-tune or prompt-engineer? How do you handle latency at scale? Where does the model end and your product logic begin?

These questions have real tradeoffs, and getting them wrong early costs months. The fractional CTO is the person who’s seen the failure modes and can help you avoid the expensive ones.

What I actually do week to week

In practice, the work looks like this:

The ratio of meetings to code is high. That’s by design.

When it makes sense

If you have a CTO, you probably don’t need me. But if you’re a technical co-founder who’s also running product and sales, or a non-technical founder who needs someone to own the technical roadmap without a six-figure salary commitment, a fractional CTO is the right structure.

The engagements that work best: early-stage AI startups with 2–10 engineers, a clear product thesis, and a founder who wants a technical partner rather than a manager.


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