Over the past few years, I’ve been thinking a lot about how the role of a CTO is evolving—especially with how quickly AI is changing how we build software.
To be honest, I never really liked titles like CEO, CTO, CMO in early-stage startups. They always felt a bit off to me. How can you be the Chief Technology Officer when you’re the only one building?
But I’ve started to look at it differently lately.
Instead of a title, I think of CTO more as a responsibility—especially in smaller, AI-assisted teams. This is not an exclusion of vibe code type prototype founders, just a way to grow beyond that.
The gap I keep running into
Working with early-stage teams, there’s a pattern I keep seeing:
You don’t necessarily need a full-time CTO yet,
but you do need help making good product and technical decisions early on.
At the same time, AI tools are making it easier than ever to build with really small teams.
Which is great—but it also makes it easier to:
- Head in the wrong direction
- Overbuild too early
- Or just lose focus
Speed isn’t the problem anymore. Direction is.
What we’ve been trying
Because of that, we’ve been experimenting with a slightly different approach.
We’re building what we’re calling an AI-Native CTO network through CollabHub.
It’s still early, and we’re figuring it out as we go, but the idea is pretty simple:
Instead of relying on one person to have all the answers,
we try to combine:
- Experience
- Collaboration (Inside our Community and Out)
- Open systems
- And AI where it actually helps
A few principles we’re leaning on
Nothing here is groundbreaking on its own, but together it’s been interesting.
1. CTO as a collaborator, not a gatekeeper
I’ve always felt the best technical leadership happens when decisions are shared and visible.
So instead of one person making all the calls, we try to bring in:
- Different perspectives
- Community input
- Real discussion
It usually leads to better outcomes—and fewer blind spots. This contrary to advice I had been giving startups in the past, don't hire a CTO hire a lead developer, but AI has made it easier to hire both in one.
2. AI as an accelerator, not a replacement
We use AI a lot, but mostly for:
- Scaffolding
- Repetitive work
- First drafts
The harder parts—like product thinking, system design, and tradeoffs—still need people.
AI helps you move faster, but it doesn’t decide where you should go. Creative code, decision making and innovation happens when devs are creating it and AI is removing the obstacles.
3. Keeping things open
We’ve been trying to build this in a more open way.
Not just code, but:
- How we think about features
- How we plan work
- How decisions get made
It’s not always the fastest path, but it creates better feedback and learning over time. Using our RAD Process we build in the open first then make things private where it's needed.
4. Learning by doing
We recently started putting together an AI-Native CTO certification, but we didn’t want it to just be another course.
So instead, it’s based on:
- Working on real projects
- Collaborating with teams
- Actually delivering something
We use a lot of guardrails for AI development via pull requests and focus on communication.
What we’re noticing so far
It’s still early, but one thing that keeps coming up:
When teams have even a bit more clarity around:
- What they’re building
- Why they’re building it
- And how they’re approaching it
Things start to move a lot more smoothly.
AI helps with speed, but alignment seems to matter more.
Still figuring it out
We definitely don’t have this all figured out yet.
But it’s been a really interesting direction, and I’m curious how others are thinking about it.
- Does the idea of a “CTO” still make sense in small teams?
- How are you using AI in your workflow right now?
- What’s actually working (or not)?
If you’re exploring similar ideas, I’d genuinely be interested to hear about it.
And if you’re curious about what we’re building, feel free to check out CollabHub—we’re slowly growing a small, open community around this.
Top comments (0)