I keep having the same conversation with founders. They want to discuss AI adoption strategy. I ask them why they think they need AI.

The silence that follows tells me everything.

Most Series A companies don’t need AI. They think they do because investors are asking about it, because competitors are claiming AI features, because it feels like falling behind not to have an AI strategy.

But need and want are different things. And premature AI adoption is expensive in ways that aren’t obvious until the damage is done.

I prepared technical due diligence materials for Blockdaemon’s Series A, B, and C funding rounds. Three times I had to answer the question: how do we know your technology actually works at scale?

The answer that got us to $3.25 billion valuation wasn’t the one most CTOs think matters.

VCs don’t care about your velocity metrics. They don’t care about story points. They barely care about your deployment frequency.

What impressed them was simple: we were running infrastructure for Meta and JP Morgan across five continents, and it worked.

After Blockdaemon hit $3.25 billion valuation, I started talking to companies about permanent CTO roles.

None of them felt as challenging as what I’d just done. Scaling from 10 to 187 engineers. Building infrastructure that Meta and JP Morgan trusted. Deploying validators across five continents. Managing 96% retention through hypergrowth.

I could wait for another opportunity like Blockdaemon. But I’d probably wait forever.

So I went fractional instead.

Why fractional made sense

The decision wasn’t complicated. I’d spent years building hard-earned knowledge about scaling technical teams, infrastructure, and organisations. I could apply that knowledge to help multiple companies rather than waiting for one perfect opportunity.