10 Questions for AI Leaders: Sean Mullaney

Every product was designed around the constraints of its time. Sean Mullaney's entire AI philosophy rests on that single observation - and the implication is radical: remove the constraints, and almost everything is worth rebuilding. The Seapoint founder and former Stripe CIO is doing exactly that.

Sean Mullaney: The ecosystem. When I started out, I don't think there was a single VC in the UK at the time. If you wanted to build a company, you were essentially on your own. I spent ten years across three startups with no institutional capital. My friends were doing MBAs. I was taking cash advances on my credit card to make payroll.

What's also changed is the speed at which you can build. We started Seapoint with a small team using AI first across all of our software development. That wasn’t possible five years ago.

What hasn't changed is the hard part. Finding a problem worth solving and building a team that's genuinely committed to it.

Every product that exists was designed around the constraints of the time it was built - AI removes most of those constraints, which means almost everything is worth redesigning from first principles.

It's not changing the fundamentals as much as people think.

The bar for getting a meeting hasn't changed. You still need a credible team and a problem worth solving, but the bar for looking prepared has risen sharply.

What AI has done is make research faster. You can analyze a VC's portfolio in minutes instead of hours. You can iterate materials more quickly. That's genuinely useful, but generic decks stand out now more than ever. If your materials could have been written by anyone, you've lost before the meeting starts.

The moves that actually work are things like using tier-one VCs for feedback even though they won't invest and compressing your timeline to build momentum. Don't prioritise speed over strategy.

There's a short-term and a long-term answer. Long term, the impact on the economy and our way of life will be even more substantial than we think today.

Short term, it's very hard to understand the speed of disruption - and in any big technology wave, whether it's the internet, PCs, or mobile, there's always difficulty in timing it.

We may get ahead of ourselves in the next year or two, but that's part of the innovation disruption cycle. Enterprise adoption always lags consumer adoption substantially, and enterprise is where I think we're going to see the largest magnitude of impact on the economy.

Managing. The world used to require individual contributors to perform the craft themselves - coding, writing PRDs, building screens. Going forward, individual contributors are going to act more like managers, directing agents who are doing a lot of the work.

Strategy, direction, feedback, context, prioritisation, QA, those are all traditionally manager skills. If you're great at managing people, you can be great at managing agents. And as an individual contributor, your output can be five times bigger than it was before.

The people building at the application layer. Every big technology wave, it's the folks solving problems in software and apps that end up winning - the platform technology gets commoditised.

Right now, it's the lowest part of the stack extracting most of the value, just like Intel in the PC era or Cisco in the internet era. But eventually the value shifts up. Bill Gates defined a platform as something where the participants building on it make ten times the money as the platform itself. AI is a platform technology.

The application developers, the software builders, the people solving real problems in industry, they're the ones who end up being the big winners.

Hiring. It's the highest-leverage decision you ever make in a business. Every single interview, every hire or no-hire call - each one feels small in the moment. But in the early stages of a company, those decisions have an outsized impact on everything that follows.

The obvious disadvantage is fragmentation. Each European market has localisation requirements (language, currency, regulation) that don't exist in the US, where one product can serve the whole market.

But the advantage is less competition. Because of that fragmentation, there are fewer large companies serving the entire European market, which means more opportunities for startups and disruptors to have a real impact.

Recursive self-learning.

I let an agent loose on our transaction enrichment algorithm over a weekend - it ran for 36 hours, conducted 78 different experiments, tested different models, features, and architectures, kept a running log of what it learned, and consulted five different reasoning models as a panel of peers to generate new research ideas.

I came back to something 15% better than what we had before. That autonomous, self-improving loop, that's the thing that changed how I think about what's actually possible.

In love with their work.

All of the stressful and boring parts will have been automated. People will be able to think bigger, more strategically, more ambitiously about what they can achieve. I'm a rare optimist right now, but I genuinely believe that's where this goes.

Sean Mullaney is the founder and CEO of Seapoint, an AI-native financial operations platform for European startups. He was previously European CIO at Stripe and CTO of Algolia. He has spent his career building financial infrastructure that actually works.

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