Q&A: Killian Brackey on Building AI That Holds Up

Killian Brackey has spent his career building financial technology from the inside out. As a cofounder of Sezzle and now its SVP of AI, he brings a rare combination of institutional knowledge and hands-on engineering experience to one of fintech's fastest-moving frontiers—Buy Now, Pay Later.

After stepping away to lead large-scale platform work as CTO at Happy Money, Brackey returned to Sezzle last year with a clear focus: applying AI thoughtfully, and prioritizing durability, trust, and real-world impact over the hype cycle many tech companies fall into.

In this conversation, Brackey shares how his early curiosity shaped his approach to technology, why AI demands more discipline than spectacle, and how Sezzle is building systems designed to hold up when people rely on them.

You've always been known as a builder. Where does that instinct come from?

As a kid, I wasn't focused on using tools so much as understanding how they worked. I was always curious about what was happening underneath the interface and why things produced the results they did. While learning, if you gave me a calculator or computer I'd disappear for hours, writing programs and pulling apart the logic underneath with a curiosity on how it worked, less about what I could build with it.

That instinct to look under the hood never really went away. As I built more software, it just turned into pattern recognition. You start to see how systems are put together, what tends to work, and where things usually break.

Did AI change that sense of predictability for you?

It did. Viable consumer AI was the first time in a while that I felt genuinely surprised by a technology.

It felt almost magical because I couldn't immediately see the constraints. To me, I saw this as a sign I needed to slow down and really understand what was happening underneath before applying it.

A lot of teams responded to that moment by moving faster. You didn't.

Right. Instead of rushing to use AI everywhere, I spent time looking at where it worked well, where it broke down, and how those weaknesses would show up once real users depended on it.

The question for me wasn't, Can we do something impressive with this? It was, Does this actually hold up when people rely on it every day?

That's where a lot of AI efforts fall apart. Demos can look great in isolation, but real products need consistency and predictability. Companies are throwing AI around for the attention and flashiness of the buzzword, but our focus has been on integrating AI carefully and intentionally at Sezzle, in ways that are designed to last.

How does that philosophy show up at Sezzle today?

Good technology earns trust by being boring where it matters.

At Sezzle, we deal with inherently complex financial problems: budgeting, underwriting, fraud detection, payment scheduling, shopping, and support workflows. Our job is to absorb that complexity into the system so users experience clear guardrails, simple reminders, and intuitive flows.

If AI is adding friction or confusion, we've missed the mark. The goal isn't to impress people with technology. It's to make complex financial tasks feel simpler and reduce the risk of mistakes.

You were one of Sezzle's cofounders, then left, and eventually came back. Why step away in the first place?

Leaving was a natural step in my career. After Sezzle, I joined Happy Money, where I worked on large-scale platform rearchitecture and embedded lending products, eventually moving into the CTO role.

That experience broadened how I think about scale, and long-term system design. Working across different products and customer use cases gave me a deeper appreciation for how financial tools support people at very different stages of their lives, and how important it is to build systems that can grow and adapt over time.

It reinforced a belief I've carried with me throughout my career: lasting progress comes from building durable foundations, not just moving quickly.

So why return to Sezzle?

When Sezzle reached out in 2025, the timing made sense.

I already understood the platform, the data, and the customer. What had changed was the opportunity to apply a more disciplined approach to AI inside a product where trust really matters.

AI has enormous potential in financial services, but only when it's built on strong foundations. I saw Sezzle was at a really strong point where its platform and data could support AI in a responsible way.

You've spoken about the "illusion of progress" with AI. What do you mean by that?

AI can make output feel effortless. You can generate code, content, or workflows much faster than before.

But the work doesn't disappear. It shifts. Reviewing, validating, fixing, and maintaining AI-generated output often takes longer than people expect.

Traditional productivity metrics can be misleading. Lines of code are not a great developer productivity metric, but they are helpful to demonstrate a reality of working with AI tooling today. You might write 5,000 lines of code one week and another 5,000 the next. But if half of that second week's work is rewriting what didn't work the first time, the actual output is lower, even if the "numbers" look good.

If you're constantly reworking what you just built, the system isn't truly moving forward. It just appears that way. And while that can make for good hype, it doesn't ultimately serve the people relying on the product.

How does that shape your approach to AI leadership?

It pushes us to be very intentional. We focus on building things that last, not just things that look impressive in a demo, and we put our users' trust ahead of theatrics.

The question we keep coming back to is whether what we're building will still work months from now, under real-world pressure, for people who depend on it.

AI should make products clearer, calmer, and more reliable. If it adds confusion or fragility, it's not doing its job.

What's your north star as AI becomes more embedded at Sezzle?

The goal isn't to showcase AI. It's to use it responsibly in service of clarity and long-term trust.

A good product feels simple because the complexity has been handled responsibly behind the scenes.

We're still in the early stages of Sezzle's AI journey, but we're taking a deliberate approach—focused on building products that deliver real outcomes for users. That's what's most exciting to me.