Neal Sample on Why All Architecture Is Wrong, the Power of Curiosity, and AI as a Game‑Changer

Business leaders, technology experts, and former champion debaters explain why all architecture is wrong and why you shouldn't over‑invest in your own ideas.

Neal Sample’s career spans digital‑native companies such as Yahoo and eBay, as well as large brands like American Express, Express Scripts, and most recently Northwestern Mutual. He holds a Ph.D. in Computer Science from Stanford and combines deep technical expertise with a strong focus on the human side of IT, including building rewarding and inclusive workplaces.

During a Tech Whisperers podcast, Sample discussed his new script, leadership philosophy, and how his technical and academic experiences shape his approach as a C‑level executive. The conversation below has been lightly edited for clarity.

Dan Roberts: With so many things to keep up with, how do you stay on top of trends and turn them into business impact?

Neal Sample: I do a lot of self‑learning—reading blogs, following industry trends, and attending diverse conferences that aren’t limited to a single technology. I rely heavily on listening to experts—security, application development, infrastructure—because they often know their domains better than I do. Occasionally we bring in external specialists when we need to explore new territory.

Curiosity is essential. If you think you have all the answers and ask no questions, you stop learning and growing. Many things I knew ten or twenty years ago are no longer true, which is a unique aspect of technology.

Even if the business doesn’t change, the tools we use do, and staying relevant as a CIO requires constant attention to those tools.

You’ve worked with many companies and industries. What’s the one thing most people say about their architecture that you hear often?

All architecture is wrong because everything we build evolves over time. I recall my first large‑scale architecture—things we’d do today would be unthinkable then. Even concepts like servers feel outdated now. So I’d boldly say: all architecture is wrong; we just haven’t realized it yet.

Given that, what can a CIO do to guard against it?

I follow many principles, especially the Open/Closed Principle: open for extension but closed for modification. If you resist change, you’ll make disruptive changes when underlying implementations shift. Being open for extension lets others build on top of your work safely.

I coined the term “Chernobyl‑ization” to describe wrapping legacy components in concrete, effectively sealing them for ten thousand years until they become less radioactive. It’s a forced version of the Open/Closed Principle—closing development in areas that no longer have strategic value.

When you talk about “the power of data versus personality worship,” what do you mean?

When you sit in the “big chair,” your voice can be loud and unintentionally influential. I hear people say, “Neal wants this,” even if I haven’t expressed a preference. That’s the danger of personality‑driven decisions.

Organizations that deliberately focus on data perform very differently from those that don’t. With a data‑science background, I encourage my team to ask for data, understand it, and let it guide decisions. If you think you have no data, you’re missing insights and may overlook alternative hypotheses.

Do your employees get surprised when you dive deep into technology?

Many CIOs from traditional companies act as cost centers, focusing on budgets and project management rather than deep technical work. I’m fortunate to have experience both in early‑stage tech firms and academia—I taught advanced database design at Stanford. That blend lets me stay hands‑on with modern tools while appreciating the broader business context.

What less obvious skills did you learn from university debating and national championships that you apply as a C‑level executive?

One skill is forming a hypothesis quickly and testing it at speed. In debate, you get a topic fifteen minutes before the round and must develop a case or rebuttal rapidly, learning to identify critical issues and filter out noise.

Another skill is switching between affirmation and negation each round, forcing you to argue both sides. This teaches self‑lessness—if you’re wrong, you admit it and move on—preventing over‑investment in any single idea.

Which technology do you think will deliver game‑changing results for your enterprise in the next 12‑24 months?

For us, and many other companies, it’s machine learning and artificial intelligence. Our backend processes already leverage the world’s best mortality and persistence results, built on algorithms and 165 years of accumulated data. AI can process far more data than we imagined a decade ago and generate and test hypotheses a million times faster than humans.

Some companies still view ML, cloud computing, and big data as natural extensions, but for many traditional firms this will be a true transformation.

Thank you for following, sharing, liking, and watching.