Every technical leader I know is drowning in options right now. New AI tools, new platforms, new architectures, all moving faster than anyone can evaluate. It’sEvery technical leader I know is drowning in options right now. New AI tools, new platforms, new architectures, all moving faster than anyone can evaluate. It’s

Choosing Wisely in an AI World: Using Systems Thinking to Navigate Technology Trade-Offs

2026/02/28 04:25
5 min read

Every technical leader I know is drowning in options right now. New AI tools, new platforms, new architectures, all moving faster than anyone can evaluate. It’s something I think about constantly as the founder of Inphra.ai, where we’re building AI-driven tools for infrastructure reliability. What I’ve learned is that decision quality depends less on which tool you pick and more on how you think through the decision itself.

A while back, my team needed to add semantic search to our platform. We found an AI tool that could handle it, and the plan was to run it on our Kubernetes cluster. We had the servers. It seemed like the obvious move.

Then I started asking different questions. I stopped thinking about whether the tool worked and started thinking about what would happen when it broke under load. Thousands of users searching at once, memory spiking, servers running out of RAM. Our team was small. If that tool went down during high traffic, we’d be debugging for hours while users lost access entirely.

We outsourced the AI layer to AWS instead. It cost more on the pricing page, but it bought us something the self-hosted option never could: a large team keeping it healthy around the clock. That decision came from looking past the demo and into the reality of living with the tool for years.

The demo problem

Most leaders evaluate technology by its happy path. They read the docs, see a polished demo, and make a decision. Nobody asks what breaks first under load or how long it takes to debug at 2 a.m. The first thing I look at when evaluating any infrastructure option is its failure mode. The second is operational cost. How much attention does this system need? A tool that saves you money on paper but requires three engineers to maintain is not actually saving you anything.

Most people pick tools based on features. I pick tools based on what I can afford to maintain at 80% capacity.

When framing goes wrong

The most common mistake I see in organizations is treating a symptom as the problem. The team is slow, so they add a project management tool. Incidents keep happening, so they add more monitoring. The real question is always why something is happening and what would actually change it.

I see this with Kubernetes adoption constantly. Teams adopt it because it makes their architecture sound sophisticated. Then they spend six months learning it and two years managing the complexity. Most startups don’t need that level of orchestration.

Framing shapes the options you even consider. Bad framing gives you a good answer to the wrong question.

Mapping what you inherit

At Bettermode, we had a performance problem caused by one large customer overwhelming our shared database. The obvious fix was to change how the application routes data, but that meant rewriting logic across more than twenty services. Every future change would inherit that complexity permanently. We found a simpler solution at the infrastructure layer, in a single place we controlled.

The question that changed my direction is one I now ask about every major decision: if we do this, what do we inherit forever?

Defaulting to reversible

When I evaluate a technology choice, I’m usually looking for the option I can walk away from. If a decision is reversible, the cost of being wrong is low. You try it, it doesn’t work, you change course. But when a decision is structural and shapes everything after it, you need a different level of rigor before committing.

Most people spend their energy thinking about the success case. I spend more time on the failure case, because that’s where the real cost lives. If a reversible option gets us most of the way there, I’ll take it every time.

The other habit that has saved me is starting with the smallest possible proof. Instead of implementing a full solution, I look for the smallest change that tells us whether this approach works. If it fails, you’ve lost days instead of months.

The hidden cost of building too early

The trade-off that surprises people most is the reliability cost of over-engineering for scale you don’t have yet. Teams build for ten times their current traffic before they have product-market fit. The system becomes distributed, observability gets complex, and debugging a simple bug takes a senior engineer half a day. You paid for scale with reliability and speed, and that trade-off was invisible because everyone was excited about the architecture.

I surface these early with a simple exercise. I ask the team to walk me through what happens when this system breaks. If nobody can answer clearly, the system is already too complex for its current stage.

Finding clarity in the noise

When a CTO comes to me overwhelmed by technology choices, I ask them to describe the problem they are actually trying to solve. Not the symptoms. The root problem. That conversation alone usually cuts the option space in half.

From there: what do you know for certain, and what are you assuming? Most technology decisions are made on assumptions treated as facts, and separating those changes the decision.

The last step is always the same: describe the simplest version that would actually work. The version that solves the immediate problem with the least new complexity. Usually, that is the answer.

The principle behind what we’re building at Inphra.ai is the same one I apply to every decision. Clarity comes from narrowing, not from evaluating more options. The best technology decision I’ve made was rarely the most exciting one. It was the one I could maintain, reverse if needed, and explain to my team on a Monday morning.

_____________________________________________

Leo Rezaei is the Founder of Inphra.ai and an engineering leader focused on infrastructure reliability, AI-driven solutions, and building teams that thrive without heroics.

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