I spent the first part of my career as a civil engineer. I designed structures that carry loads, resist forces, and absolutely cannot fail without warning. Bridges, buildings, foundations — the kind of systems where getting it wrong means people get hurt.
Then I left that career to build AI infrastructure.
This is not a pivot story about following your passion. It is about discovering that the most valuable skill in engineering — thinking in systems — transfers to a completely different domain. And that the new domain moves fast enough to actually build the things you imagine.
What Civil Engineering Teaches You
Civil engineering is constraint-first thinking. Every design starts with what you cannot do:
- The soil can only bear this much load
- The steel can only take this much stress
- The budget can only cover this much material
- The regulations require this much safety margin
You do not start with what you want to build. You start with what the world allows, and you design the best possible thing within those boundaries.
This is exactly how good software architecture works. You have latency constraints, memory limits, API rate limits, and user attention spans. The engineers who ignore constraints build systems that look impressive in demos and fall apart in production.
Failure Modes
In structural engineering, we design for failure. Not because we expect things to break, but because we need to know how they will break when they do. A well-designed beam does not shatter — it bends. A well-designed foundation does not collapse — it settles predictably.
I think about software the same way. Every system I build has a failure mode I have thought about. What happens when the API is down? What happens when the context window fills up? What happens when two agents disagree?
If you have not designed your failure modes, your users will discover them for you. Usually at the worst possible time.
Systems Thinking
The biggest transfer from civil engineering to software is systems thinking. A building is not a collection of independent parts. The foundation affects the structure. The structure affects the envelope. The envelope affects the HVAC. Everything connects to everything.
PAI — the AI infrastructure I built — works the same way. The memory system feeds the skill system. The skill system triggers the hook system. The hook system captures signals for the memory system. It is a feedback loop where every component makes the others better.
You cannot design this by thinking about individual features. You have to think about how the whole system behaves when the parts interact.
The Switch
I did not wake up one day and decide to become an AI builder. It happened in stages.
First, I started automating parts of my engineering workflow. Calculations, report generation, data processing. Python scripts at first, then more structured tools.
Then I discovered what large language models could actually do when you gave them structure and context. Not the party tricks — not “write me a poem” or “explain quantum physics.” The real work: decomposing problems, generating code, analyzing data, managing complexity.
I realized I was spending more time building tools than using them. And the tools I was building were more interesting than the structures I was designing.
So I made the switch.
Building StudioAIT
I founded StudioAIT to do this work professionally. The company helps businesses build and ship AI-powered systems. Not consultancy decks. Not proof-of-concepts that never see production. Actual infrastructure that runs, scales, and delivers value.
The work ranges from e-commerce automation — Shopify stores with AI-powered product management, listing optimization, and inventory systems — to custom AI workflows for companies that need production-grade intelligent systems.
Every project reinforces the same lesson: the hard part is not getting an AI to do something impressive once. The hard part is getting it to do something useful reliably, at scale, every single time.
That is an infrastructure problem. And infrastructure is what I know how to build.
PAI: Building My Own Infrastructure
The most important thing I built at StudioAIT was not a client project. It was PAI — Personal AI Infrastructure.
PAI started as a collection of scripts to make my Claude Code sessions more productive. Save session context. Load project information. Route notifications. Basic productivity tooling.
Then it grew. Skills for different domains. A memory system that persists across sessions. Hooks that automate lifecycle events. An agent system that delegates work in parallel. A product owner skill that decomposes features into epics and stories.
Today, PAI is the system I use to build everything else. When I build a client’s Shopify store, PAI manages the project, researches keywords, writes copy, and validates the deployment. When I write this blog post, PAI loads the content schema, checks the brand voice guidelines, and handles the SEO optimization.
It is a continuously improving algorithm for getting work done. And it gets better with every session because the memory system captures what works and what does not.
What I Learned
Three things stand out from the transition:
1. Constraints Transfer
The civil engineering mindset — start with what you cannot do, then design the best thing possible within those boundaries — is exactly right for AI systems. Context windows have limits. APIs have rate limits. Users have patience limits. Design for the constraints, not the ideal case.
2. Systems Beat Features
A bridge is not a collection of beams. A good AI system is not a collection of prompts. The value is in how the parts interact. Memory feeding skills feeding agents feeding memory — that feedback loop is where the real leverage is.
3. Infrastructure Compounds
In civil engineering, infrastructure enables commerce, transportation, and communication. In AI, infrastructure enables productivity, creativity, and capability. Both compound over time. The road you build today carries traffic for decades. The skill you teach your AI today makes every future session more productive.
Why I Write About This
I write because the transition from traditional engineering to AI building is one more people should consider making. Not because AI is trendy. Because the skills transfer perfectly, the work is meaningful, and the field is young enough that you can build real infrastructure — not just features on top of someone else’s platform.
If you are an engineer who thinks in systems, who designs for failure modes, who cares about what happens in production rather than just in demos — this field needs you. The current AI landscape has too many demo builders and not enough infrastructure builders.
I am documenting my journey because I wish someone had documented theirs when I was making the switch. The technical posts on this site cover the systems I build. The building-in-public posts cover the business and personal side. Together, they tell the full story.
If any of this resonates, reach out. I am always interested in talking with people who build things.
Max Kruisbrink is the founder of StudioAIT and creator of PAI. He writes about AI infrastructure, agentic systems, and the craft of building at maxplaining.com.