BuildingSOL Archives

A personal journey into the future of human intuition, where our value is measured not by what we do, but by how we see and judge.

I’ve always loved robots. To me, they are an extension of my being—tools that allow me to perceive and act in places I could never safely—or practically—go myself.

The Toy That Got Away

My interest started with the tactile basics of LEGOs and Erector sets. But the one that got away was the Big Trak. I wanted it so badly as a child, but it never happened—until I finally spotted one in a thrift shop 20 years later. It was a nostalgic handshake with my younger self.

Way before drones were a household name, I was operating quadcopters through the AMA. But the real shift happened in 2008 with the Surveyor Corp SRV-1. I still work with this unit regularly; it didn't just move through a room—it taught me spatial engineering. Today, it’s a critical partner in my building inspections, allowing me to perceive structural nuances from angles no human could reach.

The Reliable Apprentice vs. The Intuitive Partner

In my career, I’ve seen the evolution from the PLC (Programmable Logic Controller) to the autonomous robots of today. The difference is a shift in the very nature of labor:

• The PLC is the Reliable Apprentice: It is the good soldier of the 20th century. It executes predefined tasks with flawless, thoughtless repetition.

• The Spatial Robot is the Intuitive Partner: It doesn't just perform; it perceives. It demands that I, its human partner, provide the context, nuance, and ethical judgment it lacks. I don’t just program its path; I collaborate with its perspective.

A Historical Prophecy Realized

This shift isn't accidental. During the automation crisis of the 1960s, the boundary between human and machine began to blur (Hong, 2003). As machines mastered technical rationality, scholars like Erich Fromm warned that the heart of humanity would have to shift from the realm of pure intelligence to the realm of emotions and feelings (Fromm, 1941).

We are seeing this play out today. While 20th-century automation targeted physical drudgery, today’s AI and robotics target cognitive labor. This forces us to move from the "analytical line" to a judgment-based economy—a shift I now see daily in inspection work, where interpretation matters more than measurement (Mendon-Plasek, 2020).

The Responsibility of the Handler

We often hear the question: "Will a robot take my job?" But that's the wrong frame. Think of it like a pet owner who lets their dog roam free to behave and terrorize the neighborhood; that is an abdication of responsibility. Unlike the set it and forget it nature of early automation, modern robotics require an active handler—a partner who provides the situational awareness and moral oversight the machine cannot possess on its own.

The Call to Arms

We are not moving toward a world where robots think for us. We are moving toward a world where they see for us, reach for us, and act for us—forcing us to become better judges, more empathetic analysts, and more nuanced decision-makers.

The question isn't "Will a robot take my job?" It's "What human capacity within me—what intuition, what empathy, what wisdom—is this new tool waiting to amplify?"

Find the tool that expands your judgment. It's the call you've been waiting for since you first dreamed of that toy that got away.

Let’s Build the Future Together

I am currently #OpenToWork and looking for my next challenge. If your team is looking for someone who understands that the best automation is built on a foundation of human judgment and decades of hands-on experience, I’d love to connect.

#Robotics #SpatialEngineering #Automation #AI #FutureOfWork #OpenToWork #TechHistory #HumanIntelligence

References

Fromm, E. (1941). Escape from freedom. Farrar & Rinehart.

Hong, S. (2003). Man and machine in the 1960s. Techné: Research in Philosophy and Technology, 7(3).

Loaiza, I., & Rigobon, R. (2025). The limits of AI in financial services. MIT Sloan School of Management.

Mendon-Plasek, A. (2020). Mechanized significance and machine learning: Why it became thinkable and preferable to teach machines to judge the world. In The Cultural Life of Machine Learning.