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Welcome to The Wave Engineering Newsletter, your weekly guide to the cutting edge of engineering. Whether you're a seasoned professional, an eager student, or simply curious about innovation, we’re here to inform, inspire, and connect.

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How are you using AI to level up your work?

I’ve been browsing engineering jobs in the mechanical R&D and manufacturing in various industries. Once thing has stood out: a lot of companies want the typical Solidworks + extensive GD&T experience, but buried in these job descriptions was also mention of experience with coding languages preferred. The first time that I saw it, I figured it was a copy-paste error, but the more it pops up the more I realize that this is a fundamental shift in the industry. Hardware engineers that can code are no longer unicorns, but becoming the norm.

This pattern is hard to ignore. When I talked to professors at local engineering schools, they told me they're scrambling to add programming courses to mechanical engineering curricula because their graduates kept getting turned away from interviews for lacking software skills. Startup founders I know can't build hardware products anymore without significant firmware components, data collection systems, or IoT connectivity. Even traditional manufacturing companies want engineers who can automate their design workflows and build custom analysis tools.

It hit me that we're living through a fundamental shift in what it means to be a hardware engineer. The coolest innovations today - whether it's Tesla's self-updating cars, SpaceX's reusable rockets, or medical devices that learn from patient data - all exist at the intersection of atoms and bits. Pure hardware knowledge isn't enough anymore, but neither is pure software expertise. The future belongs to engineers who can speak both languages fluently.

This isn't just about adding a programming language to your resume. It's about understanding how software is reshaping the entire hardware development process, from initial design through manufacturing and into the field. Over the next four weeks, we're going to explore this convergence from a hardware engineer's perspective - not how to become a software developer, but how to remain a great hardware engineer in a world where the two domains are inseparably linked.

I still remember sitting in my mechatronics lab during junior year of college, staring at a blank Arduino IDE screen like it was written in hieroglyphics. The mechanical aspects of our semester project were straightforward. Design a gripper mechanism, calculate the forces, machine the components. I could do that work in my sleep.

Beyond the mechanical components, our gripper needed sensor feedback, servo control, and a state machine for autonomous operation, I felt my stomach drop. Programming wasn't just difficult for me, it was like trying to think in a completely foreign language. While my classmates were debugging code and discussing interrupt handlers, I was still trying to figure out why my servo wouldn't move.

I ended up getting help from some of my computer science friends and told myself after that experience that I was a “hardware” guy. Coding just wasn’t for me.

Nearly a decade later, I find myself elbows deep in an AI app development project. An idea that is far removed from my traditional hardware job and journeying into a type of engineering I never thought I could do, Code! This is all made possible because I have an AI assistant helping me write my code, debug errors and even help me plan out my projects MVP. I’m amazed how diving in and using the AI tools we have available now won’t replace your engineer skills, but instead amplify them by removing the barriers that previously kept me locked into a single discipline.

Engineering education creates specialists. Mechanical engineers learn thermodynamics and materials science. Electrical engineers master circuit analysis and signal processing. Software engineers focus on algorithms and data structures. This specialization makes sense for developing deep expertise, but it creates artificial barriers in real-world product development.

Modern products don't respect these academic boundaries. Every smartphone is a masterpiece of mechanical packaging, electrical design, software optimization, and manufacturing engineering. Every electric vehicle requires expertise in battery chemistry, power electronics, structural analysis, thermal management, and control software.

The traditional approach to interdisciplinary challenges has been to assemble teams with different specialists. This works, but it limits innovation. The most creative solutions often emerge at the intersection of disciplines, where mechanical insights inform software architecture or where electrical constraints drive novel mechanical designs.

Think of AI as the ultimate translator, not between human languages, but between engineering disciplines. Just as Google Translate allows you to read a scientific paper written in German without learning the language, AI allows mechanical engineers to implement sophisticated control algorithms without becoming computer scientists.

This translation happens at multiple levels. When I describe my plant watering requirements in plain English to an AI assistant, it doesn't just generate code. It translates my mechanical engineering mindset into software architecture. I think in terms of flow rates, pressure differentials, and response times. The AI translates this into variables, functions, and control loops.

The magic isn't in the code generation itself. It's in how AI preserves my engineering intuition while expressing it in an unfamiliar medium. When I tell the AI "the pump should run slower when soil moisture is only slightly below target," it understands I'm describing a proportional control response and generates appropriate PID controller code. I don't need to know what PID stands for or how to tune the coefficients. I just need to understand the physical behavior I want.

Imagine a thermal engineer working on heat dissipation for wearable medical devices. The physics of heat transfer are familiar territory, but creating computational fluid dynamics simulations traditionally required weeks of software training just to set up basic models. With AI-powered simulation tools, that same engineer could describe thermal challenges in comfortable engineering terms: "The device needs to dissipate 2 watts while maintaining skin contact temperature below 35 degrees Celsius."

The AI translates these requirements into simulation parameters, generates appropriate mesh geometries, and suggests design modifications based on results. The engineer can focus on thermal engineering while AI handles computational complexity. Instead of learning simulation software syntax, they can have conversations with AI about thermal performance, asking questions like "What happens if I add cooling fins here?" and getting immediate visual feedback.

Consider how an electrical engineer might approach designing custom enclosures for control panels. Traditional mechanical CAD software can feel overwhelming with countless features and unfamiliar workflows. But AI-powered design tools allow specification of requirements in electrical terms: "I need an enclosure that accommodates these circuit boards, provides access to these connectors, and maintains this IP rating for environmental protection."

The AI translates electrical engineering requirements into mechanical design constraints, generates multiple enclosure options, and ensures proper heat dissipation for internal electronics. The engineer can evaluate designs based on electrical performance criteria they understand, while AI handles mechanical engineering details they've never formally studied.

What makes this translation so powerful is that it preserves domain expertise while removing implementation barriers. Engineers don't become less skilled in their core disciplines by using AI assistance. They become more effective because they can explore more design options and validate intuitions more quickly across domains that were previously inaccessible.

The translation works both ways too. When mechanical engineers use AI to generate code, the AI often suggests approaches that purely software-focused programmers might miss. A mechanical engineer thinking about system dynamics might describe control requirements that lead to more robust and physically realistic software implementations than traditional programming approaches would produce.

This bidirectional translation creates opportunities for innovation that wouldn't emerge from single disciplines working in isolation. When thermal engineers can easily explore geometric modifications through AI-assisted CAD, they discover heat transfer solutions that pure mechanical designers might not consider. When electrical engineers can quickly prototype custom enclosures, they create integrated solutions that optimize both electrical and mechanical performance simultaneously.

The key insight is that AI doesn't replace domain expertise. It amplifies it by removing the barriers between domains. Engineers can maintain their core strengths while expanding their creative reach into previously inaccessible areas. This leads to more integrated, innovative solutions because the artificial boundaries between engineering disciplines start to dissolve.

Engineering is about solving, innovating, and connecting ideas to make a difference. Progress is a collective effort and your curiosity is what drives it forward. Thank you for exploring the dynamic world of engineering with all of us at Pipeline Design & Engineering and The Wave.

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“Creativity is just connecting things. When you ask creative people how they did something, they feel a little guilty because they didn’t really do it, they just saw something. It seemed obvious to them after a while.” - Steve Jobs

In collaboration and creativity,
Brad Hirayama
Blueprinting tomorrow, today