Why AI hasn't transformed hardware engineering (yet)

If you look at what is happening with AI right now in industries like law, medicine, or software development, the contributions are massive. 

But those industries all share one underlying trait: the data they process is primarily text-based or code-based. AI is an expert at handling that.

Hardware engineering is different. 

Historically, we have relied on siloed, proprietary CAD and computer-aided engineering (CAE) software. 

These are heavy, UI-driven tools. And because they aren't natively code-based, they have been the primary limiting factor in allowing AI to truly contribute to the hardware design process.

Because of these tool limitations, the traditional engineering workflow is essentially just a validation process. 

You design something, you run a simulation to see how it works, and you iterate. You design, simulate, and repeat until you have a match.

The problem with this approach — especially in highly complex, strongly coupled fields like MEMS and microelectronics — is that you end up exploring less than 1% of the actual design space. 

You might find a solution that works, but it is rarely the optimal solution.

Code is the bridge to AI

To change this, the underlying tools have to speak the AI's language.

What we’ve come to realize at Quanscient is that when you build a simulation system where everything can be represented as Python code or JSON files, you completely open the door for AI to interact with your engineering pipelines.

This enables a shift toward what you could call "prompt-based engineering." AI agents can start coordinating the tedious parts of the process. But to actually build this automated workflow, you need three foundational pillars in place:

1. Massive computational scale: AI needs data. To get it, your solver needs the throughput to run massive Designs of Experiments (DoEs) in parallel across the cloud. This is how you generate huge chunks of physics-aware data.
2. Programmatic control: You need to move away from manual user interfaces and run your physics in a purely code-based environment. This allows you to deploy AI agents that can script and adjust your geometries, while you keep a "human in the loop" to control and validate the physics.
3. AI-driven optimization: Once you have that strongly coupled multiphysics data, you can train surrogate models that run in a millisecond. We laid the groundwork for this last November with the release of Quanscient MultiphysicsAI, showing how teams can instantly evaluate hundreds of thousands of candidates to find the best solutions.
   
   

In our 2025 Quanscient Multiphysics Simulation Report, nearly 70% of respondents stated they believe AI will have the biggest impact on multiphysics simulation over the next five years. The three pillars outlined above might just be the key to realizing that potential.

Completing the automated loop

MultiphysicsAI gave teams the ability to see the best possible designs. 

Now, by wrapping that intelligence in a purely code-driven environment with massive cloud compute, we are fully automating the end-to-end workflow.

When you have these three pillars working together, it creates a massive shortcut in R&D. 

We, as engineers, are no longer forced to ask the question: "This is my design... how will it work?" 

Instead, we can finally ask the question that actually matters: 

"These are my specifications... what is the optimal design?"

To see it is to believe it

I know this is a significant shift from how traditional simulation is done. And for engineering leaders, seeing is believing.

On April 30th, I am hosting a live event with our co-CTO, Dr. Andrew Tweedie, to show you exactly how this workflow operates in practice. 

We will be showing an end-to-end MEMS demonstration where you can see exactly how each step works and what the outcomes are. 

We’ll also explore what else this approach makes possible and answer your questions live.

If you are ready to see what code-driven hardware engineering looks like, I invite you to join us.