Hardware is Hard: Why We Are Excited About Hardware Engineering
By Megan Cain and Michaela Gordon
Few can argue that the U.S. is in the early innings of a manufacturing boom, driven by macro trends like reshoring, electrification, and historic government legislation (i.e., IRA, BIL / IIJA, CHIPS Act) passed over the last three years. But what does this mean for the broader economy? Which sectors will be impacted the most? And what types of innovation can we expect to see this decade?
We can’t predict the future, but we do know that increased spending on manufacturing capacity means more physical products will be coming to market. The pace of innovation has also never been faster as the convergence of next-generation GPUs, generative AI, 5G/6G wireless communication, and sustainability trends all underpin growing demand for new product development (“NPD”) throughout the economy.
U.S. Manufacturing Boom
Since H2 2022, construction spending on factory buildout has been through the roof — surpassing $200Bn in annualized spending in 2023. Within the Clean Energy economy alone, more than 450 manufacturing facilities for solar, EV, battery, and offshore wind have been announced. Many experts believe that the “manufacturing supercycle” will continue throughout the decade, leading to continued capital spending, NPD, and job growth.
However, as R&D teams grapple with aggressive production goals, they will be forced to compete with other high-growth technology sectors to attract and retain top engineering talent, which is already in short supply. Rising demand for NPD coupled with engineering shortages means that R&D executives will be forced to invest in advanced engineering tools to help enhance productivity and streamline the entire product development process, from design to manufacturing.
An Industry Ripe for Innovation
Most standard tooling for electrical and mechanical hardware design was developed in the 1980’s and continues to operate on the same legacy codebases. Some of these monoliths have added new functionality over the years, however their core architecture is ill-equipped to navigate the complexities of modern hardware design.
If you ask any systems, mechanical, electrical or design engineer about the pain points of working with legacy tooling, you will hear — painfully slow, limited collaboration, and lack of interoperability with other design platforms.
In contrast to the lack of innovation in the hardware design space over the last 30 years, the software engineering landscape has experienced impressive growth and innovation leading to web-based collaboration platforms like Figma and Github that offer myriad time-saving developer tools.
We believe the future of hardware design and manufacturing sits at the intersection of the software design space and AI. Web-based, generative design tools have the potential to streamline the entire product development process by automating repetitive tasks, enhancing collaboration across stakeholders, reducing data silos, and compressing verification, testing and procurement timelines — all helping R&D teams bring new products to market faster, cheaper, and more efficiently.
Opportunities with Generative Design & Digital Manufacturing
Let’s first unpack the hardware design value chain and then explore specific opportunities for innovation with generative design and digital manufacturing.
Requirements & Planning: The process for defining and managing hardware requirements is incredibly complex and requires advanced systems engineering to ensure that a given design incorporates all functional, safety, certification, and reliability requirements. Legacy requirements software is clunky, overly complex, and leads to data siloes.
- Opportunity for Innovation: Design requirements must be dynamically linked to engineering tooling (models) so that adjustments to top-level requirements (i.e., length, width, mass) automatically flow through downstream models — avoiding the need to re-verify design requirements each time the design changes.
- Notable Startups: Flow Engineering, Stell Engineering, Enviate, Prewitt Ridge, Valispace
Architecture & Schematic Design: Many legacy computer-aided design (“CAD”) programs are deployed on-premises, making real-time collaboration virtually impossible. They also generally operate GUI click-based interfaces, versus code-based interfaces, resulting in low-value repetitive tasks and high error rates as engineers are forced to manually click on a screen.
- Opportunity for Innovation: CAD programs must support both code-based and click-based interfaces. AI code generation enables engineers to automatically convert text to code that drives CAD model development. In the way that GitHub and Figma enable engineers to collaborate, track changes, and resolve conflicts in real-time, CAD programs must also have native integrations with web-based integrated development environments (“IDEs”) that contain reusable code libraries, compilers, and debuggers to prevent starting a model from scratch.
- Notable Startups: Flux, SnapMagic, Celus, KittyCAD, AllSpice, JITX, Rollup, InfinitForm
Testing & Verification: Design verification is one of the most time-intensive steps in NPD. Some experts estimate up to 80% of an engineer’s time can be allocated to verification.
- Opportunity for Innovation: Generative AI can streamline the verification process for hardware design and ultimately streamline signoff flow by optimizing design performance, generating synthetic data for testing, or enabling a model to self-verify that a design incorporates key functional, performance, and safety specifications.
- Notable Startups: Collimator, Vinci 4D, Quilter, CADY, Generative Engineering
Integration & Workflow Tools: Most legacy design tools support only certain programming languages, making integration overly complicated and inefficient. Lack of interoperability between systems forces engineers to use separate programs to specify requirements, generate the design schematic, and test & verify performance — requiring manual model re-runs to refresh calculations each time a design changes.
- Opportunity for Innovation: Data integration and workflow tools help to eliminate data silos by aggregating and normalizing data across design platforms. They do this by compressing and removing duplicative code blocks to speed processing times, track changes with version control, and streamline coordination between stakeholders.
- Notable Startups: Violet Labs, Silsync, ShapeCI, Duro, Colab, Quarter20
Procurement & Assembly: Vendor sourcing and management is a critical and time-consuming step in the NPD process. Engineers are often forced to allocate 6+ hours per week on vendor sourcing, price quotes, and other procurement-related activities.
- Opportunity for Innovation: Digital manufacturing practices, like Design for Manufacturability (“DFM”), should be adopted earlier in the design process to ensure that all design attributes can be manufactured on time and within budget — avoiding costly change orders and additional design iterations that could add months to the development timeline.
- Notable Startups: Fictiv*, Diagon, Config, Prima, GCDee
TLDR
Access to engineering talent will become a universal bottleneck for high-growth technology companies trying to bring new products to market. We believe that advanced engineering tools leveraging generative design and digital manufacturing will streamline the product development process, enabling R&D teams to bring new products to market faster, cheaper, and more efficiently.
Hardware design and engineering tools are complex and we are constantly learning. Please reach out if you’re building something in the space or want to discuss further!
[*] Represents a Westly Group portfolio company
