Early-Stage | DLR Engineer Founding Startup to Bring Vibe Coding to Hardware Design

Background and Context The landscape of industrial software development has long been characterized by high barriers to entry, a structural bottleneck that has stifled innovation for small teams and startups. Industry giants such as Dassault Systèmes and ANSYS have established dominant positions by leveraging exorbitant licensing fees and steep learning curves, effectively creating a walled garden that requires engineers to spend years mastering complex toolsets. This traditional paradigm stands in stark contrast to the rapid democratization of software development seen in the consumer and web sectors, where the emergence of "vibe coding"—a natural language-driven approach to software creation facilitated by tools like Cursor—has fundamentally altered how applications are built. Orthogonal, a new startup founded by Ji Yang, aims to bridge this divide by applying the principles of vibe coding to the rigid domain of hardware design. Ji Yang brings nearly two decades of deep industry experience to this venture, having worked extensively at the German Aerospace Center (DLR) and within major industrial firms. His background is not merely academic; he was directly involved in the development of core features for Dassault simulation tools and led the electrical system development for the Airbus A350. His career trajectory includes key roles at global technology and manufacturing leaders such as KUKA, BMW, Siemens, COMAC, and Huawei. In 2023, he was recognized as a TUM Ambassador by the Technical University of Munich, underscoring his standing in the engineering community. These experiences provided him with a front-row seat to the inefficiencies of legacy industrial software, motivating his transition from corporate engineering to entrepreneurship. The core insight driving Orthogonal is that the complexity of traditional industrial toolchains has become a critical bottleneck for the industry. While software development has evolved to allow individuals to create complex systems through natural language prompts, hardware design remains tethered to cumbersome, expensive, and highly specialized software ecosystems. Ji Yang argues that this disconnect is no longer sustainable. The traditional model, which demands deep specialization in specific software suites, is ill-suited for the modern era where agility and cross-disciplinary knowledge are paramount. Orthogonal’s mission is to dismantle these barriers by introducing a new workflow that prioritizes intent over interface, allowing developers to focus on engineering logic rather than software navigation. ## Deep Analysis Orthogonal’s strategic positioning hinges on the premise that AI can serve as a paradigm-shifting force in hardware engineering, much like it has in software. The startup is not merely attempting to automate existing workflows but is fundamentally rethinking the interaction between human engineers and hardware design tools. By leveraging large language models to interpret natural language inputs, Orthogonal seeks to abstract away the intricate syntax and procedural requirements of traditional computer-aided design (CAD) and simulation software. This approach aligns with the "vibe coding" philosophy, where the developer describes the desired outcome or system behavior, and the AI generates the underlying code or design specifications. For hardware, this could mean describing a circuit’s function or a mechanical component’s constraints in plain language, with the AI handling the complex geometric and electrical validations. The significance of this shift is amplified by the changing nature of smart hardware companies. Ji Yang observes that AI-enabled teams are becoming smaller and more versatile. In this new ecosystem, a single engineer is expected to master multiple domains, from mechanical design to electrical engineering and software integration. Traditional industrial software, with its siloed modules and steep learning curves, actively works against this trend by forcing specialization. Orthogonal’s platform aims to lower the cognitive load required to switch between these domains, enabling a "full-stack" hardware engineer to operate efficiently. This democratization of capability allows smaller teams to tackle projects that previously required large, specialized departments, potentially disrupting the market share held by incumbents who rely on their entrenched tool ecosystems. Furthermore, the choice of founders and advisors plays a crucial role in the viability of this approach. Ji Yang’s direct experience with Dassault’s core simulation tools provides him with an intimate understanding of the technical hurdles involved in AI-driven hardware design. He knows exactly where the pain points lie and what level of accuracy and reliability is required for industrial adoption. This insider perspective is critical because hardware development involves physical constraints and safety regulations that do not exist in pure software development. A bug in code can be patched; a bug in hardware design can lead to costly recalls or safety hazards. Therefore, Orthogonal’s success will depend on its ability to integrate rigorous engineering validation into its AI-driven workflow, ensuring that the convenience of vibe coding does not compromise the precision required in aerospace, automotive, and industrial manufacturing sectors. ## Industry Impact The potential impact of Orthogonal’s approach extends beyond individual efficiency gains to reshape the competitive dynamics of the industrial software market. For decades, companies like Dassault and ANSYS have maintained their dominance through high switching costs and network effects. Engineers are trained on these platforms, and corporate workflows are built around their specific file formats and processes. By introducing a natural language interface that abstracts these underlying complexities, Orthogonal challenges the necessity of these legacy tools for early-stage design and prototyping. If successful, this could erode the monopoly of incumbents, forcing them to either adopt similar AI-driven interfaces or risk losing market share to more agile, user-centric alternatives. This shift also has profound implications for the structure of hardware startups and R&D departments. The traditional model of hiring specialized engineers for specific software tools is becoming obsolete. As AI handles the technical execution of design tasks, the value proposition of an engineer shifts from software proficiency to systems thinking and architectural vision. This could lead to a more inclusive engineering workforce, where individuals with strong conceptual understanding but less formal training in specific CAD tools can contribute meaningfully to hardware projects. It lowers the barrier to entry for innovation, allowing smaller teams to compete with larger corporations that previously relied on their resource-heavy engineering departments to maintain a competitive edge. Moreover, the integration of AI into hardware design accelerates the iteration cycle. In traditional workflows, making a design change often requires navigating through multiple layers of software, re-running simulations, and manually updating documentation. With an AI-driven system, changes can be proposed and validated almost instantaneously. This rapid feedback loop encourages experimentation and innovation, as engineers are less penalized for exploring alternative designs. For industries such as aerospace and automotive, where time-to-market is critical, this acceleration can translate into significant competitive advantages. Orthogonal’s focus on vibe coding in hardware is thus not just a technological novelty but a strategic response to the industry’s need for speed and flexibility in an increasingly complex technological landscape. ## Outlook Looking ahead, the trajectory of Orthogonal and similar ventures will depend on their ability to establish trust and reliability in high-stakes environments. Industrial sectors such as aerospace, automotive, and medical devices have stringent regulatory requirements and a low tolerance for error. The adoption of AI-driven design tools will require extensive validation, certification, and integration with existing industry standards. Ji Yang’s background in these sectors positions Orthogonal well to navigate these challenges, as he understands the critical importance of accuracy and compliance. However, the path forward is not without obstacles. Building user trust in AI-generated hardware designs will take time, and the startup must demonstrate that its tools can handle the full complexity of real-world engineering problems without introducing subtle errors that could have catastrophic consequences. The broader industry trend suggests that the demand for such tools is growing. As hardware becomes more software-defined, the line between software and hardware development continues to blur. Companies are increasingly looking for ways to integrate AI across their entire product development lifecycle, from initial concept to final manufacturing. Orthogonal’s early-stage focus on vibe coding for hardware design places it at the forefront of this convergence. If the startup can successfully scale its platform to support complex, multi-disciplinary projects, it could become a standard tool in the hardware engineer’s arsenal, much like CAD software is today. Ultimately, the success of Orthogonal will signal a broader transformation in how physical products are designed and built. It represents a move away from tool-centric workflows toward intent-centric design, where the focus is on what needs to be built rather than how to use the software to build it. This shift has the potential to unlock a new wave of innovation in the hardware sector, empowering smaller teams and individuals to create sophisticated products that were previously beyond their reach. As AI continues to mature, the distinction between software and hardware development may further dissolve, leading to a more integrated and efficient approach to engineering that leverages the full power of artificial intelligence to solve complex physical world challenges.