Early Project | DLR Engineer Founding Orthogonal, Bringing "Vibe Coding" to Hardware Development

Background and Context The landscape of industrial software has long been defined by high barriers to entry, characterized by steep learning curves and exorbitant licensing fees. For nearly two decades, Ji Yang, the founder of the early-stage startup Orthogonal, has navigated this complex environment from the inside out. His career trajectory offers a rare cross-section of the highest echelons of aerospace engineering and industrial software development. Ji Yang began his tenure at the German Aerospace Center (DLR), where he did not merely operate existing tools but co-developed core features for Dassault Systèmes simulation software. This foundational experience in the underlying architecture of computer-aided engineering (CAE) tools provided him with a technical depth that is uncommon among entrepreneurs. Following his time at DLR, Ji Yang moved into the aerospace manufacturing sector, where he served as a core leader in the electrical system design for the Airbus A350. This role required managing the intricate integration of electrical systems within one of the most advanced commercial aircraft in history. The complexity of the A350’s electrical architecture demanded rigorous precision and a deep understanding of legacy industrial workflows. Later, his career expanded into broader industrial and technology sectors, with key roles at global giants including KUKA, BMW, Siemens, and Huawei. This diverse portfolio of experience allowed him to observe the persistent inefficiencies in hardware design workflows across different industries, from automotive to consumer electronics. Orthogonal was founded on the premise that traditional industrial software is fundamentally too difficult to use for the modern era. Ji Yang and many of his peers in the industry share the sentiment that the current tools, dominated by incumbents like Dassault Systèmes and ANSYS, create a monopoly that stifles innovation. The high cost of entry and the rigidity of these legacy systems prevent agile teams from iterating quickly. Ji Yang views the current moment not just as a time for incremental efficiency gains, but as a critical juncture to reshape the entire paradigm of industrial software. By leveraging artificial intelligence, Orthogonal aims to dismantle the walls built by these giants, offering a new pathway for hardware development that is both accessible and powerful. ## Deep Analysis Orthogonal’s core value proposition lies in its application of artificial intelligence to lower the technical threshold for hardware design. Ji Yang identifies a significant gap in the market: while software development has embraced agile, intuitive workflows, hardware engineering remains tethered to heavy, complex tools. Traditional CAE and electronic design automation (EDA) tools require extensive training and specialized knowledge, creating a bottleneck for small teams and startups. Orthogonal seeks to disrupt this status quo by integrating AI directly into the design workflow, effectively automating the tedious and complex aspects of hardware engineering. The startup is drawing inspiration from the concept of "Vibe Coding" in the software world, where developers can generate functional code through natural language prompts and intuitive interactions. Orthogonal aims to bring this same level of abstraction and ease of use to hardware design. By allowing engineers to describe their intent in a more natural, high-level manner, the AI-driven platform can handle the underlying complexity of simulation and design rules. This approach reduces the need for deep expertise in every specific tool, enabling engineers to focus on innovation rather than tool mastery. This shift is particularly relevant given the changing structure of modern hardware companies. The trend is moving toward smaller, more agile teams that require members to possess cross-disciplinary skills. A single engineer today may need to understand mechanical design, electrical systems, and software integration simultaneously. The traditional model, where specialists rely on siloed, expert-level tools, is no longer efficient for these lean organizations. Orthogonal’s AI-native workflow is designed to support this multi-hyphenate reality, providing a unified platform that adapts to the user’s needs rather than forcing the user to adapt to the software’s rigid constraints. Furthermore, the integration of AI allows for a more iterative and flexible development process. In traditional workflows, running simulations or checking design rules can be time-consuming and resource-intensive, often slowing down the design cycle. By automating these checks and providing real-time feedback, Orthogonal enables faster iteration. This speed is crucial for startups that need to validate concepts quickly and pivot based on market feedback. The platform essentially democratizes access to high-fidelity engineering tools, which were previously the exclusive domain of large corporations with substantial budgets for software licenses and specialized staff. ## Industry Impact The emergence of Orthogonal signals a potential shift in the competitive dynamics of the industrial software market. For years, companies like Dassault Systèmes and ANSYS have maintained their dominance through a combination of technological superiority and entrenched customer relationships. Their tools are deeply integrated into the workflows of major aerospace, automotive, and electronics manufacturers. However, this dominance has also led to complacency in terms of user experience and accessibility. Orthogonal’s entry challenges this monopoly by offering a more user-friendly, AI-driven alternative that appeals to a new generation of hardware developers. For the broader ecosystem of hardware startups and small-to-medium enterprises (SMEs), Orthogonal represents a significant reduction in operational friction. The high cost of traditional industrial software has always been a barrier to entry, limiting the ability of smaller players to compete with larger incumbents. By lowering the cost and complexity of hardware design, Orthogonal levels the playing field. This could lead to a surge in innovation, as more teams are able to experiment with new hardware concepts without being constrained by the limitations of legacy tools. The impact extends beyond just cost savings. The ability to iterate faster and with greater ease can accelerate the time-to-market for new products. In industries where speed is critical, such as consumer electronics and renewable energy, this advantage can be decisive. Orthogonal’s platform allows teams to move from concept to prototype more quickly, enabling them to respond to market changes and customer feedback with greater agility. This shift towards a more agile hardware development process could redefine how products are designed and brought to market. Additionally, the adoption of AI-native workflows may lead to a change in the skill sets required for hardware engineers. As tools become more intuitive and automated, the emphasis may shift from manual tool operation to higher-level system design and problem-solving. This could open up the field of hardware engineering to a broader range of talent, including those with backgrounds in software and data science. The convergence of these disciplines could lead to more innovative and integrated hardware solutions, as teams are no longer limited by the silos of traditional engineering roles. ## Outlook Looking ahead, Orthogonal is well-positioned to capitalize on the growing demand for more flexible and efficient hardware design tools. The trend towards smaller, more agile hardware companies is likely to continue, driven by the need for rapid innovation and cost efficiency. As these companies seek to reduce their reliance on expensive legacy software, Orthogonal’s AI-driven platform offers a compelling alternative. The startup’s focus on lowering barriers to entry and enabling intuitive workflows aligns perfectly with the needs of this emerging market segment. The success of Orthogonal will depend on its ability to deliver on its promise of high-fidelity engineering capabilities while maintaining ease of use. The challenge lies in ensuring that the AI-driven tools can handle the complexity of real-world engineering problems without sacrificing accuracy or reliability. If Orthogonal can achieve this balance, it could establish itself as a key player in the industrial software landscape, challenging the dominance of established giants. Moreover, the integration of AI into hardware design is likely to evolve rapidly in the coming years. As machine learning models become more sophisticated, the capabilities of AI-driven engineering tools will expand, offering even greater levels of automation and insight. Orthogonal is at the forefront of this trend, leveraging its founder’s deep expertise in both industrial software and AI to create a platform that is both innovative and practical. The startup’s ability to adapt to changing technological landscapes and customer needs will be crucial to its long-term success. Ultimately, Orthogonal represents a significant step towards a more open and accessible future for hardware development. By breaking down the walls of traditional industrial software, the startup is enabling a new era of innovation where creativity and agility are not constrained by the limitations of legacy tools. As the industry continues to evolve, Orthogonal’s vision of an AI-native hardware design workflow could become the new standard, reshaping how hardware is designed, developed, and brought to market.