Laurent Giraid is a seasoned technologist whose career has been defined by a deep-seated fascination with how machine learning can solve the world’s most invisible problems. With a background rooted in natural language processing and the ethical implementation of artificial intelligence, Giraid has spent years advocating for technology that doesn’t just live on a screen but actively improves the physical world. His expertise lies in identifying the “unglamorous” sectors of the economy—those vital systems like global logistics and heavy industry—where traditional software has failed to keep pace with modern complexity. Today, he shares his insights on a major shift in the industrial landscape: the movement toward AI-native operating systems that are beginning to manage the very components that keep our global infrastructure functioning.
This discussion explores the radical transformation of the spare parts supply chain, a sector where billions of dollars in transactions are still managed by outdated manual processes. We dive into the limitations of legacy ERP systems, the financial implications of autonomous decision-making in inventory management, and the broader mission of using AI to extend the life of physical assets while reducing global waste.
How do legacy systems and manual processes currently hinder the efficiency of the spare parts industry, and what are the specific risks of staying with these traditional methods?
The global economy essentially runs on a physical backbone of machinery, and spare parts are what keep that backbone from collapsing. Despite the fact that $4 billion in spare parts are transacted every single day just in the automotive industry, much of this sector is stuck in a time warp using software designed thirty or forty years ago. When you look at a distributor trying to manage hundreds of thousands of individual SKUs, you see teams buried under fragmented spreadsheets and relying on phone conversations or static images to make vital calls. This manual review process isn’t just slow; it’s a massive bottleneck that creates a reactive environment where businesses are constantly surprised by rising fuel costs, shifting tariffs, or unpredictable repair volumes. By sticking to these fragmented systems, companies risk obsolescence and massive financial leakage, as they simply cannot process the sheer volume of unstructured data needed to stay competitive in a world of increasingly complex vehicles and machines.
What distinguishes an AI-native operating system from a standard dashboard, and how does it fundamentally change how a business handles its inventory and pricing?
The fundamental difference is that we are moving away from simply showing data to a human and toward a system that can make and execute decisions autonomously. Most traditional software adds another “dashboard” of complexity that an employee has to review, which doesn’t solve the underlying problem of scale. An AI-native approach, like the one recently backed by $11 million in seed funding, acts as an intelligence layer that integrates directly into existing ERP systems to automate workflows like demand prediction and dynamic pricing. This technology has already processed over $10 billion in parts demand—a figure so large it could provide new spark plugs for every passenger car currently on the road in North America. By working quietly in the background, the AI understands the extraordinary complexity of what fits where and when it is needed, allowing businesses to move from periodic, manual reviews to a state where inventory levels update continuously as market conditions change.
Beyond the technical implementation, what kind of measurable results are companies seeing when they move toward these automated, AI-driven workflows?
The shift from manual oversight to an automated intelligence layer is producing results that are honestly hard to ignore, with many customers achieving a return on investment of more than 10x. This isn’t just a marginal gain; it’s a total overhaul of how profitability is managed in a high-volume, low-margin environment. When a system can autonomously manage obsolescence and adjust pricing in real-time, it removes the massive waste associated with holding onto parts that no longer have a market. We are seeing businesses across the United States, the United Kingdom, and Europe leverage this technology to transform fragmented information into concrete, executable decisions. The founders behind this movement, including a University of Oxford-trained physicist and an AI academic from University College London, have brought deep domain expertise to ensure the AI isn’t just a generic tool but a specialized engine that understands the nuances of the $4 billion daily parts market.
In what ways does optimizing the spare parts supply chain contribute to a more sustainable global economy and the longevity of physical assets?
There is a profound environmental story here that often gets lost in the talk of spreadsheets and SKUs, as a more efficient supply chain directly extends the useful life of our existing assets. When parts are easier to find and repair cycles are faster, we keep vehicles on the road and machinery in the field longer, which reduces the need for new manufacturing. By using AI to predict demand accurately, we keep more parts in active circulation and prevent them from becoming “dead stock” that eventually ends up in a landfill. This mission is about making the physical economy more resilient by ensuring that the components we’ve already built are used to their maximum potential rather than being wasted due to poor data visibility. It is a vital step in creating a circular economy where the “intelligence layer” ensures that every component is exactly where it needs to be at the moment it is required.
What is your forecast for the role of AI in the physical economy over the next decade?
I believe that over the next ten years, the “intelligence layer” will become the invisible nervous system for the entire global supply chain, moving far beyond simple recommendation engines. We will see a total transition where manual spreadsheets are viewed as relics of a primitive era, replaced by autonomous operating systems that manage trillions of dollars in physical assets with zero human intervention. This transformation will not only make supply chains significantly faster and smarter but will also enable us to maintain infrastructure in ways we previously thought impossible, perhaps even supporting robots on Mars. The best user experience in the future will be one where the user does nothing at all, as the AI handles the complexity of the physical world in the background. Ultimately, this will lead to a more efficient, less wasteful world where the products we rely on are delivered faster and maintained better than ever before.
