Lessons from the Shift Between Luxury and Mass-Market Automaking

Interview with Samuele Sois | Principal Design Engineer and Principal Data Science & AI Engineer | Former McLaren, Jaguar Land Rover, Bentley and Tata Motors Design Tech Centre | PhD researcher, University of Birmingham

Published: November 3, 2025 · Anecdotes are the expert's personal recollections. Updated to correct a statement on EU recycling targets.

At McLaren, a gap of half a millimetre was the norm. In a high-volume plant, every extra decimal of precision is a line item. Samuele Sois, who has spent more than two decades in aerospace and automotive engineering across Europe, the Americas and Asia-Pacific, explains how luxury and mass-market carmakers reach quality by opposite routes, and why AI and recycled materials are pulling them closer together.

Over-Engineering vs. Clever Engineering

In luxury engineering, perfection is the standard. "At McLaren, I remember consistently applying tolerances around half a millimetre, when not less," Sois recalls. That precision is unaffordable at high volume, where tighter tolerances translate directly into cost.

Mass-market engineers rely instead on dimensional variation analysis: techniques that hide or minimize visible inconsistencies so parts look right even with wider tolerances. "When money is not budgeted for advanced tolerance analysis, it's time to use engineering acumen and sheer talent." One segment can afford over-engineering. The other wins through intelligent compromise.

Late Changes: A Luxury Only Luxury Can Afford

In the luxury segment, a single opinion can reset a program. Sois recalls that late in the McLaren P1 prototype cycle, the company's leadership asked for a major redesign after seeing the finished prototype.

In a mass-market company, that would be a disaster. "Late changes cost real money in mass-market OEMs, and the allowed leeway reduces significantly at the end of every major milestone." The contrast shows how differently engineering governance works: luxury design is iterative and visionary, mass-market development is structured and risk-averse.

Recycled Materials Are Now an Engineering Problem

Luxury cars introduce exotic materials such as carbon fibre, hand-stitched leather and bespoke metal finishes. Mass-market cars imitate them. "High-volume vehicles simply have to evoke such materials, with coatings, films and even plastic injection techniques that mimic these."

The bigger investment now goes elsewhere. "Where significant expense is done is in material research and development; not in the pursuit of quality or luxury but looking to obtain eco-compatible materials with the lowest possible ecological impact and the highest possible circularity."

Regulation is a big reason. In the EU, the End-of-Life Vehicles Directive requires that at least 85% of a vehicle's weight be reused or recycled, and at least 95% be reused or recovered, at the end of its life. The EU is also negotiating a regulation to replace that directive, which would add minimum recycled-content requirements for plastics in new vehicles.

Recycled inputs bring variability, lower strength and inconsistent behavior, but also weight savings and circular manufacturing. "Sustainability is quite a forced march right now. It's both a complication and an accelerator."

Perceived Quality Is Showmanship

Craftsmanship is about perception as much as precision. "Perceived quality is like the good showmanship of a seasoned magician," Sois says. "The trick is capturing the attention of the audience and letting them ignore what you don't want them to see."

Engineers tune every touchpoint: the feel of a switch, the softness of a dashboard, the sound of a closing door. "Nobody would know what lies behind the firewall of the car, or between the floor and the underbody, but everybody would hear, and appreciate, the nice sound of the door closing." Those details are how an affordable car can still feel premium.

AI Helps, Until It Hallucinates

Simulation and AI tools give engineers faster, data-driven insight and take over repetitive design work. They also make mistakes. "AI is prone to hallucinations," Sois warns. He describes a recent presentation by an EV maker in which, in his reading, an AI model handled pedestrian-impact deformation on the boot surface correctly but extended it to positions where the pedestrian would not actually touch the car.

The greater risk, in his view, is losing human judgment. "AI, simulation and data science are not shortcuts but tools to reduce risk and achieve a better vision. They should free engineers from repetitive work, enabling them to focus on the aspects that customers truly notice."

Cheaper Luxury, Harder Governance

Automated tolerance analysis, texture synthesis and topology optimization are already helping mass-market cars approach luxury-level precision. "The price of luxury is bound to go down, thanks to cost-efficient optimization."

The trade-off is governance. Sois lists the risks he sees:

• Model governance becomes more complex
• Human oversight can weaken when AI is involved
• A fully automated design AI can work as a "black box"
• Suppliers are at very different levels of maturity

His answer is transparency and explainable AI. "The entire AI pipeline, from dataset creation and training to the neural networks' inner workings, must be laid bare for all to see. AI is, for all intents and purposes, just glorified statistics. We can and should explain it to everyone as such."

Three Principles From Sois

1. The human touch is irreplaceable. The sound of a latch, the motion of a door and the feedback of a control define emotional quality.
2. AI needs governance, not mystique. Transparency, consent and safety are the basis of trustworthy innovation.
3. Technology should elevate creativity, not replace it. Automation should free engineers to focus on design, vision and craftsmanship.

Samuele Sois is an engineer with more than two decades of international experience in aerospace and automotive, including McLaren, Jaguar Land Rover, Bentley and Tata Motors Design Tech Centre. He has led programs on interior and exterior trim, kinematics and perceived quality and developed real-time ergonomics tools and PLM/BI architectures. He works as a Principal Design Engineer and Principal Data Science & AI Engineer and is a PhD researcher in Automotive AI and Cybersecurity at the University of Birmingham.

Sources

• Directive 2000/53/EC on end-of-life vehicles
• European Commission proposal for a Regulation on circularity requirements for vehicle design and end-of-life vehicle management (2023)

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