German Automotive Is Not Losing to China. It Is Losing to Itself.

Insights from the Scaling Business Summit 2026, Ho Chi Minh City.

The session was billed as a fireside chat. It turned into something closer to a diagnosis. Philipp Raasch, Founder of The German Autopreneur and the most widely read independent voice in German automotive, sat down with Lars Jankowfsky, Founder at Gradion, to talk about whether the industry that built the modern car can still save itself.

Philipp spent nearly a decade inside Mercedes-Benz before walking away to build a newsletter and media platform that now reaches virtually every CEO in the German auto sector. He has seen both worlds: the machine from the inside, and the wreckage from the outside.

What followed was an honest conversation about speed, culture, and survival, and why the biggest threat to German automotive has nothing to do with China.

1. Three Shifts at Once and the Hardest One Has No Technology Solution

The automotive industry has faced disruption before. But Philipp argued that what is happening now is different in scale. Three major technological shifts are colliding simultaneously:

• The move from combustion engines to EVs.
• The move from hardware-defined to software-defined vehicles.
• The move from driver assistance to full autonomous driving.

Any one of these would be a generational challenge. All three at once, on an accelerated timeline, is something else entirely. “We are experiencing three major technological shifts at the same time,” Philipp said. “And the third one, autonomous driving is also the direct link to AI.”

But the technology is not the hardest part. The hardest part is the organizational culture that grew up around hardware, around engineering precision, around century-old process thinking and now has to become something it was never built to be.

Lesson 1: The technology problem is solvable. The culture problem is the one that will decide who survives.

2. German Automotive Is Not Losing to China. It Is Losing to Its Own Mindset.

German quality is real. Philipp was direct about this. Drive through Ho Chi Minh City and you will still see German flags on German cars, because the brand promise still carries weight. A century of engineering reliability does not disappear overnight.

But quality alone is not enough when the speed gap is structural. “We are moving at hardware speed,” Philipp said. “China moves at software speed. That’s the shift. We have to become software-first companies.” Chinese manufacturers are iterating on a cycle that German companies cannot match, not because German engineers are less capable, but because German organizations were not designed for this pace.

The deeper problem is that the shift from hardware to software is not just a technical change. It is a change in how decisions are made, how products are defined, how success is measured. That is a cultural transformation, and it does not respond to engineering solutions.

Lesson 2: German engineering is still an asset. German operating culture is the liability.

3. The Battery Mistake. When You Outsource the Heart of the Product

About a decade ago, German automotive made a strategic decision that is now costing the industry dearly. When the shift from combustion to electric became clear, companies had to decide: is the battery a core competency, or something we buy from suppliers?

German manufacturers chose to buy. BYD chose to own. “BYD owns the complete supply chain for batteries. They can manufacture cars at a much lower cost because they don’t have to pay margin to someone else for the most critical component.” For German automakers, every electric car sold means paying that margin to a supplier, a structural cost disadvantage that compounds with every vehicle produced.

The Porsche case makes the same point from a different angle. Porsche’s identity was built on a specific engine sound, on the feeling of a combustion drivetrain. An electric Porsche is not a worse Porsche, it is a question the brand has not yet answered. What does it stand for when the thing that defined it is gone? Philipp pointed to two paths: double down on combustion as a premium niche, like a Rolex strategy, or reinvent what the brand means in a software-defined world. Neither is easy. Both require a decision.

Lesson 3: When you outsource a core component to reduce complexity, you outsource your cost advantage too. The savings are temporary. The dependency is permanent.

4. The Level 3 Dead End. What Hardware Thinking Costs in a Software World

This was the sharpest story in the session. Mercedes-Benz and BMW spent years developing Level 3 autonomous driving - the certification, the engineering, the infrastructure. They were the only companies in the world to achieve legal certification for Level 3 on public roads. It was a genuine engineering achievement.

It was also largely wasted effort. While German engineers were solving Level 3 as a hardware challenge, perfecting algorithms for every edge case, Chinese manufacturers and Tesla were asking a different question: how do we collect as much driving data as possible? “They said: autonomous driving is not an engineering challenge. It’s a software challenge. It’s a data challenge. It’s an AI challenge.” The answer was to deploy Level 2 at mass scale, often free, harvest billions of kilometres of real-world data, train a model, and skip Level 3 entirely on the way to Level 4.

Level 3 cost extra. Level 2 was free. Mass deployment of Level 2 generated the data. The data trained the AI. The AI enabled Level 4. German manufacturers invested in the wrong rung of the ladder not because their engineers were wrong about the technology, but because they were solving for hardware perfection when the real competition was about data scale.

Lesson 4: In AI-driven industries, the team with the most data wins. Engineering perfection at an earlier stage is not a shortcut to scale, it can be a trap.

5. The Pivot Playbook. What a Tier-2 Supplier Should Do Right Now

Lars pushed Philipp directly: if you were the CEO of a traditional German tier-2 supplier making high-quality metal parts, what would you change in 2026? The answer was methodical.

First, understand your exposure. If your revenue depends on combustion engine production, your addressable market is on an X-curve, shrinking as EVs grow. The question is not whether this happens, but how fast. Second, ask the hard question: “What is my unfair advantage? Where do I have a lead over anyone who just has capital and decides to start?” Competency, supply chain access, and institutional knowledge are real assets. The question is which adjacent market they transfer to.

Philipp identified three credible pivot directions. Defense spending is rising, and the manufacturing overlap between auto parts and defense components is significant. Robotics is growing fast, Philipp noted a 70% supply chain overlap between automotive manufacturing and humanoid robotics production, and the robotics market is projected to exceed the automotive market in total size. And finally, geography: German suppliers locked into European OEMs could look at Chinese manufacturers as new clients, supplying into a growing market rather than a contracting one.

“If you wait until the market is ready for EVs and then make the shift, you are too late. You have to attend two parties at the same time.”

Lesson 5: Your manufacturing competency does not expire, your market does. Find the adjacent market where your skills are an unfair advantage, and move before the core business forces the decision.

The CEO Execution Playbook: What to Do Tomorrow

1. 1. Map your three shifts. Identify which transitions, EV, software-defined, autonomous most directly threaten your product or business model. Assign a probability and a timeline to each. That is your strategic risk register.
2. 2. Audit your cultural operating model. List the five decisions that take the longest in your organization. If they are hardware-cycle decisions applied to software-speed problems, the bottleneck is structural. Name it before you try to solve it.
3. 3. Find your battery equivalent. Identify the one component or capability that will define cost competitiveness in your market over the next decade. Ask honestly: are you building it, or buying it from someone who will eventually outcompete you with it?
4. 4. Reframe your AI strategy as a data strategy. Before you ask what AI can do for your product, ask what data you are collecting at scale today. The companies winning on AI are winning on data volume first. What is your equivalent of the Level 2 fleet?
5. 5. Run the pivot analysis now. List your top three manufacturing or operational competencies. Map them against defense, robotics, and new geographies. One of those combinations is likely a better bet than waiting for your current market to recover.

→ Watch the full session on YouTube