Artificial Intelligence and Industry 2026: Why Germany is Losing the Transformation Marathon

Executive Summary

German industry is losing massive competitiveness because it researches innovations instead of implementing them – while China systematically makes electric vehicles, batteries, and robots market-ready. Prof. Gunther Dück, former CTO of IBM, sees exactly the same patterns that pushed IBM to the margins in the 1990s: holding onto expensive, outdated products too long while missing disruptive technologies. 51,000 job losses in 2025 in the automotive industry demonstrate the urgency. The key issue is not AI anxiety, but a cultural implementation problem: Germany conducts excellent basic research but doesn't build profitable businesses from it. In 2026, AI won't just replace jobs – it will make CEOs replaceable by rule algorithms, while manual and creative work will increase in value.

People

• Prof. Dr. Gunther Dück (Mathematician, former CTO IBM Germany)
• Leonhard Schmedding (Host, Everlast AI)
• Sundar Pichai (Google CEO)
• Elon Musk (Tesla, The Boring Company)

Topics

• Artificial intelligence and the labor market
• German automotive industry and electric mobility
• Corporate culture and innovation
• AI competency and executive development
• Humanoid robots and robotaxis

Clarus Lead

Germany is experiencing a silent crisis: while Chinese manufacturers like BYD and Xiaomi expand globally, the German automotive industry loses over 51,000 jobs in 2025 – the worst figure since 2011. This is not a cyclical problem but a structural failure in implementing innovations. Prof. Gunther Dück describes the phenomenon from personal experience: IBM could have invented cloud computing but feared disruption of its own business model and lost market leadership to Amazon. Today this drama is repeating in the automotive industry – with AI as an additional catalyst.

Clarus Original Research

• Clarus Research: The transcript reveals a systematic pattern: German companies invest hundreds of millions in research (e.g., 50 km overhead catenary infrastructure for trucks), produce excellent doctoral theses, then discover the business model doesn't work – and terminate the project. China, by contrast, executes 5–10-year plans, accepts zero revenue for the first 5 years, and then dominates the market.

• Classification: The core risk is not technological incompetence but cultural paralysis through rule-orientation. 85% of managers think process-oriented (Excel sheets, budgets, hierarchies), while real innovation comes from 15% of "nerds" who need to be given freedom. AI exacerbates the problem: CEOs can be replaced by algorithms that make better decisions based on data – without the emotional/political distortions of humans.

• Consequence: Those who want to benefit in 2026 must understand: Craftsmanship remains future-proof (physical work cannot be automated), but classical managerial positions will become hollow. University graduates without practical orientation face greater risk than skilled workers. The "appropriate treatment of humans" (Dück's core concept) requires AI support, not AI replacement – whoever understands this will be a winner.

Detailed Summary

The IBM Scenario 2.0: Why Germany Fails

Dück was CTO at IBM when cloud computing was invented (2006). IBM managers said: "Two cents per hour? That's too cheap, we can't do business with that." Amazon Web Services launched in 2006, took 15 years to breakthrough (2021), and is now many times larger than IBM. We see the same paralysis today with electric vehicles and batteries.

The German automotive industry still earns billions with expensive combustion engines. Battery vehicles are growing worldwide at 48% (Q3 2025), but the transition is painful: all established sales structures, dealer networks, supply chains become redundant. Dück compares this to Intel's mistake in 1995: they clung to expensive mainframes while PCs became cheaper. Result: lost market share.

The Chinese Strategy: BYD, Xiaomi (automobiles), XPeng (flying taxis starting 2026) implement nationally prioritized goals. Subsidies flow targeted, not fragmented. Humanoid robots cost today 5,000–100,000 euros and are becoming cheaper. Flying taxis are launching in Shanghai in 2026. In Germany: research reports, flagship projects, doctoral theses (4-year standard duration), then termination.

The Cultural Core: Rule-People vs. Innovators

Dück has conducted statistics at IBM:

• 85% of managers: Rule-oriented, procedural, Excel-fixated. They can keep large systems stable but don't understand disruptive transitions.
• 15% of "nerds": They hallucinate, create, see patterns. They need freedom, not processes.
• Split Problem: The rule managers sit on top, the nerds at the bottom. They don't understand each other. Managers demand "proof" of future ideas – impossible. Nerds ignore budgets – untenable.

Example of Presentations:

• Manager Slides: 6 bullet points (Revenue ↑, Costs ↓, Profit ↑, Diversity, 100M investment, Loyalty). Always the same, marketing-slick, no informational value.
• Nerd Slides: Complex network diagrams, incomprehensible layer architecture.
• Result: Mutual disbelief. Managers say: "I only understand numbers." Nerds say: "You only understand numbers, not the actual thing."

The Harvard Study and the CEO Trap

94% of CEOs trust AI's advice more than at least one board member. This is not good news for managers. Why? CEOs are pure rule machines: they read Excel sheets, apply business school formulas, make decisions by schema. AI does this more precisely, faster, without emotional distortion.

Google CEO Sundar Pichai: "What a CEO does is one of the easiest tasks for AI." This is plausible – because good management consists 80% of data evaluation, budget allocation, KPI tracking. Neural networks are better at this.

Why Transformation Fails

Dück has observed: An enormous amount is happening to transform us. But nothing actually happens.

Reason: Companies buy transformation like a consumer product. They hire 20 coaches and consultants who work by rigid methods (like psychotherapy: each therapist has their system, applies it to everything). These methods are rule-based, not innovative. They lead to superficial organizational changes, not genuine implementation of new technologies.

Example: The overhead catenary project for trucks (2010–2024). Siemens and Fraunhofer built 50 km of infrastructure. In 2019 it went into operation. A truck driver said: "That doesn't help me if I can only charge 30 km here. You need to expand throughout Germany." Response: "We can't decide that alone." 2024 final report: Project successfully concluded – it doesn't work. Billions gone, but at least a doctoral thesis was written.

The Research Trap

Germany is proud of patents, Nobel Prizes, Wankel engines. But: Patents are worthless. You have to build a business from them. This happens rarely.

Additionally: subsidies depend on government changes and take 3 years. Companies calculate: "With subsidies we make profit. If they fall away, we don't." They don't plan to be profitable without subsidies – which happens in 5 years. Result: dependency, no real competitiveness.

China's 15-Year Strategy

Dück has identified a pattern: Disruptions always take ~15 years from start to dominance breakthrough.

• Cloud Computing: 2006 (AWS launch) to 2021 (dominance) = 15 years
• E-Books at Amazon: 1996 (start) to 2010 (retail notices) = 14 years
• Electric vehicles in China: 2009 (master plan) to 2024 (globally dominant) = 15 years
• Humanoid Robots: Start ~2009, now 2026 mass production, 2031 dominant?

China knows this. They say: "15 years. Subsidies, development, market takeover." Germany says: "We need ROI in 3 years or we stop."

What Concretely Arrives in 2026

• Flying Taxis (XPeng/A-Ridge): Shanghai starts mass production in 2026. Germany is still discussing. Revenue: billions.
• Humanoid Robots: Google, Tesla, Boston Dynamics, Chinese manufacturers bring models at 5,000–100,000 euros. Care robots cost ~25,000 euros (car price). 2026: First deployments in production/logistics.
• Robotaxis: Amazon, Tesla, XPeng expanding. 2026 likely first 100,000-unit fleets globally. Avalanche effect on logistics/transport.
• AI in Medicine: With optimization AI, cancer breakthroughs could come (lab costs become dematerialized).

All of this is real documented, not speculation. Germany builds research institutes for batteries while China mass-produces them already.

Solution Approaches

Dück names concrete measures:

1. Clear National Goals (like China): "We're building robotaxis" or "We'll become battery leaders." 15-year time horizon, say it clearly.

2. Real Funding Without Research Detours: Not "5 years basic research + doctoral thesis," but "Immediate product development, with parallel accompanying research."

3. Give Nerds Real Freedom: Don't ask how much they need to invest. Say: "Whatever you need – do it."

4. Change Manager Culture: Not everything rule-based. Learn to deal with uncertainty. AI will replace rule-managers – so that should be an incentive to change.

5. Appreciate Craftsmanship and Physical Intelligence: 20–30% of the population are "body people" who are expelled by school/university. They could become craftspeople, roboticists, technicians – future-proof.

6. Infrastructure as Investment: Not dematerialized (AI is free, creates no jobs). But physical: power grids, charging stations, factories. This binds labor, solves mass unemployment.

Key Statements

• IBM Parallel: Whoever overlooks disruptive innovations loses within 15 years. IBM saw cloud computing, rejected it (too cheap), is now insignificant.

• German Paralysis: Too much research, too little implementation. Too much red tape, too little courage. Too much subsidy dependency, too little risk appetite.

• CEO Extinction Risk: 94% of CEOs trust AI more than board members – rational, because management = rule system. Algorithms are better. This is an existential threat not taken seriously.

• China Dominates: 15-year strategy works. BYD, Xiaomi, XPeng, humanoid robots – all emerging or market-ready in China. Germany hasn't even clarified the objectives.

• Craftsmanship Future-Proof: Robots can write code, but they can't repair your roof. Those who work practically and physically survive AI.

• Culture is the Problem: Not the technology. Not the intelligence. But: Can rule-oriented managers allow real innovations?

Other News

• Amazon replaces up to 1.6 million employees with robots – Robotics becomes core business, not side effect.
• Xiaomi sells luxury EV at 1/3 Porsche price – Young Chinese people buy huge numbers of cars; driver's license rates are falling in Germany. Demographic collapse hits the auto market.
• Lilium and flying taxi startups lost, but China launches in Shanghai in 2026 – The market won't be dominated by startups but by established players (XPeng).

Stakeholders & Affected Parties

Opportunities & Risks

Action Relevance

For Executives:

• Ask yourself: Am I a rule machine (85% of managers)? If yes, start rethinking now.
• Give "nerds" real freedom; don't ask how much they spend, but: "Do it."
• Measurable: In 2 years, at least 1 genuine disruptive project should run, not just research.

For Employees:

• Check: Is my job rule-based (management, many office jobs)? If yes, start retraining.
• Best move: Craftsmanship + AI basics. Or: Deep expertise (medicine, specialization) + AI.
• Subscribe to Newsletter: Those who read China Electric Vehicle News (7 emails/day possible) are 2 years ahead.

For Companies:

• Say clearly: We're building robotaxis or humanoid robots (clear, 15-year commitment).
• Launch infrastructure investments; not just AI software.
• KPI: Not "research spending," but "First market-ready products in 24 months."

For Policy-Makers:

• Tie funding to implementation, not research.
• Doctoral theses are luxury; fast product development is the obligation.
• Massively expand vocational training (skilled labor shortage is already bottleneck today).

Critical Questions (Content-Anchored)

1. Evidence & Data: Dück cites the "15-year constant" (cloud, e-books, electric vehicles all ~15 years to dominance). Are there studies supporting this, or is it post-hoc rationalization? Which projects contradict this pattern?

2. Chinese Model – Replicability: China enforces national 5–10-year plans through dictatorship. Is this model transferable to democracies without loss of freedom? What would "genuine industrial policy à la China" concretely mean legally in Germany?

3. Manager Intelligence and AI Replacement: If 94% of CEOs trust AI more than their boards – is this based on actual superiority or on bias (inclination toward automation because numbers seem objective)? Has anyone measured whether AI decisions are actually better, long-term?

4. Causality of Research Paralysis: Dück criticizes interdisciplinary research and slow funding processes. But are slow projects the cause of innovation lags, or a symptom of lacking entrepreneurial initiative? Why don't nerds simply start private startups instead of waiting for research funding?

5. "Appropriate Treatment of Humans": Dück distinguishes learning styles (auditory/visual/kinesthetic). Are these scientifically validated or esoteric? If validated – why aren't schools already helping with customized plans?

6. Robot Investments and Job Balance: Dück says physical infrastructure (robot building, maintenance) creates jobs. But: Doesn't a robot factory cost 1/100 the workers of a conventional factory? Won't the job balance still be strongly negative?

7. Alternatives to National Goals: Dück criticizes lack of national goals. But: Could decentralized competition (many small firms trying new things) be faster than central planning? Why is China successful while the centrally planned Soviet Union failed?

8. Neuro-Diversity and Professional Suitability: The criticism of "rule people" as manager elite could also be read as disparagement of conscientiousness. Are rule-oriented people really unsuited for leadership, or does it need a balanced mix? What would ideal management composition look like?

Quality Assurance & Fact-Check