philipp klöckner

The 75% selloff in software stocks is an emotional overreaction. Systems of record (SAP, ServiceNow, Salesforce, Oracle) embed deep compliance, tax, and integration logic that AI cannot easily replicate; switching costs are 2-4 year projects with C-level career risk. AI-native startups may never need these systems, but incumbents are protected for the foreseeable future.

In a 10-15 year horizon where AI and robotics commoditize white/blue-collar work, the scarce resources are energy and raw materials — where China/Russia dominate. Germany lacks both but retains world-class scientific talent and universities. Biotech/pharma (low energy, high human capital) is the optimal sector: AI accelerates drug discovery, and Germany was historically the 'pharmacy of the world'. Goal: build 10 more BioNTechs.

Without a sovereign LLM player, Europe must rely on open-source foundation models (currently mostly from China) to build application-layer value on its industrial base. If Europe becomes dependent on US closed models, the digital advertising history repeats: value extraction by US platforms, weak local tech ecosystem, and adverse tax dynamics.

Large enterprise inertia slows AI adoption (20-year digitalization analogy). Germany's demographic gap (500k workers/year) means 1% annual automation is absorption, not displacement. The real signal: CS enrollments dropping (660k→600k in US) because students avoid careers they perceive as automated (junior dev, customer service), not because companies stop hiring juniors.

Consumer agentic commerce (shopping via chat) is unlikely to replace window-shopping behavior; adoption follows generational replacement (1%/year). Early use cases: B2B tactical procurement (automating compliance-heavy purchasing) and consumer rights automation (flight/train delay claims, appointment booking). Payment infrastructure improvements help but don't change human preference for comparison shopping.

Capital follows quality: Stripe-level companies attract global capital instantly. Europe's problem is not capital supply but insufficient ambitious company creation. Talent immigration bureaucracy blocks: fragmented 20+ tax/legal regimes, slow visa processes (months/years vs US hours), and a cultural preference for safe corporate careers over entrepreneurial risk (80-90k€ at Porsche vs startup).

When AI builds AI and robots build robots (10-15 years out), the only binding constraints are energy cost and access to iron, copper, coal. China and Russia hold structural advantages here; Germany has neither cheap energy nor resources. This makes manufacturing and robotics unwinnable for Germany, reinforcing the case for energy-light sectors like biotech and software.

Just as web servers, databases, Android, and cell tower software are open source, the foundation model layer must become open source so European companies can deploy models on their own infrastructure and data. Current open-source models are predominantly Chinese; Europe needs this layer to avoid rent extraction by US closed-model providers.