The true threat is not necessarily a sci-fi superintelligence destroying humanity.
A much more immediate risk is that AI and automation destabilize the economic systems on which countries such as Germany have built their welfare states.
1. The Asymmetric Tax Trap
Germany finances a large part of its state and social-security system through taxes and contributions connected to human labor.
That creates an asymmetry.
If AI and automation substantially reduce the amount of human labor required in administration, banking, software, logistics and manufacturing, productivity may continue to increase while the fiscal base supporting the welfare state begins to weaken.
We do not need a Mad Max scenario for this to become dangerous.
A persistent increase in structural unemployment or a large shift from well-paid employment into lower-paid, less stable work would attack the system from both sides: fewer labor-related revenues entering the state while transfer expenditures rise.
The critical question is therefore not whether AI eliminates every job.
It is whether technological substitution can erode the taxable wage base faster than governments can redesign the system financing pensions, healthcare, unemployment insurance and public administration.
2. Megawatts and FLOPS vs. Labor Hours
Economic productivity may increasingly be determined not by human working hours, but by energy, compute, machines and autonomous software.
That represents a fundamental change in the production function.
If increasingly capable AI systems can perform cognitive work continuously and robotics progressively removes human labor from physical production, economic output can become increasingly decoupled from human employment.
And that is where the existing distribution mechanism becomes unstable.
Industrial capitalism distributes a large share of purchasing power through wages: humans sell labor, receive income and use that income to consume the products the economy produces.
Automation can weaken that loop.
Production can continue increasing while the amount of human labor required to produce it falls.
For some digital goods, marginal production costs can already approach zero. Physical production will never be costless energy, compute, chips, infrastructure, raw materials and machines remain scarce but the labor component of marginal cost can collapse.
The problem is therefore not that markets simply disappear.
The problem is that an economy can become extraordinarily productive without automatically distributing that productivity through wages.
A system optimized around exchanging human labor for income then faces a structural contradiction.
3. The Onset of Digital Colonialism
At the same time, control over the new productive infrastructure is becoming strategically important.
The United States currently dominates much of the frontier AI ecosystem and hyperscale computing infrastructure. China has built enormous manufacturing capacity and is aggressively scaling robotics and industrial automation.
Europe enters this transition with comparatively expensive energy, weaker hyperscale computing capacity and a regulatory model that places substantial obligations on AI deployment.
The EU AI Act is now progressively entering into application. Whether that framework ultimately improves trustworthy adoption or slows European deployment remains contested.
But the strategic risk exists independently of that debate:
If Europe fails to build competitive compute, energy infrastructure, AI systems and robotics of its own, it may become increasingly dependent on foreign infrastructure for intelligence itself.
That would be a new form of economic dependency.
Instead of importing primarily oil, gas or manufactured goods, European economies could increasingly rent compute, models, cloud infrastructure, autonomous systems and robotics from companies headquartered elsewhere.
The productive assets would exist.
Europe simply would not own enough of them.
That is the real meaning of digital colonialism.
The transition therefore goes deeper than unemployment.
If human labor becomes a progressively smaller component of production, an economy organized primarily around wages, labor taxation and consumption generated by employment eventually needs another distribution architecture.
By 2030, the relevant political question may no longer be how to preserve classical performance capitalism unchanged.
It may be how ownership, access and purchasing power are distributed in an economy where machines perform an increasing share of both cognitive and physical work.
If ownership of compute, energy, AI and robotics becomes highly concentrated while labor loses bargaining power, one plausible outcome is an increasingly neo-feudal economic structure: enormous productive capacity controlled by a relatively small number of infrastructure owners, while states struggle to tax and redistribute value through mechanisms designed for an economy dominated by human labor.
No superintelligence is required.
No robot uprising is required.
The destabilizing mechanism is much simpler:
Our institutions were designed for an economy in which humans were the scarce productive resource.
AI and automation are beginning to challenge that assumption.
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