World Congress 2026 Europe - Virtual Stage • Jul 1, 2026 • Session details

A True Story About Speeding Up the Wrong Things

Jo Hasenau , Petra Hasenau

Are you shipping code faster, or just accelerating your mistakes? Discover why surviving AI-generated complexity means prioritizing explicit steering systems and operational memory over raw output speed.

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#1 about 4 min

The historical pattern of confusing speed with progress

Unchecked technological acceleration frequently outpaces our ability to maintain feedback, responsibility, and control within dynamic systems.

#2 about 3 min

Questioning the rush for speed over strategic direction

Accelerating local metrics without understanding broader systemic optimization goals often generates more chaos than value.

#3 about 3 min

Learning from historical instances of unchecked market acceleration

Previous technology bubbles reveal that expanding reach and scale only amplifies underlying structural weaknesses.

#4 about 2 min

Abstracting complexity and the illusion of distributed responsibility

Financial markets and software architectures wrap risk in elegant abstraction layers that ultimately hide unmanageable exposure.

#5 about 3 min

The widening gap between automated action and systemic control

Executing tasks at machine speed turns crucial human oversight into retrospective archeology instead of active steering.

#6 about 5 min

Applying cybernetics and the law of requisite variety

Internal system variety must continually increase to successfully absorb the rising complexity of external operating environments.

#7 about 3 min

Identifying plausibility and output inflation in AI workflows

Treating plausible generation as absolute truth artificially deflates coding costs while drastically increasing the burden of judgment.

#8 about 2 min

The risks of replacing understanding with agentic brute force

Relying on automated trial and error loops bypasses the deep reasoning required for sustainable software engineering.

#9 about 1 min

Connection explosion and the multiplying complexity of direct integrations

Letting every automated tool and agent interface directly generates an impossible web of undocumented dependencies.

#10 about 1 min

Vanishing shared learning in private AI coding assistants

Moving development troubleshooting into private chat windows destroys the observable paths and failures that teams learn from.

#11 about 1 min

Navigating responsibility blur in automated software delivery pipelines

Inserting generative agents into agile processes diffuses accountability until nobody understands the system's final shape.

#12 about 3 min

Moving past governance theater to actively steer AI

Superficial compliance documents completely fail to constrain computational models that execute directly against strict production deadlines.

#13 about 3 min

Designing steerable architectures with explicit commitment layers

Isolating suggestion generation from verified state changes ensures models remain bounded by strict operational boundaries.

#14 about 5 min

Building a federated memory layer to preserve engineering knowledge

Retaining the rejected attempts of agentic workflows saves computational energy and strictly prevents repeating known mistakes.

#15 about 3 min

Governed service layers and open source control mechanisms

Centralizing routing policies and demanding open control primitives reduces the absolute chaos of uncontrolled model agent interactions.

#16 about 3 min

Practical steps for treating reasoning as an engineering artifact

Incrementally auditing single workflows ensures your engineering culture actively preserves vital feedback and operational memory.

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Managing the impact of AI on software trust

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Pausing hyper-speed AI deployment for responsible leadership

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Addressing institutional inertia and AI pilot failures

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