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World Congress 2026 Europe - Virtual Stage • Jul 2, 2026 • Session details

Using AI Without Losing Your Skills

Jen Callou

Passive AI reliance silently weakens your cognitive muscles. Are you ready to stop outsourcing your reasoning and start using LLMs to build deep technical intuition?

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#1 about 3 min

The risk of AI weakening developer skills

Relying on generative AI for technical work can quietly degrade the problem-solving abilities developers need most.

#2 about 1 min

Distinguishing output generation from skill building

Understanding the difference between getting a correct AI answer and developing independent reasoning for future decisions.

#3 about 2 min

The autopilot trap in passive AI writing

Outsourcing the ideation and drafting phases to AI prevents professionals from practicing argument structure and context adaptation.

#4 about 2 min

How passive AI assistance reduces cognitive engagement

Brain connectivity research indicates that relying on large language models for writing weakens mental engagement and content ownership.

#5 about 3 min

Using AI as a reviewer to preserve writing skills

Prompting AI to provide feedback on human-written drafts ensures that professionals practice vocabulary, structure, and reasoning.

#6 about 3 min

The vending machine trap in software debugging

Pasting errors into AI skips the critical diagnostic steps needed to build robust debugging intuition.

#7 about 3 min

How direct AI coding solutions bypass mental models

Studies from Anthropic and Microsoft demonstrate that accepting AI code without prior diagnosis lowers mastery and critical thinking.

#8 about 2 min

Prompting AI to act as a diagnostic pair programmer

Configuring AI assistants to ask diagnostic questions and generate tests keeps developers actively engaged in the reasoning loop.

#9 about 3 min

The solver mode trap and the illusion of competence

Using AI to instantly solve problems before self-testing creates fragile familiarity rather than consolidating deep memory traces.

#10 about 2 min

Applying a time-boxed learning loop for AI assistance

A structured 25-minute cycle of defining problems, formulating hypotheses, and using AI for critique builds independent problem-solving muscles.

#11 about 2 min

Categorizing AI tasks by cognitive risk and learning value

Delegating low-risk boilerplate to AI while maintaining friction for core architecture ensures valuable domain skills are retained.

#12 about 2 min

Team practices for maintaining code explainability and ownership

Software engineering teams must enforce transparency, rigorous human-owned testing, and thorough reviews for all AI-assisted code.

#13 about 3 min

Actionable habits to maintain independent reasoning and expertise

Drafting independently, using AI for targeted feedback, and strictly time-boxing problem attempts will shorten the path to feedback without replacing expertise.

Matching moments

1:58 min

Balancing AI productivity gains with developer responsibility

Jakov Semenski · LIVE

11:41 min

Generative artificial intelligence and programming fundamentals

Chris Heilmann +2 · LIVE

1:55 min

Shifting developer workloads and realistic AI productivity gains

Chris Heilmann +2 · LIVE

7:59 min

Navigating the necessity of learning code in AI environments

Chris Heilmann +1 · LIVE

2:22 min

Reviewing and troubleshooting code generated by AI assistants

Cassidy Williams Cassidy Williams · Coffee With Developers

3:33 min

Navigating developer bottlenecks and human accountability

Werner Vogels Werner Vogels +1 · World Congress 2026 Europe

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