#03 of How Software Becomes Driving – Daniel Langkilde – When Autonomy Becomes a Data Problem

How Software Becomes Driving is a BayInCo podcast series exploring how real-world AI systems are built, deployed, and scaled as vehicles transition from mechanical products into intelligent, software-defined and data-driven systems.

In this episode, Kailash talks to Daniel Langkilde, Co-founder & CEO of Kognic, a technology leader working at the intersection of machine learning, real-world systems, and automotive autonomy.

Together, they explore:

• Why many autonomy challenges are information problems rather than modeling problems

• The difference between what AI systems can express and what they can realistically learn

• How human feedback underpins perception, labeling, and emerging multimodal systems

• The impact of distribution shift and non-stationary environments on safety

• The real role of simulation and synthetic data in closing, or hiding, learning gaps

• How humans will remain essential teachers for machines as autonomy evolves

Daniel brings a first-principles perspective on intelligence, uncertainty, and scale, highlighting why autonomy depends not just on data pipelines, but on understanding learning limits and designing systems that can operate responsibly in the real world.

A must-listen for anyone working with autonomous systems, AI infrastructure, automotive software, or safety-critical machine learning.

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