Will Schenk is a technology strategist and co-founder associated with TheFocus.AI, an organization focused on advising technology companies on artificial intelligence implementation and organizational strategy. His work emphasizes practical approaches to building AI-native companies and avoiding architectural pitfalls common in software development transitions.

Schenk has positioned himself as an advisor to technology startups navigating the transition to AI-native architectures and operational models. His expertise centers on the strategic and technical decisions required when building companies from inception with artificial intelligence as a core component, rather than bolting AI capabilities onto existing software systems. This distinction represents a significant shift in how technology companies approach product development and organizational structure in the era of large language models and autonomous agents.

His work at TheFocus.AI involves consulting with founders and executives on how to structure their technical infrastructure and business processes to take full advantage of AI capabilities while avoiding the accumulated technical debt that often characterizes companies attempting to retrofit AI into legacy systems ¹⁾.

Schenk's approach to AI startup development emphasizes several core principles that distinguish AI-native companies from traditional software organizations:

Agent Systems Design: Schenk advocates for building simple, composable agent systems rather than monolithic AI applications. This reflects broader trends in AI architecture where autonomous agents handle discrete tasks with clear inputs and outputs, enabling better error handling, observability, and incremental capability development ²⁾.

Context Management and Legibility: A central theme in Schenk's perspective involves the importance of legible context in AI systems. Context legibility refers to the ability of systems and humans to understand what information is being presented to AI models at any given time, which he argues is essential for building reliable and debuggable AI applications. This principle addresses one of the fundamental challenges in working with large language models: the difficulty of understanding exactly what semantic information influences model outputs ³⁾.

Avoiding Legacy Patterns: Schenk emphasizes that startups building with AI have an advantage in avoiding the technical patterns and organizational structures that legacy software companies accumulated over decades. Rather than attempting to maintain backward compatibility with older architectures or organizational silos, AI-native companies can design systems from the ground up around AI capabilities ⁴⁾.

The strategic insights Schenk provides address a critical moment in technology development. As organizations increasingly incorporate AI into their operations, the question of whether to build incrementally on existing systems or redesign from first principles represents a major strategic decision. His advice suggests that startups without legacy constraints can move faster and build more coherent systems by making architectural decisions that prioritize AI capabilities rather than maintaining compatibility with pre-AI software patterns.

This perspective aligns with broader industry observations about the competitive advantages of companies founded during the AI era, which can make different assumptions about data flow, user interfaces, computational requirements, and organizational structure than companies founded before the capabilities and economics of large language models became viable ⁵⁾.

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