Decision Altitude
Why technically good decisions become organizationally expensive when capabilities live at the wrong layer—and three tests for determining where ownership belongs.
M01 Media + speaking
Gevorg A. Galstyan is a data and engineering leader who explains how complex platforms, AI systems, and organizations can create durable leverage.
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M02 Signature topics
Each topic can stand alone or become part of a broader conversation about architecture, platforms, data, and engineering leadership.
Why technically good decisions become organizationally expensive when capabilities live at the wrong layer—and three tests for determining where ownership belongs.
A practical framework for distinguishing AI-enabled products from organizations whose differentiated value, operating model, and learning loops are genuinely AI-native.
Why platform teams should expose durable capabilities instead of implementation technologies, and how that change improves adoption, migration economics, and internal customer trust.
Why availability is not adoption, how to treat internal data consumers as customers, and which lead measures indicate whether a data platform will matter.
How incentives, bottlenecks, constraints, local optimization, and the cost of optionality change technical and organizational decisions.
M03 Host-ready copy
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Gevorg A. Galstyan is a data and engineering leader who explains how complex platforms, AI systems, and organizations can create durable leverage.
Gevorg A. Galstyan is a data and engineering leader working at the intersection of architecture, platforms, data, and organizational design. Through Systems That Scale, he develops practical frameworks for data platforms, AI-native architecture, platform enablement, and engineering leadership. He examines technical systems through ownership, incentives, constraints, and the total cost of change.
Gevorg A. Galstyan is a data and engineering leader working at the intersection of architecture, platforms, data, and organizational design. He focuses on a recurring problem: a technically sound decision can still become expensive when ownership, incentives, or boundaries are wrong. His work treats architecture as more than a collection of technologies. It is also a system of constraints, decision rights, and change costs. Through Systems That Scale, Gevorg develops practical frameworks that help technology leaders see where complexity is accumulating, decide which layer should own a capability, and preserve room to change the implementation later. Gevorg is based in Canada. His opinions are his own.
M04 Interview guide
Choose a focused path or combine adjacent questions into a broader systems conversation.
What is decision altitude, and how can leaders recognize an altitude error?
When should repeated product code become a platform capability?
What is the difference between an AI-enabled and AI-native organization?
Why do platform teams struggle to explain their value internally?
Which lead measures indicate data-platform adoption?
How do incentives, constraints, and local optimization change the way you evaluate architecture?
What lessons transfer between digital health, consumer technology, and developer infrastructure?
How can leaders reduce complexity without creating a central platform bottleneck?

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