EQ vs IQ vs AI: What Really Matters in Tech Leadership?

Sep 3, 2025

41:13

forum

Ask episode

view_agenda

Chapters

auto_awesome

Transcript

info_circle

Episode notes

In this episode of The Tech Leader's Playbook, Avetis Antaplyan sits down with Andrew McVeigh, veteran technology leader and Chief Architect, whose career spans transformations at Hulu, Riot Games, and beyond. Andrew has navigated multi-billion-dollar shifts across industries from finance to gaming and healthcare, leaving behind architectures that still power companies today.

The conversation dives deep into some of the most pressing questions in modern tech leadership: What matters most—EQ, IQ, or AI? Should organizations rebuild systems from scratch or evolve incrementally? Andrew shares candid stories, including lessons from Riot Games, the pitfalls of full rewrites, and the importance of balancing optimism with realism.

Listeners will gain insight into how domain expertise and generalist skills complement one another, why EQ becomes more critical than IQ at senior levels, and how AI is reshaping engineering work without eliminating the need for human craft. Andrew also reflects on personal resilience, leadership missteps (like literally flipping a table), and the value of building systems and cultures that endure. This episode offers a rare inside look into decades of architectural wisdom and leadership lessons applicable to anyone guiding teams through complexity and change

Takeaways

EQ often outweighs IQ at senior leadership levels when managing large teams.
Losing emotional control may feel satisfying in the moment but erodes long-term trust and outcomes.
Generalists and specialists both play vital roles—large-scale architecture requires a mix of both.
Domain expertise is valuable but shouldn’t be an absolute barrier to hiring strong engineers.
Successful engineers learn to work at the level of intention rather than just tasks.
Psychological safety fuels better performance and innovation in teams.
AI augments, not replaces—engineers must learn to collaborate with it effectively.
Craft and fundamentals (e.g., programming) remain essential even as AI automates repetitive work.
The Pareto principle (80/20) applies broadly—focus on high-leverage outcomes, not perfection.
Full rewrites often fail; incremental evolution with a defined “North Star” strategy is safer.
Optimism in leadership can shift cultures and reframe challenges as opportunities.
Balancing results with humanity ensures people want to work with you again.