Pitching Go in 2025

December 10, 2024

61 min episode · 2 min read

Kent Quirk,Christian Gabrielson

Episode

61 min

Read time

2 min

Topics

Fundraising & VC

AI-Generated Summary

Published Dec 19, 2025

Key Takeaways

✓Enterprise Migration Barriers: Switching from established platforms like MuleSoft requires rebuilding security, pipelines, and infrastructure - organizational friction often outweighs technical benefits when proposing new languages.
✓Go's Stability Advantage: Go's backward compatibility promise means decade-old code still compiles and runs, while JavaScript projects from three years ago often break - critical for long-term maintainability.
✓AI Tooling Reality: ChatGPT and Copilot help with unfamiliar languages but require engineering judgment to write useful specifications and validate output - they amplify productivity without replacing core skills.
✓Language Feature Usage: Generics, iterators, and advanced Go features serve specific use cases like building abstractions - most line-of-business applications don't need them, and that's perfectly acceptable.
✓Scale-Dependent Choices: Python works well for startups and small problems but creates deployment and performance issues at scale due to GIL limitations - Go handles enterprise scaling better.

What It Covers

Go Time explores whether Go remains relevant in 2025's programming landscape, examining enterprise adoption challenges, AI tooling impact, and language longevity versus newer alternatives.

Key Questions Answered

•Enterprise Migration Barriers: Switching from established platforms like MuleSoft requires rebuilding security, pipelines, and infrastructure - organizational friction often outweighs technical benefits when proposing new languages.
•Go's Stability Advantage: Go's backward compatibility promise means decade-old code still compiles and runs, while JavaScript projects from three years ago often break - critical for long-term maintainability.
•AI Tooling Reality: ChatGPT and Copilot help with unfamiliar languages but require engineering judgment to write useful specifications and validate output - they amplify productivity without replacing core skills.
•Language Feature Usage: Generics, iterators, and advanced Go features serve specific use cases like building abstractions - most line-of-business applications don't need them, and that's perfectly acceptable.
•Scale-Dependent Choices: Python works well for startups and small problems but creates deployment and performance issues at scale due to GIL limitations - Go handles enterprise scaling better.

Notable Moment

Christian switched to a Go team within his company that was considered a "dumpster fire" due to AWS configuration issues, reinforcing management's preference for stable MuleSoft over promising but problematic implementations.

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