# Pitching Go in 2025

Page: https://stenobird.com/podcast/go-time-golang-software-engineering/pitching-go-in-2025
Text version: https://stenobird.com/podcast/go-time-golang-software-engineering/pitching-go-in-2025.md
Podcast: [Go Time: Golang, Software Engineering](https://stenobird.com/podcast/go-time-golang-software-engineering)
Published: 2024-12-10T21:45:00+00:00
Episode link: https://changelog.com/gotime/339
Audio file: https://op3.dev/e/https://cdn.changelog.com/uploads/gotime/339/go-time-339.mp3
Processing state: processed
JSON: https://stenobird.com/v1/public/podcasts/go-time-golang-software-engineering/episodes/pitching-go-in-2025
Duration seconds: 3676

## Resource

Evaluating the long-term viability of Go in an era of rapid language emergence and AI-driven development. The discussion explores the tension between choosing the 'right' tool for scale versus the 'fastest' tool for prototyping.

## Highlights
- Main idea: Language choice should be driven by long-term scalability and maintenance needs rather than short-term prototyping speed
- Failure mode: The 'Innovator's Dilemma' in engineering, where the time spent converting a codebase to a new language can result in losing market position
- Practical takeaway: Use high-level tools like Ruby, Python, or Retool for rapid prototyping, but rely on Go or Java for systems requiring high concurrency and predictable performance
- Technical insight: AI tools are excellent for generating boilerplate and solving immediate problems, but they lack the architectural foresight to prevent unmaintainable code
- Maintenance lesson: Senior engineers must prioritize long-term readability and '3 AM maintainability' over clever, overly generic, or complex implementations

## Topics

Go programming language, Software architecture, Postgres, AI development, Technical debt, Scalability, Programming language selection, Software engineering management

## Chapters
- 1:00 — Postgres as an AI Foundation: An exploration of why Postgres's extensibility makes it a primary choice for AI, vector search, and RAG applications.
- 5:40 — The Cost of Language Migration: Discussing the risks of proposing new languages within a team and the potential for falling behind during the transition.
- 10:20 — The Innovator's Dilemma in Software: Analyzing the danger of spending too much time on technology conversion at the expense of market delivery.
- 15:15 — The Burden of Team Transition: The hidden expenses of switching languages, including the need for team-wide upskilling and code review competency.
- 24:20 — Prototyping vs. Production: Comparing the speed of modern frontend stacks and low-code tools against the necessity of robust backend engineering.
- 33:25 — Choosing Tools for Survival: A discussion on selecting languages based on industry requirements and the necessity of surviving the initial business phase.
- 51:25 — Maintainability and the Role of AI: Reflecting on the ease of refactoring Go code and the importance of avoiding 'clever' code that hinders long-term maintenance.

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## Transcript

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