Why AI Commentary Channels Are Exploding (And Tutorials Aren't)

Something interesting is happening on YouTube. While traditional tutorial channels struggle to maintain engagement, commentary and analysis channels in the AI space are seeing explosive growth.

We analyzed 30 days of cross-platform data—Reddit, Hacker News, Twitter, and Bluesky—to understand what topics are resonating with audiences. The pattern was clear: commentary content is outperforming tutorial content by a 2:1 margin in engagement velocity.

The Data: Commentary vs Tutorial Engagement

Looking at posts from r/artificial, r/LocalLLaMA, and Hacker News over the past month:

| Content Type | Avg Velocity Score | Avg Comments | Cross-Platform Spread | |--------------|-------------------|--------------|----------------------| | Commentary/Analysis | 78 | 145 | 2.3 platforms | | Tutorial/How-to | 34 | 62 | 1.4 platforms | | News Reaction | 92 | 189 | 2.8 platforms | | Product Review | 56 | 98 | 1.9 platforms |

The velocity score measures engagement per hour—how fast a post is gaining traction. Higher velocity indicates content that's capturing attention quickly.

Why Commentary Wins

1. Tutorials Compete With Documentation

When someone releases a new AI tool, the official documentation and quickstart guides are usually excellent. Tutorial creators are competing against free, authoritative content from the source.

Commentary fills a different gap. People don't just want to know how to use Claude or GPT—they want to know what it means, whether they should care, and how it compares to alternatives.

2. Perspective Is Scarce

Anyone can explain how to set up a LangChain pipeline. Fewer people can explain:

• Why you might choose LangChain over LangGraph
• What the tradeoffs actually are in production
• Whether the complexity is worth it for your use case

This perspective comes from experience, and it's what audiences are searching for.

3. Commentary Ages Better

A tutorial on "How to use GPT-4 API" becomes outdated with every API update. But a video on "Why context windows matter more than model size" stays relevant because it's teaching principles, not procedures.

What This Means for Creators

If you're making AI content, consider shifting your ratio. Instead of pure tutorials:

Add analysis layers:

• "I tried the new Claude model for a week—here's what I learned"
• "3 AI tools that are overhyped (and what to use instead)"
• "The real cost of running local LLMs vs API calls"

Lead with perspective:

• Start with why something matters before how it works
• Include your opinion—that's what people can't get from docs
• Compare tools based on actual use, not just features

Finding Commentary Opportunities

The best commentary topics come from conversations already happening on social platforms. When you see heated debates or confusion on Reddit and HN, that's signal for content.

Some current high-velocity topics we're tracking:

1. AI agents vs workflows (velocity: 89) — People confused about when to use autonomous agents vs deterministic pipelines
2. Local LLM cost analysis (velocity: 67) — Real cost comparisons, not just "free vs paid"
3. RAG vs fine-tuning (velocity: 81) — Persistent confusion, lots of outdated advice

These topics have high engagement velocity but relatively low YouTube supply—exactly where commentary can shine.

The Takeaway

The AI content space is saturated with tutorials. What's scarce is thoughtful analysis and honest perspective. If you can provide that, you're filling a gap that's hard to compete with.

Use engagement velocity as your guide. When a topic is generating fast discussion across platforms, that's your signal to add your voice—not just your explanation.