"Vibe coding" (building software by describing it to an AI and accepting whatever comes back) has been the loudest topic in programming YouTube for over a year. The videos are mostly hype or hot takes. The comment sections are where developers argue with each other, so I collected them and counted.
The dataset: 13,674 top comments from 22 of the most-watched videos about vibe coding and AI coding: explainers, interviews (Karpathy, DHH, Anders Hejlsberg), "senior dev vs vibe coder" challenges, and the recent "company X vibe coded their product and it broke" stories. Together those comments collected 322,744 likes.
Disclosure: I collected the comments with a tool I built, the YouTube Comments Scraper on Apify. The method and code are at the end so you can check or repeat it.
1. The crowd's verdict is a joke, not an argument
The top 1% of comments (137 of them) collected 78% of all the likes. And most of those winners aren't arguments at all. They're punchlines:
- "I just vibe-coded my entire repo. Now it commits to me." (9,300 likes, the most-liked comment in the whole dataset)
- "We are finally learning what it feels like to be a manager: you have no understanding of how your product works…" (4,100 likes)
Humor is how this community processes the topic. If you want to know what people believe, you have to look past the top 10.
2. Skeptics outnumber fans, and collect twice the likes
I tagged comments with two simple keyword lists: skeptical (bugs, security, tech debt, "slop", hype, skill loss, "won't replace") and positive (shipped, faster, productive, game changer, "the future"). Crude, but consistent across videos.
| Share of comments | Share of likes | |
|---|---|---|
| Skeptical | 8.3% | 10.9% |
| Positive | 5.7% | 5.6% |
| Both / mixed | 1.1% | 2.4% |
| Neither (jokes, reactions, questions) | 84.9% | 81.1% |
Skeptical comments are about 1.5× as common as positive ones, and together they collect about twice as many likes. The audience doesn't hate AI coding. It rewards people who point out what breaks.
3. Experienced developers aren't the haters you'd expect
321 comments start with some version of "as a senior engineer" or "I've been coding for 20 years". You'd expect them to be the most negative group. They're the most opinionated instead:
- 10.9% skeptical (vs 8.3% overall)
- 9.3% positive (vs 5.7% overall)
Senior developers are more likely to take a side, and the sides are nearly even. Many of the positive ones describe a narrow workflow rather than a revolution. One of the most-liked practical tips: start every prompt with "don't code anything, just explain to me…" (2,500 likes).
4. What people worry about: understanding, then jobs, then bugs
| Theme (keyword match) | Share of comments |
|---|---|
| Learning and understanding ("fundamentals", "skills", "lazy", "brain") | 11.6% |
| Jobs and careers ("junior", "hire", "replace") | 8.5% |
| Bugs and breakage | 6.8% |
| Money and startups | 3.4% |
| Maintenance and tech debt | 2.7% |
| Security | 2.0% |
The most common worry isn't losing a job. It's losing the ability to understand your own code. Security is rare as a topic but punches above its weight: one of the most-liked serious comments in the dataset says security is "the best thing to learn right now" because everyone will need to fix their vibe-coded apps (5,300 likes).
And from a video about AI on dev teams: "The bottleneck was never writing code. It is figuring out what the stakeholder wants…" (3,200 likes).
5. The video frames the comments
The share of skeptical comments ranged from 16% to 2% depending on the video:
- Most skeptical: a reaction to everyone vibe coding their own software (16%), a team's "6 months of AI coding" retrospective (12.9%), and "I'm done coding with AI" (12.4%).
- Least skeptical: the Karpathy and DHH interviews (3.5% and 4.9%) and challenge videos where a vibe coder competes with a pro (3–4.5%).
When a respected builder explains how they use AI, the comments discuss workflows. When a video shows something breaking, the comments pile on. Neither tells you much on its own; together they're a decent map of where the community is.
6. Which tools come up
Mentions across all 13,674 comments: Claude 295, ChatGPT 129, Cursor 87, Replit 49, Gemini 46, Copilot 32, Windsurf 18, Lovable 8, Bolt 6. Treat this as "what people bring up when they argue," not market share.
Method (and how to repeat it)
- Videos: 22 popular English-language videos about vibe coding and AI coding from YouTube search, a mix of explainers, interviews, challenges and news.
- Comments: each video's Top comments, up to 1,500 per video for the first batch and 1,000 for the second, without replies. YouTube ends its Top list early on some videos, so counts per video range from 60 to 1,007. Collected October 21, 2026.
- Tagging: regular-expression keyword lists, not a language model. It misses sarcasm and counts some jokes as opinions, so read the percentages as rough.
- Cost: 13,674 comments at the Store price of $0.40 per 1,000 is about $5.50, and the two runs took about 8 minutes.
from apify_client import ApifyClient
import pandas as pd
client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("sblabs/youtube-comments-scraper").call(run_input={
"videos": ["https://www.youtube.com/watch?v=Tw18-4U7mts", "https://www.youtube.com/watch?v=_2C2CNmK7dQ"],
"maxComments": 1000,
"sort": "top",
})
df = pd.DataFrame(client.dataset(run["defaultDatasetId"]).list_items().items)
df = df[df.status == "ok"]
skeptic = df.text.str.contains(r"\bbugs?\b|secur|tech debt|slop|hype|skill loss|won'?t replace", case=False)
print(f"{skeptic.mean():.1%} of comments, {df[skeptic].likes.sum() / df.likes.sum():.1%} of likes")
Want to try it without code? This one-click example fetches the top 200 comments of a video. Paste your own link and click Start.
What would you measure next: replies (where the real arguments happen), or how the mood changes over time?
