Quantity Versus Quality in Growth | Generated by AI
Question: Is “posting a lot” (e.g., 10k short videos or dozens of AI notes per day) the main reason for traffic growth in the AI era, like in cases such as Gary Vee-style content, your blog (~8000+ notes), or open-source projects that gain popularity through heavy output?
Answer:
There is some truth, but the conclusion “more posts = more traffic = success” is incomplete and sometimes misleading.
A more accurate way to think about it is:
1. Volume helps discovery, but does not guarantee value
High output (like Gary Vee’s short-form content strategy or frequent blog/notes publishing) increases:
- surface area for search engines (SEO)
- algorithmic exposure (TikTok/YouTube/X feeds)
- chance of hitting “viral or long-tail hits”
- compounding indexing (especially for blogs)
So yes, volume increases probability of traffic, not certainty of impact.
But traffic quality depends on:
- topic relevance
- clarity and structure
- uniqueness / insight density
- distribution channels
A lot of content without signal still gets ignored.
2. Your blog example (8000+ notes, 70k+ page views/month)
If those numbers are accurate, that actually illustrates an important principle:
It’s not just “posting a lot”, it is:
- long-term accumulation (content compounding)
- searchable atomic notes (AI notes work well for indexing)
- consistent niche signal (AI/chat/code learning content)
Search engines especially reward:
- breadth + internal linking
- repeated topical authority signals
- long-tail keyword coverage
So your result is more like:
“high-frequency + structured knowledge database” not just raw posting volume.
3. AI era changes the equation
In AI-native content systems:
- distribution is more algorithmic
- indexing is faster
- content creation cost is lower
So yes, “posting more” becomes easier—but also:
- competition increases exponentially
- average content quality decreases
- filtering becomes stricter (ranking systems get more selective)
So the advantage shifts from:
“who posts more” to “who generates more useful, reusable, retrievable knowledge per unit content”
4. “大力出奇迹” — partially correct, but incomplete
The idiom “大力出奇迹” means:
brute force / massive effort can produce surprising results
In content + open source + AI ecosystems, it works like this:
✔ Works when:
- system rewards accumulation (GitHub, blogs, HuggingFace datasets/models)
- content is structured and discoverable
- iteration improves quality over time (feedback loop)
✘ Breaks when:
- content is noisy or repetitive
- no distribution strategy exists
- audience cannot distinguish value signal
So it’s not just “force”, but:
force + direction + feedback loop
5. Hermes / HuggingFace-style open source analogy
You’re also right that many successful projects (agents, fine-tunes, models) come from:
- rapid experimentation
- many iterations
- public release cycles
But the hidden factor is:
- community visibility + reuse by others
- not just internal output volume
Projects become popular when:
others can build on them easily
Bottom line
- Posting a lot helps exposure
- But traffic growth comes from compounding useful structure + discoverability + iteration
- “More output” is only effective when it increases signal coverage, not noise
A better mental model than “大力出奇迹” is:
high-frequency iteration inside a system that preserves and reuses value
If you want, I can break down why your “AI notes blog” model is actually closer to a knowledge graph SEO system than a normal blog — that’s usually where the real advantage comes from.