r/FacelessVideos

Hey everyone, I started a faceless channel called The Obedience Files, where I cover real psychology experiments and true stories about obedience, authority, and extreme situations narration, no face on camera.

Just posted my first video, about a 1967 classroom experiment where a teacher turned his students into a small fascist movement in just 5 days, to show them how ordinary Germans could claim they didn't know about the Nazi concentration camps. Would really appreciate feedback on the hook, pacing, thumbnail, or anything else that stood out good or bad. Still learning, so brutal honesty is welcome.

Thanks for taking a look!

Name of video...He Built a Cult in 5 Days link bellow

https://www.youtube.com/watch?v=ZObb3ysZPAk

side project: watches YouTube channels, flags videos that are massively outperforming that channel's own norm, then writes a shootable script based on what the winning video's audience asked for and never got. built it for my own channel, opened it up recently.

the interesting bug: early version treated every source video the same — find the information gap, fill it. fed it a comedy skit (dumb premise, a fridge that guilt-trips you out of snacking). it came back as a serious explainer about intermittent fasting. citing studies that don't exist.

turns out "find what's unanswered" is the right move for an explainer and the wrong one for a joke — a skit doesn't have an information gap, it has a premise and an escalation. fixed it by classifying what kind of video it's looking at before doing anything else, then applying a different contract per genre (comedy gets a premise, explainer gets fact-checking against the actual source, etc).

live at scriptdeck.net if anyone wants to poke at it. genuinely curious how others handle "one model, wildly different input types" did you build separate paths per type, or one prompt that adapts?

I made this project to use it myself, but why not open source it. It's a way to go from script or voiceover to a short, with the click of one button.

It uses stock images and videos as a baseline, but can later be replaced with AI images or videos or ones that you make yourself as well. I tried to make it as intuitive as possible, but I'm sure I have some bias so please let me know any and every problem you see with it, if you give it a try!

github.com/iGlitchz/ShortsStudio

I’m building an AI documentary channel called ORIGIN and I’m starting to think about the business side of it.

The idea is pretty simple: use AI video tools like Seedance to create short documentaries around interesting stories, events, people, inventions, places, etc.

I’ve been looking at the monetization potential across YouTube Shorts, TikTok and X.
The numbers are interesting, but it’s also made me realise that chasing “$ per million views” probably isn’t the smartest way to look at it.
If a video gets 10M views but the audience doesn’t stick around, you haven’t really built much.

The bigger play seems to be:
Short-form → discovery → YouTube long-form → audience → sponsorships/other revenue.
I’m curious what people who are already running faceless/AI-assisted channels think.
Would you focus heavily on Shorts/TikTok for reach, or put more effort into building long-form YouTube from the beginning?

I run Scope Creep, a video game essay channel (~3K subs, growing steadily). I write, record, and capture everything myself — I'm looking for an editor to cut finished VO against footage.

**The work:** 25–40 minute video essays and game critiques. Each one combines gameplay capture I provide, licensed stock footage, and trailer/press-conference clips. Long-form criticism — not shorts, not gameplay compilations, not reaction content.

Reference for tone and cadence: https://youtu.be/dbZBSnUm7M0

**Rate:** $300 per video, split deposit + on delivery, paid via [WISE/PayPal]. Ongoing work if it's a good fit — I publish regularly and have a slate mapped out for the next several videos.

**What I provide:** a written brief, all gameplay capture, a labeled VO script, and a paid stock footage subscription. I ask for short test slices as we go rather than one big delivery at the end — keeps us aligned and saves you revision rounds.

**To apply:** send a reel or 2–3 relevant long-form samples. In your message, watch the linked video and tell me one 30-second stretch you'd have cut differently, and why. Short answer is fine — I want to see how you think about footage, not a pitch.

Applications without that answer won't be reviewed.

So i started this channel and warmed up for 10 days. Then i uploaded a long form video of about 8.30 minutes. Its been two days since the upload. Is this the right trajectory for a new channel with just 14 subscribers?

The job title survives longer than almost anyone attached to it.

That's the part nobody puts in the internal memo when they call a role "AI-assisted."

 

Scott Galloway put a real number on it, talking to Steven Bartlett on The Diary Of A CEO.

He says he'll cut legal fees by a third this year — not because the law changed, but because a prompt now does the $400–$2,000 contract review a name-brand firm used to bill him for, at a fraction of the junior associate markup.

 

Bartlett went further with his own fund.

They planned to hire five analysts.

They hired one — Molly.

Two agents, two Mac Minis, and she screens inbound deals, scores them against a framework, and preps them for the investment committee herself.

Five jobs, one person, same org chart line.

 

Same ratio on executive assistants: ten planned, three hired.

One runs travel, one runs scheduling, one meets people at the door.

 

I've watched this exact pattern before, minus the AI.

I was a Technical Manager for a China Construction company here in Malaysia.

I contributed a lot into their technical and tendering work — helped build up a real chunk of their documentation and tendering process.

But about six months in, I'd exhausted all my know-how for them, I guess.

Then the announcement came at the end of my year there.

My contract wasn't renewed.

I was just let go, just like that.

I remember what Deng Xiaoping said: "无论白猫，或者黑猫，会抓老鼠的就是好猫" — black cat, white cat, doesn't matter, so long as it catches mice.

I guess they think I'd outlived my usefulness.

Can't catch mice anymore.

 

That's the mechanism underneath "AI-assisted" that nobody names out loud.

It's not that the work got automated.

It's that the one person left is now doing what used to justify five headcounts, and the fifth person's job title is the only part of the org that didn't change.

 

Actually, this reminded me of something — a former SpaceX CIO cut a 175-person engineering team down to 6 using the same compression math, and the ratio held there too.

 

Drop your take — did you know your own job has a ratio like this attached to it?

 

If you want to know how this mechanism works for me, it's the same math running under my own setup — it's just one link away.

 

Clip credit: Global Talks — full video on their channel.

DM for credit or removal requests.

There's a specific kind of quiet that happens right before a margin call.

I don't think enough people talk about that part — not the number, the quiet.

 

Leopold Aschenbrenner's AI-focused fund went from zero to $45 billion, then down to $10 billion.

Not all of it was margin, but enough of it was, and it was concentrated — five correlated AI-infrastructure-adjacent positions (SMCI, SanDisk, Micron, CoreWeave among them) dropping together in the same month, ~35% in one stretch.

That's the mechanism people skip past: it wasn't one bad bet, it was one bet wearing five different tickers.

 

He was also short Adobe. Adobe went up.

Being partially right doesn't save you — a hedge only protects what it's actually sized to protect, and this is the part that got him: three prime brokers (per CNBC's reporting) started calling at once.

Not one call. Three, simultaneously.

That's the system's own alarm bell going off.

 

Here's mine, since we're talking about leverage used well versus leverage that uses you:

>Years ago I was on the tender team for a major Malaysian construction job, competing against the two other largest contractors in the country. My GM, Mr. Chung, spotted the one contractual gap the client's own consultants hadn't caught. When they tried to pump him for the fix for free, he didn't fall for it — he told them straight: we can help you solve it, cheaply, with minimum delay, but we have to get the contract first, before we can go in and assess it. That's not a bluff. That's understanding your own leverage well enough to use it on your own terms, not the other side's. We won the contract months later. Leopold never got that chance — the market decided his terms for him, not the other way around. He still walked away with $300 million, which is the part nobody in the clip stops to sit with. That's not the actual takeaway, though. The takeaway is knowing exactly how much leverage you're carrying, on purpose, before someone else decides it for you.

 

Ken Griffin (Citadel) stepped in as the liquidity provider — what Tom, PBD's co-host, called a "white knight" on the show.

Half the room called that predatory.

Half called it structural necessity.

Both readings are true at once, and that's the actual lesson:

>Understanding why rescuers exist structurally is a different literacy than just watching one show up.

 

Clip credit: PBD Podcast — full video on their channel. DM for credit or removal requests.

 

Drop your take: predator, or necessary?

Genuinely curious where this sub lands on it.

 

If you want the actual mechanics of how leverage gets used for you instead of against you — the kind of structural read Mr. Chung had — it's just one link away

He calls it BC/AC.

Before Claude, after Claude.

 

I've watched enough of these clips land in the last few weeks that I started keeping a mental tally of which AI release date people cite like it's a diploma.

Matt Van Horn's is Thanksgiving last year — Opus 4.5, then Codex a few weeks later.

Before that, he says, his agentic coding was "Hello World" in Cursor, half-working, most of the time not working at all.

After it, he shipped Agent Cookie and says flatly he has no idea how it actually functions under the hood.

 

The part worth sitting with isn't the tooling.

It's what he says about himself getting there: "suit my entire career," never shipped anything of value beyond a high-school web page, dozens of unlaunched ideas gathering dust because he wasn't the one who could build them.

That's not a startup-guy humblebrag — that's the exact ceiling a lot of operations people, BD people, anyone who's ever had to write a ticket instead of just doing the thing themselves, know from the inside.

 

The credential that used to decide who got to build stopped mattering right around the time the tools did.

Not "got easier to climb."

Stopped existing.

 

Same shape happened to me with a guitar, at 41, zero training, cornered into it because the young players in my church all left for university at once.

Those early days, my wife's ears really paid for it (刚开始的那些日子，我的太太的耳朵有够受罪).

Felt like being tossed into open water and told to swim myself back to shore (好像被丢去深海里，自己学会游泳游回来).

Same thing happened again with video editing, then with building this whole posting system, one post at a time, getting corrected by Reddit comments the entire way.

And I swam back stronger each time.

 

Actually, this tracks with something I posted here a few weeks back — the guy who literally coined "vibe coding" saying on stage he's never felt more behind as a programmer, because the scarce skill moved from writing code to directing it with taste.

 

Clip credit: MSP Mindset (Damien Stevens) — full video on their channel. DM for credit or removal requests.

 

Drop your take — did the credential wall ever hold you back, or did you just build around it?

 

If you want the actual mechanism behind how people with zero engineering background are shipping real products right now, it's genuinely just one link away.

Disclosure first, since this sub asks for it: I’ve generated millions of views across short and long-form content, and I’m building a small tool around the research process below. Bias applies, but you can do all of this manually.

One thing I stopped doing a while ago was choosing topics based on total views.

A video with 500k views isn’t automatically interesting if that channel normally gets 1 million.

But a video with 60k views from a channel that usually gets 4k is worth looking at.

That’s normally where I start.

I open a group of channels in the niche and go through their recent uploads. I don’t calculate everything perfectly. I just get a rough idea of what each channel normally gets, then look for videos doing 3x, 5x or 10x more than usual.

When I find one, I look at the topic, title, opening hook, video length and upload date. Then I check whether other channels are suddenly getting unusual results with similar topics.

That last part matters more than the raw view count.

One outlier can be luck. Maybe the creator got picked up by the algorithm, maybe there was an external event or maybe their audience was already interested in that exact topic.

But when several smaller channels start outperforming with similar ideas, there is usually a real signal behind it.

I also try to separate a trending topic from a niche that is actually worth entering.

A niche can generate millions of views and still be difficult to monetize. I check whether creators have sponsors, affiliate links, products, communities or services. Comments help too. People asking where to buy something or how to solve a problem are normally a better sign than thousands of generic reactions.

After finding an outlier, I don’t copy the video.

I try to understand why it worked.

Was the topic new? Was it a familiar topic with a better angle? Did the title create a strong information gap? Was it connected to something that had just happened?

If a video about why Dubai keeps building empty islands suddenly performs far above the channel average, the opportunity probably isn’t remaking that exact video.

The broader signal could be failed megaprojects, expensive places nobody uses or strange infrastructure decisions.

That gives you several original angles instead of one copied idea.

This process works, but it gets pretty slow when you’re checking hundreds of videos across different niches. I was doing most of it manually with tabs and spreadsheets, so I started building it into a tool called Vabulo Stats.

If you want to try it: https://vabulo.com

It compares videos against the creator’s normal performance, finds unusual outliers and shows niches that seem to be moving. I’m also experimenting with turning that research into hooks, scripts and thumbnail prompts, but the research part is what I care about most.

The point isn’t to generate another generic script.

It’s having better information before deciding what the script should even be about.

I’m still trying to understand which parts actually save creators time and which parts just create more data to look at.

For people who research content regularly, how do you decide whether an outlier is repeatable or just luck?

And where do you lose the most time right now: finding niches, checking competitors, choosing an angle or turning the research into an actual video?

Solo founder. AMA.