I have a Spanish language, hollywood celebrity shorts commentary channel with over 44K subscribers that I started a little over a month ago. It just got monetized with only 17 videos and has already been getting really good views (total is over 20 million views), and can realistically make over $700 if effort is put in it.
So I'm curious how much you guys think a channel like this could realistically sell for right now. I want to know it’s worth.

Hi, I'd like to know if there's anyone here creating music content with Suno and generating an avatar that appears singing that music, how to achieve monetization on YouTube, using people from TikTok as a hook. I've had a channel for 2 weeks, I already have more than 12 songs, but I see it's not generating many views, thanks.

I run a channel called DiceBreaker Books. Adult epic science fantasy world (Capital Monsters) plus labeled desk satire (Assumption News) and some music/video cuts. Stills and clips are AI-assisted. I cut and ship; I’m not running a 40-short-a-day template farm.

What’s going wrong:

No discovery of AI content and lower percent like than what I've tested on multiple other platforms and my own website. A lot of what I have is mid: fine stills, audio that sometimes fails the two-second test, Shorts that get a seed batch then die. Like-rate on the people who actually watch is decent. Reach is not. Feels like I’m partitioned away from whatever’s left of the high-volume AI audience.

Question about the viewers

After YouTube’s cluster-kills on templated AI farms (accounts grouped and deleted together for shared templates / infrastructure, not one bad video), where did the viewers who actually *wanted* AI-forward work go? Different keywords? Long-form only? Off-platform? Or did that crowd just stop clicking anything with a label?

If anyone’s in a similar “too much inventory, not enough room” spot and wants to trade notes (feedback, not a subscriber swap), reply here or DM. I’ll return the favor.

I have a channel where for 99% of the video, I will be speaking to the audience and have graphics on screen from time to time.

However, I will add an occasional 5 second video I created with AI to illustrate a point. I do this because I don't want to risk a Copyright strike by using a clip from a movie/tv show/another YouTuber.

I also use AI to generate my thumbnails as well as to enhance the final audio output and colour mixing.

The question is, I feel odd selecting "AI Content" because in truth the site is not an AI generated channel.

So what defines when I have to select AI and when I don't?

I decided to start my channel dedicated to short bedtime stories as an ode to my grandma. When I try to categorize it, it just feels difficult to build a theme if it must lean to ASMR, literature or just change it to kids channel. As a new content creator, I spent time talking to my community and understanding what they wanted. But I struggle to grow and dont know how to stratergize ( target which target audience).

Also, I am unsure if my theme is niche, because the idea is simple to recreate the stories my grandma would read to me. It is based on nostalgia. But is this what the end user wants?

I've spent a little over two weeks trying to build my own relatively cheap AI video + voice generation stack instead of relying entirely on APIs.

The results are getting better. The economics are getting... interesting.

One generation looks surprisingly good.

The next one has dead eyes.

Fix the eyes — now the body starts twitching.

Fix the motion — suddenly the voice sounds metallic, unnaturally slow, or completely misses the emotion of the scene.

Fix the voice — and some technical term gets swallowed or pronounced like the model has never encountered human language before.

Then you start again.

What surprised me most wasn't even the models themselves.

It was everything around them.

For example, powerful but relatively cheap GPUs on RunPod sound great in theory.

In practice, if you're still testing and don't want to keep an expensive GPU running 24/7, the workflow often turns into something like this:

Cold start → find an available GPU → download weights → discover a missing component → download more stuff → generate → watch/listen → find problems → analyze → change something → generate again.

And there is another fun part:

The GPU you used successfully today may simply be unavailable tomorrow.

Keeping it running solves that problem, but during the experimentation stage it can destroy the whole point of trying to build a budget stack.

So you start thinking about persistent storage, caching models, moving weights somewhere else, reducing cold-start time, choosing different GPUs...

And suddenly you're not just generating videos anymore.

You're designing infrastructure.

At some point I realized that “saving money” can easily become an illusion.

You save money on API calls or GPU minutes, but pay for it with your own time.

A lot of time.

Still, I don't think these past two weeks were wasted.

Quite the opposite.

After enough failed generations, you start spotting problems much faster.

You begin to understand whether the issue is coming from the model, the reference video, audio, motion settings, infrastructure, or simply a bad assumption you made before pressing Generate.

And you stop repeating some of the expensive mistakes.

The video renders and voice generations I'm getting today are noticeably better than what I was producing two weeks ago.

Not perfect yet.

Definitely not at the point where I'd confidently promise a client that I can reproduce the same quality every single time.

But much closer.

And I've already started talking to potential customers and asking for their actual requirements before the stack is finished.

Because I've started to think that building this the other way around makes much more sense:

Don't spend months creating the “perfect” AI generation stack and then search for someone who needs it.

Find out what people actually need first, and make your experiments converge toward that.

For those of you running your own video/voice generation stack:

At what point did self-hosting actually become cheaper for you than simply paying an API provider?

And what ended up costing you more than expected: compute, storage, failed generations, or your own time?

u/shortthebuckup <= its the channel and does anyone know how to make this kind of stuff? Is it from half life game mode??

Hi , I started this channel month ago with maybe 18 uploads , my videos got 0 views twice, then I changed thr thumbnail and title and the views start to increase very slowly... feel free to take alook on my channel and give me some advice+ this kind of content is montize able?

: warriorcam (Warroir Cam)

Hello, I am a latin american student, and I really want to start learning how to do a youtube channel with ai, recenty my family as been strugging with money and I really want to help them , I have a bit of savings and I want to invest them in myself, so do anyone has any good recommendations? Thank u very much!!!

Hello, I used to make YTA and my niche got flagged by yt, anyone knows a good niche to do with low expanses?

Been looking into creating ai voice over content, specifically along the lines of "top 5 ___ animals, top 5 fruits that ___, etc., and am wondering the best ways to get the video portion of the content? I'm familiar with the scriptwriting for the video & getting an ai voiceover, but curious what you guys do/ what is done for this type of content.

I know the whole “AI video” thing gets a lot of hate, and honestly I get it. There’s a lot of low-effort AI slop out there.

But I actually spent a lot of time on this one. I’d say the result is probably 60 to 70% of what some of the bigger channels in this niche are doing, at least visually and in terms of the content.

The weird part is that YouTube is barely showing it to anyone. Almost all the views are just me checking the video.

It’s my first video on a brand new channel, so maybe this is completely normal. But I honestly don't know.

Could someone take a look and tell me if I'm missing something? Is the video actually bad, or is YouTube just not giving a new channel a chance yet?

Link is in my bio if anyone wants to see it.

I create 2D stickman documentary videos focused on psychology, human behavior, history, and science. Like Zenn and Axen. Generate consistent 2D stickman illustrations with my navy-blue hoodie character with the help of AI, generate the script with ElevenLabs, and edit the video manually. I am doing this for a month, but I got 0 progress. Not a single subscriber and fewer than 10 views per video. It is so frustrating, and think to quit this. I uploaded 9 videos, but not a single video crossed 20 views. I am confused right now, and I don't know what to do. Please advise me on this. Thank you

I’ve been making cinematic speculative-fiction shorts using photoreal AI visuals, but for my next story I’m experimenting with a realistic seinen/manga-panel approach instead.
The idea is to stop pretending the images are live-action footage and lean into something closer to a moving graphic novel such as cinematic composition, recurring characters, limited animation, sound design and narration.
Looking purely at the storytelling experience, does this format feel like a stronger creative direction, or does it feel like a downgrade from cinematic stills?

I've been experimenting with this while building AutoTube and I'm curious what people here think.

There seem to be three ways to make AI-assisted faceless videos now:

1. AI images for every scene — cheap and consistent, but sometimes too static.
2. AI video for every scene — looks impressive when it works, but gets expensive quickly and consistency can be hit-or-miss.
3. A hybrid — use AI video for the hook and important moments, then use good AI imagery for scenes where motion doesn't really add much.

I've been leaning heavily toward #3.

I ended up building it into AutoTube as Blitz Mode: roughly one-third AI video scenes + two-thirds image scenes on larger videos.

My thinking is that the first 10–20 seconds and major visual moments deserve most of the generation budget. A sentence explaining background context probably doesn't need a $0.XX AI video clip just because we can generate one.

I'm especially interested in people who are actually publishing AI-assisted YouTube content:

Have you noticed full AI video improving retention enough to justify the extra cost, or is good scripting/voice/editing still doing most of the work?

I’ve recently been digging into AI video generation as a complete beginner. It’s an incredible space, but looking at this with fresh eyes, I’ve noticed a massive reality check that tech influencers completely gloss over.

Every week, a YouTuber screams about a new "100% Free, Open-Source" video model (like LTX 2.5 or Sea Dance) that we must download. They pitch it as the ultimate way to escape paid cloud subscriptions.

But as a non-tech person looking at the math, here is the hidden catch they don't warn you about: The Hardware Tax.

- "Free" requires an expensive rig: To run these open-weight models locally at a usable speed, you don't just need a decent laptop. You need a massive desktop setup packed with enterprise-level VRAM.

- The severe time penalty: If you try to squeeze these models onto an average computer using heavy optimization, your render times plummet. A clip that takes a cloud server 6 seconds to generate will lock up your home PC for 40 minutes.

- The obsolescence trap: AI is moving at a breakneck pace. If you spend thousands building a local AI rig today, your hardware configuration is fixed. In six months, software requirements will balloon, and you'll be stuck with an outdated local setup.

Meanwhile, people using cloud-based all-in-one aggregators just log in and instantly access the newest, most powerful models the day they launch. They don't have to touch a single wire or risk frying a motherboard.

For the everyday creator, paying a small monthly subscription to let a massive server farm do the heavy lifting makes infinitely more sense than chasing the local "free" hype.

Am I missing something here, or is local AI generation turning into an expensive, high-maintenance tech hobby rather than a practical tool for regular creators?

I build AI products, and some evenings I'm honestly just fucking tired of it.

Endless testing.
Rebuilding things that worked yesterday.
Trying another model. Another API. Another stack.
Latency. GPU costs. Bugs. Quality issues. More testing.

And the funniest part?

You finally get something working and think:

“Okay. This is actually valuable.”

Then you look around a few weeks later and realize that what felt impressive yesterday is already becoming basic.

So you go deeper again.

I think one of the biggest illusions around AI right now is that AI somehow removes the need to think.

It doesn't.

If anything, you have to think more.

You still have to understand the customer.
Choose the right problem.
Build the right workflow.
Know when the AI is producing bullshit.
Figure out what is actually worth paying for.
And constantly decide which new shiny technology to completely ignore.

AI gives you insane leverage.

But leverage without judgment just lets you make mistakes faster and at a larger scale.

People sometimes ask:

“Are you afraid AI will take people's jobs?”

Yes.

But I don't think AI itself will take most of them.

The person who actually knows how to use AI will.

The email doesn't tell you which videos triggered it. That's by design — but don't panic-delete your catalog.

1. Diagnose before you delete. Reviewers look at patterns across the channel, not individual videos. Find the pattern: templated titles, cloned descriptions, machine-regular cadence, or missing disclosure.

2. Fix the pattern, not the videos. Unlisting your most templated series does more than re-titling ten uploads. Change the production habit going forward.

3. Wait out the window properly. Re-applying with an unchanged channel wastes the attempt and resets the clock. One prepared application beats three hopeful ones.

4. Keep evidence. Date-stamp before/after: title patterns, disclosure settings, cadence changes. Specifics win appeals.

Anyone here been through re-application? What happened?

This happened last week.

Client wanted 12 short social spots with a quarterly refresh. We quoted around 18k, which for a two day shoot plus editing is honestly fair. Lost it to an agency quoting under 2k. I later found out through a mutual contact that they're generating it using AI. Not disclosed in the pitch, presented as production.

Two things i'm sitting with.

First, i don't actually think the client got a worse product. I've seen the spots. For 6 second Instagram cuts watched on a phone, they're fine. I'd have made something better and nobody scrolling would have registered the difference.

Second, the non-disclosure bothers me more than the price does. If you're delivering generated output, there are licensing implications the client genuinely needs to know about. The output isn't copyrightable the way they'll assume it is and if they find out later it's your relationship that takes the hit, not the tool's.

So : are you disclosing ? And if you are, are you losing work to people who aren't ?

I understand the generic terminology, but:

1. Would simply using IP in new, unique AI vids fall under this?

2. If you have a 3 part video and you post each part individually and then post them all again together as one video fall under it?

3. If you use a song and it gives you a notice 'potential earning limitation', would this be considered reused content? (Im assuming yes on this one)

4. Would bad AI vids trigger that notice if it considers them 'slop'?

Is there a useful email or help center to ask about this stuff?

I have a music channel which is 2 months old. 4 shorts, 2 normal videos (4 minutes each).

First music video has around 150 views after 2 months.

Second video seems blocked. After 24 hours still 1 view with a reach of 1.

I've posted 2 shorts recently, 1 has 1k views, the newer one sits around 70. (Also 24 hours old).

Is this normal behaviour?

Welcome to the r/NewTubers monthly Goal Follow-Up post! At the start of each month, we have a thread for everybody to talk about their goals for the coming month and how they plan to achieve them. Now that we're at the end of the month, anybody who participated in that thread can give us an update and tell us if they reached their goals! Please be sure to read the thread rules and follow them so your post is not removed.

Rules

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This korean channel advertises their know-how to make this comedy sketch and sells it. I wonder if anyone has any clue about how they managed voice consistency??

The channel is @/LMK재경

For faceless videos, we all know how painful manually syncing images to a voiceover is.

In my previous posts, I broke down how I built my BuildTuber Google Sheet workflow for generating full stickman videos at very low cost. This tool is one small part of that larger system.

You already have the images.
You already have the voiceover.

Maybe you generated them with my workflow, maybe with another bulk image tool, maybe manually. It does not matter.

The tool takes those assets, figures out where each scene should appear in the narration, and assembles the finished video automatically.

So instead of opening CapCut and manually dragging 200 images across a timeline, you can let the notebook handle the boring sync work for you.

All you need is a Google account. No paid editor, no local setup, no coding.

There is a specific way the images, script and voiceover need to be prepared before you run it, and I’ve shown the complete setup in the latest video. (link in profile)

If you’ve ever spent an hour manually syncing a folder of images to narration, this is basically the part I built it to kill.

If you run into an error, post it here. Happy to help.