local AI's biggest problem... they solved it

Boxing Legends

**Video Description:**  Local AI’s biggest problem… and how they finally solved it! 🤖🚀  In this video, we explore one of the biggest challenges facing local AI systems and the innovative solution that could change the way we use artificial intelligence. Discover how AI technology is evolving, why running AI locally can be challenging, and how new advancements are making local AI more practical, efficient, and accessible.  If you're interested in Artificial Intelligence, machine learning, AI tools, and the future of technology, this video is for you!  👍 Like the video, 💬 share your thoughts in the comments, and 🔔 subscribe for more AI and technology updates.  **#LocalAI #ArtificialIntelligence #AI #MachineLearning #AITools #TechNews #GenerativeAI #OpenSourceAI #Technology #Innovation**

Transcript

00:00This is the first Wi-Fi GPU running an NVIDIA 5090, and it's somehow over Wi-Fi showing up as

00:06this laptop's GPU. I'm playing Cyberpunk on it right now. Also, I'm running Blender on it,

00:11but that's not even the craziest thing it's doing. Over here, I have a Mac also using the GPU. Not

00:15only is it generating images and videos very fast, all local AI, but it's also talking to this

00:20DeepSeq 4.1 Flash, a 750 billion parameter model running at full quality on a box with 32 gigabytes

00:26of VRAM. This doesn't make any sense. You would normally need over 500 gigabytes of memory to run

00:31this. It's called Wi-Fi, wireless computing infrastructure. Get your coffee ready. I cannot

00:35wait to show you how it's doing all of this. Now let's get this right out in front. This video

00:40is

00:40sponsored by Wi-Fi, but don't let that deter you. I heavily vet every sponsor and product I look at.

00:44This one is crazy, which is why I said yes. And here we are. They are a new company and

00:49they're

00:49doing something pretty bold. And when they told me about this thing, claiming to be the first

00:53wireless GPU, and it can run trillion parameter models. I had to try it. Like really, does this

00:58thing work? And in this video, we will be trying everything, video editing, gaming, and impossible

01:02local AI inference with image and video generation. Like it really doesn't make any sense what it's

01:06doing. And when I got it, like when it arrived, it was inside a suitcase, inside a box full of

01:11bubble

01:11wrap. That's how new this thing is. And here it is an early engineering model inside this box is an

01:16NVIDIA RTX 5090. There's also a mini PC as well, and it's actually running open WRT for its networking.

01:22That's how it shows up on my network. Now, if I get brave enough, I will undo this thing.

01:26So if you're seeing that B roll now, I did it, but I'm going to save it for the end

01:29of the video.

01:29So I don't break it before I make this content. Now, the big idea with this thing is local,

01:33no cloud, right? I'm already sold, but they taken it a step further, a step that I didn't think

01:38I wanted to take. This isn't just one device with a crazy, powerful GPU. Like I already have that

01:43in my studio. Instead, they just have the crazy GPU that multiple devices can use. Think about that.

01:49It's like a family GPU kind of like how we used to have one family computer for everyone.

01:53Yes. That was a thing. Comment below if you had one family computer. So the idea is that if you

01:57have a laptop, a tablet, a phone, it can now use this desktop class GPU, like it's its own.

02:03And it's not just throwing a job over to it. It's actually using it like playing cyberpunk on this

02:07little bitty laptop really hits it home for proof. You can actually hear the jet engine over here taking off.

02:15Oh, this thing's taken off and seeing that 5090 show up in the task manager, like it belongs to

02:20that laptop is so weird and kind of wonderful. How are they doing this? Let's actually hit that first

02:25because there are two things that were breaking my brain and I have to figure it out. How are they

02:29doing this over wifi? Because wifi is not the greatest connection and how is this laptop suddenly

02:33believing that it has a 5090 and we'll get to the whole trillion parameter model thing later.

02:37Now this will honestly be the craziest part of this video. I even built this visualizer that will show

02:41you exactly what's happening with the model on that GPU. Now, by the way, have you hacked the

02:45YouTube algorithm today? Let's make sure you do hit that like button, subscribe, notification bell,

02:49comment. You got to hack YouTube today. Ethically, of course. Okay. So let's talk about your GPU.

02:53Typically your GPU, your gaming GPU needs a very, very fast way of talking to your PC. And it does.

02:59Here's our fun little 5090 flying around. Can you keep an eye on it? It's fast. When you slot that

03:03sucker into your gaming PC, this is going to be a gaming PC right here. You're putting that into the

03:07PCIe port.

03:08This sucker's 512 gigabits per second answering requests in under a microsecond. Now let's get

03:13this gaming PC out of here. We're using a laptop now now over wifi instead of PCIe, we're talking

03:17about one gigabit per second answering at around 1.5 milliseconds. And that's under the best conditions

03:23because wifi is kind of, you know, and that is the technical term. Now don't miss this. It's this

03:27right here, this round trip time, the time it takes to answer those requests that becomes a huge problem.

03:33And it actually might not seem that bad at first, right? Like 1.5 milliseconds. That's super fast.

03:37No, it's not. If you compare that to a microsecond, I'll give you an analogy that helped my brain

03:41process this. If PCIe handed you a picture in one second with wifi, that would take 25 minutes.

03:48And that's a big problem because the game and the GPU, they're pretty chatty. They're sending

03:52instructions and data and answers to create a frame thousands of times every second.

03:56If every one of those answers had to wait on wifi, we'd be playing with PowerPoint, but we're not.

04:01Look at this 92 frames per second over wifi. And I'm rendering this in blender. And this is not remote

04:07desktop. It's running on this laptop on this GPU over wifi. How did YSI solve it? Well, they created

04:14their own protocol. The team at YSI has a networking background. So that tracks. The first thing they

04:18had to do is convince this laptop that he has a real GPU, that he's actually using this 5090 over

04:22here.

04:23Like this is your real GPU, buddy. Even though you have no room for it. They did that by installing

04:27a fake

04:27driver. And what's crazy is cyberpunk. The game treats this GPU. Like it's right there. Like it's the real

04:32one. It does not know the difference. Cool. But they still didn't solve the problem of goodness.

04:37This right here is super slow. This communication. So what they did was kind of genius. They just told

04:42them to stop talking so much. Like literally that's what they did. They figured out which messages

04:45actually need answers and then stopped waiting on the rest for instructions. They bundled them,

04:51compressed them and sent them without waiting. So for data, it only sends what's changed

04:55and doesn't keep repeating itself for answers. It doesn't even wait for the question. It knows

05:00what's going to be asked. So it just sends it. This is the wifi version of that meeting could

05:04have been an email. And then the GPU sends compressed video back to the laptop. These four things they

05:08did took what would actually need to be like eight gigabits per second needed to now all they need is

05:13400 megabits per second. Proof is in the pudding right now. We are gaming at 92 frames per second.

05:18Look at the wifi right now. It's about 400 megabits per second going across and get this out of your

05:22brain.

05:23This ain't cloud gaming. This also isn't remote desktop. The game isn't being streamed to us from

05:27the cloud. The game is still happening right there on the laptop, but it's using the GPU over there

05:33over wifi, which is just the craziest thing to say. Now what's funny is some of the team at YSci

05:37actually used to work in cloud gaming. I work in the cloud gaming team intensive before.

05:41And it was around the time that game, uh, black myth wukong, I think that's how you say it came

05:45out

05:46that they had this idea, the YSci idea. At a moment of black myth, we were thinking,

05:50can we still leave the front end on the laptop, but pushed back end to a remote place. That's very

05:58started. But then they discovered something kind of cool. And later on, we found that AI has a way

06:03more scenarios than gaming. Okay. This is the part that got me most excited when I first heard about

06:08YSci. Sure. The wifi gaming is neat, but local AI that's where things are happening. That's where I'm

06:12most excited about everything. And this is legit a new way to approach using big models with local AI.

06:19So YSci actually did two things, solve two big problems with local AI. I think they might be a

06:25game changer. Prove me wrong in the comments, do it. And some of this might be news to you because

06:29I'm not like a GPU expert. I don't study this stuff, but I did on this video, by the way,

06:34these videos take a minute because when someone sends me a product like this, I don't just go neat.

06:38Here are the talking points. And like, I actually get into it. I learned the technology. I learned

06:42how it works so I can pass this along. And when it's something that I think is interesting,

06:46I do want to pass it along. I get excited. So the two things that you actually solve

06:50or two problems that exist when you're already familiar with, but this first one,

06:53you're probably not GPUs don't like to share what we'll cover that here in a moment. And two,

06:58this is the obvious one consumer GPUs can't run big models because they don't fit.

07:02They don't have enough memory and yeah, they solve that. So for the first one,

07:05GPUs don't like to share. What does that mean? I mean, sure.

07:07This 5090 is great at running local AI. Have you tried it? It's fast.

07:12Hold up Chuck from the future here. I'm going to do a quick edit on this video for you because

07:16right

07:16here, I started to talk about how the GPU doesn't share, but I cannot wait for you to see the

07:20local

07:21AI stuff. So I'm going to move that right over to now. I just want you to see it. And

07:26also I want you

07:26to see that visualizer I made. It's so sick. It's like looking in the brain of deep seek.

07:32Here you go. So again, our premise is that you can't run large AI models at home,

07:36consumer GPUs. Like the one inside this box just don't have enough memory to run them yet.

07:40This box can how let's break it down. Now, first local AI is in a fun space right now.

07:45You can run a ton of amazing models on pretty much any hardware and you can do some cool things.

07:50But when we're talking about the smart ones, the frontier, like ones that get close to what

07:55you might experience with the Opus 5.5 or GPT six. And there are local models that are like that,

08:00like Kimmy K three or GLM 5.3 or deep seek 4.1. These local models do give you a

08:05level of

08:05intelligence. That's pretty dang close to these frontier models. And the best part is that it's

08:10unlimited and it's free and it's yours. It sounds great, right? Yeah. Well, you can't do it.

08:15Normal people can't run this stuff. It requires a lot of memory, like a data center level of memory.

08:21And have you seen the price of memory? The model I want to focus on right now is the deep

08:24seek 4.1

08:25flash to run the sucker full quality. You need over 500 gigabytes of memory. There is nothing

08:30consumer level that can do that. No GPU, no gaming PC that can support that much system memory.

08:35Even the new M five max studios with 512 gigabytes of Ram can't really run this comfortably. So again,

08:41how can this thing run it? Because we're not using memory. We're using the SSD because inside this box

08:47right now is a four terabyte SSD and it's using it like memory. Now by the numbers,

08:53this is a terrible idea. It's such a bad idea. Why are they doing this? Sure. SSDs,

08:59they give us more capacity. You most likely can buy a four terabyte SSD. How much are they right now?

09:05Field trip, four terabyte SSD on new egg. That's going to cost us 500 bucks. So capacity,

09:12we're getting that, but speed we're losing. And that's our main problem right now. And you need speed

09:17when working with AI. Let me show you what I'm talking about and how bad it normally would be with

09:21an

09:21SSD. If we didn't have what Y site was doing and what they're doing is pretty sick. So just for

09:26a

09:26comparison, like what we're talking about, because there are different types of memory.

09:29I keep throwing them around memory V Ram, which is the Ram that you're going to see inside your

09:34GPU. When we're talking about the 50, 90, we're going to be around 1.8 terabytes per second,

09:39wicked fast standard system, Ram, most modern stuff, DDR five, 90 gigabytes per second. And then we get

09:45down to the SSD 14 gigabytes per second. So if V Ram hands you a page in one second, Ram

09:51will take 20

09:52seconds. The SSD will take two minutes. It's like that sloth from Zootopia. How does Y site deal with

09:58this? First, I want you to imagine the best scenario possible. Imagine that somehow this 50, 90 has

10:04550 gigabytes of V Ram. Can you imagine? No, you can't, but we're going to do it right now.

10:10With that, we can run the full deep seek 4.1 flash with 500 gigabytes of required Ram.

10:15It also has 750 billion parameters. Pretty smart dude. And in this dream situation,

10:21speed is our ally for every word or token that this deep seek LLM generates.

10:25It has to transfer all 750 billion parameters to the chip. This equates to roughly about 508 gigabytes.

10:33This transfer right here, that's 1.8 terabytes per second. That's given us 3.5 words or tokens

10:39per second. That's great. But if we face reality and we get real sad, we have to realize we can't

10:44run

10:44a model that size purely on a GPU and we have to put it on the much, much, much, much,

10:48much, much,

10:49much slower SSD. Let's play that out. So goodbye, 550. We now are back to 32. By the way, this

10:55is

10:55actually going to be worse than you think. Watch. So we can actually still use the GPU in this measly

11:0032 gigabytes, but we still have to put 475 gigabytes of these parameters of these weights onto SSD.

11:05And again, for every single stinking word, we have to transfer all the weights from the SSD

11:13to the CPU. Now I know all you nerds in the comments, you're going to go, actually, Chuck,

11:16it's supposed to go to the Ram first and then to the CPU. And I know, but just for simplicity,

11:21we're just going to remove that stuff. Okay. So for one word, we transfer it, it spits it out.

11:25What's the damage? We might think, well, Chuck, we're getting 14 gigabytes per second, right?

11:30No, no, we're not. We're not even getting that. This is the rated speed for large transfers.

11:35What SSDs are meant for at least hundreds of kilobytes. What's actually happening is the

11:40transfers here are small, lots of tiny transfers, four kilobytes, millions of tiny four kilobyte

11:46transfers per word per token. So we're actually getting more around one to five gigabits per second,

11:53which means at its worst, oh, we're getting eight minutes per word. Yeah. Unusable, right?

12:00But I know what you're doing. I can see you typing in the comments right now. You're about to type,

12:05well, Chuck, actually deep seek 4.1 flash is a mixture of experts model. So we'll actually be

12:10faster. And you know what? You're right. Deep seek 4.1 flash is not one of those dense models

12:15where every parameter is used for every word or token. It's an MOE model or mixture of experts model,

12:20where only some of the parameters are used for every word or token. By the way, most of the best

12:25local open source models are mixture of experts like Kimmy K3, Quinn, GLM. And it does that by

12:31dividing the model up into experts. This one has 384 experts and kind of imagine each one being good

12:37at something like a few might be really good at answering questions about badminton. I don't know.

12:41It's not quite like that, but it's going to help illustrate it. So if you asked it a question,

12:44like what the junk is badminton, the model actually has a router built in. So let me actually draw this

12:49out. It's kind of fun. It has a router built in and out of the 384 experts, it will choose

12:53the top six

12:54that are really good at answering questions about badminton and they will be used to generate the

12:58next word. Now, this is amazing because we'll watch this with only six experts being used.

13:03We only need around 15 gigabytes of weights and files to be transferred for each word

13:07instead of the massive 508 gigabytes. So in our fictional world where we could put the entire

13:11model on one GPU and it had 550 gigabytes of VRAM, things would scream with that MOE model.

13:16We're talking eight milliseconds per word, but in the real world with an SSD at its worst,

13:21it might be around 15 seconds per word better, but it still sucks. And there's no getting around this

13:27because even if we are only using six experts to generate the next word, you might think, oh,

13:31we just put all those six experts on the GPU. It'll be fast. No, we don't know which six are

13:36going

13:36to be chosen for each word. So we have to store the entire model. So finally, we're at what YSI

13:42is

13:42doing to make this manageable, make it work. And it's actually pretty sick. And by the way,

13:46you made it to this. Think about that for a second. You learned a lot just now. You learned

13:51that file transfer speed is actually really important for LOMs. And it's kind of our main

13:55bottleneck right now. You learn what a mixture of experts model is and why it's kind of awesome

13:58for local AI. Now learn this YSI did four things. First, they rewrote the model files for the SSD

14:04drive so that every expert is the same size aligned chunks, which sounds gross, but it is a good thing.

14:10By the way, if you call me chunk in the comments, I forgive you, but it happens often. And now

14:14that I said that,

14:15I know my editors are going to have a lot of fun with it. With these chunks aligned,

14:20they now speed up the transfer reading at 64 chunks at a time. I've been saying chunk too much.

14:25Also pretend these are 64 chunks having too much fun with this. They essentially optimize the model

14:29file so the SSD can transfer them faster. And now that everything's chunky, sorry, we've gone from one

14:35gigabyte per second to around 11.5 gigabytes per second. That's a huge jump, but we're not done yet.

14:41Remember how those six experts are chosen by the router for every word? YSI keeps the

14:45most popular ones sitting on VRAM and RAM. So if most of the experts that are going to be generating

14:50the words are coming off VRAM and RAM with this design, only around one gigabyte worth of experts

14:55is being pulled from the SSD. So even though it is the slowest thing and they've, they've done their

14:59job to speed it up, we only have to worry about this much. And lastly, they split the math. If

15:03the

15:03weights are coming off the SSD or RAM, the CPU is going to handle it. But if the weights are

15:07coming off

15:07VRAM, the GPU is going to handle it. With these four optimizations, let's see what actually happens.

15:11Oh, how fast is it? I'm gonna run some deep seek y'all. It's getting really hot in here with

15:18my

15:18gaming machine and this bad boy keeping me toasty. Okay. Time to test the sucker. Now, a couple of

15:22things are happening right now. I didn't think I could do this. It's actually pretty sick. What

15:25you're seeing on the screen right now is a way for us to visualize what's happening when we're talking

15:29to deep seek on this GPU. We can see what's on SSD on RAM, GPU, and watching the experts be

15:34chosen.

15:34Hey, shot from the future again. I just realized I did not tell you what this thing is actually doing.

15:39It's not just some fancy graphic. I'm actually running this off of that wireless GPU. And we're

15:45actually looking into the brain of deep seek and how it's being used on the GPU and the SSD and

15:50the

15:50RAM and the VRAM. It's actually watching it pick the experts. And I did this by looking at trace logs.

15:55It's a whole thing, but it's running live and it's awesome. Anyways, I just had to say that. Now,

15:59one thing you might be like, uh, Chuck, that doesn't make any sense. It says all 508 gigabytes or 15,

16:06thousand 360 experts live here. Wait, Chuck, you said there's only 384 experts. Yes. But that's per

16:12layer. I simplified it in the explanation, but the principle still stands. Now I'm recording also on

16:17this Mac, which is wirelessly connected to this GPU. Let's load up deep seek ready, set load. Oh,

16:23we can see stuff happening. Look at that. Whoa, look at it go. This is pretty sick. It's ready. How

16:28is coffee roasted? Explain that process. Oh, look at that. Whoa. Look at all the experts being chosen.

16:38Notice most of them are coming from the GPU, dude. And we're getting like, look at this,

16:43like 10, we were at 15 tokens a second, our expert tiers full of coffee experts,

16:48but getting this level of speed off one GPU running off an SSD. Are you kidding me? By the way,

16:54I'm so proud of this quick little coding idea I had to visualize the experts being chosen.

17:00It's so awesome. Let's try a different question. Boom. And there it goes, picking the experts.

17:04And notice at the top, it's reading around 10 to 11 gigabytes per second. It's fluctuating though.

17:10For some reason on this round, we've slowed down a bit around three to four tokens a second.

17:14I wonder why. Maybe I can ask a new question in the actual web interface I have here.

17:19Why are a coffee beans green? Oh dang. It's going fast here.

17:27Look at that go. Reading the question, man, it's actually very fast.

17:32And notice 61% of the answers are coming from the GPU. Oh, this is fun. Hey,

17:37it's Chuck from the future again. Now it's finally time to talk about how GPUs don't share.

17:41And I bet you didn't know that here it is. GPUs don't like to share. What does that mean?

17:45I mean, sure. This 5090 is great at running local AI. Have you tried it? It's fast.

17:50I watch over wifi. I'll connect to it right now with this Mac. Tell them Mac. Hey,

17:53this is your GPU now. And I can generate images with Figma, which I don't know you could do that.

17:57I'm not sure how they did that. This is really cool. And I can make videos with this love art

18:01app.

18:01And I can even edit videos with the Vinci resolve and then render them.

18:05So I'm editing on the Mac and then I'm rendering it with that massive GPU. Like that's just cool.

18:10But these are all like single tasks. And if we want to treat the GPU, like that family computer,

18:15it needs to be that one centralized resource, which is their big idea. I think it's a fun idea.

18:19The GPU has to be able to share, but it won't, or at least it's not designed to it's stingy.

18:24It never actually had to share. The idea is that, you know, when you're playing a game,

18:28the game is using the GPU. Like that's what's happening when you're rendering a video,

18:31that's using the GPU. Most of it, no one taught it how to share because it didn't need to share.

18:36But now it does because all these AI apps are like, Hey, I want it. I want to play with

18:41it.

18:41I want to use it right now. They all want to use the GPU at the same time. And the

18:45problem is it

18:46takes time to switch because any one of these models, image model, video model, chat model

18:50can fill up the entire 32 gigabytes of VRAM. It has just by itself. And if you want to switch

18:56the app or the model, you have to move that sucker, all of it out of VRAM and then move

19:02the other

19:03one in. And if we're talking about the shared family idea, that's untenable. Like that's crazy

19:07slow. Now YSI is solving this by building what they call a scheduler. The scheduler runs on the box.

19:12And what it does is when you're want to switch an app, let's say we have the image app figma

19:15or

19:16something, but you want to switch to video generation. The scheduler will move that model down

19:20to the SSD. It's kind of like putting the app in its pocket. Didn't put it down. It didn't put

19:24it

19:24away. It's just somewhere else. And when that app needs to generate another image,

19:28it can quickly bring it out of the SSD and start it up again. In theory, this should make sharing

19:34the GPU a much better experience. And it makes it feasible for a phone or a laptop or a tablet

19:39to share it all at once. But that's just a theory. Let's test it. Now I'll code it up a

19:43quick demo.

19:44So you can actually see this visually. Here's the GPU and its memory SSD right here. So you can see

19:49the parked copies. And I'm actually going to show you without the scheduler first to show you how it

19:53kind of feels the speed difference. So we'll load the chat. So what it's going to do is it's going

19:57to load the Gemini model right now and ask it a quick question. And this is the Mac using the

20:01GPU scenario right now. Now I'm going to switch to an image model and it's going to generate a quick

20:05image. Ready, set, go. Now here's what I want you to pay attention to. And it's when I switch back

20:10to the chat app, let's switch back. It should take a minute.

20:16Notice it was pretty much the same time as loading it from scratch. I put it on my desk now,

20:20so you can

20:20kind of hear it when it spins up now, time for the parked option or the scheduler option. And look

20:25right

20:25now it already has the stuff loaded. So it's got the chat app loaded. It's got the parked models

20:30on the SSD, kind of like a copy of them, just ready to go. So I'll load the chat. It's

20:33already loaded.

20:34So it answered right here, as you can see, boom. Now I'm going to switch to image and let's see

20:38how

20:38fast this is.

20:42I mean, look at that.

20:46So, whoo, calm down, buddy. Before it took about what's 18 seconds. And this took us about 8.6.

20:52Let's switch back to chat. Boom. Woo. What took 17 seconds before. And now it takes around four

20:59seconds. Let's try again. Switch back to image. Pretty consistent. Okay, cool. So the family GPU,

21:08what do you think for me? I love the vision. I know what this can be. And after using it,

21:13the numbers don't lie. Like what they've done is impressive.

21:15Why site has created a bunch of amazing novel solutions to consumer GPU problems and running

21:21local AI. And it's not just that, like being able to game on a wireless GPU, like that's crazy,

21:26but then also run giant AI models that nobody has any business running by themselves on something like

21:32this and getting around eight tokens a second, eight words per second. That's good. This is an early

21:39engineering model. I imagine it's going to get better. And then using figma with this, that was cool.

21:44And actually that part I'm excited to see more of like mainstream apps, being able to use this GPU,

21:48using an app on your phone that believes it has a 50 90 and it's using it. Like that's crazy.

21:55The tech is exciting. I nerded out like crazy digging into this to figure out how it works.

22:00And by the way, you can buy one of these right now. They're selling them. It comes in this version

22:05with a 50 90. They also have a 50 60 TI version, which is a bit cheaper. Here's the pricing

22:09right here.

22:10They'll ship in about eight weeks. So get your orders in now, but man, this has been a wild ride.

22:16I'll catch you guys next time. Hey, you made it to the end at the end of my videos.

22:20I like to pray for you, my audience. Now, my camera's about to die.

22:23So I'm gonna pray for you real quick. Father, I thank you for the person on the other side of

22:26the screen.

22:28I thank you for them just being here. This is not an accident to be at the end of the

22:32YouTube video.

22:33Who makes it to the end? So God bless them now and their family. Um, I thank you that they

22:39are

22:39excited about tech. And I pray that this, this local AI thing, even if they're not gonna use one of

22:47these Y side devices, just the idea that local AI is accelerating, let this get them excited about

22:52what they can do in their community. Uh, what they can do for their, their home, what they can do

22:56for

22:56their businesses, what they can do for their church. God show them the cool ways we can accelerate

23:02humans, 10 X humans and do things that we could never do before and let them not be anxious about

23:07it, but be excited about it. God revealed to them ways they can use AI, but also at the same

23:14time

23:15reveal to them things they need to keep human,

23:20slow thinking and learning. We need that.

23:27Bless them indeed. Again, bless their families and their lives and their jobs and everything.

23:30I ask this in your name, Jesus. Amen. Whoo. Thanks for that. Thanks for letting me do that.

23:35I'll catch you guys next time.

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