AI Industry STUNNED As Deepseek SHOCKS The Entire Industry (DeepSeek R1)

So if you haven't heard already, there is potentially going to be a new AI king, and that is of course, the Deep Seek AI system. Now, this thing has absolutely taken over social media and as someone with an AI YouTube channel that explicitly focuses on AI content, I actually want to give you guys my perspective on where I think this is headed because a lot of people are missing several points and the story is a little bit deeper than you may think. So of course most people by now would have heard that deepseek, the AI assistant, has taken over the Internet because allegedly this is a system that is remarkably more efficient than ChatGPT and even so, probably around 95% cheaper. And it got to the point where this app has actually overtaken ChatGPT as the most downloaded free app in the iPhone app store. Now that is a crazy, crazy feat. As you have to understand, ChatGPT has had market dominance ever since the release in November of 2022. So if ever there was a company that had managed to overtake them, it would definitely be making headlines. And certainly that's exactly what has occurred. Now, some people might just think that this isn't just the start. And I would agree. This is a serious problem for OpenAI. And let me explain to you guys why. If you guys can take a look at this, you can see here that literally in the past day, for the first time ever, this is the only company to have, you know, challenged essentially OpenAI's market dominance in terms of AI services. You have to understand that this is a tremendous deal because one of the points that many of us made in the AI community was that whilst other companies sometimes get access to frontier models and maybe even create them themselves, they don't have as much publicity and brand image as ChatGPT. But currently, after today's fiasco, it seems like DeepSeek is rapidly experiencing user growth. And for those of you that think this is just for today, in terms of the trends, if we look back at the last seven days, we can see that the search volume and of course this is just Google trends. So take this data point with a grain of salt. But this is something that overall should show you the scope of how big this situation truly is. Now, this entire situation has been absolutely incredible because if a company can make exactly what you're making for 95% of the cost and it's even faster, that is going to have some serious forward looking implications. Now, some of the OpenAI employees of course, are not too happy about this. For example, one OpenAI employee, Steven Heidel, said Americans sure love giving their data away to the CCP in exchange for free stuff. Now, of course, a community note said Deepsea can be run locally without an Internet connection, unlike OpenAI's models. Now that is quite true. You can run this model locally, although I do doubt that most people will have the know how and the will in order to do that. And of course, in his defense, when we do, take a look at this Tweet that gained 2.3 million views earlier today. It spoke about how Deep Seq AI actually collects your IP, keystroke patterns, device info, etc. And actually stores it in China where all that data is vulnerable to arbitrary requests from the Chinese state. From their own privacy policy, we can see that they are collecting data. It says automatically collected information. We automatically connect information from you when you use the services, including Internet or other network activity information such as your IP address. But I think this is the image that, you know, tended to scare a lot of people because it says right here, the personal information we collect from you may be stored on a server located outside side of the country where you live. And we store the information we collect in secure servers located in the People's Republic of China. And I don't know about you, but I know a lot of people aren't too happy about this. Now, whether or not you think they're storing our data, they're doing this with it, they're doing that with it. We cannot deny that this is a monumental achievement in the AI industry. When you take a look at the benchmarks that the system has been able to achieve, it is clear that there is some kind of disruption going on and, and this is some kind of innovation we must pay attention to. And the craziest thing about all of this is that this cost a fraction of what all of these companies spending billions and billions of dollars every single year just to be able to pump out models that are currently on par with other companies for marginal gains. So this is a very, very huge announcement. And when we take a cross look at these benchmarks, even the distilled models managed to outperform some of the standard models like GPT4O. So many people could reach, realistically, even run a 70B model at the level of GPT4O. And all that means is that someone can literally run ChatGPT, a really smart model, on their home device, not connected to the Internet, with pretty low latency and all the privacy in the world. If that isn't a very big problem, then I don't know what is now. It's actually pretty crazy because this has had some incredible effects worldwide. Like for example, Nvidia was down seven times, guys, that is billions and billions of dollars in value for that company. The valuation has been slashed by 17%. This was a 3 trillion dollar company at one point and now the market is reacting to this news unfavorably. I mean, it's understandable if you know you can train a model and make a model that is just as effective as a model that you need. Billions and billions of dollars on people are like, wait a minute, aren't we about to move to a new paradigm where maybe we don't need as many GPUs? And and of course, if that is the case, we know Nvidia is the world's biggest GPU supplier. So for me this was a surprising thing. But I'm actually going to have a little bit more about this later because I think there are some market forces that most people aren't thinking about now. In addition, this actually stunned the industry because it wasn't just Nvidia that was at the peril. We saw that it was a chip bloodbath. If we look at the heat map for semiconductor and related devices, we can see that AMD down 5%. AVgo down when we look at Broadcom as well, down 15%. Of course, Nvidia at the time of making the screenshot it was down 14% and other semiconductor and related devices were also down. I mean, I gotta be honest guys, this is the kind of, I wouldn't say black swan event, but the kind of announcement that definitely the market didn't take a liking to. Being able to wipe billions and trillions of dollars on an entire market is really, really significant. This is the news that impacted the entire industry. And for the first time that probably isn't clickbait. Now we have to understand what other individuals within the AI space were saying. Someone that I'm actually going to give credit to here is Gary Marcus. Now this is quite the controversial figure in the AI community. If you aren't familiar with Gary Marcus, he is essentially someone that the AI people either love or either hate. And I'm 5050 on this guy because he makes incredible points about AI companies. But sometimes the he does them aren't in the best frame and it can seem like he is hating a little bit. But in this blog post that he actually shows, he actually called this a year early and he's been early when it comes to predicting a variety of different things within the AI industry. One year ago. Today he actually called it, he said OpenAI actually lacks profits and a moat. The systems that they have built are hugely expensive to operate because they require no massive amounts of compute. And at the same time, the general principles for building them have become fairly well known in the industry. Large language models such as ChatGPT may quickly become commodities, which means we can expect price wars and profits may continue to be elusive or even modest at least. And basically what he's saying here is that, look, the secret of ChatGPT is out. We have tons of open source models. So what is a defining thing that's going to keep OpenAI afloat? And all of these other American AI companies, if someone can go on the App Store, download something that is faster, cheaper and better, I mean, what kind of chance do they have? Now you can see right here that he talks about they require massive amounts of compute for training, estimated in the tens or hundreds of millions of dollars for GPT4, and that this is going to be a problem. And of course there are going to be price wars and profits are going to be elusive at best. Now, he wasn't the only one that had something to say. The AI Cesar David Sachs says that Deepseek R1 shows that the AI race will be very competitive and that President Trump was right to rescind the Biden EO which hamstrung American companies, without asking whether China would do the same, which obviously they wouldn't. I'm confident in the us but we can't be complacent. Now he's basically stating here that, look, we need to ensure that we win this race because if we're not, then of course China can race ahead. And the AI landscape is becoming increasingly competitive with China's rapid advancement, Exemplified by DeepSeek R1. He's basically supporting the decision to rescind Biden's executive order, implying that it imposed unilateral constraints on US AI companies without guaranteeing reciprocal actions from China. And he basically just says, look, we basically need to lock in here because if we don't, it's going to be a terrible scenario. Now another thing, the President of the United States was actually surprised. He said the release of Deep Seq AI from a Chinese company should be a wake up call for our industries, that we should be laser focused on competing to win. We have the best scientists in the world and this is very unusual. We always have the ideas and we are always first. So what on earth has happened this time? And I think the us, maybe they just got too complacent the dynamics in these two different countries are remarkably different. In China, you have people that are willing to work incredibly hard, not that they're working incredibly hard in Silicon Valley, but those salaries are definitely a lot higher and those jobs definitely seem a lot more comfortable. And this is something that someone actually did speak about a few days ago. I mean, it was leaked with regards to Meta. I'm not sure if I have the information, but it was pretty crazy. They were basically saying that, look, these AI companies are floundering at the moment. They're currently worried because they, this has just thrown a complete, complete spanner in the works. But take a listen to what Trump said, because what he said is important. I mean, it's the President.

Last week I signed an order revoking Joe Biden's destructive artificial intelligence regulation so that AI companies can once again focus on being the best, not just being the most woken today. And over the last couple of days I've been reading about China and some of the companies in China, one in particular coming up with a faster method of AI and much less expensive method. And that's good because you don't have to spend this much money. I view that as a positive, as an asset. So I really think if it's, if it's fact and if it's true, and nobody really knows if it is, but I view that as a positive because you'll be doing that too, so you won't be spending as much and you'll get the same result, hopefully. The release of Deep Seek AI from a Chinese company should be a wake up call for our industries, that we need to be laser focused on competing to win because we have the greatest scientists in the world. Even Chinese leadership told me that. They said, you have the most brilliant scientists in the world and Seattle and various places, but Silicon Valley, they said there's nobody like those people. This is very unusual when you hear a Deep seek, when you hear somebody come up with something, we always have the ideas, we're always first. So I would say that's a positive. That could be very much a positive development. So instead of spending billions and billions, you'll spend less and you'll come up with hopefully the same solution. Under the Trump administration, we're going to unleash our tech companies and we're going to dominate the future like never.

Now, the crazy thing about this is that Eric Schmidt, a former Google CEO, actually confirmed, and he actually said a few months ago that it's actually critical that we win this race. So regardless whether or not you think about AI from OpenAI or Deep Seek. I think you guys have to understand that the reason that Eric Schmidt actually said that it's critical that we win this race. And this is why I think a lot of people are not realizing this. This is not the situation of, okay, China just created another chat model that is better than OpenAI's. They've actually done something really incredible in terms of, like, this is a wake up call for the United States because they initially thought that they were, you know, essentially maybe six to 12 months ahead. Some of the times they were thinking they were two years ahead. But if they are on par with where they currently are, that means the United States needs to speed up their production in terms of the AI that they're building. Because you have to understand, AGI or asi, artificial superintelligence is absolutely game changing. That is going to be the number one military asset in future societies. And whichever society, okay, in terms of, you know, the country or the state, whichever you are, you need, need. And I can not state how important this is in terms of military defenses. ASI is going to be the ultimate thing that these countries are going to be using for their national defense. And this is why he said that it's critical that we win this race. And most people don't realize that now we are literally racing towards superintelligence and AGI.

I've done this for 50 years. I've never seen innovation at this scale. There's literally remarkable human achievement of intelligence and the things that we can do and the advances in science and on and on. There's a point at which maybe in the next year or two, where the systems can begin to do their own research. They're called AI scientists, as opposed to human scientists. So you go from having a thousand human scientists to a million AI scientists. I think that increases the slope. When you're moving at this space, it's very hard for your competitors to catch up. That's the race. It is crucial that America wins this race globally and in particular ahead of time.

And so this is something that I think we need to pay attention to as a potential side effect of the current race. I think this year is probably going to move the quickest as companies are going to start deploying more rapidly and iterating even more efficiently than they had before. Now, one of the things that I did want to know was, is this actually real? Well, I was doing some digging and I came across this tweet right here. It speaks about how they actually replicated the DeepSeek R10 and DeepSeek R1 training on a 7B model with only 8,000 examples. And they said the results are surprisingly strong. The reason I'm actually talking about this research is because it seems like the deep seq method was a lot more efficient than OpenAI's method. And if that is the case, they should be able to use similar methods on smaller models. And that's essentially what they did. They used QEN 2.5 math as a base model and then they performed some reinforcement learning on it directly. No reward model, no fine tuning, just 8k math examples for verification and achieves a pass at 1 33% on a really decent benchmark, 62% and then 77.2% outperforming the other model. So they're basically stating that, look, this is something that actually works and the self reflection thing that we spoke about in the Deepseek paper, it actually emerges. So when we actually take a look at these results here, it may look a little bit confusing, but basically they're stating that, look, the technique that Deepseek had employed here, this is something that does work with these models. So it's, it's pretty crazy. Now another thing that I think most people did miss, I mentioned this in a previous video, but the fact that Deep Seek was just a side project for a quant company where they essentially just had some spare GPUs, and I think this is a giant wake up call. If China can do something like this with spare GPUs, I mean, it's going to be a real shock to the industry. Now the crazy thing about all of this is that the thing is, is that it's still not over. They are not done. They actually recently dropped Janus and this is essentially a multimodal model that produces images in stunning resolution at a very cheap rate. Now the thing here is that, you know, I think these AI companies are definitely concerned. The AI industry is one that is rife with innovation. Innovation that is so crazy that you almost can't keep up with it. And trust me, I'm coming from a place where sometimes I just think I had eight video topics that I need to upload today. And it is almost impossible to, for people to keep up with the AI news, let alone myself. And this is something that is going to be more and more prevalent as time goes on. And I want to talk about, you know, how there are similar things going on in the electric vehicle market. Many people may know that electric vehicles are in a situation where people are starting to realize that, wait a minute, Chinese cars are, you know, costing $20,000 and they are remarkable in terms of the technology, the usability, the efficiency, all of these gizmos and gadgets that we get. But over in the EU and over in the west, we have to pay a huge markup for these cars due to the tariffs. And it's like what happens when the software is going to be at a stage where it's just a commodity due to China ramping those costs down. You have to remember the user is always going to be doing what's best for themselves and it's going to be increasingly hard to prevent you to be able to download open source software, especially if it's remarkably more effective than the current software that you're using now. Of course, Nvidia, the big dog, the big players in the AI industry have officially spoken. They've said that Deepseek is an excellent AI advancement and a perfect example of test time scaling. Deepseek's work illustrates how new models can be created using that technique, leveraging widely available models and compute that is fully export control compliant. You have to understand that, you know, for Nvidia, I still don't think this is a bearish piece of information. You have to understand that there are rumors, which I probably will get into another video, that they actually have significantly more GPUs that they are letting on. And I think if that information does come to surface, then the market may adjust its expectations on what they think about these models. But overall it goes to show that Nvidia has been paying attention and they show that, look, Nvidia isn't backing down. They are fully invested in the space and they're like, look, this is of course innovation and innovation is great and we're going to continue selling these GPUs. And the craziest thing about all of this is that labs are starting to freak out. Actually saw this post from Meta. Now, Meta is a company that heavily, heavily invested in AI and, and they've kind of reaped the rewards. Their stock has been soaring through the roof. But the problem is, is that most people don't realize that Deep Seek may have just stolen Meta's entire pie. See, most people don't realize that there is this entire open source industry. If you're watching this and you're just a normal person who's interested in the AI industry, Meta has a huge, huge hold on the open source industry because they release open source models all the time that are pretty small and very nifty. To run on a variety of different devices. But you have to understand Meta is a billion dollar company. You can't have Chinese companies making things super cheap and super efficient for a fraction of the cost that's going to eat into their bottom line. And Meta was hoping that, you know, the Llama four, which is their next iteration of their open source model, was going to be the thing that, you know, just crushes the open source industry. And they are basically the industry leaders and then they can make a ton of money from partnerships and other collaborations. However, this post from a few days ago, which I didn't really want to comment on because it was pure speculation and I will show you that it actually is true. It talks about how, you know, Basically deep seek V3 rendered llama 4 already behind in benchmarks, and this was basically Deep Seek's prior model. So this wasn't even the R1 model that everyone is freaking out about now. And you can see right here, it says adding insult to injury was the fact that it managed to put Llama 4 behind the benchmarks and an unknown Chinese company with a $5.5 million training budget. And it says the engineers are moving frantically to dissect Deep Seek and copy anything and everything we can from it. And I'm not even exaggerating. And it says that management is worried about justifying the massive cost of a generative AI Org. How would they face leadership when every single leader of Gen AI Org is making more than what it costs to train Deepseek entirely? And we have dozens of such leaders now. Most people don't realize just how much people in the AI industry are getting paid. Now, honestly, I think it's worth it because these people are ridiculously smart and AI talent is truly scarce and these companies have billions of dollars, so they're not going to skimp out on salaries when they are looking to ensure they have the brightest minds for, you know, essentially securing the AI future. And the crazy thing now is that they're like, wait a minute, we pay 12 people maybe $50 million a year collectively. And now we look at that as what on earth are we doing when we can literally just get a model done for a fraction of the cost of that training run? Like, what on earth is. Like, what on earth are we doing here? So this is something that is, you know, freaking them out. And the crazy thing about this is that they didn't even realize that Deepseek R1 was even coming. And Deepseek R1 basically made things even scarier. It says, I can't reveal confidential information, but it should be soon, public info anyways. And you can see right here that this article that has come out today, in fact, that's the wrong slide. Let me show you the real slide. You see right here that it says Meta is reportedly scrambling war rooms of engineers to figure out how Deep Seeks AI is beating everyone at a fraction of the price. So this is something that I think is a serious, serious issue. You know, you can see Mark Zuckerbergs has a stern face here. Of course that's probably what he looks like right now. But you have to understand like Meta is a billion dollar company. They staked their entire future on this. Now, Meta aren't completely screwed because they do have the distribution, but I personally think the entire AI industry is about to change in a way that you don't think. And I'm going to get to that at the last point of the video because I think most people are going to miss what this changes for the entire AI industry. Now the crazy thing about this is that Nvidia have actually spoken. They've said that Deep Seek is an excellent AI advancement and a perfect example of test time scaling. That's apparently what an Nvidia spokesperson told CNBC on Monday. Deepseek's work illustrates how new models can be created using that technique, leveraging widely available models and compute that is fully export control compliant. So basically what they're stating here is that this is good for them because they realize that, look, it might be more efficient, but you still need more compute. Now, most people don't realize that what Nvidia are saying is actually the truth. You see, remember how I said that, you know, they made deepseek incredibly good, but they did it at a fraction of the cost. And because they did it at a fraction of the cost, people were like, wait a minute, we don't need these stocks anymore. Why are we buying so many chips if you can achieve it for like, you know, a fifth of that cost? But here's the thing, okay, now I'm going to show you guys a tweet from Andrej Karpathy. This is someone who is incredibly smart and you know, was one of the original people who worked on the ChatGPT team in terms of like the GPT2 team, those early research papers. He was a key figure and he said, okay, so here's this statement. It's really important. He said, I don't have too, too much to add on top of this earlier post on V3. And I think it applies to R1 too, which is more on the recent thinking equivalent. But I will say that deep learning has a legendary ravenous appetite for compute like no other algorithm that has ever been developed in AI. You may not always be utilizing it fully, but I would never bet against computer as the upper bound for achievable intelligence in the long run. Not just for a final individual training run, but for the entire innovation experimentation engine that silently underlines all the algorithmic innovations. Basically, he's stating there that look, whilst yes, it is crazy that they managed to do this on a shoestring budget, I would never bet against the fact that you're still going to need loads and loads of compute if you truly want to achieve artificial superintelligence. There simply are probably no way around this. And the crazy thing about this is that I think, okay, when we actually look at what the researchers are saying is that maybe this situation might be overblown to an extent that most people simply haven't priced in yet. Now, of course there was this statement and I was like, okay, things might be getting out of hand. They said, you know, unusual worlds. This is someone that tracks market events. And they said that Deep Seek could be an extinction level event for some venture capital firms. But per Axios, now that's a crazy statement, an extinction level event. But I think what they're stating is that maybe these guys who are investing in AI companies invested in the wrong ones. You see, you have two kind of AI companies that you can invest in. You can invest in companies that are on the LLM layer, which is essentially you're building the actual software behind the, you know, systems like ChatGPT. So you're, you know, spending millions of dollars and you are putting all of that capital into the training runs and, and it's super expensive and you're essentially just hoping for return sometime in the near future. Now if you were invested that did that, honestly, you are going to be in some serious trouble once those costs start to come down because those companies are going to struggle to make money. But here's the thing about this. I think the companies that are on the, you know, wrapper level, so companies that are just interacting with the AI systems, maybe like character AI or other software verticals on top of that AI. I think those, those ones are going to come out on top because their valuations are simply based on how useful that technology is to the user. You can see it says right here, analysts are now asking if multi billion dollar capital investments from companies like Microsoft, Google and Meta for Nvidia based AI infrastructure are being wasted when the same results can be achieved more cheaply. And earlier this month, Microsoft said it's spending $80 billion on AI infrastructure in 2025 alone, while Mark Zuckerberg said last week that the social media company had planned to invest between 60 to $65 billion in capital expenditures in 2025 as a part of its AI strategy. So this is something that could completely change the landscape in terms of AI investment, because right now in the labs, they've probably put a pause on a lot of these developments because they're thinking, wait a minute, before we spend all of these billions of dollars, let's actually probably pump the brakes a little bit and see if we can actually figure out if, if what they're saying is true, if these benchmarks are really true, if they did manage to do this. And honestly, I'm going to continue to do some digging and I'll be covering the entire thing on my channel. If you guys did enjoy this video, I post videos every single day and hopefully you guys understood the situation a little bit more and you understand all the inner workings behind the situation. I personally find this to be a super insightful moment for the AI community. I personally think the situation is a little bit overblown in terms of the stocks declining, but I do think that it is going to change the way companies think about their AI implementation. I think right now companies are going to be rallying to get multiple different products shipped out as quickly as possible, as that might be the final moat that these companies do have. So if you are a consumer, you can probably expect within the next two to three years millions and billions of different AI products hurled at you. But it's just going to make your life easier because all of these products are going to be relatively cheap, considering many companies are going to be basically competing.