AI Experts React: Elon’s Grok 4 Is Now #1 in AI — This Changes Everything w/ Emad Mostaque, Salim Ismail & Dave Blundin | EP #182

How impressive is Grok4 for you?

If you look at the AIME benchmark, which is an Advanced math quiz, Grok4 scored 100% on it. You're literally running out of benchmarks.

It's got to be driving Google nuts that Elon got this done in 28 months from a cold start when he.

Said he was going to put this huge cluster together. Every AI expert in the world said, you cannot get power, laws and coherence at that scale. You just can't do it everywhere. Every AI expert is like, oh, God dang, he did it.

The amount of compute and resources again are going exponential. Now it's the real quality that differentiates the top models between each other.

My big question is, where do we go from here?

Now that's a moonshot.

Ladies and gentlemen.

Everybody, welcome to Moonshots. An episode of WTF just happened in tech this week. Special episode today following the release of Grok 4. It is large language model release month. An extraordinary string of new models coming up. I'm here with my moonshot mates, Dave Blunden, the head of link XPV. Salim Ismail, the CEO of OpenExo, and a special guest to help us dissect all of this is Imad Mustaq, the founder of Intelligent Internet. Guys, it was a pretty epic day yesterday. Good to see you all. Pleasure to have you.

Yeah, likewise.

This is our special Grok 4 edition. Imad, you're in London, Yes?

Yep.

Fantastic. And Salim, where on the planet are you, buddy?

New York.

Okay. Dave's in Boston, I'm in Santa Monica. All right, let's get going. So just to jump in, goal here is dissect what happened yesterday, blow by blow. What's Grok 4 all about? And just to shadow what's coming, we've got a few new model releases coming with Gemini 3, GPT 5 and probably a few others. So let's kick it off with this video.

Grok4 is postgraduate, PhD level. Everything better than PhD. But like, most PhDs would fail, so it's better. That said, I mean, at least with respect to academic questions, I want to just emphasize this point. With respect to academic questions, Grok4 is better than PhD level in every subject. No exceptions. Now, this doesn't mean that it's, you know, at times it may lack common sense and it has not yet invented new technologies or discovered new physics, but that is just a matter of time. I think it may discover new technologies as soon as later this year, and I would be shocked if it has not done so next year.

All right, Dave, you want to take the first bite.

It's awesome. This is actually a golden moment in time because it is an absolutely brilliant assistant that can do almost anything you want it to do. But like Elon said, it's not reasoning yet, so it's not coming up with the fundamental. This is what we should build and this is why. So that's still in the hands of the creator, the human operator. And so this moment in time is actually really, really golden. It feels just like an Iron man movie where you've got Jarvis, Jarvis will build the suit for you. You have to decide how you're going to save the world. It's a really, really fun time to be using these. Brand new, like you said, there'll be three of these in the next month or so. This is the first round and he's dead right. You know, the PhD level solution, it's all measured in the benchmarks we'll get into in a minute, but it does virtually anything. Mind blowing capabilities, but it doesn't decide what to do and why.

Imad, I would love your take on this. You've been plugged into this world like intimately for a while. How impressive is Grok 4 for you?

I think it is very impressive. I think, you know, picking up what Dave said, I think it is reasoning, but it's not planning as yet. And there was a question as when we got to this ronaflop level, I think that's the term 10 to 28, I think flops. Would we continue to see improvements? And part of that is the compute and part of that is the data as we'll get to later. And the answer is yes. And again, like Elon said, getting above graduate level in every sub postgraduate level in every subject it can now execute and it can reason it doesn't have planning yet.

So I mean isn't that AGI? Isn't that the sort of like kind of definition of AGI? We passed through the Turing test without noticing. Are we going to pass through AGI without noticing too?

It's like this hedonic adaptation. You're like, of course it's fine, you know already. Again, if you want to get a job done, it will do the job for you of summarizing a book. Like it will do the job for you of like writing a summary of something or translating, et cetera. And life is just the same so far because you haven't got that final step that Dave said. And there's a few extra bits that we need for full agentic above that. But we're nearly there because we have that final building block now with this next level of model where it's reliable.

Distinction, by the way, that it is reasoning it has to be to solve these really hard PhD level problems. But it's not planning. That's a great way to phrase it. Run a flop is 10 to the 27th. So that's the scale of these algorithms.

That was the level the AI act said they wanted to ban, by the way. So this would be the first ban model.

Yeah, that's a great point.

I think one of the things that's happening is the absolute beauty of capital where you've got big juggernaut companies fighting it out for supremacy and throwing, taking massive risks, choosing design paths, taking huge gambles and really, really going for it. I think it's really magical to watch this happening.

Yeah, I love this tweet from Sawyer merritt. It says Xai was founded in March of 2023. Just 28 months later, it's now the number one model in the world, verified by independent testing. Incredible achievement. I mean, it is insanely fast compared to everything else that's being built. I remember when in May, two years ago, when Elon was first raising money and I had a chance to sit in on a investor pitch in the first round for Xai and he said, I'm going to have 100,000 GPUs, H1 hundreds operating by the end of the summer. And everybody's like, no friggin way. And he did just that and he's not slowed down. So here we see in this image, Artificial analysis intelligence index. Grok 3 was placing like 5th or 6th Grok 4 leaps to the front of the line. Are we going to continue seeing this? Imad this just leapfrogging each other, Leapfrogging each other. Is there, is there no end in sight?

It's getting very difficult because if you look at the benchmarks they have there, if you look at the AIME benchmark, which is an advanced math quiz, Grok 4 scored 100% on it. So you're literally running out of benchmarks in order to do that. And the amount of compute and resources again are going exponential because you need to squeeze that out as well as have good data as well as have good algorithms. So before you could just chuck everything into a pot, slosh it around, now it's the real quality that differentiates the top models between each other. And it's become more of an engineering and quality challenge than just a brute force challenge.

Insane.

Can I please. So what for a Second, please. Okay, so I've got a problem. I would suggest that if I'm trying to answer that problem or get a solution to it, I could go to any of these and they're going to give me marginally roughly the same answer. So we're at a point where the, the new step is. I'd love to. I want to get into the details of Grok to figure out why is it so radically different from any of the others. Right. And I. That's where I think the fun will come.

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Well, the funny thing is we're using, you know, we're basically going to Einstein and asking him to summarize a poem for us. I mean, it's like there's such massive level intelligence and the utilization for the general public is de minimis. Let's look at what's next on this. GROK outperforms the highest level test. Humanity's last exam up until now we've seen O3 was at 21%, Grok4 was at 25.4%, Gemini 2.5 at 26.9%. Then Grok4 and then Grok4 Heavy comes in at 44.4%. We were talking about this a little bit earlier. Imad, can you speak to humanity's last exam for us?

Yeah, this was come up by scale, AI and kind of a few others to have an exam that even the most polymathic people in the world would find difficult. So they estimated that some of the smartest people in the world would score maybe 5% on it. Maximum 10%. And the top models at the time, which was probably like half a year ago, nine months ago, scored 8%. Now you have a qualitative leap above to that 44% level. And I think it's interesting because as kind of Salim was referring to what are these models for? They're at this super genius level. It's like having a mega liberal arts program. Then the next step is going to be to have really useful people in the workforce on one stream and then the other stream will be to take the sub components of this and just push up to superhuman reasoning, discovering new things at a level that we could never have before. I think this is one of the indications of that because again, I tried to read some of the questions. I didn't even understand the questions.

Examples. I literally just gave a presentation on this yesterday, so I have it right in front of me. Tell me, humanity's last exam, 2700 questions. When the slide says for reference humans can score 5%, that means the very best humans in any given domain can score 5% within just the domain they understand. And I'll tell you why. Like here's, here's an example question. Compute the reduced 12th dimensional spin boardism of the classifying space of the Lie group G2 and then it goes on from there.

Most people can't even understand one word of that.

Exactly. Here's another one. Take a five dimensional gravitational theory compactified on a circle down to a fourth dimensional vacuum. So yeah, these are the hardest questions and that's why this exam is supposed to last for a long time. A 44% score is just way outside the range of human ability because nobody has that broad knowledge that spans all this, all these topics.

So how far, how long before we hit 100% here too? IMOD. Any bets?

Two years max. I would say probably next year.

So you know, there was a conversation years ago about AI getting to a point where you can't understand the questions it's asking and answering. And we're not far from that. So I mean we're unable to actually at some point we're unable to measure how rapidly it's advancing. That becomes a little bit frightening.

It's got to be driving Google nuts that, that Elon got this done in 28 months from a, from a cold start.

Absolutely.

Largely because, you know, Elon is phenomenal at large scale manufacturing, large scale organizational management and you know, people working 4 or 5am sleeping in tents on the, on the factory floor. That's, that's his wheelhouse and that's Tesla. That's SpaceX and because all the intellectual property was more or less open sourced by the research community at Google and Meta, he was able to pick up all that brilliant thinking and just plow it into implementation.

Also small teams, right? It's not. I mean, Google's a massive organization.

Yeah.

I think there's something else here though. Remember we talked about this last time when Grok 3 came out, right. But when he said he was going to put this huge cluster together, every AI expert in the world said, you cannot get power, laws and coherence at that scale. You just can't do it. And he went right back to first principles, created new kind of connections between the chips and whatever, and did it. And every AI expert is like, oh God dang, he did it. And so this is the, this is the incredible ability he has to go into a domain with a beginner's mind, go to first principles and just re engineer the heck out of it to achieve massive performance. And I think this is an indication of that. My big question is, as you mentioned earlier, Dave, where do we go from here? Right? Like, what does it mean to have a 50% versus 44% on this test? Yeah, yeah.

I think if I can just give a little bit of context, in 2022, Amazon built us the 10th fastest public supercomputer in the world. 4000 a one hundreds.

Quite great too.

And that was 2022. That was the 10th fastest in the world. Well, of any supercomputer that we were training on. And there was an instance where literally hundreds of the chips melted because of the scaling. Now they've managed to, by turning into engineering problems, scale the hardware, but also the inside of the model, which I think is this really important thing. The reason it's above PhD level in each of these areas is that was a computation scale problem. And so what happens is that if you could scale a liberal arts person all the way up to postgrad in everything, you would. And then you specialize down and then you look at some of these things and Salim's question there, you've got the base down.

Just for reference, everybody, the XAI cluster now has 340,000 GPUs.

Just about $30,000 or more each.

Yeah, do the math.

10 billion.

A lot. I mean, this is why we're seeing a billion dollars a day going into AI and why Jensen said there'll be a trillion dollars a year by 2030 and it's not slowing down. So here's another image from the little conversation Elon had yesterday. These are the benchmarks his team put up. I don't know if you want to hit on any of these, Imad or Dave or Salim, Any of the favorites for you?

Well, my favorite One is the AIME25. 100%. You're down, you know, GPQA. These are all hard benchmarks.

I think Elon would want to go to 110%. He likes 11 as 11.

I don't recognize is on the bottom right. Imad, do you know what that is? The USAM025?

I think it's the USA Mathematical Olympiad.

Oh, right, of course.

So it's about to happen. But again, these are novel hard benchmarks, effectively all of them and they're being saturated because ultimately the AI can reason mathematics and science better than we can. Again, it can't plan just yet. It doesn't have the same memory capacity and the building blocks haven't been put together. But it's already superhuman narrow capability in many narrow areas. So it's inevitable, I think. What happens next?

We glossed over his quote there. Discover new physics. Wouldn't surprise me if it's this year, certainly no later than the end of next year. Alex Wisner Gross has been having a field day with that all day.

I bet.

First of all, what does it mean to discover new physics? That's. That's pretty interesting by itself.

Well, I mean, you know, Alex, Alex has been saying we're going to solve all of math and then physics comes next. Chemistry and biology follow quickly. I mean this is the most exciting for me. This is the most exciting thing of these models are will they literally unwrap the present of the universe before us right here, right now, during our lives in the next five or 10 years?

Well, there's a couple of specific applications that I think I've been watching. I want to see an AI break and solve the quandary of the wave particle duality of light. That would be interesting. And seeing what exactly is going on in this. The second one would be molecular manufacturing. And how do we new techniques for doing molecular map. Because you crack that then you crack all assembly and manufacturing of all kinds of.

Right.

Then every.

The cost of anything becomes about a dollar a pound per weight. A computer. A dollar a pound.

Yeah.

And now you're in an amazing process.

I mean, listen again, going back to Ray Kurzweil's predictions, right. How he does it. I still, you know, you know, he's mentored you. He's mentored me. But you know, his predictions that we're going to have nanotech in the early 2000-30s. Where is it? Where is it? Well, this is probably its parents.

Yeah, well, the one, the one that's really fun to think about. You know, the quantum teleportation, Peter, that you brought up at meetings. So how do you reconcile the fact that two entangled particles can be infinitely far apart, yet still communicating in real time with the fact that the speed of light can't be transcended? So. So Alex's speculation is if we can solve physics in the next year or two or three, and it turns out that you can communicate using quantum teleportation, that we instantly discover all these other intelligences around the universe.

Yeah, we've just been listening at the wrong frequency with the wrong codex. These are the key takeaways. I'm going to just read these out loud and we can talk about them. They spent just as much on fine tuning training the AI after the initial phase as they did on pre training. So that's a big change. Imad. You want to dissect that for us?

Yeah. So it used to be that everything was basically you take a snapshot of the Internet and then you put it into this giant supercomputer mixer and it figures out all the connections, the latent spaces, to guess the next word. Then you had this very weird AI that came out that was a little bit crazy. It's like a disheveled graduate student without his coffee. And then you had to tidy him up with the reinforcement learning. That was the post training and that was 1% of the compute. Then with Deepseek it was 10% of the compute and now it's moved to equal because they figured out how to chain reasoning strips. And in fact, I think part of what they did, we've seen this with other labs, is they use their frontier model to make data for the next frontier model. So having large amounts of compute to create your own training data in a structured manner allows you to take that latent space, the landscape, and make it smarter and smarter and smarter. Just like your brain adapts as you learn more and more reasoning as you see more and more things, rather than having to have these massive scrapes of the Internet or whatever, it's more and more structured data making up these models, which is making them smarter reasoners.

So the 50% additional compute dedicated to the fine tuning, does that mean we have a more sane version of grok?

Fingers crossed. It doesn't necessarily mean that because you can still get all sorts of mode collapse within it in terms of if the latent space goes. But probably because again, you're training it just on a certain field of things, as opposed to Reddit and other things. In terms of order, I'd say this is probably like $100 million each. So it probably adds up to one meta AI researcher.

A new unit of measure, the AI world. That's funny. So let's comment on the cost here. $3 per million tokens, $15 per million output tokens, and can handle long context windows of 56,000 tokens. How does that measure up, Dave, in your mind?

Well, it's pretty normal these days. It's a longer context. A lot of the claimed context windows aren't real under the covers. The dimension of the neural net is much smaller than the claimed context window. So I suspect at this scale that this is the true dimension of the network, but I don't really know. We'll have to dig in over the next couple of days and find out. But what it means is you can feed in 100 books worth of information concurrently. It instantly digests all that knowledge and then gives you an intelligent answer based on all of that information in one pass. So it's just the next step in what's been going up sequentially from model to model to model.

Imad, do you expect we're going to be constantly reducing the price per token? Is this a demonetizing curve for a while to come?

100%. I mean, so the cost of this is about the same as the cost of Claude 4 Sonnet, which is the second model of anthropic or O3's cost, but it's better than both. It's about 0.7 words per token to give you an idea. And so the cost of a million very good words that are smart is $20. But next year with Vera Rubin, the next generation chip, they're going to whack in there just by the hardware. It will be three times to four times cheaper and they'll probably figure out some more stuff around that. So equi intelligence. The cost probably drops by around five to ten times a year. So it'll be a buck for a million amazing words.

It's hard to believe the most powerful technology in the world is de minimis in cost. It's crazy.

I want to put a comparator though here. This is amazing. We could put hundreds of our books into the thing and it would hold all of that in real time, as Dave said. But let's note that a single human cell has several billion operations going on in it at any point in time. Right? So we're kind of several orders, multiple orders of magnitude from modeling one cell. And so we've got a long way to go. Try and model life or get to really big, big, big things.

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Let's talk about Super Grok Heavy. You know, got to love Elon's terminology, right? It's we've got Falcon Heavy now we've got Super Grock Heavy. He loves his terms and I love them too. Actually. It makes me I smiled when I saw that.

Why heavy, by the way? Is there a reason for that consistent.

Across the Elon verse?

Yeah. No, I mean like, you know, Falcon Heavy was able to have, you know, three boosters to launch a heavier payload to orbit. So why not, why not talk about heavier capacity. So I mean in reality, right, Falcon Heavy had multiple boosters and this has multiple agents. So super.

So next one will be heavier and the one that will have to next.

Will be Grok Starship.

It'll be bfg.

So the price point here sets a new high bar and that's going to scare a lot of people. Say the same thing I said last time, you know, try it. Burn the 300 bucks for one month, you can turn off the subscription. But you got to try it to know what you're, what you're missing or not missing a lot of the use cases, you know, the day to day use cases, it won't matter much. But if you're building something complicated, writing code or designing mechanical parts or whatever, you're going to get addicted to it. What I'm really curious about is the margin at 300 bucks a month, are they actually chewing up all that money on compute for you or do they have significant margin at that price point? Because one Thing I've been predicting for a long time, it's inevitably going to happen soon is the use cases where you need that extra intelligence. Like when you're, when you're building a software product and you're prompting it, you absolutely need that extra level of intelligence. It makes you dramatically more efficient in moving forward. And if you look at the cost of an engine software engineer's time, you can afford to go up another factor of 10 or, or even more in price point for this and still be glad that you paid it. And so I think the escalation of pricing is, is going to come soon. The counter argument is that the competing models will then commoditize it, but I think people will pay a lot for marginally better improvement because the, the effective product you get out the other side, it really accelerates your time to development or the quality of the design or whatever. The solution to the math problem is right rather than wrong. Makes a big difference.

My guess is they're losing money.

You think so?

That's what, that's what OpenAI said for their pro level. Whereas the level below, they make money. So I think the way that I view this is a loss leader because if someone's paying 300 bucks, you enterprise sell them up and then you do team things to get everyone doing it. Because basically right now what we have is a UI problem. The reasoner is there, the way to hook it up and make it usable for as many people on your team isn't there. This is what Andrej Karpathy calls context engineering. What are the new UIs that will enable us to use this most efficiently and get our data in there? If you can crack that, then 300 bucks a month for a high level knowledge worker is nothing. Just like used to pay 1000, 2000 bucks a month for Bloomberg when I was a hedge fund manager, mostly for instant messaging. But you know, like again it's just not quite there, but it's about to flip there.

Yeah, well like a lawyer will cost you that much per hour or even three, five times that per hour. Will this do the job of your legal document better?

I can't wait.

That's the one profession I would love to replace, lawyers. All right, you mentioned enterprise level mod. Let's go there right now.

What else can GROK do? So we're actually releasing this grok if you want to try it right now to evaluate run the same benchmark as us. It's on the API, has 256k contact length, so we already actually see some of the early adopters to try graph 4 APIs. So our Palo Alto Neighbor ARC Institute which is a leading biomedical research center is already using seeing like how can they automate their research flows with Grok 4 it turned out it performs. It's able to help the scientists to sniff through millions of experiments logs and then just pick the best hypothesis within a split of seconds. We see this as being used for the CRISPR research and also Grok4 independently evaluated scores as the best model to exam the chest X ray. Who would know in the financial sector we also see the graph 4 with access to all the tools, real time information is actually one of the most popular AIs out there. So our graph four is also going to be available on the hyperscalers. So the XAI enterprise sector is only started two months ago and we're open for business.

Open for business. So Imad, you've been working on medical related AI. The block here isn't the tech, it's going to be the regulations. It's going to be when will an AI be able to fully replace a radiologist or fully replace any profession in the medical world? How do you think about that?

Well, I think it's the augmentation. First reduce errors, increase outcomes and then eventually it's replacement. Because Google had their AI medical expert study which showed that it was Dr. Dr. Plus Google search Dr. Plus AI and then AI by itself. Yeah, but just in self driving cars. Just gotta get.

I just want to touch on that because it was a really important article that came out. If you Again, the physician by themselves was getting something like 80% of the cases. Correct. The centaur, the physician plus the AI was getting like 87%. The numbers are approximate. And then the AI without the human bias, without the human biasing the output, the AI by itself was outdoing all of them at like the early 90%. Extraordinary.

Well again that's why I said it's better than any postgrad at the moment. But right now I think it's about the empowering and the acceleration in terms of the integration. And you're a way off the liability profile of replacement. I don't think you need replacement right now. What we need is less errors in something like medicine. Right.

I think the Dr. Number by itself, Peter, was 70% because I remember Daniel Kraft saying when you go to the doctor, you get the wrong diagnosis about 30% of the time. Right. That's a staggering number of errors. By the way, that means out of four of us, one and a half got the wrong diagnosis. Last time we went to the doctor, I mean, we need to figure out who that was. That's really ridiculous. And so you need an AI to take over that whole field. Well, especially human bias. And getting human bias out of that is also even more important. As we can see, the number of.

Types of scans and sensors you can do is way, way outstripping any human ability to look at all the data that comes out of it. So a lot of. A lot of it isn't trying to beat a doctor. It's trying to assimilate data that never could have gotten into the diagnosis before.

It's a great point. That's a great point.

Yeah. Just.

All right, let's go on to. Let's go on to our next. Next one. So available for an API. All right, we've covered these areas already. Let's move on.

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All right, I love this. Elon is a gamer and so it's not unreasonable for him to be talking about using Grok to make games. Let's take a listen.

Yeah, so the other thing, we talked a lot about having Grok to make games, video games. So Danny is actually a video game designer on X. So we mentioned, hey, who wants to try out some Grok for preview APIs to make games? And Danny answered a call. So this was actually just made first person shooting game in a span of four hours. So some of the. Actually the unappreciated hardest problem of making video games is not necessarily encoding the core logic of the game, but actually go out source all the Assets, all the textures of files and, you know, to create a visually appealing game.

I think one of the challenges is what we do with all of our time in the future. And we may be playing a lot of video games.

You know, this could actually light up the entire Metaverse world because building the Metaverse world and building those environments was the big limiting factor. And now you can do it at a very rich level. This could be really interesting to see what comes from this.

Yeah.

When did you guys first hear that Grok4 was going to come out? Last night?

Well, he said a few days ago, didn't he?

I mean, he's about a week ago.

I mean, he was saying it was going to be this weekend and then it got pushed to yesterday.

Yeah, because I feel like we had about 48 hour notice, plus or minus a day or two. If you look at the presentation, the raw presentation from last night and compare it to Google I. Okay, Google I O was, was scripted and staged with multiple presenters and, you know, clearly planned way in advance. This last night was like, is it done yet, guys? Is it done?

Does it work?

Okay, if it works, we're launching tonight. Let's go get on stage. Let's go. And I think that's the way it's going to be in the future because, you know, it seems like getting to market one day, two days sooner actually matters a lot in this horse race. So this is kind of the dynamic we should expect going forward.

But by the way, that narrator, that's the AI voice of a geek who is living and breathing it. And that's what you want in there.

Let's take a listen on Elon on video games and movie production.

For example, for video games, you'd want to use Unreal Engine or Unity or one of the main graphics engines and then generate the art, apply it to a 3D model, and then create an executable that someone can run on a PC or a console or a phone. We expect that to happen probably this year, and if not this year, certainly next year. So that's. It's going to be wild. I would expect the first really good AI video game to be next year, and probably the first half hour of watchable TV this year and probably the first watchable AI movie next year.

It's amazing what the fragmentation of those industries is going to be. Incredible because, you know, normally we think of a video game coming out in a release. All of your friends get the exact same release. It's a release that's maybe good for a year or more. And you're all on like FIFA 23 now or whatever, 25. But here, because it's only four hours to create the next iteration, then you can say, well, no, I want a customized version or I want. There's going to be all this fragmentation. And the version of the movie that I saw isn't the same ending that the one that Salim saw. So now we're debating on how it. We're not even on the same page and how the movie ends because we saw a different AI generated version. And it's going to be great. It's going to be really, really cool because everything's.

We're going to have a lot to.

Do with our time. I mean, Iman, listen, you spent so much time as CEO of Stability in this market arena of entertainment and video production and such. When I asked you earlier whether Hollywood is, you know, going to be disrupted, you said no. Can you explain that, please?

So I think the thing that won't grow is people's attention. So if you look at Netflix, their biggest competitor is video games, which is why they're going into video games. You only have so many hours in a day, and you're a consumer. Video game sector right now I think is $450 billion. The movie sector is 70 billion. That's how fast it's grown. Like education around the world is like 10 times larger. So it's 10% of education in terms of size. So if you think about that, then for Hollywood studios, this is great because the costs are coming down and it's been a dramatic shift. To give you an idea, the first video models, stable video, I think, was pretty much the first. We released that in 2023. And now with VO3 from Google and others, you're pretty much at Hollywood level, close to it, but you need one more generation to get there. And the average Hollywood Click length is 2.5 seconds. It used to be 12 seconds, now it's 2.5. And we can generate 8. And soon we'll be able to generate more. So you're getting to this point where you can make that. But again, people like having common stories to talk about, Barbie, Oppenheimer and things like that. So these marquee things, they can get the license of Cary Grant from back in the day and make him a star again. You know, you're going to see Marilyn Monroe.

Don't you think that there's going to be so much supply? And if I have a chance to watch, you know, a new episode of classic Star Trek, but, you know, I'm the character playing Captain Kirk and. And you know, you're playing Spock and my friends are taking the roles. I mean, I don't know why I would not be buying that entertainment from a source other than outside of Hollywood.

Well, you'll buy that too. But I think one of the things we've seen in the AI world, what's it about? Distribution. Distribution. Distribution. So you'll buy your interactive games and put yourself in the game, but you'll still have your marquee things. And the cost of that will reduce dramatically and the distribution cost will decrease dramatically and the impact will increase. So again, for companies, this is all great. For the individuals working in the industry, this is terrible. And so I think this is the key thing. For the individual creators, this is great because you can finally tell the stories. So we'll see richer stories. But you've still got to distribute them. It's like one of the examples I had to give is Taylor Swift, bless her heart, it's not the best music in the world, but she still causes earthquakes.

Yeah, no, your point that I think the video game industry bypassed all other media combined. I think I read that. And it's on a much faster growth trajectory as well. But I think the video games are far more compelling with AI components, AI players, AI voices, voices that are talking directly to you. And so that interactive media is going to get even more accelerated by this trend. So whether you call it movies or video games or other, the media is going to change, right? It always does. So it may not fit exactly in those swim lanes, but it's clearly the interactive talk to me part is going to grow much, much faster than passive watching part.

Yeah, I think it's the quality part and it's the feedback for you to find flow. So the movie industry has grown from like 50 billion to 60 billion in the last 10 years. Average IMDb score, 6.3. Video game industries like doubled inside, quadrupled. It was 170 billion. Now it's like 500 billion. The average score has gone from 69% on Metacritic to 74%. Games are good now and you need to be good to compete. And again, I think what we can see from this technology is I as a creator can create the best things better because I can control every pixel. This is what Jensen has said. Every pixel will be generated exactly what's in your mind. Maybe you don't have to use a keyboard. It just comes straight from your mind, can be on that screen. You can tell the stories you want. And on the other Side, you've got the fast food, so you know, the general content farms get even better. So you've got your gourmet and you've got your fast food and both of the quality of those will increase.

All right.

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All right, back to the episode. Of course, Grok for coding. Let's take a quick listen.

Right, so if you think about what other applications out there that can really benefit from all those very intelligent, fast and smart models, and coding is actually one of them. Yeah. So the team is currently working very heavily on coding models. I think right now the main focus is we actually trained recently a specialized coding model which is going to be both fast and smart. And I believe we can share with that model with all of you in a few weeks.

I still remember, Imad, when you were on stage with me like three years ago at the Abundance Summit and you said, no more coders in five years. It was, it was front page throughout India.

I got hate mail about that.

You know, oh my God, you scared the daylights out. And it's true. I mean, there's. I mean, it's a big issue. It's a big issue.

Why would you be able to talk to a computer better than a computer can talk to a computer? Yeah, you know.

Well, hold on, let me drill into that just for a second. Don't you think we'll end up with really good coders just creating 100 times more code?

No, because what you'll have is really good context. Engineers directing to build things. Code is an intermediate step of language because the computers and the compilers couldn't handle the complexity of what we wanted to talk about. Now you can talk to the AI all day long about anything and it understands to a reasonable degree what you actually want. And once we get the feedback loops really going, as we've seen with cursor and other things like that, like there's a reason it's got to $500 million in revenue in a year. You know there's a reason that anthropics got to $4 billion. Probably 2/3 of that is code.

Crazy. Disappointing that we won't have this for a couple weeks. We'll have to get back on the pod and check it out when it's out. Somebody told me you can get to it through cursor. Right now I'm looking at cursor as we speak and I don't see it popping up as a, as an option.

But I mean cursor is very much linked towards Anthropic so it probably like lobotomize it. But Grok 3 or Grok 4 already heavy is a pretty good code. It writes clean code and the coding model I think will be even better. But again how much better are you going to get when you can output a 3D video game like that or just about anything? And I think this comes to think if you're trying to create content, the AI is good enough already for just about anything. If you're trying to create something creative, this is the final part that requires planning and coordination and multi agent systems and the UI UX isn't there yet for the feedback loops, et cetera.

Yeah, I can use all the horsepower they can give me though because when you're writing a little code module it's all pretty much perfect already. But right now I can go to the best Claude model and say build me a dashboard for this function and just give it that prompt and most of the time it comes back great and even thinks of things that I wouldn't have thought of for that dashboard. And I can use another step up of capability in that area. So I'll use it up as quickly as it comes out, believe me.

All the tokens to Dave.

Okay, let's hear from Elon about his video model training. What's coming on input output.

We expect to be training a video model with over 100,000 GB2 hundreds and to begin that training within the next three or four weeks. So we're confident it's going to be pretty spectacular in video generation and video understanding.

So 100,000 GB200 more than anybody's thrown at this Imad. What is that? How does that, how does that hit you?

So when we trained the state of the Art first video model two years ago. Two years ago, that's right we use 700, 700, 700 patients, H1 hundreds. So like let's say they're three times slower. So the equivalent of 200 of the chips that he's about to use. Because these are the integrated GB chips from Nvidia, the top level models. Right now if you look at the Lumas of the world, the ByteDance models of the world, the VO3s use 2 to 4,000.

Wow.

He's about to use 100,000 of those. And the thing about video is when you train, a video model actually learns a representation of the world through computation. So once we made a video model, we extended it to a 3D model that could generate any 3D assets. It understands physics and more. So actually, video models are world models that can be used to do all sorts of things like improve self driving cars by creating whole worlds and other things like that as well. I think that's the reason why, given they've got 300,000 chips, they're putting 100,000 of these to the video model.

Well, and they're planning a million GPUs by the end of this year. It's like no small dreams here.

Ahmad, when you pioneered this just a couple years ago, like you said, the video model was trained completely separate from the large language model because it was just too much. You couldn't put everything into one mega model. Is he going to do a monster retraining of this model with video data, or is it a separate set of parameters and a separate model entirely?

This will be a separate model. So we took the image model and then we created the video model from that, and then we created the 3D model from that. Now they're doing from scratch training because the technology we developed for stable diffusion 3, the diffusion transformer matching it is able to do that all at once. And this is similar to what VO3 and others use. And with optimizations you can just pop that all straight in. Now the arch that they use, like the GROK model for the image is actually the same architecture as for the language. And they may do the same thing. I'm not sure how they're going to train this model because again, they're super smart, but it's a different model entirely. But they may all end up being the same model. Because if you want a model that understands physics and the wonders of the universe and what's the question? To get to the answer 42, you probably want to train on everything that a human sees and more because it will train on everything a million humans can see and understand and read and all sorts of stuff.

I mean, I'm excited about the idea of. There's so many of my favorite science fiction books that have never been made into movies or TV series. Right? I mean the ability to just say, hey, one of my favorite books is the Bobiverse series by Dennis Taylor and I love it. It's a four book series. It's extraordinary. Make it into a movie for me. Make it into a 20 part TV series for me. Here's 100 bucks.

Actually, if you took, took the, the best books that have ever been turned into movies already and use that as training data. So like this book turned into this killer movie. Make the changes necessary to get from point A to point B. Okay, now here's a book that never got made into a movie. From what you learned about those patterns, make the movie that's most compelling.

The thing is, you won't even have to do that just with the pace of chip improvements as we go through the generations. In two years you will have live 4K TV. So you've already seen some people do live low resolution stuff, interactive stuff. When Jensen says every pixel will be generated, he literally means it. With the next generation chips and a bit of more improvement in the algorithms and optimization of the, the models, you can have live streaming, 3D or video where every single pixel is generated on your screen within a few years. And so you can just say, stop, try this, adjust this and that'll be the feedback loop.

It'd be fun to take some old movies and make them way better. Like take the old Conan the Barbarian movie and make it really a proper movie.

You know, it hits me. We're sitting here having this conversation in four different cities around the world where, you know, we've taken so much for granted in this video channel and like, you know, 10 years ago, what do we have? We had just barely had Skype and now, you know, it's, it's crazy. So we humans adapt so rapidly to awesomeness and we take it, we take.

It, we normalize it very fast.

It's like your second way Metal Ride, right?

Yeah.

Your first one's like, wow. When you signal, it was like, okay.

Oh, for sure. So, any, any closing thoughts on Grok?

I have a question, I have a question for Iman. You've been in the space for a while now. We have Grok 4, right. What are the types of things that Grok 5 will be able to do?

So Grok 5 will be a multi agentic system, but rather than having four boosters, it will have 60 or 600 or 6,000, depending on what you want. It'll probably have a world model plugged in and it'll have Interconnectivity. And this is something that Elon mentioned yesterday to every major type of system. So it knows how to use Maya, it knows how to use advanced physics simulators, it will write its own lean code and optimize it for mathematics. And so it's just going to be like an incredibly versatile worker. And just like he's going to unleash millions of optimus robots, he's going to unleash billions, if not trillions of these things, GPU demand withstanding into the economy and that's going to be a bit crazy. And I think the way that you'll interact with Grox 6 probably Grok 5 is you'll have a zoom call with it just like you have now.

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Gemini 3 and, and GPT 5. Let's talk one second about what you expect there. Are these going to just leapfrog Grok4? Are they going to be, you know, sort of diverting in different directions? Imod your thoughts?

I think they'll probably all be kind of the same plateau. Now it's really about the UI UX and then how you wrap these into agents and then multi agent systems and then how you make it so just easy for anyone to use like this.

So you know Google in the work that they've done with their AR glasses, you know, enabling you to have a conversation with your AI and being able to have it see what you see, that's a great step forward. OpenAI with their voice mode has been fantastic. Are there any versions of user interface that we haven't seen yet? I mean BCI will be one of them for sure.

I mean I personally think again the interface is just the interface that you have with the remote worker and all the technology is almost in place for that.

Get on a call, hit him a slack pretty much.

And you just don't know. That's my AGI. My AGI is actually more like actually useful intelligence. Right. Like this is I think probably what Salim would like. Just, I don't know it's an AI or not, it just gets the job done and it doesn't sleep. And this final part of it as well is that the task length of these AIs has gone to like seven hours now. I think I've seen from various entities now they're getting that up to almost arbitrary length so you can set teams away and they have organizing AIs and others, they get the job done, they check in whenever they're unsure about something and then this is that next step up for all these technologies. But I think the 10 to the 27 models will, as you said, all be pretty much similar because they're already above PhD and everything. Now it's about making them super useful and getting them out there. And the demand for that is in the billions of agents.

David, what I find interesting is Elon's got basically a limitless capital supply.

Yeah.

You know, it's every time he's gone to raise money, you know, I've asked well how much can I get in the next round? And it's like, well, we're over subscribed already.

Yeah, yeah, it's another constraint isn't going to be the money, it's going to be the GPUs. I have a question for you Maud, about that actually, because if you say okay, the, the, you know, GPT5 will be out soon, couple of weeks, hopefully it'll be on the same plane, probably leapfrog, but in the same genre. And then Gemini 3 will come out and it'll be somewhere similar, maybe a little better. But the chip supply, you know, Google has huge amounts of GPU and, and a massive cloud computing platform plus they make their own TPUs. Then you know, you got a million chips going to Elon, we just talked about that. Sam at OpenAI has had a little bit of trouble with Microsoft recently. There's, there's definitely some kind of falling out there. And the way OpenAI got ahead of everyone in the first place is getting access to the compute from Microsoft and so is he going to have a problem getting, catching up to a million concurrent GPUs, training a single massive model?

I mean, I think Stargate is in that order of magnitude when you look at the kind of gigawatts. And now Amazon's just announced Paul Anthropic using Trainium for something that's even bigger than Stargate with their latest kind of chip supply. Google's the leader in this. So they have 3 million odd. But the thing that I come back to is OpenAI basically slowed down when everyone was making Ghibli memes. And so if you think about order of compute of Ghibli memes compared to order of compute for useful work, it's that versus that Google is okay because Google are actually landing millions of their own TPUs and they have the full stack and it has better interconnect for large context length. It's actually really good seventh generation hardware. Elon will get the supply because he's a beast. And I think again OpenAI have the capital but they're moving more and more towards consumer with the Jony I've acquisition and things like that. The dark horse here is probably again Meta, to be honest, because Zucker is going to drop $100 billion on this. He dropped 30 billion on the glasses on the Metaverse. He thinks AGI is coming and Meta is a $1.7 trillion stock. He will easily drop 100 billion.

Yeah, he's got $70 billion of free cash right now to use and can pump it up.

Well, I did an interview of Yann LeCun at MIT not super long ago and they had committed and already bought a million GPUs for internal use in Meta. So he, he had those on order already then. I'm sure they're in house now. So he has the, the compute in house.

So basically all the top guys can get a million. The next step is 10 million.

Well, there's only 20 million in the world, so this is where it runs into a bottle.

You can't even keep a straight face, can you?

Well, but again, think about every pixel being generated and think about again the economic accident activity of actually having a single useful teammate or account. I mean we're talking about like accountants and lawyers and other things like that on the other side of the screen. We're not even talking about super genius PhDs.

Is Nvidia just going to just keep going, going, going? Is anybody going to displace their, their production at all?

All of the top chip manufacturers are good enough to run these models. The only question is who has enough gating supply. So the reason for the hopper thing was actually the packaging of the chips, the co ops. So you have different supply channel constraints. Just like robots. In two years, robots will be good enough to do what? 90%, 95% of human labor. The only reason the entire global economy on labor isn't going to flip over from $2, $1 robots is supply chains. What we've got is a complete replacement of the capital stock of the economy from GPUs for virtual workers and robots. And it's just supply constraints. So Nvidia number one, you don't go wrong, you don't get fired at getting Nvidia, but you'll get chips from wherever you can get them because those chips are orders of magnitude cheaper than your team members.

I just asked actually Gemini in the background here what it costs today's market rate to train a run a flop. So one of these models just the compute cost is 312 million. So like you said, Ahmad, it's, it's like one signing bonus over at OpenAI these days. So that's not the cost, is not the issue. It's, it's who has access to the compute. What's amazing to me in this entire conversation, we haven't said the word Apple once. Yeah, and Apple controls about a third of the manufacturing capacity at TSMC for their M3 line M2 line chips. So they could easily become a player in the, the get a big data center up and running game. They'd have an incredible asset having that manufacturing toehold with tsmc. Just incredible that they haven't done that.

Well, I think this comes down to the thing, these models have economies of scope in that once you train a model that's good enough, do you really need another one? And then it becomes like electricity, it becomes a utility. So your genius models become utilities. And then what matters is the model that runs on the M3 or whatever, you know, like liquid AI just releasing edge models, those things become even more important because the M3 has capacity, M4s have capacity.

Yeah, yeah, that's a really big deal. By the way, liquid is, I didn't appreciate how big a deal it was until recently, but people are going to want to use this stuff immediately. And it's so addictive. And the inference time compute is severely constrained. And liquid, you know, runs fine on the edge on these M3s. Runs really, really fast. It runs on the chips in the cars and it's about, you know, they say about 100 times more efficient than just trying to run a brute force transformer. So that could be a huge unlock for people having access to AI, you know, at least more access to keep up with the Demand.

Exactly. Because you'll have your gated stuff and then they might increase prices because they have to, because there'll be so much competition for chips even as you get them cheaper. And then you just got this AI with you. But that AI will be smart enough to do your day to day and so you'll have a whole curve of intelligence. Just like sometimes you need to have steady workers and sometimes you need your geniuses.

I forgot you were actually the first guy to see Liquid when it was just a research project.

Yeah, I gave them all the compute to get going.

Yeah, that's right.

Amazing. And now they're what, 2 billion dollar valuation? So listen Imad, when you come back and join us next week, I think we have it scheduled. I want to hear all about the intelligent Internet. I'd love you to break the news on what you've been working on in secret for the last year or so. I've seen pieces of it, it's awesome. But hopefully you'll spill the whole master plan for us. Dave Saleem, my moonshot mates. Thank you guys. Grok 4 special edition.

See you at Crock 5.

Yeah, we got Gemini 3 in like three weeks.

We'll be back online soon.

All right, see you all. Thank you for joining us.

Take care folks.

Bye.

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