Short answer: Jordan Metzner says there is no AI-made Netflix movie yet and probably zero AI songs in the top 100 despite Suno's valuation, so quality is not high enough. He expects watermarking of fabricated images.
Watch this answer from Episode 50 (starts at 24:20) · Episode 50 notes and transcript
From Episode 50: Physics Simulation for Chip Design Used to Take Days. This AI Does It in Seconds., starting at 24:20.
Jordan Metzner [24:20]: Okay. Cool. I you know, first of all, obviously, coming from Facebook, I think in general, there's a little hesitation on, the meta laboratory. And I think in the case of image generation, where you have, a company who's banking on social network and content generation that, there's a little bit of a questionability just in general, just, I guess, some hesitation in general. But, I think what's interesting is that, since we've seen these models produce high quality images and video I mean, we've seen video been around for a long time. We have not seen a TV show. We have not seen mean, I you know, we've seen people make some trailers. I've seen some TV commercials. I saw the Monopoly Go game the other day said they used AI generation in the commercial. But, we still haven't seen kind of like the, hey, we used AI and made this movie on Netflix and, blah. And, we produced this whole thing. And, the question is is why? Why haven't we seen that? And my guess is that we're just still early too early to produce high enough quality content that, producers or directors or creative, developers believe that is enough to keep people exciting. And I know, like, we internally have made a bunch of, like, AI videos and commercials and images and things like that.
Jordan Metzner [25:31]: But, it ends up like, after the fact, it comes up ends up being, like, kinda suboptimal to reality. Right? And I think we're seeing the same thing in music too. Right? Suno is raising at, high valuations, but how many AI songs are in the top 100? And it's probably zero. Right? So, we haven't really crossed that schism on to your point, kind of on LLMs we have, we now trust in, like, their belief and all that. But, from a creative side, whether it's images, videos, music, and, what else beyond that, it seems like we still haven't crossed the schism of high quality enough that people are, willing to kind of consume it at a high level. So I don't know about this one, but it does feel like in general that we're gonna move to a point where whether it's through regulation or not, there's gonna be some type of watermarking or some type of easily detectable mechanism to say that, this image has been fabricated, basically.
Jordan Metzner [26:22]: Yeah.
Hardik Kabaria [26:22]: Absolutely. And I think this we see the physical world very similar because if we are generating physics, then you can say we are generating design. How do we know that this is, say, a design generated by company x y z? Right? Because we care about now it even more. Those designs are gonna show up and actually drive the robot. Some of those robots might be doing very critical tasks at the level of inference and now you can see how these things are going to get very complex and could have drastic actions taken in the real world. That's what happens when the AI gets into hardware. And that means as the industry walls around different modalities just like image, videos, I think the regulations and guardrails will show up. And that's what will make the real application layer more robust. Right now, I do think we are in the early days of can the raw intelligence exist? I think we are starting to rate the answer is yes, it does. Then it has to become a useful product. That era I think now we are all going to discover for the modality that is not language. We think about physics the same way.
Hardik Kabaria [27:32]: There are transient phenomena. So I tell my team that's sort of like video. You are seeing temperature as a movie every few frames per second. But the user who is using it cares about really high fidelity answers. So you have to treat them with a different level of attention, accuracy requirements on the product versus a creative user. Both the users are important, but the product layer, I think, is going to be significantly different and that will drive innovation up and down the stack.
Jordan Metzner [28:01]: Yeah. That's awesome. Yeah. I mean, you could take a picture of an inside of an iPhone, and even if you block all the, labels of the parts, like any LLM or even most hardware engineers will tell you that's an iPhone, and they will know that it's not a Samsung phone or an LG phone or something else. Right? Because, we've seen enough inside of iPhones and how Apple designs their hardware that, you can recognize that. And so I can imagine that's gonna get all the way down to the PCB level and
Hardik Kabaria [28:27]: All the way down to somebody who's designing a memory because that is that's the secret sauce of the companies who are designing. So they will also care about, hey, we have a way to do these things. And it's starting to matter and we want to develop more on it. We want to take advantage of the fact that we have been designing memory for twenty five years. Right? So how will those companies stay ahead? Because they do have something and they wanna take advantage of it. And the startups might say, hey. We have we don't have the institutional knowledge for twenty five years, but we wanna explore the technology stack ground up. So I think we will see innovation on both the sides just like we have seen in software.
Jordan Metzner [29:04]: Yeah. That's awesome. Yeah. I mean, they just look at, like, NVIDIA's chips. This Rubin chip has hardly even been implemented in a single data center yet. I can't even imagine what, power it does when it's implemented at scale to even just NVIDIA themselves and being able to leverage it for future development of other tools. So, know, you it's this vicious cycle of, like, yeah, use your old technology, develop something new. Now that something new has to get used to get to then develop something new again and again. So this was an awesome episode. It was really great to hear about your product. And, obviously, again, early days in showing how kind of AI is implemented in a different part of the space, which is, hardware development. So I thought it was super cool and really awesome demo, and thanks for joining us.
Sam Nadler [29:44]: Yeah. Hardik, where can people find you? On LinkedIn? On X? Where can people follow-up?
Hardik Kabaria [29:49]: I mean, of course, our website is easy, getvinci.ai. You can reach out to us, winchie physics on Twitter. But, yeah, we'll be happy to show you guys what we have. And if, technologies of assistance to your ambitious hardware program, we would love to help you out.
Sam Nadler [30:05]: Perfect. Thanks, everyone. And we're here every week, and thanks for joining.
