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4:28
Today is Thursday, July 23rd, 2026.
4:30
Jordy is wearing a bandana.
4:35
>> What corporate logo is on that bandana?
4:38
>> TBPN collab here >> with AMD Advanced Micro Devices. Uh yeah, beautiful.
4:43
Thank you to the team for hosting us.
4:44
Thank you to the AMD team for having us here at AMD Advancing AI.
4:49
Uh we're on the road in SF today.
4:49
Uh we have a bunch of great guests for you.
4:54
Lisa Sue is joining in about 90 minutes.
4:57
Uh but let's you take you through a little bit of the news of what AMD is announcing, what's happening today.
5:01
So uh Lisa of course will be joining us at 1:30 p. m.
5:05
We have Oliver Cameron from Odyssey, Muhammad from Ideogram, and Anne from AMP coming back to the show for round two.
5:12
Uh round two or round three.
5:14
We've probably talked to him a few times.
5:16
It might be the third time.
5:17
Uh, Advancing AI is AMD's biggest AI event of the year where it reveals new chips, software partnerships, and how it plans to compete with Nvidia.
5:23
According to Jackson Force TV newsletter, >> we just found this out this morning.
5:27
AMD is actually powering the new >> mod retro M64. >> Very cool. >> It's very Yeah.
5:35
>> Crossover Palmer Lisa. >> Yeah.
5:37
So, we're going to be spending all 30 minutes of our time with Lisa Sue on gaming specifically.
5:41
How is How are they going to bring and suits? Yes.
5:43
wear a fantastic suit collection.
5:47
>> Got a fantastic suit on today.
5:49
>> Uh so the three key stories that people are focused on out of AMD advancing AI are AMD unveil un unveils Helios the new rack scale AI system powered by the MI450 accelerator.
5:59
Uh we'll talk to Lisa about that.
6:02
Also a new partnership with a Frontier Lab.
6:05
AMD and Anthropic are partnering to deploy up to two gigawatts of AMD Instinct Mi450s.
6:12
>> Sizegong >> sizegang five billion dollar deal uh and a whole bunch of clo uh a whole bunch of uh collaboration across compute and the software stack.
6:21
The big question around the software ecosystem around AMD is always a hot topic.
6:26
I'm sure there's a ton of developments there.
6:28
Only >> stage just a little bit ago, they were talking about how um Anthropic's own models are effectively speeding up their ability to ramp up new hardware. Yep.
6:38
>> Which is something that uh has been talked about for years now.
6:41
Some questions around Yeah. Uh the CUDA mode, right?
6:45
Uh if you assume that making great software will just get easier and easier, uh that that's been a big question.
6:52
Uh and they're starting to see uh real movement there. >> Yeah.
6:56
But also a lot of that speed up requires actual deep partnership like this because there are some close source packages that you might need to uh you might need to work on.
7:05
This is the old George hot interaction.
7:07
Um talking about little bugs that he was frustrated with wound up working with AMD developer team who we've talked to on the team on the show.
7:16
Um fantastic outcome there but interesting uh to see that flywheel just get tighter and tighter and tighter and I expect it will.
7:22
Uh also AMD is expanding its partnership with Cerebras teaming up on AI inference.
7:26
Uh we love the team over at Cerebras.
7:28
Uh they introduced a powerful disagregated inference solution pairing the right engine to each phase of the inference pipeline.
7:35
Uh this is what Agentic AI has been waiting for.
7:38
The fastest production inference at massive scale.
7:40
So uh excited to talk about that as well.
7:43
But uh our first guest is here uh Oliver Cameron from Odyssey.
7:48
I have a whole bunch of fun backstory. you can come on down. Um, >> here he is.
7:53
>> I first met Oliver, uh, almost 5 years ago.
7:57
You were working at Cruz doing easy stuff, self-driving cars.
8:00
Uh, and you took a ton of time to talk to me and walk me through exactly uh, how you were thinking about self-driving.
8:08
You explained a lot of different stuff to me about what what's being done in machine learning, what's being done with C++.
8:14
Very excited to have you back on the show. How are things going?
8:17
>> Things are going great.
8:17
Thank you so much for having me.
8:18
Great to have Uh, congratulations on the massive progress. Last round was what? 300 something billion. A million. A million raised.
8:27
It's gonna be billion next round. >> Billion. >> Yeah. >> 310 million. >> 310 million. Fantastic.
8:30
And And where is the product right now?
8:33
Like how are you describing uh how much is in the research phase?
8:37
How much are you ready to deploy this with early adopters?
8:39
How are you thinking about that?
8:43
>> We are in the GPT2 phase of world models. Yeah.
8:45
And what we're building is foundational world models.
8:47
The idea being that this is a technology that can be useful and applicable to tons of different industries.
8:53
Uh whether that's for example robotics or science or gaming or driverless cars. >> Yeah.
9:00
>> And so we've been on this journey now for 3 years.
9:02
>> Is there still a market in self-driving cars?
9:04
Because it feels like the world models, a lot of that research was done at Cruz and Whimo and Tesla and that was where I was first introduced to them.
9:10
I was like, "Oh, they built a video game version of San Francisco and of course they're training it there."
9:15
Uh is there a world where you wind up powering future self-driving initiatives and autonomy initiatives? >> I think so.
9:22
I think world models can power all sorts of virtual and physical systems.
9:26
The virtual system systems might be gaming for example, >> uh might be education.
9:32
>> Uh and then the physical systems will be driverless cars, robotics, drones, all that sort of stuff. >> Sure.
9:37
>> What I believe is that driverless cars today and lots of automated systems look very much like NLP systems in the 2010s.
9:44
M >> they are these very complex very handtuned systems.
9:45
They're intelligent but they're intelligent in sort of isolated ways >> and that those systems hold back what these technologies can do.
9:53
And so I think you can replace these very handtuned driverless car systems with a single world model that has this very deep understanding of physics of cause and effect of human behaviors and just lightly tune that world model to the task of driving.
10:07
Y >> what we're seeing early evidence of is you can indeed do that.
10:09
You can take these very big Jammer Wolf models, tune them with just a few hours of experience and then the car drives itself. Pretty cool.
10:17
>> So what what are your interactions and relationships, partnerships look like with uh various types of robotics companies?
10:24
So think of like if someone's building a humanoid or we we were talking with Travis Kalanick yesterday doing autonomy and mining.
10:28
So like what what does an Odyssey relationship look like with those companies today? >> Absolutely.
10:34
So we very much believe that the robot companies are customers of ours.
10:38
We provide that base intelligence.
10:40
They then have their robot, their embodiment, their task and they can take that base intelligence and tune their model to make that task amazing.
10:46
And what we see earlier evidence of is that using a world model versus a vision language action model, the number of examples you need to show the world model is dramatically less than a vision language action model, which means it's just much more adaptable to the world. >> Sure.
11:01
>> Than a vision language action model. >> Makes sense.
11:04
>> Talk about data right now.
11:04
Um, a lot of these world model systems, it feels like they were bootstrapped on Unreal Engine systems or just video game systems generally.
11:13
Uh, I imagine that the amount of data that's getting poured in the pot and mixed together is huge at this point.
11:19
But what are some of like the power law data sources generally that are useful in building world models? >> Yes.
11:25
So I I'll get to the point eventually, I promise. >> Yeah, take your time.
11:29
So what I observed in driverless cars, yeah, and we've seen in robotics >> is that you typically train those systems on very narrow data distributions, right?
11:36
So you're a driverless car company.
11:37
You collect lots of data from the road, >> dash cams, basically >> dash cams, >> but also cameras around the car. >> Exactly.
11:45
>> We've all seen the street view cars. >> Exactly.
11:47
And you're really teaching the car just from the perspective of a car, right?
11:52
It's it's solely seeing what cars see.
11:55
It's got the blinders on.
11:57
>> And what those cars don't see in training is video games or is inside a conference or in an office or in a home >> uh or just the multitude of scenarios that exist in the world.
12:10
>> And what we very much believe is that a world model shouldn't learn from a narrow distribution of the world like a series of uh driving examples or robot examples.
12:17
It should be that you learn from every possible thing that could exist in the world and then you tune the model to the task of driving.
12:24
So you have a base that's just very general and then you provide driverless car data at the very last step.
12:30
And so really our belief is that that will produce a more robust more intelligent system than these very narrowly trained models. >> I love that. Makes sense.
12:37
Completely tracks with a GPT2 GPT3 progression and language models.
12:42
Uh there was no point when people said oh well we really want to solve IMO level math.
12:46
So, let's get rid of all the fiction.
12:48
No, it was always include everything, right? >> Exactly.
12:52
You need Harry Potter to uh solve math problems. >> Hilarious.
12:56
Very odd scenario, but it is true. Absolutely.
13:00
>> Uh I'm interested in uh the more expensive pieces of data that I could imagine you'll be acquiring in 6 months, maybe a year, maybe 5 years, I don't know.
13:10
But we've heard about data labelers making seven figures creating RL environments for very specific tasks, very niche use cases, >> just seven. >> Yeah.
13:23
>> I mean, it's going to be eight next next month, next quarter.
13:24
But uh but but truly like the idea that uh the data labeling that went into early driverless cars, but also early reinforcement learning uh human feedback on text LLMs was just is this acceptable? Is this readable? Does this have typos?
13:41
And now it's does this actually track to case law or does this look like a financial model that I could turn in at an investment bank.
13:52
uh what is the world model equivalent of that?
13:54
Can you imagine what that will look like? >> I can.
13:57
So I think if you look at Alpha Star, which was Deep Mind's uh >> training of agents inside Starcraft to then beat Starcraft players, >> which was incredible. >> Yeah.
14:09
>> Really what you had was agents learning inside this environment that doesn't update or improve. Starcraft is Starcraft.
14:16
The agents then have a ceiling on their performance because it's always Starcraft that they're learning within. >> Yeah.
14:21
And so I think what world models really promise is the ability to be a learning environment that's continuously improving and adapting to the agents intelligence.
14:29
So for example, an agent should be able to learn within a world model that is cons consistently improving over time.
14:35
It's getting more robust.
14:37
It's getting more diverse. There's more scenarios.
14:38
And so the agent's intelligence being trained inside that world model should also get more robust more.
14:44
So fascinating because yeah, training a model to win at a video game is one thing, but the real world is never fixed in place the way a video game is. Right. >> Exactly. Yes.
14:53
And a world model really can be thought of as a sort of infinite infinite simulation, right?
14:57
It's continuously generating new types of environment that the RL agent can explore and adapt to and fight within and all these these cool things.
15:06
And so I think actually one of the the key or the killer applications of world models will be a learning environment for AIS.
15:13
language models, other types of AIS can all learn within world models.
15:15
It gets very much um philosophical. >> Exactly. >> Yes. Staying on philosophy.
15:20
Uh if there are there are many many labs that have made the bet that uh text is the universal interface scale and maybe coding software only singularity.
15:33
There's a number of different buzzwords to sort of define this idea that like LLMs are on the path.
15:38
Then you have the folks who are arguing that everything from LLMs are a dead end to deep learning is a deep end dead end blah blah blah.
15:44
But uh what I'm interested in is your if you're looking at there's clearly progress going on in textbased codebased LLMs that feels like a path to something called AGI.
15:53
Maybe that happened two years ago.
15:56
Maybe it's happening two years from now.
15:57
But it feels like there's a trajectory there.
16:01
Are world models like the next step? Is it a separate curve?
16:04
Will they come together and work together to produce whatever the next the next uh paradigm is?
16:11
How do these two technologies and like paths converge or diverge on the tech tree?
16:17
>> One slight tangent is uh one of those critics for language models, Gary Marcus.
16:21
>> Yes, I wasn't going to call by name, but you're welcome to.
16:26
>> Me and him and many others in driverless cars have been bickering for >> Oh, yeah.
16:29
Because he's driving he's an Uber guy. I [laughter] forgot.
16:30
Well, before language models were the debate, it was all about driverless cars.
16:34
And when they would come to Mark, is it 2050? Is it 2040?
16:36
It's like, no, this is going to happen much sooner.
16:40
Conveniently, he's now moved on from that being the debate.
16:41
But anyways, >> so my my belief is that language models are >> there's some new there was some new cope yesterday [laughter] as well that I was seeing that maybe I shouldn't bring up. >> Cope. Okay.
16:52
Anyway, let's [laughter] continue.
16:54
>> My my belief >> cope is evolving almost as basically as fast >> fast takeoff >> as the models themselves. >> Okay. Yeah. Yeah. Makes sense.
17:01
>> That's the frustrating thing.
17:01
The goalpost is just moving moving moving.
17:04
>> We have physical ghost that we move around the studio because there's always a new go new goal. >> It's ridiculous.
17:10
>> But that's the that's what makes it fun.
17:12
It would be boring if we reached the goal and we were done.
17:13
We want to keep building. >> That is true.
17:16
>> Uh and so what I very much believe is that language models are going to continue on the trajectory.
17:19
They're exceptional technologies.
17:20
They will lead to a form of super intelligence that's amazing.
17:24
Changes the world of course.
17:26
Um, what I believe they learn from though primarily is a sort of biased representation of the world.
17:32
>> And it's our writing of the world.
17:35
>> And it's also a remarkably, I think, inefficient representation of the world.
17:38
If you were to describe what's going on here in text, imagine everything, every detail was somehow describable by text.
17:45
How long would that representation be? It would be huge, right?
17:47
I pull out my phone, I captured two, three seconds.
17:51
I've captured so much detail of humans intermingling and everything.
17:53
So I very much believe that they are almost distinct somewhat complimentary technologies. >> Yeah.
18:01
>> There'll be cases like coding incredible for language models.
18:04
I don't think world models will play in that market too much. >> Yeah.
18:07
>> And creative writing and many other emails and things like that.
18:09
Amazing for language models.
18:11
World models I think will play this role of operating within either virtual or physical worlds and that those will be just distinct applications. it.
18:21
A world model will prove to be a better driver of a car than a language model.
18:25
A world model will prove to be a better operator of a robot than a language model, flyer of a drone, creator of a video game.
18:33
>> Uh educational experiences even I think they'll prove to be distinct.
18:36
I do think there is a convergence somewhat of the the learning environment thing I mentioned that world models will serve as this universe for language models to uh learn within. >> Yeah.
18:47
>> Uh and that's maybe where it converges at some point. >> Selfish question.
18:51
uh on autonomous driving.
18:51
How do you see the market evolving from here?
18:54
I've been having a lot of conversations recently where people are um buying buying Teslas, not specifically because they want a Tesla, but because they want the uh autonomous functionality.
19:05
And I feel like a lot of the manufacturers are in a lot more trouble than maybe they even realize because there's plenty of drivers today where if you have a maybe 10 20 minute commute daily, you're not really thinking about how do I solve this problem?
19:21
But for people that are driving any longer than that, it's starting to be like top of mind.
19:24
That's like the number one factor.
19:25
Um how how do you see this evolving?
19:28
Is it going to be like language models and that there's a bunch of different providers with like pretty good autonomous driving?
19:34
Uh we're not seeing it quite yet.
19:37
I think you can see, >> you know, the >> two wildly different companies that are both exceptional. Right. Right.
19:42
I mean, right now you have Whimo and Tesla. Yeah.
19:46
And >> but like wildly different >> tertiary players. >> Yeah.
19:51
So I see it going something like this which is that firstly consumers word of mouth matters and as many people are now trying Teslas it's going to become almost um insurmountable for many of the car companies to compete with just how good a Tesla is. Yeah.
20:05
All things not just driverless car driverless functionality.
20:11
>> And so I I think we'll see the continued sort of um attrition in the smaller car brands converging into Tesla. That's one thing.
20:20
Second thing is I think it's becoming almost um what's the right word here?
20:24
Like uh um a a national crisis to not enforce driverless technology.
20:32
>> Uh I have two kids and it's uh today still the leading cause of childhood death is car crashes >> and the idea that we have this technology now where I can literally walk out here and be driven fully driverlessly back to my house like 40 miles away.
20:46
But can you can you put on a a sympathetic hat and try and have some empathy for the ambulance chasers? >> The trial lawyers. >> The trial lawyers. >> They are people too.
20:57
>> What are you I mean Yeah, you have to >> live to think that. No.
20:59
Uh [laughter] >> I didn't know where that was going.
21:04
>> Take uh coming back to uh diffusion and self-driving.
21:07
I want to uh go back to that idea of like 2050 prediction.
21:11
It sounds ridiculous because you can walk out on the street and see one driving around San Francisco.
21:15
But if you rephrase the question on driverless cars to a billion driverless cars, full diffusion such that there are, you know, like they make themselves available everywhere over the world.
21:28
It's the dominant vehicle pattern that actually does take time.
21:32
It's more of an industrial process than a technology breakthrough.
21:35
Um, how are you processing diffusion of AI tools, LLMs, and will world models follow the same trajectory as LLM diffusion, which is really fast, everyone uses them for everything, but they haven't taken all the jobs.
21:47
And we're sort of in this interim thing where it's amazing, but it's additive and it was sort of unexpected to everyone.
21:53
Both the people that were like, it's fake, and both the people that were like, it's God, you know, like we kind of got the middle case, right?
21:58
>> It's just like, >> okay, things are going well, keep building. >> Exactly.
22:02
I specific to world models, I think that there'll be a very similar sharp progression of adoption that we've seen with language models.
22:08
There'll be some killer applications that become very clear very soon.
22:13
>> The idea that you could type a prompt and get out a AAA level game doesn't seem crazy to me. Multiplayer games too.
22:19
>> The idea that you could have generally capable robots in your house in offices and everything else enabled by these models becomes clear.
22:25
>> And then yes, driverless uh technologies and many others.
22:27
Um and so yeah, I think as what becomes the thing I always lent on in driverless cars, >> yeah, >> is even if you didn't believe in the technology, you just have to look at the volume of people and the smarts of the researchers behind it and how much dollars was going into investing in that category to assume it's going to get figured out at some point, right?
22:46
It's not an impossibility that we'll have driverless cars and so you put enough smart people behind it's going to get done.
22:51
And I I think the same with humanoids for example like there's just so much capital, so much talent flowing into that space.
22:57
It's going to get solved, right? It's just inevitability. >> You just keep going.
23:00
And I think uh this is why I have such faith that yeah, we should I mean there's no reason now we shouldn't have a billion driverless cars in the road.
23:08
>> But timelines by 2030 will I be able to walk in and get a Honda and have it have autonomous driving capabilities that's as good as Tesla's is today? >> Okay.
23:18
Not even not even not even what Tesla will be in 2030, but like a Tesla Ford, you know, these other manufacturers.
23:25
>> 2030 is like two years away.
23:25
[laughter] >> No, but but but I'm just saying like it's like I I I'm curious because I think >> every passing year all these manufacturers are just in more trouble. >> Maybe. Yeah. >> Yeah.
23:37
I think they get in their own way, right?
23:39
So I think a company will build a technology that they could sell to a Honda or other companies that is driverless ready on that scale in 2013. Absolutely.
23:47
I mean, we've got literal super intelligence coming that can write code of crazy quantities and such.
23:52
So, I I think so then it's really the car company's decision about >> do I want this?
23:56
Will I adopt it or will I continue to >> burn money internally failing at building it myself? >> Last question.
24:02
Uh how important is at least developing some of the application muscle, the go to market like uh Sunno just went viral in our office.
24:12
We're all addicted to Sunno now.
24:14
Uh and and there's been these moments where like okay everyone's on midjourney it's really fun and like it's this is this platform and then it cruise a lot of value it can sometimes be baggage because you're building you're you're like okay now I'm a consumer company.
24:26
Um how how do you think about uh value capture and how much you want to be the front end to just prompt get a get a game. >> Exactly.
24:37
>> Exactly. So my answer would be that uh I'm actually not happy with my answer but sometimes I get asked what are we good at >> Odyssey right and so my answer to that is that we we need to be great at everything >> and the reason I say that is because if you're building a very foundational technology it really does need to be
24:53
great at all of these different tasks >> um and we need to stay focused on that instead of getting really focused on a minute case and um so yeah long story short I very much believe that by building this foundational technology along the way you'll will discover by talking to companies, talking to researchers. Really cool stuff. Really cool stuff.
25:10
>> Some of them might be partners, some of them might be in house.
25:12
Uh well, I'm very excited for it. I'm so ready.
25:13
I can feel that the moment's coming.
25:17
Congratulations on all the progress and thank you for all the work you're doing.
25:20
Thanks for coming on the show. >> Thank you.
25:21
>> Have a great time here.
25:23
Uh while we bring in our next guest, let me tell you about RAMP. Time is money. Save both.
25:28
These use corporate cards, bill pay, accounting, and a whole lot more all in one place.
25:31
And we are we have a soundboard here.
25:34
We are joined by the founder and CEO of Ideogram, Muhammad. Welcome to the show. How are you doing? Thanks for having me. Looking good.
25:42
>> Thanks for looking good, too.
25:44
>> Yeah, it's uh it's the right place for us.
25:45
Um uh why don't you uh introduce the company a little bit?
25:48
I I I I know a fair amount, but for the viewers who don't, the the shape of the company and uh the product and uh sort of the the target market at this particular juncture. >> Yeah.
25:59
So, if you think about it, we already have content creation in front of us.
26:02
If you think you know we had the early days of image >> and if you look through your social media feed you see a lot of video >> but we think there's an inflection point that design marketing can also take advantage of yeah >> generative AI and that's what we're building at the AI foundation for enterprise adoption of the next uh phase of content and design.
26:27
So >> do you think that something like image generation can be fully solved within a shorter period of time than let's say intellectual intelligence?
26:42
>> It's kind of interesting because uh when you think of image generation you can have uh a very detailed document and diagram and really technical design as part of an image >> you know. Oh yeah.
26:55
>> It's not >> at some point if you're saying generate a blackboard with a solution to a IMO level math problem on it.
27:01
The model needs to understand that level of math. >> Yeah.
27:06
Like think of the circuit design or architecture design and and that's the same level of complexity as >> a lot of the reasoning problems we're trying to solve now.
27:16
So >> one of the first >> how you look at it. >> Yeah. Yeah.
27:19
I was asking from the lens of of I think everyone on Earth now has now viewed an AI image, not known it was >> not known that it was AI and just assumed like okay looks like a a normal image to me.
27:31
And I think when you're talking about like if Pepsi is working on a campaign, they don't need like as soon as it looks photoreal, they don't need something that's necessarily more real.
27:43
And so I just wonder what that means for the shape of Idoggram and where at at some point like what the what the core uh competency will need to be for Idoggram to continue to evolve as a business if that makes sense. >> Yeah. A few a few things.
27:59
One is we still have issues with consistency.
28:01
It's not at a level that a brand can use for their >> uh marketing for their design.
28:07
>> uh marketing for their design. And we focus a lot on image generation and not as much on design generation which is okay I have certain typography I want to translate across languages have my logo it has to be 100% accurate you know uh
28:24
with product photography it has to be exactly correct I can't buy a piece of clothing and then it doesn't it doesn't match the picture >> uh so when it comes to consistency uh we're still lacking but then part of the dream is to help with the design of future products. You know, the creative
28:42
You know, the creative part of uh design requires a lot of understanding of the context uh and then the brand DNA and putting them together to design the next generation of cars or design the next generation of shoes.
29:01
And that's where we are focusing on and uh we are working with brands to help supercharge their design and uh production.
29:11
>> How are >> to me that is I will just say the most >> my most addictive AI experiences is designing products that don't exist yet.
29:20
just anything I could possibly imagine in my head and then the process of like >> prompting and and you know getting getting close and you know getting to maybe 95% and then trying to get it again [laughter] you know the last 5% ends up taking you know 10 times more time than than getting to the 95. >> Yeah.
29:37
Um, >> so related to that, what are enterprises looking for specifically from you like this month?
29:44
Because we just went through the token maxing up and down where enterprises were just, you know, spraying tokens all over.
29:53
You can use whatever model for anything.
29:55
Check the weather with, you know, the best advanced model.
29:58
Um, but I imagine that your customers are concerned about cost, but also speed, quality, consistency.
30:02
Uh I know you're partnering with AMD obviously, but uh walk me through a little bit of like the problems that you're hearing from enterprises and what you're focused on solving in the short term.
30:15
>> Yeah, one thing is data sovereignty and obviously when it comes to design, IP is extremely important.
30:20
>> Lots of competitive industries that okay, I can't let my competitors see the design of my future car [laughter] >> and imagine sending that over to the cloud and then uh that that creates a lot of potential.
30:33
Does that mean they want to run your models on prem or does that mean that they're just want to know that you're hosting it in a certain certain secure way with a certain SLA around?
30:41
We're not going to train on this. >> I would say both. >> Both. Okay. >> Both.
30:44
And uh with the newest model that we released, it's actually a relatively compact model that's at the frontier and it's open vein. >> Wow.
30:52
>> And that creates a new >> set of opportunities for us in terms of licensing and partnership where companies can host these models on prem. Yeah.
31:01
that brings down their their cost but also data sovereignty is really important.
31:05
So on the design side that's one one thing we're hearing a lot. Yeah.
31:08
>> And then on the brand side this is still uh consistency is an issue but we think we're going to solve that in the next few months. >> Yeah.
31:15
There's a big there's a big debate over uh openw weightight models, geopolitical discussions and one of the themes that's coming up is like o American openweight companies might be at a disadvantage if uh other countries openweight models have less respect for intellectual property.
31:32
And so I would think that you would be fighting with one arm time tied behind your back.
31:36
But based on your business clients, they might not want a a model that infringes on IP because that could wind up getting them in trouble.
31:46
So, is it less of an issue for you or do you see uh like sort of the move fast and break things that may or may not be happening internationally actually being a like a headwind to your business?
31:58
>> On the image side, actually seems like American companies are doing really well >> and we are at the frontier.
32:02
So, so it's not as big of an issue.
32:06
side as long as we give them the protection they need.
32:11
>> But one other set of customers that we are seeing is actually >> generating data to train really sophisticated models for manufacturing problems for really unique uh defense problems where where you don't have a ton of the training data.
32:28
>> And these ones are getting to the quality that they can generate images of defects. Yeah.
32:33
uh images of really rare scenarios that you can't find in the real world. Yeah.
32:38
And that's another example where again having it on prem is important because uh they don't want to >> Yeah.
32:44
share that out >> share that data with everyone else. Yeah. >> Interesting. Interesting.
32:48
Um what what has it been like working with AMD?
32:50
We've talked about the software ecosystem around AMD.
32:54
you're obviously g give me some like benchmarks on what you've actually seen from your performance and then I want to know about the process to actually optimize one of your models for an AMD stack. >> Right.
33:07
So it started with us testing some of the early models. It was MI300.
33:12
>> Uh and then we got to know about the road map.
33:15
It's a really impressive road map with uh >> MI450 and and the following ones uh 455.
33:23
So we started testing it.
33:23
we got good results and uh with AI agents it's become so easy to you know give it the the architecture and then get it to be optimized for uh for the chip and uh the AMD team has been really accommodating in terms of helping us out whenever >> some issues uh arise and then we went live >> a few months ago with our four 4. 0 model.
33:48
How much does the road map at AMD and other semiconductor companies uh affect how you're designing your product?
33:58
Do you work backwards from this model, I know it's going to be able to do X, Y, and Z, hold this sort of memory, this flops like, and then okay, let's design the best model for that rack or that chip. >> Yeah, exactly.
34:10
So we we often >> customize the details of an architecture based on >> price per performance latency >> and then work backwards and train the model when we uh get to the uh details of the architecture. >> Yeah.
34:27
>> Uh there are certain changes that you can make uh that wouldn't have an impact on the quality of the model but would have an impact on the downstream performance and latency.
34:35
So >> I think everybody does that in the frontier AI that you kind of work backwards.
34:40
But the problem is you also don't know too much about the future chips.
34:43
[laughter] Um >> yeah, maybe that's why you got to come here, meet the people, and whisper. Oh, really?
34:50
That's what it's going to be?
34:51
Okay, we'll work backwards from that.
34:51
Or maybe you just have a proper, you know, deal and everything's above.
34:55
>> We didn't get to this, but uh before you leave, what were you doing before this?
34:59
>> Uh oh, I was at Google before this and before that I was a computer programmer, then turned AI researcher. Wow.
35:05
>> And then uh started idoggram.
35:05
At Google I started image generation.
35:08
I started image generation. this brand called imagine and I thought I can have a bigger impact uh you know >> uh how much of the team is researchers at Ideogram now >> it's about 50/50 >> 50/50 >> yeah we have a relatively small team it's primarily engineering >> that's really cool
35:24
>> and half of it focusing on the product and half of it is focusing on the model >> sounds like a dream well congratulations on the progress thank you so much to you >> great to meet you >> thanks for coming on we'll talk to you soon >> cheers >> uh let me tell you about MongoDB what's the only faster than the AI market your business on MongoDB. Don't just build
35:41
Don't just build AI, own the data platform that powers it.
35:46
And I'll also tell you about Codeex.
35:49
Codex is a powerful workspace for getting work done with AI agents.
35:51
Whether you're writing code, analyzing data, creating content, or automating business with workflows, Codeex helps you move projects forward from start to finish. >> Welcome back. How you doing? Great to see you. How you doing? I love it. >> Just saying hi. >> Amazing. Thank you, man. >> What's happening? >> Live. What's up, boys? Good to see you again. >> Good to see you.
36:15
>> Good to see you again. I think this third time. >> Oh, man. That jacket is so sharp. >> The green. >> The TVPN green.
36:21
>> I That's the TVPN green. Okay.
36:24
>> We'll We'll coordinate.
36:24
We'll connect you to our tail.
36:26
>> Do Do you have a Do you have a brand color yet for AMP?
36:29
>> We have We have like the >> the cream and the for right there.
36:31
We actually have a Forest Screen coming. >> Okay. There you go.
36:34
There's a a new video model that just got announced this morning called Flux 3 >> that's training on the AMP grid and uh it's from Black Forest Labs. >> Very cool.
36:44
>> And they're up in the Black Forest like near Fryberg in Germany.
36:47
>> Wait, say Amp Grid again. >> The Amp Grid.
36:51
>> That's a good sound effect for the AMP Grid. >> I like it. It's a good branding.
36:55
>> We'll get you We'll give you a sound effect.
36:57
>> We'll have our tailor talk to your tailor. We'll figure it all out. >> Yeah.
37:00
>> Careful what you wish for, man.
37:00
I would love the recommendation on that.
37:02
Black Force Labs, their color is forest green. Okay. >> Yeah. Because etc.
37:06
Um and I find it's a very soothing color. >> Yeah.
37:11
>> There's some like I think there's some like neurosychology around like green immediately calms you down. >> Yeah.
37:15
I I >> You don't want caution yellow or you know high viz orange that's a little bit more aggressive.
37:21
>> Well, there's some companies that make orange work. >> Yeah.
37:23
Well, that's not a Anthropic does not have a high viz orange.
37:25
Although a high viz orange is maybe the one uh white space in in branding. A16Z is orange.
37:32
>> But that's not high visive orange.
37:35
You know what I'm talking about? You see it on the road. That is an aggressive.
37:39
That's hard to pull off as a brand.
37:40
>> How are you thinking about the brand?
37:42
You do you have a whole brand book yet?
37:44
>> Uh we're we're going with the vibes and the aura, man. It's like >> I like it.
37:48
as as I've learned just the vibes generally the the vibes like are in the space of Frontier which you guys have been ex an extraordinary voice on stuff changes every day. >> Yeah.
38:06
And so nobody's got any time to look at the first principles, the details, what's actually happening, what's like which model's coming out, what's the difference between Fable 5 and like CEX blah blah blah like >> and so it's all aura based decision-m right now >> because the brain just needs some easy heristic to >> I'm surprised we haven't seen a chief aura officer.
38:27
>> That's that's the next like chief chief brand officer.
38:29
But when somebody does that sell everything.
38:31
So, [laughter] it's it's probably too much once there's a new >> but you make a good you make a good point which is that uh if if you if you have uh if you have motion doesn't matter what your site looks like or what your logo looks like people just like attach their own feel what they feel when they're interacting or uh to to whatever you're putting out. >> Motion is underrated.
38:53
There's a there should be an AI company research in motion. That would be good.
38:58
>> Wasn't that Blackberry?
38:59
>> Yeah, [laughter] anyway. I'm not that.
39:01
So, but but I want to answer your question which is what is the brand?
39:04
The brand is what you know you stand for and we stand for output maxing. >> Okay. >> Right. The the >> I love it.
39:10
>> There's just too much like what are we living through right now?
39:13
We're living through the AI scaling era. >> Yeah.
39:15
>> Bitter lesson holds >> that scale scale scale.
39:18
>> Y >> but the brain is naturally anchored to large numbers. >> Yeah.
39:23
But nobody like if if you actually deconstruct what's going on this space there's so much wastage because nobody's doing the output maxing efficiency right which is what's the output divided by the unit of input. >> Yeah.
39:36
>> And if you me start measuring businesses or model capabilities or [clears throat] whatever >> through efficiency >> the story is completely different >> than than what just the headlines say. >> Yeah. >> Right.
39:46
>> A lot of people were token maxing and I think we will look back on that era as really valuable exploration.
39:53
actually totally worth it.
39:53
Uh, but I like output maxing as the correct coinage as opposed to token maxing.
39:58
I was trying to say token minmaxing, token optimiz.
40:02
It's way better output maxing. I love it.
40:04
>> It's like what are we all here for? It's to try to grow GDP. Not at all expenses.
40:08
It' be grow GDP in the compute optimal way. Yeah. >> Right.
40:11
You cannot beat China at pure scaling.
40:14
>> It just not going to happen.
40:14
Industrial man like industrial scale coordination where they go, you know what? Ban the H100s, H200s.
40:20
H100s, H200s. all of you use Huawei and like get together and figure out how to scale with even if you don't we don't have the leading edge chips put out Kim K3 I don't care I'm Xiinping like I just want for us to be at the frontier >> and make you know 100 new nuclear plants whatever >> build build build baby like you know
40:39
build scale whatever is required you guys the permitting who needs permitting just like you know [laughter] they will just clear all the bottlenecks that they have been at a scale we don't understand right they will bring at least 100 gawatts of new energy capacity online in the next decade without even blink blinking. >> Yeah. >> Yeah. >> Yeah.
40:54
>> Meanwhile, they were already they weren't even AI pill and they were planning on doing it anyway. They're like, "Yeah." >> Yeah.
41:00
>> Uh so what do we have to do? We have to be smarter.
41:02
>> We have to do output maxing.
41:02
We got to be more efficient.
41:03
Only 15% >> of every single tenant data center today >> in the United States is being utilized.
41:11
>> Net flops utilization >> Oh.
41:13
>> is less than 20% in the United States. >> It is crazy.
41:17
Is is that just downtime from chips not being used at the right time like misconfigurations like the water supplies out or something? What's going on?
41:26
>> Basically, for every dollar of long-term lease that an AI lab takes today, you lose about 30 40% flops in just bad scheduling.
41:32
The nodes are just straight up not allocated.
41:34
Y >> and then it's there, but it's just not getting work at the right time. >> Correct.
41:39
>> Then within the chip, >> you only have like 15% of the chip being utilized.
41:42
That's called MFU model flop utilization cuz the chip is waiting around for some other process to complete.
41:47
storage, memory, networking or some other chip to hand it off.
41:50
>> So as a result, if you compound those two things, >> of every dollar, only 15% of flops are actually being utilized.
41:56
>> That 85% that's a national security crisis. >> Yeah.
42:00
>> Because that's what's keeping us from staying at the frontier. >> Yeah.
42:02
>> That's why we all keep complaining about, oh, communicate 3 came out.
42:03
Sure, you could talk about distillation.
42:05
Distillation is definitely happening.
42:07
However, everybody who feels computed in the United States should be asking, am I doing both?
42:13
Am I getting more capabilities?
42:14
Am I am I building up more supply and thank god Lisa is is on it? >> Yeah.
42:21
>> But also are we utilizing the existing capacity to get the maximum output possible >> and both those things are what's going to keep us at the frontier. Yeah. >> Does that make sense? >> Yeah. >> Yeah.
42:29
It's sort of I mean it's natural cycle.
42:31
Any company that >> that has grown really quickly or any industry that's grown really quickly, there's just ends up being all this waste and misutilized resources and all this stuff.
42:40
And then but but the key and I think what you're pushing for is like even if you have this exponential growth you can still try to be more it's still important to be more efficient now so that we don't give up the advantages that we do have.
42:53
>> I don't think we have a choice because the supply chain is backed up for like two years.
42:56
So we're just not like we at AMP the AMP grid we're procuring energy and new capacity for 2030. >> Wow.
43:04
>> And we and there's not enough sites. >> Yeah.
43:07
>> Um we we're going to need nuclear.
43:08
Where's >> are you spending time in nuclear companies?
43:10
>> Yes, actually there's Seth right over there. Hi, Seth. >> Hi, Seth. How you doing?
43:14
>> Seth is a founder of a company called Maver that we we've just invested in. I don't think. >> Good to meet you.
43:18
>> He was pop on the show.
43:18
[laughter] Pop on the show after >> Yeah. After he gets off. Well, yeah. We'll talk to you. >> Go over there. Talk to >> me. A good time. >> Yeah. >> Yeah. Yeah. Talk to the team. Seth was at the DOE. >> Yeah.
43:29
um and was one of the people responsible for a bunch of permitting that just finally got like approved for new nuclear projects in the United States. Cool.
43:39
>> And he's Thank you for your service, sir.
43:41
>> Okay, we'll we'll give you a proper mic in a couple minutes.
43:43
We got one more question. We I know you >> Yeah.
43:46
So So walk us through the shape of your business right now.
43:49
You have the fund, you're making new investments.
43:53
uh you got a big allocation in I think obviously every you know anthropic ground to date uh but otherwise like what is the shape of the business you're building out like you're you're to me it's like two different businesses they work they're very interlin but how are
44:09
you spending your time >> yeah um so we've got AMP foundry which is our venture capital firm and capital business and we raise venture capital funds in >> you don't make a single chip there >> we do not make any chips yet [laughter] not yet who knows cuz I I wouldn't put it past you. >> Lisa's a great partner. Uh on the but
44:24
>> Lisa's a great partner.
44:24
Uh on the but our our infrastructure arm, which uh we're actually >> um spinning out pretty soon as an independent >> entity. Oh, interesting.
44:35
>> Uh and we're not ready to announce the name yet, but you guys are going to love it.
44:39
>> So, that's for a followup.
44:39
Um is building out about 2 GW of capacity in the United States. >> Okay.
44:44
>> And uh we're not making our own chips yet, but we're building out new uh there's new sites.
44:48
Y >> uh there's new >> Yeah.
44:50
You're you're marshalling the capital, getting the right partners in place so everything can come together and the compute can come online. >> Yes.
44:57
>> Uh speaking of two gigawatts, uh what can you tell us about this anthropic AMD deal? I think it's 2 gawatts. $5 billion investment. >> Oh, six gigawatts. Is that what it is?
45:07
>> Um but I'm interested in first, it just seems like another positive sign for the industry for anthropic and AMD.
45:12
Uh and it feels like we're also I don't know.
45:14
I haven't checked all the reactions, but it feels like are we post uh post circular deal FUD?
45:20
Uh this seems like a very logical deal.
45:23
You know, everyone's seen the ARR charts. Everything's growing.
45:27
Businesses are using the tools.
45:30
Like the full it's a full ecosystem.
45:30
The economy is growing and so this part this type of partnership makes sense.
45:35
But what do you tell to what how are you telling an earlier stage founder if they're trying to do a strategic deal?
45:42
Um well well past the circular part I mean these were all concerns maybe two years ago right agree um and so if you look at the value creation when you have like entropic go from zero literally when we started the business like five years ago >> to well north of 40 billion in run rate
46:00
>> right in then and and most of that revenue is coming from >> everyday customers using cloud to code >> that's not circular that's net just new value creation right >> now the question is how How do you keep a healthy independent ecosystem of all kinds of frontier capabilities churning? >> Sure. >> Sure.
46:17
>> And the problem is that the the core problem one of many core bottlenecks is that private uh credit markets do not see startups as investment grade counterparties.
46:31
>> So when they're trying to procure capacity to compute capacity like 3 four years out, they just can't get those contracts are not financable.
46:36
And so what we need in the United States is a financing program that says, "Okay, we're going to be able to um turn startups, I mean, Antropic was a startup not too long ago that was not considered investment grade.
46:49
Um at this stage, >> they actually likely are still not investment grade.
46:53
Neither's OpenAI, but they're starting to become more underwritable by the financial markets, right?
46:59
>> And I think what we've got to do in the United States is do a little bit of innovation to go, how do you get the ecosystem to understand the quality?" Yeah. >> of of these startups.
47:09
>> Um they're the engine of innovation.
47:11
There's where you know Anthropic is 5,000ish people today.
47:15
>> Google is uh last time I checked like 60,000 people. >> Accenture is 75,000.
47:19
[laughter] >> And neither Accenture nor Google are responsible for frontier model innovation today.
47:26
Like Google still hasn't put out a anthropic grade coding model. >> Sure.
47:32
>> And they're trying their best.
47:32
But it's a focus thing, right?
47:33
It's a focus culture like talent dance teams that are that are focused >> get more done.
47:40
>> Speaking of that, are large founding teams underrated?
47:44
>> Are large founding teams underrated?
47:46
>> Because there's a world where anthropics like the exception of the rule, it's working for them. Crazy.
47:49
I mean, young company, but huge company.
47:52
All the founders still there.
47:54
>> And is another great example.
47:55
>> And as five uh and you know, strong team.
47:58
Uh there was a while where you know the the the Silicon Valley wisdom was like two maybe three, >> right?
48:04
Um yeah I most startup advice as you guys know is designed for the media >> fun for podcast. [laughter] >> Yeah. Yeah.
48:13
But the outlier businesses >> they they have large founding teams.
48:19
They have family founding teams >> like the stripe brothers.
48:21
The conventional wisdom is don't start you know like business. Yeah.
48:27
and Dario and Danielle are brother and sister.
48:29
The Collison's are brother and sister.
48:32
>> Um, Black Force Labs is 12 co-founders. They're all researchers. >> I had no idea. >> Yeah.
48:36
So, I you know, I think uh >> outliers do their own thing. >> Yeah.
48:41
>> And that's that's the point is like >> whatever it takes to compete at the frontier >> and as long as you're super aligned and tight, >> I think that's what matters. >> Okay.
48:51
>> Uh let's say you're let's say you're talking uh you do have a panel in a second.
48:54
I wish we could go longer.
48:54
uh you're talking to somebody who is now willing to admit that this AI thing is real.
49:01
They see the revenue ramps.
49:01
Let's say they're using various models.
49:06
They're like this is very capable, but they're getting into it and they're saying like okay, a lot of a lot of the revenue is tied to coding right now.
49:11
Uh these tokens uh are deflationary over time.
49:17
What is the next leg up in consumption?
49:21
Where is it going to come from? Right?
49:23
because like finance is an interesting category but it's I I don't see uh you know 200 billion of you know financial modeling uh spend on tokens right so uh where where are you predicting or thinking about these sort of like next legs next legs up after coding >> um so the the gift that keeps giving still is reinforcement learning like Arl is just working >> like as a technology technology. It's so simple.
49:54
>> It's like training training a a dog, right?
49:56
Like, hey, fighter, go fetch. You did it right.
49:58
You know, here's a treat. Here's a treat.
50:01
>> And reward modeling is um is used to be like this bespoke craft thing like two years ago.
50:08
>> Now we're we're getting to the stage of industrial grade like repeatable reinforcement learning as a service.
50:12
And so I think taking um models and doing post- training where you have the weights and doing post- training on data that you care about on some small set of samples that are high quality where the reward is very clear and you're doing RL gets you your own jagged frontier very fast. >> Yeah.
50:31
>> You know because you guys know this like capabilities there's a jagged frontier.
50:34
You know coding is clearly one area because of formal verification with unit tests.
50:37
The progress has been super fast.
50:40
Um, I think verification is people just still haven't truly internalized like how generalizable this concept of like the context feedback loop with verification built in is >> and the the algorithm is so simple like RL just works that wherever you can do like formal verification and RL you're going to see crazy capabilities.
51:00
So one example is Periodic Labs.
51:01
It's uh one of our portfolio companies.
51:05
>> They're um trying to discover room temperature superconductor.
51:09
The pace of progress there has been extraordinary in the last like six months.
51:12
They've got a 40,000 foot facility in Menllo Park where you got AI models that are predicting new materials, new candidates, >> and then you have robots synthesize the material and then test with an X-ray defraction machine.
51:24
Does the material have the superconducting properties that >> the model said it would?
51:29
And because that's verification from reality, like from nature, like physics, you can do X-ray defraction to test does it have it or not?
51:36
um you the feedback is the signal is very clear and then you take that signal and pipe it back into RL like as many times as you need >> and the the the capabilities ladder is is extraordinary.
51:48
>> I want to go way deeper with you on science and what's next in AI.
51:49
We got to let you get to your panel but thank you so much for coming on and hanging out. Always a pleasure.
51:56
>> I hear I'm just the opening act for Lisa.
51:58
So she's next and have fun. >> Yeah.
52:00
And thank you for the introduction.
52:01
We'll bring in your portfolio founder CEO.
52:03
First, I'm going to tell you about Public Public.
52:05
com investing for those that take it seriously.
52:09
They got stocks, options, bond, crypto, treasuries, and more with great customer service. >> Welcome to the show. Introduce yourself.
52:15
>> How much do you bench?
52:15
[laughter] >> Uh, this morning 335. There we go. >> Amazing.
52:20
Can you lift this microphone up to your mouth? Thank you.
52:24
>> And move maybe move it over a little bit. >> There we go.
52:25
Uh, the other way like this. Loud room. Loud room.
52:27
Anyway, uh, loud and clear. Give us an introduction.
52:32
Yourself and the company. >> Hey guys.
52:33
Uh my name is Seth Cohen.
52:33
I am one of the co-founders of Ma Inc.
52:36
We are an AI infrastructure company and we're looking to tackle some of the biggest problems in the space and we're so thrilled to be working with an >> Yeah.
52:45
>> Uh before this I was uh chief counsel of nuclear policy at the Department of Energy. >> Oh no way.
52:52
>> Um the uh depending on who you ask either a senior adviser or the conservator of the nuclear regulatory commission >> and an adviser at NASA. >> Oh cool.
53:01
>> Um so I was the Doge operative.
53:01
um one of two responsible for implementing the president's nuclear executive orders across the government. >> Very cool.
53:08
Yeah, we >> were covering before that because that doesn't seem like the first job.
53:13
>> Uh Supreme Court and appellet litigation at a firm called Kirkland. >> Oh, cool. Heard of that. Yeah.
53:18
>> Um so the hat says nuclear.
53:18
You introduced the company as AI infrastructure.
53:22
Like is that because AI is the hot trend and nuclear is just like the byproduct or are you a pure nuclear company?
53:29
walk me through where the ven diagrams overlap between just AI infrastructure broadly, what you'll do and what you don't do, and then nuclear, what you do and don't do.
53:38
>> Yeah, so one of the things um that uh my co uh my Doge team leader who's now my co-founder um Adam Blake, one of the two things that we or three things that we first honed in on um with Nuclear were a set of bottlenecks that we thought were really going to be uh >> were going to prevent the anything we had done we did from actually taking effect.
53:59
we needed to solve a couple of other things. >> Yeah.
54:02
>> Um and one of them was narrative, right?
54:04
Nuclear is this incredibly incredible technology that has so much potential for the world.
54:07
And yet for 60 years, it's faced this strange opposition, including around nuclear waste.
54:13
And by the way, if we if we can, I'd really like to talk about nuclear waste.
54:16
It's my favorite thing in the world.
54:18
>> We love talking about nuclear waste.
54:18
So, you're in the right place.
54:21
>> Uh it's good to be among friends.
54:21
Um but uh when so what we did with nuclear waste was actually we shifted the narrative and went from Yucka Mountain being the only place where you could put level waste completely off limits to where we are now.
54:36
27 governors applied for this program that Adam and I started called the nuclear life cycle innovation campus.
54:43
>> And there are I I can't tell you the exact number but uh it fits on two hands.
54:47
the number of governors currently fighting to host the full backend fuel cycle.
54:53
>> Would that be one additional nuclear waste storage plant to be one of three?
54:57
And these are going to be cities, by the way, that that if everything goes according to plan, we'll be competitive with our major metropolitan areas because we're going to re-industrialize. >> Yeah. Yeah. Cities.
55:08
I feel like you're just talking about a hole in the ground.
55:11
I need like two guys to protect it.
55:11
What else is going on around a nuclear waste protection site?
55:15
Well, so it's not, you know, to take a step back. >> Yeah.
55:21
>> Is it too late to offer up my backyard, [laughter] >> the ultimate emb?
55:27
>> No, I I I think what So, first off, when we talk about nuclear waste, that's really we we have this strange idea and it's probably The Simpsons fault. >> Oh, yeah. >> Um >> glowing. >> Yeah.
55:39
Uh I have seen the blue glow of a spent fuel rod, which is actually very very cool, but there's no green goo. >> Yeah. >> Right.
55:45
So almost all waste in the in the United States is medical stuff.
55:48
It's like gloves because some person went behind a wall and put pressed an X-ray button. >> Oh, interesting.
55:54
I didn't realize that that falls in there.
55:56
You think of it as like the pellets, but it it's really anything that touches that.
56:00
So, there needs to be a whole processing facility for that. That makes sense. >> Yeah, that's most.
56:03
But the second is commercial spent fuel.
56:05
And just so we're clear, it's it is fuel, but it's not spent by any means.
56:10
about 97% of the potential energy remains in a fuel rod after it's done and has to be taken out of a reactor.
56:18
So >> why why take it out if there's if 90% of the the rod's potential is still still there? >> Mhm.
56:26
So there are two main isotopes, one of which is getting split and and used up.
56:31
And so as that potential goes down, the ability to sustain a fishision reactant also goes down.
56:34
And then you accumulate byproduct material.
56:36
So it's everything from plutonium to some really really interesting stuff.
56:42
So uh but like before we get there, right, it's that let's talk about like that remainder. >> Mhm. >> Right.
56:52
So natural uranium is around.
56:52
9% of this really good isotope.
56:55
Most commercial fuel is 3 and a half to five.
57:00
>> When you're done with a rod, it's around. 7.
57:02
>> We have a 100,000 tons of this stuff.
57:05
And this is from the lifetime of the commercial fleet. Yeah.
57:08
>> Which by the way, that fits in one football field. >> Yeah.
57:12
>> Like 60 years of energy, >> but also should be repurposed in some way. >> Yeah. >> Yeah.
57:16
>> That there is four times more energy sitting on those pads outside of our reactors than Saudi Arabia has in its proven oil reserves.
57:24
>> That's actually crazy.
57:25
>> But that's not where the money is. >> Yeah. So where is the money?
57:27
Where are you spending your time?
57:28
How much time is in DC lobbying like leveraging the old stuff?
57:33
>> Wait, but but close close the loop here.
57:34
So there's there's all this sort of like latent energy potential.
57:36
I imagine you're doing something with it.
57:40
>> Um well, we got this program into motion and I just wanted to talk about it because I'm so excited about what's going on here and I think that we're on the cusp of of a real energy revolution in the United States.
57:51
But I think the to understand why we're shifting, right? Uh it's it's the weave.
57:58
Um >> there [laughter] you go.
58:02
the to understand why we're shifting here.
58:04
Um, you have to understand that what we really were successful at is getting people to see this thing that historically has been treated as a liability as a real asset, an asset where governors are going out and staking their name on joining this like Utah and Tennessee both made their applications public and they are awesome.
58:25
>> Well, that is a successful narrative shift.
58:28
So that's bottleneck one to AI deployment is energy.
58:33
>> So now we can back away from nuclear.
58:33
We actually think that's moving in the right direction. >> So what's next?
58:37
Well, >> right now data centers are facing a tremendous amount of issues in the United States. A lot of it is public.
58:47
>> Deeply popular with like 5% of the country, >> less >> on a good day.
58:52
But these things are really I mean they're critical.
58:56
These are critical national assets and so we want to help shift the narrative in order to make AI uh deployment in the United States possible and make sure that we are able to roll out and remain competitive with China.
59:09
I am dancing around what it is we exactly do and part of that is because we're we are in stealth as he was >> we'll come back on the show as soon as you're ready to come out of stealth. >> Yeah.
59:19
Thank you guys so much for having me on. Pleasure.
59:21
Great to meet Enjoy the enjoy the conference. Great to meet you.
59:25
>> I will tell everyone about Shopify.
59:27
Shopify is the commerce platform that grows with your business and lets you sell in seconds online, in store, on mobile, on social, on marketplaces, and now with AI agents.
59:33
Uh let's go through the rest of the news, get you up to speed before Lisa Sue joins in about 25 minutes.
59:41
Uh Google has the muscle to overpower spending worries, says the Wall Street Journal.
59:46
uh uh Google has put up uh some very uh impressive goo uh cloud growth numbers.
59:53
There of course was some back and forth on um on the nature of that revenue uh with semi analysis sort of jokingly maybe posting Google cloud generates revenue primarily from the sale of TPU systems.
1:00:06
from the sale of TPU systems. The actual line that did change in the SEC filing that's drawing attention is that uh there's a line in there that says Google Cloud generates product revenues primarily from the sale of TPU systems which is a little confusing because you
1:00:23
think of a company's product as their business but of course they have services as well and so the it's not that Google cloud is fully pivoting to TPU sales exclusively it is that uh they >> it's becoming meaningful >> yes yes yes they They've sold them to a number of labs and they're seeing lots of process. >> And that was that was rewind a year.
1:00:42
>> And that was that was rewind a year.
1:00:44
That was still a question. >> Yes. Yes.
1:00:45
Um we can go through uh there's a number of different uh posts here.
1:00:52
We can take you through uh Eric Suffort talking back and forth about uh souvenirators uh putting a a little bit of truth zone on uh Google ads growth. What's going on there?
1:01:04
They saw a lot of uh growth in monetized LLM queries.
1:01:07
First, I'm going to tell you about the New York Stock Exchange.
1:01:10
Want to change the world?
1:01:11
Raise capital at the New York Stock Exchange. >> Just do it.
1:01:15
>> Um, so it's a bit of a longer post, but uh it all started with Sundar Pachai's news.
1:01:20
He said Q2 was an amazing quarter with our AI investments redefining what's possible across every part of our business.
1:01:26
Alphabet revenue grew 24% year-over-year and Google Cloud accelerated to 82% growth, which is insane.
1:01:32
And that needs the fog horn for sure.
1:01:34
Uh we saw a exciting momentum across the board from search to YouTube to the Gemini app.
1:01:38
Uh our model APIs are processing 22 billion tokens a minute.
1:01:44
Uh and at this point I'm like the numbers have gotten so big on tokens per minute, tokens per month that it's very hard to keep uh keep in sync with what's actually uh impressive until you actually see a full chart.
1:01:56
Um but that's driven by their workhorse flash model which makes sense.
1:01:59
They've been focused on very efficient token generation because they uh vend them into so many Google systems for effectively free because they're still, you know, at monetizing via advertising.
1:02:08
So, uh Max Anderson said, "As someone who has personally spent $500,000 a month plus on Google ads for years, I can tell you with certainty, [laughter] >> this revenue growth in search is artificial and extremely unhealthy for Google's business long term."
1:02:24
Little bit of fear, uncertainty, doubt.
1:02:27
Search volumes are declining as legacy search is it being increasingly cannibalized by non-monetized LLM queries.
1:02:32
Google's response manufacture revenue growth via short-sighted highly extractive customer hostile tactics i. e.
1:02:38
charge advertisers more for lower quality clicks including clicks they do not want and explicitly do not approve Google to charge them for.
1:02:47
Uh he gives a few examples.
1:02:47
He's going back and forth saying that uh this is uh you know shortterm optimization.
1:02:55
But the souvenirator comes in.
1:02:55
He says, "Unfortunately, every claim made in this viral thread is either false or mix characterizes Google's policy.
1:03:00
One, search volumes are declining as legacy search is being increasingly cannibalized by non-monetized LLM queries.
1:03:08
Uh, Google stated in its Q2 earnings that search usage hit an all-time high during the World Cup this year.
1:03:13
It also stated in Q1 earnings that search queries reached an all-time high that quarter.
1:03:17
Ads and AI overviews monetize in parody with those in legacy search and Google's search revenue increased by 19%.
1:03:22
uh and uh in Q1 and 17% in Q2.
1:03:26
So a little deceleration but still growing.
1:03:29
Um he also debunks the idea that Google silently deprecated the second price auction.
1:03:35
Google still uses generalized second price auction for search.
1:03:39
He also debunks the idea that Google previously had precise keyword targeting settings that allowed advertisers to pick individual search phrases to bid on.
1:03:47
uh there's a different thing but suenator goes back and forth and uh and adds some more context to the Google earnings that people are digging into and we can dig into more later this week.
1:03:56
Uh let me tell you about Railway.
1:04:00
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1:04:03
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1:04:07
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1:04:11
Over in the Wall Street Journal, there's a couple articles.
1:04:13
Uh, Alphabet rides cloud business revenue jumps 24%.
1:04:16
But data center outlays push cash flow into the red.
1:04:20
I think it was the first quarter for negative cash flow, but this has been uh telegraph for a long time.
1:04:26
They're investing in the AI boom, not a surprise.
1:04:29
And there's been talks from all the hyperscalers around debt issuances, equity issuances, all different things to fund the AI buildout.
1:04:35
So, Google parent company Alphabet posted 24% revenue growth year-over-year in the second quarter, fueled by its booming cloud business.
1:04:42
But concern over the company's heavy spending on AI infrastructure damped investors enthusiasm.
1:04:47
Alphabet sales came in at 119.
1:04:50
8 billion exceeding analyst expectations.
1:04:54
Its cloud business brought in 24.
1:04:54
8 billion and its search business generated 63 billion.
1:04:58
Uh net income was 112 billion uh for the April June period also ahead of analyst expectations.
1:05:06
So they beat uh primarily driven by its gains in other companies, stocks that Alphabet owns.
1:05:13
They own I think almost a hundred billion dollars of SpaceX.
1:05:15
I'm not sure about the current valuation, but um in other AI news in the Wall Street Journal.
1:05:22
Uh two big uh AI infrastructure stories.
1:05:26
You have Lisa Sue herself right here.
1:05:29
Uh AMD anthropic sign AI gear deal.
1:05:32
Uh we talked to AJ a little bit about that.
1:05:36
an OpenAI boost cloud spend to 750 billion.
1:05:38
New projects and Georgia data center uh will be part of efforts to increase capacity.
1:05:44
OpenAI is scaling up its data center ambitions and its budget for spending on them.
1:05:48
The artificial intelligence company has raised its projected spending uh on computing power to around 750 billion through 2030 up from a projection of roughly 600 billion.
1:06:01
roughly 600 billion. a huge number obviously uh a lot of discussion about that but given the most recent leg up based on coding agents uh a lot of people are looking at that positively is a sign of continued uh faith in the progress and uh the scaling law of course because we are in the scaling era >> serious momentum
1:06:20
>> yeah the scaling uh the the increase reflects new agreements with cloud computing providers as open AI races to lock up the enormous amounts of computing capacity it needs to develop and run its AI models openai spend on cloud computing has become a central focus of chief executive Sam Alman's leadership team and has been a source of tension between him. Um the company said
1:06:38
Um the company said Wednesday would invest 20 billion in uh to kick off a data center called Project Camila in Effingham County, Georgia. So that's new new news.
1:06:49
Also, Nvidia has a supplier that's >> You ever been to Georgia?
1:06:53
>> Yes, I've been to Atlanta. It's a great town. >> Got to visit. >> You've never been? >> Maybe on a layover. It's like LA.
1:06:59
>> I don't think I've been outside.
1:07:00
>> It's like Los Angeles.
1:07:00
You're in the car for half an hour driving in traffic to get everywhere everywhere.
1:07:05
>> But it's uh it's a really nice city.
1:07:07
>> Uh Leang, when Fong over at Deepseek had an investor [clears throat] call, I I don't have context on how this >> uh actually came up because they're >> they're a hedge fund and they have Deepseek still a private.
1:07:22
>> They're raising outside capital now.
1:07:23
>> They're raising outside capital. Yeah.
1:07:25
>> So this was probably part of that road show.
1:07:27
I think at a certain point he needs to raise enough money that you got to just get on Zoom and uh sell the dream.
1:07:33
>> But he was grinding for 4 hours just monologuing for four.
1:07:35
I don't know if he's monologuing for all four, but it is it is sort of a long call.
1:07:38
Uh but >> that's bullish though.
1:07:41
>> It is >> because if you can go and basically do a Joe Rogan podcast episode [laughter] just monologuing.
1:07:46
>> Uh no, it's just like can somebody actually carry a conversation for that long?
1:07:51
Because after you know this has always been your take on Joe Rogan.
1:07:53
It's like it gets interesting on Joe Rogan because by the the a after an hour people go off script. Yeah.
1:08:01
>> And so if a CEO can carry a conversation for four hours straight Yeah.
1:08:05
>> and still be compelling, it's uh it's bullish.
1:08:07
But uh there are some details that Zephr uh shared. He's over at Catrini.
1:08:12
Uh their inference margins are around 85% strong.
1:08:17
They only had around 20,000 hopper equivalents until May.
1:08:19
um still sort of I can see how they would say that.
1:08:27
>> That doesn't mean that they don't have access to a lot more power globally. Yeah.
1:08:31
Some of which may or may not be above board, but who knows?
1:08:34
Uh they have 1 billion in API revenue is enough to turn the company cash flow positive. >> Yeah.
1:08:40
>> And they have a GPU payback period of 10 months and their hardware uh is uh depreciated over 3 to 5 years.
1:08:45
So looking uh looking quite good over the pond.
1:08:51
He also shared a lot of philosophy that felt sort of derivative of western AI lab leaders thought.
1:08:58
I mean he was focused on uh you know some of the same language around we don't want to moni we don't want to optimize for pure profit.
1:09:07
We're on the path to AGI first.
1:09:07
It's something that you've heard from a lot of lab leaders.
1:09:11
Uh he did say that he's very focused and he wouldn't be doing world models or image generation or video generation or even a chat app.
1:09:17
He said he doesn't want to be the next Alibaba is purely focused on the path to AGI sort of taking this focused approach and so we'll see.
1:09:27
Uh they've they've sort of been out of the game with Moonshot sort of coming from behind with K3 and a lot of hype there and then GLM of course as well.
1:09:37
Uh so it'll be it'll be interesting to see uh what he has up his sleeve uh on the back of this uh investor call that everyone's paying attention to.
1:09:46
>> Before we move on, let me tell you about console.
1:09:48
You see it on the laptops.
1:09:51
Console builds AI agents that automates 70% of IT, HR, and finance support, giving employees instant resolution for access requests and password resets. >> Gong moment.
1:10:01
Cognition is acquiring interaction. The makers of Poke. >> Huge.
1:10:07
>> Uh I think Poke has been >> an acquisition target or been talked about as an acquisition target for a long time now.
1:10:14
they've just made been able to deliver delightful novel products.
1:10:20
Uh I wouldn't have expected them to go to cognition.
1:10:22
I think a lot of people were thinking that they'd get picked up by a big uh consumer company.
1:10:28
Maybe a you know an Apple.
1:10:30
>> Were people saying Apple?
1:10:31
>> An Apple would have been amazing.
1:10:31
Having the Poke team working on Siri would be would be quite cool.
1:10:34
Uh could have seen them go into OpenAI or really any, you know, even even an an Amazon, right?
1:10:38
Uh so a little bit unexpected here.
1:10:41
Uh but not surprised that Scott saw potential in the team and I'm very interested to see uh how these businesses end up actually merging.
1:10:51
Um will they keep Poke around?
1:10:55
Will it be um will it be just sort of folded into Cognition?
1:10:58
But uh either way uh very excited to see what they built. >> Yeah.
1:11:03
Let me tell you about Figma agents meet the canvas.
1:11:06
Your AI agents can now create and modify your Figma files with design system context.
1:11:10
Um, there's an older article in the Wall Street Journal that we never got a chance to read through, but it's fun because it tells a story of a young person who basically started literally listened to a podcast and started a search fund and made I guess millions of dollars. So, >> did the meme. >> Yeah. Did the meme.
1:11:30
Uh, this shortcut to private equity riches is minting young millionaires.
1:11:34
And I saw Sal Khan from Khan Academy talking about something sort of adjacent to this uh because Khan Academy of course sort of open-source education.
1:11:45
You can basically learn everything that you learn in college from Khan Academy just watching videos um and doing their their online open coursework.
1:11:54
I I believe it's nonprofit.
1:11:56
>> It's still crazy that that Khan Academy exists and and plenty of people still turn it down. >> Yes. Oh, they do. They do.
1:12:02
Um, yeah, this person didn't though.
1:12:06
Basically, uh, go-getters are backing to buy are backing to buy up HVAC outlet outfits and specialized manufacturers.
1:12:15
If they make it back alive, there's a lot of money to be made.
1:12:19
What a quote from the Wall Street Journal.
1:12:21
So, uh, in 2015, Bakari Akil was a homeless college dropout with a single focus, figuring out how to get rich.
1:12:31
Sleeping in wei works >> on the subway in an airport waiting areas.
1:12:37
Ail watched videos, listened to podcasts, let's give it up for some podcasts, and poured over personal finance books.
1:12:44
He needed to own a business, he decided.
1:12:46
But how to do it with how do you do it with no money?
1:12:49
Then Akil read a Harvard Business School case study that described how MBA graduates were running around the country trying to buy companies with money from something called a search fund.
1:12:58
He felt like he had discovered a secret.
1:13:01
In the decades since Akil, now 37, has bought two multi-million dollar companies.
1:13:06
He has spent each month of the past three years living in a different country.
1:13:09
So he's doing the the nomad HVAC companies.
1:13:13
>> I guess we'll get into it.
1:13:13
Uh he's now worth seven figures.
1:13:15
Akil is part of a growing wave of people looking for a shortcut to private equity riches.
1:13:19
Armed with grit and determination, they are ditching the well-worn path from spreadsheet wielding associate to managing director and finding ways to buy HVAC and plumbing outfits, specialized manufacturers, and other small and mid-size businesses.
1:13:31
Financing from for deals financing such deals is far cry from traditional private equity where firms use funds they raise from institutional investors and the ultra wealthy plus debt.
1:13:43
Instead, these wouldbe business owners scrape together financing on a deal-by-deal basis.
1:13:47
They use loans from the U US Small Business Administration, seek funds raised from specialized investors, or cobble together money from friends, family offices, private equity funds, and SBA licensed small business investment companies.
1:14:00
And that's just the start.
1:14:02
Convincing the owner to sell and executing an improvement plan is a daunting task, even for the most experienced dealmakers.
1:14:06
While these buyers typically >> you're overseas constantly.
1:14:12
Yeah, I mean I imagine that he's overseas now, but during the heyday he was like actually going and you know knocking on doors.
1:14:20
But uh the reason I brought up Salcon was that he was uh advocating for instead of college buy your child a business or or take a group of of peers your ch your child's age and they can buy a business together.
1:14:39
Because if you look at the cost of college, >> yeah, >> you know, $200,000, $250,000 sometimes.
1:14:46
If you get four students and they're effectively the executive team and you say, "Look, we're we have a million dollars in cash.
1:14:53
>> We're going to lever something up.
1:14:53
So, we're going to of of net income." >> Yeah.
1:14:58
>> And we're going to lever it up. >> Yeah.
1:14:59
And the cash flow is going to pay your salary.
1:15:00
So, you're going to have a job. Yeah.
1:15:03
>> From 18 to 22 when you graduate.
1:15:03
and your job is just to learn everything on the fly.
1:15:09
And best case, you wind up with a fantastic business that makes a bunch of money, but worst case, you're no worse off than the money that you would have spent on college.
1:15:17
And so, you get to learn all these skills on the on the fly. I don't know.
1:15:21
I don't know if it'll take off.
1:15:23
It feels like alpha school adjacent.
1:15:25
Uh, >> well, when the time comes, >> yeah, >> maybe we can get all the kids and say, >> I mean, it's hard to predict what the world would be like in 15 years or 20 years if you're just having kids now, but uh it doesn't seem that crazy to imagine, but I think I I I think the aura of uh of of certain schools will endure and that will be the right path for some people.
1:15:46
Uh anyway, uh I view these guys as home the homesteaders of the Wild West, said Albertch, who previously represented a big private equity firm in a law firm, Gibson and Dunn.
1:15:56
Uh they're heading out without a dollar to their name, and if they make it back alive, there's a lot of money to be made.
1:16:02
Many caught the bug at a top business school where entrepreneurship through acquisition courses are now a standard part of the curriculum.
1:16:08
They are drawn by the promise of a more flexible schedule, a conviction in their own operational knowhow and desire to avoid grunt work that ends up lining the pockets of others, fueling also fueling their rise, a perception that it isn't as easy as it once was to make big money on Wall Street. It makes sense.
1:16:25
A lot of the big private equity firms were founded decades ago, have founders that have and partnerships that have accured a lot of the value and and it's much harder to just scale up something new.
1:16:36
Uh, the private equity industry is struggling to profitably unload companies, causing mid-level employees dubious of their chances of receiving pay tied to deal performance to jump ship.
1:16:44
Some 77 search funds, money from specialized investors to back in individuals looking to buy businesses were raised in 2025.
1:16:51
77 search funds, new search funds listed by the uh according to an annual study conducted by researchers at Stanford in 2025.
1:16:58
That feels low based on how big the meme was all throughout 2025.
1:17:03
Maybe >> there's no there's no way that that's accurate. >> Yeah.
1:17:08
Maybe there's some that are that aren't being caught in like the in the in the definition or they they they have defined their company as a as a true private equity firm instead of a search fund.
1:17:18
So they said, "No, we're not a search fund even though they might actually be acting as one."
1:17:23
>> But that is historically high.
1:17:23
Although down from the 2023 peak in of 101, there was 101 search funds founded in 2023.
1:17:33
Meanwhile, the number of firms doing deals as independent sponsors without a fund has roughly doubled to,400.
1:17:36
So, there's a whole bunch of different uh stuff going on.
1:17:41
Um there there's other stories in here.
1:17:44
Caroline Sabat bought a Boston area plumbing business in 2025 and merged it with a smaller one her and her husband had started.
1:17:51
Uh they got they got joint degrees from Harvard's business and government schools after nearly 10 years in the US Navy.
1:17:58
He was a former Navy Seal and he met launched Minuteman Plumbing, Heating and Cooling with a master plumber.
1:18:03
He met through a Harvard mentor between graduation and starting his job at a consulting firm.
1:18:09
Uh they took over running the business and grew it.
1:18:12
All of a sudden, I'm working 15our days on this plumbing business.
1:18:15
Caroline, now 35, who managed a crew of nine as a flight naval officer.
1:18:19
It was going well, but not well enough that you can light your highpaying consulting gig on fire.
1:18:23
Caroline decided to buy a business to kickstart growth.
1:18:25
She found one that they could finance with an SBA loan.
1:18:29
These typically come with a lower interest rate than a bank loan.
1:18:30
And because they are but governmentbacked, uh they did have to pay personally guarantee the loan.
1:18:34
Carolina also had to take out a life insurance policy.
1:18:36
She she bought the business for 1.
1:18:38
8 times uh IBIDA for a tiny fraction of what most uh private equity firms pay.
1:18:44
So not not too bad to get very subscale. >> Yeah.
1:18:49
Otherwise, >> working for someone younger than me and having to live life one PowerPoint slide at a time was torture, he said, and he quit his consulting job.
1:18:57
Uh anyway, uh there's another interesting Google news.
1:19:04
Uh Google did a they released this massive massive study.
1:19:07
I think it's like a hundred pages um about the impact of AI and they have very a very very unique data set obviously because Google is vended like that AI is vended into Google search and all the different tools and the API and uh they say AI is helping workers not replace them.
1:19:24
We've been hearing glimmers of this and people sort of saying they're backing off of what they were saying before like the massive job displacement.
1:19:32
Maybe it's not coming right now.
1:19:35
>> If you use your own eyes and ears instead of reading uh >> like thought pieces or sci-fi or something. Yeah. Oh yeah, that too. Yeah.
1:19:43
You know, >> it's all over here.
1:19:46
>> Then you know >> Oh, what? Okay, cool.
1:19:47
Um we will be joined by Lisa Sue in a little bit.
1:19:50
Uh but for now we'll go back to the Google study that says AI is helping workers not replacing them.
1:19:57
So uh one of the looming worries about artificial intelligence is that it will automate away jobs.
1:20:02
This uh worry certainly has been looming.
1:20:05
Uh a report from researchers at Google released Thursday says that so far the technology is mainly being used as an aid to workers.
1:20:13
It's a yes and technology.
1:20:13
Uh it is finding uh it is a finding that if tr if holds true as AI continues to develop could point to a future where the demand for highly skilled workers is accentuated rather than diminished.
1:20:24
Just because you're using AI doesn't mean it's going to automate your job.
1:20:28
Said Google economist Scott Strand, one of the researchers, the stakes are high for Google parent alphabet.
1:20:33
Google operates Gemini, one of the most popular AI platforms.
1:20:37
Public backlash to AI is intensifying with stark fears about job loss and anger over the buildout of data centers across the country.
1:20:43
Some large corporations that have laid off employees. This was your point.
1:20:46
Uh in the past year have said that they were able to downsize because of AI.
1:20:50
Yet the US unemployment rate overall remains relatively low.
1:20:54
and oh okay um and uh the uh and economists remain split on whether the technology will ultimately eliminate jobs or simply make workers more productive.
1:21:09
We can continue chatting about this in a minute but let me tell you about Crowd Strike.
1:21:12
Your business's AI their business is securing it.
1:21:16
CrowdStrike secures AI and stops breaches.
1:21:20
Um we are going to be adjusting our audio setup.
1:21:22
I think we will be joined in just a few minutes.
1:21:27
Um, are you are you ditching your Are you going full uh are you going full uh full bandana for the interview?
1:21:33
Is that what you're thinking? I don't know.
1:21:35
Uh anyway, um I think >> I got to get the backstory on the bandanas.
1:21:41
You don't typically think of AMD and this sort of western themed bandana, but now I do. >> I mean, I don't know.
1:21:49
John, did you know >> cowboys?
1:21:53
>> That we landed on the moon before we put wheels on luggage? >> That's impossible.
1:22:00
>> That's what they're saying, John.
1:22:00
We put a man on the moon before anyone put wheels on suitcases. >> How is that possible?
1:22:05
No one thought to put wheels on. This is aim to lab.
1:22:10
>> People were just like >> that 1970 was the first time that we thought, why don't we put >> It would be great to put those things from the car on my luggage.
1:22:16
If only we could miniaturize the technology.
1:22:21
>> If it can be done, we should do it.
1:22:23
>> Yeah, we >> But if no one's done it before, it might be impossible.
1:22:26
>> It's sort of a moonshot project.
1:22:26
That is that is actually a crazy crazy statistic.
1:22:30
I mean, that's like the what?
1:22:32
Sharks are older than trees. Isn't that a thing?
1:22:35
>> I think sharks have existed in the ocean since before trees or something.
1:22:37
That's one of those like uh like brain teaser things.
1:22:42
Um anyway, uh what else is in the timeline?
1:22:45
I'm trying to pull up this uh AI study because there were some interesting details in here, but uh the researchers characterize the use of AI as shallow in most occupations.
1:22:52
Workers are only using AI for a relatively small slice of the tasks they do.
1:22:57
Uh we were talking to Travis Kalanick about this yesterday.
1:23:01
Uh truck driver, what's the job?
1:23:03
Is it to drive the vehicle or is it also to protect the vehicle?
1:23:05
Is it also to do small repairs on the vehicle?
1:23:07
Uh handle logistics with the vehicle.
1:23:11
uh driving is just one of the tasks.
1:23:14
And so even if you have a uh a level four, level five self-driving system, you might still have someone in the vehicle for a long time.
1:23:23
And that's like the most automatable uh task that people have been working on for 20 years now and it's still not quite there.
1:23:29
Um the other uh the other interesting you were asking a about uh what is the next leg up?
1:23:37
what is the next thing that causes another order of magnitude boom in uh in token consumption basically or like revenue or whatever metric you want to uh measure the AI boom through.
1:23:47
Um and there are there are just like diffusion elements right where uh you know the number of people that are using coding agents is still small and and you could just see diffusion that way but uh there is >> yeah I think there's some data house US households that pay for AI products is still >> well below two digits somewhere around 5%.
1:24:13
Yeah, I I I do think there that uh >> part of part of that is like there's just a lot of households that >> are not going to pay for AI at least directly.
1:24:23
>> There there is just the uh like going [snorts] back to the meter chart uh which has showed the amount of time that an AI agent can work on its own.
1:24:31
Um, you know, we're in this like reliable agent paradigm, but I think people might be underounting what, you know, a willingness to run a prompt that will run for a week and give you a result.
1:24:47
Like, we're not quite there.
1:24:47
Or maybe it needs to be more communicative.
1:24:52
Maybe the system needs to get back and forth with you.
1:24:54
But people just aren't uh like it's it's it's we're still very very early adopter to uh the type of person that fires something off.
1:25:01
Most people at least want to check in with the system in uh in 20 minutes in 1 hour increments.
1:25:09
So, >> well, you know who's optimistic about AI, John? >> Who?
1:25:14
>> Friend of the show, Mark Zuckerberg. >> Oh, yeah.
1:25:17
>> Mark has launched a new campaign that is focused on AI optimism. Mhm.
1:25:23
>> Uh, in Axios Medio, Mark Zuckerberg on Thursday laid out an optimistic view of the agentic future, arguing the company's focus on connecting the world will only be strengthened by new AI tools and technologies.
1:25:36
Axio says why it matters.
1:25:36
His position is framed as a stark contrast to some of Meta AI's competitors who, according to a video ad posted with Zuckerberg's comments, promote fear and a dystopian vision of the future.
1:25:50
>> I thought it was a cool video. I liked it.
1:25:52
It's interesting to imagine watching it not as a fan of social media [laughter] though because it's leaning a lot on like we've connected people, we bring people together and I certainly see it that way when I log on to Meta Platforms.
1:26:06
I'm sending you funny reels, we're having a blast, but a lot of people see it as brain rot, right?
1:26:10
And they don't and and they're like, "Oh, if you're going to do what you just did to the next thing, I'm not certain. I'm not fully on board."
1:26:17
So, I don't know how it'll be received.
1:26:19
Yeah, I don't know how production value was fantastic.
1:26:20
It's definitely the correct angle. The opposite is worse.
1:26:24
Like clearly like you want to be optimistic and there's a lot of I'm really glad that he's not like mometically going down the fearbased path. >> Oh god. Yes.
1:26:32
[laughter] That would be very very rough.
1:26:35
>> Working for some players.
1:26:38
[snorts] >> Um anyway, so we'll see how it lands. >> Mhm.
1:26:42
>> Um what else is going on?
1:26:42
Paramount is moving into micro dramas.
1:26:47
This is the moment you've been waiting for.
1:26:50
You love >> dramatic movies. >> I don't know.
1:26:55
>> Can I interest you in some micro dramas?
1:26:57
Have you tried any of the >> real shorts? >> Yeah. Yeah. Yeah. Yeah.
1:27:01
I watch a true crime one and I I felt like >> I was actually fine with the AI video and voice over element of it.
1:27:11
What I felt was missing was when you're when you're tracking a true crime story and you turn on a podcast about that true crime, you know, whatever it was.
1:27:20
Uh, I like being uh led through a meandering idea maze of what is interesting to that host.
1:27:30
So maybe they find the the town and the details about the town where something happened particularly interesting and so they're just putting in random facts that flesh out the story in a particular way.
1:27:43
I felt like the one that I watched at least was very much like the Wikipedia level summary or like the headlines just condensed and it was too generic and it wasn't giving me enough uh novelty and like new facts.
1:27:53
So I even though like the video wasn't quite there. It was obviously AI.
1:27:59
It wasn't it wasn't super visually striking.
1:28:02
The audio was sort of you know jilted.
1:28:05
Uh that all would have been fine if it had been someone who actually went and found some really deep interesting novel way to tell the story or novel details that otherwise you wouldn't have heard of from the headlines. So I don't know.
1:28:19
Still early but I don't think they're going to get me.
1:28:21
There's something about going to the theater. It's an experience.
1:28:23
It's like it's the anti-brand.
1:28:24
You put your were to turn the screen sideways. >> They've done that. They've done that.
1:28:30
There's there somebody in Brooklyn, I think, put together like a whole like we're going to watch Instagram reels together or like Tik Toks.
1:28:36
It was like short form film festival or something. >> Yeah.
1:28:40
I mean, at some point you have to do a premiere.
1:28:42
Kareem from Subway Takes was talking about that.
1:28:43
He was like, >> you know, I've had this show. It's a massive hit. It's breakthrough.
1:28:46
Like where's my premiere?
1:28:47
And like maybe there should be a premiere for so for the next season of Subway Takes whenever that happens.
1:28:53
I mean, it's also just the hard thing with uh with social media shows is that there isn't really a a season.
1:28:59
It's just sort of always on.
1:28:59
We sort of fake this with What are [laughter] you laughing at?
1:29:04
>> Something I'm not going to read.
1:29:04
Tyler, who's back in the studio, said, "I've been watching this on real short and I'm just not going to read the title because [laughter] >> Oh, no.
1:29:12
You can imagine it gets pretty clickbaity in there."
1:29:15
You can definitely imagine that.
1:29:17
Uh >> yeah, this doesn't look uh this looks like if Apple knew that this was on there, they might uh they might ask them to take it down >> potentially. Potentially.
1:29:27
Yeah, I do wonder uh where the line will be drawn.
1:29:33
There's a whole new there's a whole new uh class of problems that uh Apple will have to grapple with.
1:29:39
I thought this hilarious uh I thought this uh this screenshot from Var epsilon was very funny where uh it's just I couldn't solve the re reman hypothesis.
1:29:48
I couldn't solve the hodge conjecture.
1:29:50
I couldn't solve P versus NP.
1:29:52
I couldn't solve Navier strokes.
1:29:54
This is just uh uh every really really challenging millennium prize or or unsolvable math problem.
1:30:01
We've been working on our own conjectures.
1:30:02
We're we're creating because all the Yeah.
1:30:05
All the conjectures are solved.
1:30:06
Well, I mean, we actually we built it out. I think it was 2009.
1:30:11
>> Yeah, I remember back.
1:30:11
But we've been letting it sit because we just didn't have the technology.
1:30:15
We didn't have the capability. >> Yeah.
1:30:18
>> We needed AI to to advance and I think we're going to be close to cracking it.
1:30:23
Um, but we should release the conjecture and let everybody kind of take their own shot.
1:30:27
>> Speaking of conjecture, there's a lot of conjecture about what OpenAI is releasing today.
1:30:30
Tibo said, "Unbelievably excited for what's coming together.
1:30:34
Tomorrow is feeling codeexy."
1:30:36
Uh people are speculating that it might be a a cerebrous a faster version of soul turbo mode.
1:30:41
It is okay >> voice is now in the desktop app.
1:30:44
Control your computer and direct multiple agents running GPT work or codecs using just your voice.
1:30:50
>> It's powered by GPT live.
1:30:50
So it can speak listen and coordinate work in the app at the same time.
1:30:56
>> And then also health is rolling out to users everywhere. health in chat GPT.
1:31:01
You can securely connect Apple Health and supported medical records to understand your information.
1:31:07
>> Oo, got to get it on there for sure.
1:31:10
>> Um, there's also uh uh a new law introduced, a bipartisan AI kill switch bill following the OpenAI cyber incident.
1:31:18
The bill enters Congress as lawmakers remain divided on how aggressively the federal government should regulate artificial intelligence.
1:31:26
I'm sure this will be a a longer conversation on a future show.
1:31:28
Um, >> uh, Sheil Monot is sharing some interesting nuggets in an information article on Stripe, >> which we will be getting to.
1:31:42
>> Let me tell you about Cisco >> next episode.
1:31:45
>> Critical infrastructure for the AI era.
1:31:47
Unlock seamless real-time experiences and new value with Cisco.
1:31:49
And we have Lisa Sue joining us in just a minute.
1:31:54
We're about to bring her on to the show.
1:31:57
Um, and thank you for tuning in.
1:31:57
Thank you for everyone who makes the show possible.
1:32:02
>> This is the moment we've all been waiting for. >> It is. It is.
1:32:04
Um, and thank you to Ramp for making it possible. Time is money. Say both.
1:32:10
>> Welcome to the show, Lisa.
1:32:10
Thank you so much for coming on.
1:32:12
We appreciate you being here. Such a busy day. Thank you so much.
1:32:16
Uh, please grab a seat if you wouldn't mind putting that headset on.
1:32:22
We will hear you loud and clear.
1:32:22
Uh >> how is today going?
1:32:28
>> It is a fantastic day. >> Yes.
1:32:30
What have been the highlights?
1:32:32
>> Well, you know, it's wonderful to see just everyone, you know, all of our community here.
1:32:36
So, customers, partners, um you know, certainly a lot of developers here and >> you know, we're seeing a lot of excitement.
1:32:44
So, >> take us back to 2014. How is 2026 different?
1:32:48
[laughter] just a little bit different >> not just in terms of the AI boom but uh as a leader uh emotionally is is this a more stressful time because everything's happening so fast or is this just a more exciting time like what is your life like as CEO of AMD right now?
1:33:06
Well, I have to say um when I think about this entire arc of 2014 until now, >> the thing that has been perhaps most interesting to me, it's like the world's like a different place.
1:33:18
Like >> every year, like every two years, you see the tech trends are different.
1:33:22
You see, you the customer, you know, sort of needs are different.
1:33:26
You see the marketplace changing.
1:33:28
And that's what just kept it really exciting for me because it's one of those things where like you're never going to get bored.
1:33:34
>> Um it is certainly not less stressful.
1:33:36
So I can tell [laughter] you that um I think what's different about AI right now >> I think the rate and pace of change is actually much much faster.
1:33:44
I mean semiconductors has always been a fast-paced environment but the rate and pace of change of uh the adoption curve of AI and then how the technology is changing and then frankly how the ecosystem is changing has uh has us constantly on the hey we got to go faster. Yeah.
1:34:01
I mean that's the the primary thing is we have you know we're in a business where you make decisions you know three to five years in advance on your technology road mapaps. >> Yeah.
1:34:10
>> And you realize man we can even go faster and and that's what the market uh wants.
1:34:16
>> What is >> and I think there's so much more external pressure in a way that the computing industry and the technology industry in general >> just hasn't had.
1:34:26
>> Well I view it as actually a really good thing.
1:34:27
I mean people ask me that from time to time and you know the reason I view it as a good thing is look >> you know we're in a place where everybody needs compute.
1:34:35
Yeah, >> everybody wants compute.
1:34:37
You know, every country wants compute.
1:34:39
Every hyperscaler wants compute.
1:34:42
Um, you know, all of us are using it.
1:34:44
And so, uh, it is not, you know, we're the the components under covers.
1:34:48
We're actually, you know, helping drive, uh, you know, sort of how technology is being consumed.
1:34:54
And so, I think that's the difference, right?
1:34:56
It's it's much more front and center, you know, versus just something that only techies know about. >> Yeah.
1:35:02
>> Yeah. uh culturally how is what is the state of AMD's culture because in there's one view where uh you're at the center of the AI boom it's the best place to work and then at the same time things are changing you have to move faster there's a little bit of anxiety
1:35:17
across every employee base around what my job will look like in a decade how are things changing for the employees of AMD >> well I think I'd take a step back and say hey um why do I wake up every day um what are people at AMD really excited about and we're really excited about
1:35:36
putting great tech out there and I'm not kidding you know our our mantra like our number number one mantra is you know build great products yeah and it's about how do we keep pushing the envelope on that so you have a day like today or you know this week which has been a
1:35:50
culmination of years and years of work you know you think about launching Helios you think about launching Venice you think about bringing together um the entire ecosystem and you're like It's days like today that we that that that we're working so hard for. And I think
1:36:04
And I think the culture is one of >> um we want to be um really the best in the industry.
1:36:11
I mean our goal is to drive the future of technology and you know we do it the AMD way.
1:36:15
And what does the AMD way mean?
1:36:17
Um we do it in partnership.
1:36:20
We do it in collaboration.
1:36:22
We believe in open ecosystems.
1:36:22
Uh we believe in uh using the best technology for each workload.
1:36:28
You know, so this idea that, you know, one company's going to have the killer chip. >> Yeah.
1:36:35
>> I don't think that's the world we're in today, right? The world we're in.
1:36:37
>> Is that always the the AMD way?
1:36:37
We we had uh Travis Kalanick on the show yesterday and he started off saying, you know, the Uber way and then he stopped himself and he said, well, the Travis way and he he laid out sort of his approach.
1:36:50
Was that something was this approach something that you feel like you the the company had in 2014 or or or was that or these sort of pillars things that you felt like were important going forward and you've added added some over time?
1:37:06
Well, I would say what has been the foundation of AMD, you know, back to, you know, Jerry Sand Sanders, our founder, has always been about pushing the envelope and, you know, in some sense, um, you know, being risktakers, you know, wanting to have the best road maps out there.
1:37:22
Uh, that has always been the foundation of AMD.
1:37:24
the foundation of AMD. Um I think the last 10 plus years you know hopefully what um you know I've brought to it what Mark Papermaster our CTO has brought what the leadership has brought is a sense of not only are we going to put the best technology out there but we're going to be predictable we're going to
1:37:40
be great partners uh and we're going to do it in a way that um you know brings together the ecosystem and you know I have to say everything about tech like I was so happy to see you know to have you know Tom Brown of Anthropic with us you know Santo Meta Satchin of OpenAI, you know, you know, Jeremy at AT&T, uh, G2 was here. And what you kind of get from
1:38:02
And what you kind of get from all of that is, you know, partnership is not just a word.
1:38:06
Like partnership is kind of our foundation.
1:38:09
And I'm a big believer in OnePlus 1 is greater than three.
1:38:12
So, you know, obviously we have to have great tech. >> Yeah.
1:38:16
>> But we also have to be able to, you know, kind of kind of see the future through the broad lens.
1:38:20
And the way you do that is having, you know, great partners um along the way.
1:38:25
So that's very much, I think, the foundation of today's AMD, you know, culture. >> Yeah.
1:38:31
>> What were what were some of the highlights of uh of Tom's talk earlier?
1:38:35
He was talking about how AI is actually speeding up their ability to adopt new hardware and bring new systems online.
1:38:42
But what were the highlights for you?
1:38:44
>> Well, I'm super excited uh to be able to talk about it.
1:38:46
You know, we've been I I kid you not, we have um very much wanted anthropic on AMD for a long time and we just had to find the right intersection point, the right intersection point of our technology uh with you know where they are.
1:38:59
I mean you know Claude has been incredibly incredibly successful and probably the highlight for me was um I think he told that story on stage about the MI355 and that's a very true story.
1:39:10
It's a very true story >> and you've been busy today but that's what the internet's talking about. >> Is that right? Okay.
1:39:14
I I haven't seen that, but I I can tell you a few months ago, um my guys were like, "Hey, uh we think Anthropic is is on, you know, 355s and and they're doing work on it."
1:39:24
And I'm like, "Okay, well, you know, make sure you know what they're doing.
1:39:26
Make sure we're helping them."
1:39:29
>> And they're like, "Lisa, they don't really want our help.
1:39:31
They don't really need our help."
1:39:32
They're like, "They're able to do it with Claude."
1:39:33
And I'm like, "Wow, >> that's incredible."
1:39:37
So he I think he told you the um uh the story, but it's it's just a lot about how the the ecosystem has evolved over time, right?
1:39:46
Um I'm a big big believer in AI's incredible force multiplier.
1:39:50
It's true across every industry, but it's especially true across our world, >> which is, you know, putting, you know, great technology out there.
1:39:59
uh if we can reduce our time to market, you know, sort of the the time it takes from us to start an idea to when we actually uh finish and ship a produ product, if we can shave three months off of that or six months off of that, that has tremendous value for our customers.
1:40:14
Like you heard, you know, Satchin say like more compute faster. Yeah.
1:40:20
>> Um that is a frequent conversation I have. More compute faster. Yeah.
1:40:24
>> And these are really complicated systems.
1:40:26
I I will absolutely say they're really complicated systems and we need to make sure that uh we are you know able to you know put it all together and that's where AI can be tremendously helpful on both hardware and software. >> Yeah.
1:40:39
You mentioned uh not focusing on just like the one single chip.
1:40:42
AMD is a lineage in CPU, GPU, FPGA, many other systems.
1:40:48
uh what are the advantages of having that breadth of product uh scope and are there any uh disadvantages to that?
1:40:59
Well, I you know I think the the interesting thing is you know every so often you hear like this is the killer chip, >> right?
1:41:07
I mean you guys have heard like GPU >> see that on the model side too.
1:41:11
>> GPUs are going to take over the world.
1:41:13
Like >> everything's going to be >> CPUs are dead.
1:41:14
Like we're going to move every single workload >> over.
1:41:18
>> And the world just doesn't work like that.
1:41:20
And so our thought process has always been, you know, people asked me very early on, hey Lisa, why don't you just focus like you decide like whether it's CPUs or GPUs, why do you need both?
1:41:32
>> And um, you know, I I think about each one of these questions deeply and you know, we fundamentally believe that there is no one-sizefits-all.
1:41:39
Like the world is a heterogeneous world.
1:41:41
Like you are different than I am. We have different needs.
1:41:46
We have different workloads.
1:41:47
Our businesses are different.
1:41:50
you're going to need different compute and I think the portfolio that we have you know CPUs GPUs we acquired XYlink so we brought the physical >> um AI component in there we have a rich you know PC you know ecosystem I think allows you to truly pick the right compute >> now it's a little bit harder right we have um a tremendous uh number of R&D priorities uh but I think we've done it
1:42:16
in a very you know kind of I want to say smart way in the sense that each part of our uh product portfolio builds on each other like we talk about um you know triplets being a big thing that we do um we do that across a portfolio so we do that across our CPU portfolio our GPU portfolio um and it it just is an example of how we're able to leverage getting the right compute for the right workload. >> Yeah. uh talk about uh the other slice >> Yeah.
1:42:42
uh talk about uh the other slice of the business, the other segmentation between uh consumer, proumer, enterprise.
1:42:51
Uh how valuable, how important is it to uh have a clear chain of products in particular with uh talent?
1:43:01
It feels like uh a lot of folks will start on a consumer rig and they might build some small model or they might be doing CGI work or something else and they're using a thread ripper to render something locally and then eventually they move to the cloud and eventually they start working in an enterprise.
1:43:18
Uh how how do how is that going to evolve?
1:43:21
I I think um it is extremely important like you said because >> people access technology in different ways. Yeah.
1:43:28
You know not everyone is going to a you know big cloud instance to experience AI.
1:43:34
Uh and so you know the fact that we do have a consumer road map a proumer road map are uh you know Ryzen our radon um you know frankly we're putting um a lot more emphasis on you know some of these um other ways to access you know AI.
1:43:50
So, uh, we made a big investment in AI PCs.
1:43:53
I still believe that local AI is going to be, uh, one of, you know, the key enablers, um, to truly get, uh, all of the tokens that you need in different places.
1:44:03
So, our Ryzen AI max, uh, portfolio, um, I think physical AI is another place where, um, you know, we just launched these um, you know, small uh, robotics form factors so that people can can experiment with it.
1:44:16
So I I I do think it's um quite important >> to have um let's call it low barrier of entry like I want people to experience AMD >> and have a wonderful experience and hey they may end up you know being in you know the largest frontier model companies or you know frankly the amount of startups that are doing just amazing technology uh is is fantastic and and we want to support those as well. How do you >> on that?
1:44:44
How do you think about longshot R&D?
1:44:47
You have your existing roadmap, but we talk to new chip companies very frequently on the show.
1:44:53
It feels like every week there's somebody new that has a billion dollars and they've >> they're on to something and they're and uh I can't imagine you haven't thought of it or at least the AMD team hasn't at least thought of it.
1:45:03
And so how do you think about uh how do you think about some of these investments in R&D that that um are maybe like higher risk but uh uh high potential over the long run?
1:45:15
>> Well, we are in a world where u there is a long arc on R&D like I really do like to say you know judge us on what we're doing today.
1:45:25
The ideas were you know really thought of three to five years ago. >> Yeah.
1:45:30
>> Yeah. So chiplets networking optimization CPU and GPU uh putting that together in Helios I mean that's really unfolded over the last you know three or four years and today you know we're thinking about you know what's you know
1:45:44
beyond um uh the roadmap and I think both are very important so we do have a tremendous number of new ideas u no question you know very active research team um our team is working on uh not just MI500 today MI600 lots and lots of ideas. We're spending
1:46:00
We're spending time with our top customers talking about, hey, tell me where the models are going.
1:46:06
What are you thinking the workloads will need so that we can put that um flexibility and capability into uh into our roadmap.
1:46:13
Um but I want to give credit where credit is due.
1:46:15
Uh there there are a growing number of startups.
1:46:20
It it used to be that people only did software startups >> because hardware was too hard. >> Yeah.
1:46:26
>> It took too [clears throat] long. >> Yeah. And now it's easy. So, everyone's doing it.
1:46:28
[laughter] >> It It is not easy. >> I'm joking.
1:46:33
>> It is not easy, but it's appreciated.
1:46:35
That's what I want to say. It's appreciated. >> Yeah.
1:46:38
>> And and [clears throat] part and part of this like software singularity that it that it feels like in some ways we're already in.
1:46:44
>> VCs are just they feel like more confident underwriting risk. >> Yeah.
1:46:49
>> Uh on the hardware side. >> Yeah.
1:46:52
I don't I don't know that I would feel more or less, but I would definitely say that there's been a change.
1:46:56
you know, people didn't necessarily have the patience for hardware before. Yeah.
1:47:01
>> Because it's a long arc on hardware.
1:47:01
And now I think there's an appreciation that fundamentally, you know, hardware, you know, silicon software systems when you optimize together, you're going to get >> a uh significantly better result.
1:47:12
So my view is lots of good ideas out there.
1:47:14
Um I think one of the things I pride myself on within AMD is uh we are quite quite open um to new ideas.
1:47:23
you know, we've gotten to the place where um I'm really happy with the acquisitions that we've done.
1:47:29
We we've actually brought in some incredibly talented individuals who have had a huge huge >> uh impact on our AI roadmap.
1:47:35
You know, certainly I think um a lot of people know a nouch out there.
1:47:38
So, one of our >> uh one of our acquisitions who is doing incredible job trying to make sure that um everyone who wants to use Rockom is is going to get our support.
1:47:48
Uh but a number you know our Pensando acquisition, our XYlink acquisition, our ZT acquisition, all of these were to kind of bring the components in. Yeah.
1:47:57
>> That help us uh build the full system solution.
1:48:00
>> Well, we know you're busy. Thank you so much. We have a gong.
1:48:02
We'd love you to hit for you to hit it >> to uh on this >> commemorate day. Do I get to do this?
1:48:08
>> Please, as hard as you want. >> Boom. There we go. Right here. >> Thank you so much.
1:48:13
And speaking of hardware companies, >> consumer hardware, >> they're hard, we have an AMD powered chromatic from Mod Retro, the M64.
1:48:21
Uh, you want to sign right here next to AMD? >> Perfect. >> There we go.
1:48:27
>> Is this one of yours or is this >> uh It's Dylan's over there. >> Dylan. All right.
1:48:32
We're We're good friends of the team over there.
1:48:34
We love uh >> love just consumer hardware and all these things.
1:48:37
So, thank you for powering it and for coming on the show and congratulations on everything. Thanks. We'll talk to you soon. Have a great day.
1:48:45
Um, and that's our show, folks.
1:48:47
Thank you so much for tuning in to TBPN live from AMD.
1:48:50
Uh, we are traveling tomorrow.
1:48:53
We'll be back on Monday, 11:00 a. m. Pacific.
1:48:57
>> Thank you to the team here who flew up and thank you to everyone at AMD for making this possible.
1:49:01
We really appreciate >> dialing in these IRL shows.
1:49:05
>> Yeah, we're getting there. Uh, one step at a time.
1:49:08
>> The team makes it the team makes it look easy.
1:49:10
>> Leave us five stars in Apple Podcast and Spotify.
1:49:11
Sign up for our newsletter at tbpn.
1:49:13
com and we will see you on Monday 11 a. m. sharp for another one. >> Goodbye. >> Cheers.