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Inside the Trillion-Dollar AI Buildout | Dylan Patel Interview

Inside the Trillion-Dollar AI Buildout | Dylan Patel Interview

35 segments available

Dylan Patel, AI and semiconductor expert, joins Patrick O'Shaughnessy to dive deep into the past, present, and future of compute. They explore the massive capital requirements driving AI development, from OpenAI's $300B Oracle deal to NVIDIA's strategic partnerships. The conversation covers power infrastructure challenges, US-China competition in AI, talent wars reaching unprecedented levels, and why the current AI boom differs from previous tech bubbles. Dylan shares insights on which companies will capture value in the AI stack, from hardware makers like NVIDIA to application companies like Meta and Google, while examining the geopolitical implications of the global AI race. Timestamps: 00:00 Intro 00:39 The OpenAI and Nvidia Deal: The Infinite Money Glitch 03:04 OpenAI's Compute Challenge and Capital Requirements 04:42 Oracle's $300 Billion Bet on OpenAI 06:06 Nvidia's Strategic Investment and Deal Mechanics 06:53 Understanding the Demand Dynamics 07:41 Scaling Laws and Diminishing Returns Debate 09:17 Why Bigger Models Aren't Always Better 10:51 The Economics of Tokens and Serving Capacity 15:07 Rate Limits and the Adoption Curve Problem 19:01 The Tokenomics of AI 22:02 Inference Latency vs Cost Trade-offs 23:59 Over-Parameterization and Model Learning 28:10 Building Environments for AI Training 32:16 AI in Everyday Life 34:46 The Future of Reasoning and Compute Scaling 38:45 Memory and Context in AI: Short-term vs Long-term 44:34 The Spectrum of AI Optimism 46:29 Timeline to AGI 47:56 Physical Intelligence and Embodiment 49:25 Talent Wars 58:37 Power Dynamics in the AI Ecosystem 01:00:51 Microsoft and OpenAI: A Shifting Power Balance 01:03:23 Nvidia's Dominance and Balance Sheet Strategy 01:12:32 The Middle Layer 01:14:47 The Risk Spectrum 01:18:42 AI for Material Science and Hard Tech 01:22:21 Building Infrastructure 01:27:11 Grid Regulations and Backup Power Challenges 01:29:55 US vs China: Who Really Needs AI to Win? 01:37:42 Favorite AI Bears 01:43:57 Hardware Innovation Beyond Accelerators 01:48:08 Speed Round: Company Impressions 01:55:11 The Death of Traditional SaaS Business Models 02:00:24 The Kindest Thing #AI #Semiconductors #OpenAI #NVIDIA #ComputeInfrastructure #TechInvesting #ArtificialIntelligence #MachineLearning #DataCenters #TechStrategy #claude #podcast Presented by Ramp: https://ramp.com/business-cards?utm_s Sponsored by AlphaSense and Ridgeline: https://www.alpha-sense.com/invest/ https://www.ridgelineapps.com/ ****** Patrick O'Shaughnessy is the CEO of Positive Sum. All opinions expressed by Patrick and podcast guests are solely their own and do not reflect the opinion of Positive Sum. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of Positive Sum may maintain positions in the securities discussed in this podcast. To learn more, visit psum.vc

Segments Timeline

1
0:00 - 0:39
0:39 duration117 words

Intro

"If the models don't improve, we're absolutely screwed. And in fact, the US economy will go into a recession. >> It's about the highest stakes like capitalism game of all time. >> Godsend in terms of l..."

2
0:39 - 3:05
2:25 duration562 words

The OpenAI and Nvidia Deal: The Infinite Money Glitch

"I was going to lay out this idea of going through the past, present, and future of compute as like the big big idea for our conversation, but since it just happened, I don't think you've heard you tal..."

3
3:05 - 4:42
1:37 duration385 words

OpenAI's Compute Challenge and Capital Requirements

"slow to wake up and then you know they were slow to pivot their data center operations. They were slow to do everything and so there while they could have way more compute than anyone by a humongous d..."

4
4:42 - 6:06
1:24 duration342 words

Oracle's $300 Billion Bet on OpenAI

"at the end of the day. OpenAI is committing to fiveyear deals. These five-year deals cost X amount of money. It's 10 to 15 billion dollars per gigawatt of data center capacity that you pay a year. And..."

5
6:06 - 6:53
0:47 duration167 words

Nvidia's Strategic Investment and Deal Mechanics

"and more debt so this this game now now Nvidia's kind of got the same conundrum right it's like well Google and Amazon are doing these these deals whether it's uh to two other vendors for TPUs or for ..."

6
6:53 - 7:41
0:47 duration217 words

Understanding the Demand Dynamics

"from >> I want to dig into the underlying assumptions driving this on the training and inference side because obviously there's the willingness like Zuckerberg just needs to go down the hall to a CFO ..."

7
7:41 - 9:17
1:35 duration381 words

Scaling Laws and Diminishing Returns Debate

">> given it's a log log chart right uh scaling laws are right given there's no model architecture improvements you just throw more compute data model size at it it gets better at this pace >> but you'..."

8
9:17 - 10:53
1:36 duration395 words

Why Bigger Models Aren't Always Better

"right? Um, and so I'm I'm holding those sort of off to the side for now. But that that iterative like performance improvement in the model is is like I like I mentioned earlier, right? It's like a six..."

9
10:53 - 15:08
4:14 duration1044 words

The Economics of Tokens and Serving Capacity

"know already in it's the fastest revenue ramp we've ever seen for anything of this >> and it's basically all code related >> right I mean like you know some of it's their own cloud code product some o..."

10
15:08 - 19:01
3:53 duration846 words

Rate Limits and the Adoption Curve Problem

"model for thinking about that today? What most interests you in the in the growth of just broad? >> So so the thing I like to call it is tokconomics and I I stumbled upon the word actually it's like a..."

11
19:01 - 22:03
3:01 duration731 words

The Tokenomics of AI

"also like how can Can I get people to adopt it if I don't let people use it? And so, so open had this tremendous problem with GPD 40, right? 4 and then 4 turbo was smaller than 4 and 4 was smaller tha..."

12
22:03 - 23:59
1:56 duration494 words

Inference Latency vs Cost Trade-offs

">> Inference is like always it's it's it's a curve again, right? Like all of these things are curves and it's a trade-off, right? Everything in engineering is a trade-off. So So you have inference lat..."

13
23:59 - 28:11
4:12 duration1010 words

Over-Parameterization and Model Learning

">> Yeah. And as a result, we're just going to probably have to wait a little bit longer to see what the bigger models are in practice in a way that to to see what consumers actually do with them becau..."

14
28:11 - 32:16
4:05 duration1003 words

Building Environments for AI Training

"here's a medical case, what's wrong with it? And then you have another model say, well, here's here's your instructions on how you would grade the result of a case. What looks like they didn't even tr..."

15
32:16 - 34:47
2:30 duration613 words

AI in Everyday Life

"learners >> and and the so what of let's say we fast forwarded we're in the seventh inning of that or something like this. What do you think the way that the average person will most feel that differe..."

16
34:47 - 38:45
3:58 duration887 words

The Future of Reasoning and Compute Scaling

">> before asking even more holistically kind of your view on where we're going there there's a third category which is the reasoning part of the equation so we've got pre-training we've got RL and env..."

17
38:45 - 44:34
5:49 duration1344 words

Memory and Context in AI: Short-term vs Long-term

"smart. >> On the topic of like embodiment, uh, and continuing with the human analogy, how do you think about things like short and long-term memory in a human versus just like raw model capacity or so..."

18
44:34 - 46:30
1:55 duration490 words

The Spectrum of AI Optimism

"I add all of this up and you know hold the mirror up, it's it seems like I would put you in the category of like unbelievably bullish on what these things are going to be able to do in 10 years time o..."

19
46:30 - 47:56
1:26 duration365 words

Timeline to AGI

"cloud the world is how much more efficient? Hey, making making all these random applications and like automated reports and like stop using Excel as a database, but instead like you can make a real da..."

20
47:56 - 49:25
1:29 duration410 words

Physical Intelligence and Embodiment

"intelligence is doing? attacking the whatever you want to call it large movement model or large robot model or something. >> What are they actually doing today is like holy [ __ ] it's so simple in te..."

21
49:25 - 58:37
9:11 duration2187 words

Talent Wars

"we're squeezing down the fewer and fewer number of people that really matter that will have all the impact on where we go uh in terms of like net new research and that means that all this crazy spendi..."

22
58:37 - 1:00:51
2:14 duration539 words

Power Dynamics in the AI Ecosystem

"structural stuff like just the scale the industrial scale of some of these things which just takes forever to build or whatever. How do you think about um even smaller zoomed in examples like okay cur..."

23
1:00:51 - 1:03:24
2:32 duration608 words

Microsoft and OpenAI: A Shifting Power Balance

"Microsoft, the most crazy power dynamic that's going on in the world. um where they signed aou that said they had an understanding of like what the deal would actually be for them converting to for-pr..."

24
1:03:24 - 1:12:32
9:07 duration2308 words

Nvidia's Dominance and Balance Sheet Strategy

"one around Nvidia and the hyperscalers, right? Nvidia is the king. All of the gross profit is going to them today, right? Pretty much all of it. Sure, TSMC makes some, sure, SKH makes some, but they h..."

25
1:12:32 - 1:14:47
2:15 duration502 words

The Middle Layer

"companies in the middle? We've talked a lot about Nvidia and then like people at the end serving applications. What about these companies like together and base 10 and fireworks and you mentioned Nebi..."

26
1:14:47 - 1:18:43
3:55 duration948 words

The Risk Spectrum

"amazing margins when I sell to OpenAI, but they don't have a balance sheet. So how can I be sure that they're actually going to pay the thing that they've signed up to? Right? So, so you know, I know ..."

27
1:18:43 - 1:22:22
3:39 duration870 words

AI for Material Science and Hard Tech

"than the revenues go up, >> right? But the revenues still go up. That's sort of like the fundamental basis of semiconductors, of tech, everything, right? Are we doing things that we can't do before wi..."

28
1:22:22 - 1:27:12
4:50 duration1144 words

Building Infrastructure

"beginning and power. What are your thoughts on like what is going on here and how like humanity is responding to this crazy new demand for just raw power? The first approximation is that like we're be..."

29
1:27:12 - 1:29:55
2:42 duration702 words

Grid Regulations and Backup Power Challenges

"Daario and then you take a few steps and it's like it's like ML researchers, the average ML researcher, then it's like me and then it's like you in terms of how bullish we are on AI and the guy at the..."

30
1:29:55 - 1:37:42
7:47 duration1714 words

US vs China: Who Really Needs AI to Win?

"of this between the US and China so you know power semis models applications etc where do you think the most interesting differences are like what are the what are the story lines between us and China..."

31
1:37:42 - 1:43:58
6:15 duration1440 words

Favorite AI Bears

"and import doing the same thing they've done forever, which is like prepare at the base level and be behind at the customer side and the value the value happens close to the customer. But then like yo..."

32
1:43:58 - 1:48:09
4:11 duration938 words

Hardware Innovation Beyond Accelerators

"but it would take hell of a badass thing. But I think there's a lot of individual parts of the supply chain which are not spaceaged, right? Um, Nvidia space age, yes, it's the biggest value value owne..."

33
1:48:09 - 1:55:12
7:02 duration1647 words

Speed Round: Company Impressions

"you just give me like you know a sentence or two on like your impression of them just like just like how how you feel about them in this moment. Yeah, >> start with Open AI. >> Oh, yeah. Super awesome..."

34
1:55:12 - 2:00:24
5:12 duration1141 words

The Death of Traditional SaaS Business Models

"constantly get asked is like, okay, Dylan, you know, you're lucky your obsession is that you loved hardware and you like followed it and you followed the supply chain and and you built this business o..."

35
2:00:24 - 2:02:23
1:59 duration457 words

The Kindest Thing

">> Done for me? H I mean, it have to be my brother. Everything he's done in my life. Um I've been a [ __ ] my whole life and I still am a [ __ ] Um, and so like every time he like pulls me back on pat..."

Inside the Trillion-Dollar AI Buildout | Dylan Patel Interview — Dylan Patel | Searchlore