Dylan Patel runs Semianalysis, the leading publication and research firm on AI hardware: https://www.semianalysis.com/. Jon Y runs @Asianometry, the world’s best YouTube channel on semiconductors and business history. 𝐄𝐏𝐈𝐒𝐎𝐃𝐄 𝐋𝐈𝐍𝐊𝐒 * Transcript: https://www.dwarkeshpatel.com/p/dylan-jon * Apple Podcasts: https://podcasts.apple.com/us/podcast/dylan-patel-jon-asianometry-how-the-semiconductor/id1516093381?i=1000671564456 * Spotify: https://open.spotify.com/episode/6q1XODE2L5bqqBwe7434S7?si=seXQ6K_LQZeAV6776H6MhQ * Me on Twitter: https://twitter.com/dwarkesh_sp 𝐒𝐏𝐎𝐍𝐒𝐎𝐑𝐒 * Jane Street is looking to hire their next generation of leaders. Their deep learning team is looking for FPGA programmers, CUDA programmers, and ML researchers. To learn more about their full time roles, internship, tech podcast, and upcoming Kaggle competition, go here: https://jane-st.co/dwarkesh * Stripe builds financial infrastructure for the internet. Millions of companies from Anthropic to Amazon use Stripe to accept payments, automate financial processes and grow their revenue. Learn more here: https://stripe.com/ If you’re interested in advertising on the podcast: https://www.dwarkeshpatel.com/p/advertise 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 00:00:00 – Xi’s path to AGI 00:05:05 – Liang Mong Song 00:09:10 – How semiconductors get better 00:12:01 – China can centralize compute 00:19:35 – Export controls & sanctions 00:33:36 – Huawei’s intense culture 00:39:36 – Why the semiconductor industry is so stratified 00:41:43 – N2 should not exist 00:46:38 – Taiwan invasion hypothetical 00:50:06 – Mind-boggling complexity of semiconductors 00:59:58 – Chip architecture design 01:05:21 – Architectures lead to different AI models? China vs. US 01:10:57 – Being head of compute at an AI lab 01:17:09 – Scaling costs and power demand 01:37:50 – Are we financing an AI bubble? 01:51:05 – Starting Asianometry and SemiAnalysis 02:06:55 – Opportunities in the semiconductor stack
Nvidia’s $5 billion investment in Intel is one of the biggest surprises in semiconductors in years. Two longtime rivals are now teaming up, and the ripple effects could reshape AI, cloud, and the global chip race. To make sense of it all, Erik Torenberg is joined by Dylan Patel, chief analyst at SemiAnalysis, joins Sarah Wang, general partner at a16z, and Guido Appenzeller, a16z partner and former CTO of Intel’s Data Center and AI business unit. Together, they dig into what the deal means for Nvidia, Intel, AMD, ARM, and Huawei; the state of US-China tech bans; Nvidia’s moat and Jensen Huang’s leadership; and the future of GPUs, mega data centers, and AI infrastructure. Timecodes: 0:00 Introduction 0:29 Nvidia and Intel: Unlikely Allies 2:11 Investment and Capital in Semiconductors 4:27 The Impact on AMD and ARM 5:21 China’s AI Chip Race: Huawei’s Rise 14:01 The HBM Bottleneck and Manufacturing 19:00 Nvidia’s Global Competition: The Huawei Threat 22:32 Jensen’s Next Move: Nvidia’s Strategy 29:44 Nvidia’s Moat: How They Built It 36:15 How Jensen Has Changed Over the Years 39:40 Jensen Huang’s Leadership and Company Culture 46:37 The Future of Nvidia: Cash, Data Centers, and AI Infrastructure 56:11 The Hyperscalers: Amazon, Oracle, and the Cloud Wars 1:03:01 The Era of Mega Data Centers 1:07:40 Hardware Cycles: GB200, Blackwell, and the Next Generation 01:16:03 xAI’s Colossus 2 01:22:06 Recommendations to Start-Ups 1:34:49 The State of the GPU Market Today Resources: Find Dylan on X: https://x.com/dylan522p Find Sarah on X: https://x.com/sarahdingwang Find Guido on X: https://x.com/appenz Learn more about SemiAnalysis: https://semianalysis.com/dylan-patel/ Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://x.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711 Follow our host: https://x.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details, please see a16z.com/disclosures.
Dylan Patel, founder of SemiAnalysis, provides a deep dive into the 3 big bottlenecks to scaling AI compute: logic, memory, and power. And walks through the economics of labs, hyperscalers, foundries, and fab equipment manufacturers. Learned a ton about every single level of the stack. Enjoy! 𝐄𝐏𝐈𝐒𝐎𝐃𝐄 𝐋𝐈𝐍𝐊𝐒 * Transcript: https://www.dwarkesh.com/p/dylan-patel * Apple Podcasts: https://podcasts.apple.com/us/podcast/dylan-patel-deep-dive-on-the-3-big-bottlenecks-to/id1516093381?i=1000755126873 * Spotify: https://open.spotify.com/episode/5qiibwoBWY5rXyflK7WJzH?si=SX4ajSKXT-KeNtaHsiTNzw 𝐒𝐏𝐎𝐍𝐒𝐎𝐑𝐒 - Mercury has already saved me a bunch of time this tax season. Last year, I used Mercury to request W-9s from all the contractors I worked with. Then, when it came time to issue 1099s this year, I literally just clicked a button and Mercury sent them out. Learn more at https://mercury.com - Labelbox noticed that even when voice models appear to take interruptions in stride, their performance degrades. To figure out why, they built a new evaluation pipeline called EchoChain. EchoChain diagnoses voice models’ specific failure modes, letting you understand what your model needs to truly handle interruptions. Check it out at https://labelbox.com/dwarkesh - Jane Street is basically a research lab with a trading desk attached – and their infrastructure backs this up. They’ve got tens of thousands of GPUs, hundreds of thousands of CPU cores, and exabytes of storage. This is what it takes to find subtle signals hidden deep within noisy market data. If this sounds interesting, you can explore open positions at https://janestreet.com/dwarkesh To sponsor a future episode, visit https://dwarkesh.com/advertise. 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 00:00:00 – Why an H100 is worth more today than 3 years ago 00:24:52 – Nvidia secured TSMC allocation early; Google is getting squeezed 00:34:34 – ASML will be the #1 constraint for AI compute scaling by 2030 00:55:47 – Can't we just use TSMC's older fabs? 01:05:37 – When will China outscale the West in semis? 01:16:01 – The enormous incoming memory crunch 01:42:34 – Scaling power in the US will not be a problem 01:54:44 – Space GPUs aren't happening this decade 02:14:07 – Why aren't more hedge funds making the AGI trade? 02:18:30 – Will TSMC kick Apple out from N2? 02:24:16 – Robots and Taiwan risk
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
Patrick O'Shaughnessy sits down with Dylan Patel, founder of SemiAnalysis, to explore the explosive supply and demand dynamics of the AI revolution. Dylan shares how his firm's token spend skyrocketed to $7 million a year, completely transforming their productivity and highlighting a new era where execution is cheap, but high-quality ideas are at a premium. They dive into the implications of Anthropic’s frontier models like Opus 4.7 and "Mythos," the hidden bottlenecks in the semiconductor supply chain (including memory, TSMC, and CPUs), and the economic phenomenon of "phantom GDP." Finally, Dylan shares his bold prediction on the societal impact of rapid AI scaling, including why large-scale anti-AI protests might be just around the corner. Timestamps: 0:00 Intro 1:00 Surging AI Spend 10:27 Token Demand 16:21 When Ideas Are Cheap and Execution is Easy 20:46 Model Hoarding 22:34 Robotics 27:03 The Compute Bottleneck 30:26 The AI Permanent Underclass 31:39 Supply Chain Reality 37:47 CPUs 42:54 Predictions: Public Backlash #AI #Investing #Semiconductors #Anthropic #OpenAI #GPUs #TechTrends #FutureOfWork #MachineLearning #SemiAnalysis Presented by Ramp: https://ramp.com/invest Sponsored by Vanta, WorkOS, Rogo, and Ridgeline: https://www.vanta.com/invest https://workos.com/ https://rogo.ai/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
The battle for AI supremacy has ignited a fierce talent war, with companies like Meta offering salaries in the tens of millions for top researchers. In this clip, Dylan Patel explains the incredible economics behind these bidding wars. Discover why a single researcher who can make a process just 5% more efficient can save a company billions in compute costs, and how this makes even a $100 million salary a potential bargain. #AI #Semiconductors #OpenAI #NVIDIA #TechInvesting #ArtificialIntelligence #MachineLearning #TechStrategy #claude #podcast #talentwar #salary #jobs 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