Chipstrat

If you enjoy our pod, please check out our sponsor @CrusoeAI using the link below. Sign up quick or log in with Google and get $5 in credit! When you claim that $5 in credit, this show keeps going! 🙏 semidoped.com/crusoe-x

🎙️ NEW EPISODE: MicroVCSELs vs MicroLEDs, a MicroExplainer Austin and Vik host an explainer on the two competing microemitter technologies for AI interconnects, microLEDs and microVCSELs. They break down Credo's surprising pivot at OCP from its microLED acquisition to a microVCSEL product, and the physics that give microVCSELs better speed and reach. - The new battleground is the 5-50 meter "reach gap," too long for copper limited to ~7 meters and too short for expensive, supply-constrained InP optics - MicroLEDs are limited to ~3-5 Gbps over 5 meters because their broad light spectrum causes chromatic dispersion that smears the signal - Hitting 1 Tbps with microLEDs takes ~300 slow lanes, and coupling hundreds of fibers adds a lot of manufacturing complexity - MicroVCSELs can hit 50 Gbps+ over 50 meters. Stimulated emission gives a narrow beam with less dispersion, for 10x the speed and reach - MicroVCSELs use a mature Gallium Arsenide (GaAs) process on 8-inch wafers, shared with cell phones, for a cost and scale advantage over InP - MicroLEDs' niche is heat. Gallium Nitride (GaN) is highly temperature-insensitive, a candidate for short-reach optics inside a hot server tray This episode is presented by @CrusoeAI. Crusoe's Serverless Fine-Tuning trains an open model like Llama, DeepSeek, or GLM on your own data, and you pay per million tokens, not per GPU hour. About $2 buys roughly 5M training tokens on smaller models. Get $5 in credit to start: semidoped.com/crusoe-x Chapters: 0:00 The Credo Pivot 3:49 The Talk of OCP 8:13 The 5-50 Meter Reach Gap 12:52 How MicroLEDs Work 21:10 MicroLEDs' Parallelism Problem 22:55 How MicroVCSELs Work 26:57 MicroVCSEL Performance Specs 31:51 The Physics of VCSELs 33:35 The Market Chooses MicroVCSELs 35:45 MicroLEDs' Niche: Heat 38:16 Teasing Future Interviews Mentioned in this episode: - PicoJool’s Al Yuen: The Case for GaAs VCSELs in Scale-Up Interconnects (Semi Doped episode): daily.semidoped.com/p/picojo… - A MicroLED Field Trip to Avicena (Vik's Newsletter, paid): viksnewsletter.com/p/a-micro… - MicroLEDs vs. Lasers: The Linewidth Tradeoff (Vik's Newsletter, free): viksnewsletter.com/p/microle… - Understanding Energy Bands in Semiconductors (Vik's Newsletter, free): viksnewsletter.com/p/energy-… Get more of Austin and Vik daily, free! Sign up: daily.semidoped.com/ Connect with Vik and Austin: Vik's Paid Substack: viksnewsletter.com Austin's Paid Substack: chipstrat.com @austinsemis @vikramskr

🎙️ NEW EPISODE: Synopsys Will Take a Royalty on Amazon's Chips and Sell GPT-Synopsys: Ravi Subramanian Ravi joins Austin to discuss why the company is moving to a royalty-based model for its custom "application optimized IP", starting with a billion-dollar deal with Amazon. Ravi also unpacks the OpenAI partnership and AI "agent engineers", and explains why physics-based simulation remains the ground truth. Insights: - Because 1-2 applications drive most of a hyperscaler's revenue, companies like Amazon now want custom "application optimized IP" instead of standards-based IP - The custom model has three parts. There is a license fee, a "customization fee" to prioritize scarce engineers, and for the first time a royalty on the final chip - The royalty model mirrors the Arm-Amazon partnership from a decade ago and fits 12-month design cycles. It also expands Synopsys's role to system-level bring-up - Customers can "hire" an AI agent engineer with a specific skillset, which fits existing budgets and team structures - GPT-Synopsys with OpenAI bundles a specialized LLM, the compute, and forward-deployed engineers - A CFET needs 200x more device equations than a planar FET, so physics-based simulators stay the ground truth This episode is presented by @CrusoeAI. Try Crusoe's managed inference for open models with $5 in free credits: semidoped.com/crusoe Chapters: 0:00 Intro: The Amazon & OpenAI Deals 2:39 Application Optimized IP 6:04 The New Royalty Business Model 12:14 How Standard IP Differentiates 18:20 The Arm-Amazon Analogy 22:00 Synopsys's Role in Test Chips 26:06 The Three Stages of Chip Design 31:27 The 'Agent Engineer' AI Model 36:20 The GPT-Synopsys Partnership 39:27 In AI We Believe, In Physics We Trust Get more of Austin and Vik daily, free! Sign up: daily.semidoped.com/ Connect with Vik and Austin: Vik's Paid Substack: viksnewsletter.com Austin's Paid Substack: chipstrat.com @austinsemis @vikramskr @Synopsys

🎙️ NEW EPISODE: Why calls to "pace" AI are a red herring for GPU demand. AI safety work doesn't slow the hardware buildout. The real governor on AI is data center construction pushback. Today's sponsor is G2i — expert training data, evals, and RL environments for AI labs: fandf.co/4wZvKHS Chapters: 0:00 Introduction: Pacing AI 1:36 Recapping Dario Amodei's Essay 7:42 Hot Take: Anthropic vs. OpenAI 11:34 The John Deere Playbook 15:22 The Problem with Interpretability 17:53 Pulling the Plug 19:03 The Real Bottleneck: Data Centers 22:03 The Geopolitical Blind Spot 23:41 Counter-narratives: Meta & Open Source 27:26 The Investment Thesis: Pacing Increases Compute 32:29 The Financial System Analogy 36:05 AI Safety as the New Cybersecurity Get more of Austin and Vik daily, free! Sign up: daily.semidoped.com/ Connect with Vik and Austin: Vik's Paid Substack: viksnewsletter.com Austin's Paid Substack: chipstrat.com @austinsemis @vikramskr