🎙️ NEW EPISODE: Copper vs Optics vs .... RF ?!! A novel third option is emerging between short-reach copper and power-hungry optics, the Active RF Cable (ARC), which sends high-frequency radio signals over a flexible plastic waveguide. Point2 CEO Sean Park joins Vik Sekar to explain. - ARC sends 100-200 GHz signals over a plastic waveguide for the 5-10 meter reach AI scale-up requires - 4x cable density over copper. One plastic core instead of a differential pair (2x), then two channels per core with frequency division multiplexing (another 2x) - A 1.6T ARC module draws <10 W (~5 pJ/bit) with no laser, DSP or TIA, and the roadmap targets <2 pJ/bit for co-packaged versions - No DSP means sub-nanosecond latency, 1,000x better than DSP-based optics, which matters for all-reduce - The plastic waveguide has theoretically unlimited bandwidth with no skin effect, so future speed gains come from silicon shrinks alone - 4.7 mm bend radius, tight enough to wrap around a finger and easier to route than fragile fiber 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 Intro: RF Over Plastic 3:06 The Third Option 8:27 Investor Validation 13:20 How E-Tube Works 15:16 The 4x Density Advantage 18:28 Inside the OSFP Module 29:51 4.7mm Bend Radius 34:20 Roadmap to Co-Packaging 36:09 The No-Gearbox Advantage 42:00 Biggest Challenges 49:57 The Physics of Scaling 51:00 Origin Story: Wallpaper TV Mentioned in this episode: - Plastic Cables for AI Clusters: Can Point2’s E-Tube Disrupt Copper and Optics? (Vik's Newsletter, paid): viksnewsletter.com/p/plastic… 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

Building semiconductor leadership requires both innovation and the manufacturing capacity to bring it to scale. In his latest piece, Naga Chandrasekaran highlights Intel Foundry’s growing U.S. footprint, including progress on our Ohio site. Read more: ms.spr.ly/6010avfyk #IntelFoundry

Optics Primer, Part 4: Coherent Optics How amplitude, phase, and polarization carry data hundreds of kilometers, why that costs more than the optics inside a data center, and where coherent-lite fits newsletter.chipstrat.com/p/o…

Micro-VCSELs vs micro-LEDs This was a hot topic at OCP 2026 @austinsemis and I sat down on @semidoped right after the show to discuss the future of short reach optics (even if it means I'm in a garage, because some things are too important to delay 😅) Enjoy the show! Lots of visuals in there, so make sure you watch on YouTube or right here on 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

The @AMD AI DevDay world tour kicks off next Thursday, Oct. 29, in Austin, Texas. Register for an event in a city near you! amd.cventevents.com/event/8c…

I’m extremely AI pilled and use AI in most facets of life with steady state usage always driving higher I say that so no one accuses me of being overly bearish but I think the “why you need to stay / keep using” factor is sorely missing for most AI tools for the every day user Most people don’t have the imagination / creativity / desire to want to pursue coding or even side projects. Daily assistant seems fine but they don’t / won’t offload all their day-to-day to them. It’s a neat parlor trick or treat for one off specific use case (subscription audit, book a trip, etc.) You’re stuck in this spot where the average person doesn’t know the power of agentic tool capabilities but at the same time the labs/AI companies can’t force people to do more projects with the tools. Need to create power users that come BACK to the products every day

🎙️ NEW EPISODE: @Microsoft Maia 300 with Saurabh Dighe Why Maia 300 makes memory bandwidth and capacity a first-order design principle. - Moving data to feed the compute engine can cost two orders of magnitude more energy than the compute itself - Maia 200 had six HBM3E stacks at 7 TB/s. Maia 300 has eight HBM4 stacks at 18 TB/s - Compute grows about 35% while rack-level memory bandwidth grows 5x - 7x token throughput per rack over Maia 200 at 2.5x perf per watt - Prefill and decode are disaggregated, and the KV cache moves over the scale-up fabric instead of the slower scale-out network - Memory interface chiplets "extend the shoreline" so the TSMC N2P compute die stays one efficient die 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 Intro: Saurabh Dighe 1:39 Microsoft's Silicon Philosophy 5:52 Workload Shift: Agentic AI 9:32 The Data Movement Problem 14:20 Crusoe Ad Read 17:47 Maia 300 Specs 24:07 Chiplets & HBM4 29:12 Prefill/Decode Disaggregation 30:33 Two-Tiered Network 35:45 NPO vs. CPO 36:46 Rack-Scale Power & Cooling 41:35 Agentic Kernel Optimization 49:08 What's Next: Maia 400 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