Chipstrat

Semis, infra, optics/networking, physical AI, and more from @austinsemis. If you like Stratechery and SemiAnalysis, you're in the right place

[Chipstrat @chipstrat]

Advanced Packaging: Intel's EMIB vs TSMC's CoWoS
https://t.co/mgOLRmCYPS

[Semi Doped @semidoped]

🎙️ NEW EPISODE: Intel's Earnings Disaster: Bad Luck, Bad Planning, or Both?

Intel had a strong quarter. Its outlook told a different story.

@vikramskr and @austinsemis unpack Intel’s Q3 2026 results and the gap between demand for its chips and its ability to deliver them. They cover the AI memory squeeze hitting PCs, the substrate and chiplet bottlenecks limiting server CPUs, and the fallout from the Fab 28 accident. Then they debate what Intel could have done differently and whether its long-term foundry story still holds.

Presented by @CrusoeAI: Fine-tune and serve open models without managing a single GPU. When we sat down with Crusoe’s product team, Austin asked: what will you do with my data? Their answer: They don’t touch or see it. Your training data is encrypted so even Crusoe’s own admins can’t see it, and the fine-tuned model is yours. Deploy on Crusoe in one click, or download and run it anywhere. Upload a dataset, pick an open model, and pay per token rather than per GPU hour. Run a few versions, compare them, and download the one that works best. Accounts are free, and get $5 in credit when you sign up. Try Serverless Fine-Tuning: https://t.co/aanT3Zx8G1

This episode is supported by @meter. You have the GPUs and the power. Now who builds the network, and who do you call when it breaks? Meter designs, deploys, and runs frontend, storage, and management networks on its own switches and its own network OS. One call reaches the team that designed the hardware and wrote the software. https://t.co/3fw4WImJAl

Chapters:
0:00 Intel's 35% Stock Drop
1:22 The Pre-Earnings Bull Case
4:03 Reason 1: Weak PC Market
12:25 The Flawed Q2 Pivot Plan
17:17 The IDM Model's Failure
20:23 Reason 2: Substrate Bottleneck
23:18 Chiplet Design's Fragility
25:18 Reason 3: The Fab 28 Explosion
40:46 The GPU Intelligence Gap
45:51 The Foundry Thesis Question
50:22 Packaging as a Hope Story
51:19 Four Lessons from the Meltdown

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Vik's Paid Substack: https://t.co/yLJKcE3WBM
Austin's Paid Substack: https://t.co/bTnS0lvmpm

@austinsemis @vikramskr

[Chipstrat @chipstrat]

AMD reported Q3 FY26 on November 3. I thought it was a great call, so I wanted to share some of my thoughts:

https://t.co/fEZCulN5EM

[Chipstrat @chipstrat]

When Agents Are the Interface

Agent economics, who pays for inference, vertical integration, and what it means for Google, Amazon, Microsoft, Meta, OpenAI, Anthropic, Apple and semis

https://t.co/FIQpHvkPGT

[Semi Doped @semidoped]

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! 🙏

https://t.co/khcMStn6jd

[Chipstrat @chipstrat]

Great deep dive into EMIB with $INTC

[Semi Doped @semidoped]

🎙️ NEW EPISODE: How Intel Took EMIB From Science Fiction to High Volume: Intel Foundry's Ravi Mahajan

Ravi Mahajan, a distinguished engineer at Intel Foundry, joins Austin to detail EMIB's 20-year journey from a "science fiction" concept to a high-volume manufacturing reality.

- Organic package wire escape density at the die edge improved only about 10% per generation, so EMIB puts fine-pitch silicon in small patches at the die edges and leaves power to the substrate's thick metal

- Production required five technologies to converge, including a custom 4-layer silicon process and dies half the thickness of an A4 sheet of paper

- HBM was the first driver. Solve the highest-performance memory interconnect and the technology can "waterfall down" to everything else

- The advantage over CoWoS is manufacturing scale. Small bridges replace a full-sized silicon interposer and its high cost and poor wafer utilization on large, multi-reticle AI packages

- EMIB-T was in the original patent, using TSVs to fix the "torturous" power path around the bridge
- EMIB is an Intel Foundry exclusive, and the roadmap includes top-side optics

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: https://t.co/aanT3ZwAQt

Chapters:
0:00 Intro: Ravi Mahajan
1:44 Pathfinding & The HIR Roadmap
7:58 The Two Drivers of EMIB
15:15 Decoupling I/O and Power
16:27 The 'Science Fiction' of EMIB
20:36 HBM as the Killer App
25:06 EMIB vs. CoWoS
31:44 Chiplets, UCIe, and Packaging Tiers
34:08 The EMIB Portfolio: T, M, Active
37:01 An Intel Foundry Exclusive
41:21 AI's Role in Packaging
43:26 Future: Top-Side Optics

Get more of Austin and Vik daily, free!
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Connect with Vik and Austin:
Vik's Paid Substack: https://t.co/yLJKcE3oMe
Austin's Paid Substack: https://t.co/bTnS0luOzO

@austinsemis @vikramskr @Intel_Foundry

[Chipstrat @chipstrat]

New article: Right-Sizing Memory
My take on Micron's webinar, from despeccing to who captures value in custom HBM
https://t.co/6kIU1l2e09

[Chipstrat @chipstrat]

https://t.co/t6lH8LBiiu

[Chipstrat @chipstrat]

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

https://t.co/jwaB6d7GTM

[Semi Doped @semidoped]

🎙️ 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: https://t.co/aanT3ZwAQt

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): https://t.co/hnzQDoPdNc
- A MicroLED Field Trip to Avicena (Vik's Newsletter, paid): https://t.co/Lk5VlPYjzN
- MicroLEDs vs. Lasers: The Linewidth Tradeoff (Vik's Newsletter, free): https://t.co/HWXZaPyifL
- Understanding Energy Bands in Semiconductors (Vik's Newsletter, free): https://t.co/cr7STSFmUi

Get more of Austin and Vik daily, free!
Sign up: https://t.co/7trwA2esP9

Connect with Vik and Austin:
Vik's Paid Substack: https://t.co/yLJKcE3oMe
Austin's Paid Substack: https://t.co/bTnS0luOzO

@austinsemis @vikramskr

[Chipstrat @chipstrat]

Dell's Tailwinds.
Why I drive my agents run from a Dell XPS, and fine-tuned models for Enterprise AI
https://t.co/y5x5XsnMnT

[Semi Doped @semidoped]

🎙️ 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: https://t.co/3uvRnFWq2d

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

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Vik's Paid Substack: https://t.co/yLJKcE3oMe
Austin's Paid Substack: https://t.co/bTnS0luOzO

@austinsemis @vikramskr @Synopsys

[Chipstrat @chipstrat]

@MoneyCrptBunny @gabyllorenzi 🙌🏻

[Austin Lyons @austinsemis]

Fun chat! Check it out!

[Chipstrat @chipstrat]

Is EDA dead? My takeaways from @Synopsys Investor Day and the GPT-Synopsys deal with @OpenAI.

https://t.co/RH2SiGJRXj

[Austin Lyons @austinsemis]

Aw yeah! @semidoped is now presented by @CrusoeAI 🚀

Love the pod? Support us! Sign up for Crusoe's managed inference at https://t.co/pfgcJKCxgM and get $5 in free credits. You can even fine-tune your own model with just a few clicks!

[Chipstrat @chipstrat]

What does Jev from @typesafeai imply for the AI infra buildout? After all, there's no decode needed...

https://t.co/LVfOx1cAKz

[Semi Doped @semidoped]

🎙️ 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: https://t.co/BXzvDtajcg

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!
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Connect with Vik and Austin:
Vik's Paid Substack: https://t.co/yLJKcE3oMe
Austin's Paid Substack: https://t.co/bTnS0luOzO

@austinsemis @vikramskr

[Chipstrat @chipstrat]

Product innovation from @meta, @bot, and Instinct show a path for normie adoption of agentic AI.

And that's great for the AI infrastructure supply chain.

https://t.co/fSLQZusGwJ

[Chipstrat @chipstrat]

Why do most semiconductor markets have ~3 main companies?

https://t.co/zyZr8SvN7B

[Austin Lyons @austinsemis]

AI diffusion needs open competitive frontier models