Semi Doped

Jensen Huang resigned as Nvidia CEO this morning, and Austin and Vik recorded two hours after the news broke. They argue that a board must prioritize corporate integrity over short-term shareholder value, even for a legendary founder. The discussion then turns to Nvidia's critical lack of a succession plan and the long-term risk of the company's unique, founder-led culture decaying into bureaucracy.
Presented by Crusoe: Fine-tune and serve open models without managing a single GPU. 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. Accounts are free, and get $5 in credit when you sign up. Try Serverless Fine-Tuning: https://semidoped.com/crusoe
Key Takeaways:
- A board's first duty is governance, not shareholder value. Even a founder CEO must be forced out for breaking company rules, because keeping them sends a message that lying and rule-breaking are acceptable.
- The hardest job demands the highest standard. The immense pressure of being Nvidia's CEO is the reason for strict conduct rules, not an excuse for leniency.
- Nvidia has no succession plan — Ian Buck is the only plausible internal CEO candidate. Unlike competitors, Nvidia's 'all Jensen, all the time' media strategy has left its leadership bench visibly thin.
- Jensen's unique management style is the biggest succession risk. His flat structure with ~60 direct reports is impossible for a successor to replicate, creating a vacuum that invites bureaucracy.
- The real long-term risk is Nvidia decaying into a spreadsheet-CEO culture. The fear is a slide into the managerial, process-driven leadership that hampered competitors like Intel.
- A leadership crisis at Nvidia is AMD's opportunity to attack. For a competitor like Lisa Su, Nvidia's moment of weakness is the time to 'put my foot on the gas,' not switch teams.
- Arm CEO Rene Haas is a credible external successor. As an Nvidia alumnus, he understands the culture but also brings proven CEO experience from a different silicon leader.
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://semidoped.com/meter
Chapters:
0:00 Introduction
0:23 The News Flash
4:12 Why the Board Had to Act
9:43 Shareholder Value vs. Integrity
14:05 The Pressures of the CEO Role
15:08 The Highest Standard
15:57 Short-Term Stability
19:08 Long-Term Risk: Bureaucracy
19:24 Founder vs. Professional CEO
22:38 Candidate: Lisa Su
26:50 Candidate: Rene Haas
28:14 Candidate: Ian Buck
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Intel just had its worst day on the stock market in 55 years, with shares falling nearly 30%. But here's the strange part: Intel actually reported a strong quarter. Revenue grew, its data center business surged, and the company beat expectations. So what went wrong?
In this episode of Semi Doped, Vik Sekar and Austin Lyons dig into Intel's Q3 2026 earnings and the problems behind its disappointing outlook.
Three issues are at the center of Intel's troubles: a weakening PC market driven by rising memory prices, a shortage of advanced packaging substrates that limits server CPU production, and a tragic factory accident in Israel that disrupted chiplet supply.
But how much of this was bad luck, and how much was poor planning?
We debate whether Intel should have anticipated the surge in demand for server CPUs from agentic AI, whether its manufacturing strategy can respond quickly enough, and what these problems mean for its ambitions to become a leading-edge foundry alternative to TSMC.
We also step back to examine what Intel's experience tells us about the broader semiconductor market.
Topics covered:
- Why Intel's stock crashed despite strong earnings
- How the AI memory boom is hurting the PC market
- Why substrate shortages are constraining server CPU production
- The consequences of Intel's Fab 28 accident
- Whether Intel misjudged demand for agentic AI CPUs
- Intel 18A, 14A, and the long-term foundry opportunity
- Why strong demand doesn't necessarily translate into revenue
- Why investors are increasingly demanding returns from the AI infrastructure buildout
The bigger question: Is Intel facing a temporary supply-chain problem, or are its troubles a warning sign for the broader AI semiconductor industry?
Presented by Crusoe: 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://semidoped.com/crusoe
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://semidoped.com/meter
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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The fundamental physics of data movement are forcing a shift to new memory architectures for AI inference. Sudeep Bhoja, co-founder and CTO of d-Matrix, joins Vik to detail the company's journey from a non-consensus bet on memory-bound inference to a full-rack 3D-stacked DRAM solution. Sudeep explains the pivot from analog to digital in-memory compute, the system-level disaggregation strategies for their Corsair card, and how the next-generation Raptor chip integrates with Nvidia's ecosystem.
"Essentially, we got 16 times the capacity of SRAM, while we retained SRAM bandwidth."
— Sudeep Bhoja, d-Matrix
Presented by Crusoe: 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://semidoped.com/crusoe

Key Takeaways:
- The core problem in AI inference is a 1000x energy gap. Compute costs femtojoules per operation, but moving data from memory costs picojoules per bit.
- d-Matrix's first analog in-memory compute chip failed because it couldn't maintain 32-bit accumulation precision, forcing a pivot to a digital architecture.
- The Corsair card's digital in-memory compute delivers 150 TB/s of on-chip SRAM bandwidth — over 8x more than an Nvidia Blackwell GPU's 18 TB/s HBM.
- The next-gen Raptor chip stacks a logic die on a DRAM die, delivering 16x the capacity (32 GB) of the prior SRAM design while retaining ~100 TB/s bandwidth.
- This 3D stacking is over 10x more energy-efficient than HBM4, consuming just 0.37 pJ/bit versus an estimated 5 pJ/bit for an HBM4-plus-GPU system.
- Choosing the stacking order is a 'pick your poison' trade-off. d-Matrix put logic-on-top for better thermals, accepting a harder power delivery challenge through the DRAM.
- The go-to-market product is a full rack with 144 Raptor packages, aggregating 2.3 TB of high-bandwidth memory designed to drop into Nvidia's VR rack infrastructure.
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://semidoped.com/meter
Chapters:
0:00 Intro: The Cost of a Token
3:28 The 2019 Bet on Inference
7:43 Nighthawk: Analog In-Memory Compute
12:08 Why Analog Failed
19:02 Corsair: Digital In-Memory Compute
26:04 System Strategy: Speculative Decoding
37:05 Raptor: 3D Stacked DRAM
39:40 The 'Free Lunch' of 3D Stacking
45:00 Trade-off: Logic-on-Top vs DRAM-on-Top
54:14 Go-to-Market: The Nvidia VR Rack
1:03:17 The Future: Lightning & 4-High Stacks
1:12:45 The End State of Inference
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Marvell's potential acquisition of Lumentum is a strategic vertical integration play to capture the massive value optical components now unlock in the AI era. Andrew Schmitt and Scott Wilkinson of market research firm Signal AI join Austin and Vik to break down the deal's core drivers. They explain how Marvell could run the 'Broadcom playbook' by bundling DSPs and optics, even at the cost of competing with its own customers, and why Lumentum's future-facing assets like Optical Circuit Switches (OCS) and high-power lasers are just as critical as its current-gen transceivers.
This episode is presented by Crusoe. 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://semidoped.com/crusoe
Key Takeaways:
- The deal's driver is vertical integration, not securing laser capacity. Marvell wants to control the supply chain from chips to optics to co-design superior products for the fast-moving AI market.
- The economics have flipped. Optics margins have doubled from ~25% to nearly 50%, making them attractive for chipmakers to acquire — even if it means Marvell pays 20-30x what it might have cost years ago.
- Lumentum's real moat is manufacturing scale. While others can build a hundred 'unicorn' devices like 200G EMLs, Lumentum's advantage is its ability to produce them reliably by the million.
- The acquisition creates channel conflict. Marvell's DSP customers will now have to compete with a vertically integrated Marvell-Lumentum, a dynamic that has already pushed some players out of the coherent market.
- This is also a bet on future architectures. The deal secures Lumentum's critical assets for next-gen data centers, including Optical Circuit Switches (OCS) and high-power lasers for CPO and NPO.
- Lumentum has the capacity to do both. After a 4x expansion of its Indium Phosphide fab, it can supply a vertically integrated Marvell and continue selling lasers to the open market.
- A bidding war is likely. The guests speculate the news leak was intentional to attract other suitors like Nvidia, who could run the same vertical integration playbook.
Chapters:
0:00 Intro: Marvell & Lumentum
7:38 The Vertical Integration Driver
9:17 The Channel Conflict Problem
14:58 Transceiver Value Chain
17:38 Why Would Lumentum Sell?
21:23 Lumentum's InP Capacity
24:52 The Cost of Waiting
27:00 Manufacturing Scale Is the Moat
31:30 How AI Changed Component Value
33:43 CPO: The Integration Endgame
36:50 The 'Broadcom Playbook'
38:58 Prediction: A Bidding War
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Chapters:
0:00 Intro: Marvell & Lumentum
7:38 The Vertical Integration Driver
9:17 The Channel Conflict Problem
14:58 Transceiver Value Chain
17:38 Why Would Lumentum Sell?
21:23 Lumentum's InP Capacity
24:52 The Cost of Waiting
27:00 Manufacturing Scale Is the Moat
31:30 How AI Changed Component Value
33:43 CPO: The Integration Endgame
36:50 The 'Broadcom Playbook'
38:58 Prediction: A Bidding War
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Intel's EMIB technology represents a scalable, cost-effective advanced packaging solution that now serves as a core strategic differentiator for its foundry services. 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. They discuss the principle behind EMIB, interconnect where you need it and only where you need it: small silicon bridges carry the fine-pitch die-to-die signals, while the organic substrate's thick metal still delivers power and everything else. They also cover its manufacturing advantages over CoWoS and Intel's forward-looking roadmap for active bridges and top-side optics.
This episode is presented by Crusoe. 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://semidoped.com/crusoe
"When first envisioned, this was almost science fiction because the bridge I was talking about is roughly half the thickness of an A4 sheet of paper. So imagine being able to manipulate tiny strips of silicon like that."
— Ravi Mahajan, Intel Foundry
Key Takeaways:
- EMIB was invented to outpace organic packages, whose wire escape density at the die edge improved only about 10% per generation. The principle Ravi keeps returning to is interconnect where you need it and only where you need it: fine-pitch silicon in small local patches at the die edges, with the organic substrate's thick metal still carrying power and everything else. Fine wires are a poor way to deliver power anyway, and small bridges cut from a wafer keep the silicon cost down.
- Bringing EMIB from concept to production required the convergence of five distinct technologies, including a custom 4-layer silicon process and handling dies half the thickness of an A4 sheet of paper.
- Intel strategically chose HBM as EMIB's first driver, betting that if it could solve for the highest-performance memory interconnect, the technology could then "waterfall down" to all other less demanding applications.
- EMIB's advantage over CoWoS is manufacturing scale. Silicon only where you need it means small bridges instead of a full-sized silicon interposer, avoiding its high cost and poor wafer utilization, which is critical for large, multi-reticle AI packages.
- The EMIB portfolio was planned from the start. The original patent included EMIB-T, which uses TSVs to solve the "torturous" power delivery path of routing power around the bridge instead of through it.
- EMIB is an Intel Foundry exclusive. Packaging dies from other foundries is logistically straightforward, positioning advanced packaging as a key strategic differentiator for Intel's services.
- Intel's future roadmap includes top-side optics, using the top surface of the package for optical I/O to complement the electrical I/O on the bottom, dramatically increasing total package bandwidth.
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
Mentioned in this episode:
- NEW EPISODE: Synopsys’s Amazon Deal and GPT-Synopsys: Ravi Subramanian (Semi Doped episode): https://daily.semidoped.com/p/new-episode-synopsys-will-take-a
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