In this episode, Austin and Vik discuss if Intel is finally back with CPU partnerships with Google, and heterogeneous inference with SambaNova, while market cap soars above $300B. Vik tries to get his OpenClaw instance to dream every night.

Chapters

00:00 Anthropic's New Direction: Chip Development
02:30 Navigating Subscription Changes and Token Costs
05:25 Exploring Alternative AI Models
08:10 The Economics of AI: Rent vs. Buy
10:56 Intel's Resurgence and Market Dynamics
15:23 Intel's Strategic Partnerships and Market Positioning
19:37 The Role of IPUs in Modern Computing
25:08 Coexistence of x86 and ARM Architectures
29:55 Innovations in Chip Architecture and Future Prospects

Reiner Pope is the co-founder and CEO of MatX, the startup building chips designed from first principles for LLMs. Before MatX, Reiner was on the Google Brain team training LLMs, and his co-founder Mike Gunter was on the TPU team. They left Google one week before ChatGPT was released.

A counterintuitive throughput insight from the conversation:

“Low latency means small batch sizes. That is just Little’s law. Memory occupancy in HBM is proportional to batch size. So you can actually fit longer contexts than you could if the latency were larger. Low latency is not just a usability win, it improves throughput.”

We get into:

• The hybrid SRAM + HBM bet, and why pipeline parallelism finally works

• Overcoming the CUDA moat

• Why frontier labs are willing to bet on an AI ASIC startup

• Memory-bandwidth-efficient attention, numerics, and what MatX publishes (and what it does not)

• Why 95% of model-side news is noise for chip design

• Why sparse MoE drives MatX to “the most interconnect of any announced product”

• How MatX uses AI for its own chip design

• The biggest challenges ahead

Chapters:

00:00 “We left Google one week before ChatGPT”

00:24 Intro: who is MatX

01:17 Origin story: leaving Google for LLM chips

02:21 GPT-3 and the “too expensive” problem

04:25 Why buy hardware that is not a GPU

05:52 Overcoming the CUDA moat

08:46 Early investors

09:35 The name MatX

09:59 The chip: matrix multiply + hybrid SRAM/HBM

12:11 Why pipeline parallelism finally works

14:22 Reading papers and Google going dark

15:20 Research agenda: attention and numerics

17:06 Five specs and meeting customers where they are

19:24 Why frontier labs are the natural first customer

20:32 Workloads: training, prefill, decode

22:18 Little’s law and the throughput case for low latency

24:29 Interconnect and MoE topology

26:35 Inside the team: 100 people, full stack

28:32 Agentic AI: 95% noise for hardware

30:35 KV cache sizing in an agentic world

32:11 How MatX uses AI for chip design (Verilog + BlueSpec)

34:23 Go to market: proving credibility under NDA

35:12 Porting effort for frontier labs

36:34 Biggest skepticism: manufacturing at gigawatt scale

37:32 Hiring plug

Austin Lyons @ Chipstrat: https://www.chipstrat.com

Vik Sekar @ Vik's Newsletter: https://www.viksnewsletter.com/

Intel Foundry just partnered with Elon Musk’s Terafab. What is Terafab anyway, why vertically integrated fabs make sense but the economics don’t (yet!), and what Intel is doing here (hint: no idea).

Then: OpenAI acquires TBPN for an estimated $100-300M. Not sure why, but the more interesting thing is the value of niche audiences when five companies control a trillion dollars in AI capex.

And finally, Citrini Research sent an analyst to the Strait of Hormuz with a Pelican case full of spy gear, $15K cash, and Cuban cigars. The most unhinged research trip in Substack history.

Austin Lyons — Chipstrat (https://chipstrat.com) Vik Sekar — Vik's Newsletter (https://www.viksnewsletter.com)

Subscribe for weekly episodes on semiconductors, AI, infrastructure, and the business of chips.

In this episode, Austin and Vik analyze NVIDIA's $2 billion investment in Marvell NVLink Fusion, exploring its implications for AI infrastructure, interconnect protocols, and the broader chip ecosystem. They also discuss the current memory market surge, DRAM pricing, and Intel's strategic fab buyback, providing deep insights into industry trends and future directions.

On Substack
Vik: https://www.viksnewsletter.com/
Austin: https://www.chipstrat.com/

Chapters

00:00 NVIDIA's $2 Billion Investment in Marvell
20:11 The Memory Market Crisis
20:16 The Future of Memory Pricing and Consumer Impact
22:55 The Cycle of Supply and Demand in Memory
27:23 AI's Impact on Memory Demand
31:46 Long-Term Agreements and Market Stability
35:07 Intel's Strategic Fab Buyback
40:44 Monopoly Analogy: Intel's Market Strategy

In this episode, Austin and Vik analyze recent developments in GloFo patent lawsuits, the impact of TurboQuant on AI inference, and ARM's strategic move into silicon for agentic AI workloads.

Read Vik's substack: https://www.viksnewsletter.com
Read Austin's substack: https://www.chipstrat.com

Chapters

00:00 Patent Wars in Semiconductor Industry
07:14 Understanding TurboQuant and Its Implications
24:42 Innovations in Memory Management
28:00 The Rise of ARM AGI CPUs
32:56 Agentic AI and CPU Compatibility
39:54 Performance Metrics in Agentic AI
44:52 ARM's Market Timing and Challenges

Austin and Vik break down a packed week in semiconductors, covering GTC, OFC, and Micron earnings. The conversation kicks off with Jensen Huang's bold claim that engineers should spend $250K/year on AI tokens, and whether companies will buy tokens or token generators (i.e., on-prem hardware like the Dell Pro Max with GB300). They dig into the CapEx vs OpEx tradeoffs, data security concerns, and how sharing GPU resources might end up looking a lot like the old EDA license model.

Next up: Micron crushed earnings and appears to be designed into Vera Rubin for HBM4 — despite months of rumors saying otherwise. Austin and Vik unpack the nuance around HBM pin speeds, memory node base dies, and what Micron's massive new fab investments in Taiwan, Singapore, Idaho, and New York mean for the memory cycle.

The back half of the episode dives into optical interconnects for AI scale-up. A new industry consortium (OCI-MSA) has formed with Meta, Broadcom, NVIDIA, and OpenAI to standardize optical components. Vik explains why traditional indium phosphide lasers might be overkill for short-reach scale-up, and makes the case for micro LEDs — a "slow but wide" approach that could fill the gap between copper and conventional optics. They also touch on Credo's expanding product portfolio (and the infamous purple-to-orange cable saga), plus Lumentum's new VCSEL work for scale-up.

Vik - https://www.viksnewsletter.com/
Austin - https://www.chipstrat.com/

CHAPTERS
0:00 Intro & GTC/OFC Conference Overload
2:09 Jensen's $250K Token Budget Per Engineer
5:08 On-Prem Inference vs. Cloud Token Spending (Dell Pro Max, CapEx vs OpEx)
6:44 Sharing GPU Resources Like EDA Licenses
8:16 Data Security & On-Prem Privacy Concerns
9:53 Matthew Berman's Fine-Tuned Open Claw Agent
10:35 Vik Sets Up Open Claw on a Home Server
11:53 Always Be Clauden (ABC) – Managing Agents from Your Phone
13:34 Micron Earnings & HBM4 in Vera Rubin
16:39 HBM Pin Speeds & the Micron Design-In Debate
20:17 Micron's New Fab Investments & Memory Cycle Fears
23:49 Why AI Drives a Step Change in Memory Demand
26:30 Optical Compute Interconnect MSA (OCI-MSA)
29:48 Scale-Up Optics: Do We Need New Technology?
30:58 Micro LEDs – The "Slow but Wide" Approach
35:45 Micro LEDs vs. Copper vs. Traditional Optics
36:55 Credo's Product Spectrum & the Purple Cable Story
39:31 VCSELs & Lumentum's 1060nm Scale-Up Play

Vik and Austin unpack the Nvidia GTC keynote with fresh, top-of-mind takes while trying to breakdown key announcements, what matters and what doesn't. They discuss Groq's LPX, optics+copper for scale up, new CPU requirements, CPO for networking, and what agents means for software, and much, much, more.

Check out Austin's substack: https://www.chipstrat.com
Check out Vik's substack: https://www.viksnewsletter.com

Chapters

00:00 Introduction and Keynote Context
03:18 Keynote Highlights and Gaming Innovations
06:18 Generative AI: The Three Eras
09:28 Inference: The New Revenue Generator
12:21 NVIDIA's Tiered Approach to AI Models
15:30 The Grok Chip and Its Role
18:35 Vera Rubin System: A Full Data Center
21:18 CPU Demand and Performance
24:31 Networking Innovations and Future Directions
32:32 Innovations in PCB Technology
34:06 Scaling GPU Systems
36:57 Understanding the STX Rack and AI Storage
38:23 The Rosa CPU and Its Significance
40:07 Digital Twin Platforms and AI Factories
43:53 NVIDIA's New Software Innovations
47:09 The Future of Token Budgets in AI
54:15 Balancing CapEx and OpEx in AI Deployments

Austin recaps moderating an agentic AI panel at Synopsys Converge, then gives an in-depth technical breakdown of Meta's MTIA custom silicon. Why they're building it, how chiplets let them ship a new chip every 6 months, and how the roadmap is shifting toward gen AI inference. Vik digs into Applied Optoelectronics (AAOI), the vertically integrated Texas laser shop whose stock went from $1.48 to $100+, and whether history is about to rhyme.

Austin Lyons: https://www.chipstrat.com
Vik Sekar: https://www.viksnewsletter.com/

Topics covered:
• Agentic AI in chip design — how it changes roles for junior and senior engineers
• Optical circuit switching and what it means for Arista's business model
• Meta's ad-serving pipeline: Andromeda, Lattice, and the GEM foundation model
• Why custom silicon (MTIA) makes sense at Meta's scale
• MTIA chiplet strategy — 4 generations in 2 years
• AAOI's vertical integration, Amazon's $4B warrant deal, and the 2017 parallel

Chapters:
0:00 Intro
1:26 Synopsys Converge — Agentic AI Panel
9:44 Vik's Article: Optical Circuit Switching & Arista
14:43 Meta MTIA — A New Chip Every 6 Months
21:32 Why Custom Silicon Makes Sense for Meta
27:22 MTIA Chiplet Strategy & Roadmap
33:56 Gen AI Fits Meta's Business Model
36:31 How Meta Ships Chips So Fast
40:30 Applied Optoelectronics (AAOI) Deep Dive
45:02 Amazon's $4B Warrant Deal
48:54 Can AAOI's Lasers Compete with Lumentum?
53:16 AAOI's Aggressive Capacity Buildout
55:35 History Rhymes: AAOI's 2017 Boom & Bust
1:00:55 Wrap-Up

#semiconductors #chips #tech #meta #MTIA #AAOI #optics #inference #AI