Semi Doped

This week, we move from optics technology to optics companies. We walk the AI optical supply chain from bottom to top. Main debate: Who has a moat? Who is already priced for perfection?  *Not investment advice, do your own due diligence*
AXTI - Indium phosphide substrate supplier. Critical bottleneck in the laser stack. Major China export-control risk. Massive stock run vs thin earnings.
Tower Semiconductor - Leading silicon photonics foundry. 5x capacity expansion with customer prepayments. Strong process lock-in. Pure-play optics exposure.
GlobalFoundries - 300mm monolithic photonics platform + Chips Act support. Optics growing fast but still small piece of overall business.
Lumentum - Dominant EML laser supplier. Explosive AI demand. Strong technical moat. Valuation and capex sensitivity are key risks.
Coherent - Vertically integrated from substrate to module. 6-inch InP push could lower costs structurally. Execution and margin mix matter.
Fabrinet - Optics assembly partner. High NVIDIA exposure. Scales with industry, but dependent on upstream supply.
Corning - AI data centers require far more fiber than traditional cloud. $6B Meta deal adds visibility. Timing of scale-up optics is the swing factor.
Timestamps
00:01 Intro
06:59 AXT $AXTI
13:38 Tower Semiconductor $TSEM
23:58 GlobalFoundries $GFS
32:43 Lumentum $LITE
39:38 Coherent $COHR
47:09 Fabrinet $FN
54:07 Corning $GLW
Austin's Substack: https://www.chipstrat.com/
Vik's Substack: https://www.viksnewsletter.com/

Austin and Vik delve into the evolving landscape of optics and networking, particularly in relation to AI and data centers.

The conversation covers various scales of networking, including scale across, scale out, and scale up, while also addressing the demand-supply dynamics in laser manufacturing and the future of optical circuit switches.

The episode highlights the technological advancements and market opportunities in the optics sector, emphasizing the significance of these developments for the future of AI.

Takeaways
Silicon photonics is becoming crucial for data center connectivity.
Optics is essential for overcoming copper's limitations in speed and distance.
Scale across technology is vital for connecting data centers.
Scale out optics is the standard for connecting GPUs between racks.
Co-packaged optics can reduce energy consumption in data centers.
The scale up market for optics is emerging as a new opportunity.
Indium phosphide wafers are a critical bottleneck in laser manufacturing.
Optical circuit switches are gaining traction in data centers.
2026 is anticipated to be a pivotal year for optical networking. 

Chapters

00:00 Introduction to AI and CPU Bottlenecks
03:00 The Rise of Silicon Photonics
06:01 Understanding Optical Networking and Data Centers
08:49 Scale Across: Connecting Data Centers
11:56 Scale Out: Optimizing Data Center Connectivity
14:53 Scale Up: The Future of GPU Connectivity
23:32 The Shift from Copper to Optical Connections
26:13 Challenges and Reliability of Lasers
30:47 Understanding Co-Packaged Optics
34:17 Market Dynamics: Demand and Supply of Lasers
40:46 Emerging Technologies: Optical Circuit Switches

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

In this episode of the Semi Doped podcast, Austin and Vik delve into the current state of the semiconductor industry, focusing on the memory crisis driven by increasing demand from AI applications. They discuss the implications of rising memory prices, the impact of hyperscaler spending on the market, and the strategic moves of major players like Google, Microsoft, Meta, and Amazon in the AI landscape.

Takeaways
Memory prices are skyrocketing, impacting consumer electronics.
The memory crisis is affecting the production of lower-end devices.
DRAM prices have doubled in a single quarter, creating challenges for manufacturers.
Nanya Tech's revenue growth indicates a booming memory market.
AI applications are driving unprecedented demand for memory.
Hyperscalers are significantly increasing their capital expenditures for AI infrastructure.
The integration of AI into advertising is reshaping business models for companies like Google and Meta.
Chapters

00:00 The State of Memory in Semiconductors
03:08 Nvidia's GPU Dilemma and Market Dynamics
06:13 The Impact of AI on Memory Demand
09:08 NAND Flash and Context Memory Trends
11:59 The Future of Memory Supply and Demand
15:12 AI Infrastructure and CapEx Spending
17:47 Google's Strategic Investments in AI
20:58 The Advertising Business Model and AI Integration
30:26 Revenue vs. Expenses: A Balancing Act
31:08 The Future of TPUs vs. GPUs in Cloud Computing
35:31 Microsoft vs. Google: AI Investments and Market Reactions
38:22 AI Integration in Enterprises: Microsoft’s Unique Position
39:57 The Power of Microsoft’s Reach in AI
40:30 GitHub: A Hidden Gem for Microsoft’s AI Strategy
43:52 Meta’s AI Strategy: Advertising and Revenue Growth
51:18 Amazon’s Massive CapEx: Implications for the Future
54:00 Looking Ahead: Predictions for 2027 and Beyond

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

In this episode, Vik and Wayne Nelms discuss the emerging financial exchange for GPU compute, exploring its implications for the AI infrastructure market. They discuss the value of compute, pricing dynamics, hedging strategies, and the future of GPU and memory trading. 
Wayne shares insights on partnerships, the depreciation of GPUs, and how inference demand may reshape hardware utilization. The conversation highlights the importance of financial products in facilitating data center development and optimizing profitability in the evolving landscape of compute resources.
Takeaways
Wayne Nelms is the CTO of Ornn, focusing on GPU compute as a commodity.
The value of compute is still being defined in the market.
Hedging strategies are essential for managing compute costs.
The pricing of GPUs varies significantly across providers.
Memory trading is becoming a crucial aspect of the compute market.
Partnerships can enhance trading platforms and market efficiency.
Depreciation of GPUs is not linear and varies by use case.
Inference demand may change how GPUs are utilized in the future.
Transparency in pricing benefits smaller players in the market.
Financial products can facilitate data center development and profitability.
Chapters
00:00 Introduction to GPU Compute Futures
03:13 The Value of Compute in Today's Market
05:59 Understanding GPU Pricing Dynamics
08:46 Hedging and Futures in Compute
11:52 The Role of Memory in AI Infrastructure
15:14 Partnerships and Market Expansion
17:46 Depreciation and Residual Value of GPUs
20:57 Future of Data Centers and Compute Demand
24:01 The Impact of Financialization on AI Infrastructure
27:04 Looking Ahead: The Future of Compute Markets
Keywords
GPU compute, financial exchange, futures market, data centers, AI infrastructure, pricing strategies, hedging, memory trading, Ornn 
Follow Wayne Nelms (@wayne_nelmz on X)
Check out Ornn's website: https://www.ornnai.com/
Check out Vik's Substack: https://www.viksnewsletter.com/
Check out Austin's Substack: https://www.chipstrat.com/

Vik and Val Bercovici discuss the evolution of storage solutions in the context of AI, focusing on Weka's innovative approaches to context memory, high bandwidth flash, and the importance of optimizing GPU usage.

Val shares insights from his extensive experience in the storage industry, highlighting the challenges and advancements in memory requirements for AI models, the significance of latency, and the future of storage technologies.

Takeaways
Context memory is crucial for AI performance.
The demand for memory has drastically increased.
Latency issues can hinder AI efficiency.
High bandwidth flash offers new storage capabilities.
Weka's Axon software enhances GPU storage utilization.
Token warehouses can significantly reduce costs.
Augmented memory grids improve memory access speeds.
Networking innovations are essential for AI storage solutions.
Understanding memory hierarchies is vital for optimization.
The future of storage will involve more advanced technologies.
Chapters

00:00 Introduction to Weka and AI Storage Solutions
05:18 The Evolution of Context Memory in AI
09:30 Understanding Memory Hierarchies and Their Impact
16:24 Latency Challenges in Modern Storage Solutions
21:32 The Role of Networking in AI Storage Efficiency
29:42 Dynamic Resource Utilization in AI Networks
30:04 Introducing the Context Memory Network
31:13 High Bandwidth Flash: A Game Changer
32:54 Weka's Neural Mesh and Storage Solutions
35:01 Axon: Transforming GPU Storage into Memory
39:00 Augmented Memory Grid Explained
42:00 Pooling DRAM and CXL Innovations
46:02 Token Warehouses and Inference Economics
52:10 The Future of Storage Innovations
Resources
Manus AI $2B Blog: https://manus.im/blog/Context-Engineering-for-AI-Agents-Lessons-from-Building-Manus
Also listen to this podcast on your favorite platform. https://www.semidoped.fm/

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