Austin and Vik discuss the emerging trend of AI agents, particularly focusing on Claude Code and OpenClaw, and the resulting hardware implications.

Key Takeaways:

• 2026 is expected to be a pivotal year for AI agents.
• The rise of agentic AI is moving beyond marketing to practical applications.
• Claude Code is being used for more than just coding; it aids in research and organization.
• Integrating AI with tools like Google Drive enhances productivity.
• Security concerns arise with giving AI agents access to personal data.
• Local computing options for AI can reduce costs and increase control.
• AI agents can automate repetitive tasks, freeing up human time for creative work.
• The demand for CPUs is increasing due to the needs of AI agents.
• AI can help summarize and organize information but may lack deep insights.
• The future of AI will involve balancing automation with human oversight.

Chapters
00:00 The Rise of Agents in AI
03:04 Exploring Claude Code and Its Applications
05:58 Integrating AI with Google Drive and Email
08:56 Optimizing Workflows with AI Agents
12:01 The Future of AI Agents and Local Computing
14:50 Security and Infrastructure Implications of AI Agents

Deploy your secure OpenClaw instance with DigitalOcean:
https://www.digitalocean.com/blog/moltbot-on-digitalocean

Visit the podcast website: https://www.semidoped.fm
Austin's Substack: https://www.chipstrat.com/
Vik's Substack: https://www.viksnewsletter.com/

Maia 100 was a pre-GPT accelerator.
Maia 200 is explicitly post-GPT for large multimodal inference.

Saurabh Dighe says if Microsoft were chasing peak performance or trying to span training and inference, Maia would look very different. Higher TDPs. Different tradeoffs. Those paths were pruned early to optimize for one thing: inference price-performance. That focus drives the claim of ~30% better performance per dollar versus the latest hardware in Microsoft’s fleet.

Intereting topics include:
• What “30% better price-performance” actually means
• Who Maia 200 is built for
• Why Microsoft bet on inference when designing Maia back in 2022/2023
• Large SRAM + high-capacity HBM
• Massive scale-up, no scale-out
• On-die NIC integration

Maia is a portfolio platform: many internal customers, varied inference profiles, one goal. Lower inference cost at planetary scale.

Chapters:
(00:00) Introduction
(01:00) What Maia 200 is and who it’s for
(02:45) Why custom silicon isn’t just a margin play
(04:45) Inference as an efficient frontier
(06:15) Portfolio thinking and heterogeneous infrastructure
(09:00) Designing for LLMs and reasoning models
(10:45) Why Maia avoids training workloads
(12:00) Betting on inference in 2022–2023, before reasoning models
(14:40) Hyperscaler advantage in custom silicon
(16:00) Capacity allocation and internal customers
(17:45) How third-party customers access Maia
(18:30) Software, compilers, and time-to-value
(22:30) Measuring success and the Maia 300 roadmap
(28:30) What “30% better price-performance” actually means
(32:00) Scale-up vs scale-out architecture
(35:00) Ethernet and custom transport choices
(37:30) On-die NIC integration
(40:30) Memory hierarchy: SRAM, HBM, and locality
(49:00) Long context and KV cache strategy
(51:30) Wrap-up

OpenAI's partnership with Cerebras and Nvidia's announcement of context memory storage raises a fundamental question: as agentic AI demands long sessions with massive context windows, can SRAM-based accelerators designed before the LLM era keep up—or will they converge with GPUs?

Key Takeaways
1. Context is the new bottleneck. As agentic workloads demand long sessions with massive codebases, storing and retrieving KV cache efficiently becomes critical.
2. There's no one-size-fits-all. Sachin Khatti's (OpenAI, ex-Intel) signals a shift toward heterogeneous compute—matching specific accelerators to specific workloads.
3. Cerebras has 44GB of SRAM per wafer — orders of magnitude more than typical chips — but the question remains: where does the KV cache go for long context?
4. Pre-GPT accelerators may converge toward GPUs. If they need to add HBM or external memory for long context, some of their differentiation erodes.
5. Post-GPT accelerators (Etched, MatX) are the ones to watch. Designed specifically for transformer inference, they may solve the KV cache problem from first principles.

Chapters
- 00:00 — Intro
- 01:20 — What is context memory storage?
- 03:30 — When Claude runs out of context
- 06:00 — Tokens, attention, and the KV cache explained
- 09:07 — The AI memory hierarchy: HBM → DRAM → SSD → network storage
- 12:53 — Nvidia's G1/G2/G3 tiers and the missing G0 (SRAM)
- 14:35 — Bluefield DPUs and GPU Direct Storage
- 15:53 — Token economics: cache hits vs misses
- 20:03 — OpenAI + Cerebras: 750 megawatts for faster Codex
- 21:29 — Why Cerebras built a wafer-scale engine
- 25:07 — 44GB SRAM and running Llama 70B on four wafers
- 25:55 — Sachin Khatti on heterogeneous compute strategy
- 31:43 — The big question: where does Cerebras store KV cache?
- 34:11 — If SRAM offloads to HBM, does it lose its edge?
- 35:40 — Pre-GPT vs Post-GPT accelerators
- 36:51 — Etched raises $500M at $5B valuation
- 38:48 — Wrap up

Innoviz CEO Omer Keilaf believes the LIDAR market is down to its final players—and that Innoviz has already won its seat.

In this conversation, we cover the Level 4 gold rush sparked by Waymo, why stalled Level 3 programs are suddenly accelerating, the technical moat that separates L4-grade LIDAR from everything else, how a one-year-old startup won BMW, and why Keilaf thinks his competitors are already out of the race.

Omer Keilaf founded Innoviz in 2016. Today it's a publicly traded Tier 1 supplier to BMW, Volkswagen, Daimler Truck, and other global OEMs.

Chapters
00:00 Introduction
00:17 Why Start a LIDAR Company in 2016?
01:32 The Personal Story Behind Innoviz
03:12 Transportation Is Still Our Biggest Daily Risk
04:28 The 2012 Spark: Xbox Kinect and 3D Sensing
06:32 From Mobile to Automotive: Finding the Right Platform
07:54 "I Didn't Know What LIDAR Was, But I'd Do It Better"
08:19 How a One-Year-Old Startup Won BMW
10:04 Surviving the First Product
11:23 From Tier 2 to Tier 1: The Volkswagen Win
13:47 Lessons Learned Scaling Through Partners
14:45 The SPAC Decision: A Wake-Up Call from a Competitor
16:42 From 200 LIDAR Companies to a Handful
17:27 NREs: How Tier 1 Status Funds R&D
18:44 Why Automotive-First Is the Right Strategy
19:45 Consolidation Patterns: Cameras, Radars, Airbags
20:31 "The Music Has Stopped"
21:07 Non-Automotive: Underserved Markets
23:51 Working with Secretive OEMs
25:27 The Press Release They Tried to Stop
26:42 CES 2025: 85% of Meetings Were Level 4
27:40 Why Level 3 Programs Are Suddenly Accelerating
28:33 The EV/ADAS Coupling Problem
29:49 Design Is Everything: The Holy Grail Is Behind the Windshield
31:13 The Three-Year RFQ: Grill → Roof → Windshield
32:32 Innoviz3: Small Enough for Behind-the-Windshield
34:40 Innoviz2 for L4, Innoviz3 for Consumer L3
36:38 What's the Real Difference Between L2, L3, and L4 LIDAR?
38:51 The Mud Test: Why L4 Demands 100% Availability
40:50 "We're the Only LIDAR Designed for Level 4"
42:52 Patents and the Maslow Pyramid of Autonomy
44:15 Non-Automotive Markets: Agriculture, Mining, Security
46:15 Closing

Austin and Vik discuss why LiDAR is important for autonomy, how modern systems work, and how the technology has evolved. They compare Time of Flight and FMCW architectures, explain why wavelength choice matters, and walk through the tradeoffs between 905 nm and 1550 nm across eye safety, cost, and performance. The discussion closes with a clear-eyed look at competition, Chinese suppliers, and supply chain risk.

Chapters

(00:00) Introduction to LiDAR and why it matters

(05:40) The case for LiDAR in autonomous vehicles

(12:41) Wavelengths, eye safety, and system tradeoffs

(15:38) How LiDAR works: Time of Flight vs. FMCW

(20:12) Mechanical vs. solid-state LiDAR designs

(27:31) Market dynamics, competition, and geopolitics

Episode Summary

Austin and Vik break down NVIDIA’s CES 2026 keynote, focusing on Vera Rubin, DGX Spark and DGX Station, uneducated investor panic, and physical AI.

Key Takeaways

• DGX Spark brings server-class NVIDIA architecture to the desktop at low power, aimed at developers, enthusiasts, and enterprises experimenting locally.
• DGX Station functions more like a mini-AI rack on-prem: Grace Blackwell for inference and development without full racks
• The historical parallel is mainframes to minicomputers, expanding compute TAM rather than displacing cloud usage.
• On-prem AI converts some GPU rental OpEx into CapEx, appealing to CFOs
• NVIDIA positioned autonomy as physical AI with vision-language-action models and early Mercedes-Benz deployments in 2026.
• Vera Rubin integrates CPU, GPU, DPU, networking, and photonics into a single platform, emphasizing Ethernet for scale-out. (Where was the Infiniband switch?)
• The new Vera CPU highlights rising CPU importance for agentic workloads through higher core counts, SMT, and large LPDDR capacity.
• Rubin GPU’s move to HBM4 and adaptive precision targets inference efficiency gains and lower cost per token.
• Context memory storage elevates SSDs and DPUs, enabling massive KV cache offload beyond HBM and DRAM.
• Cable-less rack design and warm-water cooling show NVIDIA’s shift from raw performance toward manufacturability and enterprise polish.

Austin and Vik discuss key insights from the IEDM conference.

They explore the significance of IEDM for engineers and investors, the networking opportunities it offers, and the latest innovations in silicon photonics, complementary FETs, NAND flash memory, and GaN-on-silicon chiplets.

Takeaways

• Penta-level NAND flash memory could disrupt the SSD market
• GaN-on-Silicon chiplets enhance power efficiency
• Complementary FETs
• Optical scale-up has a power problem
• The future of transistors is still bright
  
  

Key Topics

• What Nvidia actually bought from Groq and why it is not a traditional acquisition
• Why the deal triggered claims that GPUs and HBM are obsolete
• Architectural trade-offs between GPUs, TPUs, XPUs, and LPUs
• SRAM vs HBM. Speed, capacity, cost, and supply chain realities
• Groq LPU fundamentals: VLIW, compiler-scheduled execution, determinism, ultra-low latency
• Why LPUs struggle with large models and where they excel instead
• Practical use cases for hyper-low-latency inference:
  • Ad copy personalization at search latency budgets
  • Model routing and agent orchestration
  • Conversational interfaces and real-time translation
  • Robotics and physical AI at the edge
  • Potential applications in AI-RAN and telecom infrastructure
• Memory as a design spectrum: SRAM-only, SRAM plus DDR, SRAM plus HBM
• Nvidia’s growing portfolio approach to inference hardware rather than one-size-fits-all
  
  

Core Takeaways

• GPUs are not dead. HBM is not dead.
• LPUs solve a different problem: deterministic, ultra-low-latency inference for small models.
• Large frontier models still require HBM-based systems.
• Nvidia’s move expands its inference portfolio surface area rather than replacing GPUs.
• The future of AI infrastructure is workload-specific optimization and TCO-driven deployment.