Semi Doped

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 lands...

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 c...

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 late...

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 w...

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 p...

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 codeba...

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 al...

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 supp...

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 Blackw...

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 effic...

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 mode...