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Vik Sekar

Co-host, Semi Doped; Author of Vik's Newsletter, Semi Doped

Search every verified Vik Sekar interview, podcast appearance, and on-the-record quote — each transcript cross-checked by AI and human review to confirm speaker identity. Vik Sekar, co-host of the Semi Doped podcast and author of Vik's Newsletter on Substack, has recently discussed developments in AI infrastructure and semiconductor technology. In his podcast appearances, he covered topics including Point2 Technologies' Active RF Cable as a third interconnect option for data centers, the industry convergence on microVCSELs over microLEDs for AI interconnects, and the physical limits of embedded flash and the potential of ReRAM as a replacement. He has also stated on the show that he views memory as AI's biggest bottleneck, given the memory-heavy nature of large language models. Sekar has offered opinions on industry trends, such as arguing that the debate over "pacing" AI for safety is unlikely to slow compute demand and that physical data center bottlenecks, like local pushback in Iowa, are more likely governing factors. He has also reacted to OpenAI's Jalapeño inference chip announcement at Hot Chips, speculating that the chip's architecture may be designed for OpenAI's specific needs and that AI-assisted EDA tools could lower the barrier to chip design. In discussing ASML's high NA EUV roadmap, he elaborated on the technical shift to 6x12-inch photomasks and its implications for supply chain coordination.

Recent appearances

  • RF Over Plastic Is a Third Option Between Copper and Optics: Point2's Sean Park

    October 23, 2026 · 📺 Semi Doped

    A novel third option is emerging for data center interconnects between short-reach copper and power-hungry optics. Point2 CEO Sean Park joins Vik Sekar to explain his company's Active RF Cable (ARC) technology, which sends high-frequency radio signals over a flexible plastic waveguide. Park details how this approach targets the critical 5-10 meter "scale-up" problem for AI clusters, claiming significant advantages in cable density, power efficiency, and latency over existing solutions. This episode is presented by Crusoe. Crusoe's Serverless Fine-Tuning trains an open model like Llama, DeepSe…

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  • MicroVCSELs vs MicroLEDs, a MicroExplainer

    October 20, 2026 · 📺 Semi Doped

    Austin Lyons and Vik Sekar host a micro-explainer on the two competing micro-emitter technologies for AI interconnects: microLEDs and microVCSELs. They break down Credo's surprising pivot at OCP from its microLED acquisition to a microVCSEL product, revealing the core physics that give microVCSELs superior speed and reach. The episode establishes the technical reasons why the industry is converging on microVCSELs for the critical 5-50 meter gap in AI data centers. This episode is presented by Crusoe. Crusoe's Serverless Fine-Tuning trains an open model like Llama, DeepSeek, or GLM on your own…

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  • Why Memory Is AI's Biggest Bottleneck with Vikram Sekar | EP 170

    October 02, 2026 · 📺 Frictionless with Logan Jastremski

    Vikram Sekar, writer of Vik's Newsletter and co-host of Semi Doped, joins me to discuss AI agents, memory, and the hardware behind the data center buildout. We break down why training and inference need different hardware, why copper becomes harder to use as GPUs move farther apart, and why the industry is turning to optical interconnects. Vik explains the tradeoffs across SRAM, DRAM, high bandwidth memory, and storage. We discuss why useful work per token matters more than speed alone, how much context an agent needs, and why he sees memory as AI's biggest bottleneck. Enjoy. 00:00 Highlig…

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  • Every Muse User Gets 2 CPUs and 8GB in Meta's Cloud. Your Phone Already Has That

    October 01, 2026 · 📺 Semi Doped

    The push to run AI agents on edge devices will not be driven by model labs or hardware vendors, but by service providers like Meta whose ad-based business models create a powerful financial incentive to offload massive cloud compute costs onto the user's own powerful-but-idle hardware. Austin Lyons and Vik Sekar break down why the failed "AI PC" push has given way to a new, economically-driven strategy for on-device AI. They discuss how Qualcomm's acquisition of Modular provides the key software layer and why the ultimate vision is an "ecosystem of you" where the device acts as a context-aware…

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  • Flash Hit Its Physical Limit. ReRAM Is the Replacement: WeeBit Nano's Ilan Sever

    September 29, 2026 · 📺 Semi Doped

    As embedded flash hits a physical and economic scaling wall below 22nm, a new on-chip memory is needed. Ilan Sever of WeeBit Nano joins to explain how Resistive RAM (ReRAM) is positioned to become the new standard by offering a low-cost, high-endurance alternative. Ilan details how ReRAM's simple back-end-of-line integration, automotive qualification, and future applications in AI in-memory compute create a compelling replacement for the incumbent technology. "In geometries below 28 or 22 nanometer, flash reached its physical limit, actually. So physically you cannot implement embedded flash…

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  • Pacing AI Means More Compute, Not Less

    September 18, 2026 · 📺 Semi Doped

    The debate over "pacing" AI for safety is a red herring, since more safety work doesn't slow things down; it increases demand for compute hardware. Austin Lyons and Vik Sekar unpack Dario Amodei's call to slow down frontier AI development, arguing that the true governors on AI are not voluntary agreements but physical data center bottlenecks and geopolitical realities. They conclude that the push for more safety and interpretability will ultimately drive more sales of both training (GPUs) and inference (XPUs) hardware. Today's sponsor is G2i — expert training data, evals, and RL environments…

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  • TSMC Will Buy High NA After All: ASML's New 6x12-Inch Mask

    September 14, 2026 · 📺 Semi Doped

    The emergence of user-friendly AI agents is creating massive new hardware demand, which in turn is forcing the semiconductor industry into a complex, multi-year transition to High NA EUV lithography and an entirely new 12-inch photomask standard. Austin Lyons and Vik Sekar unpack the physics and economics of ASML's move to High NA, explaining why it necessitates a shift from 6-inch to 6x12-inch masks and what this means for the timelines of TSMC, Intel, and Samsung. "This is where you have to get the whole supply chain coordinated around this. And this is why you really ultimately need ASML's…

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  • OpenAI’s Jalapeño! Feeling Hot Hot Hot!

    August 28, 2026 · 📺 Semi Doped

    Austin and Vik react to OpenAI's Jalapeño announcement at Hot Chips. Plus extra spicy questions like should OpenAI sell it, how much of this was AI-written RTL, and where is Anthropic's chip? Key Takeaways: The chip's core design philosophy is "dark silicon is cheaper than idle accelerators" — using one balanced chip and power-gating unused blocks is more efficient than a two-chip (e.g. GPU + LPU) solution. The unprecedented nine-month RTL-to-tapeout cycle was enabled by AI for EDA tools, serving as a wake-up call that small, expert teams can now develop Rubin-class chips in under a year.…

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  • Grok Bots and How CPUs are used in Agentic AI

    August 24, 2026 · 📺 Semi Doped

    The rise of user-friendly agentic AI platforms will create a massive new demand category for dedicated, high-core-count "agentic CPUs" to execute tasks in parallel, fundamentally reshaping the server CPU market beyond just feeding GPUs. Key Takeaways: The 'Mac Mini Craze' wasn't about having a GPU on your desk — it was also about security, as users needed a sandboxed machine to run untrusted agent code like OpenClaw, a problem cloud VMs solve too. In AI servers, the GPU is the 'genius' doing the thinking, while the host CPU is the 'assistant' whose primary job is keeping the GPU fed, requir…

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  • NEWS TAKE: China's Optical Ban, Volta's $10B Anthropic Deal, AMD's Earnings

    August 11, 2026 · 📺 Semi Doped

    Austin Lyons and Vik Sekar break down three stories hitting the semiconductor industry this week. They analyze the technical and economic fallout of a proposed US ban on Chinese optical transceivers, unpack why AMD's stock fell despite record earnings, and explain the novel financing model behind Volta Infrastructure's massive $10 billion compute deal with Anthropic. Key Takeaways: The US ban on Chinese optical transceivers is based on a flawed security premise; the hardware lacks the capability to install malware, much like a USB cable cannot infect a computer on its own. Banning Chinese o…

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  • How Retimers Built an $80B Company: The Story of Astera Labs

    August 01, 2026 · 📺 Semi Doped

    A retimer is a small, unglamorous chip. Astera turned it into a high-margin franchise. Austin and Vik cover why copper gives up at PCIe speeds, what a retimer does that a redriver can't, and how one H100 socket became a moat. Then Scorpio: can Astera take switch share from Broadcom? Key Takeaways: 30cm of PCB trace at Gen 5's 32 GT/s and the signal is gone. Signal integrity is an inside-the-server problem, not just a rack-to-rack one. Gen 6 swaps NRZ for PAM4. Four voltage levels instead of two — a third the eye height, ~9.5 dB of SNR gone. A redriver amplifies, noise included. A retimer reco…

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  • News Take: Hyperscaler CDS, SK Hynix Earnings, China's DUV

    July 29, 2026 · 📺 Semi Doped

    Austin Lyons and Vik Sekar break down the market's recent volatility. They discuss the rising fear around hyperscaler debt, visible in credit default swap (CDS) premiums, and the counter-argument that GPU ROI is actually understated. They then analyze the paradox of SK Hynix's massive stock drop despite record growth, and explain why China's new DUV lithography tool makes 3D chip stacking essential to their strategy. Key Takeaways: Hyperscaler debt fears are quantified by rising credit default swap (CDS) premiums, signaling investor nervousness that the AI CapEx firehose is shifting from cash…

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Verified interview transcripts

Notable quotes

“So if they can only go three or 5 Gbits per second, but we're talking about a single lane wanting to, you know, first there was 50G, then 100G, and now 200G, and then eventually we want to go to 400G. How do you get 224 gigabits per second if an individual LED can only send at like three gigabits per second? Yeah. But essentially if you have such a slow data rate you have to now put, you know, multiple parallel highw”

“So in general I think a lot of these models that run personal assistance can do with I don't know set level models or even if you go open source I don't know it could be the cost could be even lower to run these things. Um and from the so the inference wise I think the token count is going to go up but it's not like everybody's going to be running the frontier model. So that has its own consequences right. So maybe w”

“The memory is the biggest problem. There is literally the LLM are a memory heavy method and memory is everything. So one of the biggest problems that people should solve is like how do you get the same performance without using as much memory. You know once you solve that problem a lot of things fall into place right because if you can if you don't have the memory limitation and the memory then probably that if if yo”

“The more it, so I was like, oh man, I'm using those, you know, all this stuff that was provisioned for me in the cloud, but then I stopped using it, and I'm like, is it just sitting there? Are they sharing with other people? But I was thinking like, man, if I get this just cranking all the time for me, Muse is so handy, so convenient because it's on my phone. And so I was actually checking it when I was in Hawaii. Yo”

“I wrote a whole article on this. It's totally free to read on my Substack and it says that the AI capex build will just continue as is. And the infrastructure buildout will just continue as is because for one, all of these things that we spoke about are very difficult to implement. It's literally like where do we start? Do I agree that it's an important problem that security is a problem to be solved? Yes, I do agree”

“I think from where I live in Iowa, the thing that's going to pace frontier training, the thing that's going to slow down frontier training, which by the way, let's have a conversation about whether it actually slows it down and if it actually impacts the amount of compute and whatnot, but the thing that's going to pace it is called the human pushback to building the data centers that train this AI. These data centers”

Other executives at Semi Doped

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