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In this episode, Dr. Patrick Pilarski, co-author of the Alberta Plan alongside Richard Sutton and Michael Bowling, makes the case for a radically different kind of AI: one that never stops learning, builds its own understanding from raw experience, and gets more out of limited compute rather than more data centers. A Canada CIFAR AI Chair (Amii) and professor at the University of Alberta, Pilarski also leads a lab that builds robotic prosthetic limbs that learn to anticipate what their users intend to do before they do it. In this conversation, Pilarski joins TechSurge to unpack why his co-author Rich Sutton has publicly called LLMs a dead end, how the Alberta Plan’s “temporal uniformity” differs from how today’s AI labs bolt reinforcement learning onto pre-trained models, and why he believes AI safety problems in today’s LLM agents are really reinforcement learning problems the field has studied for decades. The conversation covers: ✅ Why Richard Sutton argues large language models are a dead end ✅ The four core commitments of the Alberta Plan for AI research ✅ Why “temporal uniformity” rejects the idea of separate training and deployment phases ✅ Patrick’s upcoming move to ExperienceFlow.AI as Head of Edge Superintelligence ✅ Why he doesn’t believe the AI data center build-out locks the industry into one path ✅ How reinforcement learning’s lessons on exploration and reward specification apply to AI agent safety ✅ Inside his lab’s work on AI-powered prosthetic limbs that predict user intent ✅ Supernumerary limbs and the future of human augmentation Guest Links: Patrick Pilarski: Canada CIFAR AI Chair and Fellow at Amii, Professor in the Department of Medicine at the University of Alberta, co-author of the Alberta Plan, and co-lead of the BLINC Lab. Incoming Head of Edge Superintelligence at ExperienceFlow.AI. University of Alberta faculty page: https://sites.ualberta.ca/~pilarski/#teaching BLINC Lab: https://blinclab.ca Further Reading and Resources The Alberta Plan – Pilarski’s contrarian roadmap for AI, co-authored with Richard Sutton and Michael Bowling, built around continual learning, temporal uniformity, computational efficiency, and multi-agent interaction. arXiv paper: https://arxiv.org/abs/2208.11173 The Bitter Lesson – Richard Sutton’s influential essay, referenced in the conversation, arguing that scaling compute consistently beats hand-crafted human knowledge in AI research. http://www.incompleteideas.net/IncIdeas/BitterLesson.html Reward Is Enough – The paper discussed in the episode, arguing that sufficiently rich reward signals alone can give rise to the full range of abilities associated with intelligence. ScienceDirect: https://www.sciencedirect.com/science/article/pii/S0004370221000862 AI Agent Safety is a Reinforcement Learning Problem – A paper co-authored by Pilarski, mapping AI agent safety failures onto established reinforcement learning challenges like safe exploration and reward misspecification. OpenReview: https://openreview.net/forum?id=wvga9chmb6 ExperienceFlow.AI – The company Pilarski is joining as Head of Edge Superintelligence, whose Superintelligence Research Laboratory focuses on continual learning and is led scientifically by Richard Sutton. Press release: https://www.businesswire.com/news/home/20251105563930/en/ExperienceFlow.AI-Announces-the-Launch-of-Its-Research-Laboratory-to-Advance-Experience-powered-Decentralized-Superintelligence AMD’s Acquisition of World Labs – Referenced in the episode’s discussion of “world models,” AMD’s $8.2 billion deal to acquire Fei-Fei Li’s company. CNBC: https://www.cnbc.com/2026/09/28/amd-fei-fei-li-world-labs.html Chapters: 00:00 – Welcome to Edmonton: AI, Free Will, and the Rodeo 00:57 – Why AI Researchers Name Papers Better Than Other Scientists 02:37 – Should We Anthropomorphize AI? 08:03 – The Alberta Plan, Explained 11:25 – “LLMs Are a Dead End”: Contrasting the Alberta Plan with Today’s AI Labs 14:41 – Human Babies, Giraffes, and Built-In Knowledge 17:34 – Four Years Later: What the Alberta Plan Got Right 19:57 – Who’s Actually Pursuing the Alberta Plan Today 21:03 – Breaking News: Patrick Joins ExperienceFlow.AI as Head of Edge Superintelligence 21:40 – What “Edge” Computing Really Means 24:00 – Are We Locked Into the Data Center Build-Out? 26:21 – Reinforcement Learning as Hill Climbing: Rewards and Evals 28:38 – AMD, Fei-Fei Li, and the World Models Debate 33:29 – Why AI Safety Is a Reinforcement Learning Problem 37:27 – Inside the Lab: AI-Powered Prosthetic Limbs 44:20 – How Prosthetics Learn Fast Without “Knocking Over Every Glass” 46:43 – Robot Hands With Eyeballs and Edge Intelligence 47:14 – A Breakthrough: Canada’s First Bone-Anchored Prosthetic Surgery 49:40 – Supernumerary Limbs and the Future of Human Augmentation 53:41 – What’s Underfunded in AI Research Right Now About TechSurge: TechSurge Podcast shares the latest insights directly from legendary Silicon Valley leaders, daring new founders, and visionary technologists. 🔔Subscribe for weekly conversations into the intersection of technology advancement, market dynamics, and founder journeys.

In this episode, host David Goldman speaks with legendary graphics chip architect Raja Koduri, who explains why every gigawatt of AI infrastructure now costs $50 to $60 billion, and why China's goal of doing it for under $10 billion is the real threat to Western AI. Raja twice led graphics at AMD, directed Apple's graphics architecture and was chief architect at Intel. He argues that the real AI race isn't Nvidia vs. Google vs. Broadcom but China vs. the rest of the world, and that the new bottleneck isn't compute. It's memory. In this conversation, Raja joins TechSurge to talk about his new startup Oxmiq, which aims to turn "electrons to tokens super efficiently." He covers how 3D-stacked, hybrid-bonded memory could deliver 10x the bandwidth of today's HBM, why AI agents are changing how chips get designed, and why he thinks the next disruption to AI data centers "comes from the bottom." The conversation covers: ✅ Why every gigawatt of AI infrastructure costs $50 to $60 billion, and where the $45 billion in hardware spend goes ✅ The $24 trillion capital question: 400+ gigawatts of new compute needed by 2030 ✅ How China's under-$10 billion per gigawatt target creates a 5 to 6x cost gap ✅ Why memory hierarchy, not raw compute, is now the real bottleneck in AI ✅ How advanced packaging can unlock 10x bandwidth and 10x token generation, even on older 7nm nodes ✅ Why OpenAI's Jalapeño chip shows that AI can speed up silicon design ✅ Why the value of experienced engineers has gone up 100x in the age of AI coding agents ✅ Leadership lessons from Steve Jobs at Apple and Lisa Su at AMD ✅ Why Intel's decision to kill 3D XPoint memory came at "the wrong time" ✅ Boom or bust: the financial engineering risk behind the AI infrastructure buildout ✅ Token factories vs. token banks: why "the more boring you make it, the more it becomes fabulous" Guest Links: Raja Koduri: Founder of Oxmiq. LinkedIn: https://www.linkedin.com/in/raja-koduri-3a51611X: https://x.com/RajaXgOxmiq: https://oxmiq.ai  Further Reading and Resources OpenAI and Broadcom announcement: https://openai.com/index/openai-broadcom-jalapeno-inference-chip/ High Bandwidth Memory (HBM): The memory technology Raja's AMD team helped bring to market with HBM1 and HBM2, and the benchmark Oxmiq's 3D-stacked approach aims to beat by 10x. https://en.wikipedia.org/wiki/High_Bandwidth_Memory  Intel 3D XPoint (Optane): The discontinued memory technology Raja says could have made Intel a major player in the inference era. https://en.wikipedia.org/wiki/3D_XPoint  Chapters 00:00 - A Gigawatt of AI Now Costs $60 Billion 01:58 - Introducing Raja Koduri 02:02 - What Oxmiq Builds: Electrons In, Tokens Out 05:18 - The $24 Trillion AI Infrastructure Bill 06:05 - China vs. the Rest of the World 09:10 - Memory Is the New Bottleneck 22:03 - How AI Agents Are Changing Chip Design 36:30 - Lessons From Steve Jobs and Lisa Su 44:54 - Advanced Packaging, Memory Prices, and Intel's Mistake 55:38 - Boom or Bust: The Future of Token Factories About TechSurge: TechSurge Podcast shares the latest insights directly from legendary Silicon Valley leaders, daring new founders, and visionary technologists. 🔔 Subscribe for weekly conversations at the intersection of technology advancement, market dynamics, and founder journeys.

Almost 2% of U.S. GDP will be spent on AI infrastructure this year, nearly double 2025's figure. But beneath those headline numbers, the composition of that spending has quietly flipped: for the first time, dollars spent on running models in production now outweigh dollars spent training them.   In this episode of TechSurge, host David Goldman speaks with Austin Lyons, a semiconductor analyst at Creative Strategies, co-host of the Semi Doped podcast, and author of the Chipstrat newsletter. Lyons previously worked as a hardware engineer at Intel and as a product manager on John Deere's autonomous tractor and Blue River Technology teams before turning to full-time chip industry analysis.   The conversation opens with why AI buyers have moved from assembling commoditized parts to buying entire pre-integrated systems, tracing how Nvidia's rack-scale approach, exemplified by its 72-GPU Grace Blackwell racks, made turnkey deployment the default, and why that raises the bar for any chip startup trying to compete. Lyons and Goldman then unpack how inference workloads have split into two distinct problems, prefill and decode, and how that split created an opening for SRAM-based challengers to outperform general-purpose GPUs on decode speed.   From there, the discussion turns to the rise of neoclouds, the GPU-rental companies that grew into public businesses worth well over $100 billion combined, and why so many traditional investors missed them. Lyons and Goldman work through the circular financing debate head-on: the mechanics of Nvidia's equity stakes, GPU-backed debt, and hyperscaler off-take agreements that critics compare to dot-com-era vendor financing, and the counterargument that demand is simply outrunning fixed supply.   The episode closes on Lyons's own framework for identifying the next trillion-dollar chip company, built on four conditions including the ability to run trillion-parameter models at rack scale, beat an incumbent on a key performance metric, and land a frontier anchor customer, along with a look at how AI-assisted chip design is lowering the barrier for more companies, from OpenAI to electric vehicle makers, to design their own custom silicon.   Sign up for our newsletter at techsurgepodcast.com for updates on upcoming TechSurge Live Summits and future episodes.   Speaker Profiles and Links   David Goldman: Partner, Celesta Capital   Austin Lyons: Senior Analyst, Creative Strategies; Founder, Chipstrat; Co-host, Semi Doped LinkedIn: https://www.linkedin.com/in/austinlyons/ Newsletter: https://www.chipstrat.com   Further Reading and Resources   Nvidia DGX GB Rack Scale Systems documentation: https://docs.nvidia.com/dgx/dgxgb200-user-guide/ OpenAI and Broadcom – "OpenAI and Broadcom Unveil LLM-Optimized Inference Chip": https://openai.com/index/openai-broadcom-jalapeno-inference-chip/ Chipstrat – Austin Lyons's newsletter: https://www.chipstrat.comSemi-doped: https://semidoped.com/   Timestamps   00:00 — No One's Brought a Chip to Market Built for LLMs 01:21 — Introducing Austin Lyons 02:16 — Why AI Buyers Now Buy Whole Systems, Not Parts 08:18 — Nvidia's Margins and the Case for System Simplicity 10:10 — Can a Startup Compete When You Have to Sell Systems? 14:12 — Prefill vs. Decode: Splitting the Inference Workload 24:51 — Fragmentation vs. Consolidation in AI Silicon 28:22 — Why Investors Missed the First Wave of Neoclouds 38:18 — The Circular Financing Debate 48:29 — Lyons's Four Conditions for the Next Trillion-Dollar Chip Company    About TechSurge: TechSurge Podcast shares the latest insights directly from legendary Silicon Valley leaders,daring new founders, and visionary technologists.Subscribe for weekly conversations into the intersection of technology advancement, market dynamics, and founder journeys. #AISilicon #LLMHardware #Nvidia #AIInference #TechPodcasts #AIInfrastructure

In this episode, Nobel Prize-winning physicist Dr. John Martinis reveals how his breakthrough in superconducting qubits made quantum physics real at macroscopic scale and what it means for the future of technology. The former lead of Google's Quantum AI lab explains why quantum computing is so fragile, why a lot of hype has a low chance to work, and why his fabless company Qolab could be the Nvidia of quantum computing In this conversation, Dr. Martinis joins TechSurge to explain the science behind macroscopic quantum coherence, the engineering challenges of scaling quantum computers, and how hybrid quantum-classical computing will shape the future of technology. The conversation covers: ✅ How the superconducting qubit breakthrough won the Nobel Prize in Physics✅ Why Nature wants to destroy quantum coherence and why quantum is fragile✅ From academic physics to building Google's quantum computer✅ The engineering challenge of scaling quantum computing beyond the lab✅ Why a lot of quantum computing hype has a low chance to work✅ How Qolab's fabless model could scale quantum hardware Guest Links: John Martinis: 2025 Nobel Prize laureate in Physics, superconducting-qubit pioneer, formerGoogle quantum-hardware researcher, and founder and CTO of Qolab.Nobel Prize profile: https://www.nobelprize.org/prizes/physics/2025/martinis/Qolab: https://qolab.ai/ Further Reading and Resources Google Sycamore Quantum Processor - Google’s 2019 experiment used a 53-qubitsuperconducting processor to perform a specific random-circuit-sampling task substantiallyfaster than the then-known classical approach. Nature research paper:https://www.nature.com/articles/s41586-019-1666-5 Google Research explanation:https://research.google/blog/quantum-supremacy-using-a-programmable-superconducting-processor/ Artificial Intelligence and Transformers – The Transformer architecture discussed in thepodcast was introduced in the paper “Attention Is All You Need.” Original paper:https://arxiv.org/abs/1706.03762 AlphaFold and Protein Structure Prediction – AlphaFold demonstrated how classical AI canpredict protein structures with high accuracy, illustrating the distinction between present-day AIand potential future quantum applications. Nature paper:https://www.nature.com/articles/s41586-021-03819-2 Google DeepMind – AlphaFold:https://deepmind.google/science/alphafold/ Quantum Computing Hardware Approaches – The podcast compares superconductingqubits, semiconductor spin qubits, neutral atoms, trapped ions and photonic systems. Google Quantum AI:https://quantumai.google/ Intel Quantum Computing:https://www.intel.com/content/www/us/en/research/quantum-computing.html QuEra – Neutral-atom quantum computing:https://www.quera.com/ Atom Computing:https://atom-computing.com/ Quantum Manufacturing and Scaling – Qolab is focused on improving the fabrication, wiring and scalability of superconducting quantum processors through industrial partnerships. Qolab:https://qolab.ai/ Qolab and Applied Materials collaboration:https://thequantuminsider.com/2025/03/18/qolab-secures-investment-from-applied-ventures-and-announces-collaboration-to-advance-quantum-computing-manufacturing/ Applied Materials:https://www.appliedmaterials.com/ Quantum–Optical Networking – The podcast discusses the challenge of convertingmicrowave signals used by superconducting qubits into optical signals suitable for fiber-optic communication. Microwave-to-optical conversion research:https://www.nature.com/articles/s41567-019-0650-1 Chapters: 00:00 – The Quantum Computing Hype: Physics vs Engineering04:06 – Introducing Nobel Prize Winner John Martinis12:09 – Schrödinger's Cat Explained13:12 – Can Quantum Effects Exist at a Macroscopic Scale?17:45 – The Experiment That Changed Quantum Computing34:33 – The Biggest Challenge: Scaling Quantum Computers43:21 – John Martinis on Google's Quantum Supremacy45:51 – AI vs Quantum Computing01:01:45 – Can Quantum and Classical Computers Work Together?01:07:36 – The NVIDIA Model for Quantum Computing About TechSurge: TechSurge Podcast shares the latest insights directly from legendary Silicon Valley leaders,daring new founders, and visionary technologists.Subscribe for weekly conversations into the intersection of technology advancement, market dynamics, and founder journeys. #quantumcomputing #quantumphysics #nobelprize #technology

Silicon Valley was built on semiconductors, but for nearly two decades, venture capital shifted its attention towards software. Today, AI is changing that as the demand for compute, memory and networking explodes, hardware is once again at the centre of the industry's biggest bets.  In this episode of TechSurge, host Michael Marks speaks with Lip-Bu Tan, CEO of Intel and one of the semiconductor industry's most influential investors and executives. The conversation traces Tan's journey from studying nuclear engineering at MIT to leading Cadence's turnaround, investing in more than 500 technology companies, and now steering Intel through one of the most significant transformations in its history. Tan shares his VC conviction on backing semiconductor startups when most venture investors favored software, and why he believes AI's next breakthroughs will come from advances in memory, packaging, photonics, cooling and high-speed connectivity. He also opens up on the leadership philosophy that defined his time at Cadence, where listening to customers and building a culture of responsiveness became the foundation of the company's revival. Wearing his CEO hat, Tan explains Intel's long-term strategy, why vertical integration still matters, how the company plans to reconnect with the startup ecosystem, and why missing another technology wave is not an option.  Speaker Profiles and Links Lip-Bu Tan: CEO of Intel Corporation, Chairman of Walden International, Founding Managing Partner of Walden Catalyst Ventures LinkedIn: https://www.linkedin.com/in/lip-bu-tan-284a7846/celesta.vc bio link Intel ceo bio link  Further reading and resources Reuters – “Intel’s new CEO plots overhaul of manufacturing and AI operations”https://www.reuters.com/technology/intels-new-ceo-plots-overhaul-manufacturing-ai-operations-2025-03-17/ Intel – https://www.intel.comCelesta Capital – https://www.celesta.vcSIA – “Global annual semiconductor sales increase 25.6% to $791.7 billion in 2025” – https://www.semiconductors.org/global-annual-semiconductor-sales-increase-25-6-to-791-7-billion-in-2025/Infercom – “What is an RDU? Reconfigurable Dataflow Unit” – https://infercom.ai/glossary/rdu/SemiconductorX – “Advanced Packaging: CoWoS, Foveros, EMIB, 3D IC” – https://semiconductorx.com/packaging-overview.htmlTWIML AI Podcast – “Dataflow Computing for AI Inference [Kunle Olukotun]” – https://twimlai.com/go/751 Chapters: 00:00- Introduction03:03- Lip-Bu Tan's Journey to Silicon Valley04:12- Betting on Semiconductors Before AI06:25- Why Hardware Matters Again07:35- Investing in Deep Tech09:31- Learning Through Boardrooms12:10- Building the Next Generation of AI Infrastructure17:03- The Cadence Turnaround19:03- Customer Obsession as a Leadership Strategy23:02- Rebuilding Intel26:03- AI's Next Bottlenecks30:32- Looking Ahead: The Future of Computing

Artificial intelligence is often discussed through models and GPUs. This episode looks beneath that surface, at the power delivery and networking required to make AI work at scale. Host Sriram Viswanathan speaks with Rajiv Khemani, a serial deep tech entrepreneur whose career has tracked several major infrastructure cycles: internet networking, cloud switching, blockchain compute and now AI networking. Khemani reflects on his early work at NetBoost and Intel, his operating role at Cavium, and the founding of Innovium, which Marvell agreed to acquire for $1.1 billion in 2021. He also explains how work on low-power blockchain silicon led his team toward the infrastructure demands created by generative AI. The discussion examines why incumbents often overlook emerging markets, why purpose-built hardware can outperform systems inherited from an earlier technology cycle, and how founders decide whether to keep financing a company or sell while the outcome remains attractive. Khemani describes the concentration risk of selling to a small number of hyperscalers, the fragility of semiconductor supply chains, and why leading-edge chip development now demands much larger balance sheets. The conversation then turns to AI’s emerging bottlenecks. Large models require many accelerators to operate as one computer, making low-latency scale-up and scale-out networks central to performance. The episode explores heterogeneous compute, open networking standards, memory scarcity, AI’s growing electricity demand, and the competition between AI and Bitcoin mining for energy.  Speaker Profiles and Links Sriram Viswanathan: Founding Managing Partner, Celesta Capital — https://www.linkedin.com/in/onesriram/ Rajiv Khemani: Co-founder and Executive Chairman, Upscale AI; deep-tech entrepreneur and IIT Delhi alumnus LinkedIn: https://www.linkedin.com/in/rajivkhemani/ Profile and contribution to the IIT, Delhi, Yardi School of Artificial Intelligence : https://scai.iitd.ac.in/rajiv-khemani  References Mentioned and Further Reading Upscale AI : https://upscaleai.com/ Upscale AI Launch Announcement : https://upscaleai.com/press-release/ Velaura AI : https://velaura.ai/ Acquisition of Innovium and cloud data-centre switching rationale, Marvell: https://www.marvell.com/company/newsroom/marvell-to-acquire-innovium-accelerates-cloud-growth-with-expanded-ethernet-switching-portfolio.html Cavium combination and infrastructure semiconductor strategy, Marvell:  https://www.marvell.com/company/newsroom/marvell-and-cavium-to-combine-creating-an-infrastructure-solutions-powerhouse.html Energy and AI, International Energy Agency: https://www.iea.org/reports/energy-and-aiEnergy demand from AI, International Energy Agency: https://www.iea.org/reports/energy-and-ai/energy-demand-from-ai Tokenisation in the context of money and other assets, Bank for International Settlements : https://www.bis.org/cpmi/publ/d225.pdf Leveraging tokenisation for payments and financial transactions, Bank for International Settlements : https://www.bis.org/publ/othp92.pdf Collective communication for clusters exceeding 100,000 GPUs, Meta researchers : https://arxiv.org/abs/2510.20171 Load balancing for AI training workloads, UC Berkeley researchers : https://arxiv.org/abs/2507.21372 Reliability in large-scale machine-learning clusters : https://arxiv.org/abs/2410.21680 Bitcoin: A Peer-to-Peer Electronic Cash System : https://bitcoin.org/bitcoin.pdf  Timestamps: [Timestamp] Chapter Title 00:00 - Highlights and welcome02:28 - From IIT Delhi to Silicon Valley07:50 - Building Through Major Technology Waves10:19 - Why Incumbents Miss Emerging Markets And Where Start-Ups Win12:47 - Building Innovium for the Cloud19:37 - Supply Shocks and Strategic Exits27:28 - From Bitcoin Chips to AI30:09 - Bitcoin, Tokenisation and Energy42:37 - Agentic AI and Future Networks53:21 - Memory, Capital and Founder Resilience

Canada produces world-leading science, engineering, and AI research. So why does so much of that research still commercialize outside of Canada? In this episode of TechSurge, host Nic Brathwaite puts that question to four leaders at two of Canada's top research universities: Mary Wells (Dean of Engineering) and Chris Houser (Dean of Science) at the University of Waterloo, and Heather Sheardown (Dean of Engineering) and Gianni Parise (VP Research) at McMaster. At Waterloo, Mary Wells traces how the university's origin produced one of the world's most influential co-op programs and a creator-owned IP policy that lets inventors keep their ideas, making the school a talent engine for global tech. The group digs into Canada's AI paradox, foundational research and talent but far less of the economic value, and what quantum, robotics, and advanced manufacturing show about getting research to market. McMaster runs a different model, built on health sciences, nuclear research, and problem-based learning. Heather Sheardown explains the McMaster Method and why it matters in an AI-shaped future. Gianni Parise argues for commercialization as a core university function, with work spanning AI-assisted drug discovery, inhaled vaccines, critical-mineral-free motors, and a campus nuclear reactor that supplies much of the world's iodine-125 for prostate cancer treatment. They also unpack Fusion Pharmaceuticals, the McMaster spin-out acquired by AstraZeneca, and what it reveals about university commercialization. Together, these conversations ask what universities must become in an era defined by AI, deep tech, national competitiveness, and the urgent need to move ideas from the lab into the world. Sign up for our newsletter at techsurgepodcast.com for updates on upcoming TechSurge Live Summits and future episodes. Speaker Profiles and Links Mary Wells - University of Waterloo Profile: https://uwaterloo.ca/engineering/about/dean-engineerinChris Houser - University of Waterloo profile: https://uwaterloo.ca/earth-environmental-sciences/profile/chouserHeather Sheardown - McMaster Engineering profile: https://www.eng.mcmaster.ca/chemeng/faculty/dr-heather-sheardown/Gianni Parise - McMaster Experts Profile: https://experts.mcmaster.ca/people/parisegChapters: 0:00 Highlights0:56 Welcome 2:39 Waterloo’s origin story 4:51 Creator-owned IP and the Waterloo model 7:42 The Co-op Flywheel 8:56 Canada’s AI paradox: world-class research, slower domestic value capture 10:21 AI, Regulation, Trust, and Canadian Competitiveness 17:09 Rethinking the PhD for Commercialisation 21:43 Inside Waterloo’s labs 30:14 What Waterloo wants to be in ten years: builders of the country 32:39 Meet McMaster: health sciences, nuclear capability, and research intensity 34:12 The McMaster Method 35:11 Research, Health, and Commercialisation 40:45 McMaster Labs: Heat, Motors and Health Innovation 47:13 Bioinnovation, Nuclear Research and Fusion Pharmaceuticals 58:43 The university of 2035: less lecture, deeper societal impact  References Mentioned and Further Reading University of Waterloo Policy 73 - Intellectual Property Rights: https://uwaterloo.ca/secretariat/policies-procedures-guidelines/policies/policy-73-intellectual-property-rightsUniversity of Waterloo - Our IP policy: https://uwaterloo.ca/entrepreneurship/our-ip-policyUniversity of Waterloo Co-op programs: https://uwaterloo.ca/future-students/co-opUniversity of Waterloo - Academy of Research Commercialization: https://uwaterloo.ca/conrad-school-entrepreneurship-business/graduate-students/academy-research-commercialization-arcOpen Quantum Design: https://openquantumdesign.org/Institute for Quantum Computing, University of Waterloo: https://uwaterloo.ca/institute-for-quantum-computing/CIFAR - Pan-Canadian Artificial Intelligence Strategy: https://cifar.ca/ai/Government of Canada / ISED - Pan-Canadian Artificial Intelligence Strategy: https://ised-isde.canada.ca/site/ised/en/pan-canadian-artificial-intelligence-strategyStatistics Canada - Understanding Canada’s innovation paradox: https://www150.statcan.gc.ca/n1/pub/36-28-0001/2024007/article/00002-eng.htmCouncil of Canadian Academies - Innovation and Business Strategy: Why Canada Falls Short: https://cca-reports.ca/wp-content/uploads/2018/10/2009-06-11-innovation-report-1.pdfKPMG / University of Melbourne - Trust, attitudes and use of artificial intelligence: https://assets.kpmg.com/content/dam/kpmg/ca/pdf/2025/07/trust-in-ai-en-report.pdfMcMaster - Our approach to teaching and learning / Problem-Based Learning: https://provost.mcmaster.ca/teaching-learning/our-approach/McMaster - Evidence-based medicine: https://fhshrwelcome.mcmaster.ca/did_you_know/evidence-based-medicine/McMaster Nuclear Reactor - Medical Isotopes: https://nuclear.mcmaster.ca/medical-isotopes/McMaster Industry Liaison Office - IP and commercialization FAQ: https://research.mcmaster.ca/mcmaster-industry-liaison-office-milo/ip-education/intellectual-property-guides/faqs/McMaster - AstraZeneca to acquire McMaster-supported Fusion Pharmaceuticals: https://news.mcmaster.ca/astrazeneca-to-acquire-mcmaster-supported-fusion-pharmaceuticals/Fusion Pharmaceuticals - FACIT investment and CPDC spin-out background: https://fusionpharma.com/facit-announces-investment-in-fusion-pharmaceuticals-and-alpha-emitting-radiotherapeutics/PubMed - Evidence-based medicine and problem-based learning at McMaster: https://pubmed.ncbi.nlm.nih.gov/31617018/ScienceDirect - Fifty Years on: The first problem-based learning programme at McMaster:

TechSurge is sponsored by Notion. From product roadmaps to investor updates, Notion is where modern teams plan, write, and ship together. Get started at http://notion.dev/techsurge. Search began as a way to find pages. AI is turning it into a way to ask, reason, decide, and act. Search has always been more than a technical problem. It is a way of organising knowledge, connecting intent with information, and increasingly, turning questions into actions. In the age of artificial intelligence, that basic function is being redefined. In this episode of TechSurge, host Sriram Vishwanath speaks with Prabhakar Raghavan, Chief Technologist at Google, about the long arc of search: from the early web and link analysis to knowledge graphs, language models, transformers, Gemini, and the unresolved question of how AI will change the way we find, trust, and use information. Prabhakar reflects on his career as a computer scientist, researcher, and technology leader, beginning with his time at IBM Research, where he worked on algorithms, optimization, databases, and early information retrieval. He explains how the explosion of unstructured data on the web created a new class of technical and economic problems. Search was not simply about indexing pages; it was about imposing structure on a chaotic information environment and building mechanisms that could connect supply, demand, relevance, authority, and trust. The conversation traces how early search evolved through link analysis and PageRank, drawing on ideas from scholarly citation analysis, graph theory, and algorithmic ranking. Prabhakar describes why authority and trust became central to search as the web grew, and why users themselves changed alongside the technology. As search engines became more capable, people moved from looking for simple webpages to asking richer, more contextual questions that required intent understanding rather than mere document retrieval. Sriram and Prabhakar then explore the transition from classical search to AI-infused products. Through examples such as Gmail Smart Reply, Smart Compose, Google Drive recommendations, and knowledge graphs, Prabhakar shows how prediction, context, and language modelling were already reshaping user experiences well before the current generative AI wave. These systems were early signals of a broader shift: computers moving from retrieving information to anticipating what users might need next. The episode also offers a technical tour of the major algorithmic milestones that led to today’s AI systems, including deep learning, sequence-to-sequence models, attention mechanisms, transformers, and the compute architectures needed to train and serve large models. Prabhakar explains why attention changed the quality of language modelling, why AI systems appear increasingly conversational, and why compute remains one of the central constraints in the field. At the heart of the discussion is the central tension facing search today: if AI systems can generate answers directly, what becomes of search as we know it? Prabhakar does not frame AI as the end of search, but as its next transformation. The future of search may be less about finding a page and more about understanding intent, synthesising knowledge, reasoning through ambiguity, and helping users complete complex tasks. The conversation closes with deeper questions about AI world models, hallucination, test-time compute, diffusion models, recursive self-improvement, theorem proving, and whether AI systems can ever reason with the same grounded understanding as humans. For Prabhakar, the challenge is not only to build more powerful models, but to understand their limits, failure modes, and relationship to truth. This episode is a wide-ranging exploration of how search became one of the defining technologies of the internet age—and how artificial intelligence may now force us to rethink what it means to search at all. Sign up for our newsletter at techsurgepodcast.com for updates on upcoming TechSurge Live Summits and future episodes. Links: Prabhakar Raghavan - Google Research profile: https://research.google/people/prabhakarraghavan/?&type=googlePrabhakar Raghavan - Google blogs and writing: https://blog.google/authors/prabhakar-raghavan/References Mentioned During the Discussion Brin and Page - The Anatomy of a Large-Scale Hypertextual Web Search Engine: https://research.google/pubs/the-anatomy-of-a-large-scale-hypertextual-web-search-engine/Page, Brin, Motwani and Winograd - The PageRank Citation Ranking: https://ilpubs.stanford.edu:8090/422/1/1999-66.pdfJon Kleinberg - Authoritative Sources in a Hyperlinked Environment: https://www.cs.cornell.edu/info/people/kleinber/auth.pdfManning, Raghavan and Schutze - Introduction to Information Retrieval: https://nlp.stanford.edu/IR-book/ Google - Introducing the Knowledge Graph: things, not strings: https://blog.google/products-and-platforms/products/search/introducing-knowledge-graph-things-not/Google Help - How Google's Knowledge Graph works: https://support.google.com/knowledgepanel/answer/9787176Further Reading Krizhevsky, Sutskever and Hinton - ImageNet Classification with Deep Convolutional Neural Networks: https://proceedings.neurips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.htmlVaswani et al. - Attention Is All You Need: https://papers.neurips.cc/paper/7181-attention-is-all-you-needDevlin et al. - BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding: https://aclanthology.org/N19-1423/Chen et al. - Gmail Smart Compose: Real-Time Assisted Writing: https://arxiv.org/abs/1906.00080Kannan et al. - Smart Reply: Automated Response Suggestion for Email: https://arxiv.org/abs/1606.04870Hoffmann et al. - Training Compute-Optimal Large Language Models: https://arxiv.org/abs/2203.15556 Tay et al. - Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers:

Semiconductors have moved from the background of the technology stack to the center of the AI economy. What used to be a specialized industry discussed mostly by engineers and investors is now shaping the speed, cost, and strategic direction of modern computing. In this episode of TechSurge, host Michael Marks speaks with Stacy Rasgon, Managing Director and Senior Analyst covering U.S. semiconductors and semiconductor capital equipment at Bernstein Research. Stacy has spent years analyzing the chip industry across cycles, but argues that the current moment feels different in scale: AI demand has created an unprecedented scramble for compute, memory pricing has surged, and companies across the stack are being forced to rethink capacity, architecture, and capital allocation. The conversation explains the 4 different kinds of semiconductor cycles—supply, inventory, product, and demand — and why Stacy believes the industry is currently in a demand cycle of unusual magnitude. The discussion also unpacks the distinction between DRAM and NAND, why high-bandwidth memory is becoming strategically central to AI systems, and how the physical realities of wafer capacity and silicon area are constraining supply in ways the broader market often misses. Stacy and Michael also discuss the hardware economics behind the current boom, with Michael pressing Stacy on why compute remains so scarce and how companies are improving performance through packaging and system design. Michael then moves the conversation beyond market headlines to the core business questions: who is actually paying for this compute, which use cases are generating real revenue, and whether AI spending is creating durable economic value or simply shifting costs elsewhere. Together, these questions highlight two of the episode's clearest insights: coding may be one of the earliest AI applications with meaningful willingness to pay, and inference, not training, is the real test of whether the current buildout becomes a lasting business or just another expensive wave of infrastructure. Stacy explains the concentration of power among the major wafer fabrication equipment players, the rise of ASICs as a meaningful share of AI silicon, Broadcom's rapidly expanding AI opportunity, and the growing role of Chinese companies as new entrants, especially in memory and semiconductor equipment. Along the way, the conversation asks the defining question facing the sector: is this just another semiconductor upswing, or the first true supercycle the industry has seen? Stacy believes that this might be the biggest supercycle he has seen in his career. Sign up for our newsletter at techsurgepodcast.com for updates on upcoming TechSurge Live Summits and future episodes. Links: Stacy Rasgon on LinkedIn: https://www.linkedin.com/in/stacy-rasgon-6924963Bernstein: https://www.alliancebernstein.com/corporate/en/home.htmlReferences Mentioned During the Discussion NVIDIA Blackwell Platform: https://www.nvidia.com/en-us/data-center/blackwell-platform/High Bandwidth Memory (HBM) overview from Micron: https://www.micron.com/products/memory/hbmDRAM overview from IBM: https://www.ibm.com/think/topics/dramNAND flash overview from IBM: https://www.ibm.com/think/topics/nand-flash-memoryFurther Reading McKinsey on the semiconductor industry outlook: https://www.mckinsey.com/industries/semiconductors/our-insights/the-semiconductor-industry-in-2025Semiconductor Industry Association: 2025 State of the U.S. Semiconductor Industry: https://www.semiconductors.orgNVIDIA on the Blackwell architecture and AI infrastructure roadmap: https://www.nvidia.com/en-us/data-center/blackwell-platform/Broadcom AI investor materials and infrastructure commentary: https://investors.broadcom.comASML on lithography and advanced chip manufacturing: https://www.asml.com/en/technologyMicron on HBM and AI memory demand: https://www.micron.com/products/memory/hbmChapters [00:00:00] — Highlights[00:00:26] — Welcome to  the Episode[00:01:29] — Meet Stacy Rasgon[00:02:01] — Is This the First Real Semiconductor Supercycle?[00:05:33] — Inside the Strongest Memory Cycle in History [00:09:14] — Can Innovation Keep Up With AI Demand?[00:11:33] — Chiplets, Blackwell, and the New Economics of Compute [00:12:37] — What Could Signal the Cycle Is Slowing[00:14:26] — Vertical Integration at the Hyperscales [00:16:36] — The Difference between Apple and Meta[00:17:15] — What is Vertical Integration Being Done For?[00:18:15] — Will other bottlenecks develop as This Progresses? [00:21:13] — Oligopoly Pricing in the Market[00:22:22] — Any New Entrants into Memory?[00:23:46] — Why the Industry Must Pivot From Training to Inference[00:25:10] — Agentic Coding and the First Real AI Revenues[00:26:57] — Groq, Low-Latency Inference, and What GPUs Cannot Do Alone[00:29:28] —-Could The Smaller Companies All be Bought Up ?[00:30:19] — Why Semiconductor Equipment Matters More Than Ever [00:31:00] — How Semiconductor Equipment is Affected by the Cycle[00:32:55] — A Long Upcycle for Semiconductor Equipment Guys?[00:33:13] — The Big Five and the Rise of Chinese Equipment Players[00:34:24] — The Effects of Geopolitics[00:35:02] — Broadcom’s Quiet AI Breakout[00:40:46] — ASICs vs GPUs and the Next Wave of Custom Chips[00:41:06] — Intel, Foundry Strategy, and the Long Turnaround[00:46:46] —-The Risks the Market May Still Be Underestimating[00:49:32] — Where Startups Still Have Room to Win[00:50:39] — What the Semiconductor Industry Could Look Like Next Year

For most of human history, space has been a place we visited. The next chapter may be about building there. For decades, space was the domain of governments, astronauts, and science fiction. Today, falling launch costs, reusable rockets, and a new generation of ambitious founders are turning orbit into something else entirely: a place to build. The question is no longer whether humanity can construct large-scale infrastructure in space, but what we should build first—and why. In this episode of TechSurge, host Sriram Vishwanath speaks with Dr. Ariel Ekblaw, Founder and CEO of Aurelia Institute, Research Affiliate at MIT’s Space Exploration Initiative, and founder of Rendezvous Robotics. Ariel has spent her career exploring one of the most fundamental challenges of the emerging space economy: how to build structures in orbit that are far larger than anything that can fit inside a rocket. Ariel explains the origins of TESSERAE, her pioneering work on autonomous self-assembling space architecture, and how ideas borrowed from biology, swarm intelligence, and modular construction could unlock a future of massive solar arrays, communications infrastructure, orbital laboratories, and eventually human habitats in space. The conversation explores the rapidly emerging market for in-orbit infrastructure, including AI data centers in space, space-based solar power, and the technologies needed to support a permanent industrial presence beyond Earth. Ariel breaks down the engineering realities behind these ideas—why cooling data centers in space is harder than most people assume, how autonomous assembly could solve the scale problem, and why the future of orbital infrastructure may look more like a business park than a collection of standalone satellites. Sriram and Ariel also discuss the broader implications of humanity’s return to space: the economics unlocked by reusable launch systems, the opportunities created by dramatically lower transportation costs, and the second-order innovations that may emerge from building an industrial ecosystem in orbit. Along the way, they examine space debris, stewardship of the orbital commons, artificial gravity, and what it will take to make long-term human habitation in space viable. At the heart of the discussion is Ariel’s belief that space is not an escape from Earth’s problems, but a tool for solving them. Whether through advanced manufacturing, new energy systems, biotechnology research, or entirely new industries, she argues that the next era of space exploration should be focused on improving life here at home. Sign up for our newsletter at techsurgepodcast.com for updates on upcoming TechSurge Live Summits and future episodes. Links: Ariel Ekblaw on LinkedIn:https://www.linkedin.com/in/arielekblawAurelia Institute:https://www.aureliainstitute.orgRendezvous Robotics:https://www.rdvrobotics.comMIT Space Exploration Initiative:https://www.media.mit.edu/groups/space-exploration/overview/ How Aurelia is Designing Self-Assembling Space Stations: https://www.fastcompany.com/91242689/how-the-aurelia-institute-is-designing-a-self-assembling-space-stationOverview Energy (Space-Based Solar Power): https://www.overviewenergy.comStarCatcher Industries (Space-to-Space Power Transmission): https://www.starcatcherindustries.comImpulse Space (Orbital Transportation): https://www.impulsespace.com References Mentioned During the Discussion Earthrise - The Apollo 8 Photograph: https://www.nasa.gov/image-article/apollo-8-earthrise/Carl Sagan’s “Pale Blue Dot”: https://www.planetary.org/worlds/pale-blue-dotBuckminster Fuller Institute: https://www.bfi.org Watch Ariel’s Talks & Interviews Aurelia Institute YouTube Channel: https://www.youtube.com/@AureliaInstituteAriel’s TED Talk: https://youtu.be/IHrGK3Mu5K4?si=QwGHq1BEoB-QMUjkSpace Business Podcast - Self-Assembling Space Habitats with Ariel Ekblaw: https://spacebusiness.podbean.com/e/137-self-assembling-space-habitats-ariel-ekblaw-founder-ceo-aurelia-institute/ Further Reading NASA’s Artemis Program: https://www.nasa.gov/artemisInternational Space Station (ISS): https://www.nasa.gov/international-space-stationAurelia Institute’s Vision for Humanity’s Future in Space: https://www.aureliainstitute.orgMIT News: Supporting Mission-Driven Space Innovation: https://news.mit.edu/2025/supporting-mission-driven-space-innovation-aurelia-institute-0710 Timestamps: [00:00] Highlights [00:34] Welcome to the Episode [02:33] The New Space Race Begins [04:10] Meet Dr. Ariel Ekblaw [06:30] Why We Explore Space?  [12:53] How She Discovered Self-Assembly at MIT  [17:10] How TESSERAE Tiles Build Themselves [20:14] How the Tiles Coordinate Like a Swarm [24:47] Repairing and Reconfiguring Structures in Orbit [28:32] Why the Space Industry Is Exploding Now [34:25] The Case for AI Data Centers in Space [45:21] How Much Compute Will Move to Space? [48:40] Why This Space Era Is Different [52:24] The Growing Problem of Space Debris [55:14] Building the Next SpaceX [57:27] What Could Go Wrong in Space? [59:33 ] How Many Hours of Gravity Do Humans Need? [01:00:38] Why We Should Build in Low Earth Orbit First [01:05:09] Should We Really Colonize Mars? [01:11:27] Could You Commute to Space for Work? [01:13:50] Who Makes the Rules in Space? [01:22:30] What's Overhyped and Underhyped in Space [01:26:57]What's the Real Story in Space?

For years, the United States told itself a reassuring story: China could manufacture and copy, but it couldn't innovate. That story is no longer credible. From DeepSeek's compute-efficient AI model to BYD's dominance of the global EV market, China is producing both volume and quality across sectors that matter. The question is no longer whether China can compete — it's whether the United States is playing its own hand well. In this episode of TechSurge, host Michael Marks speaks with Vivek Chilukuri, Senior Fellow at CNAS, where he focuses on U.S.–China technology competition, AI policy, and digital geopolitics. Vivek's path from counter-terrorism work at the State Department to tech policy in the Senate gives him an unusually grounded perspective on how government actually functions — and where it keeps failing itself. Vivek and Michael work through the full competitive landscape: the wake-up moments that shifted Washington's focus from manufacturing to technology dominance, why the dual-use nature of advanced technology has pulled the national security community into conversations once left to industry, and what Made in China 2025 actually achieved — and where it fell short. The conversation goes deep on America's policy toolkit: what the CHIPS Act accomplished and why it wasn't enough, how export controls on advanced semiconductors are working and what they're missing, and why Washington is far too weighted toward restriction at the expense of the "run faster" side of the equation. Vivek is also candid about what DeepSeek really tells us — not just about Chinese innovation, but about the gap between building a model and deploying AI at scale. They also explore the global dimension: China's "easy button" approach to technology exports, what the U.S. AI exports program is trying to do in response, the rise of "AI sovereignty" movements from Brussels to Delhi, and why the talent and immigration decisions of the past year amount to a serious self-inflicted wound. The United States still holds the best hand in the world for this competition. The question Vivek keeps returning to is whether we're playing it well — and right now, his honest answer is no. Sign up for our newsletter at techsurgepodcast.com for updates on upcoming TechSurge Live Summits and future Season 2 episodes. Episode Links: Connect with Vivek: https://www.linkedin.com/in/vivekchilukuri/Learn more about CNAS: https://www.cnas.orgTimestamps: [02:11] Wake-Up Calls: Chips & 5G [04:17] Atoms vs Bits in AI [07:27] China's Innovation Surge [10:57] Systems Capital vs Planning [14:14] Made in China 2025 Scorecard [17:23] US Tools: Chips & Controls [24:12] DeepSeek & Compute Scarcity [26:47] Energy Constraints & Scaling [29:01] AI Exports & the Easy Button [32:43] Allies & AI Sovereignty [36:13] Talent Flows & Immigration [39:04] Beyond AI: The Biotech Frontier [43:30] Founder Advice: Global South [45:20] Wrap-Up & Key Takeaways

For thirty years, the United States largely ignored critical minerals. We mined less, processed less, and stockpiled less — while China quietly built the most dominant mineral supply chain in modern history. When China imposed rare earth export restrictions in 2024, manufacturers from Detroit to Tokyo scrambled. The invisible inputs powering electric vehicles, semiconductors, AI data centers, and defense systems had suddenly become visible — and vulnerable. In this episode of TechSurge, host Sriram Viswanathan speaks with Dr. Gracelin Baskaran, Director of the Critical Mineral Security Program at the Center for Strategic and International Studies. A mining economist with over a decade of field experience across Africa, Latin America, Asia, and the Middle East, Gracelin is one of the sharpest minds working on how the world secures the raw materials that make advanced technology possible. Gracelin brings a clarifying perspective to a topic that is often framed as a geopolitical contest: the real challenge, she argues, is economic. Until mining in allied countries is genuinely profitable — until the capital, energy infrastructure, processing technology, and policy stability are all in place — supply chain security remains aspirational, regardless of how many executive orders get signed. Sriram and Gracelin work through the full landscape: what critical minerals actually are and why the term matters, how China built its dominance not just through geology but through industrial strategy and foreign policy, and why the 29-year average timeline from mineral discovery to production creates a fundamental tension with the pace of technology investment. They examine the gap the CHIPS Act left unfilled, the case for aggregating allied demand to change the economics of new mines, and what tech CEOs are dangerously wrong to assume about their own supply chains. They also dig into the emerging policy architecture: Project Vault as a demand-driven civilian stockpile, the critical minerals ministerial that brought 55 countries to Washington, and the role of recycling and AI-driven exploration in accelerating a supply chain that cannot be built on mining alone. Ultimately, Gracelin argues that America's greatest advantage is not its geology — it is its capacity to innovate. But innovation without investment, and investment without durable policy, will not be enough. The window is open. The question is whether the commitment holds. If you enjoy this episode, please subscribe and leave us a review on your favorite podcast platform. Sign up for our newsletter at techsurgepodcast.com for updates on upcoming TechSurge Live Summits and future Season 2 episodes. Episode Links: Connect with Gracelin: https://www.linkedin.com/in/gracelinbaskaran/ Learn more about CSIS Critical Minerals Security Program: https://www.csis.org/programs/energy-security-and-climate-change-program/critical-minerals-securityTimestamps:[00:00] China’s Rare Earth Wake-Up Call[02:57] The Origin Story Behind Gracelin[05:02] What “Critical Minerals” Actually Means[08:17] Saudi Arabia’s Mineral Strategy Playbook[10:33] Why Economics Matters More Than Geology[13:54] Why New Mines Take Decades to Build[16:42] China’s Supply Chain Dominance Explained[24:57] America’s Workforce and Processing Problem[27:05] Innovation vs Scale in the Mineral Race[29:54] Can the US Rebuild Mineral Processing?[33:10] Startups, Capital, and the Mining Challenge[35:02] Belt and Road, Security, and Global Supply[41:19] The CHIPS Act’s Missing Ingredient[46:21] The Policy Blueprint for Critical Minerals[51:59] Project Vault Explained[53:54] Rapid-Fire Takeaways and Final Reality Check

For years, crypto policy in the United States was defined less by clear rules than by the threat of enforcement. Startups and institutions building in the space operated in a gray zone: no clear guidance, no path to compliance, and always the possibility of a regulatory hammer coming down. In 2025, that began to change. In this episode of TechSurge, host Sriram Viswanathan speaks with Commissioner Hester Peirce of the U.S. Securities and Exchange Commission — one of Washington's most closely watched voices on digital asset policy. Known informally as "Crypto Mom" for her consistent advocacy that markets work best with clear rules and room to innovate, Commissioner Peirce was designated in 2025 to lead the SEC's first Crypto Task Force, signaling a more structured, collaborative approach to digital asset regulation. Commissioner Peirce brings a rare perspective: a regulator who believes that ambiguity does not protect investors — it protects incumbents and rewards bad actors. In this conversation, she explains what has actually changed in 2025, what it means for companies building in crypto, and what it will take to make this regulatory progress durable beyond any single administration. Sriram and Commissioner Peirce work through the full landscape: why "crypto" is not one thing but several, how the SEC thinks about Bitcoin as a commodity, what tokenization of traditional securities actually requires, and where real policy gaps remain. They also examine the role of stablecoins and CBDCs, the tension between investor protection and permissionless innovation, and how vertical integration in crypto markets raises the same questions the financial system has always faced — just with new architecture underneath. Ultimately, Commissioner Peirce argues that the best regulatory framework is one that lets markets identify where technology is useful, enforces rules fairly and consistently, and makes enough room for people to build real things that solve real problems. Once those products exist and are woven into daily economic life, she argues, they become durable — regardless of who is in office. If you enjoy this episode, please subscribe and leave us a review on your favorite podcast platform. Sign up for our newsletter at techsurgepodcast.com for updates on upcoming TechSurge Live Summits and future Season 2 episodes. Episode Links Connect with Hester: https://x.com/HesterPeirce Learn more about the SEC Crypto Task Force: sec.gov/crypto Timestamps 00:00 Permissionless Innovation 02:05 Crypto Basics Explained 09:25 State of US Crypto Policy 11:13 Howey Test and Tokenization14:25 Crypto as Strategic Advantage23:17 2025 Policy Turning Point 30:06 DeFi Consumer Protection 40:01 Bitcoin’s Unique Role

Digital imaging is so ubiquitous today that it’s easy to forget how improbable it once was. In this episode of TechSurge, guest host Nic Brathwaite sits down with Dr. Eric Fossum, inventor of the CMOS active pixel image sensor, to unpack the breakthrough that made it possible to embed cameras into billions of devices and the deeper lessons behind it. Eric explains how his work began not with consumer electronics, but with a NASA constraint: how to shrink a refrigerator-sized space camera into something small enough for spacecraft. The solution required a fundamental shift in architecture. By moving from CCD-based imaging to CMOS, where sensing and processing could happen on a single chip, he enabled a level of miniaturization and scalability that transformed cameras from standalone systems into embedded infrastructure. But the conversation goes far beyond the invention itself. Nic and Eric explore what it takes to commercialize deep technology, from the early days of Photobit to its acquisition by Micron, and the critical role ecosystems play in turning breakthroughs into global platforms. They discuss why intellectual property is less about protection and more about leverage, and why even the most important inventions require manufacturing scale, capital, and partnerships to succeed. The episode also looks forward. As AI systems increasingly rely on visual and physical data, sensors are shifting from tools designed for human perception to components optimized for machine intelligence. Eric highlights the challenges of pushing intelligence to the edge, the limitations of current architectures, and the growing importance of sensing technologies beyond traditional imaging—including molecular detection and new materials that go beyond silicon. While much of today’s investment is concentrated in models and compute, this conversation makes the case that the next wave of innovation may come from deeper layers of the stack, where machines interact directly with the physical world. The future of AI may depend not just on how systems think, but on how they see, detect, and understand their environment. If you enjoy this episode, please subscribe and leave us a review on your favorite podcast platform. Sign up for our newsletter at techsurgepodcast.com for updates on upcoming TechSurge Live Summits and future Season 2 episodes. Episode Links Connect with Eric and learn more about his work and recognition: https://engineering.dartmouth.edu/community/faculty/eric-fossum Learn more about CMOS image sensors: https://www.spacefoundation.org/space_technology_hal/active-pixel-sensor/Timestamps 02:00 From CCD to CMOS: Rethinking How Images Are Captured06:45 The NASA Problem: Shrinking a Camera for Space12:30 From Refrigerator to Coffee Cup and Beyond19:30 From Lab to Market: Founding Photobit26:00 Scaling the Technology: Micron, Manufacturing, and Cost31:00 The Role of IP in Deep Tech: Leverage vs Protection39:30 From Human Vision to Machine Perception44:30 Edge AI vs Centralized Compute: Where Intelligence Lives49:30 Beyond Imaging: Molecular Sensing and New Frontiers53:30 What Comes Next: Materials, Sensors, and the Limits of Silicon

As artificial intelligence becomes a strategic capability for nations as well as companies, questions of governance, safety, and geopolitical competition are moving to the forefront. In this episode of TechSurge, host Sriram Viswanathan speaks with Helen Toner, Interim Executive Director of the Center for Security and Emerging Technology (CSET) at Georgetown and a former OpenAI board member, about the rise of sovereign AI stacks and the global implications of increasingly powerful AI systems. Helen brings a rare vantage point from both inside the frontier AI ecosystem and the policy world. She reflects on lessons from her time on the OpenAI board, including the governance challenges that arise when nonprofit missions intersect with enormous commercial incentives and rapid technological progress. As AI capabilities accelerate, she argues that the industry is still grappling with deep uncertainty about how these systems work, how they will evolve, and what responsibilities companies and governments should carry. The conversation explores the idea of sovereign AI; the growing push by countries to control key layers of the AI stack, including compute infrastructure, models, and data. Helen explains why governments increasingly view AI as a strategic national resource, comparable to past transformative technologies like electricity or the internet. At the same time, she cautions that full technological independence may be unrealistic for most nations, given the complexity and global interdependence of the AI supply chain. Sriram and Helen also examine the evolving US–China AI competition, the role of export controls and semiconductor supply chains, and how different countries, from China to emerging AI hubs in the Middle East, are positioning themselves in the race to build advanced AI capabilities. Along the way, they discuss whether the industry should slow down development, how companies are experimenting with “safety frameworks” for frontier models, and why installing guardrails may be more realistic than attempting to halt progress altogether. Ultimately, Helen argues that society is entering a period of profound uncertainty. AI is transitioning from a research discipline into a foundational system that will shape economies, security, and daily life. Navigating that transition will require not just technical breakthroughs, but new approaches to governance, transparency, and global cooperation. If you enjoy this episode, please subscribe and leave us a review on your favorite podcast platform. Sign up for our newsletter at techsurgepodcast.com for updates on upcoming TechSurge Live Summits and future Season 2 episodes.-- Episode Links Connect with Helen: linkedin.com/in/helen-toner-4162439a Learn more about CSET: https://cset.georgetown.edu/--Timestamps 03:00 Lessons from the OpenAI Board: Governance in the Age of Frontier AI 05:00 The Big Unknowns in AI Development: Why Experts Still Disagree 12:05 Public Trust and the Risk of an AI Backlash 14:20 When AI Became Infrastructure: From Research Field to Societal System 16:00 Is AGI a Meaningless Term Now? Rethinking the Goalposts19:05 AI’s True Scale: Internet-Level Impact or Something Bigger?23:15 Why Frontier AI Labs Struggle to Slow Down 24:40 What “Sovereign AI” Actually Means for Nations 28:10 Mapping the AI Stack: Chips, Cloud, Models, and Applications33:38 The US–China AI Competition: Who’s Ahead and Why39:44 China’s Progress in AI: Compute Constraints and Fast Followers44:03 US AI Policy: Export Controls, Regulation, and Federal Preemption48:40 Frontier AI Safety Frameworks: How Labs Define Dangerous Capabilities51:36 The Future of AI: Utopia, Industrialization, or Something Worse?56:04 Rapid Fire: AI Misconceptions, Governance Reforms, and Regions to Watch