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by Celesta Capital | Deep Tech Venture Capital Firm
The TechSurge: Deep Tech VC Podcast explores the frontiers of emerging tech, geopolitics, and business, with conversations tailored for entrepreneurs, technologists, and investment professionals. Presented and hosted by the Celesta Capital team. Each discussion delves into the intersection of technology advancement, market dynamics, and the founder journey, offering insights into the vast opportunities and complex challenges ahead. Episode topics include AI, data center transformation, blockchain, cyber security, healthcare innovation, VC investment trends, tips for first-time founders, and more. Tune in to hear directly from Silicon Valley leaders, daring new founders, and visionary thinkers. Past guests include Intel CEO Lip-Bu Tan, Micron CEO Sanjay Mehrotra, VC investor Vinod Khosla, and executive leaders from OpenAI, Microsoft, Google, and other leading tech companies.
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- 1 weeks ago57 min
The Path to Superintelligence: Alberta Plan Co-Author On Reinforcement Learning & AI-Powered Prosthetics
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 augmentationGuest 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/#teachingBLINC Lab: https://blinclab.caFurther Reading and ResourcesThe 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/2208.11173The 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.htmlReward 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/S0004370221000862AI 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=wvga9chmb6ExperienceFlow.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-SuperintelligenceAMD’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.htmlChapters:00:00 – Welcome to Edmonton: AI, Free Will, and the Rodeo00:57 – Why AI Researchers Name Papers Better Than Other Scientists02:37 – Should We Anthropomorphize AI?08:03 – The Alberta Plan, Explained11:25 – “LLMs
- Sep 29, 20261h 0m
Gaming Chip Pioneer Raja Koduri on China's AI Cost Advantage and Moving Beyond the GPU
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 ResourcesOpenAI 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 Chapters00:00 - A Gigawatt of AI Now Costs $60 Billion01:58 - Introducing Raja Koduri02:02 - What Oxmiq Builds: Electrons In, Tokens Out05:18 - The $24 Trillion AI Infrastructure Bill06:05 - China vs. the Rest of the World09:10 - Memory Is the New Bottleneck22:03 - How AI Agents Are Changing Chip Design36:30 - Lessons From Steve Jobs and Lisa Su44:54 - Advanced Packaging, Memory Prices, and Intel's Mistake55:38 - Boom or Bust: The Future of Token FactoriesAbout 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.
- Sep 16, 20261h 12m
The Race to Build the Next Trillion-Dollar AI Chip Company
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 DopedLinkedIn: 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 LLMs01:21 — Introducing Austin Lyons02:16 — Why AI Buyers Now Buy Whole Systems, Not Parts08:18 — Nvidia's Margins and the Case for System Simplicity10:10 — Can a Startup Compete When You Have to Sell Systems?14:12 — Prefill vs. Decode: Splitting the Inference Workload24:51 — Fragmentation vs. Consolidation in AI Silicon28:22 — Why Investors Missed the First Wave of Neoclouds38:18 — The Circular Financing Debate48:29 — 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
- Sep 1, 20261h 8m
Nobel Laureate John Martinis on Quantum Computing’s Turning Point
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 computingIn this conversation, Dr. Martinis joins Tech Surge to explain the science behind macroscopicquantum 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 hardwareGuest 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 ResourcesGoogle 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/s41586-019-1666-5Google 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/1706.03762AlphaFold 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/s41586-021-03819-2Google 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.htmlQuEra – 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/s41567-019-0650-1Chapters: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 ComputingAbout 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
- Aug 11, 202635 min
Intel CEO Lip-Bu Tan on 40 Years of Contrarian Bets in Semiconductors
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 LinksLip-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 resourcesReuters – “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/751Chapters: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
- Jul 28, 20261h 11m
The Moving Bottleneck: Networking, Power, Memory, and the Race to Win AI
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 LinksSriram 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 ReadingUpscale 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:</
- Jul 15, 20261h 9m
Physical AI, Quantum, and Bio-Innovation: Inside Canada’s Research Frontier
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 LinksMary 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 ReadingUniversity 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
- Jun 30, 20261h 18m
Google's Chief Technologist on Intelligent Search in the Age of AI
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/Refer
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About TechSurge: Deep Tech Podcast
The TechSurge: Deep Tech VC Podcast explores the frontiers of emerging tech, geopolitics, and business, with conversations tailored for entrepreneurs, technologists, and investment professionals. Presented and hosted by the Celesta Capital team. Each discussion delves into the intersection of technology advancement, market dynamics, and the founder journey, offering insights into the vast opportunities and complex challenges ahead. Episode topics include AI, data center transformation, blockchain, cyber security, healthcare innovation, VC investment trends, tips for first-time founders, and more. Tune in to hear directly from Silicon Valley leaders, daring new founders, and visionary thinkers. Past guests include Intel CEO Lip-Bu Tan, Micron CEO Sanjay Mehrotra, VC investor Vinod Khosla, and executive leaders from OpenAI, Microsoft, Google, and other leading tech companies.
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It's a free AI-powered email that summarizes new episodes of TechSurge: Deep Tech Podcast as soon as they're published. You get the key takeaways, notable quotes, and links & mentions — all in a quick read.
How does the TechSurge: Deep Tech Podcast podcast summary work?
When a new episode drops, our AI transcribes and analyzes it, then generates a personalized summary tailored to your interests and profession. It's delivered to your inbox every morning.
Is this an official TechSurge: Deep Tech Podcast product?
No. Podzilla is an independent service that summarizes publicly available podcast content. We're not affiliated with or endorsed by Celesta Capital | Deep Tech Venture Capital Firm.
Can I get summaries of other podcasts too?
Absolutely! The free plan covers up to 3 podcasts, any in the catalog. Basic raises that to 15, Pro to 40 and Max to 100. Browse our full catalog at /podcasts.
How often does TechSurge: Deep Tech Podcast release new episodes?
TechSurge: Deep Tech Podcast publishes biweekly. Our AI generates a summary within hours of each new episode.
What topics does TechSurge: Deep Tech Podcast cover?
TechSurge: Deep Tech Podcast covers topics including Technology, Business, Investing. Our AI identifies the specific themes in each episode and highlights what matters most to you.
Start getting TechSurge: Deep Tech Podcast summaries tomorrow morning.
Free forever for up to 3 podcasts. No credit card required.
Free forever for up to 3 podcasts. No credit card required.