TechSurge: Deep Tech Podcast

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 Tech Surge 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, former
Google 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-qubit
superconducting processor to perform a specific random-circuit-sampling task substantially
faster 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 the
podcast 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 can
predict protein structures with high accuracy, illustrating the distinction between present-day AI
and 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 superconducting
qubits, 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 converting
microwave 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 Engineering
04:06 – Introducing Nobel Prize Winner John Martinis
12:09 – Schrödinger's Cat Explained
13:12 – Can Quantum Effects Exist at a Macroscopic Scale?
17:45 – The Experiment That Changed Quantum Computing
34:33 – The Biggest Challenge: Scaling Quantum Computers
43:21 – John Martinis on Google's Quantum Supremacy
45:51 – AI vs Quantum Computing
01:01:45 – Can Quantum and Classical Computers Work Together?
01:07:36 – The NVIDIA Model for Quantum Computing
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#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.com
Celesta Capital – https://www.celesta.vc
SIA – “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.html
TWIML AI Podcast – “Dataflow Computing for AI Inference [Kunle Olukotun]” – https://twimlai.com/go/751

Chapters:

00:00- Introduction
03:03- Lip-Bu Tan's Journey to Silicon Valley
04:12- Betting on Semiconductors Before AI
06:25- Why Hardware Matters Again
07:35- Investing in Deep Tech
09:31- Learning Through Boardrooms
12:10- Building the Next Generation of AI Infrastructure
17:03- The Cadence Turnaround
19:03- Customer Obsession as a Leadership Strategy
23:02- Rebuilding Intel
26:03- AI's Next Bottlenecks
30:32- 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-ai
Energy 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 welcome
02:28 - From IIT Delhi to Silicon Valley
07:50 - Building Through Major Technology Waves
10:19 - Why Incumbents Miss Emerging Markets And Where Start-Ups Win
12:47 - Building Innovium for the Cloud
19:37 - Supply Shocks and Strategic Exits
27:28 - From Bitcoin Chips to AI
30:09 - Bitcoin, Tokenisation and Energy
42:37 - Agentic AI and Future Networks
53:21 - 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.
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Speaker Profiles and Links
Mary Wells - University of Waterloo Profile: https://uwaterloo.ca/engineering/about/dean-engineerin
Chris Houser - University of Waterloo profile: https://uwaterloo.ca/earth-environmental-sciences/profile/chouser
Heather 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 Highlights
0:56 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-rights
University of Waterloo - Our IP policy: https://uwaterloo.ca/entrepreneurship/our-ip-policy
University of Waterloo Co-op programs: https://uwaterloo.ca/future-students/co-op
University of Waterloo - Academy of Research Commercialization: https://uwaterloo.ca/conrad-school-entrepreneurship-business/graduate-students/academy-research-commercialization-arc
Open 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-strategy
Statistics Canada - Understanding Canada’s innovation paradox: https://www150.statcan.gc.ca/n1/pub/36-28-0001/2024007/article/00002-eng.htm
Council 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.pdf
KPMG / 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.pdf
McMaster - 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:

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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.
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Links:
Prabhakar Raghavan - Google Research profile: https://research.google/people/prabhakarraghavan/?&type=google
Prabhakar 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.pdf
Jon Kleinberg - Authoritative Sources in a Hyperlinked Environment: https://www.cs.cornell.edu/info/people/kleinber/auth.pdf
Manning, 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/9787176
Further Reading
Krizhevsky, Sutskever and Hinton - ImageNet Classification with Deep Convolutional Neural Networks: https://proceedings.neurips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html
Vaswani et al. - Attention Is All You Need: https://papers.neurips.cc/paper/7181-attention-is-all-you-need
Devlin 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/1906.00080
Kannan et al. - Smart Reply: Automated Response Suggestion for Email: https://arxiv.org/abs/1606.04870
Hoffmann 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: