TechSurge: Deep Tech Podcast

Before 2020, few people thought about supply chains. The pandemic put them on every boardroom agenda, and trade and geopolitics now make them critical issues for every global company. Few businesses have a better view into what changed than FedEx.

In this episode of TechSurge, host Michael Marks speaks with Raj Subramaniam, who has run FedEx since 2022 as only the second CEO in the company’s history after founder Fred Smith. The company moves close to 18 million packages a day across more than 220 countries and territories, roughly $2 trillion of goods a year.

Subramaniam shares his perspective on  “re-globalization”, describing a fundamental shift in trade patterns that began in 2025. He walks through the operational shock of the US ending de minimis treatment for low-value imports, which forced FedEx to formally clear six times as many packages and hire more than 2,000 people in a matter of weeks, and explains why the company has quietly opened new hubs and routes across Asia, the Middle East, and Latin America to stay ahead of shifting trade flows.

The conversation turns to data and AI: FedEx scans 18 million packages roughly 20 times a day, generating two petabytes of data daily, which the company uses for everything from predictive logistics for hospital deliveries to an AI tool that picks the correct customs classification code for international shipments. Subramaniam also discusses FedEx’s expanding use of physical AI, including robotic trailer-loading partnerships with Dexterity and Berkshire Grey and nearly a million miles of autonomous trucking with Aurora in Texas, and makes the case that AI is adding jobs at FedEx rather than eliminating them.

Marks and Subramaniam also discuss why Amazon is a FedEx customer rather than a competitor, the company’s recent spinoff of FedEx Freight, its new supply chain orchestration partnership with the US Army, and Subramaniam’s own 35-year career at FedEx, including his path as an immigrant who rose to lead the company.

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Speaker Profiles and Links

Michael Marks: Founding Managing Partner, Celesta Capital

Raj Subramaniam: President and CEO, FedEx
 Wikipedia: https://en.wikipedia.org/wiki/Raj_Subramaniam

LinkedIn
https://www.linkedin.com/in/rajeshsubramaniam1/

Further Reading and Resources

• White House – Executive Order 14324, ending de minimis duty-free treatment: https://www.whitehouse.gov/presidential-actions/2025/07/suspending-duty-free-de-minimis-treatment-for-all-countries/
• Supply Chain Dive – FedEx’s 2025 shipping agreement with Amazon: https://www.supplychaindive.com/news/fedex-amazon-new-shipping-contract-2025/747942/
• FedEx Newsroom – FedEx and Dexterity’s physical AI trailer-loading deployment: https://newsroom.fedex.com/newsroom/global-english/fedex-and-dexterity-expand-physical-ai-deployment-for-autonomous-trailer-loading-at-hagerstown-hub
• FedEx Newsroom – FedEx and Aurora autonomous trucking partnership: https://newsroom.fedex.com/newsroom/fedex-teams-up-with-aurora-and-paccar-to-test-autonomous-linehaul-technology/
• U.S. Army – Strategic Capital Initiative with FedEx Dataworks: https://www.army.mil/article/294805/army_launches_flagship_strategic_capital_initiative_with_fedex_dataworks_to_modernize_logistics_infrastructure
• Fortune – Profile of Raj Subramaniam: https://fortune.com/2026/07/15/fedex-ceo-raj-subramaniam-tariffs-supply-chain-shipping-fortune-500-titans-and-disruptors-of-industry/
  
  

Timestamps

 [00:00] Why Supply Chains Became a Boardroom Issue
 [00:38] Introducing Raj Subramaniam and FedEx
 [02:27] Is Today’s Trade Environment “Business as Usual”?
 [02:50] Megatrend One: Changing Trade Patterns
 [05:44] Megatrend Two: The $1.8 Trillion Supply Chain Problem
 [07:37] What Actually Changed in 2025
 [08:00] The End of De Minimis: A 6x Customs Surge
 [10:35] Opening New Hubs: Reactive or Predictive?
 [11:42] Tariffs and Pricing: Just a Pass-Through
 [14:17] Vertical Integration and the FedEx Freight Spinoff
 [17:18] FedEx’s Network Moat
 [18:18] Amazon: Customer, Not Competitor
 [19:45] The Three Vectors of FedEx’s Growth
 [21:06] Two Petabytes a Day: AI and FedEx’s Data Advantage
 [23:20] Predictive AI and FedEx’s #1 Position in Healthcare
 [25:30] AI-Powered Customs Codes and Supply Chain Orchestration
 [26:27] FedEx’s New Deal with the US Army
 [27:43] Physical AI: Robots Loading Trucks and Driverless Trucks in Texas
 [28:45] Why AI Is Adding Jobs at FedEx, Not Cutting Them
 [29:13] Investing in Startups: FedEx as a Platform
 [32:06] Raj’s 35-Year Career at FedEx
 [34:35] Inside FedEx’s People-Service-Profit Culture
 [35:30] On Immigration and the American Dream
 [37:48] Global Mobility Inside FedEx
 [39:46] FedEx Cares: Delivering Vaccines and Oxygen During COVID

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-3a51611
X: https://x.com/RajaXg
Oxmiq: 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.com
Semi-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, 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

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.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

1. Upscale AI : https://upscaleai.com/ 
2. Upscale AI Launch Announcement : https://upscaleai.com/press-release/ 
3. Velaura AI : https://velaura.ai/ 
4. 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 
5. Cavium combination and infrastructure semiconductor strategy, Marvell:  https://www.marvell.com/company/newsroom/marvell-and-cavium-to-combine-creating-an-infrastructure-solutions-powerhouse.html 
6. Energy and AI, International Energy Agency: https://www.iea.org/reports/energy-and-ai
7. Energy demand from AI, International Energy Agency: https://www.iea.org/reports/energy-and-ai/energy-demand-from-ai 
8. Tokenisation in the context of money and other assets, Bank for International Settlements : https://www.bis.org/cpmi/publ/d225.pdf 
9. Leveraging tokenisation for payments and financial transactions, Bank for International Settlements : https://www.bis.org/publ/othp92.pdf 
10. Collective communication for clusters exceeding 100,000 GPUs, Meta researchers : https://arxiv.org/abs/2510.20171 
11. Load balancing for AI training workloads, UC Berkeley researchers : https://arxiv.org/abs/2507.21372 
12. Reliability in large-scale machine-learning clusters : https://arxiv.org/abs/2410.21680 
13. 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.

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-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/pariseg

  Chapters:

  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:

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.

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Links:

1. Stacy Rasgon on LinkedIn: https://www.linkedin.com/in/stacy-rasgon-6924963
2. Bernstein: https://www.alliancebernstein.com/corporate/en/home.html

References Mentioned During the Discussion

1. NVIDIA Blackwell Platform: https://www.nvidia.com/en-us/data-center/blackwell-platform/
2. High Bandwidth Memory (HBM) overview from Micron: https://www.micron.com/products/memory/hbm
3. DRAM overview from IBM: https://www.ibm.com/think/topics/dram
4. NAND flash overview from IBM: https://www.ibm.com/think/topics/nand-flash-memory

Further Reading

1. McKinsey on the semiconductor industry outlook: https://www.mckinsey.com/industries/semiconductors/our-insights/the-semiconductor-industry-in-2025
2. Semiconductor Industry Association: 2025 State of the U.S. Semiconductor Industry: https://www.semiconductors.org
3. NVIDIA on the Blackwell architecture and AI infrastructure roadmap: https://www.nvidia.com/en-us/data-center/blackwell-platform/
4. Broadcom AI investor materials and infrastructure commentary: https://investors.broadcom.com
5. ASML on lithography and advanced chip manufacturing: https://www.asml.com/en/technology
6. Micron on HBM and AI memory demand: https://www.micron.com/products/memory/hbm

   Chapters

[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