May 21, 2026

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

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.
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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.
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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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