The Moving Bottleneck: Networking, Power, Memory, and the Race to Win AI

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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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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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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