Executive hiring can make or break a scaling company. Travis Hann, co-founder and managing partner of Pender & Howe, explains why leadership hires fail, how founders can assess candidates beyond the résumé, and why they should hire for where the business is going, not where it is today.

Learn what to define before opening an executive search, which early leadership roles matter most, and the signals that reveal judgment, curiosity, consistency, communication, and real operating ability. Travis also breaks down how AI is changing executive search, where it improves the process, and why human judgment still matters in high-stakes hiring.

Topics

01:10 Why Leadership Hiring Goes Wrong

09:05 What to Define Before You Hire

12:32 How to Assess Beyond the Résumé

21:13 The First Leadership Hires

28:13 Will AI Replace Executive Search?

32:38 Where Human Judgment Still Matters

35:49 How AI Is Changing Org Charts

39:37 Building an Unconventional Firm

41:34 Hiring People Different From You

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AI demos are easy. Building AI that actually works inside a business is much harder.Andrew Brooks, founder and CEO of Contextual.io joins Ian Bergman to unpack the AI hype tax, the hidden cost of confusing ChatGPT, Copilot, and impressive prototypes with real business transformation.They explore what makes an AI system truly production ready, why even 99% accuracy can create serious problems at scale, and where human judgment, guardrails, governance, and accountability still matter.You’ll also learn how to decide when to build AI capabilities versus rent existing tools, why differentiated data and workflows should guide that decision, and how AI can amplify expertise instead of simply replacing people.For founders, operators, and innovation leaders trying to turn AI experimentation into measurable business value, this conversation offers a practical framework for separating hype from what actually works.

Key Topics

⏱️ 04:14 What is the AI hype tax?

💸 09:50 Where companies are wasting money on AI

🧠 16:18 Human + AI: why the goal is augmentation, not replacement

🏗️ 22:20 Why simply turning on Copilot is not enough

🏰 27:23 Build vs. rent: when custom AI actually makes sense

📊 29:56 Accountability, governance, and measuring AI ROI

🏠 34:35 What enterprise AI can learn from the smart home era

💡 40:52 How employees can become internal AI champions

🔮 46:39 What Andrew was wrong about AI two years ago

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Emmanuel Vallod, Partner and Head of Venture Research at Hivemind Capital, joins Ian Bergman to explore the funding gap between academic research and startup formation. They discuss Dark Matter Lab, a new model for supporting frontier technologies before a company fully exists, and why AI infrastructure, deep tech, robotics, and other research-heavy projects may require a different approach to pre-seed investment.

The conversation also goes inside some of the technologies being developed today, from the engineering challenges behind space-based data centers to new computing architectures and robotics designed around how humans actually interact with intelligent machines.

🧭 1:36 Meet Emmanuel Vallod

🚀 2:20 Why Space Is Hard for AI Data Centers

🛰️ 4:15 What Space-Based Compute Could Actually Look Like

⚙️ 10:13 The Technology Needed to Make It Work

💡 15:16 When Breakthrough Research Has No Company Yet

🧪 17:12 Why Valuable Research Gets Stuck

💰 20:09 The Idea Behind Dark Matter Lab

🔬 26:07 How to Evaluate Research Before There Is a Product

🏛️ 29:39 Why Grants Are Not Enough

🧠 32:17 Why Frontier Tech Needs Technical Investors

📈 36:14 Can Private Capital Fill the Research Funding Gap?

🤖 39:44 Why Humanoid Robots May Be Wrong for the Home

🚀 45:07 What Changes If Dark Matter Lab Works?

🔎 45:51 Where to Follow Emmanuel and Dark Matter Lab

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This episode offers a practical look at how technical risk gets reduced, why promising research gets stuck, and what investors need to understand before backing the next generation of deep tech companies.
Topics