Why do some transformation efforts take hold while others require constant pushing?

Jennifer Charbonneau, COO of Cache Energy, shares what she learned leading innovation, AI adoption, safety, and organizational change inside complex, high-stakes environments.

Learn how to build trust and buy-in, turn small wins into momentum, choose AI projects based on business value, and create room for experimentation without ignoring risk. Jennifer also explains why successful transformation starts with people, not technology, and what her move from a major energy company to long-duration energy storage taught her about moving faster.

Topics & Timestamps

00:00 Making Transformation Real

03:15 Leading Change Differently

09:02 Lessons From Innovation and Safety

14:00 Why Transformation Breaks Down

19:04 Innovating Without Ignoring Risk

24:57 Why AI Pilots Fail

29:55 AI Plus Human Expertise

34:02 Building Change Around People

39:12 Rapid Fire

41:11 Lessons From the Journey

Connect with Jennifer Charbonneau:

https://www.linkedin.com/in/jennifer-charbonneau-75018b14a/

Learn more about Cache Energy:

https://cacheenergy.com/

Cache Energy on LinkedIn:

https://www.linkedin.com/company/cache-energy/

Connect with Ian Bergman:

https://www.linkedin.com/in/ianbergman/

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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

The technologies reshaping our lives today, from GPS and semiconductors to AI and self-driving cars, often began decades before they became commercial products.

In this episode of the Innovators Inside Podcast, Ian Bergman talks with Arati Prabhakar, former director of the White House Office of Science and Technology Policy (OSTP), DARPA and NIST, about the public research, institutions, and long-term investments behind some of America’s most important technological breakthroughs.

Arati explains how government-funded R&D helped create the foundations for GPS, the internet, advanced semiconductors, mRNA vaccines, autonomous vehicles, and modern AI. She also breaks down why DARPA consistently produces breakthrough technologies, how organizations can avoid the expertise trap, and why some of the biggest opportunities for AI may lie beyond business productivity.

They also explore a bigger question: What happens to innovation when a country stops investing in ideas that may take decades to pay off?

Key Topics

00:00 🎙️ Introduction

01:47 👋 Meet Arati Prabhakar

03:07 📍 The Hidden Innovation Behind the GPS Blue Dot

08:00 📱 How Public Research Built the Modern Smartphone

10:01 🔬 Why Government R&D Matters for Innovation

12:55 🤖 The 70 Years of Research Behind Modern AI

17:25 🚀 How DARPA Creates Breakthrough Technologies

22:15 🧠 Avoiding the Expertise Trap in Innovation

27:05 ⚠️ Why America’s R&D System Is at Risk

33:35 🇺🇸 What Should America Invest in Next?

38:24 🤖 Why AI Is Not Just a Marketing Problem

42:57 💡 Creating Value Beyond Business Productivity

43:35 🚗 How DARPA Helped Launch Self-Driving Cars

46:30 ⏳ Why Breakthrough Innovation Takes Decades

47:02 🏛️ How Public Research Funding Actually Works

50:30 🔮 Rebuilding America’s Innovation Future

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Dave Lambert, founder and managing director of Right Side Capital Management, explains why early-stage companies are reaching revenue faster, operating with smaller teams, and potentially needing far less venture capital.

Drawing on insights from more than 2,000 portfolio companies, Dave joins Ian Bergman to explore what investors now look for in AI startups and how founders can compete in a market where software is easier than ever to build.

Key Topics

🤖 How AI is reshaping startup economics

📉 The falling cost of building and scaling software

🚀 Why AI startups are reaching revenue faster

⚙️ AI agents that execute work, not just assist humans

💰 Capital efficiency and the changing need for venture funding

📊 What investors now look for in early-stage companies

🏢 Why enterprises are adopting startup technology faster

🛡️ Building products that remain defensible as AI improves

🔧 The return of hardware, marketplaces, and tech-enabled services

📣 Why branding and customer acquisition will matter more

🌱 When founders should avoid traditional venture capital

📈 Profitability, EBITDA, and the future of startup valuations

🧠 The psychology behind investor decisions

🔮 Technology hype, adoption, and predicting the future

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Most AI projects do not fail because the technology is not ready. They fail because companies choose the wrong problem, keep iterating without shipping, or never move beyond the pilot stage.

Ingrid Curtis, CEO of Sparq, explains how leaders can identify their highest-impact opportunity, get AI solutions in front of real users, and connect innovation to measurable growth.

Key Topics

🎯 How to identify the right AI opportunity

🚧 Why AI pilots stall before production

⚙️ Spark’s ACT framework

🚀 Moving from “problem zero” to a live solution

📈 Using AI for growth, not only efficiency

🧠 Why human judgment still matters

🏢 How AI may reshape teams and accountability

🔮 What an AI-native company could look like

📺🎧 Enjoying the conversation?

Discover more stories of innovation and transformation at alchemistaccelerator.com/podcasts — and explore how today’s leaders are shaping the future.

Garnet Heraman, co-founder and GP at Aperture Venture Capital, joins Ian Bergman to explain how investors can separate real technology shifts from hype. They explore vertical AI, enterprise adoption, quantum infrastructure, and why historical patterns still matter in fast-moving markets.

Garnet shares how his journey from startup operator to venture investor shaped the way he evaluates emerging technologies. He breaks down why generic enterprise AI may struggle, why vertical AI can create deeper value in industries like healthcare, insurance, and finance, and how adoption often lags behind aspiration.

The conversation also looks ahead to quantum computing, including why AI utilization may drive demand for quantum infrastructure, why software could be the next major opportunity in the quantum stack, and how quantum could transform asset management, climate risk, and financial services.

For founders, operators, and innovation leaders, this episode offers a practical way to think about technology cycles, enterprise behavior, and where the next wave of opportunity may emerge.

Key Topics

🚀 Garnet Heraman’s path from startup operator to venture investor

🧠 Why startups went from job to career to lifestyle

🤖 Vertical AI vs. generic enterprise AI

📈 The gap between AI aspiration and real adoption

🏥 How AI can improve healthcare payment integrity

🔁 Why historical patterns still matter in emerging markets

💡 How investors identify clusters, momentum, and market timing

⚛️ Why quantum infrastructure may follow AI utilization

🧩 The next opportunity in quantum software, middleware, APIs, and orchestration

🌍 Quantum’s potential role in climate finance and risk management

💼 What founders and innovation leaders can learn from market cycles

Chapters

🚀 Introduction to Garnet Heraman 00:01:05

🧭 From immigrant roots to startup leadership 00:02:08

💼 When startups became a lifestyle 00:04:19

🔁 From operator to venture investor 00:07:12

🤖 How Aperture thinks about AI investing 00:10:04

🧠 Why vertical AI matters more than generic AI 00:12:20

📈 Enterprise AI adoption and the utilization gap 00:15:18

🏥 Healthcare AI and payment integrity 00:20:36

⚡ Where we are in the AI adoption cycle 00:23:11

📚 Why history helps investors spot patterns 00:26:02

🔮 Predictive AI and the future of communication 00:30:23

⚛️ Why quantum is becoming impossible to ignore 00:34:00

🧩 The link between AI utilization and quantum infrastructure 00:35:29

📊 How Garnet identifies market clusters and momentum 00:37:06

💻 Why quantum software may be next 00:39:04

🧪 Learning the quantum market through experts and research 00:41:05

💰 Quantum finance, asset management, and trading 00:45:00

🌍 Climate finance and quantum risk management 00:46:19

🎙️ Final thoughts and where to follow Garnet 00:49:06

📺🎧 Enjoying the conversation?

Discover more stories of innovation and transformation at alchemistaccelerator.com/podcasts — and explore how today’s leaders are shaping the future.