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

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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

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AI is changing how businesses make decisions, manage teams, and compete. In this episode of Innovators Inside Podcast, Ian Bergman and Geoff McQueen, Founder and CEO of WorkSights AI, explore how AI can move beyond individual productivity and workflow automation to become a new layer of intelligence for the entire organization.

Geoff shares why the next big opportunity in AI is helping leaders understand what is really happening inside their business, using existing digital signals, audit logs, and company context to surface insights that dashboards often miss.

They also discuss what this means for managers, founders, and innovation leaders as AI reduces busywork, improves visibility, and raises the bar for entrepreneurial leadership.

You’ll learn:

• Why individual AI tools are only the first phase of business transformation

• How AI agents and workflow automation may reshape productivity

• Why Geoff believes the third wave of AI is organizational intelligence

• How leaders can use AI to move from status updates to better coaching

• Why qualitative context matters more than dashboards alone

• What entrepreneurs should know about building in an AI-powered world

• Why go-to-market is getting harder, even as building products gets easier

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Matt Higgins, CEO and co-founder of RSE Ventures, joins Innovators Inside to unpack what it really takes to build boldly when there is no fallback plan. From Shark Tank and Harvard Business School to defense tech, indoor air sanitation, venture capital, and West Palm Beach’s rise as an innovation hub, Matt shares practical lessons for founders, operators, and leaders making high-stakes decisions.

In this episode, Matt breaks down why “burning the boats” is not about reckless risk. It is about committing fully to the right goal, staying self-aware, and being willing to abandon the wrong tactics fast.

This conversation is for founders, innovation leaders, and operators who are building through uncertainty, betting on emerging categories, or trying to understand when bold commitment becomes a real advantage.

Key Topics:

🔥 Burn the Boats and what bold commitment really means

🧠 How to tell the difference between goals and tactics

🚀 What Matt Higgins looks for in great founders

📉 Why self-awareness matters when going all in

🛩️ Building Performance Drone Works before drones became obvious

🏙️ Why West Palm Beach could become a major innovation hub

💡 How strong innovation ecosystems are built from scratch

🦠 The future of indoor air sanitation with Izyum

💰 Venture capital, category creation, and early market signals

❤️ Turning survival, hardship, and purpose into impact

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How do great companies lose their mission, and how can founders stop it before it starts?

In this episode, Eric Ries, author of The Lean Startup and Incorruptible, explains how mission drift, shareholder pressure, false metrics, and weak governance push organizations away from real value creation.

Eric breaks down why so many companies optimize for financial proxies instead of human flourishing, why “best practices” often lead to mediocre outcomes, and what founders, executives, and operators can do to build organizations that stay principled as they scale. He also shares practical ideas on governance, board structure, public benefit corporations, culture, metrics, and how leaders can defend their values when pressure mounts.

If you are building, leading, or reinventing a company, this conversation will challenge how you think about profit, trust, growth, and long-term performance.

Key topics

📘 Why Eric Ries wrote Incorruptible after The Lean Startup

🧭 How mission drift turns great companies into hollow institutions

💰 Why shareholder primacy became the default logic in modern business

📊 How false proxies and financial metrics distort decision-making

🏗️ What makes an organization truly incorruptible

🛡️ Governance moves founders can make early to protect the mission

🧪 How Lean Startup principles fit with values, truth, and long-term thinking

🌱 What “human flourishing” means in practice for builders and leaders

🧲 Why principled companies attract better talent, customers, and outcomes

⚔️ How founders can respond when boards or investors pressure them to compromise

🔁 Whether large incumbents can recover after corruption takes hold

🚀 Why long-term company resilience requires more than founder-led intensity

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Category creation can help founders and innovation leaders stop competing in crowded markets and start defining new ones. In this episode of Inside Podcast, Ian Bergman and Layne Fawns talk with Kevin Maney, co-founder of Category Design Advisors, and author of The Category Creation Formula, about how companies create and lead new market categories.

Kevin breaks down his framework: context + missing + innovation = new category. He explains why dominant design matters more than first-mover advantage, how Netflix and Tesla shaped customer behavior, and why category strategy can align product, marketing, and leadership around a bigger opportunity.

Key Topics

🚀 Category creation for founders and innovators

📐 Context + missing + innovation

🏆 Dominant design vs. first-mover advantage

🎬 Lessons from Netflix, Tesla, Zamboni, and Chrysler

🤖 AI, creativity, and authenticity

🏢 Innovation inside large companies

Chapters

🎙️ Introduction to Kevin Maney and Category Creation 00:00

🤖 AI, Creativity, and the Human-Machine Partnership 02:11

🎨 Authenticity, AI, and the Future of Creative Work 08:24

📘 Kevin’s Journey from Tech Journalism to Category Design 15:48

🚀 What Category Design Means and Why It Matters 20:33

📐 The Category Creation Formula 21:02

🧠 What a New Category Really Is 23:15

🏆 Why Competitors Can Validate a Category 26:01

🥇 Dominant Design vs. First-Mover Advantage 28:03

🚗 The Zamboni Story and Hidden Category Creation 31:31

🔁 Market-Product Fit vs. Product-Market Fit 35:42

🧭 How to Understand What Customers Really Need 38:41

🏷️ Category vs. Brand 41:20

📣 How to Know a Category Is Catching On 43:38

🎬 Netflix, Timing, and the Adjacent Possible 46:11

⏱️ How to Know When the Market Is Ready 54:31

🏢 Category Creation Inside Large Companies 56:19

💡 Final Takeaway for Founders and Innovation Leaders 01:00:38

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How do leaders create real change when the problem has no clear answer?

In this episode, Ian Bergman and Layne Fawns sit down with John Michael Schert, founder of JMS & Company, to explore adaptive leadership, creativity, organizational change, and why the hardest part of innovation is often letting go of what used to work.

John Michael brings a rare perspective shaped by professional ballet, nonprofit leadership, entrepreneurship, Harvard-trained adaptive leadership, and years advising teams through complex change.

This conversation breaks down why creativity is not just a soft skill, but a practical tool for founders, operators, and innovation leaders facing uncertainty.

Key topics

🎯 Why people are not afraid of change, they are afraid of loss

🏔️ How success can become a trap for founders and organizations

🧩 The difference between technical problems and adaptive challenges

💡 Why creative choices are fragile before they can be proven

🛡️ How leaders can protect teams while they work through uncertainty

👂 Why listening and diagnosis matter before jumping to solutions

🤝 How to build coalitions instead of trying to drive change alone

🎭 What organizations can learn from artists without romanticizing creativity

🔄 Why innovation often starts by letting go of the 10% that no longer serves the system

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In this episode, neuroscientist, entrepreneur, and author Vivienne Ming explains why the biggest opportunity in AI is not automation, but helping people think better, ask better questions, and solve harder problems.

Vivienne breaks down the difference between well-posed and ill-posed problems, why most companies are using AI the wrong way, and how leaders can build cultures that reward exploration instead of safe answers. She also shares practical ideas on human and AI collaboration, learning, innovation, and preparing people for a future where routine work matters less and judgment matters more.

Key topics

🤖 AI augmentation vs automation

🧠 Well-posed vs ill-posed problems

🚀 Why better questions drive innovation

🏢 How leaders can reward productive failure

👥 Human and AI collaboration in real work

📚 How to build better thinkers, teams, and kids for the future

Chapters

00:00 🧠 Intro to Vivienne Ming

01:55 ⚡ Rapid fire and the nature of real innovation

05:37 🔍 Why facts are not enough anymore

08:16 🤖 AI, well-posed problems, and ill-posed problems

15:58 🚀 Why the future belongs to people who explore the unknown

20:26 🏢 How leaders build cultures that reward productive failure

21:51 🔥 The efficiency lie in AI

27:00 👥 AI augmentation vs automation

29:52 👶 How to prepare kids for an AI-shaped future

33:27 🗺️ Designing tools that make people better

35:56 📚 Why AI tutors should not give answers

37:14 🛠️ What leaders should do differently right now

43:36 🌍 Vivienne’s realistic view of the future of AI

47:55 📊 Using AI to uncover human behavior and hidden patterns

57:40 🧭 How people really make decisions

01:01:28 🧪 Hybrid intelligence and human + AI teams

01:07:10 💡 Why human + AI teams outperform

01:10:20 ✅ The real choice: stay shallow or go deep

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In this episode, Wendy Gonzalez, CEO of Sama, breaks down why high-quality data, human-in-the-loop systems, and clear evaluation standards are essential for building AI that actually works at scale.

Wendy shares how enterprises train, validate, and improve AI models in the real world, from autonomous vehicles to e-commerce recommendations and generative AI. She also explains why dirty data, edge cases, and weak quality standards can quietly kill AI performance, trust, and adoption.

You’ll also hear a sharp conversation on responsible AI, model bias, regulation, language inclusion, and why founders and innovation leaders need to define what “good” looks like before shipping AI products.

If you are building, buying, or leading AI initiatives, this episode offers practical insight on AI deployment, trustworthy AI, training data, model accuracy, and the human systems behind production-grade machine learning.

Key topics

🤖 How to get AI into production and keep it there

🧠 Why human-in-the-loop systems still matter in modern AI

🧹 What dirty data is and how it hurts model performance

🎯 Why edge cases define real-world AI success

📊 How enterprises think about AI quality, validation, and ROI

🚗 Lessons from autonomous vehicles, safety, and model training

🛒 How recommendation engines and search relevance depend on better data

🌍 Why language, culture, and context matter in AI models

⚖️ Responsible AI, regulation, and the tension between policy and speed

🔍 How users can think critically and decide when to trust AI outputs

👥 How Sama connects AI training work with economic opportunity and impact

💼 Leadership lessons on humility, growth, and “firing yourself” as a CEO

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