What if insurance isn’t actually about protection—but about who understands risk better than anyone else?

Tanner Hackett has spent his career at the edge of massive shifts—helping build Lazada into Southeast Asia’s e-commerce backbone, then co-founding Button to ride the mobile commerce wave. Now he’s the founder and CEO of Counterpart, an insurtech company rebuilding small business insurance with AI-driven underwriting, real-time risk insights, and a growing dataset across 35,000+ policies.

In this episode, Tanner breaks down why insurance—one of the largest financial markets in the world—still runs on outdated assumptions, and how Counterpart is quietly rewriting the math.

In Today's Episode We Discuss:

00:01 – Introduction and Tanner Hackett Background

00:33 – Early Career: Lazada and Southeast Asia E-commerce

01:30 – Building Button and Mobile Commerce Insights

02:03 – Founding Counterpart and Shift to Insurance

02:19 – Initial Idea: HR Tech to Insurtech Pivot

04:11 – What’s Broken in Insurance Today

06:15 – Why Previous Insurtech Startups Failed

08:16 – Understanding MGA and Insurance Business Model

09:16 – Why Now Is the Right Time for Counterpart

11:49 – Rethinking Value in Insurance

13:07 – Insurance Categories Counterpart Focuses On

17:28 – Distribution vs Underwriting Bottlenecks

19:45 – Data Advantage and Underwriting at Scale

21:25 – Insurance Basics for Startups

27:02 – Counterpart’s End-to-End Platform Approach

29:28 – AI, Data Infrastructure, and Pricing Risk

33:48 – Building a Data Moat in Insurance

35:10 – Founder Advice: Reducing Risk and Lowering Premiums

38:05 – Real-World Claims and Insurance Stories

39:21 – Fintech and HR Tech Companies Tanner Admires

40:58 – Long-Term Vision for Counterpart

42:29 – Closing Thoughts and Future of Insurance

“Insurance is a math problem—but most of the industry is still pricing to averages.”

“Every dollar paid out on a bad claim gets passed on to the next customer. That’s the system we’re fixing.”

Tanner’s journey started with marketplaces and marketing tech. Now he’s building a company where the product isn’t just coverage—it’s understanding risk at a level traditional insurers never could. Because in the end, the companies that win in insurance aren’t the ones with the biggest balance sheets. They’re the ones who see the risks others miss.

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What if your AI didn’t just answer you… but interrupted you, remembered you, and slowly became someone you knew?

Vish Hari is building toward that future. A former Facebook AI researcher with roots in astrophysics, he’s spent nearly a decade inside deep learning—from training early models in 2013 to now founding Ego AI, an applied research lab focused on one idea: AI should feel human, not mechanical.

Ego isn’t chasing superintelligence. It’s going after something most labs ignore—behavior, personality, and relationships. The bet: the next breakout consumer platform won’t be the smartest AI. It’ll be the one you actually want to talk to.

In Today's Episode We Discuss:

00:01 Introduction to Vish Hari & Ego AI

00:45 Background in Astrophysics and Early AI Work

03:00 From Research Labs to Founding Ego AI

05:30 The Problem with Current AI Interactions

08:00 OpenAI, Agents, and Personal AI Limitations

11:30 Video Games as AI Training Grounds

15:00 Human Behavior vs Machine Intelligence

18:30 Ego AI Architecture and Product Vision

22:00 Utility vs Personality Tradeoff

26:00 Vision for AI Companions and Relationships

30:00 AI, Society, and Behavioral Shifts

34:30 Near-Fatal Assault and Recovery

40:00 Lessons on Time, Resilience, and Focus

43:00 Why Current AI Agents Fall Short

47:00 Consumer AI vs B2B AI Debate

50:00 Future of Work and AI Impact

53:30 Founder Mindset and Building Ego AI

56:00 Closing Thoughts and Where to Find Ego AI

Pull quotes:

“The problem we’re solving is entirely human-ness. AI does not feel human.”

“We don’t need superintelligence. We need something that feels like a person.”

If Vish is right, the future of AI isn’t a better tool.

It’s a new category of relationship.

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What do you learn after building companies across five generations, and investing in thousands more?

David S. Rose has been early to nearly every wave: PCs, the internet, mobile, and now AI. As the founder of Gust, he didn’t just invest in startups, he built the infrastructure that powers how startups raise money globally.

He’s a serial entrepreneur, one of the most active angel investors in the world, founder of New York Angels, and the architect behind Gust, which now supports over 2 million founders and most major angel networks worldwide.

In Today's Episode We Discuss:

00:01 – Intro & David S. Rose background

00:24 – Early entrepreneurial beginnings

02:30 – College ventures & first hustles

03:30 – First job with Senator Moynihan

04:40 – Real estate career & early tech adoption

05:20 – Inventing proptech

06:50 – First startup experiences

08:00 – WristMac and early wearable tech

10:30 – Mobile messaging startup

13:30 – Accidental Series A raise

16:00 – Wireless software & early telecom

17:30 – Internet disruption

18:00 – AirMedia & wireless internet vision

22:00 – Dot-com boom & expansion

23:40 – Dot-com crash & shutdown

24:40 – Transition to angel investing

25:30 – Founding New York Angels

26:30 – Building Gust platform

29:00 – Gust Launch & company formation

30:00 – Writing books & Quora

33:00 – AI impact on startups

35:00 – USREM & real estate marketplace

37:00 – Current ventures & roles

39:00 – Singularity University origins

42:00 – Exponential technology & Ray Kurzweil

49:00 – AI, AGI, and future predictions

52:00 – Impact of AI on venture capital

54:00 – Future of work & unemployment thesis

58:00 – UBI and economic restructuring

01:00:00 – Abundance & societal shifts

01:01:30 – Entrepreneurship in AI era

01:03:30 – Healthcare, robotics, and tech progress

01:05:00 – Founder mindset & entrepreneurship

01:07:30 – Startup success traits

01:09:30 – Investment decision framework

01:11:30 – Closing thoughts & where to find David

Along the way, he shares lessons from decades of building through booms, crashes, and paradigm shifts—and why today’s founders are playing a completely different game.

“If a machine can do your job better, faster, and cheaper… by definition, that job disappears.”

“Entrepreneurs are like cockroaches. You can’t kill us.”

From selling firewood on his first day of college to powering global startup ecosystems, David’s story is a reminder: the tools change, the stakes get higher, but the mindset that builds companies stays constant.

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What happens when Google stops sending traffic… and AI starts deciding who gets discovered?

Jochen Madler is betting that most companies are not ready for that shift.

He’s the co-founder and CEO of SiteFire, a YC W26 startup building marketing infrastructure for the agentic web. After a background in statistics and reinforcement learning, he left his PhD to build a company helping brands show up inside AI systems like ChatGPT, Claude, and Gemini—where discovery is already moving.

And the shift is not theoretical. Some companies are already seeing AI traffic grow fast, while others are losing distribution without knowing why.

In Today's Episode We Discuss:

00:01 Introduction & SiteFire Overview

00:28 Jochen’s Background in Statistics & Energy Systems

01:18 Founding SiteFire & Early YC Journey

02:14 Moving to San Francisco & Ecosystem Exposure

02:50 YC Experience & Key Takeaways

04:00 Advice for Founders Applying to YC

05:25 From Reinforcement Learning to AI Marketing

06:20 Identifying the AI Search Opportunity

07:35 Google AI Overviews & Traffic Disruption

08:30 Rise of AI as the New Interface

09:17 Why AI Search Isn’t SEO 2.0

10:53 Rational Search vs Human Behavior

12:29 Marketing as a Math Problem

14:01 AI Search vs Traditional Channels

15:26 Websites Becoming Agent-Focused

16:33 Human Traffic vs Agent Traffic Trends

17:46 Adoption Gap & Real-World Usage

19:14 Long-Term Evolution of Search & AI

21:01 GEO vs AEO Debate

21:43 Agent Experience as the New Frontier

22:02 What Winning in AI Search Means

23:12 Distribution Risk if Google Disappears

24:10 OpenAI’s Role in Search Distribution

24:41 GEO vs SEO Debate Revisited

25:11 Markdown, Content Structure & AI Readability

25:58 SiteFire Product: From Monitoring to Execution

28:47 How AI Models Rank & Retrieve Content

31:00 Early Product Traction & Breakthrough Results

32:12 Content Strategy: Domain vs Platforms

34:00 Product vs Services Approach

34:58 Hardest Technical Challenges

36:31 Misconceptions About AI Marketing

38:25 Content Commoditization & Future of SEO

39:17 Competition & Incumbent Risk

42:27 Future of AI Agents Replacing Search

43:27 AI Agents Executing Transactions

44:56 Agent Reviews & API Ecosystems

45:47 Where Value Accrues in AI Stack

48:15 Vision for SiteFire & Agent Funnels

51:10 Rapid Fire: Startup Lessons & Beliefs

Pull Quotes

“Websites will increasingly be visited by agents, not humans.”

“If your API is the one the agent finds and uses, there’s no reason to switch.”

Jochen started by modeling energy systems. Now he’s modeling something bigger: how attention flows when machines—not people—control discovery.

Because in a world where agents decide what gets seen, clicked, and bought…marketing doesn’t disappear. It just moves one layer deeper.

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Why do some startups get instant buy-in while others struggle to convince their first customer?

Mike MacCombie didn’t come up through the typical VC pipeline. He’s a former behavioral science consultant, Techstars mentor, and ecosystem builder who has hosted 50+ curated communities designed to change how people connect, share, and act. Today, he runs Generous Ventures, a pre-seed fund built on one core idea: the best companies don’t fight for attention—they trigger an immediate “of course” from customers.

Instead of chasing hype, Mike looks for dense, overlooked ecosystems where a company can own distribution, control data, and compound advantage over time. His portfolio spans niche vertical AI plays—from radiology cost reduction to animal health data layers—where clear value beats storytelling every time.

In this episode, Mike breaks down how he evaluates founders and markets through a behavioral lens—and why most investors are asking the wrong questions.

In Today's Episode We Discuss:

00:01 Introduction and Guest Background

00:32 Mike’s Origin Story and Early Career

01:29 Transition from Teaching to Venture Capital

02:37 Building Communities and Entering VC

03:50 Fund Strategy and Investment Thesis

05:02 Evaluating Distribution and Market Dynamics

06:05 Customer Concentration and Market Size

07:15 Behavioral Science in Venture Investing

09:06 Lessons from Early VC Experience

10:47 Founder Traits and Decision-Making

11:42 Follow-On Strategy and Portfolio Management

12:40 Community as a Competitive Advantage

14:32 Fund Structure and LP Incentives

17:02 Building and Managing High-Value Communities

19:53 Experiments in Founder-Investor Connections

22:21 Personal Story and Resilience

25:14 Evaluating Startup Potential and Risk

27:51 Portfolio Strategy and Diversification Debate

31:56 Investment Philosophy and Decision Frameworks

34:26 Founder Mistakes in Fundraising

36:42 Secondary Markets and Liquidity Strategy

39:28 Founder Priorities and Non-Negotiables

41:38 Learning, Reflection, and Continuous Improvement

44:16 Network Effects and Deal Flow Growth

47:00 Identifying High-Potential Founders

49:31 Changes in Venture Capital Landscape

51:23 Staying Relevant as an Emerging VC

53:25 Tools, Systems, and Personal Workflow

55:16 Vision for the Future

56:54 Rapid Fire Questions and Closing

Two standout lines from the episode:

“Great companies don’t need hype. Their value is obvious the moment you see it.”

“The difference between expectations set and met is the measure of happiness—so I don’t promise outcomes, I create conditions for them.”

At its core, Mike’s approach is simple but hard to execute: strip away noise, focus on first principles, and build systems where the right opportunities find you.

From teaching classrooms to building one of the most connected early-stage networks in venture, his edge comes from the same place it always has—understanding how people think, decide, and act.

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How do you secure systems when attackers are moving faster than your developers?

Derek Foster has spent his career inside that gap. From breaking video games with GameShark to securing large-scale fintech systems, he now builds what most teams still lack: a way to test, validate, and fix vulnerabilities continuously, not once a year.

As Co-Founder and CTO of Best Defense, Derek is building an AI-driven cybersecurity platform that doesn’t just flag risks, it proves exploits and ships fixes directly into developer workflows. That shift matters now. Software velocity is accelerating, attackers are faster, and the cost of a breach averages over $4M.

In Today's Episode We Discuss:

00:01 Introduction to Derek Foster & Best Defense

00:37 Derek’s Background and Early Curiosity in Systems

02:25 From Gaming to Cybersecurity Foundations

04:41 First Experiences in Security and Problem Solving

07:24 Origin Story of Best Defense

10:42 AI, Developer Velocity, and Rising Security Risks

13:15 Target Customers and User Focus

14:29 Why Traditional Security Models Are Broken

17:30 AI, Trust, and Security in Developer Workflows

21:19 Fixing Vulnerabilities vs Detecting Them

23:12 Building Automated Security Remediation

26:32 Market Trends and Investor Blind Spots

29:32 Common Security Mistakes Founders Make

31:52 Hardest Engineering Decisions at Best Defense

33:56 Open Source vs Closed Source Strategy

36:12 Long-Term Vision for Cybersecurity

39:30 Red Team vs Blue Team Explained

42:28 The Future of Cybersecurity and Automation

46:07 Seamless Security in Developer Workflows

48:30 Metrics That Signal Industry Change

49:37 Governance Debt and AI Risk

51:19 SOC 2 Compliance and Security Standards

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What happens when a former Google and Oracle engineer spends a decade studying how enterprise buyers actually make decisions—and then uses that to pick startups? You get a very different lens on what “product-market fit” really means.

Arun Penmetsa, Partner at Storm Ventures, sits at the intersection of deep technical experience and over 11 years of early-stage investing. At Storm, a firm with 25+ years of history and 200+ investments, he focuses on backing B2B startups across SaaS, cybersecurity, and digital health—helping them move from early traction to repeatable growth. Before venture, he helped build enterprise software at Oracle and was part of the early Google Apps for Enterprise team, giving him firsthand exposure to how large organizations adopt new technology.

This episode breaks down how great enterprise companies actually win—and why most founders misunderstand the hardest part of building one.

Arun explains why product-market fit isn’t about revenue or growth curves. It starts with urgency. If your product isn’t solving a “need it now” problem, enterprise buyers will stall, pilot, or churn. That’s why Storm pushes founders to obsess over whether they’ve truly hit a “hair on fire” use case before scaling.

From there, the real challenge begins: turning founder intuition into a repeatable go-to-market system. Many early teams succeed because the founder can sell. Growth breaks when that knowledge stays in their head. Storm’s approach focuses on mapping the customer journey alongside the sales pipeline—forcing teams to define what makes a buyer move from one step to the next, including the “wow moments” that earn the next meeting.

In Today's Episode We Discuss:

00:01 Intro & Arun Penmetsa Background

01:06 Early Career at Oracle & Google

03:00 Transition from Operator to VC

05:03 Is an MBA Necessary for Venture?

07:00 Joining Storm Ventures

08:26 Shifting to Investor Mindset

11:04 Storm’s Enterprise Focus

14:25 Storm’s Go-To-Market Playbook

18:55 Defining Product-Market Fit

20:52 Evolution of Go-To-Market

24:05 Investment Stage & Check Size

25:04 How Storm Evaluates Startups

27:58 Platform vs Feature Risk

30:07 Common Investment Mistakes

31:55 Cybersecurity Market Insights

33:29 Capital Efficiency in Startups

35:28 Technical Differentiation Debate

38:08 Where to Build in the Stack

42:20 Future of Work & AI Impact

43:48 SaaS vs AI Debate

46:00 Rapid Fire: Enterprise Insights

48:41 Future of Cybersecurity

50:05 Manual Workflows & Opportunities

53:21 Metrics VCs Don’t Trust

Two lines from Arun that stick:

“Product-market fit starts with urgency. If they don’t need it now, they won’t buy.”

“The moment you sign the contract is the moment of highest stress for your customer.”

That last point reframes everything. Winning the deal isn’t the finish line. It’s where risk peaks. The companies that move fastest from sale to value are the ones that build trust—and compound growth.

Arun’s journey started with building products inside large enterprises. Today, he backs founders trying to disrupt them. The throughline hasn’t changed: understanding how real buyers behave is still the advantage.

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What happens when a 22-year-old gets dropped into frontier markets to build e-commerce from scratch—and learns how to scale chaos into systems?

Christian Schroeder lived it. Today, he’s channeling those lessons into building and backing companies designed to win from day one.

Christian is the founder and CEO of 10x Value Partners and Utopia Capital, a former Rocket Internet operator, and an investor with ~40 portfolio companies and multiple exits. At Rocket, he helped launch and scale businesses across emerging markets like Pakistan and Bangladesh—often building from zero in environments where customers didn’t even know online shopping existed. Now, he applies that same “industrial company-building” playbook to venture creation and early-stage investing.

In Today's Episode We Discuss:

00:01 Introduction & Guest Overview

00:24 Christian Schroeder Origin Story

02:27 Rocket Internet & Frontier Market Experience

03:33 Key Lessons from Rocket Internet

05:32 Hiring Top Talent & Interview Signals

08:34 Applying Hiring Principles Today

09:48 10x Value Partners & Investment Approach

11:58 Longevity Thesis & Personal Health Journey

16:01 Telomeres, Biohacking & Reversal Strategy

20:50 Longevity Market Opportunities

23:32 Future of Longevity & Regenerative Science

27:49 Venture-Scale Business Models in Health

28:59 Evolution of 10x Value Partners

30:57 Venture Studio Strategy & Market Timing

32:28 Founder-Led Investing Philosophy

35:24 Venture Studio vs Accelerator Model

38:32 Macro Trends & AI Landscape

41:26 AI, Margins & Future Business Models

Two sharp takeaways from Christian:

“If an interview is over in 15 minutes because there’s nothing left to talk about, that’s your answer.”

“Don’t take VC feedback as truth. Most of the time, it’s not the real reason they passed.”

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